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.
CONCLUSION: The global burden of cancer-related deaths attributable to high BMI has increased substantially from 2010 to 2019. The greatest increase in age-standardized death rates occurred in Southeast Asia, and liver cancer is the fastest-growing cause of cancer mortality attributable to high BMI. Urgent and sustained measures are required at a global and regional level to reverse these trends and slow the growing burden of cancer attributed to high 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.
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.
METHODS: Data were used from children and adolescents aged 8-19 years in six pooled childhood cohorts (19,261 participants, collected between 1972 and 2010) to create reference standards for fasting glucose and total cholesterol. Using the models for glucose and cholesterol as well as previously published reference standards for body mass index and blood pressure, clinical cardiovascular health charts were developed. All models were estimated using sex-specific random-effects linear regression, and modeling was performed during 2020-2022.
RESULTS: Models were created to generate charts with smoothed means, percentiles, and standard deviations of clinical cardiovascular health for each year of childhood. For example, a 10-year-old girl with a body mass index of 16 kg/m2 (30th percentile), blood pressure of 100/60 mm Hg (46th/50th), glucose of 80 mg/dL (31st), and total cholesterol of 160 mg/dL (46th) (lower implies better) would have a clinical cardiovascular health percentile of 62 (higher implies better).
CONCLUSIONS: Clinical cardiovascular health charts based on pediatric data offer a standardized approach to express clinical cardiovascular health as an age- and sex-standardized percentile for clinicians to assess cardiovascular health in childhood to consider preventive approaches at early ages and proactively optimize lifetime trajectories of cardiovascular health.
METHODS: This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups.
RESULTS: After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories.
CONCLUSION: The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.
METHODS: Data from four population-based National Health and Morbidity Surveys conducted in 1996, 2006, 2010, and 2015 were pooled. Hierarchical Age-Period-Cohort (HAPC) analysis explored the trajectories of BMI and waist circumference across the life course and birth cohorts by sex and ethnicity. These models assumed no period effect.
RESULTS: Generally, BMI and waist circumference trajectories increased across age and birth cohorts. These trajectories varied by sex and ethnicity. Females have more profound increasing BMI and waist circumference trajectories than their male counterparts as they age and as cohort recency increases. Chinese have less profound BMI and waist circumference increases across the life course and birth cohorts than other ethnic groups.
CONCLUSIONS: The profound increasing cohort trajectories of obesity, regardless of sex and ethnicity, are alarming. Future studies should focus on identifying factors associated with the less profound cohort effect among the Chinese to reduce the magnitude of trajectories in obesity, particularly among future generations.
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.
CONCLUSION: Various risk factors are linked to CRC, based on the findings related to socio-demographic characteristics and BMI. Therefore, to lower the nationwide prevalence of CRC, national public health campaigns should include collaboration with the regional authorities to highlight the incidence and risk factors of CRC based on ethnicity.
MATERIALS AND METHODS: A dose-ranging analysis using SKF7® was conducted through a randomized, double-blind, multicentre, placebo-controlled, phase 2 clinical trial involving individuals with obesity (N = 133) between January 2020 and April 2021. The potential percentage of change was assessed in relation to BW, BMI, WC and WHtR.
RESULTS: Average treatment effect estimates (treatment group vs. placebo) show a statistically significant reduction in the percentage of change for BW (mean = -2.915; CI: -4.546, -1.285), BMI (-2.921; CI: -4.551, -1.291), WC (mean = -2.187; CI: -3.784, -0.589) and WHtR (mean = -2.294, CI: -3.908, -0.681) in the group with a total of 750 mg of SKF7® (p
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.
CONCLUSION: From our review, there were methodological drawbacks and very low to moderate quality of evidence across all comparisons, and hence, it is inconclusive to say that yoga can significantly improve anthropometric parameters. More well-designed trials are needed to confirm and support the beneficial effects of yoga.
METHOD: This prospective study targeted those admitted for bariatric surgery. Participants underwent the biweekly pre-habilitation program included an individualized high whey-based protein very low-calorie (VLCHP) enteral regime (600-900 kcal/day) and moderate intensive exercise before bariatric surgery. Body composition and waist circumference were assessed after fortnight. Participants were segregated into morbid obese (MOG) (BMI <49 kg/m2) and super morbid obese group (SMOG) (BMI ≥50 kg/m2) for analysis.
