Predictors Of Body Mass Index Research Articles

Childhood obesity accelerates biological ageing: is oxidative stress a link?

Obesity is multifactorial pathophysiological condition with an imbalance in biochemical, immunochemical, redox status and genetic parameters values. We aimed estimate connection between relative leukocyte telomere lengths (rLRL) - biomarker of cellular aging with metabolic and redox status biomarkers values in a group of obese and lean children. The study includes 110 obese and 42 lean children and adolescents, both genders. The results suggested that rLTL are significantly shorter in obese, compared to lean group (p<0,01). Negative correlation of rLTL with total oxidant status, TOS (Spearman's ρ = -0.365, p<0.001) as well as with C reactive protein (Spearman's ρ= -0,363, p<0.001) were observed. Principal component analysis (PCA) extracted three distinct factors (i.e. principal components) entitled as: prooxidant factor with 35% of total variability; antioxidant factor with 30% of total variability and lipid antioxidant - biological ageing factor with 12% of the total variability. The most important predictor of body mass index (BMI) >30kg/m2 according to logistic regression analysis was PCA derived antioxidant factor's score (OR: 1.66, 95th Cl: 1.05-2.6, p=0.029). PCA analysis confirmed oxidative stress importance in biological ageing caused by obesity and its multiple consequences related to prooxidants augmentation and antioxidants exhaustion and gave us clear signs of disturbed cellular homeostasis deepness, even before any overt disease occurrence.

Obesity-Related Traits Mediate the Effects of Educational Attainment on the Risk of Varicose Veins, Venous Thromboembolism, and Phlebitis.

The extent to which educational attainment (EA) influences the risk of varicose veins (VVs), venous thromboembolism (VTE), and phlebitis occurrence, whether this pathway is mediated by obesity-related traits, and the proportion of their mediation is unknown. A Mendelian randomization (MR) design was used to genetically investigate the causal effects of EA on the risk of VV, VTE, and phlebitis and to assess the mediating effect of obesity-related traits. Causal effects were estimated using primarily the multiplicative random-effects inverse variance-weighted method. This was supplemented by Cochran's Q-statistic, MR-Egger regression, MR funnel plots, and leave-one-out test to evaluate the reliability of the results. For the individual mediation effect, the coefficient product method was mainly utilized to estimate. An increase in genetically predicted EA was associated with a lower risk of VV, VTE, and phlebitis, as well as lower body mass index, basal metabolic rate, hip circumference, and waist circumference. As genetically predicted body mass index, basal metabolic rate, hip circumference, and waist circumference increased, the risk of developing VV, VTE, and phlebitis increased, respectively. Body mass index, basal metabolic rate, hip circumference, and waist circumference were identified as mediators of the protective effects of EA on VV, VTE, and phlebitis. The findings support a causal relationship between higher EA and lower risk of VV, VTE, and phlebitis. Obesity-related traits play a significant mediating role in these pathways, and there are interactions between them, with hip circumference mediating these pathways relatively independently from the other three.

Multiomics Picture of Obesity in Young Adults.

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Weight gained during treatment predicts 6-month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning.

This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program. Of 368 individuals with anorexia nervosa (209 adolescents and 159 adults), 160 individuals had data available for a 6-month follow-up analysis. Participants were treated in a 6-day-per-week partial-hospital program. Participants were assessed for eating disorder-specific and non-specific psychopathology. The analyses used established machine learning procedures combined in an ensemble model from support vector machine learning, random forest prediction, and the elastic net regularized regression with an exploration (training; 75%) and confirmation (test; 25%) split of the data. The models predicting body mass index (BMI) at 6-month follow-up explained a 28.6% variance in the training set (n = 120). The model had good performance in predicting 6-month BMI in the test dataset (n = 40), with predicted BMI significantly correlating with actual BMI (r = .51, p = 0.01). The change in BMI from admission to discharge was the most important predictor, strongly correlating with reported BMI at 6-month follow-up (r = .55). Behavioral variables were much less predictive of BMI outcome. Results were similar for z-transformed BMI in the adolescent-only group. Length of stay was most predictive of weight gain in treatment (r = .56) but did not predict longer-term BMI. This study, using an agnostic ensemble machine learning approach in the largest to-date sample of individuals with anorexia nervosa, suggests that achieving weight gain goals in treatment predicts longer-term weight-related outcomes. Other potential predictors, personality, mood, or eating disorder-specific symptoms were relatively much less predictive. The results from this study indicate that the amount of weight gained during treatment predicts BMI 6 months after discharge from a high level of care. This suggests that patients require sufficient time in a higher level of care treatment to meet their specific weight goals and be able to maintain normal weight.

