Chemerin in Participants with or without Insulin Resistance and Diabetes.

Chemerin is a chemokine/adipokine, regulating inflammation, adipogenesis and energy metabolism whose activity depends on successive proteolytic cleavages at its C-terminus. Chemerin levels and processing are correlated with insulin resistance. We hypothesized that chemerin processing would be higher in individuals with type 2 diabetes (T2D) and in those who are insulin resistant (IR). This hypothesis was tested by characterizing different chemerin forms by specific ELISA in the plasma of 18 participants with T2D and 116 without T2D who also had their insulin resistance measured by steady-state plasma glucose (SSPG) concentration during an insulin suppression test. This approach enabled us to analyze the association of chemerin levels with a direct measure of insulin resistance (SSPG concentration). Participants were divided into groups based on their degree of insulin resistance using SSPG concentration tertiles: insulin sensitive (IS, SSPG ≤ 91 mg/dL), intermediate IR (IM, SSPG 92-199 mg/dL), and IR (SSPG ≥ 200 mg/dL). Levels of different chemerin forms were highest in patients with T2D, second highest in individuals without T2D who were IR, and lowest in persons without T2D who were IM or IS. In the whole group, chemerin levels positively correlated with both degree of insulin resistance (SSPG concentration) and adiposity (BMI). Participants with T2D and those without T2D who were IR had the most proteolytic processing of chemerin, resulting in higher levels of both cleaved and degraded chemerin. This suggests that increased inflammation in individuals who have T2D or are IR causes more chemerin processing.

Lac-Phe mediates the effects of metformin on food intake and body weight.

Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. There is ongoing debate about the mechanisms that mediate metformin's effects on energy balance. Here, we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite N-lactoyl-phenylalanine (Lac-Phe) in cells, in mice and two independent human cohorts. Metformin drives Lac-Phe biosynthesis through the inhibition of complex I, increased glycolytic flux and intracellular lactate mass action. Intestinal epithelial CNDP2+ cells, not macrophages, are the principal in vivo source of basal and metformin-inducible Lac-Phe. Genetic ablation of Lac-Phe biosynthesis in male mice renders animals resistant to the effects of metformin on food intake and body weight. Lastly, mediation analyses support a role for Lac-Phe as a downstream effector of metformin's effects on body mass index in participants of a large population-based observational cohort, the Multi-Ethnic Study of Atherosclerosis. Together, these data establish Lac-Phe as a critical mediator of the body weight-lowering effects of metformin.

Proteomic Determinants of Variation in Cholesterol Efflux: Observations from the Dallas Heart Study.

High-density lipoproteins (HDLs) are promising targets for predicting and treating atherosclerotic cardiovascular disease (ASCVD), as they mediate removal of excess cholesterol from lipid-laden macrophages that accumulate in the vasculature. This functional property of HDLs, termed cholesterol efflux capacity (CEC), is inversely associated with ASCVD. HDLs are compositionally diverse, associating with >250 different proteins, but their relative contribution to CEC remains poorly understood. Our goal was to identify and define key HDL-associated proteins that modulate CEC in humans. The proteomic signature of plasma HDL was quantified in 36 individuals in the multi-ethnic population-based Dallas Heart Study (DHS) cohort that exhibited persistent extremely high (>=90th%) or extremely low CEC (<=10th%) over 15 years. Levels of apolipoprotein (Apo)A-I associated ApoC-II, ApoC-III, and ApoA-IV were differentially correlated with CEC in high (r = 0.49, 0.41, and -0.21 respectively) and low (r = -0.46, -0.41, and 0.66 respectively) CEC groups (p for heterogeneity (pHet) = 0.03, 0.04, and 0.003 respectively). Further, we observed that levels of ApoA-I with ApoC-III, complement C3 (CO3), ApoE, and plasminogen (PLMG) were inversely associated with CEC in individuals within the low CEC group (r = -0.11 to -0.25 for subspecies with these proteins vs. r = 0.58 to 0.65 for subspecies lacking these proteins; p < 0.05 for heterogeneity). These findings suggest that enrichment of specific proteins on HDLs and, thus, different subspecies of HDLs, differentially modulate the removal of cholesterol from the vasculature.

