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Insulin resistance progressing to type 2 diabetes mellitus (T2DM) is marked by a broad perturbation of macronutrient intermediary metabolism. Understanding the biochemical networks that underlie metabolic homeostasis and how they associate with insulin action will help unravel diabetes etiology and should foster discovery of new biomarkers of disease risk and severity. We examined differences in plasma concentrations of >350 metabolites in fasted obese T2DM vs. obese non-diabetic African-American women, and utilized principal components analysis to identify 158 metabolite components that strongly correlated with fasting HbA1c over a broad range of the latter (r = −0.631; p<0.0001). In addition to many unidentified small molecules, specific metabolites that were increased significantly in T2DM subjects included certain amino acids and their derivatives (i.e., leucine, 2-ketoisocaproate, valine, cystine, histidine), 2-hydroxybutanoate, long-chain fatty acids, and carbohydrate derivatives. Leucine and valine concentrations rose with increasing HbA1c, and significantly correlated with plasma acetylcarnitine concentrations. It is hypothesized that this reflects a close link between abnormalities in glucose homeostasis, amino acid catabolism, and efficiency of fuel combustion in the tricarboxylic acid (TCA) cycle. It is speculated that a mechanism for potential TCA cycle inefficiency concurrent with insulin resistance is “anaplerotic stress” emanating from reduced amino acid-derived carbon flux to TCA cycle intermediates, which if coupled to perturbation in cataplerosis would lead to net reduction in TCA cycle capacity relative to fuel delivery.

Abbreviations: A1C = hemoglobin A1C; AACE = American Association of Clinical Endocrinologists; ABCD = adiposity-based chronic disease; ACCORD = Action to Control Cardiovascular Risk in Diabetes; ACCORD BP = Action to Control Cardiovascular Risk in Diabetes Blood Pressure; ACE = American College of Endocrinology; ACEI = angiotensin-converting enzyme inhibitor; AGI = alpha-glucosidase inhibitor; apo B = apolipoprotein B; ARB = angiotensin II receptor blocker; ASCVD = atherosclerotic cardiovascular disease; BAS = bile acid sequestrant; BMI = body mass index; BP = blood pressure; CCB = calcium channel blocker; CGM = continuous glucose monitoring; CHD = coronary heart disease; CKD = chronic kidney disease; DKA = diabetic ketoacidosis; DPP4 = dipeptidyl peptidase 4; eGFR = estimated glomerular filtration rate; EPA = eicosapentaenoic acid; ER = extended release; FDA = Food and Drug Administration; GLP1 = glucagon-like peptide 1; HDL-C = high-density-lipoprotein cholesterol; HeFH = heterozygous familial hypercholesterolemia; LDL-C = low-density-lipoprotein cholesterol; LDL-P = low-density-lipoprotein particle; Look AHEAD = Look Action for Health in Diabetes; NPH = neutral protamine Hagedorn; OSA = obstructive sleep apnea; PCSK9 = proprotein convertase subtilisin-kexin type 9 serine protease; RCT = randomized controlled trial; SU = sulfonylurea; SGLT2 = sodium-glucose cotransporter 2; SMBG = self-monitoring of blood glucose; T2D = type 2 diabetes; TZD = thiazolidinedione

