Optimal Cut-Off Values for Body Mass Index and Fat Mass Index Based on Age in Physically Active Males Using Receiver Operating Characteristic Curve.

This study aims to redefine obesity cut-off points for body mass index (BMI) and fat mass index (FMI) according to the different age groups of physically active males. Healthy physically active volunteers (N = 1442) aged 18-57 years (y), with a mean BMI = 22.7 ± 2.8 kg/m2, and mean FMI = 4.3 ± 1.7 kg/m2 were recruited from various fitness centers. BMI was calculated and individuals were categorized according to the Asia-Pacific BMI criterion of ≤22.9 kg/m2 and the previous WHO-guided BMI criterion of ≤24.9 kg/m2. FMI was also calculated for the study participants with a cut-off of 6.6 kg/m2. Redefining of BMI and FMI cut-off values was carried out based on different age groups categorized with a difference of 10 y and 5 y using the receiver operating characteristic (ROC) curve and Youden's index. For the entire study population, BMI redefined cut-off points for overweight and obesity were 23.7 kg/m2 and 24.5 kg/m2, respectively, while FMI redefined cut-off points for overweight and obesity were 4.6 kg/m2 and 5.7 kg/m2, respectively. With 10 y of age group difference, a constant BMI and FMI values were observed, while with 5 y of age group difference, a constant increase in the BMI cut-offs was observed as the age group increased, i.e., from 23.3 kg/m2 in 20-24 y to 26.6 kg/m2 in ≥45 y and a similar trend was seen in FMI cut-offs. To conclude, our study suggests that age-dependent BMI and FMI cut-off points may provide appropriate measurements for physically active males as the age group increases.

Deciphering Biochemical and Molecular Signatures Associated with Obesity in Context of Metabolic Health.

This study aims to identify the clinical and genetic markers related to the two uncommon nutritional statuses-metabolically unhealthy normal-weight (MUNW) and metabolically healthy overweight/obese (MHOW) individuals in the physically active individuals. Physically active male volunteers (n = 120) were recruited, and plasma samples were analyzed for the clinical parameters. Triglycerides, HDL-Cholesterol, LDL-cholesterol, total cholesterol, C-reactive protein, and insulin resistance were considered as markers of metabolic syndrome. The subjects were classified as 'healthy' (0 metabolic abnormalities) or 'unhealthy' (≥1 metabolic abnormalities) in their respective BMI group with a cut-off at 24.9 kg/m2. Analysis of biochemical variables was done using enzyme linked immunosorbent assay (ELISA) kits with further confirmation using western blot analysis. The microarray was conducted, followed by quantitative real-time PCR to identify and analyze differentially expressed genes (DEGs). The MHOW group constituted 12.6%, while the MUNW group constituted 32.4% of the total study population. Pro-inflammatory markers like interleukin-6, tumor necrosis factor (TNF)-α, and ferritin were increased in metabolically unhealthy groups in comparison to metabolically healthy groups. Gene expression profiling of MUNW and MHOW individuals resulted in differential expression of 7470 and 5864 genes, respectively. The gene ontology (GO) biological pathway analysis showed significant enrichment of the 'JAK/STAT signaling pathway' in MUNW and 'The information-processing pathway at the IFN-ß enhancer' pathway in MHOW. The G6PC3 gene has genetically emerged as a new distinct gene showing its involvement in insulin resistance. Biochemical, as well as genetic analysis, revealed that MUNW and MHOW are the transition state between healthy and obese individuals with simply having fewer metabolic abnormalities. Moreover, it is possible that the state of obesity is a biological adaptation to cope up with the unhealthy parameters.

Effect of endurance training on copper, zinc, iron and magnesium status.