Lac-Phe mediates the anti-obesity effect of metformin.

Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. The mechanisms that mediate metformin's effects on energy balance remain incompletely defined. Here we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite Lac-Phe in mice as well as in two independent human cohorts. In cell culture, metformin drives Lac-Phe biosynthesis via inhibition of complex I, increased glycolytic flux, and intracellular lactate mass action. Other biguanides and structurally distinct inhibitors of oxidative phosphorylation also increase Lac-Phe levels in vitro. Genetic ablation of CNDP2, the principal biosynthetic enzyme for Lac-Phe, in mice renders animals resistant to metformin's anorexigenic and anti-obesity effects. Mediation analyses also support a role for Lac-Phe in metformin's effect on body mass index in humans. These data establish the CNDP2/Lac-Phe pathway as a critical mediator of the effects of metformin on energy balance.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

The relationship between insulin resistance and ion mobility lipoprotein fractions.

Objective: Insulin resistance (IR) increases risk of type 2 diabetes and atherosclerotic cardiovascular disease and is associated with lipid and lipoprotein abnormalities including high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C). Lipoprotein size and lipoprotein subfractions (LS) have also been used to assist in identifying persons with IR. Associations of LS and IR have not been validated using both direct measures of IR and direct measures of LS. We assessed the usefulness of fasting lipoprotein subfractions (LS) by ion mobility to identify individuals with IR. Methods: Lipid panel, LS by ion mobility (LS-IM), and IR by steady-state plasma glucose (SSPG) concentration were assessed in 526 adult volunteers without diabetes. IR was defined as being in the highest tertile of SSPG concentration. LS-IM score was calculated by linear combination of regression coefficients from a stepwise regression analysis with SSPG concentration as the dependent variable. Improvement in prediction of IR was evaluated after combining LS-IM score with TG/HDL-C, TG/HDL-C and BMI as well as with TG/HDL-C, BMI, sex, race and ethnicity. IR prediction was evaluated by area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV) considering the highest 5% of scores as positive test. Results: Prediction of IR was similar by LS-IM score and TG/HDL-C (AUC=0.68; PPV=0.59 and AUC=0.70; PPV=0.59, respectively) and prediction was improved when LS-IM was combined with TG/HDL-C (AUC=0.73; PPV=0.70), TG/HDL-C and BMI (AUC=0.82; PPV=0.81) and with TG/HDL-C, BMI, sex, race and ethnicity (AUC=0.84; PPV=0.89). Conclusion: For identifying individuals with IR, LS-IM score and TG/HDL-C are comparable and their combination further improves IR prediction by TG/HDL-C alone. Among patients who have undergone IM testing, the LS-IM score may assist prioritization of subjects for further evaluation and interventions to reduce IR.

Insulin resistance and accelerated cognitive aging.

Insulin resistance may be an early sign of metabolic dysfunction with the potential to lead to neuropsychiatric sequelae in the long term. In order to identify whether insulin resistance in otherwise healthy young and middle-aged adults is associated with preclinical signs of neuropsychiatric impairment, we recruited 126 overweight but nondiabetic, nondepressed individuals who completed an insulin suppression test for direct measurement of insulin resistance as well as a battery of cognitive and neuropsychiatric measures. Insulin resistance was associated with weaker performance on a fine motor task (Purdue Pegboard) as well as increases in subclinical symptoms of depression. We submit that insulin resistance in early to mid-adulthood may be an important predictor of long-term risk for metabolic, psychiatric, and neurobehavioral dysfunction.

Family history of diabetes moderates metabolic depression endophenotypes in overweight/obese adults.