Obesity is closely related to the development of insulin resistance and type 2 diabetes (T2D). The prevention of T2D has become imperative to stem the rising rates of this disease. Weight loss is highly effective in preventing T2D; however, the at-risk pool is large, and a clinically meaningful metric for risk stratification to guide interventions remains a challenge. The objective of this study is to predict T2D risk using full-information continuous analysis of nationally sampled data from white and black American adults age ≥45 years. A sample of 12,043 black (33%) and white individuals from a population-based cohort, REasons for Geographic And Racial Differences in Stroke (REGARDS) (enrolled 2003-2007), was observed through 2013-2016. The mean participant age was 63.12 ± 8.62 years, and 43.7% were male. Mean BMI was 28.55 ± 5.61 kg/m2. Risk factors for T2D regularly recorded in the primary care setting were used to evaluate future T2D risk using Bayesian logistic regression. External validation was performed using 9,710 participants (19% black) from Atherosclerotic Risk in Communities (ARIC) (enrolled 1987-1989), observed through 1996-1998. The mean participant age in this cohort was 53.86 ± 5.65 years, and 44.6% were male. Mean BMI was 27.15 ± 4.92 kg/m2. Predictive performance was assessed using the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. The primary outcome was incident T2D. By 2016 in REGARDS, there were 1,602 incident cases of T2D. Risk factors used to predict T2D progression included age, sex, race, BMI, triglycerides, high-density lipoprotein, blood pressure, and blood glucose. The Bayesian logistic model (AUC = 0.79) outperformed the Framingham risk score (AUC = 0.76), the American Diabetes Association risk score (AUC = 0.64), and a cardiometabolic disease system (using Adult Treatment Panel III criteria) (AUC = 0.75). Validation in ARIC was robust (AUC = 0.85). Main limitations include the limited generalizability of the REGARDS sample to black and white, older Americans, and no time to diagnosis for T2D. Our results show that a Bayesian logistic model using full-information continuous predictors has high predictive discrimination, and can be used to quantify race- and sex-specific T2D risk, providing a new, powerful predictive tool. This tool can be used for T2D prevention efforts including weight loss therapy by allowing clinicians to target high-risk individuals in a manner that could be used to optimize outcomes.

The American Association of Clinical Endocrinologists (AACE) has created a dysglycemia-based chronic disease (DBCD) multimorbidity care model consisting of four distinct stages along the insulin resistance-prediabetes-type 2 diabetes (T2D) spectrum that are actionable in a preventive care paradigm to reduce the potential impact of T2D, cardiometabolic risk, and cardiovascular events. The controversy of whether there is value, cost-effectiveness, or clinical benefit of diagnosing and/or managing the prediabetes state is resolved by regarding the problem, not in isolation, but as an intermediate stage in the continuum of a progressive chronic disease with opportunities for multiple concurrent prevention strategies. In this context, stage 1 represents \"insulin resistance,\" stage 2 \"prediabetes,\" stage 3 \"type 2 diabetes,\" and stage 4 \"vascular complications.\" This model encourages earliest intervention focusing on structured lifestyle change. Further scientific research may eventually reclassify stage 2 DBCD prediabetes from a predisease to a true disease state. This position statement is consistent with a portfolio of AACE endocrine disease care models, including adiposity-based chronic disease, that prioritize patient-centered care, evidence-based medicine, complexity, multimorbid chronic disease, the current health care environment, and a societal mandate for a higher value attributed to good health. Ultimately, transformative changes in diagnostic coding and reimbursement structures for prediabetes and T2D can provide improvements in population-based endocrine health care. Abbreviations: A1C = hemoglobin A1c; AACE = American Association of Clinical Endocrinologists; ABCD = adiposity-based chronic disease; CVD = cardiovascular disease; DBCD = dysglycemia-based chronic disease; FPG = fasting plasma glucose; GLP-1 = glucagon-like peptide-1; MetS = metabolic syndrome; T2D = type 2 diabetes.

This algorithm for the comprehensive management of persons with type 2 diabetes (T2D) was developed to provide clinicians with a practical guide that considers the whole patient, his or her spectrum of risks and complications, and evidence-based approaches to treatment. It is now clear that the progressive pancreatic beta-cell defect that drives the deterioration of metabolic control over time begins early and may be present before the diagnosis of T2D (1-3). In addition to advocating glycemic control to reduce microvascular complications, this document highlights obesity and prediabetes as underlying risk factors for the development of T2D and associated macrovascular complications. In addition, the algorithm provides recommendations for blood pressure (BP) and lipid control, the two most important risk factors for atherosclerotic cardiovascular disease (ASCVD). Since originally drafted in 2013, the algorithm has been updated as new therapies, management approaches, and important clinical data have emerged. The current algorithm includes up-to-date sections on lifestyle therapy and all classes of obesity, antihyperglycemic, lipidlowering, and antihypertensive medications approved by the U.S. Food and Drug Administration (FDA) through December 2018. This algorithm supplements the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) 2015 Clinical Practice Guidelines for Developing a Diabetes Mellitus Comprehensive Care Plan (4) and is organized into discrete sections that address the following topics: the founding principles of the algorithm, lifestyle therapy, obesity, prediabetes, management of hypertension and dyslipidemia, and glucose control with noninsulin antihyperglycemic agents and insulin. In the accompanying algorithm, a chart summarizing the attributes of each antihyperglycemic class appears at the end.