Child eating behavior predicts body mass index after 1 year: results from the Swiss Preschooler's Health Study (SPLASHY).

Child obesity is a growing global issue. Preventing early development of overweight and obesity requires identifying reliable risk factors for high body mass index (BMI) in children. Child eating behavior might be an important and malleable risk factor that can be reliably assessed with the parent-report Child Eating Behavior Questionnaire (CEBQ). Using a hierarchical dataset (children nested within child care centers) from a representative cohort of Swiss preschool children, we tested whether eating behavior, assessed with a 7-factor solution of the CEBQ, and BMI at baseline predicted the outcome BMI after 1 year, controlling for socioeconomic status (n = 555; 47% female; mean age = 3.9 years, range: 2.2-6.6; mean BMI = 16 kg/m2, range: 11.2-23; mean age- and sex-corrected z-transformed BMI, zBMI = 0.4, range -4 to +4.7). The statistical model explained 65.2% of zBMI at follow-up. Baseline zBMI was a strong positive predictor, uniquely explaining 48.8% of outcome variance. A linear combination of all CEBQ scales, taken together, explained 10.7% of outcome variance. Due to their intercorrelations, uniquely explained variance by any individual scale was of negligible clinical relevance. Only food responsiveness was a significant predictor, when accounting for all other predictors and covariates in the model, and uniquely explained only 0.4% of outcome variance. Altogether, our results confirm, extend, and refine previous research on eating behavior and zBMI in preschool children, by adjusting for covariates, accounting for intercorrelations between predictors, partitioning explained outcome variance, and providing standardized beta estimates. Our findings show the importance of carefully examining the contribution of predictors in multiple regression models for clinically relevant outcomes.

Socio-demographic factors and parental feeding practices predicted body mass index of Malaysian children with learning disabilities

Introduction: Overweight and obesity have emerged as significant global health concerns among children. Previous studies have provided evidence that children with intellectual and learning disabilities (LD) are at a higher risk of obesity compared to their peers without disabilities. Methods: This study aimed to predict body weight status of children with LD who attended Special Education Integration Program in Kelantan, located on East Coast of Peninsular Malaysia. Parents completed a self-administered questionnaire in Malay language, which included “Screening Tool of Feeding Problems” children’s version (STEP-CHILD) and Comprehensive Feeding Practice Questionnaire (CFPQ). The children’s body weight and height were measured to determine body mass index (BMI). Research hypothesis was tested through stepwise multiple linear regression analysis. Results: This study recruited 245 subjects with mean age of 10.5±1.7 years and mean BMI of 18.5±4.9 kg/m2. Prevalence of underweight, thinness and severe thinness was 12.2%, while overweight and obesity was 29.0%. Male children with LD (β=0.109, p&lt;0.044), older age (β=0.226, p&lt;0.001), higher child birth weight (β=0.119, p&lt;0.029), lack of parental modelling (β=-0.170, p=0.004), lower parental pressure (β=-0.266, p&lt;0.001), and higher restriction for weight control (β=0.361, p&lt;0.001) were found to predict higher BMI values. Conclusion: Positive parental feeding practices during mealtime are crucial for addressing the poor nutritional status of children with LD.

Physical activity, eating behaviour, and sleep quality as predictors of Body Mass Index in adolescents? A correlational study on high school students in East Kalimantan

Background and Study Aim. Obesity is a metabolic disease characterised by excessive fat accumulation. Indonesia ranks second after Singapore with the largest number of obese adolescents. The study aimed to assess the impact of physical activity, eating behavior, and sleep quality on Body Mass Index (BMI) in adolescents. Materials and Methods. This study was quantitative, with an ex post facto design. The study included 231 students (136 males, 95 females) aged 16-19 years, each with a Body Mass Index (BMI) ≥ 25.1. Participants were healthy and consented to the study by completing a screening questionnaire. The analysis was performed using regression techniques in the Statistical Package for Social Sciences (SPSS) version 21 software. Results. The selected regression model is feasible and demonstrates that physical activity, diet, and sleep quality collectively influence students' BMI (p-value &lt; 0.001). Furthermore, the analysis shows that physical activity (p-value &lt; 0.001), diet (p-value = 0.019), and sleep quality (p-value &lt; 0.001) independently affect students' BMI. Conclusions. We suggest that schools need to hold counseling programs in collaboration with relevant health workers to conduct counseling with the prevention of malnutrition, especially obesity. Provide additional tasks in the form of physical activity that students must do at home with parental supervision and the results will be reported to the teacher as an additional task value. Parents should also provide supervision of eating behavior and sleep patterns. For future researchers to be able to reveal other variables that can affect student BMI, because our report found only 49.90%.

