STUDY DESIGN: A wide range of socio-demographic characteristics of Chinese, Malay and Indian women attending routine gynecologic care in Singapore were prospectively collected. Physical performance was objectively measured by hand grip strength and the Short Physical Performance Battery. Percent VAT was determined by dual-energy X-ray absorptiometry. Fasting serum concentrations of glucose, insulin, IL-6, TNF- α, and hs-CRP were measured.
MAIN OUTCOME MEASURE: was insulin resistance, expressed as the homeostatic model assessment of insulin resistance (HOMA-IR).
RESULTS: 1159 women were analyzed, mean age 56.3 (range 45-69) years, comprising women of Chinese (84.0%), Indian (10.2%), and Malay (5.7%) ethnic origins. The adjusted mean differences for obesity (0.66, 95% CI 0.32-1.00), VAT area in the highest vs lowest tertile (1.03, 95% CI 0.73-1.34), low physical performance (0.63, 95% CI 0.05-1.24), and highest vs lowest tertile of TNF- α (0.35, 95% CI 0.13-0.57) were independently associated with HOMA-IR. Women of Malay and Indian ethnicity had higher crude HOMA-IR than Chinese women. However, after adjustment for obesity, VAT, physical performance, and TNF- α, no differences in mean HOMA-IR remained, when comparing Chinese women with those of Malay ethnicity (0.27, 95% CI -0.12 to 0.66) and with those of Indian ethnicity (0.30, 95% CI -0.01 to 0.66).
CONCLUSIONS: Insulin resistance was independently associated with obesity, high VAT, low physical performance, and high levels of TNF- α in midlife Singaporean women. These variables entirely explained the significant differences in insulin resistance between women of Chinese, Malay and Indian ethnicity.
METHODS: Fifty overweight/obese individuals aged 22-29 years were assigned to either no-exercise control (n=25) or HIIT (n=25) group. The HIIT group underwent a 12-week intervention, three days/week, with intensity of 65-80% of age-based maximum heart rate. Anthropometric measurements, homeostatic model of insulin resistance (HOMA-IR) and gene expression analysis were conducted at baseline and post intervention.
RESULTS: Significant time-by-group interactions (p<0.001) were found for body weight, BMI, waist circumference and body fat percentage. The HIIT group had lower body weight (2.3%, p<0.001), BMI (2.7%, p<0.001), waist circumference (2.4%, p<0.001) and body fat percentage (4.3%, p<0.001) post intervention. Compared to baseline, expressions of PGC-1∝ and AdipoR1 were increased by approximately three-fold (p=0.019) and two-fold (p=0.003) respectively, along with improved insulin sensitivity (33%, p=0.019) in the HIIT group.
CONCLUSION: Findings suggest that HIIT possibly improved insulin sensitivity through modulation of PGC-1∝ and AdipoR1. This study also showed that improved metabolic responses can occur despite modest reduction in body weight in overweight/obese individuals undergoing HIIT intervention.
METHODS: The study involved 235 Malaysian subjects who were randomly selected (66 normal weight subjects, 97 overweight, 59 obese subjects, and 13 subjects who were underweight). Serum sDPP4 and active GLP-1 levels were examined by enzyme-linked immunosorbent assay (ELISA). Also, body mass index kg/m(2) (BMI), lipid profiles, insulin and glucose levels were evaluated. Insulin resistance (IR) was estimated via the homeostasis model assessment for insulin resistance (HOMA-IR).
RESULTS: Serum sDPP4 levels were significantly higher in obese subjects compared to normal weight subjects (p=0.034), whereas serum levels of active GLP-1 were lower (p=0.021). In obese subjects, sDPP4 levels correlated negatively with active GLP-1 levels (r(2)=-0.326, p=0.015). Furthermore, linear regression showed that sDPP4 levels were positively associated with insulin resistance (B=82.28, p=0.023) in obese subjects.
CONCLUSION: Elevated serum sDPP4 levels and reduced GLP-1 levels were observed in obese subjects. In addition, sDPP4 levels correlated negatively with active GLP-1 levels but was positively associated with insulin resistance. This finding provides evidence that sDPP4 and GLP-1 may play an important role in the pathogenesis of obesity, suggesting that sDPP4 may be valuable as an early marker for the augmented risk of obesity and insulin resistance.
METHODS: Fifty-two females (21.43 ± 4.8 years) were divided into "normal" (BMI = 18-24.9 kg/m2) and "high" (BMI ≥ 25 kg/m2) BMI groups. Participants wore pedometers throughout the day for nine weeks. Pre-post intervention tests performed on anthropometric, biochemical, and nutrient intake variables were tested at p ≤ 0.05.
