Predictor Of Insulin Sensitivity Research Articles

Lipid Accumulation Product Outperforms BMI and Waist Circumference in Metabolic Disorders.

Background and Objective: Obesity is a global health issue intricately linked to metabolic syndrome (MetS), insulin resistance, and dyslipidemia. Anthropometric indices, particularly those measuring central obesity, have emerged as more reliable predictors of these metabolic disorders than general obesity indices such as body mass index (BMI). However, the relative predictive power of these indices remains debated, particularly across sexes. This study aimed to evaluate the discriminative performance of various anthropometric measures, including lipid accumulation product (LAP), BMI, waist circumference (WC), and visceral adiposity index (VAI), in predicting insulin sensitivity, β-cell function, MetS, and dyslipidemia using National Health and Nutritional Evaluation Survey III (NHANES III) data. Methods: A cross-sectional analysis of 3,706 adults from the NHANES III database was conducted. Anthropometric indices were compared against insulin sensitivity Homeostasis Model Assessment (HOMA)-S, β-cell function (HOMA-B), metabolic syndrome (MetS) status, and dyslipidemia. Receiver-operating characteristic (ROC) curves and linear regression models were used to identify thresholds for predicting metabolic abnormalities. Results: LAP emerged as the most discriminative index across all outcomes, outperforming BMI and WC, particularly in predicting insulin sensitivity and β-cell function in males. In females, BMI was superior in predicting β-cell function. VAI demonstrated the strongest association with dyslipidemia but was less effective in predicting insulin resistance. Conclusion: LAP significantly outperforms conventional anthropometric indices in identifying insulin resistance and MetS, highlighting its potential as a screening tool for cardiometabolic risk. Gender differences in the predictive abilities of these measures suggest that BMI may retain value in assessing β-cell function in females. VAI should be considered when screening for dyslipidemia but is less effective for insulin resistance.

Soluble receptors for advanced glycation endproducts are predictors of insulin sensitivity and affected by weight loss

BackgroundMice experiments have underscored the efficacy of pharmacological inhibition of advanced glycation endproducts (AGEs) through the use of soluble receptors for advanced glycation endproducts (sRAGE) in mitigating obesity-linked metabolic disruptions and insulin resistance. However, human studies have presented conflicting findings regarding the correlation between circulating sRAGE levels and insulin resistance, as well as glucose tolerance. Here, we aimed to delve deeper into the relationship between sRAGE levels and systemic insulin sensitivity.MethodsPlasma sRAGE levels, hyperinsulinemic-euglycemic clamp, and continuous glucose monitoring were measured in two independent cross-sectional case-control studies [cohort 1 (n = 180) and cohort 2 (n = 124)]. In addition, a subgroup of 42 participants with obesity were followed for 12 months. In 14 of these participants, weight loss was achieved through bariatric surgery intervention.ResultsOur results revealed a significant association between plasma sRAGE levels and both insulin sensitivity and glycemic control parameters, even after adjustments for age, sex, and BMI. Furthermore, longitudinal analysis demonstrated that interventions aimed at weight loss led to reductions in fasting glucose and HbA1c levels, concurrently with increases in sRAGE levels.ConclusionsThese findings underscore that sRAGE levels were strongly associated with insulin sensitivity and glycemic control, suggesting a possible role of sRAGE in preserving insulin sensitivity and maintaining glycemic control, which should be confirmed in further studies.

Intra-Individual Variations in How Insulin Sensitivity Responds to Long-Term Exercise: Predictions by Machine Learning Based on Large-Scale Serum Proteomics.

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise ("non-responders"). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40-65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO2max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO2max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes.

Model predictive control of blood glucose in critically ill patients using Gaussian processes

Abstract Stress-induced hyperglycemia and high glycemic variability are common in intensive care patients. Several clinical studies show the benefits of tight blood glucose control, including lower mortality. This article presents an algorithm for blood glucose control in the intensive care unit. An Unscented Kalman Filter is developed to estimate the glucose metabolism state and time-varying insulin sensitivity from blood glucose measurements. Gaussian Processes are used to predict future insulin sensitivity changes based on previous measurements. A model predictive controller is designed to estimate optimal insulin infusion based on current state, predicted insulin sensitivity and planned nutrition. The developed control algorithm allows individualized blood glucose control with reduced glycemic variability and reduced risk of hypoglycemia in the intensive care unit.