Health Fatalism Does Not Predict Body Mass Index but Is Associated with Diet Quality in Healthy Adults: A Cross-Sectional Study

Objective Obesity as assessed by body mass index (BMI) is associated with increased risk of chronic disease. Health fatalism, defined as the belief that health outcomes are outside of one’s control, is also associated with chronic disease risk. The purpose of this cross-sectional study was to understand the relationship between health fatalism and BMI in healthy adults. Secondary outcomes assessed the relationships between health fatalism and diet quality and health fatalism and physical activity. Method Healthy individuals aged 18 to 65 years were recruited via ResearchMatch, electronic mailing lists, and social media. Participants completed online questionnaires on demographic characteristics, diet quality, physical activity, and degree of health fatalism. Regression models were used to assess the primary and secondary outcomes. For the primary outcome, the model of health fatalism (predictor) and BMI (outcome) was also adjusted for diet quality, physical activity, and demographic characteristics. Results Participants (n = 496) were 38.7 ± 14.3 years old and primarily female (76%) and White (81%), with a BMI of 25.1 ± 5.2 kg/m2. Most participants had a college or post-college education (74%), stated that they always had sufficient income to live comfortably (90%), and were moderately to highly active (91%). There was no relationship between health fatalism and BMI (p > 0.05) or health fatalism and physical activity (p > 0.05); however, there was a significant relationship between health fatalism and diet quality (beta coefficient: −0.046; 95% confidence interval, −0.086 to −0.0058; p = 0.025), such that a higher degree of fatalism predicted a slight decrease in diet quality. Conclusions Although health fatalism did not predict BMI in this population, fatalistic beliefs were associated with poorer diet quality.

Genetically Predicted Body Mass Index and Mortality in COPD.

BMI is associated with COPD mortality, but the underlying mechanisms are unclear. The effect of genetic variants aggregated into a polygenic score may elucidate causal mechanisms and predict risk. To examine the associations of genetically predicted BMI with all-cause and cause-specific mortality in COPD. We developed a polygenic score for BMI (PGSBMI) and tested for associations of the PGSBMI with all-cause, respiratory, and cardiovascular mortality in participants with COPD from the COPDGene, ECLIPSE, and Framingham Heart studies. We calculated the difference between measured BMI and PGS-predicted BMI (BMIdiff) and categorized participants into groups of discordantly low (BMIdiff < 20th percentile), concordant (BMIdiff between 20th - 80th percentile), and discordantly high (BMIdiff > 80th percentile) BMI. We applied Cox models, examined potential non-linear associations of the PGSBMI and BMIdiff with mortality, and summarized results with meta-analysis. We observed significant non-linear associations of measured BMI and BMIdiff, but not PGSBMI, with all-cause mortality. In meta-analyses, a one standard deviation increase in the PGSBMI was associated with an increased hazard for cardiovascular mortality (HR=1.29, 95% CI=1.12-1.49), but not with respiratory or all-cause mortality. Compared to participants with concordant measured and genetically predicted BMI, those with discordantly low BMI had higher mortality risk for all-cause (HR=1.57, CI=1.41-1.74) and respiratory death (HR=2.01, CI=1.61-2.51). In people with COPD, higher genetically predicted BMI is associated with higher cardiovascular mortality but not respiratory mortality. Individuals with discordantly low BMI have higher all-cause and respiratory mortality compared to those with concordant BMI.

Artificial intelligence for the practical assessment of nutritional status in emergencies

AbstractThis paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid‐upper arm circumference (MUAC) and weight‐for‐height (WFH), are impractical in conflict and catastrophic disaster situations. They require well‐trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods. If MERON were to be trained and validated on a larger sample, with more extreme cases, it would provide a practical solution to a recurrent humanitarian problem.

Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions

BackgroundAge and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner.MethodsWe integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI).ResultsWe found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level.ConclusionsOverall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.

