ABSTRACT

 

Diabetic foot ulcers (DFU) are associated with significant impairment of quality of life, increased morbidity and mortality, and are a huge drain on health care resources. In Western countries, the annual incidence of foot ulceration in the diabetic population is around 2%. DFUs develop as a consequence of a combination of factors, most commonly peripheral neuropathy (loss of the gift of pain), peripheral arterial disease (PAD), and some form of unperceived trauma. Recent studies emphasize the very high prevalence of foot ulceration in people with diabetes on dialysis as a consequence of end-stage renal disease. The mortality in this patient group is higher than for most forms of cancer. All patients with diabetes should have an annual screen to identify their foot ulcer risk status: those with any risk factors require specific foot care education as well as regular contact with a health care professional, usually a podiatrist. DFUs should heal if there is an adequate arterial inflow, infection is aggressively managed, and pressure is removed from the wound and its margins. In the management of plantar neuropathic ulcers, offloading is critical and all efforts must be made to enhance patient understanding of the need for offloading. Antibiotic usage should be guided by clinical signs of infection and microbiologic analysis of deep tissue specimens: evidence now exists to show that oral antibiotics are equally efficacious as intravenous in treating most cases of osteomyelitis in the diabetic foot. Most adjunctive therapies have little evidence to support their use although recent trials suggest efficacy for a number of topical therapies including LeucoPatch (3C patch) and sucrose octasulphate; and negative pressure wound therapy has also been shown to be helpful in certain cases. There is currently no indication for hyperbaric oxygen usage, whereas recent studies suggest that topical oxygen therapies help wound healing. Charcot neuroarthropathy (CN) should be easily preventable: most important is to treat any neuropathic patient with a warm swollen foot as having CN until proven otherwise.

 

INTRODUCTION

 

At the beginning of the 21^st Century, diabetic foot problems, although eminently preventable, represent one of the commonest causes of hospital inpatient admission in Western countries. In 2005, the International Diabetes Federation realized the global importance of diabetic foot disease and chose to focus their campaign during the whole year on raising awareness with a worldwide campaign to “put feet first” and highlight the common problem of amputation amongst diabetic patients throughout the world. To coincide with World Diabetes Day 2005 (November 14, birth date of Frederick Banting), the Lancet elected to dedicate a whole issue to diabetic foot problems (1).

 

In this chapter, the global term “diabetic foot” refers to the variety of pathological conditions that might affect the feet in patients with diabetes. Foot ulcers are defined as lesions involving a skin break with loss of epithelium: they can extend into the dermis and deeper layers sometimes involving bone and muscle. Amputation is defined as “the removal of a terminal, non-viable portion of the limb”. The lifetime risk of a person with diabetes developing a foot ulcer (DFU) has been estimated to be as high as 34 % (2).

 

The suffering of affected individuals and the cost of DFUs are both equally staggering. Those individuals with DFUs usually have other complications of diabetes including nephropathy: data from the UK and the USA confirmed that the outlook for those people with foot complications who are on dialysis is very poor with a high mortality risk (1-3). Data from our group confirm that those people with diabetes who have had an amputation and who are on dialysis have a 75% two-year mortality; the majority of these were of cardiovascular etiology. Data such as these are worse than most malignant diseases, with the possible exception of lung and pancreas. There is therefore an urgent need for preventative strategies to reduce the incidence of foot complications amongst those with diabetes. With respect to costs, in 2008 Rogers et al (4) reported that in the US $18 billion was spent on the care of DFUs and US $11.7 billion on lower extremity amputations. More recently, data from the UK in 2019 suggest that a conservative estimate of the annual cost of diabetic foot problems exceeds UK £900 million which represents approximately 1% of the total budget of the National Health Service (5).

 

The importance of regular diabetic foot care in very high-risk patients is emphasized by an observational study from Arizona where the State decided to remove routine podiatry from high-risk patients to reduce their health budget. This led to an annual saving of US $351,000 but the cost of this action measured by increased hospitalization, length of stay, and amputations was $16.7 million (6).

 

This chapter will include a discussion on the epidemiology of foot problems including foot ulceration, amputations, and Charcot neuroarthropathy (CN). The etiopathogenesis will then be described and aspects of management of neuropathic, neuroischemic, and infected DFUs considered. The question of how to address primary and secondary prevention of diabetic foot problems will then be discussed followed by a section on Charcot neuroarthropathy. For more detailed discussion, the reader is referred to review articles on these topics (2,7-9).

 

EPIDEMIOLOGY OF THE DIABETIC FOOT

 

The study of the epidemiology of diabetic foot disease has been beset by numerous problems relating to both diagnostic tests used, and population selected. However, there is little doubt that foot complications are common. In the UK, the North West Diabetes Foot Care Study (a community-based study of over 15,000 people) reported that the annual incidence of foot problems amongst the population with diabetes was just under 2% (10), with similar results having been reported from the Netherlands. Similarly, when discussing amputations, the figures vary widely again due to diagnostic criteria as well as regional differences. It must be remembered that many individuals at diagnosis of type 2 diabetes have significant neuropathy: in the United Kingdom Prospective Diabetes Study, for example, 13% of patients at diagnosis had neuropathy of sufficient severity to put them at risk for foot ulceration (7).

 

With respect to ethnicity, studies from the UK suggest that foot ulcers and amputations appear to be less common in Asians of Indian sub-continental origin and Afro-Caribbean men. In contrast, reports from the USA suggest that amputation rates are more common amongst African-Americans with diabetes than amongst white Americans. Similarly, ulceration is much more common in Hispanic Americans and native Americans than in non-Hispanic whites (2). More recently, reviews have confirmed the importance of healthcare inequities in diabetic foot disease: race, ethnicity, socioeconomic status, and geography are powerful mediators of risk for DFU and lower-extremity amputation (2,11).

 

ETIOPATHOGENESIS OF DIABETIC FOOT ULCERATION

 

The foot does not break down spontaneously and in this section, the many warning signs that the feet are at risk of breakdown will be discussed. This was recognized by Elliott Joslin almost 90 years ago when he stated that “diabetic gangrene is not heaven-sent but is earth-borne” (12). It was previously believed that neuropathy, vascular disease, and infection were the main causes of ulceration: it is now recognized that infection occurs as a consequence of ulceration, and is not a cause thereof. There are many contributory factors to foot ulceration, the most important of which are diabetic neuropathy and peripheral arterial disease (PAD). These and other causative factors are listed in table 1.

 

Table 1. Risk Factors for Foot Ulceration

Peripheral neuropathy Somatic Autonomic

Peripheral arterial disease Proximal and/or distal disease

Past history of foot ulcers/amputation

Other long-term complications End-stage renal disease (especially on dialysis) Post-transplant (including pancreas/kidney transplant) Visual loss

Plantar callus

Elevated foot pressures

Foot deformity

Edema

Ethnic background

Poor social background

More common contributory factors shown in bold

 

Diabetic Neuropathy

 

Although the association between both somatic and autonomic neuropathy and foot ulceration has been recognized for many years, it is only in the last 20 years that prospective studies have confirmed these assumptions (2,8,10). It has been reported that the risk of developing the first foot ulcer is seven-fold higher in those with moderate to severe sensory loss compared with non-neuropathic diabetic individuals (13). Additionally, poor balance and instability as a consequence of loss of proprioception have been confirmed and are also likely contributory factors not only to foot ulceration, but also to Charcot neuroarthropathy (CN) (2,7,14,15).

 

Sympathetic autonomic neuropathy in the lower extremity leads to reduced sweating and dry skin that is prone to crack and fissure, and as well, in the absence of PVD, to increased blood flow, arterio-venous shunting, and the warm foot.

 

As will be discussed later, simple clinical tests may be used to identify the high-risk neuropathic foot (16). Most important in the identification of the high-risk neuropathic foot is good clinical observation and removal of the shoes and socks, with careful inspection of the feet as part of the routine follow up of all patients with diabetes.

 

Peripheral Arterial Disease (PAD)

 

A two-center study of causal pathways to foot ulceration reported that peripheral ischemia was a causal component in the pathway to ulceration in 35% of cases (17). In many Western countries, there has been an increase in the percentage of foot ulceration in which ischemia is a contributory factor (18). It is well recognized that patients with diabetes are more prone to distal arterial disease, which may be associated with a poorer outcome.

 

A detailed discussion of PAD in diabetes is outside the scope of this chapter and readers are directed to reviews on this topic (19, 20). A large follow-up study from Australia has confirmed that the strongest predictors of development of PAD in type 2 diabetes include microvascular complications (particularly macroalbuminuria and photocoagulation for retinopathy (21)).

 

Other Risk Factors

 

Of all the risk factors for foot ulceration (table 1), the most important is a past history of ulceration and/or amputation (2). In some series, the annual recurrence rate is up to 50%.

 

Other Long-term Complications

 

Those with other late complications particularly nephropathy, have an increased ulcer risk. Visual disturbance as a consequence of retinopathy is a confirmed risk factor; it is easy to understand why this should be. Those patients with sensory loss, particularly large fiber dysfunction, have poor balance and rely on vision as a secondary protective factor. Thus, those who have had for example extensive laser therapy and also have loss of proprioception, are at great risk of foot injury particularly when walking on uneven surfaces and in the hours of darkness.

 

A strong association between end-stage renal disease and foot ulceration has been emphasized in a number of studies. The temporal association between the start of dialysis treatment and foot ulceration was first confirmed by Game et al (22). A study comprising patients from both the US and the UK subsequently reported a very high prevalence of foot pathology in patients on dialysis, with 46% of patients having past or present foot ulceration and 18% were already amputees (23). The same group later confirmed that being on dialysis is an independent risk factor for foot ulceration in patients with diabetes (3,24). As noted above, preliminary data from the same group suggests that those patients who have already undergone amputation and who are on dialysis have a two-year mortality of up to 75%.

 

It must also be remembered that patients post-renal transplant or even post-simultaneous pancreas-kidney (SPK) transplant remain at very high risk of developing foot complications. There have been a number of reports of both foot ulceration and Charcot neuroarthropathy occurring in patients post-SPK (25). Theoretically, such subjects are “non-diabetic” but they remain at high risk because they invariably have a dense sensorimotor and autonomic peripheral neuropathy. They should remain under annual review and be coded as ‘diabetes in remission’.

 

Plantar Callus

 

Plantar callus forms under weight-bearing areas as a consequence of the dry skin (autonomic neuropathy), insensitivity, and repetitive moderate stress from high foot pressures. Callus itself acts as a foreign body and can cause ulceration in the insensate foot.

 

Elevated Foot Pressures

 

Numerous studies have confirmed the contributory role that abnormal plantar pressures play in the pathogenesis of foot ulceration (1, 2, 7). Most studies used sophisticated techniques such as pedobarography to assess foot pressures, but these are not required in day-to-day clinical practice.

 

Foot Deformities

 

A combination of motor neuropathy, cheiro-arthropathy, and altered gait pressures is thought to result in the “high-risk” neuropathic foot with clawing of the toes, prominent metatarsal heads, high arch, and small muscle wasting.

 

Demographics

 

In Western countries, the male sex has been associated with a 1.6-fold increased risk of foot ulcers (10). There is an increased risk of foot ulceration with increasing age and duration of diabetes.

 

Psychosocial Factors

 

There have been a few studies of psychosocial factors in the pathway to foot ulceration and it appears that patients’ behavior is not driven by the abstract designation of being “at risk”; it is driven by patients’ perception of their risk (26) . Thus, if a patient does not believe or understand that a foot ulcer lies on the path from neuropathy to amputation, are they likely to follow educational advice on how to reduce neuropathic ulcers? Moreover, a prospective study has confirmed that depression predicts first, although not recurrent, diabetic foot ulcers (27) .

 

THE PATHWAY TO FOOT ULCERATION IN DIABETES

 

As discussed and outlined in Figure 1, the pathway to ulceration is indeed complex and involves an interaction of numerous factors. Whereas none of the factors listed in the last section will alone result in ulceration, it is the interaction and combination of risk factors working together that leads to skin breakdown. In the prospective study of Reiber et al, 63% of all foot ulcers resulted from a combination of neuropathy, deformity, and trauma: in Western countries, the commonest cause of trauma is ill-fitting footwear (17). It must be remembered that as those with neuropathy have reduced sensory input, they will commonly be unable to feel the fit of a shoe until the pressure from the shoe is quite high. Thus, people with neuropathy frequently choose shoes that are too small. All such individuals should be advised to have their feet measured prior to the purchase of any “off the shelf” footwear.

 

Other simple examples of two risk factors working together in the pathway to ulceration are neuropathy and mechanical trauma (a common scenario is a neuropathic individual with a foreign body in the shoe), neuropathy and thermal trauma (holidays are particularly dangerous), and neuropathy and chemical trauma (such as inappropriate use of over-the-counter chemical corn treatments which should never be used in those with neuropathy).

 

In summary, whereas neuropathy was present in four out of five cases of new foot ulcers in the Reiber study (17), as noted above, the combination of neuropathy and ischemia is becoming more common in Western countries, and neuro-ischemic ulcers are the commonest type seen in 2023 in diabetic foot clinics.

 

FOOT ULCERATION

 

DFUs are common, associated with much morbidity and even mortality but should be eminently preventable. It used to be believed that diabetic foot ulcers were difficult to heal: this is not true: a foot ulcer will heal if it is permitted to do so and this requires attention to three factors-

A That there is adequate arterial inflow to the foot.

B That any infection is appropriately and aggressively managed.

C That all pressure is removed from the wound and its margins.

 

 

 

Despite increased knowledge of the pathogenesis and treatment of diabetic foot ulcers in recent years, it is still the third point, offloading the wound, that is poorly adhered to by health care professionals. Many forget that those with a neuropathic or neuroischemic ulcer have “lost the gift of pain”. That pain is a gift which is only realized when it is lost, as first described by Dr Paul Brand when studying leprosy (28). However, before going into more detail on management, it is important to classify wounds appropriately in order to guide therapeutic management.

 

CLASSIFICATION OF DIABETIC FOOT WOUNDS

 

Accurate and concise ulcer description and classification systems are required to improve multidisciplinary collaboration and communication, as well as for aiding treatment choices. For many years, the Meggitt-Wagner grading system was regarded as the gold standard. One problem with this system is that the ischemic status of the wound is not included. Thus, a number of new classification systems for diabetic foot wounds have been proposed and validated over the last 20 years. A commonly used system in the United States is the University of Texas Wound Classification System (29). This incorporates the Meggitt-Wagner grades but also enables the practitioner to stage the wound with respect to the presence or absence of infection and/or ischemia (Figure 2). In a comparative prospective study across two Centers, one in the UK and one in the US, the University of Texas Classification System was shown to be superior to the Meggitt-Wagner system at predicting outcomes (30). However, this study also showed that the traditional Meggitt-Wagner system was itself generally accurate in predicting outcomes.

 

Most recently, the WIFI (Wound Ischemia, Foot Infection) classification was introduced and is the most commonly used today in the USA, particularly in vascular clinics. This was developed and validated as a method to assess three variables – the wound, level of ischemia and the presence and severity of foot infection – to predict the risk of amputation (Figure 3)

 

 

EVALUATION OF THE DIABETIC FOOT ULCER

 

Clinical evaluation of the foot wound should include a detailed description of the site, size, and depth of the wound. The neuropathic and vascular status of the wound should then be assessed (for details see below). In general, neuropathic ulcers typically occur in the warm but insensate foot, often under pressure bearing areas, and are surrounded by callus. In contrast, ischemic wounds tend to occur in the cool, poorly perfused foot, and are often at lateral fifth metatarsal head regions or the medial first metatarsal head regions. In a predominantly ischemic wound, callus tissue is uncommon. In a neuroischemic wound, the morphology will depend upon the predominance of each of these two pathologies. The correct identification of the degree of ischemia is of the utmost importance when evaluating a wound. If the foot is cool with impalpable pulses, then non-invasive Doppler ultrasound studies are indicated. Conventional methods of assessing tissue perfusion in the peripheral circulation may not be entirely reliable in patients with diabetes. For example, the Ankle Brachial Pressure Index, which is routinely used to screen for PAD in individuals without diabetes, may well be falsely elevated in the those with diabetes because of medial arterial calcification. Toe pressure indices may therefore be more reliable.

 

Peripheral Arterial Disease

 

A detailed discussion of vascular procedures is outside the scope of this review, although any person being considered for radiological or surgical procedures will require arteriography. Care must be taken in the use of certain contrast media in patients with chronic renal disease. A detailed discussion of the use of bedside investigations to diagnose PAD in people with diabetes is provided in the recently published guidelines by Fitridge et al (31).

 

Is Infection Present?

 

The correct diagnosis of infection in the diabetic foot wound is critical as it is often the combination of untreated infection and PAD that lead to amputation in the diabetic foot. A systematic review that was updated in 2020 still recommend that the diagnosis of infection requiring treatment is a clinical one (32). However, appropriate tissue specimens should be sent to the microbiological laboratory for culture and sensitivity. Superficial swabs are of little use: deep tissue specimens or if osteomyelitis is suspected, bone biopsies are recommended (32) .

 

A high index of suspicion for the presence of osteomyelitis is essential when assessing the diabetic foot wound. The “probe to bone” (PTB) is often used to diagnose osteomyelitis although there has been much discussion about its accuracy. A systematic review concluded that the PTB test can accurately diagnose osteomyelitis in high-risk patients, and rule out osteomyelitis in low-risk patients (33).

 

Role of Plain X-Ray in Diagnosing Osteomyelitis

 

The plain radiograph remains the commonest first radiological investigation of an acutely presenting diabetic foot problem. Despite this, it may be dismissed because of relatively low sensitivity for acute osteomyelitis, with literature over the last 10 years concentrating on CT scanning, MR scanning, and nuclear medicine studies (particularly Gallium Citrate, labelled leucocyte scans and recently PET, PET-CT, SPECT-CT and PET-MR). These latter studies are of limited availability and are expensive, and some carry a high radiation burden. They have their own sensitivity and specificity problems and may not be available in a timely manner. The initial sensitivity of the plain radiograph for acute osteomyelitis is improved by serial studies at one to two-week intervals, during which time therapy for presumed osteomyelitis may be instigated for clinical reasons and whilst awaiting the results of further “high tech” imaging (if still required). The plain radiographic findings could then be considered of high sensitivity and specificity, but with a two-week lag, both for diagnosis and for response to treatment. Appropriate clinical information for the reporting radiologist must include that the patient is diabetic, whether the foot is neuropathic, whether an ulcer is present and if so, its precise anatomical location, and whether it probes to bone. The radiologist should be aware that most sites of acute osteomyelitis in the diabetic foot occur in the floor of an ulcer that probes to bone and that if the foot is neuropathic there may be acute fractures without a history of trauma or acute Charcot neuroarthropathy may be present.

 

Whilst periosteal reaction is an early feature of osteomyelitis, it is not commonly seen around the small bones of the foot, and if present, is most often seen around metatarsals, and may be due to fracture rather than osteomyelitis.

 

The hallmark plain radiographic feature of osteomyelitis in the diabetic foot is focal loss of bone density, almost invariably adjacent to the floor of an ulcer. Whilst sometimes described as bone destruction, it is initially bone de-mineralization that causes this appearance, which can reverse on successful treatment, with radiographic re-appearance of the apparently destroyed bone (Figure 4). Obtaining the radiographic view most likely to demonstrate the bone in the floor of an ulcer is therefore an important consideration, often overlooked now that requests are electronic and radiographic views are selected from limited drop-down menus. For example, toe-tip ulcers and ulcers on the dorsum of the inter-phalangeal joints require lateral toe views - best obtained using dental radiographs if available; the inferior surfaces of metatarsal heads are best demonstrated on sesamoid views; the heel requires both lateral and axial views. As a general rule, radiographs tangential to the bone surface at the site of suspected osteomyelitis are ideal, in addition to the standard radiographs of the region. A dedicated team of radiographers familiar with these requirements will improve the relevance and quality of the resultant radiographs.

 

Plain radiology remains an important investigation in the diagnosis and management of diabetic foot osteomyelitis, but it needs to be of high quality, with appropriate views, and regularly repeated to fulfil its potential.

 

 

MANAGEMENT OF DIABETIC FOOT ULCERS

 

The principles of management of different types of foot ulcers will be discussed in brief in this section. The University of Texas Wound Classification System (Figure 2) will be used throughout.

 

Neuropathic Plantar Ulcers (UT 1A, 1B, 2A, 2B)

 

As noted above, neuropathic ulcers tend to occur under pressure areas, particularly at the plantar surface of the forefoot. Other recognized sites include the dorsal areas of the toes, particularly the distal inter-phalangeal joint if there is clawing of the toes. In patients with marked deformities such as those caused by Charcot neuroarthropathy, ulcers may occur at other pressure points, particularly in the plantar mid-foot due to, for example, a dropped cuboid bone. When lecturing on the management of neuropathic diabetic foot problems, one is often asked “what can one put on the wound to heal it?”. The answer is invariably that one should be asking “what should one take off the foot to help heal the ulcer?”. Thus, the management of a plantar neuropathic foot ulcer that is not infected is firstly sharp debridement of the ulcer down to bleeding healthy tissue with removal of all callus tissue over the wound and the edge, and secondly, the removal of pressure from the wound while the person is walking. Pain sensation normally protects wounds from further damage causing the non-neuropathic individual to limp. Any subject with a plantar ulcer who walks into the clinic without limping must, by definition, have loss of pain sensation. A neuropathic individual with a plantar ulcer will therefore walk on the ulcer as there is no warning symptom to inform him or her otherwise. Techniques for removing pressure include the use of casts (either removable or irremovable), boots, half shoes, sandals and felted foam dressings. The total contact cast (TCC) is regarded as the gold standard. Studies that randomize patients to an irremovable TCC, a removable cast walker (RCW), or other offloading devices invariably confirm that healing is fastest in the irremovable device (2,7). Although RCWs and irremovable casts (such as the TCC) offload equally well in the gait laboratory, the irremovable device is always associated with more rapid healing in clinical practice. The problem is that those with neuropathic foot ulcers have lost the sensory cue that tells them not to walk on their active ulcer. Studies suggest that individuals are compliant with wearing the offloading RCW during the day, but feel that home is safer and therefore tend to put slippers on, or even walk barefoot at home. A subsequent trial has confirmed that if the RCW is rendered irremovable by wrapping with scotch cast for example, then the outcome is that there is no difference in healing rates between the TCC and the RCW rendered irremovable (34) . Most people with simple neuropathic foot ulcers (UT grades 1A, 2A, 1B, 2B) generally heal in less than three months although of course this does vary with ulcer size. There is no contraindication to casting neuropathic individuals with mild foot infections (UT grades 2A, 2B). It is recommended that after the wound is healed, offloading should continue for a further four weeks to enable the scar tissue to firm up.

