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. ^

Coronary heart disease risk factors among tri-ethnic students at Florida International University

The present study identified and compared Coronary Heart Disease (CHD) risk factors quantified as “CHD risk point standards” (CHDRPS) among tri-ethnic (White non-Hispanic [WNH], Hispanic [H], and Black non-Hispanic [BNH]) college students. All 300 tri-ethnic subjects completed the Cardiovascular Risk Assessment Instruments and had blood pressure readings recorded on three occasions. The Bioelectrical Impedance Analysis (BIA) was used to measure body composition. Students' knowledge of CHD risk factors was also measured. In addition, a 15 ml fasting blood sample was collected from 180 subjects and blood lipids and Homocysteine (tHcy) levels were measured. Data were analyzed by gender and ethnicity using one-way Analysis of Variance (ANOVA) with Bonferroni's pairwise mean comparison procedure, Pearson correlation, and Chi-square test with follow-up Bonferroni's Chi-square tests. ^ The mean score of CHDRPS for all subjects was 19.15 ± 6.79. Assigned to the CHD risk category, college students were below-average risk of developing CHD. Males scored significantly (p < 0.013) higher for CHD risk than females, and BNHs scored significantly (p < 0.033) higher than WNHs. High consumption of dietary fat saturated fat and cholesterol resulted in a high CHDRPS among H males and females and WNH females. High alcohol consumption resulted in a high CHDRPS among all subjects. Mean tHcy ± SD of all subjects was 6.33 ± 3. 15 μmol/L. Males had significantly (p < 0.001) higher tHcy than females. Black non-Hispanic females and H females had significantly (p < 0.003) lower tHcy than WNH females. Positive associations were found between tHcy levels and CHDRPS among females (p < 0.001), Hs (p < 0.001), H males (p < 0.049), H females (p < 0.009), and BNH females (p < 0.005). Significant positive correlations were found between BMI levels and CHDRPS in males (p < 0.001), females (p < 0.001), WNHs (p < 0.008), Hs (p < 0.001), WNH males (p < 0.024), H males (p < 0.004) and H females (p < 0.001). The mean knowledge of CHD questions of all subjects was 71.70 ± 7.92 out of 100. The mean knowledge of CHD was significantly higher for WNH males (p < 0.039) than BNH males. A significant inverse correlation (r = 0.392, p < 0.032) was found between the CHD knowledge and CHDRPS in WNH females. The researcher's findings indicate strong gender and ethnic differences in CHD risk factors among the college-age population. ^

Caffeine Intake and its Association with Disease Progression, Sleep Quality and Anxiety Symptoms and Nutritional Alterations in People Living with HIV in the Miami Adult Studies on HIV Cohort

Miami-Dade County has approximately 27,000 people living with HIV (PLWH), and the highest HIV incidence in the nation. PLWH have reported several types of sleep disturbances. Caffeine is an anorexic and lipolytic stimulant that may adversely affect sleep patterns, dietary intakes and body composition. High caffeine consumption (>250 mg. per day or the equivalent of >4 cups of brewed coffee) may also affect general functionality, adherence to antiretroviral treatment (ART) and HIV care. This study assess the relationship of high caffeine intake with markers of disease progression, sleep quality, insomnia, anxiety, nutritional intakes and body composition. A convenience sample of 130 PLWH on stable ART were recruited from the Miami Adult Studies on HIV (MASH) cohort, and followed for three months. After consenting, questionnaires on Modified Caffeine Consumption (MCCQ), Pittsburg Insomnia Rating Scale (PIRS), Pittsburg Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), socio-demographics, drug and medication use were completed. CD4 count, HIV viral load, anthropometries, and body composition measures were obtained. Mean age was 47.89±6.37 years, 60.8% were male and 75.4% were African-Americans. Mean caffeine intake at baseline was 337.63 ± 304.97 mg/day (Range: 0-1498 mg/day) and did not change significantly at 3 months. In linear regression, high caffeine consumption was associated with higher CD4 cell count (β=1.532, P=0.049), lower HIV viral load (β=-1.067, P=0.048), higher global PIRS (β=1.776, P=0.046), global PSQI (β=2.587, P=0.038), and GAD-7 scores (β=1.674, P=0.027), and with lower fat mass (β=-0.994, P=0.042), energy intakes (β=-1.643, P=0.042) and fat consumption (β=-1.902, P=0.044), adjusting for relevant socioeconomic and disease progression variables. Over three months, these associations remained significant. The association of high caffeine with lower BMI weakened when excluding users of other anorexic and stimulant drugs such as cocaine and methamphetamine, suggesting that caffeine in combination, but not alone, may worsen their action. In summary, high caffeine consumption was associated with better measures of disease progression; but was also detrimental on sleep quality, nutritional intakes, BMI and body composition and associated with insomnia and anxiety. Large scale studies for longer time are needed to elucidate the contribution of caffeine to the well-being of PLWH.