RESULT: Majority of participants were female (71%) with median age 36.0 years old (MOG) and 34.3 years old (SMOG) respectively. SMOG achieved significant greater loss in weight (-7.4 kg vs -4.0 kg), fat percentage (-4.4% vs -1.7%) and fat mass (-9.9 kg vs -3.8 kg); but MOG had a significant increment in muscle mass (3.2 kg vs 2.8 kg) as compared to SOG (p mass growth during periods of negative energy balance combined with moderately intense aerobic activity.
CONCLUSION: Individualized whey-based VLCHP enteral regime and moderate intensive exercise encourage weight loss; increases muscle mass and strength; improve function status prior to bariatric surgery.
METHODS: Population-based surveys included 30,721 Malay, 10,865 Indian and 25,296 Chinese adults from The Malaysian Cohort, and 413,737 White adults from UK Biobank. Sex-specific linear regression models estimated associations of anthropometry and body composition (body mass index [BMI], waist circumference [WC], fat mass, appendicular lean mass) with systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), triglycerides and HbA1c.
RESULTS: Compared to Malay and Indian participants, Chinese adults had lower BMI and fat mass while White participants were taller with more appendicular lean mass. For BMI and fat mass, positive associations with SBP and HbA1c were strongest among the Chinese and Malay and weaker in White participants. Associations with triglycerides were considerably weaker in those of Indian ethnicity (eg 0.09 [0.02] mmol/L per 5 kg/m2 BMI in men, vs 0.38 [0.02] in Chinese). For appendicular lean mass, there were weak associations among men; but stronger positive associations with SBP, triglycerides, and HbA1c, and inverse associations with LDL-C, among Malay and Indian women. Associations between WC and risk factors were generally strongest in Chinese and weakest in Indian ethnicities, although this pattern was reversed for HbA1c.
CONCLUSION: There were distinct patterns of adiposity and body composition and cardiovascular risk factors across ethnic groups. We need to better understand the mechanisms relating body composition with cardiovascular risk to attenuate the increasing global burden of obesity-related disease.
METHODS: One hundred-eleven adults and 105 children were consecutively recruited from three centers. The Epworth Sleepiness Scale (ESS) and ESS for Children and Adolescents were used for Risk stratifications for OSA and POSA.
RESULTS: The prevalence of patients seeking orthodontic treatment at high risk of OSA/POSA was 27.8%, where 26.1% were adults, and 29.5% were children. High risk for OSA/POSA was not associated with gender, ethnicity, age, Body Mass Index, or neck circumference.
CONCLUSION: Approximately 26% of adults and 30% of children seeking orthodontic care were at high risk for OSA and POSA. Screening for OSA and POSA among adults and children seeking orthodontic treatment is imperative.
METHODS: This study included people living with HIV enrolled in a longitudinal cohort study from 2003 to 2019, receiving antiretroviral therapy (ART), and without prior tuberculosis. BMI at ART initiation was categorized using Asian BMI classifications: underweight (<18.5 kg/m2 ), normal (18.5-22.9 kg/m2 ), overweight (23-24.9 kg/m2 ), and obese (≥25 kg/m2 ). High FBG was defined as a single post-ART FBG measurement ≥126 mg/dL. Factors associated with high FBG were analyzed using Cox regression models stratified by site.
RESULTS: A total of 3939 people living with HIV (63% male) were included. In total, 50% had a BMI in the normal weight range, 23% were underweight, 13% were overweight, and 14% were obese. Median age at ART initiation was 34 years (interquartile range 29-41). Overall, 8% had a high FBG, with an incidence rate of 1.14 per 100 person-years. Factors associated with an increased hazard of high FBG included being obese (≥25 kg/m2 ) compared with normal weight (hazard ratio [HR] = 1.79; 95% confidence interval [CI] 1.31-2.44; p 25 kg/m2 were at increased risk of high FBG. This indicates that regular assessments should be performed in those with high BMI, irrespective of the classification used.