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.

Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

The Quality of Preoperative Glycemic Control Predicts Insulin Sensitivity During Major Upper Abdominal Surgery: A Case-Control Study.

To examine the association of the quality of preoperative glycemic control and insulin sensitivity during major upper abdominal surgery. In cardiac surgery, glycated hemoglobin A1c (HbA1c), an indicator of glycemic control during the preceding 3 months, correlated with intraoperative insulin sensitivity. Furthermore, insulin resistance showed a significant association with adverse clinical outcomes. This study is a post hoc exploratory analysis of a randomized controlled trial in patients undergoing elective hepatectomy and receiving the hyperinsulinemic-normoglycemic clamp (HNC) as a potential intervention to reduce surgical site infections (ClinicalTrials.gov NCT01528189). Immediately before skin incision, the HNC was initiated by infusing insulin at the rate of 2 mU/kg/min. Dextrose was administered at rates titrated to maintain normoglycemia (4.0-6.0 mmol/L). The average of 3 consecutive dextrose infusion rates during steady state was used as a measure of insulin sensitivity. Primary outcome was the relationship between preoperative HbA1c and insulin sensitivity during surgery. Secondary outcomes were the associations of insulin sensitivity with the patient's body mass index (BMI) and postoperative morbidity. Thirty-four patients were studied. HbA1c (Y = -0.52X + 4.8, P < 0.001, R2 = 0.29), BMI (Y = -0.12X + 5.0, P < 0.001, R2 = 0.43) showed negative correlations with insulin sensitivity. The odds ratio of postoperative complications within 30 days of surgery for every increase in insulin sensitivity by 1 mg/kg/min was 0.22 (95% confidential interval, 0.06-0.59; P = 0.009). We demonstrate significant associations of the quality of preoperative glycemic control and body mass index with insulin sensitivity during hepatectomy. The degree of insulin resistance correlated with postoperative morbidity.

Nonequilibrium thermodynamics and mitochondrial protein content predict insulin sensitivity and fuel selection during exercise in human skeletal muscle.

Introduction: Many investigators have attempted to define the molecular nature of changes responsible for insulin resistance in muscle, but a molecular approach may not consider the overall physiological context of muscle. Because the energetic state of ATP (ΔGATP) could affect the rate of insulin-stimulated, energy-consuming processes, the present study was undertaken to determine whether the thermodynamic state of skeletal muscle can partially explain insulin sensitivity and fuel selection independently of molecular changes. Methods: 31P-MRS was used with glucose clamps, exercise studies, muscle biopsies and proteomics to measure insulin sensitivity, thermodynamic variables, mitochondrial protein content, and aerobic capacity in 16 volunteers. Results: After showing calibrated 31P-MRS measurements conformed to a linear electrical circuit model of muscle nonequilibrium thermodynamics, we used these measurements in multiple stepwise regression against rates of insulin-stimulated glucose disposal and fuel oxidation. Multiple linear regression analyses showed 53% of the variance in insulin sensitivity was explained by 1) VO2max (p = 0.001) and the 2) slope of the relationship of ΔGATP with the rate of oxidative phosphorylation (p = 0.007). This slope represents conductance in the linear model (functional content of mitochondria). Mitochondrial protein content from proteomics was an independent predictor of fractional fat oxidation during mild exercise (R2 = 0.55, p = 0.001). Conclusion: Higher mitochondrial functional content is related to the ability of skeletal muscle to maintain a greater ΔGATP, which may lead to faster rates of insulin-stimulated processes. Mitochondrial protein content per se can explain fractional fat oxidation during mild exercise.

Lipoprotein Insulin Resistance Index: A Simple, Accurate Method for Assessing Insulin Resistance in South Asians.

