METHODS: This cross-sectional study involved 1,404 school adolescents aged 12 years (46% boys and 54% girls). Socio-demographic, dietary and physical activity data were collected using questionnaires whilst body weight and height were measured and body mass index was classified based on WHO BMI-for-age Z-scores cut-off.

RESULTS: A multivariable linear regression model showed that BMI z-score was positively associated with parents' BMI (P<0.001), birth weight (P=0.003), and serving size of milk and dairy products (P=0.036) whilst inversely associated with household size (P=0.022). Overall, 13.1% of the variances in BMI Z-scores were explained by parents' BMI, birth weight, servings of milk and dairy products and household size.

AIM: This study aimed to compare the performance of BMI, waist circumference (WC) and waist-to-height ratio (WtHR) in predicting Malaysians with excess body fat defined by dual-energy X-ray absorptiometry (DXA).

SUBJECTS AND METHODS: A total of 399 men and women aged ≥40 years were recruited from Klang Valley, Malaysia. The body composition of the subjects, including body fat percentage, was measured by DXA. The weight, height, WC and WHtR of the subjects were also determined.

RESULTS: BMI [sensitivity = 55.7%, specificity = 86.1%, area under curve (AUC) = 0.709] and WC (sensitivity = 62.7%, specificity = 90.3%, AUC = 0.765) performed moderately in predicting excess adiposity. Their performance and sensitivity improved with lower cut-off values. The performance of WHtR (sensitivity = 96.6%, specificity = 36.1, AUC = 0.664) was optimal at the standard cut-off value and no modification was required.

OBJECTIVE: We hypothesized that people with a high BMI have altered plasma Aβ homeostasis compared with people with a lower BMI. We also tested whether reducing BMI by calorie-restriction could normalize plasma concentrations of Aβ.

METHODS: Plasma concentrations of Aβ40, Aβ42, and Aβ42/40 ratio were measured in 106 participants with BMIs classified as lean, overweight, or obese. From this cohort, twelve participants with overweight or obese BMIs entered a 12-week calorie-restriction weight loss program. We then tested whether decreasing BMI affected plasma Aβ concentrations.

METHODOLOGY: The Cochrane Central Register of Controlled Trials (CENTRAL) and PubMed (1985-January 2022) and trial registries for relevant randomised clinical trials were used. Relevant and published randomised clinical trials were reviewed and evaluated. The primary outcomes were anthropometry measurements, which were weight, waist circumference, body mass index (BMI), and body fat percentages. The secondary outcomes were changes in quality of life, psychological impact, lipid profile measurement, presence of adverse events, and changes in blood pressure and blood glucose. We assessed the data for risk of bias, heterogeneity, sensitivity, reporting bias, and quality of evidence.

RESULTS: 15 studies are included, involving 1161 participants. The analysis performed is based on three comparisons. For the first comparison between yoga and control, yoga reduces the waist circumference (MD -0.84, 95% CI [-5.12 to 3.44]), while there is no difference in body weight, BMI, or body fat percentages. In the second comparison between yoga and calorie restriction, yoga reduces body weight (MD -3.47, 95% CI [-6.20 to -0.74]), while there is no difference in waist circumference, BMI, or body fat percentage. In the third comparison between yoga and exercise, yoga reduces the body weight (MD -7.58, 95% CI [-11.51 to -3.65]), while there is no difference in waist circumference or BMI. For the secondary outcomes, yoga intervention reduces total cholesterol (MD -17.12, 95% CI [-32.24 to -2.00]) and triglycerides (MD -21.75, 95% CI [-38.77 to -4.73]) compared to the control group, but there is no difference compared to the calorie restriction and exercise group. There is no difference in the rest of the outcomes, which are LDL, HDL, quality of life, psychological impact, adverse events, blood pressure, and blood glucose. However, findings are not robust due to a high risk of bias and low-quality evidence.

METHODS: In a sample of 9448 participants followed for a mean of 15.3 years (186,158.5 person-years) from the Monitoring of Trends and Determinants in Cardiovascular Disease Augsburg/Cooperative Health Research in the Region of Augsburg population-based cohort conducted in Germany, we investigated the association of social connectivity, measured by the Social Network Index, and body mass index (BMI) with the risk of clinically validated T2D incidence using stratified Cox proportional hazards regression models adjusted for sociodemographic, life-style, cardiometabolic, and psychosocial risk factors.

RESULTS: During a mean follow-up of 14.1 years (186,158.5 person-years), 975 (10.3%) participants developed T2D. Participants with low social connectivity developed T2D at a higher rate than socially connected participants (10.0 versus 8.0 cases/10,000 person-years); however, BMI played a significant role in the association of social connectivity with T2D ( p < .001). In comparison to their socially connected counterparts, low social connectivity was associated with a higher rate of T2D incidence in normal-weight (6.0 versus 2.0 cases/10,000 person-years), but not overweight (13.0 versus 13.0 cases/10,000 person-years) or obese participants (32.0 versus 30.0 cases/10,000 person-years). Correspondingly, Cox regression analysis showed that 5-unit increments in BMI increased the risk of T2D in socially connected participants (hazard ratio = 3.03, 95% confidence interval = 2.48-3.79, p < .001) at a substantially higher rate than in low socially connected participants (hazard ratio = 1.77, 95% confidence interval = 1.45-2.16, p < .001).

MATERIALS AND METHODS: This study is an analysis of a matched case-control study with a ratio of 1:2. The case group contained 206 respondents, and the control group contained 412. All CRC cases were confirmed with the histological results. The control group was matched for links between age, sex and ethnicity with CRC. The Statistical Package for Social Sciences Statistics (SPSS) IBM version 28.0 was used to conduct descriptive analysis using chi-squared testing and simple logistic regression. The statistical significance was P < 0.05.

