Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?

BACKGROUND: Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes. METHODS: We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies. RESULTS: Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes. CONCLUSIONS: Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.

Safety and Efficacy of Insulins in Critically Ill Patients Receiving Continuous Enteral Nutrition.

OBJECTIVE: There is a relative lack of consensus regarding the optimal management of hyperglycemia in patients receiving continuous enteral nutrition (EN), with or without a diagnosis of diabetes. METHODS: This retrospective study examined 475 patients (303 with known diabetes) hospitalized in critical care setting units in 2019 in a single center who received continuous EN. Rates of hypoglycemia, hyperglycemia, and glucose levels within the target range (70-180 mg/dL) were compared between patients with and without diabetes, and among patients treated with intermediate-acting (IA) biphasic neutral protamine Hagedorn 70/30, long-acting (LA) insulin, or rapid-acting insulin only. RESULTS: Among those with type 2 diabetes mellitus, IA and LA insulin regimens were associated with a significantly higher proportion of patient-days in the target glucose range and fewer hyperglycemic days. Level 1 (<70 mg/dL) and level 2 (<54 mg/dL) hypoglycemia occurred rarely, and there were no significant differences in level 2 hypoglycemia frequency across the different insulin regimens. CONCLUSION: Administration of IA and LA insulin can be safe and effective for those receiving insulin doses for EN-related hyperglycemia.

Rosiglitazone improves insulin resistance but does not improve exercise capacity in individuals with impaired glucose tolerance: A randomized clinical study.

Dysmetabolic states, such as type 2 diabetes (T2D), characterized by insulin resistance (IR), are associated with fatty liver, increased cardiovascular disease (CVD) risk, and decreased functional exercise capacity (FEC). Rosiglitazone (RO) improves exercise capacity and IR in T2D. However, the effects of RO on FEC and other markers of CVD risk in prediabetes are unknown. We hypothesized that insulin sensitization with RO would improve exercise capacity and markers of CVD risk in participants with impaired glucose tolerance (IGT). Exercise performance (peak oxygen consumption and oxygen uptake kinetics), IR (homeostasis model assessment of IR and quantitative insulin sensitivity check index), and surrogate cardiovascular endpoints (coronary artery calcium (CAC) volume and density and C-reactive protein (CRP)) were measured in participants with IGT after 12 and 18 months of RO or placebo (PL). RO did not significantly improve exercise capacity. Glycemic measures and IR were significantly lower in people on RO compared to PL at 18 months. CAC volume progression was not different between PL and RO groups. RO did not improve exercise capacity during an 18-month intervention despite improved IR and glycemia in people with IGT. Future studies should explore why effects on FEC with RO occur in T2D but not IGT. Understanding these questions may help in targeting therapeutic approaches in T2D and IGT.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Advances in the management of type 2 diabetes in adults.

Type 2 diabetes is a chronic and progressive cardiometabolic disorder that affects more than 10% of adults worldwide and is a major cause of morbidity, mortality, disability, and high costs. Over the past decade, the pattern of management of diabetes has shifted from a predominantly glucose centric approach, focused on lowering levels of haemoglobin A1c (HbA1c), to a directed complications centric approach, aimed at preventing short term and long term complications of diabetes, and a pathogenesis centric approach, which looks at the underlying metabolic dysfunction of excess adiposity that both causes and complicates the management of diabetes. In this review, we discuss the latest advances in patient centred care for type 2 diabetes, focusing on drug and non-drug approaches to reducing the risks of complications of diabetes in adults. We also discuss the effects of social determinants of health on the management of diabetes, particularly as they affect the treatment of hyperglycaemia in type 2 diabetes.

A simple modeling framework for prediction in the human glucose-insulin system.

Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

American Association of Clinical Endocrinology Consensus Statement: Comprehensive Type 2 Diabetes Management Algorithm - 2023 Update.

