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BACKGROUND: Social relationships impact health and may affect proximal determinants such as obesity. Our review examined empirical evidence of the impact of changes in social relationships on risk of obesity in aging adults, with attention to gender. METHODS: We systematically searched Medline, EMBASE, Scopus, CINAHL, and ProQuest Dissertations and reference lists of relevant reviews and papers published up to November 2022. Longitudinal studies examining changes in marital status, living arrangement, social participation, and social networks and anthropometric changes or incident obesity were deemed eligible. Relevant data were extracted and synthesized narratively. RESULTS: We identified 41,910 eligible titles, resulting in 252 full texts and 7 included studies. All studies were conducted in the United States and assessed marital transitions only; 3 studies included both women and men. Overall, compared with people who remain non-married, entering marriage leads to a significant increase in body mass index (BMI) for both genders. Conversely, transitioning out of marriage through widowhood or divorce leads to a decrease in weight (kg/lbs), waist circumference, and BMI for both genders. All seven studies were deemed to be of medium quality. CONCLUSIONS: Our review revealed a paucity of prospective evidence on social connections as determinants of obesity among aging adults. There is limited evidence that marital transitions are associated with changes in anthropometric measurements in aging individuals, but no evidence on the effects of changes in living arrangements, social participation, and social networks on anthropometric measurements. More research attention to the social determinants of obesity is warranted.
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Purpose: When examining the underlying processes of obesity, evaluation of gut flora and energy homeostasis can be crucial since disruption of the normal gut microbiota community and energy imbalances are significant factors in the development of obesity. Therefore, this study aimed to compare the relative abundance of important obesity modulator gut microbiota (including Firmicutes, Bacteroidetes, Bifidobactrium spp., Lactobacillus spp., Bacteroides fragilis, Faecalibacterium prausnitzii, Akkermansia muciniphila, and Escherichia coli) in fecal samples of normometabilic and hypometabolic overweight/obese individuals. Methods: This matched case-control study conducted on 36 healthy women aged 18-50 years old. An indirect calorimeter and impedance body analyzer were used to assess resting metabolic rate (RMR) and body composition, respectively. Dietary intake and physical activity were assessed using questionnaires. To determine the abundance of the abovementioned gut microbiota, quantitative polymerase chain reaction (qPCR) method was performed. Moreover, ELISA kits were used to assess leptin, ghrelin, and insulin hormones. Results: The results highlighted higher load of Firmicutes (p = 0.02), F. prausnitzii (p < 0.001), and B. fragilis (p = 0.02) in the normometabolic individuals compared to the hypometabolic ones. Besides, the positive correlation between the abundance of Firmicutes (ß = 7.76 × 10-1, p = 0.01), F. prausnitzii (ß = 1.29 × 10-5, p = 0.01), and B. fragilis (ß = 4.13 × 10-6, p = 0.04) with the RMR have been shown. Whereas the abundance of Bacteroidetes, A. muciniphila, Lactobacillus spp., Bifidobactrium spp., and E. coli showed no significant difference (p > 0.05) and no significant correlation with the RMR except Lactobacillus spp. (ß = 1.73 × 10-4, p = 0.01). Conclusion: It seems that gut microbiota can be a potential target for refining host energy homeostasis and treating obesity and its consequences.
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OBJECTIVES: We aimed to validate dietary assessment methods against the gold standard, doubly labeled water (DLW), for estimating total energy intake (TEI). METHODS: PubMed, Scopus, Web of Science, and Google Scholar databases were searched until May 2023. Inclusion criteria encompassed studies involving participants aged 1-18 years, employing dietary assessment methods like food records, dietary histories, food frequency questionnaire (FFQ), or 24-h recalls estimating TEI alongside DLW to measure total energy expenditure (TEE). Data were pooled using random-effects meta-analysis models. RESULTS: Thirty-three studies were identified, with sample sizes ranging from 9 to 118 participants. Meta-analysis of 22 studies identified underestimation of TEI (mean difference [MD] = -262.9 kcal/day [95% CI: -380.0, -145.8]; I2 = 93.55%) for food records compared with TEE estimated by DLW. Other dietary assessment methods, including food recalls (n = 9) (MD = 54.2 kcal/day [95% CI: -19.8, 128.1]; I2 = 49.62%), FFQ (n = 7) (MD = 44.5 kcal/day [95% CI: -317.8, 406.8]; I2 = 94.94%), and diet history (n = 3) (MD = -130.8 kcal/day [95% CI: -455.8, 194.1]; I2 = 77.48%), showed no significant differences in TEI compared with DLW-estimated TEE. All studies were of high quality. CONCLUSION: Food records may underestimate TEI, yet additional research is needed to identify the most accurate methods for assessing children's dietary intake.