Identification of insulin resistance (IR) in South Asians, who are at a higher risk for type 2 diabetes, is important. Lack of standardization of insulin assays limits the clinical use of insulin-based surrogate indices. The lipoprotein insulin resistance index (LP-IR), a metabolomic marker, reflects the lipoprotein abnormalities observed in IR. The reliability of the LP-IR index in South Asians is unknown. We evaluated the predictive accuracy of LP-IR compared with other IR surrogate indices in South Asians. In a cross-sectional study (n = 55), we used calibration model analysis to assess the ability of the LP-IR score and other simple surrogate indices (Homeostatic Model Assessment of Insulin Resistance, Quantitative insulin sensitivity check index, Adipose insulin resistance index, and Matsuda Index) to predict insulin sensitivity (SI) derived from the reference frequently sampled intravenous glucose tolerance test. LP-IR index was derived from lipoprotein particle concentrations and sizes measured by nuclear magnetic resonance spectroscopy. Predictive accuracy was determined by root mean squared error (RMSE) of prediction and leave-one-out cross-validation type RMSE of prediction (CVPE). The optimal cut-off of the LP-IR index was determined by the area under the receiver operating characteristic curve (AUROC) and the Youden index. The simple surrogate indices showed moderate correlations with SI (r = 0.53-0.69, P < .0001). CVPE and RMSE were not different in any of the surrogate indices when compared with LP-IR. The AUROC was 0.77 (95% CI 0.64-0.89). The optimal cut-off for IR in South Asians was LP-IR >48 (sensitivity: 75%, specificity: 70%). The LP-IR index is a simple, accurate, and clinically useful test to assess IR in South Asians.

The threshold value for identifying insulin resistance (HOMA-IR) in an admixed adolescent population: A hyperglycemic clamp validated study.

To validate the homeostasis model assessment (HOMA) of insulin resistance (IR) as a surrogate to the hyperglycemic clamp to measure IR in both pubertal and postpubertal adolescents, and determine the HOMA-IR cutoff values for detecting IR in both pubertal stages. The study sample comprised 80 adolescents of both sexes (aged 10-18 years; 37 pubertal), in which IR was assessed with the HOMA-IR and the hyperglycemic clamp. In the multivariable linear regression analysis, adjusted for sex, age, and waist circumference, the HOMA-IR was independently and negatively associated with the clamp-derived insulin sensitivity index in both pubertal (unstandardized coefficient - B = -0.087, 95% confidence interval [CI] = -0.135 to -0.040) and postpubertal (B = -0.101, 95% CI, -0.145 to -0.058) adolescents. Bland-Altman plots showed agreement between the predicted insulin sensitivity index and measured clamp-derived insulin sensitivity index in both pubertal stages (mean =-0.00 for pubertal and postpubertal); all P > 0.05. The HOMA-IR showed a good discriminatory power for detecting IR with an area under the receiver operator characteristic curve of 0.870 (95% CI, 0.718-0.957) in pubertal and 0.861 (95% CI, 0.721-0.947) in postpubertal adolescents; all P < 0.001. The optimal cutoff values of the HOMA-IR for detecting IR were > 3.22 (sensitivity, 85.7; 95% CI, 57.2-98.2; specificity, 82.6; 95% CI, 61.2-95.0) for pubertal and > 2.91 (sensitivity, 63.6; 95% CI, 30.8-89.1, specificity, 93.7; 95%CI, 79.2-99.2) for postpubertal adolescents. The threshold value of the HOMA-IR for identifying insulin resistance was > 3.22 for pubertal and > 2.91 for postpubertal adolescents.

Dietary advanced glycation products and their associations with insulin sensitivity and body weight: A 16-week randomized clinical trial.

Evidence suggests that changes in advanced glycation end-products (AGEs) may influence body weight. Previous studies have focused on cooking methods as the primary way how to reduce the dietary AGEs but little is known about the effects of a change in diet composition. The aim of this study was to assess the effects of a low-fat plant-based diet on dietary AGEs and test the association with body weight, body composition, and insulin sensitivity. Participants who were overweight (n=244) were randomly assigned to an intervention (low-fat plant-based) (n=122) or control group (n=122) for 16weeks. Before and after the intervention period, body composition was measured by dual X-ray absorptiometry. Insulin sensitivity was assessed with the predicted insulin sensitivity index (PREDIM). Three-day diet records were analyzed using the Nutrition Data System for Research software and dietary AGEs were estimated, using a database. Repeated measure ANOVA was used for statistical analysis. Dietary AGEs decreased in the intervention group by 8768 ku/day on average (95% -9611 to -7925; p<0.001), compared with the control group (-1608; 95% CI -2709 to -506; p=0.005; treatment effect -7161 ku/day [95% CI -8540 to -5781]; Gxt, p<0.001). Body weight decreased by 6.4kg in the intervention group, compared with 0.5kg in the control group (treatment effect -5.9kg [95% CI -6.8 to -5.0]; Gxt, p<0.001), largely due to a reduction in fat mass, notably visceral fat. PREDIM increased in the intervention group (treatment effect +0.9 [95% CI+0.5 to +1.2]; p<0.001). Changes in dietary AGEs correlated with changes in body weight (r=+0.41; p<0.001), fat mass (r=+0.38; p<0.001), visceral fat (r=+0.23; p<0.001), and PREDIM (r=-0.28; p<0.001), and remained significant even after adjustment for changes in energy intake (r=+0.35; p<0.001 for body weight; r=+0.34; p<0.001 for fat mass; r=+0.15; p=0.03 for visceral fat; and r=-0.24; p<0.001 for PREDIM). Dietary AGEs decreased on a low-fat plant-based diet, and this decrease was associated with changes in body weight, body composition, and insulin sensitivity, independent of energy intake. These findings demonstrate positive effects of qualitative dietary changes on dietary AGEs and cardiometabolic outcomes. NCT02939638.