OBJECTIVE: This consensus statement provides (1) visual guidance in concise graphic algorithms to assist with clinical decision-making of health care professionals in the management of persons with type 2 diabetes mellitus to improve patient care and (2) a summary of details to support the visual guidance found in each algorithm. METHODS: The American Association of Clinical Endocrinology (AACE) selected a task force of medical experts who updated the 2020 AACE Comprehensive Type 2 Diabetes Management Algorithm based on the 2022 AACE Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan and consensus of task force authors. RESULTS: This algorithm for management of persons with type 2 diabetes includes 11 distinct sections: (1) Principles for the Management of Type 2 Diabetes; (2) Complications-Centric Model for the Care of Persons with Overweight/Obesity; (3) Prediabetes Algorithm; (4) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Dyslipidemia; (5) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Hypertension; (6) Complications-Centric Algorithm for Glycemic Control; (7) Glucose-Centric Algorithm for Glycemic Control; (8) Algorithm for Adding/Intensifying Insulin; (9) Profiles of Antihyperglycemic Medications; (10) Profiles of Weight-Loss Medications (new); and (11) Vaccine Recommendations for Persons with Diabetes Mellitus (new), which summarizes recommendations from the Advisory Committee on Immunization Practices of the U.S. Centers for Disease Control and Prevention. CONCLUSIONS: Aligning with the 2022 AACE diabetes guideline update, this 2023 diabetes algorithm update emphasizes lifestyle modification and treatment of overweight/obesity as key pillars in the management of prediabetes and diabetes mellitus and highlights the importance of appropriate management of atherosclerotic risk factors of dyslipidemia and hypertension. One notable new theme is an emphasis on a complication-centric approach, beyond glucose levels, to frame decisions regarding first-line pharmacologic choices for the treatment of persons with diabetes. The algorithm also includes access/cost of medications as factors related to health equity to consider in clinical decision-making.

Digital Health and Shared Decision-Making in Diabetes Care - A Survey Initiative in Patients and Clinicians.

OBJECTIVE: To assess the landscape of digital health resources in the United States, better understand the impact of the digital health on shared decision-making, and identify potential barriers and opportunities for progress in the care of persons with diabetes. METHODS: The study consisted of two phases: A qualitative phase in which one-on-one interviews were conducted virtually with 34 physicians (endocrinologists {Endos}: n = 15; primary care physicians {PCPs}: n = 19) between February 11, 2021 and February 18, 2021, and a quantitative phase in which two online, email-based surveys in the English language were conducted between April 16, 2021 and May 17, 2021: one with healthcare professionals (HCP) (n = 403: n = 200 Endos and n = 203 PCPs), and one with persons with diabetes (n = 517: patients with type 1 diabetes, n = 257; patients with type 2 diabetes, n = 260). RESULTS: Diabetes digital health tools were found to be helpful in shared decision-making, but leading barriers include cost, coverage, and lack of time by healthcare professionals. Among diabetes digital health tools, continuous glucose monitoring (CGM) systems were used most commonly and viewed as most effective in improving quality of life and facilitating shared decision-making. Strategies for increasing use of diabetes digital health resources included lower cost, integration into electronic health records, and increased simplicity of tools. CONCLUSION: This study revealed that both Endos and PCPs feel that diabetes digital health tools have an overall positive impact. Integration with telemedicine and simpler, lower cost tools with increased patient access can further facilitate shared decision-making and improved diabetes care and quality of life.

Glucocorticoid-Induced Hyperglycemia Including Dexamethasone-Associated Hyperglycemia in COVID-19 Infection: A Systematic Review.

OBJECTIVE: Optimal glucocorticoid-induced hyperglycemia (GCIH) management is unclear. The COVID-19 pandemic has made this issue more prominent because dexamethasone became the standard of care in patients needing respiratory support. This systematic review aimed to describe the management of GCIH and summarize available management strategies for dexamethasone-associated hyperglycemia in patients with COVID-19. METHODS: A systematic review was conducted using the PubMed/MEDLINE, Cochrane Library, Embase, and Web of Science databases with results from 2011 through January 2022. Keywords included synonyms for "steroid-induced diabetes" or "steroid-induced hyperglycemia." Randomized controlled trials (RCTs) were included for review of GCIH management. All studies focusing on dexamethasone-associated hyperglycemia in COVID-19 were included regardless of study quality. RESULTS: Initial search for non-COVID GCIH identified 1230 references. After screening and review, 33 articles were included in the non-COVID section of this systematic review. Initial search for COVID-19-related management of dexamethasone-associated hyperglycemia in COVID-19 identified 63 references, whereas 7 of these were included in the COVID-19 section. RCTs of management strategies were scarce, did not use standard definitions for hyperglycemia, evaluated a variety of treatment strategies with varying primary end points, and were generally not found to be effective except for Neutral Protamine Hagedorn insulin added to basal-bolus regimens. CONCLUSION: Few RCTs are available evaluating GCIH management. Further studies are needed to support the formulation of clinical guidelines for GCIH especially given the widespread use of dexamethasone during the COVID-19 pandemic.