RESULTS: Participants walked 7056 ± 1570 footsteps/day without a significant difference between normal (7488.49 ± 1098) and high (6739.18 ± 1793) BMI groups. After week 9, the normal BMI group improved significantly in BMI, body fat mass (BFM), and waist-hip ratio (WHR). Additionally, percent body fat, waist circumference (WC), and visceral fat area also reduced significantly in the high BMI group. A significant decrease in triglycerides (TG) (71.62 ± 29.22 vs. 62.50 ± 29.16 mg/dl, p=0.003) and insulin (21.7 ± 8.33 µU/l vs. 18.64 ± 8.25 µU/l, p=0.046) and increase in HMW-Adip (3.77 ± 0.46 vs. 3.80 ± 0.44 μg/ml, p=0.034) were recorded in the high BMI group. All participants exhibited significant inverse correlations between daily footsteps and BMI (r=-0.33, p=0.017), BFM (r=-0.29, p=0.037), WHR (r=-0.401, p=0.003), and MetS score (r=-0.49, p < 0.001) and positive correlation with HMW-Adip (r=0.331, p=0.017). A positive correlation with systolic (r=0.46, p=0.011) and diastolic (r=0.39, p=0.031) blood pressures and inverse correlation with the MetS score (r=-0.5, p=0.005) were evident in the high BMI group.
CONCLUSION: Counting footsteps using a pedometer is effective in improving MetS components (obesity, TG) and increasing HMW-Adip levels.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
AIM OF THE STUDY: This study aims to investigate the anti-obesity and lipid lowering effects of ethanolic extract of C. cauliflora leaves and its major compound (vitexin) in C57BL/6 obese mice induced by high-fat diet (HFD), as well as to further identify the molecular mechanism underlying this action.
METHODS AND MATERIAL: Male C57BL/6 mice were fed with HFD (60% fat) for 16 weeks to become obese. The treatment started during the last 8 weeks of HFD feeding and the obese mice were treated with C. cauliflora leaf extract at 200 and 400 mg/kg/day, orlistat (10 mg/kg) and vitexin (10 mg/kg).
RESULTS: The oral administration of C. cauliflora (400 and 200 mg/kg) and vitexin significantly reduced body weight, adipose tissue and liver weight and lipid accumulation in the liver compared to control HFD group. Both doses of C. cauliflora also significantly (P ≤ 0.05) decreased serum triglyceride, LDL, lipase, IL-6, peptide YY, resistin levels, hyperglycemia, hyperinsulinemia, and hyperleptinemia compared to the control HFD group. Moreover, C. cauliflora significantly up-regulated the expression of adiponectin, Glut4, Mtor, IRS-1 and InsR genes, and significantly decreased the expression of Lepr in white adipose tissue. Furthermore, C. cauliflora significantly up-regulated the expression of hypothalamus Glut4, Mtor and NF-kB genes. GC-MS analysis of C. cauliflora leaves detected the presence of phytol, vitamin E and β-sitosterol. Besides, the phytochemical evaluation of C. cauliflora leaves showed the presence of flavonoid, saponin and phenolic compounds.
CONCLUSION: This study shows interesting outcomes of C. cauliflora against HFD-induced obesity and associated metabolic abnormalities. Therefore, the C. cauliflora extract could be a potentially effective agent for obesity management and its related metabolic disorders such as insulin resistance and hyperlipidemia.
METHODS: Online literature search databases including Scopus, Web of Science, PubMed/Medline, Embase and Google Scholar were searched to discover relevant articles available up to 17 March 2020. We used mean changes and SD of the outcomes to assess treatment response from baseline and mean difference, and 95 % CI were calculated to combined data and assessment effect sizes in astaxanthin and control groups.
RESULTS: 14 eligible articles were included in the final quantitative analysis. Current study revealed that astaxanthin consumption was not associated with FBS, HbA1c, TC, LDL-C, TG, BMI, BW, DBP, and SBP. We did observe an overall increase in HDL-C (WMD: 1.473 mg/dl, 95 % CI: 0.319-2.627, p = 0.012). As for the levels of CRP, only when astaxanthin was administered (i) for relatively long periods (≥ 12 weeks) (WMD: -0.528 mg/l, 95 % CI: -0.990 to -0.066), and (ii) at high dose (> 12 mg/day) (WMD: -0.389 mg/dl, 95 % CI: -0.596 to -0.183), the levels of CRP would decrease.
CONCLUSION: In summary, our systematic review and meta-analysis revealed that astaxanthin consumption was associated with increase in HDL-C and decrease in CRP. Significant associations were not observed for other outcomes.