 

Wound dressings are important to keep the ulcer clean, but the placement of a large dressing on a wound may lead the person to a false sense of security by believing that dressing an ulcer is curative. Nothing could be further from the truth in the neuropathic ulcer. Unfortunately, there is little evidence from randomized controlled trials (RCTs) that any dressing is superior to another. Indeed, Jeffcoate et al (35)  randomized people to one of three dressings and could find no difference in outcome according to dressing used: the only difference was in cost. Thus, without an evidence-base, there is no indication to use some of the newer more expensive dressings.

 

Neuro-ischemic Ulcers

 

A neuro-ischemic ulcer is one occurring in a foot of a person who has both a neuropathic deficit and impaired arterial inflow: these would be classified UT 1C, 2C in the absence of infection, or 1D, 2D or 3D in the presence of infection. Such individuals warrant full vascular investigation as described above, and referral to the vascular surgery team. The principles of treatment are similar to those for neuropathic ulcers, and it has been confirmed that offloading can safely be used in non-infected neuro-ischemic ulcers under a weight-bearing area. However, antibiotics should be used if there is any suspicion of infection and casting only used with extreme caution in such cases (36). There is now evidence that one dressing, sucrose octasulphate, can improve the healing rates of neuroischemic ulcers in diabetic patients (for further details, see below under adjunctive treatments). With respect to the effectiveness of revascularization of the ulcerated foot in those with neuro-ischemic lesions, results showed that major outcomes following endovascular or open bypass surgery were similar amongst studies (37).

 

Management of Diabetic Foot Infections

 

Appropriate wound debridement and offloading together with antibiotics are important in the management of the infected neuropathic foot ulcer, although there are few data from randomized trials to guide the prescriber (32). There is however no evidence that clinically non-infected neuropathic ulcers warrant treatment with antibiotics. With respect to the choice of antibiotic therapy, the reader is directed to the helpful 2012 Infectious Diseases Society of America Clinical Practice Guideline (38).  Commonly used broad-spectrum antibiotics include Clindamycin, Cephalexin, Ciprofloxacin, and the Amoxycillin – Clavulanate potassium. Oral antibiotics usually suffice for mild infections, whereas more severe infections including cellulitis and osteomyelitis require intravenous antibiotic usage initially. Care should also be taken to optimize glycemic control, as hyperglycemia impairs leucocyte function.

 

The above statements on antibiotics refer to initial treatment: after starting with such broad-spectrum antibiotics, when the results of cultured deep tissue specimens are available, antibiotic therapy should be targeted at the likely primary infective organisms. Finally, with respect to duration of antibiotics, there are no data available from randomized trials to help guide the practitioner. Antibiotics should be continued until clinical signs of infection have resolved, but there is no indication to continue antibiotics beyond this period of time and certainly no indication to continue until the wound has healed. A recent review has identified the challenges facing us due to the increasing threat of multidrug-resistant pathogens (39) .

 

Osteomyelitis

 

Diagnosis of osteomyelitis has been discussed above both relating to the PTB test and also the use of plain radiographs. Although the treatment of osteomyelitis has traditionally been surgical, there is increasing evidence from case series and a RCT, that osteomyelitis localized to one or two bones, such as digits, may successfully be treated with antibiotics alone (40, 41). A randomized trial from Spain showed that antibiotics alone were not inferior to localized surgery (41). Again, with respect to duration of antibiotic therapy for osteomyelitis, there is no evidence-base to guide us though a recent trial suggests that six weeks’ antibiotic therapy for non-surgically treated diabetic foot osteomyelitis may be sufficient: traditionally, up to three months has been recommended (42). Lastly, many were surprised to read the results of the OVIVA (Oral Vs Intravenous Antibiotics) study (43) which randomized patients with osteomyelitis to oral vs intravenous antibiotics and showed no superiority of either delivery modality. These observations will certainly challenge the approach to osteomyelitis management in the future. A detailed updated review on infection management has been published by the American Diabetes Association in 2020 (44) .

 

Adjunctive Treatments

 

Adjunctive therapies are those which might be considered for complex diabetic foot wounds which fail to heal after 8-12 weeks of standard of care as discussed in the above sections. In recent years, many new such therapies, including skin substitutes, oxygen and other gases, products designed to correct abnormalities of wound biochemistry and cell biology associated with impaired wound healing, applications of cells, bioengineered skin and others, have been proposed to accelerate wound healing in the diabetic foot. Some years ago, an internationally conducted systemic review concluded that there was little published evidence from appropriately designed clinical trials to justify the use of such newer therapies (45).

 

However, there has been a renaissance in diabetic foot care with many RCTs of new therapies published since 2018 including topical therapies and oxygen-based treatments (46). A number of well-designed RCTs were published in 2018. The first proven therapy for neuro-ischemic ulcers, sucrose octasulfate dressings, was reported in the Explorer study (47). In the active group, 48% of wounds were closed after 20 weeks compared to 30% in the control dressing group (p<0.002). In the same issue of Lancet Diabetes Endocrinology, Game and colleagues reported the positive effect of the Leucopatch (3C Patch) device (a disc containing autologous platelets, leucocytes and fibrin) when applied to the surface of hard-to-heal foot ulcers (48).

 

Although, as noted above, the International Working Group on the Diabetic Foot (IWGDF) systematic review in 2016 (45) could not support the use of many of the therapies outlined above, this had changed by 2020 when three trials of placenta-derived products were considered (49). Although none was blinded, these were judged to be of low risk of bias as outcomes were assessed in a blinded manner. The first studied a cryo-preserved amniotic membrane allograft (50), the second an umbilical cord product (51), and the third a dehydrated amniotic membrane allograft (52): each showed significantly faster healing in the active treated group versus standard of care. Further details of all these studies referred to above can be found in the most recent American Diabetes Association (ADA) compendium on evidence-based management of complex diabetic foot wounds (53).

 

Hyperbaric and Topical Oxygen in the Diabetic Foot

 

HBO has been promoted as an effective treatment in diabetic foot wounds over many years (8). However, early RCTs have been criticized because of the small numbers of patients enrolled, and methodological and reporting inadequacies. A well designed and blinded RCT was conducted in Sweden some years ago suggesting the benefit of HBO in chronic neuro-ischemic infected foot ulcers with no possibility of revascularization (54). More recently, there have been two negative studies including a large retrospective cohort trial (55) and a multi-center Canadian study that showed no benefits of HBO whatsoever in any patient group (56). Thus, at present, the use of HBO in any diabetic foot wound has few data to support its efficacy and the multi-center trial from the Netherlands was also negative (57). The use of HBO in diabetic foot wounds was the topic of a recent debate (58).

 

There has been increasing interest in the use of topical oxygen-based therapies in wound healing in recent years. Whereas the latest studies of HBO have been negative, there have been interesting developments in the use of devices delivering topical oxygen. There is now evidence that both continuous (59) and cyclical (60) topical wound therapy may improve wound healing rates. A number of more recent studies now support the use of cyclical topical oxygen therapy (TWO2) (53) including some ‘real world’ data (61) and a number of meta-analyses and systematic reviews, the most recent of which has just been published (62). Thus, there is a body of evidence to support the use of TWO2 in the management of hard-to-heal diabetic foot ulcers that fail to respond to standard of care.

 

Negative Pressure Wound Therapy (NPWT)

 

The application of NPWT is believed to accelerate healing through reducing edema, removal of exudate, increased perfusion, self-proliferation, and the formation of granulation tissue (63). RCTs have suggested efficacy in rates of wound healing and reduced amputations, with the application of NPWT in both post-surgical and non-surgical chronic non-healing ulcers (64,65). A systematic review confirmed that there was some evidence to support the use of NPWT in post-operative wounds (49).

 

 ADA Standards of Care and IWDGF Guidelines 2023

 

The ADA publishes its standards of care and clinical practice guidelines each January in Diabetes Care. In 2023 (66), those adjunctive therapies for foot ulceration recommended and supported by level A evidence (based on large, well-designed randomized controlled trials or well-done meta-analyses of randomized controlled trials) included: negative-pressure wound therapy, placental membranes, bioengineered skin substitutes, several acellular matrices, autologous fibrin and leukocyte platelet patches, and topical oxygen therapy. The IWDGF guidelines are renewed every four years, and in 2023, for adjunctive therapies for foot ulceration, recommended, with variable levels of strength and certainty of evidence, that the following might be considered (67): - regular sharp debridement (strength of recommendation: strong), sucrose-octasulfate impregnated dressings, hyperbaric oxygen in neuro-ischemic or ischemic diabetes-related foot ulcers, topical oxygen therapy, the autologous leucocyte, platelet and fibrin patch (Leucopatch or 3C Patch), placental derived products and Negative Pressure Wound Therapy (only as an adjunct therapy to standard of care for the healing of postsurgical diabetes-related foot wounds).

 

CHARCOT NEUROARTHROPATHY (CN)

 

Charcot neuroarthropathy, although uncommon, is a potentially devastating late complication of diabetic neuropathy (68). Although the exact mechanisms resulting in the development of CN remain unclear, much progress has been made in our understanding of the etiopathogenesis of this disorder over the last two decades. CN occurs in a well-perfused foot with both somatic and autonomic neuropathy: the patient presenting with acute CN tends to be slightly younger than is usual for those presenting with foot ulcers. A history of trauma may be present though may be missed because of the severe sensory loss. Although, in its pathogenesis, there are many unanswered questions, improved understanding in recent years of the role of inflammatory pathways might lead to new pharmacologic approaches in the acute phase of the condition. The outcomes in terms of management of CN have been generally poor because of ignorance that leads to delayed diagnosis.

 

Most important in the management of this condition is recognition of the acute Charcot foot. Any patient with known neuropathy who presents with a warm, swollen foot of unknown causation should be presumed to have acute CN until proven otherwise. Contrary to earlier reports, many patients may present with painful, difficult to describe symptoms in the affected foot despite significant neuropathy.

 

In its early stages, all investigations may be normal, including the foot x-ray. The role of the radiologist in the diagnosis of acute and chronic CN is discussed in the next section.

 

Role of Radiologist in in Diagnosing CN

 

As with acute osteomyelitis (see above), the initial radiographs in acute CN may appear (almost) normal, though it is common for soft tissue swelling to be present and radiographically visible, usually over the dorsum of the foot. It is consequently imperative that both the clinician and the radiologist are aware of the possibility of this condition being present. The first more specific radiographic feature is bone demineralization, usually subchondral or periarticular, around the joint(s) involved by the acute CN process (in contrast to acute osteomyelitis, where it is related to the ulcer location). Focal peri-articular fractures may then develop (Figure 5). If CN is suspected, despite non-diagnostic initial radiographs, then the options are to treat as acute CN (see below) and perform serial radiographs at one-to-two-week intervals until the diagnosis is confirmed or no longer clinically suspected, or treat similarly whilst arranging urgent radiological investigation with a more sensitive test (whilst repeating the radiographs if the further tests are delayed). CT scanning may show small avulsion fractures around midfoot articulations that are invisible on plain radiographs, with minimal increase in the sensitivity and specificity over the plain radiograph, but MR scanning (to include fat suppressed sequences) is better, demonstrating soft tissue edema, bone marrow edema and/or ligamentous disruption. If the MR scan shows no marrow signal abnormality in the foot, acute CN is unlikely. Where the appearances or clinical presentation are complex, with both osteomyelitis and acute CN being suspected, Indium labelled white cell scans and PET/CT have a role, though both can be false positive for osteomyelitis in the presence of acute CN. In infection, MR may demonstrate soft tissue abscesses or sinus tracks that may extend to the (infected) bone surface.

 

In chronic inactive CN, plain radiographs demonstrate the features of joint distension, destruction, dislocation, disorganization, debris, increased bone density (sclerosis) and deformity. On MR scanning, marrow edema of acute CN is replaced by low signal from sclerosis of the bone. Acute osteomyelitis superimposed on chronic CN produces a mixed picture requiring careful clinical-radiological review.

 

Diagnosis of acute Charcot neuroarthropathy remains a synthesis of high clinical awareness, clinical findings and radiological findings. The latter should always include serial plain radiography and, where necessary, MR scans.

 

An overview of imaging in the Charcot foot is available online (69).

 

 

Management of Charcot Neuroarthropathy

 

The treatment of CN depends upon the stage during which it is diagnosed. The essence of treatment in the acute phase remains non-weight bearing immobilization in a total contact or below-knee cast. Duration of treatment will depend upon response and it is recommended to continue casting until the temperature differential between the active and non-affected foot is down to approximately 1.5°C. As for the foot ulcer, it is recommended that treatment in a cast be continued for up to 4 weeks after the temperature differential has settled. At present, there are no proven medical or pharmacological approaches other than casting that have been shown to improve outcome. The management of advanced CN with bone deformity requiring reconstructive surgery is beyond the scope of this chapter and the reader is referred to a detailed review (70).

 

PREVENTION OF FIRST AND RECURRENT ULCERS

 

Prevention will only be successful with the early identification of those patients who have risk factors for foot ulceration. In the 1990s, the concept of the “annual review” was developed, and all those with diabetes should, at whatever stage, be screened for evidence of complications at least annually. The principle aim of such a review is to identify those with early signs of complications and institute appropriate management to prevent progression. The “Comprehensive Diabetic Foot Examination” (CDFE), was developed by a taskforce of the American Diabetes Association (ADA) that was charged with describing what should be included in the annual review for those at risk of foot complications (16). As noted above, the most important aspect of the annual foot review is the removal of shoes and socks with very careful inspection of both feet including between toes. Many neuropathic feet can be identified by this simple clinical observation, looking for features such as small muscle wasting, clawing of the toes, prominence of the metatarsal heads, distended dorsal foot pains (a sign of sympathetic autonomic neuropathy), dry skin, and callus formation. The key components of the diabetic foot annual examination are displayed in table 2.

 

The ADA Taskforce recommended that for evidence of neuropathy, that the perception of pressure using the 10g monofilament should be used at four sites in each foot (16). An additional test which might include a vibrating 128 Hz tuning fork or others outlined in table 2 should also be used to confirm any abnormality.

 

For the vascular assessment, foot pulse palpation is most important. Again, as noted above, the ankle brachial index may be falsely elevated in many people with diabetic neuropathy and therefore listening to the Doppler signal may be more helpful as may be a more detailed non-invasive vascular assessment.

 

More recently, other simple devices for clinical screening have been described. The simplest of all is the “Ipswich Touch Test” developed by Rayman et al in Ipswich, UK. This test simply assesses the ability of the patient to perceive the touch of a finger on the toes (71). The Vibratip which is a battery-operated disposable vibrating stylus can also be used to assess vibration sensation (72), and this has the advantage of using a forced-choice methodology. Both of these tests have been validated in clinical studies (71, 72).

 

Table 2. Key Components of the Diabetic Foot Exam. Adapted from Boulton (16)

Inspection Evidence of past/present ulcers? Foot shape? Prominent metatarsal heads/claw toes Hallux valgus Muscle wasting Charcot deformity Dermatological? Callus Erythema Sweating Dystrophic nails

Neurological 10g monofilament at 4 sites on each foot + 1 of the following: Vibration using 128 Hz tuning fork Pinprick sensation Ankle reflexes Vibration perception threshold

Vascular Foot pulses Ankle Brachial Index, if indicated Doppler wave forms, if indicated

 

Prevention of Diabetic Foot Ulcers

 

Surprisingly, there is no evidence from RCTs to confirm the efficacy of preventative foot care education either in the prevention of first foot ulcers or of recurrent foot ulceration (73). This, however, should be interpreted as lack of evidence rather than evidence of no effect. For those patients with no foot ulcer history found to have any of the risk factors listed above or in table 2, they require education in foot self-care and regular podiatric attention.

 

With respect to secondary prevention, a RCT that looked at the effect of a foot care education program in those with a history of foot ulcers could provide no evidence that such a program of targeted education led to clinical benefit when compared to the usual care (74). It seems likely that those with a history of foot ulcers have such predominant physical abnormalities (e.g., foot deformity, loss of sensation, etc.) that education alone in self-foot care management is insufficient to prevent recurrent ulceration. It may be the combination of foot care education and an intervention that the individual can perform may be more effective. Lavery and colleagues, in studies supported by other RCTs demonstrated in an RCT that patients with a history of neuropathic foot ulcers who were randomized for self-foot temperature monitoring did demonstrate a reduced recurrent ulceration rate. All patients in the active group received foot care education and were provided with a skin thermometer which they used twice a day to check the temperatures of both feet. Those patients who discovered increased unilateral foot temperatures were advised to stop walking and see their health care professional. In the active group there was a highly significant reduction in recurrent foot ulceration (75): however, not all subsequent studies have confirmed this observation (76).

 

Important in the prevention of foot complications in diabetes is the team approach: members of the team commonly include the diabetes specialists, orthopedic and vascular surgeons, podiatrists, nurse educators, physiotherapists, pedorthists, and others. A study from one district in the UK was able to confirm a 62% reduction in major amputations over an 11-year follow-up period: this decrease occurred after the establishment of such a multi-disciplinary diabetic foot care team (77).

 

One of the impacts of the recent Covid-19 pandemic has been an explosion in the use of telemedicine and remote monitoring in the care of the diabetic foot (78). A number of studies are currently ongoing looking at “smart technology” in the prevention of recurrent diabetic foot ulcers. These include the use of sensors in socks or shoes to detect pressure change and also various devices to measure differentials in skin temperature: each of these might alert patients in the pre-ulcerative phase with the hope of preventing the actual ulcer from developing. It has now clearly been confirmed that a temperature differential of 2.2C between the feet using remote at home monitoring in patients at high risk of plantar ulceration is a strong predictor of ulcer development (79). Similarly, intelligent pressure sensing insole systems can reduce the incidence of plantar ulcers in those with a past history of ulceration (80). However, face to face consultations remain crucial in the screening for PAD and neuropathy in people with diabetes.

 

The Foot in Remission

 

As a recurrence is so common after the healing of neuropathic or neuro-ischemic foot ulcers, it has been suggested that those with a history of foot ulcers should be described as having “a foot in remission” rather than healed. This might better communicate risk of recurrence not only to the patient, but also other healthcare professionals (8). It is hoped that, as in cancers, aggressive treatment during the active disease together with a focus on improving care in “remission” can help to maximize patients’ function and of course improve quality of life (8,26) .

 

CONCLUSIONS

 

Although there has been much progress in our understanding of the etiopathogenesis and management of diabetic foot disorders over the last 30 years, much of what we use in clinical practice today still lacks an evidence-base. This is particularly true for example for dressings. The International Working Group on the Diabetic Foot has reported on the details required in the planning and reporting of intervention studies in the prevention and management of diabetic foot lesions (81). Details of the necessary trial design, conduct, and reporting should be taken into account when assessing published studies on interventions in the diabetic foot. Most important of all however in the management of patients with diabetic foot disorders, is to remember that the patient has frequently lost the “gift of pain” that protects most of us from developing significant foot problems but, when absent, can lead to devastating consequences.

 

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ABSTRACT

 

It is recognized that traditional measures of glucose control (such as hemoglobin A1c [A1C]) provide little information regarding the need for day-to-day changes in therapies. While intermittent self-monitored blood glucose (SMBG) provides additional information with which to make treatment decisions, significant barriers to its use exist, such asinconvenience and lack of timely and regular feedback. Furthermore, important information regarding glucose trends may be missed. Continuous glucose monitoring (CGM) has become increasingly reliable and has demonstrated efficacy in terms of improving A1C, reducing hypoglycemia, and improving the time in target glucose range. Incremental progress continues to be made toward a fully functional artificial pancreas, of which CGM will play a vital role. As more and more data are presented to patients and providers, it has become increasingly paramount that the data are organized in a standardized way and that communication of data is streamlined using patients’ mobiledevices where available and within the existing clinic infrastructure. Systems that provide immediate feedback to patients and decision support tools for patients and providers have demonstrated superior outcomes compared to routine SMBG alone. Alternate markers of glucose control may provide complementary information about glucose control and long-term prognosis. This chapter will review the latest evidence for use of professional and personal CGM, mobile glucose monitoring approaches, and biomarkers of glycemic control.