Genetic evidence for the causal relations between metabolic syndrome and psychiatric disorders: a Mendelian randomization study

Emerging evidence reveals associations between metabolic syndrome (MetS) and psychiatric disorders (PDs), although causality remains uncertain. Consequently, we conducted Mendelian randomization (MR) to systematically evaluate the causality between MetS and PDs. Linkage disequilibrium score regression estimated the heritability of PDs and their genetic correlations with MetS. In primary analyses, the main model employed inverse variance weighting method, with sensitivity analyses using various MR models to ensure robustness. Replication MR analyses, involving cohorts distinct from those in the primary analyses, were performed to validate the generalizability of the findings. Multivariable MR analyses were carried out to account for genetically predicted body mass index (BMI). As a result, genetic correlations of MetS with attention-deficit/hyperactivity disorder(ADHD), anorexia nervosa(ANO), major depressive disorder(MDD), and schizophrenia were identified. Causal effects of MetS on ADHD (OR: 1.59 [95% CI:1.45–1.74]), ANO (OR: 1.42 [95% CI:1.25–1.61]), MDD(OR: 1.23 [95% CI: 1.13–1.33]), and the effects of ADHD (OR: 1.03 [95% CI: 1.02–1.04]) and ANO (OR: 1.01 [95% CI: 1.01–1.02]) on MetS were observed in primary analyses. Results from sensitivity analyses and replication analyses were generally consistent with the primary analyses, confirming the robustness and generalizability of the findings. Associations between MetS and ADHD, as well as ANO persisted after adjusting for BMI, whereas the statistical significance of the association between MetS and MDD was no longer observable. These results contribute to a deeper understanding of the mechanisms underlying PDs, suggesting potential modifiable targets for public prevention and clinical intervention in specific PDs related to metabolic pathways.

A bottom-up approach dramatically increases the predictability of body mass from personality traits.

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

Hyperglycaemia is a causal risk factor for upper limb pathologies.

Diabetes (regardless of type) and obesity are associated with a range of musculoskeletal disorders. The causal mechanisms driving these associations are unknown for many upper limb pathologies. We used genetic techniques to test the causal link between glycemia, obesity and musculoskeletal conditions. In the UK Biobank's unrelated European cohort (N = 379 708) we performed mendelian randomisation (MR) analyses to test for a causal effect of long-term high glycaemia and adiposity on four musculoskeletal pathologies: frozen shoulder, Dupuytren's disease, carpal tunnel syndrome and trigger finger. We also performed single-gene MR using rare variants in the GCK gene. Using MR, we found evidence that long-term high glycaemia has a causal role in the aetiology of upper limb conditions. A 10-mmol/mol increase in genetically predicted haemoglobin A1C (HbA1c) was associated with frozen shoulder: odds ratio (OR) = 1.50 [95% confidence interval (CI), 1.20-1.88], Dupuytren's disease: OR = 1.17 (95% CI, 1.01-1.35), trigger finger: OR = 1.30 (95% CI, 1.09-1.55) and carpal tunnel syndrome: OR = 1.20 (95% CI, 1.09-1.33). Carriers of GCK mutations have increased odds of frozen shoulder: OR = 7.16 (95% CI, 2.93-17.51) and carpal tunnel syndrome: OR = 2.86 (95% CI, 1.50-5.44) but not Dupuytren's disease or trigger finger. We found evidence that an increase in genetically predicted body mass index (BMI) of 5 kg/m2 was associated with carpal tunnel syndrome: OR = 1.13 (95% CI, 1.10-1.16) and associated negatively with Dupuytren's disease: OR = 0.94 (95% CI, 0.90-0.98), but no evidence of association with frozen shoulder or trigger finger. Trigger finger (OR 1.96 (95% CI, 1.42-2.69) P = 3.6e-05) and carpal tunnel syndrome [OR 1.63 (95% CI, 1.36-1.95) P = 8.5e-08] are associated with genetically predicted unfavourable adiposity increase of one standard deviation of body fat. Our study consistently demonstrates a causal role of long-term high glycaemia in the aetiology of upper limb musculoskeletal conditions. Clinicians treating diabetes patients should be aware of these complications in clinic, specifically those managing the care of GCK mutation carriers. Upper limb musculoskeletal conditions should be considered diabetes complications.

The Psychological Impact of the Widespread Availability of Palatable Foods Predicts Uncontrolled and Emotional Eating in Adults.

