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: 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.
OBJECTIVE: (i) To examine the triglyceride glucose (TyG) index (Ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]) and its relationship to in vivo insulin sensitivity in obese adolescents (OB) along the spectrum of glucose tolerance and (ii) to compare TyG index with triglyceride/high-density lipoprotein TG/HDL and 1/fasting insulin (1/IF ), other surrogates of insulin sensitivity.
PATIENTS AND DESIGN: Cross-sectional data in 225 OB with normal glucose tolerance (NGT), prediabetes (preDM), and type 2 diabetes (T2DM) who had a 3-h hyperinsulinemic-euglycemic clamp and fasting lipid measurement.
RESULTS: Insulin-stimulated glucose disposal (Rd) declined significantly across the glycemic groups from OB-NGT to OB-preDM to OB-T2DM with a corresponding increase in TyG index (8.3 ± 0.5, 8.6 ± 0.5, 8.9 ± 0.6, p
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.
METHODS AND RESULTS: A total of 40 male Sprague-Dawley rats were assigned to one of five groups of varying diets as follows: standard diet, high fat diet (HFD), HFD supplemented with Lactobacillus casei strain Shirota, HFD supplemented with Bifidobacterium longum and HFD supplemented with a mixture of these two bacterial species. After 15 weeks of supplementation, the animals were examined for changes in body weight, body fat, total count of bacteria in fecal, blood serum lipid profile, leptin, adiponectin and inflammatory biomarkers. Histological analysis of the liver and adipose tissue was performed and the hepatic mRNA expression levels of genes related to lipid metabolism were measured. It was found that probiotic supplementation of either B. longum or a mixture of B. longum and LcS bacteria significantly reduced weight and triglycerides in the HFD groups. Supplementation of B. longum bacteria showed better results in terms of modulating leptin level, fat mass, adipocyte size and lipoprotein lipase expression, as well as increasing adiponectin and peroxisome proliferator-activated receptors-γ expression compared to dual species of bacteria. No significant differences were observed in the total count of fecal bacteria, glucose and inflammatory biomarker levels between supplemented groups.
CONCLUSIONS: B. longum supplementation in obesity was more beneficial in metabolic profile changes than the mixture species.
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.