1118-P: Prediction of Clamp-Derived Insulin Sensitivity in African Americans from Clinical Risk Factors

Surrogate indices of insulin sensitivity based on fasting glucose and insulin values fail to adequately predict insulin sensitivity in African-Americans (AA) , a group with high risk for type 2 diabetes. The objective of this study was to evaluate the individual and combined utility of clinically accessible risk factors (demographic, anthropometric, and metabolic traits) to predict clamp-derived insulin sensitivity in 53 AA adults without diabetes. Insulin sensitivity was measured with the euglycemic clamp (SI-clamp) at an insulin dose of 120 µU/m2/min/kg lean body mass to assess primarily skeletal muscle glucose disposal. Concentrations of lipid/lipoproteins, adipokines, glucose, insulin, C-peptide, proinsulin, and inflammatory markers were obtained from fasting sera. Individual associations between SI-clamp and predictors were evaluated with linear regression, adjusting for age and sex. Multiple regression analyses with backward stepwise selection of all candidate predictors were used to derive prediction models. Body mass, triglycerides (TAG) , glucose, insulin, C-peptide, proinsulin, and TNF-α were inversely associated with SI-clamp, whereas female sex, HDL-cholesterol, adiponectin, and IFN-γ were positively associated (P&amp;lt; 0.05) . For the prediction models, HDL-cholesterol, TAG, C-peptide, adiponectin, leptin, and IL-6 explained the highest proportion of variance in SI-clamp (AIC = 15.8 and adjusted R2 = 54.27%) . A second, more clinically applicable model was also derived using HDL-cholesterol, TAG, C-peptide, and adiponectin (AIC = 18.136 and adjusted R2 = 50.63%) . These results suggest that in AA, fasting levels of lipid/lipoproteins, C-peptide, and adiponectin are stronger predictors of insulin sensitivity than fasting glucose and insulin. Development of a surrogate index for insulin sensitivity with these predictors may aid in accurately identifying AA at increased risk for type 2 diabetes in clinical settings and epidemiological studies. Disclosure L.A.Fowler: None. V.Parcha: None. C.Couch: None. W.Garvey: Other Relationship; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Epitomee, JAZZ Pharmaceuticals, Novo Nordisk, Novo Nordisk, Pfizer Inc., Pfizer Inc. B.Gower: None. Funding NIH/NIDDK (R01DK096288) NIH/NIDDK (P30DK056336) NIH/NIDDK (P60DK079626) NIH/NIDDK (T32DK062710) NIH/NCATS CTSA (U54TR001368-01)

The Use and Effectiveness of Selected Alternative Markers for Insulin Sensitivity and Secretion Compared with Gold Standard Markers in Dietary Intervention Studies in Individuals without Diabetes: Results of a Systematic Review.

