METHODS: The study included individuals from the Northern Finland Birth Cohort 1966 (NFBC1966) who had available data for adiposity measures (body mass index and waist-to-hip ratio), alexithymia (measured by the 20-Item Toronto Alexithymia Scale: TAS-20), depressive symptoms (measured by the 13-item depression subscale of Hopkins Symptom Checklist: HSCL-13) at age of 31 years (n = 4773) and 46 years (n = 4431). Pearson's (r) correlation, and multiple linear regression were used to investigate the relationships between alexithymia, depressive symptoms, and adiposity measures. The potential mediating role of depressive symptoms was examined via Hayes' procedure (PROCESS).
RESULTS: Positive correlations were confirmed between adiposity measures (BMI and WHR) and the TAS-20 score (and its subscale), but not between obesity and HSCL-13 score. The strongest correlation was between the DIF (difficulty identifying feelings) subscale of the TAS-20 and HSCL-13 at both time points (31 y: r(3013) = 0.41, p
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).
CONCLUSION: The detrimental link between low social connectivity and increased risk of T2D is substantially stronger in participants with a lower BMI.
METHODS: We conducted systematic search in PubMed and Scopus from September 1, 2016, to July 22, 2022. Original COI studies estimating the economic cost of obesity and/or overweight in at least one country, published in English were included. To facilitate the comparison of estimates across countries, we converted the cost estimates of different years to 2022 purchasing power parity (PPP) values using each country's consumer price index (CPI) and PPP conversion rate.
RESULTS: Nineteen studies were included. All studies employed a prevalence-based approach using Population Attributable Fraction (PAF) methodology. About half of the included studies (53%) were conducted in high-income countries while the others (47%) were conducted in middle-income countries. The economic burden of obesity ranged between PPP 15 million in Brazil to PPP 126 billion in the USA, in the year 2022. Direct medical costs accounted for 0.7% to 17.8% of the health system expenditure. Furthermore, the total costs of obesity ranged from 0.05% to 2.42% of the country's gross domestic product (GDP). Of the seven studies that estimated both direct and indirect costs, indirect costs accounted for the largest portion of five studies. Nevertheless, a variety in methodology across studies was identified. The number of co-morbidities included in the analysis varied across studies.
CONCLUSIONS: Although there was a variety of methodologies across studies, consistent evidence indicated that the economic burden of obesity was substantial. Obesity prevention and control should be a public health priority, especially among countries with high prevalence of obesity.
METHODS: In this study, a systematic search was conducted across electronic databases, including PubMed, Scopus, Web of Science, Embase, ScienceDirect, and Google Scholar. The search aimed to identify studies published between December 2000 and August 2022 that reported metabolic syndrome's impact on female sexual dysfunction.
RESULTS: The review included nine studies with a sample size of 1508 obese women. The I2 heterogeneity index indicated high heterogeneity (I2: 97.5). As a result, the random effects method was used to analyze the data. Based on this meta-analysis, the prevalence of sexual dysfunction in women with obesity was reported as 49.7% (95%CI: 35.8-63.5). Furthermore, the review comprised five studies involving 1411 overweight women. The I2 heterogeneity test demonstrated high heterogeneity (I2: 96.6). Consequently, the random effects model was used to analyze the results. According to the meta-analysis, the prevalence of sexual dysfunction in overweight women was 26.9% (95% CI: 13.5-46.5).
CONCLUSION: Based on the results of this study, it has been reported that being overweight and particularly obese is an important factor affecting women's sexual dysfunction. Therefore, health policymakers must acknowledge the significance of this issue in order to raise awareness in society about its detrimental effect on the female population.
DESIGN: Cross-sectional.
SETTING: Central and eastern regions of Peninsular Malaysia.
PARTICIPANTS: A stratified random sampling was employed to select 917 secondary school-going adolescents (aged 15-17 years).
RESULTS: The prevalence of under-reporters was 17·4 %, while no over-reporters were identified. Under-reporters had higher body composition and lower dietary intakes (except for vitamin C, Cr and Fl) compared with plausible reporters (P < 0·05). Adolescents with overweight and obesity had a higher odds of under-reporting compared with under-/normal weight adolescents (P < 0·001). In model 3, the highest regression coefficient (R2 = 0·404, P < 0·001) was obtained after adjusting for reporting status.
CONCLUSIONS: Overweight and obese adolescents were more likely to under-report their food intake and consequently affect nutrient intakes estimates. Future analyses that include nutrient intake data should adjust for reporting status so that the impact of misreporting on study outcomes can be conceded and consequently improve the accuracy of dietary-related results.