OBJECTIVE: Insulin resistance (IR) is linked to depressive disorders, and there is growing evidence that targeting IR may be beneficial in treating them. We examine the association between depressive symptoms and a direct measure of IR, and whether family history of type 2 diabetes (FHx-T2DM) or major depressive disorder (FHx-MDD) moderate this relationship. METHODS: Cross-sectional data were collected from 96 primarily overweight/obese adults ages 25-50 without diabetes or clinical depression. Multiple regression and correlation analyses were used to assess the association between depressive symptoms and a direct measure of IR (steady-state plasma glucose) as well as moderating effects of FHx-T2DM or FHx-MDD. RESULTS: In the total sample, elevated depressive symptoms were positively associated with IR (p = 0.005). IR was associated with depressive symptoms in subjects with FHx-T2DM (p = 0.002) or FHx-MDD (p = 0.009) whereas BMI was associated with depressive symptoms in subjects without FHx-T2DM (p = 0.049) or FHx-MDD (p = 0.029). The odds of being in the top tertile of IR increased with elevated depressive symptoms alone (OR, 4.22; 95%CI, 1.15 to 17.33), presence of FHx-T2DM alone (OR, 3.42; 95%CI, 1.26 to 10.00), and presence of both FHx-T2DM and elevated depressive symptoms (OR, 10.08; 95%CI, 1.94 to 96.96). CONCLUSIONS: Our findings indicate that depressive symptoms are positively associated with a direct measure of IR in overweight/obese individuals without diabetes or clinical depression. This association is moderated by FHx-T2DM. Early identification of groups vulnerable to IR related to depressive symptomatology may be useful for determining personalized interventions that have the potential to reduce morbidity in later years.

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance.

BACKGROUND: Metabolic dysregulation is currently considered a major risk factor for hippocampal pathology. The aim of the present study was to characterize the influence of key metabolic drivers on functional connectivity of the hippocampus in healthy adults. METHODS: Insulin resistance was directly quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test and fasting levels of insulin, glucose, leptin, and cortisol, and measurements of body mass index and waist circumference were obtained in a sample of healthy cognitively intact adults (n = 104). Resting-state neuroimaging data were also acquired for the quantification of hippocampal functional cohesiveness and integration with the major resting-state networks (RSNs). Data-driven analysis using unsupervised machine learning (k-means clustering) was then employed to identify clusters of individuals based on their metabolic and functional connectivity profiles. RESULTS: K-means clustering identified two clusters of increasing metabolic deviance evidenced by cluster differences in the plasma levels of leptin (40.36 (29.97) vs. 27.59 (25.58) µg/L) and the degree of insulin resistance (SSPG concentration: 161.63 (65.27) vs. 125.72 (66.81) mg/dL). Individuals in the cluster with higher metabolic deviance showed lower functional cohesiveness within each hippocampus and lower integration of posterior and anterior components of the left and right hippocampus with the major RSNs. The two clusters did not differ in general intellectual ability or episodic memory. CONCLUSIONS: We identified two clusters of individuals differentiated by abnormalities in insulin resistance, leptin levels, and hippocampal connectivity, with one of the clusters showing greater deviance. These findings support the link between metabolic dysregulation and hippocampal function even in nonclinical samples.

Hippocampal volume reduction is associated with direct measure of insulin resistance in adults.

Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23-61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m2], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) µg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as risk factor for this phenotype.

Statins Are Associated With Increased Insulin Resistance and Secretion.

