SPINA Carb: a simple mathematical model supporting fast in-vivo estimation of insulin sensitivity and beta cell function.

Modelling insulin-glucose homeostasis may provide novel functional insights. In particular, simple models are clinically useful if they yield diagnostic methods. Examples include the homeostasis model assessment (HOMA) and the quantitative insulin sensitivity check index (QUICKI). However, limitations of these approaches have been criticised. Moreover, recent advances in physiological and biochemical research prompt further refinement in this area. We have developed a nonlinear model based on fundamental physiological motifs, including saturation kinetics, non-competitive inhibition, and pharmacokinetics. This model explains the evolution of insulin and glucose concentrations from perturbation to steady-state. Additionally, it lays the foundation of a structure parameter inference approach (SPINA), providing novel biomarkers of carbohydrate homeostasis, namely the secretory capacity of beta-cells (SPINA-GBeta) and insulin receptor gain (SPINA-GR). These markers correlate with central parameters of glucose metabolism, including average glucose infusion rate in hyperinsulinemic glucose clamp studies, response to oral glucose tolerance testing and HbA1c. Moreover, they mirror multiple measures of body composition. Compared to normal controls, SPINA-GR is significantly reduced in subjects with diabetes and prediabetes. The new model explains important physiological phenomena of insulin-glucose homeostasis. Clinical validation suggests that it may provide an efficient biomarker panel for screening purposes and clinical research.

An Atypical Form of Diabetes Among Individuals With Low BMI.

OBJECTIVE: Diabetes among individuals with low BMI (<19 kg/m2) has been recognized for >60 years as a prevalent entity in low- and middle-income countries (LMICs) and was formally classified as "malnutrition-related diabetes mellitus" by the World Health Organization (WHO) in 1985. Since the WHO withdrew this category in 1999, our objective was to define the metabolic characteristics of these individuals to establish that this is a distinct form of diabetes. RESEARCH DESIGN AND METHODS: State-of-the-art metabolic studies were used to characterize Indian individuals with "low BMI diabetes" (LD) in whom all known forms of diabetes were excluded by immunogenetic analysis. They were compared with demographically matched groups: a group with type 1 diabetes (T1D), a group with type 2 diabetes (T2D), and a group without diabetes. Insulin secretion was assessed by C-peptide deconvolution. Hepatic and peripheral insulin sensitivity were analyzed with stepped hyperinsulinemic-euglycemic pancreatic clamp studies. Hepatic and myocellular lipid contents were assessed with 1H-nuclear magnetic resonance spectroscopy. RESULTS: The total insulin secretory response was lower in the LD group in comparison with the lean group without diabetes and the T2D group. Endogenous glucose production was significantly lower in the LD group than the T2D group (mean ± SEM 0.50 ± 0.1 vs. 0.84 ± 0.1 mg/kg · min, respectively; P < 0.05). Glucose uptake was significantly higher in the LD group in comparison with the T2D group (10.1 ± 0.7 vs. 4.2 ± 0.5 mg/kg · min; P < 0.001). Visceral adipose tissue and hepatocellular lipids were significantly lower in LD than in T2D. CONCLUSIONS: These studies are the first to demonstrate that LD individuals in LMICs have a unique metabolic profile, suggesting that this is a distinct entity that warrants further investigation.

Lipid accumulation product (LAP) as a potential index to predict risk of insulin resistance in young, non-obese Asian Indian males from Southern India: observations from hyperinsulinemic-euglycemic clamp studies.

INTRODUCTION: We aimed to compare the predictive accuracy of surrogate indices namely the lipid accumulation product (LAP) index, homeostatic model of assessment of insulin resistance (HOMA-IR), fasting glucose-insulin ratio (FG-IR) and the quantitative-insulin sensitivity check index (QUICKI), against the M value of hyperinsulinemic-euglycemic clamp (HEC), and to determine a cut-off value for the LAP index to predict risk of insulin resistance in non-obese (body mass index <21 kg/m2), normoglycemic, Asian Indian males from Southern India. RESEARCH DESIGN AND METHODS: Data of HEC studies performed in 108 non-obese, normoglycemic, Asian Indian males was obtained retrospectively and the M value (a measure of whole-body insulin sensitivity) was calculated. The M value is the rate of whole-body glucose metabolism at the hyperinsulinemic plateau (a measure of insulin sensitivity) and is calculated between 60 and 120 min after the start of the insulin infusion in the HEC procedure. The LAP index, the HOMA-IR, FG-IR and QUICKI were calculated. Spearman's correlation and logistic regression analysis were performed. Cut-off value for the LAP index was obtained using receiver operating characteristics with area under curve (AUC) analysis at 95% CI. P value <0.05 was considered to be statistically significant. RESULTS: Significant negative correlation was observed for the M value with LAP index (r=-0.39, p<0.001) while significant positive correlation was noted with FG-IR (r=0.25; p<0.01) and QUICKI (r=0.22; p<0.01). The LAP index cut-off value ≥33.4 showed 75% sensitivity and 75% specificity with AUC (0.72) to predict risk of insulin resistance in this cohort. CONCLUSION: The LAP index showed higher predictive accuracy for the risk of insulin resistance as compared with HOMA-IR, QUICKI and FG-IR in non-obese, normoglycemic Asian Indian males from Southern India.