Macrovascular Complications.

Because macrovascular complications of diabetes are the leading cause of mortality and decreased quality of life for individuals with diabetes, prevention and risk reduction are paramount. Besides lifestyle management, contemporary therapies can significantly reduce risk for cardiovascular events in diabetes. For primary prevention, most individuals should be on statin therapy, whereas those at high atherosclerotic cardiovascular disease risk should also be on glucagon-like peptide 1 receptor agonists (GLP1RA) or sodium/glucose cotransporter-2 inhibitors (SGLT2i) at any hemoglobin A1c. For secondary prevention, addition of GLP1RA or SGLT2i, PCKS9i, rivaroxaban, and/or icosapent ethyl should be considered in addition to a statin and low-dose aspirin.

Etiology and outcomes of amputation in patients with peripheral artery disease in the EUCLID trial.

OBJECTIVE: Amputation remains a frequent and feared outcome in patients with peripheral artery disease (PAD). Although typically characterized as major or minor on the extent of tissue loss, the etiologies and outcomes after amputation by extent are not well-understood. In addition, emerging data suggest that the drivers and outcomes of amputation in patients with PAD may differ in those with and without diabetes mellitus (DM). METHODS: The EUCLID trial randomized 13,885 patients with symptomatic PAD, including 5345 with concomitant diabetes, to ticagrelor or clopidogrel and followed them for long-term outcomes. Amputations were prospectively reported by trial investigators. Their primary and contributing drivers were adjudicated using safety data, including infection, ischemia, or multifactorial etiologies. Outcomes following major and minor amputations were analyzed, including recurrent amputation, major adverse limb events, adverse cardiovascular events, and mortality. Multivariable logistic regression models were used to identify independent predictors of minor amputations. Analyses were performed overall and stratified by the presence or absence of DM at baseline. RESULTS: Of the patients randomized, 398 (2.9%) underwent at least one lower extremity nontraumatic amputation, for a total of 511 amputations (255 major and 256 minor) over a median of 30 months. A history of minor amputation was the strongest independent predictor for a subsequent minor amputation (odds ratio, 7.29; 95% confidence interval, 5.17-10.30; P < .001) followed by comorbid DM (odds ratio, 4.60; 95% confidence interval, 3.16-6.69; P < .001). Compared with patients who had a major amputation, those with a minor amputation had similar rates of subsequent major amputation (12.2% vs 13.6%), major adverse limb events (15.1% vs 14.9%), and major adverse cardiovascular events (17.6% vs 16.3%). Ischemia alone was the primary driver of amputation (51%), followed by infection alone (27%), and multifactorial etiologies (22%); however, infection was the most frequent driver in those with DM (58%) but not in those without DM (15%). CONCLUSIONS: Outcomes after amputation remain poor regardless of whether they are categorized as major or minor. The pattern of amputation drivers in PAD differs by history of DM, with infection being the dominant etiology in those with DM and ischemia in those without DM. Greater focus is needed on the prognostic importance of minor amputation and of the multifactorial etiologies of amputation in PAD. Nomenclature with anatomical description of amputations and eliminating terms "major" or "minor" would seem appropriate.

Primary Care Diabetes Fellowship Programs: Developing National Standards.

The rapid and constant increase in the number of people living with diabetes has outstripped the capacity of specialists to fully address this chronic disease alone. Furthermore, although most people with diabetes are treated in the primary care setting, most primary care providers feel under-prepared and under-resourced to fully address the needs of their patients with diabetes. Addressing this care gap will require a multifaceted approach centering on primary care training in diabetes and its complications. One-year diabetology fellowship programs are well situated to provide this training. Previous research has shown that the higher the diabetes-specific volume of patients seeing a primary care physician was, the better the quality outcomes were across six quality indicators (eye examinations, LDL cholesterol testing, A1C testing, prescriptions for ACE inhibitors or angiotensin receptor blockers, prescriptions for statins, and emergency department visits for hypoglycemia or hyperglycemia). Primary care diabetes fellowships have existed for many years, but the number of fellowships and fellowship positions has recently grown dramatically. This article proposes a standardized curriculum for such programs and makes the case for increasing their number in the United States.