 

INTRODUCTION

 

The current technology for monitoring of glucose levels has been well established since the 1980′s. This practice is beneficial to patients with diabetes from both a clinical and an economic standpoint when used optimally. Knowledgeof the glucose levels that are measured can allow a patient to select an appropriate dose of insulin or implement dietary or other lifestyle changes to regulate their glucose levels. Expert groups provide recommendations for glucose targets, including A1C, self-monitored blood glucose (SMBG), and interstitial glucose (1,2). Although targets vary, expert groups recommend individualization based upon risk of hypoglycemia, polypharmacy, comorbidities, and other characteristics that may affect long-term benefit and individual patient characteristics. The ADA has expanded recommendations for assessing overall glucose levels to include the A1C or CGM metrics such as % Time in Range(TIR, the % of time spent 70-180 mg/dl), or the Glucose Management Indicator (GMI), which is an estimate of A1C that is derived from a 14-day CGM report for routine assessment of glucose levels (1).

 

The landscape of glucose monitoring technologies is expanding and rapidly changing. For a full review of glucose monitoring technologies, the reader is referred to one of many excellent reviews referenced throughout this chapter. Several trends are emerging in glucose monitoring and will be reviewed in more detail in this chapter:

 

• CGM: This practice is becoming more widely established as evidence supporting its use has accumulated. The data available through CGM can permit significantly more fine-tuned adjustments in insulin dosing and other therapies than spot testing from self-monitoring of blood glucose (SMBG) can CGM technologies forautomatic collection of data have spurred interest in noninvasive glucose monitoring as an additional tool for obtaining information about glucose levels.
• Closed loop control (CLC): Also known as an “artificial” or “bionic” pancreas, this technology links CGM withautomatically controlled insulin The first steps toward CLC are now in use.
• Mobile Technology and Decision Support: In recent years, increasing connectivity between glucose monitoringtechnologies and mobile devices has facilitated ongoing improvements in self-care and communication of data.
• Alternate Markers of Glucose Control: Finally, the use of additional analytes besides glucose is still being established.

 

This chapter analyzes the technology, benefits, and problems with the use of intermittent SMBG and CGM, mobile technology and decision support, and alternate biomarkers of glycemic control.

 

CONTINUOUS GLUCOSE MONITORS

 

CGM measures glucose levels (typically interstitial glucose) continuously and updates the glucose level display every 5 minutes. Most CGMs consist of 1) a monitor to display the information (in some cases, this is the patient’s mobile device), 2) a sensor that is usually inserted into the subcutaneous tissue, and 3) a transmitter that transmits the sensor data to the monitor. Previously, all devices were approved for adjunctive use only due to limitations in accuracy; in this case patients must still perform fingerstick glucose monitoring in order to guide therapy and perform calibrations. However, in 2016, the FDA approved the use of the Dexcom G5 as the first CGM for stand-alone use.Newer technologies have eliminated the requirements for calibration of CGM with a fingerstick glucose. The accuracy of all commercially available CGMs is still the lowest in the hypoglycemic range, which is where the need for sensitivity and specificity is great in terms of serving as an alarm for hypoglycemia.

 

CGM can provide both retrospective as well as real-time information to detect: 1) hypoglycemic and hyperglycemic excursions; 2) predict impending hypoglycemia; and 3) wide fluctuations in glucose levels, also known as glycemicvariability. 24-hour telephone support is available for all FDA approved CGM devices. Use of CGM can help both thepatient and their medical provider make fine tune adjustments to medication therapy and provide insight to the patient on behavioral changes to achieve glycemic control. Additionally, current efforts to link CGM measurement with automatically controlled insulin delivery, has progressed incrementally toward a fully functional artificial pancreas.Systems can be divided according to their intended use as professional CGM (which is a clinic-owned device and provides either retrospective or real-time glucose data) and personal CGM (which is patient-owned and provides real-time glucose data).

 

PROFESSIONAL CGM

 

Professional CGM describes CGM data that are obtained via healthcare provider owned equipment. It does not necessarily provide the glucose results in real time, but downloads the readings after they have been collected, similar to a 24-hour cardiac Holter monitor that provides information about cardiac rhythms after they have occurred. This allows the health care provider to obtain relatively unbiased glucose patterns during typical everyday life. The Endocrine Society recommendations state that professional CGM may be of benefit in adults with diabetes to detectnocturnal hypoglycemia, dawn phenomenon, postprandial hyperglycemia and to assist in management of diabetes therapies (3). Professional CGM is more readily reimbursed than personal CGM, but interpretation of both personal and professional CGM reports by qualified healthcare professionals may be reimbursed on a monthly basis.

 

Some personal CGM systems can be operated in a blinded fashion in order to provide professional glucose data. These systems will be discussed in more detail later (see “Personal [Real-time] Continuous Glucose Monitoring”). The first device for reading blood glucose levels continuously was a professional CGM that was approved by the FDA inJune 1999. This device was the Continuous Glucose Monitor System (CGMS) manufactured by Medtronic MiniMed (Medtronic Diabetes, Northridge, CA) (4). Since then, newer models have shown improvements in accuracy and patient acceptance. In a meta-analysis of 22 articles, professional CGM resulted in a greater reduction in A1c(-0.28%, 95% CI -0.36% to -0.21%, P < 0.00001) as well as TIR (5.59%, 95% CI 0.12 to 11.06, P = 0.05) compared to usual practice (5).

 

FreeStyle Libre Pro

 

The FreeStyle Libre Pro utilizes the same sensor as the Libre personal CGM. The Libre is factory calibrated andtherefore does not require self-monitored blood glucose calibrations. This may be a potential advantage since capillary blood glucose testing is subject to various system and user errors, which in addition to the physiologic lag time between blood and interstitial glucose (which is magnified in the postprandial period) could contribute to CGM error. Itcollects up to 14 days of glucose readings, which are recorded every 15 minutes. The glucose sensor is fully disposable and a single reader is used to activate and scan multiple devices, allowing multiple patients in one office to undergo the procedure simultaneously. Reports are obtained through the LibreView website, which offers a secure cloud-based system, or the FreeStyle Libre desktop reporting software. Reports provide daily patterns, an assessmentof glucose variability and hypoglycemia risk, a daily glucose report, and an overall snapshot report.

 

The overall MARD (Mean Absolute Relative Difference which is calculated by averaging the absolute values of relative differences between CGM measurement results and corresponding comparison method results) for the FreeStyle Libre is 11.4%, 86.7% of readings were in Zone A of the Consensus Error Grid analysis, and 99.7% of results were in Zones A and B (6). It is important to note that sensor accuracy is lower on day 1 and in thehypoglycemia range (MARD 20.3% for values <72 mg/dl in one study) (7). Accuracy improves and remains steady over the 14-day wear period. The Libre utilizes glucose oxidase in a “direct signaling” approach that is not dependenton oxygen and minimizes interference by other substances, such as acetaminophen, which may falsely elevated readings on other devices.

 

Dexcom Professional

 

The Dexcom G6 Pro was approved by the FDA in March 2018 and is available in blinded or unblinded mode depending upon whether the goal is to observe glucose patterns without intervention, to provide immediate feedback to educate and inform patients about their medications and behaviors, or to facilitate decisions about pursuingpersonal CGM. The sensor, transmitter, and receiver are essentially identical to the personal Dexcom G6 system and features expedited startup time and no calibration. The device measures interstitial glucose levels every 5 minutes and is approved for 10 days of use. The device is downloaded using Dexcom CLARITY, a web-based software program that is also used to download and review personal data.

 

Analysis of Retrospective Data

 

Data from all CGM devices can be studied retrospectively after downloading (8). It is recommended that diet, activity, symptom, and insulin data are collected during professional CGM to assist with interpretation, either via patient diary,direct entry of events into the device, or use of an accompanying app, depending on the system. Three time periods should be analyzed. These are:

 

• Overnight: Out-of-target overnight glucose levels can be modified by adjusting the basal insulin dose.
• Pre-prandial Period: Out-of-target pre-prandial glucose levels can be modified by adjusting the previous meal bolus, meal, or exercise pattern.
• Post-prandial period: Out-of-target postprandial glucose levels can be modified by adjusting the immediate meal bolus, meal, or exercise pattern.

 

In certain special situations, targets may need to be adjusted. Other important elements of a professional CGM analysis are shown in Table 1. An example of a patient who used CGM is presented in Figure 1. The CGMdemonstrated high glucose levels from 6:00 PM to 11:00 PM post-supper and low glucose levels from 12:00 AM to 2AM. Recognition of these patterns allowed appropriately timed treatment interventions.

 

Table 1. Elements of Professional Continuous Glucose Monitoring Analysis

Overall Control Mean Glucose Glucose Variability (Standard Deviation, Coefficient of Variation) Daily Detail Diurnal Patterns: dawn phenomenon, overnight Meal effects Correction Exercise effects Other patterns (work days vs. weekend, menstrual cycles) Hypoglycemia Precipitating factors Corresponding meter glucose (recognition)

 

 

Ambulatory Glucose Profile

 

The ambulatory glucose profile (AGP, Figure 2) is a standardized reporting format for glucose data that was developed by an expert panel of diabetes specialists and sponsored by the Helmsley Charitable Trust and is customized for insulin pumps or injection therapy (9). The universal report is intended to simplify and facilitate interpretation of otherwise complex and lengthy reports with varying terminology. It is anticipated that a standardized report would “help clinicians develop expertise in CGM use, enhance quality of care through enhanced pattern recognition, improve practice efficiencies with minimal disruption of workflow, and engage patients, thereby reinforcing consistent use of CGM technology.” A single page report that the medical team can view and file into a patient’s electronic medical record and that can be used as a shared decision-making tool with people with diabetes wasconsidered to be of great value in the report of the 12th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2019) (10). The AGP is currently employed by many reporting systems and consists of 3 components:

 

• Statistical Summary, which utilizes standard metrics and terminology to summarize the number of values,percentage of values, and time in target, above target, and below target, as well as an assessment of glucose variability.
• Modal day report which collapses data from days or weeks to a single day in order to identify patterns by time of day. Data are presented graphically as 5 distribution curves, representing the median, interquartile range, and10^th to 90^th percentiles, on the backdrop of target range.
• Daily View, which facilitates review of within day

 

Composite Metrics

 

As a measure of the quality of glycemia, the time in range (TIR), similar to the A1C is limited in its assessment of hypoglycemia. Multiple composite metrics have thus been reported (11). However, the use of multiple metricsincreases complexity and is subject to issues with collinearity. The Glycemia Risk Index (GRI) is a composite metricthat was developed using input from 330 clinical experts who analyzed 14-day tracings from 225 adults with diabetes (12). GRI more heavily weights very high or very low glucose values and correlates with clinician rankings more closely than TIR or time below range (%time < 70 mg/dl, TBR) alone.

 

 

PERSONAL REAL-TIME CGM (RT-CGM) OR INTERMITTENTLY SCANNED CGM (IS-CGM)

 

RT-CGM devices not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. Bycomparison, is-CGM requires patient interaction with the device to obtain readings but may still provide alerts for hypoglycemia or hyperglycemia. While few head to head studies are available, some studies suggest greater reduction in hypoglycemia and improvement in TIR with RT-CGM compared to is-CGM in persons with type 1 diabetes (13,14), even up to 24 months (15).

 

Over time, accuracy with RT-CGM and is-CGM has improved substantially (16,17,18). In fact, some devices, including the Dexcom and Freestyle Libre are approved for stand-alone use, meaning that under specified conditions, the device may be used to make treatment decisions without confirmatory blood glucose measure. However, the user will still experience a tradeoff between a high alarm sensitivity and specificity for detecting hypoglycemic events, particularly where glucose levels are changing rapidly (Figure 3). Current and recent glucose levels, trend information,and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. This potential decrease in glycemic variability will not necessarily be reflected in an improved A1C value, which reflects mean glycemic levels.

 

 

Evidence- Type 1 Diabetes

 

Studies may be divided according to background therapies (insulin pump or injection therapy).

 

STUDIES UTILIZING EITHER INSULIN PUMP OR INJECTIONS AS BACKGROUND THERAPIES

 

• The seven-country GuardControl Study was the first randomized controlled trial to ever demonstrate a statisticallysignificant improvement in A1C levels with the use of RT-CGM (19). The Guardian RT was used either continuously or biweekly for three months and both regimens were compared to control treatment which did not include use of CGM. At one month and at three months the continuous users had significantly lower A1C levelsthan the controls. The biweekly users had intermediate improvement which did not reach statistical significance compared to the outcomes in the control group.
• In 2008, the Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group evaluated 322 adults and children with type 1 diabetes (either injection or insulin pump therapy) and A1C 7-10% who wererandomized to either RT-CGM or usual care (20). RT-CGM was associated with a 53% reduction in A1Ccompared to usual care (p<0.001), but was only significant among subjects over age 24 due to lack of consistent use in younger patients. Hypoglycemia was infrequent and was not different between groups.
• In 2011, 120 children and adults with type 1 diabetes on insulin pump or injection therapy and A1C <7.5% wererandomly assigned to RT-CGM (Freestyle Navigator—not available in the US) or masked CGM every other week (21). The time spent in hypoglycemia was reduced over 50% at 26 weeks, and patients spent more time in 70-180 mg/dl range.
• In the IMPACT trial, 241 adults with type 1 diabetes with an A1C less than or equal to 7.5% were randomly assigned to Freestyle flash glucose monitoring (described in more detail under “Overview of Stand-Alone Personal CGM systems”) vs. SMBG. In this group 68% of the patients were treated with multiple daily injections and 32% with CSII. The amount of time spent in hypoglycemia was decreased by nearly 90 minutes per day (P<0.0001) when patients had access to CGM data (22). It must be noted that this technology does not provide real-time alerts for impending hypoglycemia or hyperglycemia and data are accessed via a hand-held device on Ina small study of patients with hypoglycemia unawareness or recent severe hypoglycemia, RT-CGM more effectively reduced the time spent in hypoglycemia compared to flash glucose monitoring (23).
• The CITY study was a randomized study among 153 adolescents and young adults with type 1 diabetes. CGM resulted in a -0.37% greater reduction in A1C compared to usual care (p=0.01) (24). In this study, only 68% of participants used CGM at least 5 days per week in month 6, which is significantly lower than studies reported inadults (25). However, this is more than twice that reported in the pivotal JDRF study of 2008 (20). Moreover, this study utilized an earlier generation CGM which required twice daily calibration; thus, it is possible that newer technologies may support greater persistence with use.
• Among 203 older adults (median age 68) with type 1 diabetes randomized to CGM or usual care, CGM resulted in less hypoglycemia at 26 weeks (estimated treatment difference 27 minutes/day, p<0.001) as well as modest improvement in A1C (estimated treatment difference -0.3%, p<0.001) (26). The improvement in hypoglycemia was sustained over 52 weeks, at which point CGM use was still >90% (27).

 

STUDIES UTILIZING INSULIN PUMP THERAPY AS BACKGROUND

 

• In the largest study to date, the STAR3 study, 485 adults and children with A1C 4-9.5% were randomized to sensor-augmented pump therapy (Medtronic Paradigm Revel) or multiple daily injections per day (28). Sensor-augmented pump therapy resulted in better A1C reduction with between-group difference of 0.6%, p<0.001.Hypoglycemia did not differ between groups, but only short-term CGM data were available for comparison and patients with a history of severe hypoglycemia were excluded.

 

STUDIES UTILIZING INJECTION THERAPY AS BACKGROUND

 

• In 2016, a 6-month randomized controlled trial, the DIAMOND study, compared RT-CGM (using Dexcom G4system) versus SMBG in 158 patients with type 1 diabetes on multi-dose injection therapy and demonstrated a significantly lower A1C (between group difference 0.6%, p<0.0001), decrease in hypoglycemia (43 minutes vs. 80 minutes per day, p=0.0002) and less glucose variability with RT-CGM compared to SMBG. This study did not address hypoglycemia frequency in the two groups (25).
• The GOLD trial studied 161 patients with type 1 diabetes receiving multiple daily injections with either RT-CGM (Dexcom G4) or standard care in a random order cross-over trial. The mean difference in A1C was 0.43% (p<0.001), favoring RT-CGM. One subject in the CGM group compared to 5 subjects in the standard care group experienced a severe hypoglycemic event. The percentage of time spent in hypoglycemia numerically favored the CGM group but statistical analyses were not presented. There was a significant reduction in standard deviationand MAGE (measures of glucose variability). Overall well-being, diabetes treatment satisfaction, and fear of hypoglycemia improved (29).
• In the FLASH-UK study, 156 participants with type 1 diabetes were randomized to intermittently scanned glucosemonitoring or usual care (30). The intervention group had a significantly greater reduction in HbA1 (adjusted treatment difference -0.5%, p<0.001), higher % TIR, and lower % TBR.
• A randomized controlled trial among 104 adults with type 1 diabetes found that intermittently scanned glucose monitoring improved A1c (estimated treatment difference 0.3% [95% CI, 0.0%-0.6%; P = 0.04) and TIR but not TBR compared to blood glucose monitoring (31).

 

META-ANALYSES

 

A Cochrane review and another meta-analysis found modest A1c reductions, particularly among patients who were not using insulin pumps, patients under age 18, and among patients with lower adherence (32). The results were heavily influenced by the STAR3 trial, and the JDRF study did not report a difference between pump users and patients using multiple dose injection therapy. Severe hypoglycemia rates did not differ. However, the quality of most studies was limited due to small sample size, lack of blinding, and lack of sufficient data to compare hypoglycemia rates. Meta-analyses may be hampered by the inclusion of studies with obsolete technology or lack of consideration for the intended use of the device in the study (33,34). In another meta-analysis, studies that specifically enrolled patients at risk for hypoglycemia and used blinded CGM to assess it did show improvement in hypoglycemia (35).

 

More recently, a meta-analysis of 21 studies published between 2011-2020 encompassing 2149 individuals with type 1 diabetes revealed that CGM led to a significant reduction in A1C by 0.23% (p=0.0005), with larger treatment effect at higher baseline A1C (>8%), and no effect on severe hypoglycemia or DKA (36). However, the meta-analysis did not report CGM derived metrics such as TIR or TBR or clinically significant hypoglycemia. In a 2023 meta-analysis of 22 randomized controlled trials that included participants with type 1 diabetes, there was an overall improvement in A1c,TIR and TBR (37). Reduction in A1C was limited to nonadjunctive devices but all devices resulted in improvement in TIR.

 

PATIENTS WITH HYPOGLYCEMIC UNAWARENESS

 

Many older studies specifically excluded patients with a history of severe hypoglycemia or were underpowered todetect significant hypoglycemia. Recent studies have examined the use of RT- CGM in patients with hypoglycemiaunawareness, which is a risk factor for severe hypoglycemia (events requiring outside assistance to treat).

 

• In the HypoCOMPaSS trial, 96 patients with a history of hypoglycemia unawareness determined by the GOLD Score of at least 4 or more were randomly assigned in a 2x2 factorial design to insulin pump or injection therapy, both with access to a bolus insulin calculator, and either RT-CGM (Medtronic Continuous Glucose Monitoring System) or SMBG. All patients had diabetes education with a goal toward hypoglycemia avoidance (38). Theresults demonstrated a similar reduction in severe hypoglycemia and improvement in hypoglycemia unawareness and fear of hypoglycemia without a significant treatment interaction between insulin or glucose monitoring interventions. Treatment satisfaction was higher with insulin pump compared to injection therapy but similar between RT-CGM and
• The IN CONTROL trial evaluated patients with Type 1 diabetes and hypoglycemia unawareness receiving either injection or insulin pump therapy in a crossover study comparing RT-CTM (Medtronic Paradigm Veo system with a MiniLink transmitter and an Enlite glucose sensor) or SMBG (39). Hypoglycemia was significantly reduced with RT-CGM compared to SMBG (including a 9.8% reduction in events <70 mg/dl and 44% reduction in events <40 mg/dl). Severe hypoglycemic events were significantly reduced but hypoglycemia unawareness was unchanged.
• In a smaller study of 52 adults with type 1 diabetes and problematic hypoglycemia, immediate randomization to CGM was more effective for preventing severe hypoglycemia (39% fewer events, p<0.05) than a dedicated hypoglycemia avoidance education program alone (40). CGM also lead to greater reduction in A1c (treatment difference -0.47%, p<0.05), but impaired awareness was restored in 31% of both groups, supporting the conceptthat CGM assists in earlier recognition and treatment of impending hypoglycemia as opposed to effecting fundamental change in counterregulatory responses.

 

Differences between studies may be explained by differences in populations and the technologies utilized. In the InCONTROL study, contact with patients was less frequent, sensor use was greater (89 vs. 57% in HypoCOMPaSS) and there were no insulin adjustment protocols. Therefore, more studies are needed to understand the potential role of background therapy, other technologies, and clinical support.

 

PATIENT REPORTED OUTCOMES

 

Generic Quality of life scores generally do not improve with RT-CGM but treatment-specific measures, such asdiabetes distress, hypoglycemic confidence, fear of hypoglycemia and to a lesser extent, measures of convenience, efficacy and performance, may be improved (28,41,42).

 

Evidence- Type 2 Diabetes

 

In patients with type 2 diabetes, even in patients not on insulin, RT-CGM may act as a motivator and positive influence for patients to improve lifestyle. The change in behavior can potentially lead to better glycemic control and weight loss(43). Moreover, periodic (every 3 months) short- term (14 day) use of real-time CGM may be sufficient to achieve andmaintain clinically relevant improvements in A1c in this population (44).

