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      Self-Reported Eating Speed Is Associated with Indicators of Obesity in Adults: A Systematic Review and Meta-Analysis

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      Healthcare
      MDPI AG

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          Abstract

          Eating speed (ES) as a dietary behaviour has become a widely discussed factor for weight management and obesity. This study analysed the relationship between ES and anthropometric indicators of obesity, including BMI and waist circumference (WC) in adults. A search conducted of PubMed, Web of Science, Science Direct and Scopus found six longitudinal studies and fifteen cross-sectional studies published for further analysis. A quality assessment was performed with the MINORS checklist. Eight studies were included in the meta-analysis and almost all reviewed studies showed that ES was associated with BMI, and non-fast eaters had significantly lower BMI than fast eaters. Therefore, it was assumed that slowing down the ES may be an effective strategy for weight management and lowering obesity risk. There was also an association between WC and ES. Assessment of eating speed can be included in nutrition surveys to analyse obesity risk. More broadly, research is also needed to establish a validated and standardised methodology to determine eating speed. Further research needs to examine the links between eating speed, obesity, ethnicity, sex, food culture and chronic diseases.

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          Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults

          Summary Background Underweight, overweight, and obesity in childhood and adolescence are associated with adverse health consequences throughout the life-course. Our aim was to estimate worldwide trends in mean body-mass index (BMI) and a comprehensive set of BMI categories that cover underweight to obesity in children and adolescents, and to compare trends with those of adults. Methods We pooled 2416 population-based studies with measurements of height and weight on 128·9 million participants aged 5 years and older, including 31·5 million aged 5–19 years. We used a Bayesian hierarchical model to estimate trends from 1975 to 2016 in 200 countries for mean BMI and for prevalence of BMI in the following categories for children and adolescents aged 5–19 years: more than 2 SD below the median of the WHO growth reference for children and adolescents (referred to as moderate and severe underweight hereafter), 2 SD to more than 1 SD below the median (mild underweight), 1 SD below the median to 1 SD above the median (healthy weight), more than 1 SD to 2 SD above the median (overweight but not obese), and more than 2 SD above the median (obesity). Findings Regional change in age-standardised mean BMI in girls from 1975 to 2016 ranged from virtually no change (−0·01 kg/m2 per decade; 95% credible interval −0·42 to 0·39, posterior probability [PP] of the observed decrease being a true decrease=0·5098) in eastern Europe to an increase of 1·00 kg/m2 per decade (0·69–1·35, PP>0·9999) in central Latin America and an increase of 0·95 kg/m2 per decade (0·64–1·25, PP>0·9999) in Polynesia and Micronesia. The range for boys was from a non-significant increase of 0·09 kg/m2 per decade (−0·33 to 0·49, PP=0·6926) in eastern Europe to an increase of 0·77 kg/m2 per decade (0·50–1·06, PP>0·9999) in Polynesia and Micronesia. Trends in mean BMI have recently flattened in northwestern Europe and the high-income English-speaking and Asia-Pacific regions for both sexes, southwestern Europe for boys, and central and Andean Latin America for girls. By contrast, the rise in BMI has accelerated in east and south Asia for both sexes, and southeast Asia for boys. Global age-standardised prevalence of obesity increased from 0·7% (0·4–1·2) in 1975 to 5·6% (4·8–6·5) in 2016 in girls, and from 0·9% (0·5–1·3) in 1975 to 7·8% (6·7–9·1) in 2016 in boys; the prevalence of moderate and severe underweight decreased from 9·2% (6·0–12·9) in 1975 to 8·4% (6·8–10·1) in 2016 in girls and from 14·8% (10·4–19·5) in 1975 to 12·4% (10·3–14·5) in 2016 in boys. Prevalence of moderate and severe underweight was highest in India, at 22·7% (16·7–29·6) among girls and 30·7% (23·5–38·0) among boys. Prevalence of obesity was more than 30% in girls in Nauru, the Cook Islands, and Palau; and boys in the Cook Islands, Nauru, Palau, Niue, and American Samoa in 2016. Prevalence of obesity was about 20% or more in several countries in Polynesia and Micronesia, the Middle East and north Africa, the Caribbean, and the USA. In 2016, 75 (44–117) million girls and 117 (70–178) million boys worldwide were moderately or severely underweight. In the same year, 50 (24–89) million girls and 74 (39–125) million boys worldwide were obese. Interpretation The rising trends in children's and adolescents' BMI have plateaued in many high-income countries, albeit at high levels, but have accelerated in parts of Asia, with trends no longer correlated with those of adults. Funding Wellcome Trust, AstraZeneca Young Health Programme.
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            Genetics of Obesity: What have we Learned?

            Candidate gene and genome-wide association studies have led to the discovery of nine loci involved in Mendelian forms of obesity and 58 loci contributing to polygenic obesity. These loci explain a small fraction of the heritability for obesity and many genes remain to be discovered. However, efforts in obesity gene identification greatly modified our understanding of this disorder. In this review, we propose an overlook of major lessons learned from 15 years of research in the field of genetics and obesity. We comment on the existence of the genetic continuum between monogenic and polygenic forms of obesity that pinpoints the role of genes involved in the central regulation of food intake and genetic predisposition to obesity. We explain how the identification of novel obesity predisposing genes has clarified unsuspected biological pathways involved in the control of energy balance that have helped to understand past human history and to explore causality in epidemiology. We provide evidence that obesity predisposing genes interact with the environment and influence the response to treatment relevant to disease prediction.
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              Effect of Central Obesity and Hyperandrogenism on Selected Inflammatory Markers in Patients with PCOS: A WHtR-Matched Case-Control Study

              White blood cell counts (WBC), lymphocyte-to-monocyte ratio (LMR), and monocyte-to-high-density lipoprotein cholesterol ratio (MHR) are used as chronic inflammation markers. Polycystic ovary syndrome (PCOS) is a constellation of systemic inflammation linked to central obesity (CO), hyperandrogenism, insulin resistance, and metabolic syndrome. The waist-to-height ratio (WHtR) constitutes a highest-concordance anthropometric CO measure. This study aims to access WBC, LMR, and MHR in PCOS and healthy subjects, with or without CO. Establishing relationships between complete blood count parameters, high-sensitivity C-reactive protein (hsCRP), and hormonal, lipid and glucose metabolism in PCOS. To do this, WBC, LMR, MHR, hsCRP, anthropometric, metabolic, and hormonal data were analyzed from 395 women of reproductive age, with and without, PCOS. Correlations between MHR, and dysmetabolism, hyperandrogenism, and inflammation variables were examined. No differences were found in WBC, LMR, MHR, and hsCRP between PCOS and controls (p > 0.05). PCOS subjects with CO had higher hsCRP, MHR, and WBC, and lower LMR vs. those without CO (p < 0.05). WBC and MHR were also higher in controls with CO vs. without CO (p < 0.001). MHR correlated with anthropometric, metabolic, and endocrine parameters in PCOS. WHtR appeared to strongly predict MHR in PCOS. We conclude that PCOS does not independently influence WBC or MHR when matched for CO. CO and dysmetabolism may modify MHR in PCOS and control groups.
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                Journal
                Healthcare
                Healthcare
                MDPI AG
                2227-9032
                November 2021
                November 16 2021
                : 9
                : 11
                : 1559
                Article
                10.3390/healthcare9111559
                34828605
                15b56f43-c4fa-4d28-887e-091f9ccefbb3
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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