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: This is a cross-sectional comparison study whereby 225 overweight/obese children matched for age, sex, and ethnicity with 225 normal weight children participated in this study. Body image dissatisfaction, disordered eating, and depressive symptoms were assessed through a self-administered questionnaire. Blood pressure was measured, whereas blood was drawn to determine insulin, high-sensitivity C-reactive protein (hs-CRP), glucose, and lipid profiles. Homeostasis model assessment-estimated insulin resistance (HOMA-IR) was calculated using glucose and insulin levels. Wechsler's Intelligence Scale for Children-Fourth Edition (WISC-IV) was used to assess cognitive function in children. Ordinary least square regression analysis was conducted to determine the direct and indirect relationships between weight status and cognitive function.
RESULTS: A negative relationship was found between overweight/obesity with cognitive function. Overweight/obese children were on average 4.075 units lower in cognitive function scores compared to normal weight children. Such difference was found through mediators, such as body image dissatisfaction, disordered eating, depression, systolic blood pressure, triglycerides, HOMA-IR, and hs-CRP, contributing 22.2% of the variances in cognitive function in children.
CONCLUSION: Results highlight the important mediators of the relationship between overweight/obesity and cognitive function. Consequently, future interventions should target to improve psychological well-being and reduce cardiovascular disease risk for the prevention of poorer cognitive performance in overweight/obese children.
METHODS: We performed a regression discontinuity design study. A total of 46 975 adults with ≥1 cardiovascular risk factor in 2015 were included in the study. A two-stage evaluation process (stage 1: waist circumference ≥85 cm for men or ≥90 cm for women and ≥1 cardiovascular risk factor; stage 2: body mass index (BMI)≥25 kg/m2 and ≥2 cardiovascular risk factors) was applied. Changes in obesity, cardiovascular outcomes, and health care utilisation were evaluated in a one-year follow-up in the fiscal year 2016.
RESULTS: Participants who received lifestyle guidance intervention based on the waist circumference had a statistically significant reduction in obesity outcomes (Δ weight: -0.30 kg, 95% CI = -0.46 to -0.11; Δ waist circumference: -0.26 cm, 95% CI = -0.53 to -0.02; Δ BMI = -0.09 kg/m2, 95% CI = -0.17 to -0.04) but not in other cardiovascular risk factors and health care utilisation. Analyses based on BMI and results according to demographic subgroups did not reveal significant findings.
CONCLUSIONS: The provision of this intervention had a limited effect on health improvement and a decrease in health care costs, health care visits, and length of stay. A more intensive intervention delivery could potentially improve the efficacy of this intervention programme.