 

• In 2012, Vigersky et al. randomized 100 patients with type 2 diabetes on basal insulin and anti-hyperglycemic agents into either a group that used real-time RT-CGM intermittently (2 weeks on, 1 week off) or a group that recorded SMBG four times per day for 12 weeks. At 12 weeks, they found a statistically significantly greater reduction in A1c by 1.0% in the CGM group compared to 5% reduction in the SMBG group. The effect persistedup to the 40-week follow-up, 0.8% and 0.5% reduction in A1c in the RT-CGM versus SMBG group respectively (45).
• In 2017, Beck et al conducted a randomized study to evaluate benefit of RT-CGM use in 158 patients with type 2 diabetes with mean A1C of 8.5% treated using multiple daily injections (46). Over a 24-week period the A1C decreased to 7.7% in the RT-CGM group compared to 8% in the group with usual care (mean difference -0.3%,p=0.022). RT-CGM derived hypoglycemia and quality of life did not differ.
• The Dexcom MOBILE study assessed patients with type 2 diabetes on basal insulin randomly assigned to the Dexcom G6 or usual care for 8 months and reported a significant reduction in A1C, improved TIR andhypoglycemia (47). This was accomplished without an appreciable change in insulin or other medication use, indicating that CGM improves glucose levels by facilitating behavioral changes. Moreover, subsequent discontinuation of CGM for 6 months resulted in loss of about half of the improvement in TIR (48). Moreover, the benefit was similar in older (≥65 years old) vs. younger adults (49).
• In a 10-week study of 101 patients with type 2 diabetes on multiple daily injections of insulin, patients randomized flash glucose monitoring (Freestyle Libre) had greater A1C reduction (-0.82 vs -0.33%, p=0.005), found their treatment to be significantly more flexible and were more likely to recommend it to others (50).
• Among 141 adults with type 2 diabetes treated with insulin or sulfonylurea and recent myocardial infarction, thoserandomized to intermittently scanned glucose monitoring had significantly less TBR (-80 minutes, 95% CI -118, -43 minutes) at 90 days, but marginal difference in A1c or TIR, and the intervention was reported to be cost-effective (51).
• In a randomized trial of 116 adults with type 2 diabetes using non-insulin therapies, intermittently scanned glucose monitoring in combination with diabetes self-management education demonstrated superior A1c reduction at 16 weeks (treatment difference 0.3%, 95% CI 0 to 7%, p=0.048), larger increase in TIR (9.9%, p<0.01), and greatersatisfaction compared to education alone (52).

 

Real World Outcomes

 

• In a study of over 29,000 pediatric patients with type 1 diabetes in the Type 1 diabetes Exchange Registry or the German/Austrian DPV Initiative, pediatric CGM use was associated with lower mean A1C regardless of insulindelivery modality (pump or injection) (53).
• In a study of 106 UK hospitals incorporating 16,427 participants, 1241 with repeated TIR data, improvements inTIR were associated with improvement in hypoglycemia unawareness and diabetes related Moreover, TIR>70% was associated with reduced resource utilization (hospital admissions for hypoglycemia or hyperglycemia, paramedic visits, and severe hypoglycemia (54).
• In the Swedish National Diabetes Registry that included 14,372 adults with type 1 diabetes, intermittently scanned glucose monitoring was associated with a small (0.11%, p<0.0001) reduction in A1C after 15-24 months and reduction in severe hypoglycemic episodes (OR 0.79, 95% CI 0.69-0.91) (55).
• Using the French national claims database, a total of 74,011 patients with type 1 or type 2 diabetes initiatedintermittently scanned glucose monitoring and over 98% persisted with the device at 12 months (56). Following initiation of the device, patients had a 39-49% reduction in hospitalizations for acute complications and a 32-40%reduction in diabetes-related Moreover, the reduction in hospitalizations persisted after 2 years (57).
• In Belgium, a study of 1913 adults with type 1 diabetes were studied before and after nationwide reimbursement of intermittently scanned continuous glucose monitoring (58). Following the policy change, treatment satisfaction improved, there was a significant reduction in admissions for acute complications (severe hypoglycemia orketoacidosis), and there were fewer absences from work.
• Among 41,753 patients with insulin requiring diabetes in an integrated health care delivery system, 3806 patientsinitiated CGM, which was associated with a greater reduction in A1C (adjusted treatment difference 0.40%, p<0.001), emergency department or hospitalization for hypoglycemia (adjusted difference -2.7%, p=0.001), areduction in number of outpatient visits and an increase in telephone visits (59). However, there was no difference in hospitalizations for hyperglycemia or ketoacidosis.
• In a Medicare supplemental and commercial claims database study of 2463 patients with type 2 diabetes on multiple injections of insulin/day, intermittently scanned CGM was associated with a reduction in acute diabetes events (HR 0.39, 95% CI 0.30-0.51) and all cause hospitalizations (HR 68, 95% CI 0.59-0.78) at 6 monthscompared to the 6 months prior to initiation (60).

 

Recommendations

 

Patients should be adequately informed of the benefits and limitations of this technology, particularly with respect to the role for SMBG. At a minimum, structured education programs encompassing concepts such as carbohydrate counting and active insulin time (insulin on board) should be completed prior to considering RT-CGM, and payers mayrequire that patients demonstrate that they can reliably and consistently perform SMBG (61). Several expert groups have issued guidance in the use of RT-CGM.

 

• In 2016, the Endocrine Society, co-sponsored by The American Association for Clinical Chemistry, the American Association of Diabetes Educators, and the European Society of Endocrinology, published guidelines for use ofinsulin pumps and CGM. The guidelines recommended RT-CGM in adults with type 1 diabetes and any A1C who are willing and able to use the devices nearly daily. The panel suggested short-term intermittent use for patientswith type 2 diabetes (not requiring prandial insulin) who had an A1C ≥7% and are willing and able to use the device (3).
• The American Diabetes Association (ADA) Standards of Care recommend CGM in adults with type 1 diabetes andthose with hypoglycemia unawareness or frequent hypoglycemia (Table 2a). Among pediatric patients, the ADAnotes that CGM may reduce missed school days with regular usage (1).

 

Table 2a. ADA 2023 Recommendations for CGM
Group	Recommendation (Level of Evidence)
	Real-time CGM		Intermittently Scanned CGM
	Adults	Youth	Adults	Youth
MDI or CSII insulin use	Should be offered (A)	Should be offered(B-T1D, E-T2D)	Should be offered(B)	Should be offered (E- T1D)
	Should be used as close to daily as possible (A)		Should be scanned frequently, at least every 8 hours (A)
Basal insulin use	A	NA	C	NA
All	·       Devices are recommended for individuals or caregivers who can use the devices safely ·       The choice of device should be individualized based on patient centered factors. ·       People should have uninterrupted access to supplies to minimize gaps in monitoring (A) ·       Periodic RT-CGM, intermittently scanned CGM, or professional CGM can be helpful where continuous use is not possible (C)
Diabetes and pregnancy	CGM can help to achieve A1C targets in pregnancy when used as an adjunct to pre- and postprandial SMBG (B)

A=Clear evidence from well-conducted, generalizable randomized controlled trials that are adequately powered;B=Supportive evidence from well-conducted cohort studies; C= Supportive evidence from poorly controlled or uncontrolled studies; E=expert consensus.

T1D=type 1 diabetes, T2D=type 2 diabetes, SMBG=self-monitored blood glucose

 

Table 2b. AACE Recommendations for CGM by Methodology
Method	Background/Therapy	Evidence*	BEL*	Grade^
RT-CGM	•       Problematic hypoglycemia •       Lifestyle and other factors should also be considered	Low- Intermediate	1	B
isCGM	•       Newly diagnosed T2D •       Non-hypoglycemic therapies •       Motivated to scan device several times/day •       Low hypoglycemia risk, desire for more data	Low/Expert Opinion	4	D
Diagnostic/ professional CGM	•       Newly diagnosed T2D •       problematic hypoglycemia, but no access to personal CGM •       non-insulin therapies as an educational tool •       Trial use	Intermediate	1	B
Intermittent CGM	persons …who are reluctant or unable to commit to routine CGM use.	Intermediate	1	C

* Level of Evidence:

• High (1) = randomized controlled trial (RCT) or meta-analysis of RCT
• Intermediate (2) = meta-analysis including nonrandomized studies, network meta-analysis, nonrandomizedcontrolled trial, prospective cohort, case control, cross-sectional, hypothesis driven epidemiologic, open label extension, post-hoc analysis
• Weak (3) = discovery/exploratory, economic, consecutive case series, case report, safety/feasibility, high impact basic research
• None (4) = consensus, position, policy, guideline, any highly flawed study, lower impact basic science

BEL=best evidence level

^Grade is based upon evidence level, recommendation qualifiers, subjective factors, and consensus

 

Table 2c. AACE Recommendations for CGM—Patient Characteristics
Background/Therapy	Evidence rating	BEL	Grade*
3+ injections/day or CSII	High	1	A
Frequent/severe or nocturnal hypoglycemia or unawareness	Intermediate-High	1	A
Children/adolescents with T1D	Intermediate-High	1	A
Pregnant, 3+ injection/day	Intermediate-High	1	A
Gestational DM on insulin	Intermediate	1	A
Gestational DM no insulin	Intermediate	1	B
T2D, on insulin	Intermediate	1	B

* Level of Evidence:

• High (1) = randomized controlled trial (RCT) or meta-analysis of RCT
• Intermediate (2) = meta-analysis including nonrandomized studies, network meta-analysis, nonrandomizedcontrolled trial, prospective cohort, case control, cross-sectional, hypothesis driven epidemiologic, open label extension, post-hoc analysis
• Weak (3) = discovery/exploratory, economic, consecutive case series, case report, safety/feasibility, high impact basic research
• None (4) = consensus, position, policy, guideline, any highly flawed study, lower impact basic science

BEL=best evidence level

^Grade is based upon evidence level, recommendation qualifiers, subjective factors, and consensus

 

The 2021 American Association of Clinical Endocrinologists recommendations for use are summarized in Table 2band 2c. These include all adults and children with type 1 diabetes, especially those with severe hypoglycemia or hypoglycemia unawareness, and all patients with type 2 diabetes on multiple insulin injections, basal insulin, or sulfonylureas who are at risk for hypoglycemia (2).

 

In 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress organized an international consensus panel, consisting of physicians, researchers, and individuals with diabetes to analyze the existing literature and to provide guidance for utilizing, interpreting, and reporting CGM data (62). This was updated in 2019 (Table 3 and 4). These recommendations are supported by recent data from the DCCT demonstrating that a 10% reduction in time in target glucose range derived from 7-point self-monitored glucose profiles is associated with a 40% reduction in risk of microalbuminuria and a 64% reduction in risk of incident or progressive retinopathy (63).

 

Table 3. CGM-Based Targets for Different Diabetes Populations
Glucose Range	%Time in Range
	Non-Pregnant Patients
	Type 1 and Type 2 Diabetes	Older/High Risk Diabetes
>250 mg/dl (13.9 mmol/L)	<5%	<10%
>180 mg/dl (10 mmol/L)*	<25%	<50%
70-180 mg/dl (3.9-10 mmol/L)	>70%	>50%
<70 mg/dl (3.9 mmol/L)**	<4%	<1%
<54 mg/dl (3.0 mmol/L)	<1%
	Pregnant Patients
	Type 1 Diabetes	Gestational and Type 2 Diabetes #
>140 mg/dl (7.8 mmol/L)	<25%	-
63-140 mg/dl (3.5-7.8 mmol/L)	>70%	-
<65 mg/dl (3.5 mmol/L)	<4%	-
<54 mg/dl (3.0 mmol/L)	<1%	-

*Includes time >250, **Includes time <54 mg/dl, ^#Insufficient data

 

Table 4. Summary of ATTD Recommendations for CGM
Limitations of A1C	CGM should be utilized when there is a discrepancy in A1C and other measures of glucose control. CGM should be utilized to assess hypoglycemia and glucose variability.
Guiding management and assessing outcomes	CGM should be considered for patients with type 1 diabetes and insulintreated type 2 diabetes who are not achieving targets or those with hypoglycemia. All patients should receive training education regarding how to interpret andrespond to their data, utilizing standardized programs with follow-up.
Performance	No accepted standard exists for CGM system performance. However, a mean absolute relative difference ≤10% provides little additional benefit forinsulin dosing.
Definition and assessment of hypoglycemia	Clinical classification Level 1: 54-70 mg/dl with or without symptoms Level 2: <54 mg/dl with or without symptoms (clinically significant) Level 3: cognitive impairment requiring external assistance for recovery Quantification using CGM % of values or time below a given threshold (54 or 70 mg/dl) Number of events (defined as CGM readings persistently below threshold for at least15 min. with recovery defined as persistent readings over the threshold forat least 15 min.) over a given reporting period)
Glycemic Variability	Coefficient of Variation should be the primary measure
Time in Range	The % time in hyperglycemia, hypoglycemia, and target range should be reported.
CGM Metrics	Standardized reporting using the AGP and integration into electronic health records is recommended. Key metrics: ·         Number of days worn (14 days recommended) ·         % time CGM is active (70% of data from 14 days recommended) ·         Mean glucose ·         Glucose Management Indicator ·         Glycemic variability (%CV, target <36%) ·         % Time in Range (TIR): 70-180 mg/dl (3.9-10.0 mmol/L) %Time Above Range (TAR): 181-250 mg/dl (10.1-13.9 mmol/L), and >250 mg/dl (>13.9 mmol/L) ·         %Time Below Range (TBR): 54-69 mg/dl (3.0-3.8 mmol/L), and <54 mg/dl (3.0 mmol/L)

 

Also, in 2017 the ADA and the European Association for the Study of Diabetes published a joint statement providing recommendations for systematic improvements in clinical use and regulatory handling of CGM devices (64).

 

Hospital Use

 

CGM is not currently approved for use in the hospital setting. However, during the COVID-19 pandemic, the FDA announced that it would not object to their use in an effort to support reduction in use of personal protective equipmentand risk of exposures to staff. Thus, there has been increasing interest in their use. Moreover, an increasing number of patients are using these devices in the ambulatory setting and want to continue their use in the hospital. Recent randomized trials support the use of CGM on the hospital wards, where it has been shown to be safe, and may reduce the frequency of hypoglycemia (65,66). In the ICU, there is concern that CGM may be less accurate due to factors such as edema, hypoperfusion, and acidosis but preliminary studies suggest use of CGM in conjunction with periodic point of care (POC) blood glucose (BG) within well-established protocols is safe and may reduce the need for POC BG (67).

 

In 2020, the Diabetes Technology Society sponsored a panel of experts in inpatient diabetes management to reviewthe evidence for us of CGM in the hospital (68). The panel agreed that CGM had the potential to improve clinicaloutcomes, particularly for patients who are unable to communicate signs or symptoms of hypoglycemia, but use is limited by lack of data demonstrating accuracy (particularly in the hypoglycemic range or in case of diabetic ketoacidosis, poor perfusion, or acetaminophen use) and clinical utility, and a lack of decision support systems, including infrastructure for communicating results to care teams and to the electronic medical record. The panelagreed that patients who are admitted with personal CGM devices should be allowed to continue use of such devices under the condition that they are able to self-manage the devices on their own and are followed by an endocrinologist or experienced practitioner who is specifically trained in their use. In particular, the panel advised implementing institutional policies that recommend continued capillary or blood glucose monitoring, ensuring that CGM data are not used for inpatient insulin dosing (since no CGM device is FDA approved in the inpatient setting), and requiring patients to sign safety waivers which illustrate the potential risks and benefits of continued use. Devices must beremoved for any MR or CT imaging. The panel made the specific recommendations for clinical care including:

• Consider use of CGM to reduce exposures (such as for point of care glucose) and need for personal protective equipment in persons with highly contagious diseases.
• Barring use in the setting of highly contagious disease, CGM values should be confirmed with point of care (POC) glucose prior to making treatment decisions.
• Hospitals should develop implementation plans which include a process map, protocol, provider/staff/patient education and order sets.
• Providers should recognize CGM pattern caused by compression of the device, which can cause a falsely low value.
• Providers should ensure patients are not taking medications or supplements that can interfere with CGM.
• Nurses should be adequately trained on use of CGM, inspect the insertion site every shift, and set expectations that POC values are still necessary to support ongoing use of CGM (typically every 6 hours).
• Hospitals need to develop security protocols, data storage, visualization tools, and integration within the electronic medical record to support the use of CGM.
• Hospitals need to identify CGM values in the electronic medical record to distinguish values from blood glucose values.
• Hospitals need to adopt the Unique Device Identifier (UDI) to track devices in the electronic medical record.

 

Limitations of Use

 

It should be emphasized that most prospective randomized controlled trials enroll highly motivated patients. In the real-world setting, there are concerns about limited resources for training, and less motivated patients may be overwhelmed with the additional data, particularly where complex algorithms are required. Nevertheless, in the Type 1 Diabetes Exchange Registry, CGM use increased from 7% in 2010-2012 to 30% in 2016-2018, and rose more than 10-fold in children (69). A1C levels were lower in CGM users compared to nonusers. While CGM use has improved substantially over time, more than half of respondents cited cost or insurance coverage as a significant barrier to use (70). Moreover, disparities in prescribing patterns and implicit bias have been described (71,72,73). Modifiablereasons for avoiding use include the hassle of devices (47%) and aversion to having a device attached to the body (35%). Skin reactions and/or difficulty with adhesion are well known and are an important cause of discontinuation (74). Methods of addressing this barrier such as use of barriers, overlay patches, or topical antihistamines and corticosteroids have been described but additional research is needed (75).

 

In a multi-national study of 263 patients, persistent sensor use for 12 months was only 30% (76). Improvement in A1C was associated with higher A1C at baseline, older age, and more frequent sensor use. Diabetes related hospital admissions were reduced following the initiation of sensor augmented pump therapy and fear of hypoglycemia improved. In the 6-month follow- up phase of the JDRF-CGM trial, RT-CGM was initiated in the control group in a manner that more closely approximates clinical practice (77). Investigators found a significant reduction in CGM use inall age groups over time. However, increasing sensor use was associated with A1C reduction. It is likely that adherence will improve as technologies improve.

 

Other limitations include possible interference with acetaminophen, ascorbate, and other active agents in glucose-oxidase based electrochemical sensors. They are also dependent on both the sensitivity and specificity on the enzyme availability on the electrode surface. There are well known delay artifacts due to the time lag between glucose concentration in the interstitial fluid and blood glucose. These time ranges, often between 5 and 10 minutes, are not crucial to analyzing retrospective data, but can be critical when CGM is indicated for real-time decision making (78).

 

Daily Use

 

Patients must be aware that sensor readings can deviate from actual blood glucose measurements, particularly during rapid glucose changes such as that which occurs post-meal or during exercise. Calibration, where necessary, should not be performed when trend arrows indicate rapid swings in glucose. While systems are becoming more reliable, patients may need to verify sensor readings before taking action such as meal boluses or treatment of hypoglycemia depending on the device, even if a device is approved for nonadjunctive use.

 

Alarm thresholds should be set in order to maximize patient compliance, keeping in mind that the sensitivity fordetecting hypoglycemia decreases as the threshold is reduced below 70 mg/dl. Conversely, specificity improves to amuch smaller degree at lower thresholds, and thus false alarms may not be reduced substantially.

 

Several algorithms have been published that provide specific guidance to patients for responding to trend arrows and alarms and are summarized below. All algorithms are complex and are not integrated within bolus calculators ofexisting insulin pumps. Therefore, they should only be implemented in patients who have demonstrated anunderstanding of CGM technology, including lag times between CGM and BGM, calibration procedures, alerts andtrend arrows, as well as understanding of insulin action time and the risks of insulin stacking. In one small study, trend arrows were accurate approximately 79% of the time outside of mealtime windows (30 minutes before and 120 min after carbohydrate intake) but this dropped to ~60% within mealtime windows (80). Thus, algorithms are not intended for use post-meal. The use of automated insulin delivery systems should increase safety and efficacy and reduce the complexity of the trend arrow approach.

 

• The algorithm by Jenkins et al. provides tiered recommendations that are based upon the meter glucose and sensor trend arrows (81). In addition, the algorithm advises patients how to review downloads of the data periodically (weekly) and make adjustments. Patients who were randomly assigned to sensor augmented pump with the algorithm had lower A1C and reported better quality of life at 16 weeks compared to patients who did not get the The effect on quality of life persisted at the 32-week follow-up, and was associated with A1Creduction. Importantly, patients who received the algorithm at 16 weeks after initiating sensor augmented pump did not benefit.
• The DirecNet study algorithm (for use with the Navigator system) recommended that patients increase or decreasethe meal + correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms (82). Algorithm use was high in the first 3 weeks but dropped off by week 13, despite increasing insulin self- adjustments, possibly as patients became more independent over time.
• Subsequent methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend arrow (83,84).
• Klonoff and Kerr proposed a more straightforward correction dose (in 5-unit increments), based upon the trend arrow and the patient’s insulin sensitivity (85).
• A consensus statement facilitated by the Endocrine Society provides expert guidance on the use of trend arrows for making treatment decisions (86). The guidance recommends adjustment of boluses pre-meal and no sooner than 4 hours post-meal in 0.5-unit increments based upon the trend arrow and the patient’s sensitivity. The statement recommends no additional treatment within 2 hours of a previous meal bolus, and correction bolus using the bolus calculator or usual correction dose only in the 4 hours after a meal. Similar expert guidance has been developed for the Freestyle Libre system (87).
• A more recent adaptation of the Endocrine Society guidance incorporated pre-meal glucose levels in addition to theinsulin sensitivity (88) and a small randomized study demonstrated it was more effective than the incorporation of insulin sensitivity alone, particularly among insulin pump patients (89).

 

Overview of Stand-alone Personal RT-CGM and IS-CGM Systems

 

The first RT-CGM (Guardian, Medtronic^R) was approved in 2004. Since then, additional models and other devices have entered the market, and accuracy and patient satisfaction have improved. Several personal continuous glucosemonitors have been approved by the US Food and Drug Administration (FDA) for use in the United States or carry CE marking for use in Europe and are currently on the market (Table 5). For a full review of regulatory requirements for glucose monitoring devices, the reader is referred to one of several excellent reviews (90).