Background: The gold-standard techniques for measuring insulin sensitivity and secretion are well established. However, they may be perceived as invasive and expensive for use in dietary intervention studies. Thus, surrogate markers have been proposed as alternative markers for insulin sensitivity and secretion. This systematic review aimed to identify markers of insulin sensitivity and secretion in response to dietary intervention and assess their suitability as surrogates for the gold-standard methodology. Methods: Three databases, PubMed, Scopus, and Cochrane were searched, intervention studies and randomised controlled trials reporting data on dietary intake, a gold standard of analysis of insulin sensitivity (either euglycaemic-hyperinsulinaemic clamp or intravenous glucose tolerance test and secretion (acute insulin response to glucose), as well as surrogate markers for insulin sensitivity (either fasting insulin, area under the curve oral glucose tolerance tests and HOMA-IR) and insulin secretion (disposition index), were selected. Results: We identified thirty-five studies that were eligible for inclusion. We found insufficient evidence to predict insulin sensitivity and secretion with surrogate markers when compared to gold standards in nutritional intervention studies. Conclusions: Future research is needed to investigate if surrogate measures of insulin sensitivity and secretion can be repeatable and reproducible in the same way as gold standards.

A plant-based diet in overweight adults in a 16-week randomized clinical trial: The role of dietary acid load.

Evidence suggests that changes in dietary acid load may influence body weight, body composition, and insulin sensitivity. Participants (n=244) were randomly assigned to an intervention (vegan) (n=122) or control group (n=122) for 16 weeks. Before and after the intervention period, body composition was measured by dual X-ray absorptiometry. Insulin resistance was assessed with the Homeostasis Model Assessment (HOMA-IR) index and predicted insulin sensitivity index (PREDIM). Repeated measure ANOVA was used for statistical analysis. Potential Renal Acid Load (PRAL) and Net Endogenous Acid Production (NEAP) decreased significantly in the vegan group with no change in the control group (treatment effect-24.7 mEq/day [95% CI -30.2 to-19.2]; p<0.001; and-23.8 mEq/day [95% CI -29.6 to-18.0]; p<0.001, respectively). Body weight decreased by 6.4kg in the vegan group, compared with 0.5kg in the control group (treatment effect-5.9kg [95% CI -6.8 to-5.0]; Gxt, p<0.001), largely due to a reduction in fat mass and visceral fat. HOMA-IR index decreased and PREDIM increased in the vegan group. After adjustment for energy intake, changes in PRAL and NEAP correlated positively with changes in body weight (r=+0.37; p<0.001; and r=+0.37; p<0.001, respectively), fat mass (r=+0.32; p<0.001; and r=+0.32; p<0.001, respectively), visceral fat (r=+0.19; p=0.006; and r=+0.15; p=0.03, respectively), and HOMA (r=+0.17; p=0.02; and r=+0.20; p=0.006, respectively), and negatively with changes in PREDIM (r=-0.22; p=0.002; and r=-0.27; p<0.001, respectively). Dietary acid load as part of a plant-based diet was associated with changes in body weight, body composition, and insulin sensitivity, independent of energy intake. Mechanistic explanations suggest that the relationship may be causal. ClinicalTrials.gov number, NCT03698955.

Evaluation of fasting plasma insulin and proxy measurements to assess insulin sensitivity in horses

BackgroundProxies are mathematical calculations based on fasting glucose and/or insulin concentrations developed to allow prediction of insulin sensitivity (IS) and β-cell response. These proxies have not been evaluated in horses with insulin dysregulation. The first objective of this study was to evaluate how fasting insulin (FI) and proxies for IS (1/Insulin, reciprocal of the square root of insulin (RISQI) and the quantitative insulin sensitivity check index (QUICKI)) and β-cell response (the modified insulin-to-glucose ratio (MIRG) and the homeostatic model assessment of β-cell function (HOMA-β)) were correlated to measures of IS (M index) using the euglycemic hyperinsulinemic clamp (EHC) in horses with insulin resistance (IR) and normal IS. A second objective was to evaluate the repeatability of FI and proxies in horses based on sampling on consecutive days. The last objective was to investigate the most appropriate cut-off value for the proxies and FI.ResultsThirty-four horses were categorized as IR and 26 as IS based on the M index. The proxies and FI had coefficients of variation (CVs) ≤ 25.3 % and very good reliability (intraclass correlation coefficients ≥ 0.89). All proxies and FI were good predictors of the M index (r = 0.76–0.85; P < 0.001). The proxies for IS had a positive linear relationship with the M index whereas proxies for β-cell response and FI had an inverse relationship with the M index. Cut-off values to distinguish horses with IR from horses with normal IS based on the M index were established for all proxies and FI using receiver operating characteristic curves, with sensitivity between 79 % and 91 % and specificity between 85 % and 96 %. The cut-off values to predict IR were < 0.32 (RISQI), < 0.33 (QUICKI) and > 9.5 µIU/mL for FI.ConclusionsAll proxies and FI provided repeatable estimates of horses’ IS. However, there is no advantage of using proxies instead of FI to estimate IR in the horse. Due to the heteroscedasticity of the data, proxies and FI in general are more suitable for epidemiological studies and larger clinical studies than as a diagnostic tool for measurement of IR in individual horses.