Bioimpedance analysis with a novel predictive equation - A reliable technique to estimate fat free mass in birth weight based cohorts of Asian Indian males.

AIM: To validate bioimpedance based predictive equations for fat free mass (FFM) against DEXA and to derive a novel birth weight based predictive equation for FFM in a birth weight based cohort of healthy Asian Indian men. METHODOLOGY: Whole body composition was done using DEXA and bioimpedance in 117 young Asian Indian men, born of normal birth weight (n = 59, birth weight ≥2.5 kg) or low birth weight (n = 58, birth weight < 2.5 kg). Predictive accuracy of 11 different bioimpedance based equations for FFM was evaluated using Pearson's correlation analysis and the root of mean squared prediction error (RMSE) analysis. RESULTS: The mean FFM (on DEXA) and total lean mass & impedance index (on bioimpedance) were significantly higher in the low birth weight cohort. Significantly higher body fat percentage was noted on bioimpedance, for the normal birth weight cohort, but not on DEXA. In addition, the mean values of predicted FFM were significantly higher in the low birth weight cohort for 9 different predictive equations. Specifically, the mean FFM values obtained using the predictive equations of Schaefer et al., Hoot cooper et al. and Hughes et al. were in close agreement with the actual FFM values on DEXA. A novel predictive equation (CMC equation) for FFM based on birth weight was derived. FFM = 32.637 + (-0.222*age) + (-32.51*waist-to-hip ratio) + (0.33*body mass index) + (1.58 * 1 or 2 (1 = normal birth weight, 2 = low birth weight) + (0.510*waist circumference). CONCLUSIONS: Our study findings substantiate the validity of Bio-impedance analysis (BIA) as a reliable and noninvasive tool for estimating body composition measures in birth-weight based cohorts of Asian Indian males. Further, we have devised a novel BIA-based predictive equation that can be useful in larger epidemiological studies to look at alterations in body fat in this cohort.

Differential performance of surrogate indices of fasting insulin resistance in low-birthweight and normal-birth weight cohorts: Observations from Hyperinsulinaemic-Euglycaemic clamp studies in young, Asian Indian males.

AIM: To evaluate the predictive accuracy of surrogate measures of fasting insulin resistance/sensitivity like the Homeostasis model assessment for insulin resistance (HOMA -IR), Fasting glucose/insulin ratio (FG-IR), Quantitative insulin sensitivity check index (QUICKI), and the 20/fasting C peptide x fasting plasma glucose [20/(FCP × FPG)] index in comparison to M value derived from hyperinsulinaemic-euglycaemic clamp (HEC) studies in two birth weight based cohorts of Asian Indian males. METHODS: HEC studies were performed in non-diabetic Asian Indian males (n = 117), born of normal birth weight (n = 59, birth weight > 2.5 kgs) and low birth weight (n = 58, birth weight < 2.5 kgs). Anthropometry and biochemical analysis were done. Surrogate indices of fasting insulin resistance were calculated and data were analysed by Pearson's correlation and Random calibration model analysis. RESULTS: Amongst surrogate indices of fasting insulin resistance/sensitivity, the mean values for HOMA-IR, QUICKI, FG-IR, 20/(FCP × FPG) index and M value were similar between the two groups. Significant positive correlation was observed for FG-IR and QUICKI with M value (the gold standard measure of insulin sensitivity derived from HEC procedure) in the low birth weight cohort in contrast to the normal birth weight cohort, wherein no significant correlation was observed for any of the indices. Random calibration model analysis showed highest predictive accuracy for QUICKI in both the study groups. CONCLUSION: The QUICKI index showed highest predictive accuracy in the normal birth weight and the low birth weight cohorts of Asian Indian males.

Are Predictive Equations for Estimating Resting Energy Expenditure Accurate in Asian Indian Male Weightlifters?

BACKGROUND: The accuracy of existing predictive equations to determine the resting energy expenditure (REE) of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE) with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. MATERIALS AND METHODS: REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris-Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. RESULTS: Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM), waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986) and the lowest difference was 375 kcal/day (Cunninghams, 1980). Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = -164.065 + 0.039 (LBM) (confidence interval -1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40). CONCLUSION: The significant finding of this study was that all the prediction equations underestimated the REE. The LBM was the sole determinant of REE in this population. In the absence of indirect calorimetry, the REE equation developed by us using LBM is a better predictor for calculating REE of professional male weightlifters of this region.