The development of these updated guidelines was commissioned by the AACE, TOS, and ASMBS Board of Directors and adheres to the AACE 2010 protocol for standardized production of clinical practice guidelines (CPG). Each recommendation was re-evaluated and updated based on the evidence and subjective factors per protocol. Examples of expanded topics in this update include: the roles of sleeve gastrectomy, bariatric surgery in patients with type-2 diabetes, bariatric surgery for patients with mild obesity, copper deficiency, informed consent, and behavioral issues. There are 74 recommendations (of which 56 are revised and 2 are new) in this 2013 update, compared with 164 original recommendations in 2008. There are 403 citations, of which 33 (8.2%) are EL 1, 131 (32.5%) are EL 2, 170 (42.2%) are EL 3, and 69 (17.1%) are EL 4. There is a relatively high proportion (40.4%) of strong (EL 1 and 2) studies, compared with only 16.5% in the 2008 AACE-TOS-ASMBS CPG. These updated guidelines reflect recent additions to the evidence base. Bariatric surgery remains a safe and effective intervention for select patients with obesity. A team approach to perioperative care is mandatory with special attention to nutritional and metabolic issues.

Canagliflozin (Cana) is an FDA-approved diabetes drug that protects against cardiovascular and kidney diseases. It also inhibits the sodium glucose transporter 2 by blocking renal reuptake and intestinal absorption of glucose. In the context of the mouse Interventions Testing Program, genetically heterogeneous mice were given chow containing Cana at 180 ppm at 7 months of age until their death. Cana extended median survival of male mice by 14%. Cana also increased by 9% the age for 90th percentile survival, with parallel effects seen at each of 3 test sites. Neither the distribution of inferred cause of death nor incidental pathology findings at end-of-life necropsies were altered by Cana. Moreover, although no life span benefits were seen in female mice, Cana led to lower fasting glucose and improved glucose tolerance in both sexes, diminishing fat mass in females only. Therefore, the life span benefit of Cana is likely to reflect blunting of peak glucose levels, because similar longevity effects are seen in male mice given acarbose, a diabetes drug that blocks glucose surges through a distinct mechanism, i.e., slowing breakdown of carbohydrate in the intestine. Interventions that control daily peak glucose levels deserve attention as possible preventive medicines to protect from a wide range of late-life neoplastic and degenerative diseases.

Type 2 diabetes has profound effects on metabolism that can be detected in plasma. While increases in circulating non-esterified fatty acids (NEFA) are well-described in diabetes, effects on signaling lipids have received little attention. Oxylipins and endocannabinoids are classes of bioactive fatty acid metabolites with many structural members that influence insulin signaling, adipose function and inflammation through autocrine, paracrine and endocrine mechanisms. To link diabetes-associated changes in plasma NEFA and signaling lipids, we quantitatively targeted >150 plasma lipidome components in age- and body mass index-matched, overweight to obese, non-diabetic (n = 12) and type 2 diabetic (n = 43) African-American women. Diabetes related NEFA patterns indicated ∼60% increase in steroyl-CoA desaturase activity and ∼40% decrease in very long chain polyunsaturated fatty acid chain shortening, patterns previously associated with the development of nonalcoholic fatty liver disease. Further, epoxides and ketones of eighteen carbon polyunsaturated fatty acids were elevated >80% in diabetes and strongly correlated with changes in NEFA, consistent with their liberation during adipose lipolysis. Endocannabinoid behavior differed by class with diabetes increasing an array of N-acylethanolamides which were positively correlated with pro-inflammatory 5-lipooxygenase-derived metabolites, while monoacylglycerols were negatively correlated with body mass. These results clearly show that diabetes not only results in an increase in plasma NEFA, but shifts the plasma lipidomic profiles in ways that reflect the biochemical and physiological changes of this pathological state which are independent of obesity associated changes.