 

Table 5. Comparison of Subcutaneous Continuous Glucose Monitoring Devices
	Calibration required	Confirmatory Fingersticks requiredprior to treatment	Real-time alerts	Sensor Life (days)	Warm-up (hrs)	Removefor MRI, CT diathermy	Acetaminophen interference
Dexcom G5	Y	N	Y	7	2	Y	Y
Dexcom G6	N	N	Y	10	2	Y	N
Dexcom G7	N	N	Y	10	0.5	Y	N
Medtronic Guardian 3	Y	Y	Y	7	2	Y	Y
FreeStyle Libre 14 day	N	N	N	14	1	Y	N
FreeStyle Libre 2	N	N	Y	14	1	Y	N
FreeStyle Libre 3	N	N	Y	14	1	Y	N
Eversense (surgical implant)	Y	N	Y	180	NA	Y	No

 

GUARDIAN CONNECT

 

The Guardian Connect utilizes the Medtronic Guardian Sensor 3, the Guardian Connect transmitter, and the GuardianConnect app to transmit data via Bluetooth every 5 minutes to the user’s smart phone or device (initially only available on iOS devices) via the Guardian Connect App on smartphones and via CareLink personal and professional software. A separate receiver is not available with this system. Data can be shared with others remotely, and SMS messages can be sent in times of hypoglycemia. The system is only approved for adjunctive use and at least 2 daily fingerstick calibrations are required.

 

DEXCOM G6

 

The Dexcom CGM utilizes a glucose oxidase sensor at the tip of a wire that is implanted in the subcutaneous space. The data are transmitted wirelessly and are displayed on a separate receiver (personal smartphone or device specificreceiver). The Dexcom G6 has a sensor life of 10 days, no longer requires calibrations, and minimizes interference by acetaminophen. G6 is also associated with a smartphone app that allows the patient to log activity, set reminders or alarms, and physically see their glucose levels and trends throughout their time wearing the device. The Dexcom CLARITY Diabetes Management Software organizes and presents the patient’s blood glucose data. Dexcom SHARE app allows users to share data with up to 10 other individuals.

 

DEXCOM G7

 

The Dexcom G7 features a 60% smaller size (the size of 3 stacked quarters) vs. the G6, is fully disposable, and has a shorter 30-minute warm up time and a 12-hour grace period to replace completed sensors. The overall MARD was reported to be 8.2% with the abdomen and 9.1% on the arm (17). Consistent with previous studies, accuracy is lower at lower sensor glucose, higher glucose rate of change, and on day one of wear.

 

FREESTYLE LIBRE 14 Day

 

The sensor utilizes Wired Enzyme™ technology in which the enzyme and mediator are co- immobilized on the sensor. It offers factory calibration, and therefore nearly eliminates the need for fingerstick monitoring. However, patients are still advised to perform SMBG whenever an alert appears on the reader display (which occurs when the glucose isrising or falling rapidly) or whenever the glucose value does not fit the patient’s symptoms. The reader contains a built-in meter for this purpose. The sensor is FDA approved for 14 days of use. The system is not approved for use in children under age 18, or during pregnancy or in persons requiring hemodialysis. The Libre has minimized the interference by acetaminophen which is present in other devices but interference from other substances such as ascorbic acid or aspirin may be possible. The Libre 14 day differs from other CGM devices in that the system does not alert the user for glucose values surpassing a high or low threshold. In addition, glucose values are not automatically made available to the user but are easily and instantly accessed by scanning the sensor with a handheld reader or theassociated app FreeStyle LibreLink. However, this product may be attractive option for patients who are averse to the hassle imposed by other RT-CGM devices. Glucoses are measured every minute and recorded every 15 minutes. Data can be accessed using the reader or downloaded to LibreView cloud based online management system, or using the FreeStyle Libre desktop software. The MARD is reported by the manufacturer to be 9.7% overall, and as with other CGM devices, less accurate on day 1 of wear and in hypoglycemia range (91).

 

FREESTYLE LIBRE 2

 

The FreeStyle Libre 2 system offers real-time alerts for high or low glucose values and improved accuracy, approved for ages 4 years and older (92). However, users must continue to scan the device to obtain glucose readings.Moreover, similar to the Libre 14 day, the sensor memory is only 8 hours and glucose data are lost if the sensor is scanned less frequently.

 

FREESTYLE LIBRE 3

 

The FreeStyle Libre 3 is even smaller than other devices (the size of 2 stacked pennies), does not require scanningunlike older models, but does require the use of a compatible smartphone. The bluetooth range is improved from 20 to 33 feet. Accuracy is improved compared to the Libre 2, with an overall MARD of 9.2% in adults and 9.7% in children (16).

 

EVERSENSE

 

The Eversense system (Senseonics) is a 90-day implantable sensor that uses fluorescent technology to sendmeasures via a rechargeable transmitter which rests just above the skin to a smartphone app titled Eversense NOW (93). In a pivotal clinical study of 71 patients with type 1 and type 2 diabetes, there were no device-related serious adverse events, and the MARD was 11.1%, with over 99% of samples in clinically acceptable error zones A and B of Clarke Error Grid Analysis (94). One study reported interference with tetracycline and mannitol, but not with acetaminophen or ascorbic acid (95). The Eversense XL CGM system consists of a 180-day implantable sensor thathas been shown to have acceptable safety and accuracy with an overall MARD of 9.1% (96). This option may be particularly useful for patients with privacy concerns, physical disability, needle phobia, allergies or other difficulty with adhesion, or activities or professions that may be barriers to external wear (97).

 

Sensor Augmented Pumps

 

To date the largest A1C reductions have been observed when sensors are initiated with insulin pump technology. In the observational (nonrandomized) COMISAIR study, patients initiating CGM (with or without insulin pump) achieved significantly larger reductions in A1C (-1.2%) compared to subjects initiating insulin pump alone (-0.6%) or remaining on injections alone (- 0.3%) (98). There was no difference in outcomes between the DexCom G4 and Enlite sensor.

 

A reduction in time spent in hypoglycemia was observed only in patients using CGM (8% vs 6%, p<0.001).

 

STEPS TOWARDS AN ARTIFICIAL PANCREAS

 

Until recently, RT-CGM technology has operated completely independently of insulin delivery. By combining continuous basal insulin delivery during fasting periods with discrete bolus doses of insulin at mealtimes, insulindelivery can be crafted to mimic the natural pattern of pancreatic insulin release. An artificial pancreas consists of: 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a signal to send the glucose level to the body surface for continuous display onto a monitor. Limitations to full implementation include sensor accuracy and lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulinsensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness (99). However, even incremental advances improve glucose control without increasing the complexity of decision-making on the part of the patient. These include:

• Low glucose (threshold) suspend: the insulin pump suspends when the glucose decreases below a pre-set value.
• Suspend before low: insulin pump suspends when hypoglycemia is
• Hybrid closed loop: insulin delivery increases or decreases based upon the sensor glucose value but meal boluses are still required.
• Closed loop control: fully closed loop delivery without the need for meal boluses
• Dual hormone systems: these are hybrid closed loop or closed loop control systems that utilize glucagon or other peptides (such as amylin) in an effort to more closely mimic the physiology of the endocrine pancreas.

 

The long-term safety, efficacy, cost, and cost-effectiveness of an artificial pancreas are still largely unknown at this time. However, the urgency of this technology is demonstrated by the #WeAreNotWaiting movement, which has given rise to home-grown, crowd-sourced, patient driven systems that utilize existing devices which are linked by open-source software, such as Open Artificial Pancreas System, and Loop. Recently Tidepool Loop received FDA approval (100). A retrospective observational study of patients with Type 1 diabetes demonstrated lower mean glucose, higher time in target range, and less time in hypoglycemia using Open Artificial Pancreas Systems (OpenAPS) compared to sensor augmented pump use alone (101). In general, open-source systems carry safety concerns, particularly among less tech-savvy patients, in the absence of regulatory approval (102). However, the healthcare provider can providesafety recommendations as well as a back-up plan in case of system failure (103). The reader is referred to one of several reviews as a detailed review is beyond the scope of this chapter (104,105).

 

Threshold Suspend

 

Progress is expected toward a fully functional closed loop system in incremental steps. The first step toward a fully automated “artificial pancreas” is the low glucose suspend feature, which is now available. The Medtronic 530Gsystem, containing the Veo insulin pump and Enlite sensor, is the first sensor augmented pump with low threshold suspend and uses the same sensor as the more recent 630G system. The Enlite sensor accuracy is significantly improved over the previous Sof-sensor^R, with a MARD of 13.6% when used with the 530G (106). The Enlite is also one-third of the size of Sof-sensor and the filament is 38% shorter. The Enlite sensor may be worn up to six days. The low threshold suspend SmartGard^TM technology suspends the pump for up to two hours in the event of sensor detected hypoglycemia in which the user does not respond to the alarm. Prior to suspension, a “siren” sounds which is distinct from other high or low alerts, and the suspension can be overridden at any time. The MiniMed Connect mobile accessory sends sensor data to an app on a mobile device where data can be viewed (available only with the 530G system). A study that enrolled 247 patients with type 1 diabetes and documented nocturnal hypoglycemia to sensor-augmented pump with or without a low-glucose threshold-suspend feature demonstrated similar A1C between groups at 3 months but lower frequency of nocturnal hypoglycemia (107). Similar findings were demonstrated in an Australian study of 95 patients, in which the incidence rate ratio for hypoglycemia was 3.6 (95% CI 1.7-7.5, p<0.001) (108). There were no reports of DKA in either study.

 

Suspend Before Low

 

The next incremental step in closed loop systems is the suspend before low feature, currently available in theMedtronic 640G (approved only in Europe) and the 670G systems. This feature automatically suspends insulin delivery 30 minutes before a low glucose threshold is predicted and resumes delivery once the glucose recovers, without alerting the patient. In a 6-month randomized study of 154 children and adolescents with type 1 diabetes, the 640G system reduced the time spent in hypoglycemia from 2.6 to 1.5% without causing a change in A1C (109). The t:slim X2 Insulin Pump incorporates Basal IQ technology with predictive low glucose suspend using the Dexcom G5 or G6 sensor. In a randomized cross-over study of 103 participants with type 1 diabetes age 6-72 years of age, predictivelow glucose suspend resulted in a 31% reduction in time spent in hypoglycemia < 70 mg/dl without a change in meanglucose or time in hyperglycemia (110).

 

Hybrid Closed Loop (HCL)

 

This step refers to sensor glucose driven automatic adjustment of basal insulin with or without additional auto boluses,and still requires the patient to bolus for meals. In a recent consensus statement, an ideal candidate for automated insulin delivery systems (103):

• Is technically capable of managing a pump, has basic carbohydrate counting skills, and is able to implement a back-up plan (including the use of manual injections).
• Has realistic expectations of system In particular, several situations that are unique to HCL are worth emphasizing:
  • Bolusing: pre-blousing approximately 15 minutes prior to meals is critical to maintain In many systems,delayed boluses not only cause early postmeal hyperglycemia but also precipitate delayed hypoglycemia as the system has already begun to augment insulin delivery in response to hyperglycemia.
  • Exercise management: Similarly, carbohydrate loading prior to exercise while using HCL systems will only stimulate insulin delivery and thus is recommended that users implement other means for management such as setting a higher target, typically with a designated exercise mode, or exiting to manual mode with temporarybasal insulin reduction or temporary suspension of the pump.
  • Hypoglycemia management: HCL users typically need fewer carbohydrates (about half) to manage hypoglycemia since the pump has generally already suspended insulin delivery based upon glucose trends.
• Has adequate support, including diabetes education, insurance coverage, and caregiver or other social support where relevant.
• Has the ability to transmit data to the healthcare
• Is mentally and psychologically able to implement AID

 

On the other hand, there does not appear to be an ideal threshold A1c for determining candidates for HCL therapy, asthose with lower A1c may benefit by reducing TBR and those with higher A1c benefit from reductions in hyperglycemia without the perceived risk of ketoacidosis traditionally attributed to initiation of insulin pump therapy (111). Thus, less ideal candidates may obtain the greatest benefit in terms of achieving glycemic targets.

 

HCL demonstrates improvements in a range of glycemic outcomes and may confer psychological benefits as well.Most studies have enrolled patients with type 1 diabetes. More data are needed for special populations including type 2 diabetes, especially those with very high insulin requirements, pregnancy, acute illness, steroid use, renaldysfunction, and persons in assisted living facilities (112).

 

As with CGM, it is important to evaluate these systems in the real-world setting, where user experience can differ from that of the highly controlled and supportive research environment. Cost and insurance hassles, as well as user wear issues are the most commonly reported barriers to use of any diabetes related device. These barriers contribute todiabetes distress and depressive symptoms which can impede self-management behaviors (70,112). HCL systems improve glucose control but may also introduce additional alarms or alerts which are needed for safety (such as threshold alerts for hyperglycemia or hypoglycemia or HCL mode exits due to insulin delivery exceeding the system’s guardrail) or ongoing functionality such as calibration of the CGM (113). Newer systems have attempted to address many of these barriers through improved algorithms or other features (Table 6). Devices differ considerably with respect to algorithms used for insulin adjustment and a number of other features (Table 6) (114,115,116). There are few head-to-head studies comparing the efficacy and safety of available HCL systems. Details of select systems are presented next.

 

MEDTRONIC 670G

 

The first system to gain FDA approval is the Medtronic 670G, which adjusts basal insulin delivery every 5 minuteswhen in auto mode. This system utilizes the Guardian sensor 3, which offers enhanced sensor accuracy, with an overall MARD of 9.64% (117). The system was associated with a reduction in A1C from 7.4 to 6.9% and there were trends in improvement of time in target range and hypoglycemia in a non-randomized study of 124 patients with type 1 diabetes (118). A subsequent randomized trial of 151 adults and children demonstrated a significant reduction in A1c and TBR compared to insulin pump without CGM (119). There are few studies addressing long-term use however. In a 1-year prospective observational study of 84 patients, 28% stopped using auto mode by 3 months, and 33% discontinued by 12 months (120). The most common reasons for discontinuation included sensor issues (62%) and difficulty obtaining supplies (12%), fear of hypoglycemia (12%), and preference for injections (8%) or sports (8%). In astudy of 92 youth, 30% discontinued HCL, typically between 3 and 6 months after initiation, due to issues such asdifficulty with calibrations, alarms, and extra time needed for operation (121).

 

MEDTRONIC ADVANCED HYBRID CLOSED LOOP (AHCL) SYSTEM (780G)

 

This HCL system is approved for use in Europe and features substantially reduce frequency of alerts, improved time in auto mode, remote software updating, an adjustable target setting as low as 100 mg/dl and enable users to view data via an app on mobile devices (122). In a single arm study of 157 adolescents and adults the 780G system resulted in nearly 95% time in automated mode with 1.2 exits per week, improved A1C, TIR, and TBR (123). In a randomized study of 82 persons with type 1 diabetes using multiple injections per day and isCGM, AHCL resulted in improvements in A1C (-1·42%, 95% CI -1·74 to -1·10; p<0·0001) and TIR, but no difference in hypoglycemia (124). There was an improvement in treatment satisfaction, fear of hypoglycemia, and similar diabetes quality of life. By comparison in a randomized study of 41 participants with type 1 diabetes who were naïve to both CGM or insulin pump technologies, AHCL also resulted in improvements in TBR (125). In a real-world study of 3211 youth (<age 15 years) and 8874 individuals >age 15 years, ACHL demonstrated >90% treatment persistence over 6 months (126). The ACHL system was reported to have better glucose monitoring treatment satisfaction but similar diabetes distress, technology attitudes, and fear of hypoglycemia compared to the 670G system (127).

 

TANDOM CONTROL-IQ

 

This system utilizes a t:slim X2 insulin pump with a calibration-free Dexcom G6 sensor (128). In a 6-month trial of 168 patients (age 14-71) randomized 2:1 to hybrid closed loop vs. sensor augmented pump alone, the % time in target 70-180 mg/dl was increased by 11% more in the hybrid closed loop group compared to sensor augmented pump(p<0.0001), with improvements in hypoglycemia, mean glucose and A1c. Moreover, real-world outcomes among 1435 persons with type 1 diabetes included reduced impact of diabetes on life, improved device related treatment satisfaction, and improved emotional well-being (129).

 

OMNIPOD 5

 

The Omnipod 5 is a HCL system that uses the Omnipod DASH platform (130). In a single arm study, the Omnipod 5 demonstrated a reduction in A1C of 0.38%, increase in TIR of 9.3% and decrease in TBR of 1.6% (131).

 

Table 6. Comparison of Hybrid Closed Loop Systems
	Medtronic 670G/770G	Medtronic780G	Tandem T:Slim with Control IQ	Omnipod 5	iLet BionicPancreas
Insulin delivery	Tubing	Tubing	Tubing	Tubeless (pod)	Tubing
CGM	Guardian 3	Guardian 4	Dexcom G6	Dexcom G6	Dexcom G6
Reservoir capacity(unit)	300	300	300	200	180
Calibration needed	Yes	No	No	No	No
Supplies	DME company	DME company	DME company	Pharmacy	To be determined
Control via smartphone	Dataviewable from Smartphone	Data viewablefrom Smartphone	Smartphone bolus	Compatible smartphone controller	No
Algorithm initiation	48 hours in manualmode to estimate TDI	48 hours in manual mode to estimate TDI	Weight and TDI entry with maximumdelivery of 50% TDI over 2 hr	TDI estimated from programmed basal rates	Weight entry
Bolus automation	No	Yes, every 5 minutes	up to 1 auto-correction bolus//hr if glucose predicted >180 mg/dl	No	unknown
Other inputs	CIR, AIT Unable to overridebolus dose	CIR, AIT	CIR, ISF, AIT is fixedat 5 hours	CIR, ISF, AIT Boluscalculator uses CGM rate of change	“Usual for Me”, “More”, or“Less” customized by meal
Extended bolus	No	No	Yes, up to 2 hr	No	No
Algorithm adjustment	Every 6 days	Every 6days	TDI used to scalebasal changes	Every 3 days with pod change	Continuously or based on change in entered weight
Target	120 mg/dl	100, 110, 120 mg/dl	112.5-160 mg/dl	110, 120, 130, 140, 150 mg/dl, customizableby time of day	100, 110, 120, 130 mg/dl, customizable by time of day
Exercise	Temp target 150 mg/dl for 2-12 hour	Temp target 150 mg/dlfor 2-12 hour	Exercise target 140-160 mg/dl—cannotprogram duration Sleep mode: target 112.5-120 mg/dlwithout auto-correction bolus, programmable	Target 150 mg/dl, “less aggressive”,for 1-24 hr	None
Safety mode*	Yes, for 670/770G results in forced exits from HCL	Yes, exits to Safe Basal up to 4hours (5.7 vs. 1.7 x/week with 670G) (130).	Not applicable(defaults to basal rate settings)	Yes, forced exits “rare”	Yes, BG-run mode uses manuallyentered BG upto 72 hours, forces switch to alternate therapy.

PID=proportional integral derivative (system with continual change in response to error between actual and targetvalues). MPC=model predictive control (dynamic reference model serves as a basis. TDI=total daily insulin dose,CIR=carbohydrate to insulin ratio, AIT=active insulin time, ISF=insulin sensitivity factor. *Safety mode provides amechanism to ensure insulin delivery in case of loss of sensor input or threshold for insulin delivery guardrail is reached.

 

Closed Loop Systems (CLC)

 

Additional steps toward closed loop control (CLC) insulin delivery require algorithmic insulin adjustments, whicharguably present additional safety concerns. Overnight CLC insulin delivery is relatively straightforward, whereas post-meal control and exercise effects remain the most challenging of events to manage. Until recently, most randomized studies have been small and reported only short-term outcomes, often in controlled settings.

 

ILET BIONIC PANCREAS

 

The iLet Bionic pancreas was approved by the FDA in 2023. This insulin pump is initiated using the patient’s body weight and requires meal announcements (designated as small, medium, or large) but not formal carb counting and thus represents an incremental step toward a fully closed loop insulin pump. In A 13-week multi-center randomized study of 219 participants with type 1 diabetes demonstrated a greater reduction in A1c with the iLet bionic pancreas (-0.5%, [95% CI -0.6 to -0.3; P<0.001) but no difference in hypoglycemia compared to standard care (132).

 

OTHER SYSTEMS

 

Systems have utilized single hormone (rapid acting insulin only) or dual hormone (both fast- acting insulin analog and glucagon to imitate normal physiology) as directed by a computer algorithm (Figure 4) (133). At this time, there areinsufficient data demonstrating the superiority of one system or algorithm compared to others. The three most common algorithms are:

• Model Predictive Control (MPC): predicts future glucose levels and adjusts insulin delivery in response.
• Proportional Integral Derivative (PID): calculates the deviation of glucose from target to determine insulin delivery.
• Fuzzy Logic (FL): mimics insulin dosing based upon clinical

 

A meta-analysis of 40 randomized studies (35 studies using insulin alone and 9 dual hormone studies) including 1027participants with type 1 diabetes demonstrated a significant increase in % time in target range (70-180 mg/dl, weighted mean difference 9.6%, 95% CI 7.5-12%), as well as less time in hypoglycemia or hyperglycemia, regardless of type of system (134). In another meta-analysis of 24 studies and 585 participants (7 studies using dual-hormone therapy and 20 studies of insulin only) reported greater improvement in time in target with artificial pancreas systems (12.6%, 95% CI 9.0-16.2, p<0.0001), and greater improvement with dual hormone compared to single hormone systems (135). Another meta-analysis of studies with at least 8 weeks duration confirmed these findings (136). A systematic review and meta-analysis of 25 studies in 504 children demonstrated superior %TIR with CLC and bi-hormonal systems vs. single hormone systems (137).