Objective: Statin treatment reduces the risk of atherosclerotic cardiovascular disease but is associated with a modest increased risk of type 2 diabetes, especially in those with insulin resistance or prediabetes. Our objective was to determine the physiological mechanism for the increased type 2 diabetes risk. Approach and Results: We conducted an open-label clinical trial of atorvastatin 40 mg daily in adults without known atherosclerotic cardiovascular disease or type 2 diabetes at baseline. The co-primary outcomes were changes at 10 weeks versus baseline in insulin resistance as assessed by steady-state plasma glucose during the insulin suppression test and insulin secretion as assessed by insulin secretion rate area under the curve (ISRAUC) during the graded-glucose infusion test. Secondary outcomes included glucose and insulin, both fasting and during oral glucose tolerance test. Of 75 participants who enrolled, 71 completed the study (median age 61 years, 37% women, 65% non-Hispanic White, median body mass index, 27.8 kg/m2). Atorvastatin reduced LDL (low-density lipoprotein)-cholesterol (median decrease 53%, P<0.001) but did not change body weight. Compared with baseline, atorvastatin increased insulin resistance (steady-state plasma glucose) by a median of 8% (P=0.01) and insulin secretion (ISRAUC) by a median of 9% (P<0.001). There were small increases in oral glucose tolerance test glucoseAUC (median increase, 0.05%; P=0.03) and fasting insulin (median increase, 7%; P=0.01). Conclusions: In individuals without type 2 diabetes, high-intensity atorvastatin for 10 weeks increases insulin resistance and insulin secretion. Over time, the risk of new-onset diabetes with statin use may increase in individuals who become more insulin resistant but are unable to maintain compensatory increases in insulin secretion.

Effect of the glucagon-like peptide-1 analogue liraglutide versus placebo treatment on circulating proglucagon-derived peptides that mediate improvements in body weight, insulin secretion and action: A randomized controlled trial.

AIM: To examine how circulating glucagon-like peptide-1 (GLP-1) concentrations during liraglutide treatment relate to its therapeutic actions on glucose and weight, and to study the effects of liraglutide on other proglucagon-derived peptides (PGDPs), including endogenous GLP-1, glucagon-like peptide-2, glucagon, oxyntomodulin, glicentin and major proglucagon fragment, which also regulate metabolic and weight control. MATERIALS AND METHODS: Adults who were overweight/obese (body mass index 27-40 kg/m2 ) with prediabetes were randomized to liraglutide (1.8 mg/day) versus placebo for 14 weeks. We used specific assays to measure exogenous (liraglutide, GLP-1 agonist [GLP-1A]) and endogenous (GLP-1E) GLP-1, alongside five other PGDP concentrations during a mixed meal tolerance test (MMTT) completed at baseline and at week 14 (liraglutide, n = 16; placebo, n = 19). Glucose during MMTT, steady-state plasma glucose (SSPG) concentration for insulin resistance and insulin secretion rate (ISR) were previously measured. MMTT area-under-the-curve (AUC) was calculated for ISR, glucose and levels of PGDPs. RESULTS: Participants on liraglutide versus placebo had significantly (P ≤ .004) decreased weight (mean -3.6%, 95% CI [-5.2% to -2.1%]), SSPG (-32% [-43% to -22%]) and glucose AUC (-7.0% [-11.5% to -2.5%]) and increased ISR AUC (30% [16% to 44%]). GLP-1A AUC at study end was significantly (P ≤ .04) linearly associated with % decrease in weight (r = -0.54) and SSPG (r = -0.59) and increase in ISR AUC (r = 0.51) in the liraglutide group. Treatment with liraglutide significantly (P ≤ .005) increased exogenous GLP-1A AUC (median 310 vs. 262 pg/mL × 8 hours at baseline but decreased endogenous GLP-1E AUC [13.1 vs. 24.2 pmol/L × 8 hours at baseline]), as well as the five other PGDPs. Decreases in the PGDPs processed in the intestines are independent of weight loss, indicating a probable direct effect of GLP-1 receptor agonists to decrease their endogenous production in contrast to weight loss-dependent changes in glucagon and major proglucagon fragment that are processed in pancreatic alpha cells. CONCLUSIONS: Circulating GLP-1A concentrations, reflecting liraglutide levels, predict improvement in weight, insulin action and secretion in a linear manner. Importantly, liraglutide also downregulates other PGDPs, normalization of the levels of which may provide additional metabolic and weight loss benefits in the future.