Cardiovascular and Limb Outcomes in Patients With Diabetes and Peripheral Artery Disease: The EUCLID Trial.

BACKGROUND: Diabetes confers an increased risk for atherosclerotic cardiovascular disease, but less is known about the independent risk diabetes confers on major cardiovascular and limb events in patients with symptomatic peripheral artery disease (PAD) on contemporary management. OBJECTIVES: The authors sought to assess the risk of cardiovascular and limb events in patients with PAD and diabetes as compared with those with PAD alone. METHODS: In the EUCLID (Examining Use of Ticagrelor in Peripheral Artery Disease) trial, 13,885 patients with symptomatic PAD were evaluated with a primary endpoint of an adjudicated composite of major adverse cardiovascular events (MACE) (cardiovascular death, myocardial infarction, ischemic stroke) followed over a median of ∼30 months. The diabetes subgroup was analyzed compared with the subgroup without diabetes, and further examined for diabetes-specific factors such as glycosylated hemoglobin (HbA1c) that might affect risk for major cardiovascular and limb outcomes. RESULTS: A total of 5,345 patients (38.5%) had diabetes; the majority (n = 5,134 [96.1%]) had type 2 diabetes. The primary endpoint occurred in 15.9% of patients with PAD and diabetes as compared with 10.4% of those without diabetes (absolute risk difference 5.5%; adjusted hazard ratio: 1.56; 95% confidence interval [CI]: 1.41 to 1.72; p < 0.001). Every 1% increase in HbA1c was associated with a 14.2% increased relative risk for MACE (95% CI: 1.09 to 1.20; p < 0.0001). CONCLUSIONS: Patients with PAD and diabetes are at high risk for cardiovascular and limb ischemic events, even on contemporary therapies. Every 1% increase in HbA1c was associated with a 14.2% increased relative risk for MACE (95% CI: 1.09 to 1.20; p < 0.0001). (A Study Comparing Cardiovascular Effects of Ticagrelor and Clopidogrel in Patients With Peripheral Artery Disease [EUCLID]; NCT01732822).

Ticagrelor in Peripheral Artery Disease Endovascular Revascularization (TI-PAD): Challenges in clinical trial execution.

There is limited evidence to guide clinical decision-making for antiplatelet therapy in peripheral artery disease (PAD) in the setting of lower extremity endovascular treatment. The Ticagrelor in Peripheral Artery Disease Endovascular Revascularization Study (TI-PAD) evaluated the role of ticagrelor versus aspirin as monotherapy in the management of patients following lower extremity endovascular revascularization. The trial failed to recruit the targeted number of patients, likely due to aspects of the design including the lack of option for dual antiplatelet therapy, and inability to identify suitable patients at study sites. In response, the protocol underwent amendments, but these changes did not adequately stimulate recruitment, and thus TI-PAD was prematurely terminated. This article describes the rationale for TI-PAD and challenges in trial design, subject recruitment and trial operations to better inform the conduct of future trials in PAD revascularization. ClinicalTrials.gov Identifier: NCT02227368.

PCSK9 Inhibitors: Mechanisms of Action, Metabolic Effects, and Clinical Outcomes.

Atherosclerotic cardiovascular disease (ASCVD) is associated with significant morbidity and mortality worldwide. Increased serum levels of low-density lipoprotein cholesterol (LDL-C) are an independent risk factor for ASCVD, and clinical trial data have shown that lowering LDL-C generally reduces cardiovascular risk. Until recently, 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins) have been the main therapy for lowering LDL-C. However, some statin-treated patients have persistently elevated residual cardiovascular risk due to inadequate lowering of LDL-C levels or non-LDL-related dyslipidemia. In addition, adverse effects of statins may limit their tolerability and therefore the ability to attain effective doses in some patients. A new class of drugs that inhibit proprotein convertase subtilisin-kexin type 9 (PCSK9) has been developed to treat hyperlipidemia. This review discusses the history and mechanism of action of PCSK9 inhibitors, their metabolic effects, and clinical outcomes associated with these medications, highlighting recent large cardiovascular outcome trials investigating these therapies.