Comprehensive Maturity Onset Diabetes of the Young (MODY) Gene Screening in Pregnant Women with Diabetes in India.

Pregnant women with diabetes may have underlying beta cell dysfunction due to mutations/rare variants in genes associated with Maturity Onset Diabetes of the Young (MODY). MODY gene screening would reveal those women genetically predisposed and previously unrecognized with a monogenic form of diabetes for further clinical management, family screening and genetic counselling. However, there are minimal data available on MODY gene variants in pregnant women with diabetes from India. In this study, utilizing the Next generation sequencing (NGS) based protocol fifty subjects were screened for variants in a panel of thirteen MODY genes. Of these subjects 18% (9/50) were positive for definite or likely pathogenic or uncertain MODY variants. The majority of these variants was identified in subjects with autosomal dominant family history, of whom five were in women with pre-GDM and four with overt-GDM. The identified variants included one patient with HNF1A Ser3Cys, two PDX1 Glu224Lys, His94Gln, two NEUROD1 Glu59Gln, Phe318Ser, one INS Gly44Arg, one GCK, one ABCC8 Arg620Cys and one BLK Val418Met variants. In addition, three of the seven offspring screened were positive for the identified variant. These identified variants were further confirmed by Sanger sequencing. In conclusion, these findings in pregnant women with diabetes, imply that a proportion of GDM patients with autosomal dominant family history may have MODY. Further NGS based comprehensive studies with larger samples are required to confirm these finding.

Masked hypoglycemia in pregnancy.

BACKGROUND: Hypoglycemia is a major hindrance for optimal glycemic control in women with gestational diabetes mellitus (GDM) on insulin. In the present study, masked hypoglycemia (glucose <2.77mmol/L for ≥30 min) was estimated in pregnant women using a continuous glucose monitoring (CGM) system. METHODS: Twenty pregnant women with GDM on insulin (cases) and 10 age-matched euglycemic pregnant women (controls) between 24 and 36 weeks gestation were recruited. Both groups performed self-monitoring of blood glucose (SMBG) and underwent CGM for 72 h to assess masked hypoglycemia. Masked hypoglycemic episodes were further stratified into two groups based on interstitial glucose (2.28-2.77 and ≤2.22 mmol/L). RESULTS: Masked hypoglycemia was recorded in 35% (7/20) of cases and 40% (4/10) of controls using CGM, with an average of 1.28 and 1.25 episodes per subject, respectively. Time spent at glucose levels between 2.28 and 2.77 mmol/L did not differ between the two groups (mean 114 vs 90 min; P = 0.617), but cases spent a longer time with glucose ≤2.2 mmol/L. Babies born to women with GDM were significantly lighter than those born to controls (2860 vs 3290 g; P = 0.012). There was no significant difference in birth weight within the groups among babies born to women with or without hypoglycemia. CONCLUSION: Euglycemic pregnant women and those with GDM on insulin had masked hypoglycemia. Masked hypoglycemia was not associated with adverse maternal or fetal outcomes. Therefore, low glucose levels in the hypoglycemic range may represent a physiologic adaptation in pregnancy. This response is exaggerated in women with GDM on insulin.

Born with low birth weight in rural Southern India: what are the metabolic consequences 20 years later?

OBJECTIVE: Low birth weight (LBW) is common in the Indian population and may represent an important predisposing factor for type 2 diabetes (T2D) and the metabolic syndrome. Intensive metabolic examinations in ethnic LBW Asian Indians have been almost exclusively performed in immigrants living outside India. Therefore, we aimed to study the metabolic impact of being born with LBW in a rural non-migrant Indian population. SUBJECTS AND METHODS: One hundred and seventeen non-migrant, young healthy men were recruited from a birth cohort in a rural part of south India. The subjects comprised 61 LBW and 56 normal birth weight (NBW) men, with NBW men acting as controls. Subjects underwent a hyperinsulinaemic euglycaemic clamp, i.v. and oral glucose tolerance tests and a dual-energy X-ray absorptiometry scan. The parents' anthropometric status and metabolic parameters were assessed. RESULTS: Men with LBW were shorter (167±6.4 vs 172±6.0 cm, P<0.0001), lighter (51.9±9 vs 55.4±7 kg, P=0.02) and had a reduced lean body mass (42.1±5.4 vs 45.0±4.5 kg, P=0.002) compared with NBW controls. After adjustment for height and weight, the LBW subjects had increased diastolic blood pressure (77±6 vs 75±6 mmHg, P=0.01). Five LBW subjects had impaired glucose tolerance. In vivo insulin secretion and peripheral insulin action were similar in both the groups. Mothers of the LBW subjects were 3 cm shorter than the control mothers. CONCLUSION: Only subtle features of the metabolic syndrome and changes in body composition among LBW rural Indians were found. Whether other factors such as urbanisation and ageing may unmask more severe metabolic abnormalities may require a long-term follow-up.