Increasing Mortality Among Patients With Diabetes and Chronic Liver Disease From 2007 to 2017.

The age-standardized prevalence of diabetes increased from 9.8% in 1988-1994, to 10.8% in 2001-2002, to 12.4% in 2011-2012 in the United States.1 According to the National Vital Statistics System data, diabetes-related mortality has remained stable as the seventh-leading cause of death nationally since 2006.2 However, the age-standardized diabetes-related mortality decreased from 112.2 per 100,000 individuals in 2007 to 104.3 per 100,000 individuals in 2017.3 In terms of cause-specific mortality, the age-standardized mortality for cardiovascular disease, complications of diabetes, and cancer among individuals with diabetes declined annually by approximately 1%.3 In contrast, chronic liver disease-related mortality has been reported to be increasing in individuals with diabetes.4,5 However, the trends in mortality due to chronic liver disease in the setting of diabetes remain unknown. In this study, we estimated the trends in chronic liver disease-related mortality among individuals with diabetes from 2007 to 2017 in the United States.

Finding missed cases of familial hypercholesterolemia in health systems using machine learning.

Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation's FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier's predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies.

The role of insulin as a key regulator of seeding, proliferation, and mRNA transcription of human pluripotent stem cells.

BACKGROUND: Human-induced pluripotent stem cells (hiPSCs) show a great promise as a renewable source of cells with broad biomedical applications. Since insulin has been used in the maintenance of hiPSCs, in this study we explored the role of insulin in culture of these cells. METHODS: We report conditions for insulin starvation and stimulation of hiPSCs. Crystal violet staining was used to study the adhesion and proliferation of hiPSCs. Apoptosis and cell cycle assays were performed through flow cytometry. Protein arrays were used to confirm phosphorylation targets, and mRNA sequencing was used to evaluate the effect of transcriptome. RESULTS: Insulin improved the seeding and proliferation of hiPSCs. We also observed an altered cell cycle profile and increase in apoptosis in hiPSCs in the absence of insulin. Furthermore, we confirmed phosphorylation of key components of insulin signaling pathway in the presence of insulin and demonstrated the significant effect of insulin on regulation of the mRNA transcriptome of hiPSCs. CONCLUSION: Insulin is a major regulator of seeding, proliferation, phosphorylation and mRNA transcriptome in hiPSCs. Collectively, our work furthers our understanding of human pluripotency and paves the way for future studies that use hiPSCs for modeling genetic ailments affecting insulin signaling pathways.

Impact of race/ethnicity on insulin resistance and hypertriglyceridaemia.

OBJECTIVE: Insulin sensitivity affects plasma triglyceride concentration and both differ by race/ethnicity. The purpose of this study was to provide a comprehensive assessment of the variation in insulin sensitivity and its relationship to hypertriglyceridaemia between five race/ethnic groups. RESEARCH DESIGN AND METHODS: In this cross-sectional study, clinical data for 1025 healthy non-Hispanic White, Hispanic White, East Asian, South Asian and African American individuals were analysed. Insulin-mediated glucose disposal (a direct measure of peripheral insulin sensitivity) was measured using the modified insulin suppression test. Statistical analysis was performed using analysis of co-variance. RESULTS: Of the study participants, 63% were non-Hispanic White, 9% were Hispanic White, 11% were East Asian, 11% were South Asian and 6% were African American. Overall, non-Hispanic Whites and African Americans displayed greater insulin sensitivity than East Asians and South Asians. Triglyceride concentration was positively associated with insulin resistance in all groups, including African Americans. Nevertheless, for any given level of insulin sensitivity, African Americans had the lowest triglyceride concentrations. CONCLUSION: Insulin sensitivity, as assessed by a direct measure of insulin-mediated glucose disposal, and its relationship to triglyceride concentration vary across five race/ethnic groups. Understanding these relationships is crucial for accurate cardiovascular risk stratification and prevention.