A Mediterranean Diet and Low-Fat Vegan Diet to Improve Body Weight and Cardiometabolic Risk Factors: A Randomized, Cross-over Trial

Objective: Evidence suggests that both Mediterranean and vegan diets improve body weight and cardiometabolic risk factors, but their relative efficacy has not been compared in a randomized trial. Methods: In a randomized crossover trial, 62 overweight adults were randomly assigned to a Mediterranean or vegan diet for a 16-week period. Body weight, plasma lipids, blood pressure, and body composition (dual X-ray absorptiometry) were measured. Secondary measures included insulin resistance (Homeostasis Model Assessment, HOMA-IR), oral glucose insulin sensitivity (OGIS), and predicted insulin sensitivity (PREDIM) indices. Thereafter, participants were asked to return to their baseline diets for 4 weeks, after which they began the opposite diet for 16 weeks. The same parameters were measured before and after this 2nd 16-week period. Results: Overall net weight changes were 0.0 (Mediterranean) and −6.0 kg (vegan), (treatment effect −6.0 kg [95% CI −7.5 to −4.5]; p < 0.001). HOMA-IR decreased and OGIS increased on the vegan diet with no significant change on the Mediterranean diet (treatment effect −0.7 [95% CI, −1.8 to +0.4]; p = 0.21; and +35.8 mL/min/m2 [95% CI, +13.2 to +58.3]; p = 0.003, respectively). PREDIM did not change significantly in either group. Among participants with no medication changes, total and LDL-cholesterol decreased 18.7 mg/dL (0.5 mmol/L) and 15.3 mg/dL (0.4 mmol/L), respectively, on the vegan diet, compared with no significant change on the Mediterranean diet (treatment effect −15.6 [-24.6 to −6.6]; p = 0.001 and −14.8 [-23.5 to −6.2]; p = 0.001, respectively); systolic and diastolic blood pressure decreased 9.3 and 7.3 mmHg on the Mediterranean diet, compared with 3.4 and 4.1 mmHg on the vegan diet (treatment effect +5.9 [95% CI +1.0 to +10.9]; p = 0.02; and +1.8 [95% CI −4.6 to +8.1]; p = 0.58, respectively). Conclusions: A low-fat vegan diet improved body weight, lipid concentrations, and insulin sensitivity, both from baseline and compared with a Mediterranean diet. Blood pressure decreased on both diets, more on the Mediterranean diet. Clinical trial registration: ClinicalTrials.gov number, NCT03698955 https://clinicaltrials.gov/ct2/show/NCT03698955?term=NCT03698955&draw=2&rank=1

Plasma Proteomics to Predict Insulin-mediated Glucose Disposal/Uptake

Background Direct measurement of insulin sensitivity (IS) by the hyperinsulinemic euglycemic clamp (HEC) technique is considered a ‘gold-standard’ method, but is laborious and expensive. High-throughput plasma proteomics might provide an opportunity to improve and streamline the prediction of IS. Methods We measured 828 proteins using the OLINK PEA-technique in baseline fasting plasma from 968 participants of the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study. We estimated the variance explained of the HEC-derived insulin- mediated glucose uptake (M-value, per kilogram of lean body mass) by clinical covariates alone, and applied LASSO regression to develop prediction models incorporating protein levels. Models were trained in 70% of the cohort and tested in the remaining 30%. Results Clinical covariates alone (age, sex, BMI, lipids, and blood pressure) explained 19.8% (95% confidence interval 15.3-24.2%) of the variance of the M-value. LASSO selected 50 proteins, which increased the variance explained in the testing set to 44.8% (95% CI 38.1-51.3%). A separate protein-only LASSO model of 46 proteins explained a similar proportion of variance (44.1%,95% CI 37.5-50.7%) as the clinical covariates and proteins combined. Conclusion & future directions Plasma proteomic profiling substantially improved the prediction of the M-value, which may allow for accurate assessment of IS without the need of HEC. We are currently evaluating the RISC based models in a replication cohort (the Uppsala Longitudinal Study of Adult Men, ULSAM).