 

 

MINIMALLY INVASIVE AND NON-INVASIVE GLUCOSE MONITORS

 

Continuous hypoglycemia detection systems using current sensing technology must be either implanted (fully or partially, either subcutaneously or into a blood vessel). Implantation is more secure, but may be associated with biocompatibility problems or local irritation. Less invasive methods may be categorized as minimally invasive or noninvasive. Minimally invasive techniques extract fluid (tears or interstitial fluid) while noninvasive technologies do not.

 

Minimally invasive methods include electrical, nanotechnology, and optical approaches while noninvasive techniquesrely on some form of radiation without the need to access bodily fluids. Noninvasive methods frequently incorporate electric, thermal, optical, or nanotechnology methods for detection. Many noninvasive devices under development are aimed for non- continuous monitoring as they often require controlled surroundings including factors such as light, motion and temperature.

• Optical approaches utilize reflective, absorptive, or refractive properties of infrared and optical bands of the light spectrum to detect glucose. Pure optical methods under development utilize Raman and Near infra-red spectroscopy.
• Thermal methods detect glucose via the far-infrared band of the spectrum and provide noninvasive approaches for glucose monitoring.
• Electric methods use electromagnetic radiation, currents, or ultrasound approaches to detect dielectric properties of Reverse iontophoresis has been employed with early minimally invasive approaches while bioimpedance spectroscopy has been used in recent noninvasive approaches.
• Nanotechnologies aim to miniaturize existing technologies, including fluorescence and surface plasmon resonance (SPR) approaches (138).

 

Few devices (other than interstitial CGMs discussed above) have demonstrated high levels of accuracy recommendedby expert groups, though several have been approved by CE or FDA (139).

 

MOBILE TECHNOLOGY AND DECISION SUPPORT

 

It has become increasingly clear that the isolated use of glucose monitoring technologies without a specific plan toaddress the data provides minimal benefit, particularly among patients with type 2 diabetes or who are not using insulin (140). In order for glucose monitoring to provide the most benefit, patients and providers must be able to easilyobtain and communicate the data. Data must be organized in such a way that patterns can be identified, and patients must receive feedback at the point of care. The widespread use of mobile devices provides opportunities for data collection, analysis, and communication of results with health care providers as well as facilitates digital or remote clinical models of care (141). Finally, as healthcare providers are inundated with more data and spend increasingamounts of time using electronic medical records, it has also become paramount that devices and or reports from the devices communicate or interface with these systems (142).

 

Hurdles to wider implementation of mobile technology include the lack of usability (both for patients, as well as providers who may be expected to review and act upon reports), safety, efficacy (including long-term adherence), and cost-effectiveness studies (143). The lack of data is in part due to the rapidly changing technology itself, which rendersthe technology obsolete by the time a vigorous clinical trial is conducted and published. The fee for service model is amajor barrier to adapting many glucose monitoring technologies, which often require frequent feedback and treatment adjustments, efforts that are not reimbursed without an actual office visit. Finally, cyber security is a big concern for all medical devices, especially for devices that are controlled by a smartphone (144).

 

Device Downloading, Connectivity, and Interoperability

 

Manual recording of glucose data is fraught with inaccuracies (145). Most monitors can be downloaded, via a tethering cable or wireless connection, either by the patient or healthcare provider. Each glucose monitoring devicegenerally works with its own proprietary management software. However, several programs (Tidepool,Glooko/Diasend, Carelink by Medtronic, Accu- chek) are capable of downloading and organizing data for multiple different devices (146).

 

Reports are standardized across all device downloads, facilitating efficient and actionable patient and healthcare provider review. These programs also facilitate population health and telehealth strategies (discussed below). The Nightscout Project is a crowd sourced application that provides a free mobile technology platform for patients whowant to access their devices in real time on any mobile device (147). Recent data suggest that retrospective weekly review of data is associated with improved TIR (148,149) as well as patient reported outcomes including confidence in avoiding hypoglycemia, overall well-being and diabetes distress (150).

 

Direct connectivity of blood glucose or CGM levels to cell phones or other devices not only improves data integrity but may also simplify the assimilation of glucose levels with other data such as insulin use, carbohydrate intake, andactivity levels for the purpose of facilitating insulin dose adjustments in real time or retrospectively. Cell phone connectivity may also improve communication with providers. A few meters with direct cellular capability are available.

 

Devices with direct cellular or Bluetooth connections may be paired with apps that facilitate collection, communication, and analysis of a variety of data and provide tools for education (such as nutrition information) at the point of care.

 

Currently, both the Tandem t:slim X2 and Insulet’s Omnipod 5 System are FDA approved for remote blousing via a cell phone app (151,152). A regulatory pathway has been developed for alternate controller enabled (ACE) infusion pumps which can be operated in conjunction with interchangeable components, particularly CGMs (153). In 2019, theFDA approved the first such devices (Tandem t:Slim X2 and Omnipod DASH system).

 

Diabetes Apps

 

A variety of stand-alone smart phone applications that support glucose monitoring are also available. Most provide information and track data (usually manually entered), some allow insulin or carbohydrate documentation, facilitate carbohydrate or calorie counting, promote weight loss, track or promote physical activity, enhance medication adherence, and use motivational or self-efficacy approaches, and a few provide an insulin dosing calculator. Simple apps provide information or tracking functions while more sophisticated approaches incorporate gaming theory and machine learning approaches that learn from the user’s previous experiences to optimize interactions. Apps have shown limited magnitude and sustainability of effect due to a variety of factors, including user fatigue, require continuous data entry (e.g., most apps do not connect directly with a glucose meter), and lack of integration with the health care team. Moreover, most apps have not been evaluated by the FDA or other regulatory agencies. Data privacy is also a concern, as no federal regulations currently prevent app developers from disclosing data to third parties. Most apps (81% in one survey of Android apps) do not have privacy policies, and of those that do, 49% share user data with third parties (154). Expert groups have developed policy or guidance statements to improvestandardization and functionality (155,156,157).

 

Efficacy

 

While the data are still evolving with respect to mobile diabetes applications, several systematic reviews and meta-analyses demonstrate modest (~0.5%) reductions in A1C in persons with type 2 diabetes, especially among younger patients, apps that provide healthcare provider feedback, or had other features including wireless entry of data (158,159,160,161). The Agency for Healthcare Research and Quality published a systematic review of comparative effectiveness studies assessing apps or programs available through a mobile device for the purpose of diabetes self-management (162). For type 1 diabetes, 6 apps were identified, 3 of which were associated with improvement in A1c, 2 of which were associated with improvement in hypoglycemia. Five apps for patients with type 2 diabetes were identified, 3 of which were associated with improvement in A1c. Efficacy is variable, in part because app features vary but also because apps are often studied as part of a multi-component intervention, making it difficult to assess individual elements, particularly the effect of additional health care provider support. Other researchers have focusedon identifying standard evidence-based features that should be included in diabetes apps, such as education, glucose monitoring, and reminders (163,164).

 

Usability

 

In a systematic review of 20 studies, only one third of the 20 apps met the authors’ health literacy standards (165).Usability was measured in 7 studies through satisfaction surveys from patients and experts, and ranged from 38-80%. The most common usability problems were multi-step tasks, limited functionality, and poor system navigation. While many apps are rated high quality for performing a single task, most do not address diabetes self-management tasks comprehensively (166) or otherwise do not function properly (165,167).

 

Decision Support

 

The use of pattern management software improves health care provider efficiency and accuracy in identifying needed therapeutic adjustments (168,169). Software programs provide graphs or charts and may in some cases provide dosing advice, either for the healthcare provider or directly to the patient.

 

Insulin Dosing Calculators

 

Insulin dosing calculators have been used for years as a means of incorporating glucose measures into routine practice, largely in concert with continuous insulin infusion pumps. While numerous apps have become available forbolus insulin calculation and basal insulin titration, it is important to note that only a few have been formally evaluated and approved by regulatory agencies. In addition, many still require manual data entry, few integrate within existing electronic medical records, and published evidence for efficacy is limited (170). All approved insulin calculators or dose titration apps require a prescription or need to be set up by a healthcare provider. Many such apps operate in conjunction with connected meters and insulin pens, which are subject to regulatory oversight and long-term support (171). Such support ensures safety and that software is updated to address any problems with operation and device compatibility. The functionality of connected pens ranges from insulin tracking functions, including insulin on board calculations and reminders to smart insulin pens which feature bolus dose calculators and more advanced decisionsupport such as dose titration and coaching features (172). A full review of insulin dosing apps is beyond the scope of this chapter.

 

Bolus calculators are known to substantially improve dosing accuracy and glycemic control in outpatients with type 1 diabetes (173,174,175). Bolus calculators might be particularly helpful for patients with poor numeracy. A number of stand-alone smart-phone apps for bolus insulin calculation have been developed but safety and efficacy remain a concern (176,177). Though algorithms typically incorporate the current glucose level, active insulin time, and carbohydrate intake, some do not account for activity or illness. Applications that improve the accuracy of carbohydrate counting, which is a major source of error (regardless of educational level), are desirable (178). Reports from connected pens provide insight into missed or altered insulin doses and when integrated with CGM data can alsofacilitate the evaluation of timing of boluses.

 

Likewise, basal insulin calculators have been developed to recommend ongoing adjustments in therapy, either fortitration or for mealtime insulin calculations. Unfortunately, efficacy and safety studies are not currently available for most apps. Most basal insulin titration apps account only for fasting glucose measures and not overnight trends.

 

Although there are a plethora of apps available, the ultimate choice should be individualized to the needs of thepatient. Those patients only needing a resource that assists with carbohydrate counting can be referred to common apps like MyFitnessPal or Calorie King. For glucose monitoring, apps that require manual entry of data should beminimized as they are not likely to be utilized long-term. Universal platforms that can download multiple devices can increase clinic efficiency. Where possible, patients should be invited to directly link with their clinic. This is particularly useful for telehealth visits. Smart insulin pens provide assistance with insulin dosing and can also be downloaded using some universal platforms.

 

Integration within the Electronic Health Record (EHR)

 

The major limitation of patient generated data is that it does not integrate within the EHR in a meaningful way. Someopportunities exist with the integration of Apple Health Kit and Samsung S-Health which can transmit data from a variety of apps but this process requires multiple steps and can be cumbersome (179,180). Recently, a consensus report from the Integration of Continuous Glucose Monitoring Data into the Electronic Health Record (iCoDE) project was published, setting standards for integration of CGM data within the EHR (181). Under these standards, data would be accessed by placing an order in the EHR. This would generate a notice to the patient via email or electronic message to obtain consent for sharing data. Once approved, standardized report is uploaded to the EHR. Importantly,none of these mobile health tools replace frequent patient contact and feedback (182).

 

BIOMARKERS OF GLYCEMIC CONTROL

 

Hemoglobin A1c (A1C)

 

A1C is the best biomarker indicator of glycemic control over the past 2-3 months due to strong data predicting complications (1,2). In addition, the American Diabetes Association has recommended its use for the diagnosis of diabetes (1).

 

Hemoglobin A1c refers to the nonenzymatic addition of glucose to the N-terminal valine of the hemoglobin beta chain. Assays are based upon charge and structural differences between hemoglobin molecules (183,184). Therefore, variants in hemoglobin molecules may lead to analytic interferences. It should be noted that some homozygous hemoglobin variants (HbC or HbD, or sickle cell disease) also alter erythrocyte life span and therefore, even if theassay does not show analytic interference, other methods of monitoring glycemia should be utilized, as A1C will be falsely low. Individual assay interferences are available at the National Glycohemohemoglobin Standardization Program website: www.ngsp.org (185). Several commercial home monitoring kits are also available (186). The two reference methods used to standardize A1c levels are 1) HPLC and electrospray ionization mass spectrometry or 2) a two- dimensional approach using HPLC and capillary electrophoresis with UV-detection (187). A brief summary of assay methods is described below.

 

• HPLC methods utilize the fact that glycated hemoglobin has a lower isoelectric point and migrates faster than otherhemoglobin As such it has variable interference with hemoglobinopathies that alter the charge of the molecule (such as HbF and carbamylated Hb), but these may be revealed through individual inspection of the chromatograms.
• Boronate affinity methods are based upon glucose binding to m-aminophenylboronic acid and measures glycation on the N-terminal valine on the beta chain but also glycation at other sites. There is minimal interference from hemoglobinopathies but this assay is not widely available.
• Immunoassays make use of antibody binding to glucose and N-terminal amino acids on the beta chain and therefore may be affected by hemoglobinopathies with structural changes at these sites, including HbF but notHbE, HbD, or carbamylated Some newer assays have attempted to correct for these interferences.
• Enzymatic methods lyse whole blood, releasing glycated N-terminal valines which are detected using achromogenic reaction and are not affected by hemoglobin

 

An Organization with links to governmental regulatory agencies, the National Glycohemoglobin Standardization Program (NGSP) (<http://www.ngsp.org/news.asp >), evaluates every laboratory and home test for A1C, sets accuracy standards, and certifies which methods meet their standards (188). The trend in industry is for monitors to become increasingly more accurate and the trend in regulatory organizations is to require increasing accuracy for ongoing certification.

 

A1C is an analyte found within red blood cells, comprised of glycated Hemoglobin. The glycation gap (formerly known as the glycosylation gap) (GG), based on fructosamine measurement, and the Hemoglobin Glycation Index (HGI), based on mean blood glucose, are two indices of between-individual differences in glycated hemoglobin adjusted forglycemia. GG is the difference between the measured A1C test and the A1C test result predicted from serum fructosamine testing based on a population regression equation of A1C on fructosamine (189). and HGI is the difference between the measured A1C test and A1C results predicted from the mean blood glucose level (calculated from self-monitored blood glucose tests) based on a population regression equation of A1C tests on mean blood glucose levels (190). These two indices are consistent within an individual over time and reflect an inherent tendency for an individual’s proteins to glycate (191,192). Patients with high GG and HGI indices might have falsely high A1C test results and might also be at increased risk of basement membrane glycosylation and development of microvascular complications. Whether between-individual biological variation in Hemoglobin A1c is an independent risk factor, distinct from that attributable to mean blood glucose or fructosamine levels, for diabetic microvascular complications is controversial (193).

 

Because the A1C test is supposed to reflect the mean level of glycemia, attempts have been made to correlate thiswidely-accepted measure with empirically measured mean blood glucose levels. In 2008, the A1c-Derived Average Glucose (ADAG) study compared A1C and continuous glucose monitoring derived mean glucose and 7-point glucose profiles among 507 patients with type 1 and type 2 diabetes and without diabetes from 10 international centers to derive an estimated average glucose (eAG) from A1C levels using the following equation: eAG(mg/dl) = (28.7* A1C)-46.7 (Table 6).

 

Table 6. A1C and Estimated Average Glucose
A1C (%)	eAG (mg/dl)	eAG (mmol/l)
5	97	5.4
6	126	7.0
7	154	8.6
8	183	10.2
9	212	11.8
10	240	13.4
11	269	14.9
12	298	16.5

 

Several lines of evidence support this disconnect from a tight correlation between mean glycemia and A1C levels. First, improvements in mean glycemia may not necessarily be reflected by improvements in A1C in intensively treated patients (194). A1C does not reflect short-term changes in glucose control, and therefore can be misleading wherethere have been recent changes in the clinical condition. In addition, glucose fluctuations, compared to chronic sustained hyperglycemia, have been shown to exhibit a more specific triggering effect on oxidative stress and endothelial function (195,196). Glycemic variability cannot be assessed by a global measure of mean glycemia, such as A1C, but requires multiple individual glucose values, such as what can be obtained from continuous glucose monitoring or from seven-point- per-day (or greater) self-glucose testing. Third, A1C does not permit specific adjustments in therapy, particularly among patients requiring insulin titration. Finally, A1C reliability may be affected by several conditions that alter red blood cell lifespan and its use in these circumstances can be misleading. Acomparison of the features and limitations in glucose markers is presented in Table 7 (197,198,199).

 

Table 7. Comparison of Markers of Glycemic Control
	Biomarker mechanism	Interval of time reflecting glucose control	Cautions/Interferences
A1C	Hemoglobin glycation	3 months	Hemoglobinopathy (↑/↓*) Decrease in RBC survival (hemolysis, splenomegaly, pregnancy, drugs) (↓) Increase in RBC survival (Erythropoietin, iron replacement) (↑) Transfusion (↓)
Fructosamine	Protein glycation	2 weeks	Conditions resulting in hypoproteinemia (severe cirrhosis, nephrotic syndrome, enteropathy) (↓) High dose Vitamin C, severe hyperbilirubinemia/uremia/ hypertriglyceridemia (↑)
1,5-AG	Renal clearance	1 week	Chronic kidney disease (stage 4, 5) (↓) Glucosuria (pregnancy, renal tubular disorders SGLT2 inhibitors) (↓) Advanced cirrhosis (↓) High soy diet (↑)

*Assay-dependent

 

Ethnic differences in A1C have also been reported (200). For example, recent data from the Type 1 Diabetes Exchange demonstrates a 0.4% higher A1C at a given mean glucose among black patients compared to whitepatients with type 1 diabetes, but no effect of race on glycated albumin or fructosamine (201). However, NHANES data do not demonstrate an effect of ethnicity on the association between A1C and retinopathy (202). Data from the ARIC study demonstrated that A1C, fructosamine, glycated albumin, and 1,5-AG were consistent with residualhyperglycemia among blacks compared to whites, and the prognostic value for incident cardiovascular disease, end stage renal disease and retinopathy were similar by race (203). It should be noted that the range of available A1C was relatively narrow in NHANES and ARIC, and further data across an expansive range is needed. In relation to CGMs, utility of A1C is further enhanced when used as a complement to glycemic data measured by CGM (10). Other biomarkers are becoming more widely used, however, A1C remains the most common biomarker. Other measures of average glycemia such as fructosamine and 1,5-anhydroglucitol are available, but their translation into average glucose levels and prognostic significance are not as clear as for A1C (1).

 

Fructosamine

 

A short to medium-term marker (reflecting the average glucose control over the past few weeks) may be useful for determining control over a period of days to weeks since A1C does not reflect recent changes in glucose control. Alternate markers may also be useful in patients with discrepant A1C and self-monitored blood glucose readings as well as patients with other hematologic conditions known to affect A1C. Fructosamine is a term that refers to a family of glycated serum proteins and this family is comprised primarily of albumin and to a lesser extent, globulins, and to an even lesser extent, other circulating serum proteins. No product exists for home use that measures serum fructosamine. A home blood fructosamine monitor, Duet Glucose Control System, was marketed in the early 2000′sand then withdrawn from the market. No subsequent home fructosamine test has been available since then. Randomized controlled trials have reported inconsistent effects of frequent monitoring on A1C lowering, possibly due to differences in execution of therapeutic interventions (204,205). Serial monitoring of short-term markers may also facilitate timely elective surgery in patients whose procedure is delayed due to an elevated A1C. In a recent study, fructosamine was a better predictor of post-operative complications in patients undergoing primary total joint arthroplasty (206).

 

GLYCATED ALBUMIN

 

The largest constituent of fructosamine is glycated albumin. Several investigators and companies are developing portable assays for glycated albumin to assess overall control during periods of rapidly changing glucose levels. In these situations, an A1C test may change too slowly to capture a sudden increase or decrease in mean glycemia. The components of the necessary technology appear to be in place to build a commercial instrument for home testing of glycated albumin. However, there is no randomized controlled trial showing that the measurement of glycated albumin improves outcomes. In the ARIC study, fructosamine, glycated albumin, and 1,5-AG were associated with incident diabetes, even after adjustment for baseline A1C and fasting glucose. In the ARIC study, both fructosamine and glycated albumin predicted incident retinopathy and nephropathy, even after adjusting for A1C (207). However, in adults with severe chronic kidney disease, none of the markers, including A1C, fructosamine, or glycated albumin were very highly correlated with fasting glucose, and there did not appear to be an advantage of one marker over another (208). In addition, baseline glycated albumin and fructosamine were associated with cardiovascular outcomes over a 20-year follow-up period after adjusting for other risk factors, but the overall magnitude of associations was similar to A1C (209). In the Diabetes Control and Complications Trial (DCCT), glycated albumin had a similar association with retinopathy and nephropathy as A1C, but the combination of both markers provided even better prediction (210). Short-term markers are also of interest for use in pregnancy, where glucose levels are changing more quickly than can be reflected by A1C. Unfortunately, glycated albumin does not predict gestational diabetes more effectively than A1C or fasting glucose (211). However, other preliminary data suggests that glycated albumin may be a better predictor of pregnancy complications than A1C (212).

 

1,5-Anhyroglucitol

 

The aforementioned biomarkers for measuring glycemic control, (A1C, fructosamine, and glycated albumin) only reflect mean levels of glycemia. These measures can fail to portray hyperglycemic excursions if they are balanced by hypoglycemic excursions. Plasma 1,5- anhydroglucitol (1,5-AG) is a naturally occurring dietary monosaccharide, witha structure similar to that of glucose (Figure 5). This analyte has been proposed as a marker for postprandial hyperglycemia (213). An automated laboratory grade assay named Glycomark is approved in the U.S. for measuring 1,5-AG as a short-term marker for glycemic control. A similar laboratory assay has been used in Japan. During normoglycemia, 1,5-AG is maintained at constant steady-state levels because of a large body pool compared with the amount of intake and because this substance is metabolically inert. Normally, 1,5-AG is filtered and completely reabsorbed by the renal tubules. During acute hyperglycemia when the blood glucose levels exceed 180 mg/dl, whichis the renal threshold for spilling glucose into the urine, serum 1,5-AG falls. This fall occurs due to competitive inhibition of renal tubular reabsorption by filtered glucose. The greater the amount of glucose in renal filtrate (due to hyperglycemia), the less 1, 5-AG is reabsorbed by the kidneys. The 1,5-AG levels respond sensitively and rapidly torises in serum glucose and a fall in the serum level of this analyte can indicate transient elevations of serum glucose occurring over as short a period as a few days. Measurement of 1,5-AG can be useful in assessing the prior 1-2 weeks for: 1) the degree of postprandial hyperglycemia; and 2) the mean short-term level of glycemia. This assay might prove useful in assessing the extent of glycemic variability that is present in an individual with a close-to-normal A1C level, but who is suspected to be alternating between frequent periods of hyperglycemia and hypoglycemia. In such a patient, the 1,5-AG level would be low, which would indicate frequent periods of hyperglycemia, whereas in a patient with little glycemic variability, the 1,5-AG levels would not be particularly depressed because of a lack of frequent hyperglycemic periods. In the ARIC study, 1,5-AG was associated with severe hypoglycemia after adjustmentfor other variables, an observation which is consistent with the role of 1,5-AG in reflecting glycemic variability, a known risk factor for hypoglycemia (214).

