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.

Utility of anthropometric indicators in predicting osteoporosis in ambulant community dwelling rural postmenopausal women from southern India.

Osteoporosis is characterised by low bone mineral density (BMD) and is a significant public health problem in India. This cross-sectional study was done to assess the relationship between various anthropometric measures and BMD in 308 rural dwelling South Indian postmenopausal women. Anthropometric variables such as weight, body mass index (BMI), waist circumference (WC), hip circumference (HC) and neck circumference (NC) were measured. BMD was assessed by dual-energy X-ray absorptiometry (DXA) scan at the lumbar spine (LS) and femoral neck (NOF). The mean age ± SD of study participants was 60.7 ± 7.8 years. All anthropometric variables showed positive correlation with BMD at NOF and LS (P < 0.05). Weight showed the best correlation (r = 0.482 for NOF and 0.412 for LS; P < 0.001). On multivariate logistic regression, age and weight remained significant for predicting femoral neck osteoporosis while weight and WC were the best predictors for LS osteoporosis. These anthropometric measures may serve as surrogate markers for osteoporosis and thus be used to screen postmenopausal women for referral to a centre with fewer limited resources.

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.