RESULT: Overall, 618 respondents took part in this survey, of which 256 (41.4%) were female and 362 (58.6%) were male. The maximum age was 76, with a mean age ± SD of 53.17 ± 11.4. Those of Bajau ethnicity comprised 24.6% (152) of the population, followed by Dusun with 22.8% (141), Kadazan with 17.6% (109%), other North Borneo ethnic groups with 15.5% (96), Bugis with 9.7% (60), Brunei with 4.4% (27) and other predominant races with 5.3% (33). Regression analyses revealed that the incidence of CRC in North Borneo, Malaysia, was substantially correlated with income, occupation, other linked diseases and BMI.

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.

METHODS: A cross-sectional study was conducted from October 2017 to March 2018 using a multi-stage stratified sampling method among Malaysian adults aged 18 years old and above. Sodium intake was determined by 24-h urinary sodium excretion, estimated from the respondents' 24-h urinary sample. Height was obtained based on standard protocol. Weight and WC were measured twice using validated anthropometric equipment and BMI was calculated according to World Health Organization (WHO) 1998 classification. Descriptive analysis was done to describe socio-demographic characteristics. A simple linear regression and multiple linear regression tests were done to assess the relationship of 24-h urinary excretion and anthropometric measurement. All statistical analysis was done using SPSS version 22.0.

RESULTS: Of 1047 interviewed respondents, 798 respondents had done the 24-h urine collection (76.0% response rate). Majority was between 40 and 59 years old (43.5%) and married (77.7%). Simple linear regression showed a significant positive linear association between 24-h urinary excretion and household income, WC, and obese group. In the multivariate analysis, it was indicated that, an increase of 1 unit of BMI will significantly increase the sodium intake by 129.20 mg/dl and an increase of 1 cm of WC will significantly increase the sodium intake by 376.45 mg/dl.

METHODS: This is a cross-sectional study using multiple logistic regression to identify predictors of elevated CRP among pre-treatment, newly diagnosed BCa patients. Studied variables were socio-demographic and medical characteristics, anthropometric measurements [body weight, Body Mass Index, body fat percentage, fat mass/fat free mass ratio, muscle mass, visceral fat], biochemical parameters [albumin, hemoglobin, white blood cell (WBC), neutrophil, lymphocyte], energy-adjusted Dietary Inflammatory Index, handgrip strength (HGS), scored Patient Generated-Subjective Global Assessment, physical activity level and perceived stress scale (PSS).

RESULTS: A total of 105 participants took part in this study. Significant predictors of elevated CRP were body fat percentage (OR 1.222; 95 % CI 1.099-1.358; p < 0.001), PSS (OR 1.120; 95 % CI 1.026-1.223; p = 0.011), low vs normal HGS (OR 41.928; 95 % CI 2.155-815.728; p = 0.014), albumin (OR 0.779; 95 % CI 0.632-0.960; p = 0.019), and WBC (OR 1.418; 95% CI 1.024-1.963; p = 0.036).

Methods: This case-control study was carried out on 113 patients with PV and 100 healthy controls. Total cholesterol, high-density lipoprotein (HDL) and triglycerides (TG) levels were measured and low-density lipoprotein (LDL), non-HDL cholesterol (non-HDL-C) and atherogenic index of plasma (AIP) were calculated. Chi-squared test and independent Student t-test (or their alternatives) were used for group comparison.

Results: The mean age and BMI of patients and controls were 47.7 ± 14.5 and 28 ± 6.2 and, 44.5 ± 18.5 and 25.5 ± 5.1, respectively. Total cholesterol, LDL, HDL, non-HDL-C and TG were statistically different between the two groups (P values < 0.001; < 0.001; < 0.001; < 0.001 and 0.021, respectively). However, AIP was not significantly different (P-value = 0.752).

METHODS: the following databases: PubMed, CINAHL, Scopus, and Cochrane Library have been searched to retrieve randomized controlled trials that examine the impact of digital health intervention on blood pressure, body mass index, lipid profile, blood glucose, Six-Minute Walk Test, and peak oxygen consumption. filters were set to include studies published in English between 2000-2023.

RESULTS: Nineteen studies were included in this review. Six-Minute Walk Test (MD = 16.70; 95% CI: 6.00 to 27.39, p = 0.000) and maximal oxygen consumption (SMD = 0.27; 95% CI: 0.08 to 0.45, p = 0.004) significantly improved following digital health intervention, after employing the sensitivity analysis significant improvement was observed in systolic (MD = -2.54; 95% CI: -4.98 to -0.11, p = 0.04) and diastolic blood pressure (SMD = -2.0182; 95% CI: -3.9436 to -0.0928, p = 0.04) favoring experimental groups. Subgroup analysis revealed significant improvement in quality of life after three months of follow-up (SMD = 0.18; 95% CI: 0.05 to 0.31, p = 0.00), no significant differences have been observed in body mass index, lipid profile, and blood glucose.

METHODS: We estimated global and regional temporal trends in the burden of cancer attributable to high BMI, and the contributions of various cancer types using the framework of the Global Burden of Disease Study.

RESULTS: From 2010 to 2019, there was a 35 % increase in deaths and a 34 % increase in disability-adjusted life-years from cancers attributable to high BMI. The age-standardized death rates for cancer attributable to high BMI increased over the study period (annual percentage change [APC] +0.48 %, 95 % CI 0.22 to 0.74 %). The greatest number of deaths from cancer attributable to high BMI occurred in Europe, but the fastest-growing age-standardized death rates and disability-adjusted life-years occurred in Southeast Asia. Liver cancer was the fastest-growing cause of cancer mortality (APC: 1.37 %, 95 % CI 1.25 to 1.49 %) attributable to high BMI.