 

Longitudinal data from the ARIC study showed that 1,5-AG was associated with ESRD over a 19-year follow-up period, but the relationship was no longer significant after adjusting for glucose control with other markers (215). Among participants with diabetes and A1C <7%, each 5 mcg/mL decrease in 1,5-AG was associated with an increase in dementia risk by 16%, and at A1C >7%, there was also a significant association over a median 21-year follow-up period (216). There was also an association of 1,5-AG and cardiovascular outcomes in ARIC, which persisted, thoughwere attenuated after adjusting for A1C (217). Therefore, it is not yet clear whether 1,5-AG, as a measure of glucoseexcursions, provides incremental value beyond A1C for predicting long-term complications.

 

 

CONCLUSIONS

 

Many new types of technology are increasingly being developed and applied to fight diabetes and its complications. New technologies will improve the lives of people with diabetes by measuring glucose and other biomarkers of glycemic control and linking glucose levels with insulin delivery to improve the lives of people with diabetes.

 

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203. Parrinello CM, Sharrett AR, Maruthur NM, Bergenstal RM, Grams ME, Coresh J, Selvin Racial Differences in and Prognostic Value of Biomarkers of Hyperglycemia. Diabetes Care. 2016;39(4):589-95.
204. Lindsey CC, Carter AW, Mangum S, Greene D, Richardson A, Brown SJ, Essary JL, McCandless Aprospective, randomized, multicentered controlled trial to compare the annual glycemic and quality outcomes of patients with diabetes mellitus monitored with weekly fructosamine testing versus usual care. Diabetes TechnolTher. 2004;6(3):370-7.
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ABSTRACT

 

Type 1A diabetes (T1D) represents an autoimmune disorder that can affect individuals from within a year of birth until age 60. A number of genes strongly influence the development of disease, including genes found within the human lymphocyte antigen (HLA) complex. The role of non-HLA genes is being defined in recent studies, and we are beginning to identify pathways that lead to autoimmunity and eventually pancreatic islet cell destruction. Although genes can predispose one to type 1A diabetes, environmental factors may also play a significant role in the pathogenesis. These as-yet-undefined factors appear to have accelerated the onset and markedly increased the frequency of disease in many populations around the world over the last 30 years. The development of ever more sophisticated immunoassays to detect antibodies directed against pancreatic antigens have helped define the autoimmune nature of the disorder, but as importantly have also provided an opportunity to identify those individuals with prediabetes and to stratify their risk of developing overt hyperglycemia. Immunologic assays as well as intervention trials are allowing us to learn more about the immune pathways that are disordered and offer hope for future therapeutic approaches to prevent and reverse type 1A diabetes.

 

INTRODUCTION

 

In the U.S. alone, more than one million people are living with type 1 diabetes (TID) and approximately 80 people per day, or 30,000 individuals per year, are newly diagnosed (1, 2). Recent epidemiological studies demonstrate that the global T1D incidence is increasing at a rate of approximately 3-4% per year, notably among younger children (3, 4). Despite improvements in insulins, insulin delivery methods, and home glucose monitoring, the vast majority of those with T1D do not achieve recommended levels of glycemic control.  This is particularly true in childhood and adolescence, where a recent U.S. study reported mean HbA1c values exceeding 9.5%, and a high frequency of both DKA and severe hypoglycemia (5). In addition to the increased risk of morbidity and mortality, TID places significant emotional and financial burdens on individuals, families, and society. These realities highlight the need for both better TID therapies and the continued push towards the prevention of TID. In recent decades, research efforts have described the natural history of type 1 diabetes and expanded the ability to identify individuals at risk for the disease even before clinical onset, via the recognition of genetic markers or TID-specific autoantibodies. The increasing ability to identify the at-risk population affords researchers the opportunity to intervene at progressively earlier stages in the disease.  With the understanding that established islet autoimmunity, confirmed by the presence of multiple T1D autoantibodies, inevitably leads to clinical TID, investigative efforts are shifting towards the prevention or modification of autoimmunity.  Furthermore, with the mounting evidence that any amount of residual C-peptide improves long term clinical outcomes in TID, some therapies aim to preserve remaining beta cell function in those with clinical disease. In this chapter, we review the epidemiology of TID and the genetic and environmental risk factors for T1D.

 

 

EPIDEMIOLOGY OF DIABETES

 

T1D, or autoimmune diabetes, represents 5-10% of diabetes, and like autoimmunity in general, TID is increasing worldwide. The increase likely is attributable to environmental factors or epigenetic changes, as genetic changes don’t occur rapidly enough to explain such a dramatic increase. The SEARCH for Diabetes in Youth Study is a multicenter observational study investigating trends in incidence and prevalence of diabetes in American youth < age 20.  SEARCH data suggests that the prevalence of TID among non-Hispanic white youth is ~1/300 in the US by age 20 years (6). Between 2002 and 2009, the incidence of TID among non-Hispanic white youth < age 20 years increased by an average of 2.7% per year (7). Similarly, the EURODIAB study evaluated TID incidence trends in 17 European countries from 1989-2003 in youth < age 15 years, and found an average annual incidence increase of 3.9%. This trend predicts a 70% increase in TID prevalence between 2005-2020 among European youth < 15 years old (8) with the peak of diagnosis between ages 10-14 (9). While incidence and prevalence are well documented in children, TID occurs in adults as well, at a frequency that is less certain; estimates are that 25-50% of all TID cases are diagnosed in adulthood. The uncertainty likely is due to a less dramatic clinical presentation than is typically seen in children who present with TID. The incidence of TID varies tremendously by geographic location, with higher rates generally seen in countries located farther from the equator. Worldwide incidence data was reported in 2000 by the DIAMOND project (10), a WHO-sponsored effort to address the public health implications of TID. The incidence of TID between 1990 and 1994 in 50 countries is shown in Figure 1. Between 1990 and 1994, the incidence of TID in individuals aged 0-14 years in both Finland and Sardinia was 37/100,000 individuals, whereas the incidence in both China and Venezuela was 0.1/100,000 individuals, a 350-fold difference. The increased incidence coupled with reduced early mortality has contributed to the increasing prevalence of disease. 

 

 

 

 

WHAT IS THE RISK OF TYPE 1 DIABETES?

 

As is true for Cindy, 85% of individuals who develop TID have no family history of TID; nonetheless, a family history of the disease does increase an individual’s relative risk.  The prevalence of TID in the US non-Hispanic white population by age 20 is ~0.3%, as compared with ~5% of those with a relative with TID, a 15-fold increase in relative risk.   This relative risk is depicted in Figure 2.

 

 

The risk of TID among family members varies depending on who the affected family member is, as shown in Table 1.  

 

Table 1. Prevalence of TID in Individuals with a Family History of TID
Relative with TID	Prevalence at age 20	Reference
Mother	2%	(11, 12)
Father	6%	(11, 12)
Non-twin sibling	6%	(13)
Dizygotic (fraternal) twin	10%	(13, 14)
Monozygotic (identical) twin	>50%	(15)

 

The heritability pattern suggests that both genes and environment contribute to risk.  Curiously, the risk of TID in offspring is higher if the father has TID (~6%) as compared to if the mother has TID (~2%) (11, 12). Moreover, the risk to a dizygotic twin is slightly higher (~10%) than is the risk to a non-twin sibling with similar HLA risk genes (~6%) (13, 14) suggesting that the intrauterine environment and/or similar early life exposures may be important. Lastly, the risk to a monozygotic twin is upwards of ~50%; surprisingly the second twin’s diagnosis may occur many decades after the index twin, highlighting the complexities of gene and environmental interactions that underlie the disease (15).

 

 

 

THE NATURAL HISTORY TYPE 1 DIABETES

 

It is now understood that TID is an immune-mediated disease that begins in the setting of genetic predisposition and then progresses along a predictable path: early islet autoimmunity (one autoantibody), established islet autoimmunity (two or more autoantibodies), abnormal glucose tolerance, clinical TID with some remaining beta cell function, and finally, little or no remaining beta cell function. This understanding comes from decades of effort by multiple investigators and from participation by thousands of patients with TID and their family members.  George Eisenbarth’s description of TID as a chronic autoimmune disease, manifested by autoimmunity and a gradual linear fall in beta cell function until there is insufficient beta cell mass to suppress symptomatic hyperglycemia, has served for decades as the TID natural history paradigm (16). The “Eisenbarth” model has undergone refinements in recent years; namely, although autoimmunity and beta cell dysfunction do appear prior to diagnosis, these changes are often step-wise and non-linear.  Furthermore, beta cell destruction may not be absolute.  Nonetheless, the paradigm is largely correct and serves as the underlying rationale for TID trials. 

 

The long pre-symptomatic natural history of TID presents an opportunity to intervene earlier than is done currently. Diabetes-specific autoantibodies can appear many years before clinical diagnosis and may reliably be used to predict disease progression. In 2015, JDRF, the Endocrine Society, and the American Diabetes Association proposed a new TID staging system which underscores that TID begins with islet autoimmunity rather than with symptomatic hyperglycemia (17). Stage 1 TID is defined as the presence of 2 or more autoantibodies with normoglycemia; stage 2 TID is 2 or more autoantibodies, impaired glucose tolerance, and no symptoms; stage 3 TID is clinical disease. The staging system is depicted in figure 3.  

 

HOW TO DETERMINE RISK OF TID

 

Risk of TID may be determined by the identification of autoantibodies, usually in those identified as having genetic risk through HLA testing or by family history. Autoantibodies are detectable years before the onset of clinical TID. 

 

Determining Risk: Genes

 

With the knowledge that TID runs in families and with advances in technology, investigators have described the genetic risk of TID.  TID risk is strongly linked to HLA class II DR3 and DR4 haplotypes, with the highest risk in those with the DR3/DR4 genotype.  The importance of HLA genes to TID risk highlights the role of the adaptive immune system in the development of autoimmunity.  Newer studies have discovered multiple other genes that also contribute to TID risk (18). They are largely genes known also to impact immune function; however, their contribution is dwarfed by the impact of HLA genes. Interestingly, recent work suggests that HLA genes primarily contribute to development of autoantibodies, while non-HLA genes and environmental factors may be more important in the progression from autoantibodies to clinically overt disease (19, 20). The description of non-HLA risk genes (such as the genes for insulin, a major TID autoantigen) highlights other potential pathways to disease and potential therapies. 

Although the contribution of HLA class II risk genes overwhelms the contribution of non-HLA risk genes, the HLA contribution may be decreasing as the overall incidence of TID increases.  This suggests that in a population with non-HLA genetic susceptibility, the environment may have become more conducive to the development of TID. This was reported in a 2004 Lancet article by Gillespie, et al., in which the investigators compared the frequency of HLA class II haplotypes in a UK cohort of 194 individuals diagnosed with TID between 1922-1946 (the Golden Years cohort) to a cohort of 582 individuals diagnosed between 1985-2002 (the BOX cohort) (21). In this comparison, shown in Figure 4, 47% of individuals in the Golden Years cohort were positive for the highest risk genotype DR3-DQ2/DR4-DQ8, compared to 35% of individuals in the BOX cohort.

 

 

Determining Risk: Family History And Islet Cell Autoantibodies

 

Natural history studies of relatives such as Diabetes Prevention Trial (DPT-1) and Diabetes TrialNet Pathway to Prevention have helped define the risk of TID in those with a family history of TID.  Since 2000, Diabetes TrialNet has screened over 200,000 relatives of people with TID, aiming to enroll at-risk individuals in prevention trials.  Among relatives of people with TID, ~5% will have at least one of five islet autoantibodies (22). TrialNet screens for islet cell antibodies (ICA), autoantibodies to insulin (IAA or mIAA), antibodies to a tyrosine phosphatase (IA-2; previously ICA512), antibodies to glutamic acid decarboxylase (GAD), and antibodies to a zinc transporter (ZnT8).  With each additional autoantibody, the risk of TID increases predictably. Unsurprisingly, those with islet autoimmunity and abnormal glucose tolerance are at an even further increased risk of symptomatic T1D. The TrialNet strategy to identify islet autoimmunity among relatives of individuals with TID is shown in Figure 5. There are many other screening efforts ongoing outside of TrialNet. (23-25)

 

 

Natural history studies have shown not only that islet autoimmunity predicts TID risk, but also that islet autoantibodies usually appear early in life; 64% of babies destined to develop T1D before puberty will have antibodies by age 2 and 95% by age 5 (26). Furthermore, the data from both prospective birth cohort studies (27) and cross-sectional studies (28-31) is remarkably consistent and suggests that the risk of progression from established autoimmunity to clinical TID is in the range of 40% after 5 years, 70% after 10 years, and 85% after 15 years. This risk over time is depicted in Figure 6. The key understanding from natural history studies is that essentially all individuals with confirmed islet autoimmunity will eventually develop clinical T1D at a rate of 11% per year.

 

 

Identifying individuals with islet autoimmunity has two potential benefits; namely, the opportunity to monitor closely for disease progression, conferring a reduced risk of morbidity and mortality at the time of TID diagnosis, and the identification of individuals who are eligible for prevention trials.  It is perhaps underappreciated that there is potentially a direct clinical benefit to identifying those with islet autoimmunity.  Individuals with islet autoimmunity followed regularly until clinical diagnosis present with lower HbA1c and experience less DKA than those diagnosed in the community (Table 2) (32-36). For this reason, since 2009, the ADA has recommended that all individuals with a relative with T1D be counseled about the opportunity to be screened for diabetes autoantibodies in the context of a clinical research trial (37).

 

Table 2.  Individuals Diagnosed with T1D While Enrolled in a Clinical Trial have Less Morbidity at the Time of Diagnosis. (32-36)
STUDY	HbA1c at time of TID diagnosis		% with DKA at time of TID diagnosis
	Enrolled in study	Usual care	Enrolled in study	Usual care
SEARCH				25.5%
BABYDIAB	8.6%	11.0%	3.3%	29.1%
DPT-1	6.4%		3.7%
DAISY	7.2%	10.9%	< 4%
TEDDY < age 5			13.1%
SEARCH < age 5				36.4%
BABYDIAB < age 5				32.3%

 

STRATEGIES TO BRING SCREENING FOR RISK TO CLINICAL PRACTICE

 

Screening relatives does identify a population of those at risk for clinical T1D; however, at least 85% who get T1D have no relatives with disease.  Thus, to truly prevent all T1D, testing of the general population would have to occur. This could be done with current technology by testing all babies for genetic (HLA) risk at birth and then following with antibody testing. The Population Level Estimate of type 1 Diabetes risk Genes in children (PLEDGE) study enrolls newborns from the general population and offers one-time genetic testing and follow-up autoantibody testing at 2 and 4 years of age (38). The study aims to demonstrate feasibility and to develop evidence to support eventual inclusion of a T1D screening program in standard primary care.

 

Other studies, such as The Environmental Determinants of Diabetes in the Young (TEDDY) study, the Diabetes Autoimmunity Study in the Young (DAISY), and the Global Platform for the Prevention of Autoimmune Diabetes (GPPAD) are exploring similar methodologies to screen and monitor for risk (24, 39, 40).  However, with an increasing number of individuals developing T1D even without the high-risk HLA types, such approaches may still miss some destined to develop disease. 

 

An alternative risk detection strategy for those without a family history may be to perform point-of-care antibody testing in a routine pediatric visit.  Since almost all who will develop diabetes before puberty will have antibodies by age 5; such testing could be done at age 4-5 and perhaps once again in the teenage years.  This method will still miss those who develop T1D before this age, but would likely be a cost-effective approach to finding those at risk.  If these at-risk subjects are monitored regularly until development of clinical disease they would benefit from reduced morbidity at time of diagnosis even if a prevention therapy were not yet available.

 

There are many ongoing projects aimed at screening members of the general population for diabetes autoantibodies even without prior HLA testing (23, 25, 41, 42).

 

As risk-screening programs employ varying assays and recruit from different populations, interpretation and translation of results is unclear. It is not yet known whether those found to be autoantibody positive through one program will experience the same rates of T1D progression and/or benefit from the same therapies as individuals who have participated in other screening and intervention efforts.

 

 

 

PRENATAL INFLUENCES  

 

The prenatal environment can have profound effects on the developing fetus. With the recognition that antibodies often develop early in life and that essentially all those with established islet autoimmunity (two or more autoantibodies) will eventually develop TID, investigators have looked to the prenatal period to search for factors that could contribute to disease development in utero.  As shown in Table 3, decades of observational studies have yielded inconsistent results.  Yet this remains an important area of investigation and one that may lead to primary prevention strategies for T1D. The Environmental Determinants of Islet Autoimmunity (ENDIA) study is an ongoing prospective birth cohort study in Australia that enrolled infants and unborn infants of first degree relatives with T1D. Biologic samples including blood, stool, and saliva will be collected longitudinally for investigation of factors including viral exposures during pregnancy and early childhood, maternal and fetal microbiome, delivery method, maternal and early infant nutrition, pregnancy and early childhood body weight, and both innate and adaptive immune function. In 2018, the ENDIA study completed target enrollment of ~1500 subjects, who will be followed regularly until the development of islet autoimmunity (43).

 

Table 3.  Potential Prenatal Influences on TID Risk
Pre-natal or intrauterine exposure	Relative risk to offspring	Reference
Maternal age	Inconsistent data	(44-46)
Birth weight > 2 SD above norm (~4000g)	Inconsistent data	(47-51)
Birth weight < 2 SD below norm (~2500g)	Inconsistent data	(49-51)
Birth order: second and later born	Inconsistent data	(46, 52, 53)
Birth interval < 3 years	Inconsistent data	(46, 54)
Caesarean delivery	Inconsistent data	(51, 55, 56)
Pre-eclampsia	Inconsistent data	(51, 57)
Pre-term delivery (<37 weeks gestation)	Inconsistent data	(51, 58)
Maternal vitamin D supplementation	Inconsistent data	(59-62)
Maternal antibiotic use	No association	(53, 63)
maternal BMI/pregnancy weight gain	No association	(51, 64)
Maternal omega 3 fatty acid supplementation	No association	(60, 65, 66)

 

 

 

Investigators also have studied the early childhood period for clues to the causes of islet autoimmunity and TID; these have included both observational studies and randomized clinical trials. Such influences might be divided into early nutritional exposures and early microbial/infectious exposures, both of which can affect development of the normal immune system.

 

The inconsistent findings relating to environmental factors reported from observational studies and clinical trials led to the design and implementation of a large international comprehensive evaluation of genetically at-risk babies using cutting edge technologies to study genetics, genomics (gene function), metabolomics, and the microbiome. The Environmental Determinants of Diabetes in the Young (TEDDY) is an international prospective birth cohort study that recruited almost 8,000 babies at increased risk for TID (based on HLA and family history) from Finland, Germany, Sweden, and the US from 2004-2010.  Information on environmental exposures such as diet (including breastfeeding history), infections, vaccinations, and psychosocial stressors will be collected. Participants will be followed until the age of 15 for the development of islet autoimmunity or TID. The wealth of data from this study will provide a foundation for future randomized clinical trials (24). One interesting finding reported in December 2019 is that there are subtle differences in the gut microbiome—such as, persistent stool enterovirus B species--in children who develop islet autoimmunity compared to children who do not develop autoimmunity (68).

 

EARLY NUTRITIONAL EXPOSURES

 

Breastfeeding

 

The hypothesis that human breastmilk may protect against future TID development was presented as early as 1984 (69). Since then, there have been several prospective cohort studies to suggest that breastmilk lowers the risk of islet autoimmunity and TID, including the German BABYDIAB/BABYDIET study (70), the Colorado-based DAISY study (71), and the Norwegian MIDIA study (72), but others show no effect (73).  Although the data on whether breastmilk is protective against TID isn’t clear, it certainly isn’t harmful.  Given the well-established general benefits of breastfeeding, patients may safely be advised to follow the American Academy of Pediatrics’ guidelines related to infant feeding. The mechanism by which breastmilk may lower the risk of TID is uncertain, but one theory suggests that breastmilk has positive effects on the infant microbiome. The microbiome is discussed in greater detail below.  

 

Cow’s Milk And Bovine Insulin Exposure

 

In contrast to considering breastfeeding as potentially beneficial in protecting against autoimmunity, it was hypothesized that early introduction of cow’s milk or cow protein might accelerate disease.  This concept was tested in the Trial to Reduce IDDM in the Genetically at Risk (TRIGR) which asked whether weaning to hydrolyzed casein (which is free of bovine proteins including insulin) formula (n=1081) instead of regular cow’s milk formula (n=1078) in genetically at-risk infants could prevent or delay TID.  Though the TRIGR pilot study was suggestive of benefit, no benefit was seen in the fully powered study (74) (75). Similarly, The Finnish Dietary Intervention Trial for the Prevention of Type 1 Diabetes of (FINDIA) suggested that weaning to hydrolyzed cow’s milk formula was not effective in reducing the appearance of autoantibodies, though they did report that a patented cow’s milk formula specifically removing bovine insulin appeared to be beneficial in this pilot study (76).  While additional studies may be informative, current data does not support that weaning to hydrolyzed cow’s milk formula is protective against islet autoimmunity. 