Myths about Insulin Resistance: Tribute to Gerald Reaven.

Gerald Reaven was often called the "father of insulin resistance." On the 1-year anniversary of his death in 2018, we challenge three myths associated with insulin resistance: metformin improves insulin resistance; measurement of waist circumference predicts insulin resistance better than body mass index; and insulin resistance causes weight gain. In this review, we highlight Reaven's relevant research that helped to dispel these myths associated with insulin resistance.

Plasma glucose concentration 60 min post oral glucose load and risk of type 2 diabetes and cardiovascular disease: Pathophysiological implications.

AIM: The aim of this study was to gain insight into the pathophysiological significance of elevated plasma glucose concentrations (mmol/L) 60 min post oral glucose load in apparently healthy individuals. METHODS: Comparison of resistance to insulin action and associated cardio-metabolic risk factors in 490 apparently healthy persons, subdivided into those with a plasma glucose concentration 60 min following a 75-g oral glucose challenge of <8.6 versus ⩾8.6. RESULTS: Insulin resistance was significantly greater in persons with normal glucose tolerance whose 60-min glucose concentration was ⩾8.6, associated with higher blood pressure, plasma concentrations of glucose, insulin, triglyceride and lower high-density lipoprotein cholesterol concentrations. Similar differences were seen in persons with impaired fasting glucose, but not in those with impaired glucose tolerance or both impaired fasting glucose and impaired glucose tolerance. The group whose 60-min glucose was <8.6 (n = 318) contained primarily persons with normal glucose tolerance (88%), whereas the majority of those whose 60-min value was ⩾8.6 (n = 172) had prediabetes (59%) and in particular combined impaired fasting glucose and impaired glucose tolerance. CONCLUSION: Plasma glucose concentration of ⩾8.6 mmol/L 60 min post oral glucose identifies higher proportions of combined impaired fasting glucose and impaired glucose tolerance individuals as well as normal glucose tolerance and impaired fasting glucose individuals with a more adverse cardio-metabolic profile, contributing to observed increased overall risk of type 2 diabetes and other metabolic diseases.

Insulin Resistance Probability Scores for Apparently Healthy Individuals.

CONTEXT: Insulin resistance (IR) can progress to type 2 diabetes. Therefore, timely identification of IR could facilitate disease prevention efforts. However, direct measurement of IR is not feasible in a clinical setting. OBJECTIVE: Develop a clinically practical probability score to assess IR in apparently healthy individuals based on levels of insulin, C-peptide, and other risk factors. DESIGN: Cross-sectional study. PARTICIPANTS: Apparently healthy individuals who volunteered to participate in studies of IR. MAIN OUTCOME MEASURE: IR, defined as the top tertile of steady-state plasma glucose during an insulin-suppression test. RESULTS: In a study of 535 participants, insulin, C-peptide, creatinine, body mass index (BMI), and triglycerides to high-density lipoprotein cholesterol ratio (TG/HDL-C) were independently associated with IR (all P < 0.05) in a model that included age, sex, ethnicity, BMI, blood pressure, insulin, C-peptide, fasting glucose, low-density lipoprotein cholesterol, TG/HDL-C, alanine aminotransferase, and creatinine. For an IR probability score based on a model that included insulin, C-peptide, creatinine, TG/HDL-C, and BMI, the odds ratio was 26.7 (95% CI 14.0 to 50.8) for those with scores >66% compared with those with scores <33%. When only insulin and C-peptide were included in the model, the odds ratio was 15.6 (95% CI 7.5 to 32.4) for those with scores >66% compared with those with scores <33%. CONCLUSIONS: An IR probability score based on insulin, C-peptide, creatinine, TG/HDL-C, and BMI or a score based on only insulin and C-peptide may help assess IR in apparently healthy individuals.