BACKGROUND: Physical activity related energy expenditure, environmental stresses, body composition, dietary intake, etc., are key factors influencing the nutritional requirements of minerals. The present study was designed to study the nutritional status of metals with respect to extensive endurance training. METHODS: The participants of the study were navy sailors (N.=37, mean age±SD: 25.2±4.8 y) undergoing one month of endurance training. Nutritional status was assessed by determining their body composition using bioelectrical impedance (BIA), food intake and urinary excretion levels. Fasting blood samples were taken to separate plasma and red blood cells for analysis of copper, zinc, magnesium and iron and certain metal dependent enzymatic biomarkers. RESULTS: Endurance training significantly decreased the plasma levels of copper (P<0.01), zinc and iron (P<0.05) while in erythrocytes a significant (P<0.001) decrease was observed only for Mg and Zn. There was a concomitant increase (P<0.05) in urinary Zn excretion. In addition, the concentrations of certain metal dependent enzymatic biomarkers like RBC metallothionein (P<0.05) and carbonic anhydrase (P<0.01) (Zn biomarker), plasma ferritin (Fe biomarker) (P<0.001) and RBC Mg ATPase (Mg biomarker) (P<0.05) decreased after physical activity. CONCLUSIONS: The findings of the present study suggested the increased requirements of these minerals during physical activity.

Study of Metal-Metal Interactions and Their Biomarkers Using an Intestinal Human Cell Line.

From the time of dietary intake to their utilization, the number of important interactions occurs among mineral elements, which can affect their bioavailability because of similarity in physicochemical properties and common absorptive pathways. However, the studies that have analyzed the interactions among copper, iron, and zinc have conflicting results and need further exploration. HT-29 cells grown to confluence in 6-well plates were incubated with increasing concentrations (0 to 200 µM) of Cu, Fe, and Zn for 3 and 6 h for uptake studies. Interaction studies involved measuring the uptake of metal in the presence of 0:1-4:1 ratio of the other metal for 3 h using atomic absorption spectrophotometer. The concentration of metal biomarkers and cytokines was also measured in the cell lysate following extracellular supplementation. The presence of 50 µM Zn significantly decreased (P < 0.05) cellular Cu uptake in HT-29 cells at 0.5:1 Cu:Zn ratio and also the cellular Fe uptake at the ratios 0.5:1, 2:1, and 4:1 Fe:Zn. The presence of 50 µM Fe significantly (P < 0.05) decreased cellular Cu uptake at the ratios 1:1, 2:1, and 4:1 Cu:Fe. The concentration of metallothionein responded significantly (P < 0.05) to changes in extracellular Zn concentration (supplementation and depletion). There was a decrease in concentration of IL-1ß and TNF-α (P < 0.05) with an increasing extracellular concentration of Cu and Fe. The results of the study indicated that the presence of one mineral in the diet and multi mineral supplement may influence the bioavailability of the other mineral. Copper and iron may find application in promoting gut health.

Effect of physical activity and age on plasma copper, zinc, iron, and magnesium concentration in physically active healthy males.

OBJECTIVES: The concentration of nutritionally important minerals in circulation is under tight homeostatic control, however, physical activity and aging influence their body stores and nutritional requirement. The aim of this study was to evaluate the effect of both physical activity and age on plasma concentrations of copper, zinc, iron, and magnesium. METHODS: Stratified cluster sampling was used for selection of study participants (N = 360) belonging to three physical activity groups: sedentary, moderately active, and highly active on the basis of their physical activity levels as 1.53, 1.8, and 2.3, respectively. They were also divided into six different age groups (18-20, 21-25, 26-30, 31-35, 36-40, and 41-45 y). We assessed nutritional status by determining their body composition using bioelectrical impedance method and measuring intake levels. Fasting blood samples were taken to separate plasma for analysis of copper, zinc, magnesium, and iron. RESULTS: There was a major difference (P < 0.001) in the mean value of plasma copper, zinc, magnesium, and iron for the three activity groups. The plasma copper and iron concentrations were higher in the moderately active group (copper: 1.59 ± 0.05 mg/L, iron: 0.79 ± 0.22 mg/L) whereas zinc concentration was higher in the sedentary group (2.37 ± 0.29 mg/L). Both the highly and moderately active groups had higher plasma magnesium levels compared with the sedentary group. Plasma copper, zinc, magnesium, and iron levels also were influenced by age in a different pattern with respect to physical activity. CONCLUSION: Physical activity-related energy expenditure and age play a remarkable role in deciphering the plasma mineral levels in the healthy individuals.