Serum carotenoids and Pediatric Metabolic Index predict insulin sensitivity in Mexican American children

High concentrations of carotenoids are protective against cardiometabolic risk traits (CMTs) in adults and children. We recently showed in non-diabetic Mexican American (MA) children that serum α-carotene and β-carotene are inversely correlated with obesity measures and triglycerides and positively with HDL cholesterol and that they were under strong genetic influences. Additionally, we previously described a Pediatric Metabolic Index (PMI) that helps in the identification of children who are at risk for cardiometabolic diseases. Here, we quantified serum lycopene and β-cryptoxanthin concentrations in approximately 580 children from MA families using an ultraperformance liquid chromatography-photodiode array and determined their heritabilities and correlations with CMTs. Using response surface methodology (RSM), we determined two-way interactions of carotenoids and PMI on Matsuda insulin sensitivity index (ISI). The concentrations of lycopene and β-cryptoxanthin were highly heritable [h2 = 0.98, P = 7 × 10–18 and h2 = 0.58, P = 1 × 10–7]. We found significant (P ≤ 0.05) negative phenotypic correlations between β-cryptoxanthin and five CMTs: body mass index (− 0.22), waist circumference (− 0.25), triglycerides (− 0.18), fat mass (− 0.23), fasting glucose (− 0.09), and positive correlations with HDL cholesterol (0.29). In contrast, lycopene only showed a significant negative correlation with fasting glucose (− 0.08) and a positive correlation with HDL cholesterol (0.18). Importantly, we found that common genetic influences significantly contributed to the observed phenotypic correlations. RSM showed that increased serum concentrations of α- and β-carotenoids rather than that of β-cryptoxanthin or lycopene had maximal effects on ISI. In summary, our findings suggest that the serum carotenoids are under strong additive genetic influences and may have differential effects on susceptibility to CMTs in children.

Accelerated subcutaneous abdominal stem cell adipogenesis predicts insulin sensitivity in normal-weight women with polycystic ovary syndrome

To examine whether subcutaneous (SC) abdominal adipose stem cell differentiation into adipocytes invitro predicts insulin sensitivity (Si) invivo in normal-weight women with polycystic ovary syndrome (PCOS) and controls. Prospective cohort study. Academic medical center. Eight normal-weight women with PCOS and 8 age- and body mass index-matched controls. Women underwent circulating hormone/metabolic determinations, intravenous glucose tolerance testing, total-body dual-energy x-ray absorptiometry, and SC abdominal fat biopsy. PPARγ and CEBPa gene expression and lipid content of adipocytes matured invitro were compared between women with PCOS and control women, and correlated with patient characteristics, systemic Si, and adipose insulin resistance (adipose-IR). Serum androgen levels, adipose-IR, and percentage of android fat were greater in women with PCOS than control women. Stem cell PPARγ and CEBPa gene expression increased maximally by day 12 without a female-type effect. In control cells, gene expression positively correlated with fasting serum insulin levels (both genes) and adipose-IR (CEBPa) and negatively correlated with Si (CEBPa). Conversely, CEBPa gene expression in PCOS cells negatively correlated with adipose-IR and serum free testosterone, whereas total lipid accumulation in these cells positively corelated with Si. In normal-weight women with PCOS, accelerated SC abdominal adipose stem cell differentiation into adipocytes invitro favors Si invivo, suggesting a role for hyperandrogenism in the evolution of metabolic thrift to enhance fat storage through increased cellular glucose uptake.

Effect of a Low-Fat Vegan Diet on Body Weight, Insulin Sensitivity, Postprandial Metabolism, and Intramyocellular and Hepatocellular Lipid Levels in Overweight Adults

