Association between body composition, circulating irisin and telomere length in healthy individuals and individuals with Type 2 diabetes

Aims: Obesity and Type 2 diabetes are associated with accelerated ageing. The underlying mechanisms behind this, however, are poorly understood. In this study, we investigated the association between circulating irisin - a novel my okine involved in energy regulation - and telomere length (TL) (a marker of aging) in healthy individuals and individuals with Type 2 diabetes. Methods: Eighty-two healthy people and 67 subjects with Type 2 diabetes were recruited to this cross-sectional study. Anthropometric measurements including body composition measured by biompedance were recorded. Plasma irisin was measured by ELISA on a fasted blood sample. Relative TL was determined using real-time PCR. Associations between anthropometric measures and irisin and TL were explored using Pearson’s bivariate correlations. Multiple regression was used to explore all the significant predictors of TL using backward elimination. Results: In healthy individuals chronological age was a strong negative predictor of TL (=0.552, p < 0.001). Multiple regression analysis using backward elimination (excluding age) revealed the greater relative TL could be predicted by greater total muscle mass(b = 0.046, p = 0.001), less visceral fat (b = =0.183, p < 0.001)and higher plasma irisin levels (b = 0.01, p = 0.027). There were no significant associations between chronological age, plasmairisin, anthropometric measures and TL in patients with Type 2diabetes (p > 0.1). Conclusion: These data support the view that body composition and plasma irisin may have a role in modulation of energy balance and the aging process in healthy individuals. This relationship is altered in individuals with Type 2 diabetes.

Segmental body composition determination through a bioelectrical very large scale integration (VLSI) system

The purpose of this investigation was to develop new techniques to generate segmental assessments of body composition based on Segmental Bioelectrical Impedance Analysis (SBIA). An equally important consideration was the design, simulation, development, and the software and hardware integration of the SBIA system. This integration was carried out with a Very Large Scale Integration (VLSI) Field Programmable Gate Array (FPGA) microcontroller that analyzed the measurements obtained from segments of the body, and provided full body and segmental Fat Free Mass (FFM) and Fat Mass (FM) percentages. Also, the issues related to the estimate of the body's composition in persons with spinal cord injury (SCI) were addressed and investigated. This investigation demonstrated that the SBIA methodology provided accurate segmental body composition measurements. Disabled individuals are expected to benefit from these SBIA evaluations, as they are non-invasive methods, suitable for paralyzed individuals. The SBIA VLSI system may replace bulky, non flexible electronic modules attached to human bodies. ^