 

Gluten Exposure

 

Both BABYDIAB (77) and DAISY (78) were observational studies that suggested an association between introduction of gluten and islet autoimmunity.  However, these studies had different results as to the timing of gluten introduction. Similarly, no effect was found in the BABYDIET study; a randomized controlled trial that asked whether delayed introduction of gluten to 6 vs 12 months would affect the risk of diabetes autoimmunity (79, 80).

 

Vitamin D And/Or Omega 3 Fatty Acids

 

Vitamin D is an important component of a normal immune response; moreover, the higher incidence of TID in northern climates suggests that vitamin D deficiency could contribute to autoimmunity and TID.  However, data from observational studies is mixed on whether vitamin D and/or omega 3 supplementation is beneficial or not (60, 81-86). A pilot randomized trial of omega 3 supplementation to pregnant mothers and infants failed to demonstrate a profound immunologic effect of treatment (87). With routine vitamin D supplementation recommended for infants (88), it is unlikely that a fully powered randomized trial would be feasible to assess the impact on autoimmunity. 

 

MICROBIAL EXPOSURES

 

The Hygiene Hypothesis

 

Parallel to the rising incidence of TID and other autoimmune diseases, there has been a worldwide trend towards urbanization, increased standard of living, smaller family sizes, less crowded living conditions, safer water and food supplies, less cohabitation with animals, wide use of antibiotics, childhood vaccination, etc.  While these trends are generally considered improvements in human existence, the so-called “hygiene hypothesis,” proposed by Strachan in 1989 (89) suggests a possible downside; that is, that early microbial exposures might have a protective effect via the early education of the immune system and the development of normal tolerance to self-antigens. Data cited in support of the hygiene hypothesis comes from comparisons between eastern Finland and Russian Karelia (Figure 7) (90-92).

 

 

The Microbiome

 

Another possible interface between microbial exposure and human disease is through the microbiome; that is the gut flora established within the first 3 years of life (93).  It has been hypothesized that perturbations in normal early microbiome development might pre-dispose to disease whether through direct modulation of innate immunity or via alteration of intestinal permeability and the downstream effects on adaptive immunity. Interestingly, it appears that the gut microbiome is less diverse and less “protective” in individuals with islet autoimmunity or recent onset TID (94-96).  Whether this difference is cause, effect, or correlation isn’t known. Nonetheless, multiple factors might affect the early intestinal microbiome, some of which also have been shown to correlate with risk of islet autoimmunity and TID.  For example, breastfeeding can alter the intestinal microbiome of the infant by increasing the number and diversity of beneficial microbiota (97, 98). As previously discussed, multiple prospective observational studies suggest that breastfeeding protects against future development of islet autoimmunity and TID, but there’s no evidence to connect this directly to the infant microbiome.

  

Viral Infections

 

A viral etiology for initiation of autoimmunity is an attractive idea; a beta cell trophic virus could contribute to disease by directly killing beta cells, by leading to a chronic infection which triggers an immune response, or by molecular mimicry in which self-antigens are erroneously recognized as viral epitopes targeted for destruction.  Notably, these possible mechanisms would not necessarily point to a particular virus; any virus widespread in a population could theoretically lead to autoimmunity in genetically susceptible individuals if encountered at a vulnerable time in immune system or beta cell development.  With the notable exception of congenital rubella which is associated with type 1 diabetes (99), other data relating viruses to initiation of autoimmunity is less conclusive.  While some studies have reported viral “footprints” in islets from individuals who have died from TID, these have not been consistently confirmed.  Similarly, many studies have focused on enteroviruses, including coxsackie B, due to observations suggesting seasonal variation in antibody development that is reminiscent of the timing of such infections (100) (101), yet this remains controversial.  Aside from a viral role in the initiation of autoimmunity, others have proposed that acute viral infections may impact the transition from islet autoimmunity to clinical TID due to increased insulin demand during infections.  Patients commonly report an acute viral illness preceding the diagnosis of TID, and the clinical onset of TID more commonly presents in the fall and winter months in both the northern and southern hemispheres (102); but this does not imply a causal relationship.

 

Vaccinations

 

In recent decades, an increasing number of parents in Western countries have declined routine childhood vaccination of their children, which has created a situation with significant personal and public health consequences.  Multiple high-quality studies have thoroughly investigated vaccinations and TID, and none have found any association with islet autoimmunity or TID (103-107)

 

 

FUTURE CONSIDERATIONS

 

Despite advances in glucose monitoring and insulin delivery, the daily psychological and financial burden of disease on individuals, their families, and society together with the persistence of complications and reduced life span demand a paradigm shift.

 

As of 2021, we know much about the natural history of disease. We know that antibodies can develop early in life and that essentially all of those with established islet autoimmunity will develop clinically overt disease. We also know that identifying these individuals is of significant clinical benefit. Those with islet autoimmunity followed carefully until diagnosis have markedly less morbidity at the time of diagnosis and lower HbA1c values. Family members of T1D probands should be made aware of their disease risk and should be offered autoantibody screening and enrollment in monitoring trials. Correspondingly, patients with TID should be informed of the opportunity to have their relatives screened for TID risk in the setting of a clinical research study.

 

While the interaction of humans with their environment must contribute to disease; how this occurs is still being elucidated. It is likely that there are many different paths by which individual gene/environment interactions result in T1D; suggesting that dissecting this heterogeneity will provide better insights and therapies.

 

REFERENCES

 

1. JDRF Fact Sheet (accessed 2021 Dec 14) Available from: https://www.jdrf.org/t1d-resources/about/facts/.
2. Centers for Disease Control and Prevention, National Diabetes Statistics Report, 2014. 2014; (accessed 2021 Dec 14)Available from: http://www.cdc.gov/diabetes/pubs/statsreport14/national- diabetes-report-web.pdf.
3. Patterson, C., et al., Trends in childhood type 1 diabetes incidence in Europe during 1989-2008: Evidence of non- uniformity over time in rates of increase. Diabetologia, 2012. 55(8): p. 2142-2147.
4. Bell, R., et al., Diabetes in non-Hispanic white youth:prevalence, incidence, and clinical characteristics: the SEARCH for diabetes in youth study. Diabetes Care, 2009. 32(Suppl 2): p. S102-S111.
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ADDITIONAL INFORMATION

(From prior chapter by Aaron W. Michels, MD and Peter Gottlieb, MD)

 

INTRODUCTION

 

Type 1 diabetes mellitus is defined as immune mediated diabetes mellitus (1-6). It can become manifest with hyperglycemia presenting in the first days of life or in adults over the age of 60. Current estimates indicate that immune mediated diabetes represents approximately 5 to 10% of the diabetes developing in adults and that approximately as many individuals develop this form of diabetes as adults as do children (7-9). In the United States the great majority (>90%) of Caucasian children developing diabetes have type 1 diabetes; whereas, approximately 50% of African American and Hispanic American children developing diabetes lack the autoantibody and immunogenetic markers of typical type 1 diabetes (10-12). Most of these latter children appear to have variants of type 2 diabetes with a small number having specific characteristic genetic syndromes (e.g. MODY: Maturity Onset Diabetes of Youth) with identified mutations of genes such as glucokinase and HNF (Hepatic Nuclear Factors) (13). In addition, studies of the pathology of the pancreas of Hispanic and African American children who lack islet autoantibodies show that all islets have some beta cells, but in decreased numbers (11). In contrast, in the pancreas of patients with type 1 diabetes, there is lobular loss of beta cells (termed pseudoatrophic islets) (11).

 

When an individual presents with type 1 diabetes it indicates that they and their relatives have an increased risk of having or developing a series of autoimmune disorders (12). Celiac disease, hypothyroidism, hyperthyroidism, Addison's disease, and pernicious anemia are some of the most prominent associated diseases. For example, approximately 1/20 patients with type 1 diabetes have celiac disease (14,15). Most of these patients are asymptomatic and the disorder is only discovered if anti-transglutaminase autoantibodies are measured and individuals with positive antibodies biopsied. In that the therapy for celiac disease, namely gluten avoidance, is highly effective, and we routinely screen all type 1 diabetic patients. We also screen for thyroid disease, which has an incidence of approximately 20% in type 1 diabetes, with yearly TSH measurements and for Addison's disease (21-hydroxylase autoantibodies) (16).

 

GENETIC SUSCEPTIBILITY

 

Type 1 diabetes is itself heterogeneous, with several forms of immune mediated diabetes with known genetic causes as parts of autoimmune syndromes (thus likely to be classified as other Specific Forms of Diabetes). In particular, patients develop immune mediated diabetes when they have mutations of the AIRE (Autoimmune Regulator) gene (21). Mutations of the AIRE gene result in Autoimmune Polyendocrine Syndrome Type I (23,24). Most forms of type 1 diabetes are polygenic in etiology, and polymorphisms of genes within the major histocompatibility complex (HLA genes) play a major role in determining disease susceptibility (27,28).

 

The alleles of different HLA genes (e.g., DRB1 and DQB1) are non-randomly associated with each other, such that with DRB1*0401 one usually finds one of three DQ alleles (e.g., DQB1*0301, DQB1*0302, DQB1*0303) rather than any one of more than forty different DQB molecules. Such non-random association of alleles of different genes on the same chromosome is termed linkage disequilibrium. The histocompatibility complex is divided into three regions, class II, class III and class I. The most important determinants of type 1 diabetes are the HLA DQ and DR alleles. These molecules on the surface of antigen presenting cells (e.g., macrophages) bind and present short peptides that are recognized by T cell receptors of T lymphocytes (27,35,36). They are termed immune response genes in that the specific amino acid sequence of these molecules determines which peptides will be bound and to a large extent determine which peptides an individual will respond to. Each different amino acid sequence is given a number. For the DQ molecules both its alpha and beta chain gene are polymorphic, and thus to specify a DQ molecule one must specify both chains. For DR molecules only the DRB chain is polymorphic and thus only this chain is specified. Each number after the star indicates a specific amino acid sequence of the HLA allele and the letters and first number the gene (e.g., DRB1*0401, DR B chain gene number 1, allele 0401).

 

There is a tremendous spectrum of diabetes risk associated with different DR and DQ genotypes (37-39) (Figure 8). For Caucasians with type 1 diabetes the most common diabetes-associated haplotypes are DR3 and DR4. More than 90% of patients with type 1A diabetes have one or both of these alleles versus approximately 40% of the general U.S. population. With the finer sequence information that is now available, DR4 haplotypes are subdivided based on specific variants of DRB1 and DQB1. The highest risk DR4 haplotypes have DRB1*0401, DRB1*0402, DRB1*0405, while DRB1*0403 is moderately protective. The highest risk DR4 haplotypes have DQB1*0302, with DQB1*0301 and DQB1*0303 of lower risk. Thus, both DR and DQ alleles contribute to diabetes risk. DR3 haplotypes are almost always conserved with DRB1*03 combined with DQA1*0501, DQB1*0201 (40). The highest risk genotype has both DR4/DR3 DQB1*0302/DQB1*0201. This genotype occurs in 2.4% of newborns in Denver, Colorado, and between 30 and 50% of children developing type 1 diabetes. Approximately 50% of children developing type 1 diabetes early (i.e., less than age 5) are DR3/4 heterozygotes versus 30% of young adults presenting with type 1A diabetes.

 

 

There are three HLA molecules that provide dominant protection. The most common is DQB1*0602 that occurs in approximately 20% of U.S. individuals (41-43). Protection is not absolute, but less than 1% of children with type 1 diabetes have this molecule. DQA1*0201 with DQB1*0303 and DRB1*1401 also provide dramatic protection, rarely being found in patients with type 1 diabetes and rarely transmitted from a parent with the alleles to their diabetic offspring (38,39). It is noteworthy that both DR and DQ alleles can protect. The specific mechanism underlying both susceptibility and protection are not fully understood. One attractive hypothesis is that protective alleles when expressed within the thymus lead to deletion of T cells with receptors that recognize a critical islet peptide (44). With deletion of such T cells, the risk of diabetes would be reduced. In addition, it is likely that high-risk HLA alleles present specific peptides of target islet molecules to T lymphocytes (28).

 

Multiple additional loci (Figure 7) have been implicated with estimates that approximately 50% of the familial aggregation of type 1 diabetes is attributable to the HLA region, perhaps 10% to the insulin locus, with all other loci contributing much less, though in aggregate their contribution is important. In the Cox analysis (Figure 7) of approximately 700 sibling pairs the only significant LOD score was for a locus on chromosome 16q that was not given an iddm designation with earlier genome screens. Several areas implicated in the past had suggestive scores, but there is overlap with the families from which the original evidence was generated. It is likely that contributing loci may differ between populations contributing to the initial difficulty of replicating putative loci in different studies (56,57). More than 40 genetic loci contributing to diabetes risk have been implicated (Figure 7). Polymorphisms of the insulin gene are well established as contributing to risk. A repeat sequence upstream (5') of the insulin gene termed a Variable nucleotide tandem repeat or VNTR, is divided into three general repeat sizes with the longest set of repeats associated with protection from diabetes (46-48). This set of alleles is also associated with greater thymic production of insulin messenger RNA (49), leading to the hypothesis that greater thymic message and presumably greater proinsulin production dampens anti-insulin autoimmunity (49-51). A functional polymorphism of the LYP gene (Lymphocyte Specific Phosphatase; PTPN22- Protein Tyrosine Phosphatase) has been associated with type 1 diabetes, rheumatoid arthritis, and lupus erythematosus (52-54). The R620W missense mutation (tryptophan replacing arginine) disrupts the binding of the phosphatase to the molecule Csk and this blocks its ability to down-regulate T cell receptor signaling. With an odds ratio of between 1.7 and 2.0 of the "autoimmunity” allele which is relatively common (5-10% allele frequency) there is a large genetic effect that is much greater than CTLA-4 polymorphisms associated with diabetes risk (55). Combining known diabetogenic polymorphisms of LYP, the insulin gene, alleles of DP, DQ, and DR class II immune response genes, as well all of the new loci account for approximately 48% of the familial aggregation of type 1A diabetes, with DR and DQ loci accounting for 41% of this 48% (45).  A recent study suggests that for a major subset of individuals with the highest risk HLA genotype (DR3/4-DQ2/DQ8 heterozygotes) who share both HLA haplotypes with a diabetic sibling, risk of activating anti-islet autoimmunity is as high as 80% (33).

 

AUTOIMMUNITY

 

Insulin autoantibodies are usually the first autoantibody to appear in children followed from birth for the development of type 1 diabetes (84,85). These autoantibodies can appear in the first six months of life. Once insulin autoantibodies appear in such young children there is a high risk of development of additional anti-islet autoantibodies and progression to diabetes. More than 90% of children developing type 1 diabetes prior to age 5 have insulin autoantibodies while less than 50% of children developing diabetes after age 12 have such autoantibodies (86). Therapy with human insulin induces insulin antibodies that cannot at present be distinguished from insulin autoantibodies. Thus, if an individual has been treated with insulin for more than several weeks, positive insulin autoantibodies are not interpretable. For all autoantibodies measured in the first 9 months of life, the antibodies may be transplacental in origin, a particular problem if a mother has type 1 diabetes and is treated with insulin.

 

There are a number of important caveats in the utilization of anti-islet autoantibody assays. The field developed from the initial observation that patient's sera "stained” islets of cut sections of human pancreas, the cytoplasmic islet cell antibody (ICA) assay (83). This assay, given its utilization of human pancreas from cadaveric donation and subjective reading of slides, has proven the most difficult to standardize (69). The assay predominantly detects antibodies reacting with GAD65, IA-2 and ZnT8, but does not detect anti-insulin autoantibodies. Given the difficulty in standardization, reliability over time, and major overlap with defined autoantibody assays, a number of investigators no longer utilize this assay. For research purposes and potentially in older adults with what has been termed LADA (latent autoimmune diabetes of adults) the ICA assay may have utility in that there is evidence of one or more additional autoantibodies detected with this assay and not with GAD65, IA-2, ZnT8 and insulin autoantibody determination.

 

A single autoantibody, even when present on multiple occasions, is associated with only a modest risk of progression to diabetes: approximately 10% (87,88). Once two or more anti-islet autoantibodies are present in children, progression to diabetes is very high, approaching almost 100% after 15 years of follow-up (89). In addition, once multiple autoantibodies are present it is very unusual for an individual to lose all expression of autoantibodies prior to the development of overt diabetes. Following the development of diabetes, IA-2 and more slowly GAD65 (over decades) autoantibodies wane. Following islet or pancreatic transplantation expression of GAD65 and IA-2 autoantibodies can be induced in patients with long-standing diabetes (90).

 

The most specific of the autoantibodies react with the molecule IA-2, but IA-2 autoantibodies are usually detected following the appearance of insulin and/or GAD65 autoantibodies (84). Even with IA-2 autoantibodies, however, there are apparent "false” positives in terms of diabetes risk. We evaluated approximately 10 individuals with either transient IA-2 autoantibodies or normal controls with IA-2 autoantibodies. None of these individuals expressed an additional anti-islet autoantibody. In contrast to patients diagnosed with or developing type 1 diabetes, the ICA512/IA-2 autoantibodies of nine out of ten of these normal individuals did not recognize multiple ICA512 epitopes and did not react with the dominant ICA512 autoantigenic domain (91). This indicates that even with a highly specific radioassay, if one screens tens of thousands of sera, one can find sera that presumably by chance cross-react with some epitope of the IA-2 molecule. It is much less likely to find an individual with antibodies that by chance react with two different islet autoantigens using fluid phase radioassays set with specificity at the 99th percentile of controls.

 

LOSS OF INSULIN SECRETION

 

At present, beta cell mass is not readily measured over time in humans, so it is not possible to absolutely define progression of beta cell loss. There is however no doubt that measurable anti-islet autoimmunity precedes the development of diabetes in terms of anti-islet autoantibodies in humans, and autoantibodies and T cell invasion in animal models. In the NOD mouse there is evidence of some beta cell destruction and beta cell regeneration prior to the onset of diabetes (92). There is also evidence for a change in the immune system close to the time of onset of diabetes (i.e., Th2 to Th1) (93-96). This change is associated with more rapid disease progression, ability to transfer diabetes by T cells, and a time window during which a specific immunotherapy (monoclonal anti-CD3 antibodies) is effective (97). In humans the best evidence for progressive loss of beta cell function comes from studies of insulin and C-peptide secretion (98). C-peptide, the connecting peptide of proinsulin, is secreted in equimolar amount to insulin, but C-peptide is not present in insulin preparations utilized to treat diabetes. Thus, C-peptide has become an important indicator of remaining beta cell function. Following the onset of diabetes, it has long been appreciated that C-peptide secretion progressively declines, until for most patients with type 1 diabetes C-peptide becomes non-detectable, associated with true insulin dependence. In a similar manner, first phase insulin secretion following a bolus of glucose on intravenous glucose tolerance testing is progressively lost for relatives followed to the development of type 1 diabetes (99). Such metabolic abnormalities may result in part from functional inhibition of beta cell secretion, but pathologic studies indicate that beta cell mass is normal for identical twins of patients that have not activated anti-islet autoimmunity, and for new onset patients that bulk of beta cells are destroyed (100). Within the pancreas of a patient with type 1 diabetes there is heterogeneity of islet lesions, with most islets lacking all beta cells and with no lymphocytic infiltrates (pseudoatrophic islets), few normal islets with no infiltrates, and few islets with remaining beta cells and infiltrates. This is perhaps analogous to the progressive development of vitiligo in patients, with patches of skin with all melanocytes destroyed, whereas other skin is normal.

 

OVERT DIABETES

 

The development of type 1 diabetes is usually perceived as an abrupt event, and some individuals may rapidly manifest severe hyperglycemia. Now that we can follow individuals to the development of type 1 diabetes, we can see that anti-islet autoantibodies can precede hyperglycemia by years, and there is usually some deterioration in glucose tolerance more than one year prior to diabetes onset (particularly with intravenous glucose tolerance testing) (101). The majority of individuals identified to be diabetic following autoantibody testing are found to have a diabetic 2-hour glucose on oral glucose tolerance testing (>200mg/dl) rather than fasting hyperglycemia. The acute presentation with severe hyperglycemia and ketoacidosis is life threatening, and it is estimated that approximately 1/200 children die at the onset of type 1 diabetes (102,103). Such children typically have a medical history where the first health care providers have failed to make the diagnosis of diabetes; the child then presents again later and dies with cerebral edema. The classic symptoms of polyuria, polydipsia, and weight loss are usually present but the initial diagnosis is still missed. The alternative diagnosis of nausea and vomiting due to viral illness is the most common mistaken diagnosis, and with the ready availability of glucose determination from a finger or heel stick, there should be a low threshold in emergency rooms and physicians’ offices for ruling out diabetes. Though transient hyperglycemia can occur, such children obviously need close follow up. We usually arrange glucose monitoring for children thought to have transient hyperglycemia, and measure anti-islet autoantibodies (104). Of those with anti-islet autoantibodies and transient hyperglycemia, almost all progress to type 1 diabetes within several months.

 

At the onset of type 1 diabetes, almost all individuals have residual insulin secretion, and there is convincing evidence that residual insulin secretion as measured by C-peptide secretion is of clinical benefit (less hypoglycemia, less microvascular complications, and much easier diabetes management).

 

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