Excess body weight and insulin resistance lead to type 2 diabetes and other major health problems. There is an urgent need for dietary interventions to address these conditions. To measure the effects of a low-fat vegan diet on body weight, insulin resistance, postprandial metabolism, and intramyocellular and hepatocellular lipid levels in overweight adults. This 16-week randomized clinical trial was conducted between January 2017 and February 2019 in Washington, DC. Of 3115 people who responded to flyers in medical offices and newspaper and radio advertisements, 244 met the participation criteria (age 25 to 75 years; body mass index of 28 to 40) after having been screened by telephone. Participants were randomized in a 1:1 ratio. The intervention group (n = 122) was asked to follow a low-fat vegan diet and the control group (n = 122) to make no diet changes for 16 weeks. At weeks 0 and 16, body weight was assessed using a calibrated scale. Body composition and visceral fat were measured by dual x-ray absorptiometry. Insulin resistance was assessed with the homeostasis model assessment index and the predicted insulin sensitivity index (PREDIM). Thermic effect of food was measured by indirect calorimetry over 3 hours after a standard liquid breakfast (720 kcal). In a subset of participants (n = 44), hepatocellular and intramyocellular lipids were quantified by proton magnetic resonance spectroscopy. Repeated measure analysis of variance was used for statistical analysis. Among the 244 participants in the study, 211 (87%) were female, 117 (48%) were White, and the mean (SD) age was 54.4 (11.6) years. Over the 16 weeks, body weight decreased in the intervention group by 5.9 kg (95% CI, 5.0-6.7 kg; P < .001). Thermic effect of food increased in the intervention group by 14.1% (95% CI, 6.5-20.4; P < .001). The homeostasis model assessment index decreased (-1.3; 95% CI, -2.2 to -0.3; P < .001) and PREDIM increased (0.9; 95% CI, 0.5-1.2; P < .001) in the intervention group. Hepatocellular lipid levels decreased in the intervention group by 34.4%, from a mean (SD) of 3.2% (2.9%) to 2.4% (2.2%) (P = .002), and intramyocellular lipid levels decreased by 10.4%, from a mean (SD) of 1.6 (1.1) to 1.5 (1.0) (P = .03). None of these variables changed significantly in the control group over the 16 weeks. The change in PREDIM correlated negatively with the change in body weight (r = -0.43; P < .001). Changes in hepatocellular and intramyocellular lipid levels correlated with changes in insulin resistance (both r = 0.51; P = .01). A low-fat plant-based dietary intervention reduces body weight by reducing energy intake and increasing postprandial metabolism. The changes are associated with reductions in hepatocellular and intramyocellular fat and increased insulin sensitivity. ClinicalTrials.gov Identifier: NCT02939638.

95-OR: Plasma miRNAs Levels at First Trimester of Pregnancy Predict Insulin Sensitivity Estimated at the Second Trimester of Pregnancy

Background: Gestational diabetes mellitus (GDM) is the most common pregnancy complication with a prevalence of 14% worldwide and has important consequences on both the mother and her child. GDM results from imbalance between insulin secretion capacity response and decreasing insulin sensitivity in pregnancy. MicroRNAs (miRNAs) are small (19-24nt) single-stranded RNA molecules involved in post-transcriptional regulation through binding to targeted mRNAs. MiRNAs are thought to regulate many physiological processes including glucose homeostasis. Objective: Identify plasmatic miRNAs at the first trimester of pregnancy that predict insulin sensitivity (IS; as estimated with the Matsuda index) assessed between the 24th and the 28thweek of pregnancy. Methods: miRNAs were quantified by next generation sequencing in 443 plasma samples from the Gen3G birth-cohort. DESeq2 package was applied to identify miRNAs associated with IS. Glmnet package was used to compute lasso regression and find the minimum number of miRNAs predicting IS. Results: On average, participants were 28.43 ± 4.3 (18-47) years old and had a BMI of 26.1 ± 6.1 (16.6-54.1) kg/m2 at first trimester and had a Matsuda index of 8.41 ± 4.9 (0.77-37.53) at second trimester. A total of 106 miRNAs were associated with IS (p-adjusted for false discovery rate &amp;lt;0.1) when analyses were corrected for gestational age at blood collection, with 37 of them remaining significant after further correction for maternal age and BMI measured at first trimester. A lasso regression selected 27 of these miRNAs along with maternal age and BMI and overall explained 32% of the variability of IS at second trimester. Conclusion: miRNAs quantified at first trimester of pregnancy are associated with and predictive of maternal IS estimated at second trimester. These miRNAs are likely involved in the pathophysiology of GDM and provide novel promising biomarkers for predicting glucose metabolism dysregulation in pregnancy. Disclosure C. Légaré: None. V. Desgagné: None. F. White: None. M.S. Scott: None. P. Perron: None. M. Hivert: None. R. Guérin: None. L. Bouchard: None. Funding IRSC (IGH-155183)