This study aimed to investigate the role of the psychological impact of environments rich in palatable foods on three aspects of eating behavior: cognitive restraint (CR), uncontrolled eating (UE), and emotional eating (EE). The hypotheses were as follows: (a) The psychological impact (i.e., motivation to eat) of an environment rich in palatable foods will positively predict CR, UE, and EE; (b) dieting will predict CR, UE, and EE; and (c) CR, UE, and EE will positively predict body mass index (BMI). This study had a cross-sectional design in which data were collected online from 413 subjects. The psychological impact of food-rich environments (food available, food present, and food tasted) was assessed using the Power of Food Scale (PFS), and CR, UE, and EE were assessed using the Three-Factor Eating Questionnaire (TFEQ-R18). Both instruments were tested for confirmatory factor analysis. The relationship between constructs was measured using partial least-square structural equation modeling (PLS-SEM). "Food available" positively predicted all TFEQ-R18 factors (p < 0.01). "Food present" positively predicted UE (p < 0.001) and EE (p = 0.01). People currently on a diet showed higher levels of CR (p < 0.001) and EE (p = 0.02). UE and EE positively predicted BMI. Thus, CR, UE, and EE were positively predicted by the motivation to consume palatable foods in varying proximity, suggesting that the presence of food and, more importantly, its general availability may be important determinants of eating behavior, particularly UE and EE. Health strategies should consider the influence of the food environment to prevent and better manage impairments in eating behavior. Sex differences suggest that special attention should be paid to women. Furthermore, dieting was associated with higher levels of EE, which in turn was associated with higher BMI. Weight loss interventions should consider this vulnerability.

Prediction of BMI traits in the Chinese population based on the gut metagenome

BackgroundIdentifying individual characteristics based on trace evidence left at a crime scene is crucial in forensic identification. Microbial communities found in fecal traces have high individual specificity and could serve as potential markers for forensic characterization. Previous research has established that predicting body type based on the relative abundance of the gut microbiome is relatively accurate. However, the long-term stability and high individual specificity of the gut microbiome are closely linked to changes at the genome level of the microbiome. No studies have been conducted to deduce body shape from genetic traits. Therefore, in this study, the vital role of gut bacterial community characteristics and genetic traits in predicting body mass index (BMI) was investigated using gut metagenomic data from a healthy Chinese population.ResultsRegarding the gut microbial community, the underweight group displayed increased α-diversity in comparison to the other BMI groups. There were significant differences in the relative abundances of 19 species among these three BMI groups. The BMI prediction model, based on the 31 most significant species, showed a goodness of fit (R2) of 0.56 and a mean absolute error (MAE) of 2.09 kg/m2. The overweight group exhibited significantly higher α-diversity than the other BMI groups at the level of gut microbial genes. Furthermore, there were significant variations observed in the single-nucleotide polymorphism (SNP) density of 732 contigs between these three BMI groups. The BMI prediction model, reliant on the 62 most contributing contigs, exhibited a model R2 of 0.72 and an MAE of 1.56 kg/m2. The model predicting body type from 44 contigs correctly identified the body type of 93.55% of the study participants.ConclusionBased on metagenomic data from a healthy Chinese population, we demonstrated the potential of genetic traits of gut bacteria to predict an individual’s BMI. The findings of this study suggest the effectiveness of a novel method for determining the body type of suspects in forensic applications using the genetic traits of the gut microbiome and holds great promise for forensic individual identification.

Independent relevance of adiposity measures to coronary heart disease risk among 0.5 million adults in UK Biobank.

Evidence on body fat distribution shows opposing effects of waist circumference (WC) and hip circumference (HC) for coronary heart disease (CHD). We aimed to investigate the causality and the shape of such associations. UK Biobank is a prospective cohort study of 0.5million adults aged 40-69years recruited between 2006 and 2010. Adjusted hazard ratios (HRs) for the associations of measured and genetically predicted body mass index (BMI), WC, HC and waist-to-hip ratio with incident CHD were obtained from Cox models. Mendelian randomization (MR) was used to assess causality. The analysis included 456 495 participants (26 225 first-ever CHD events) without prior CHD. All measures of adiposity demonstrated strong, positive and approximately log-linear associations with CHD risk over a median follow-up of 12.7 years. For HC, however, the association became inverse given the BMI and WC (HR per usual SD 0.95, 95% CI 0.93-0.97). Associations for BMI and WC remained independently positive after adjustment for other adiposity measures and were similar (1.14, 1.13-1.16 and 1.18, 1.15-1.20, respectively), with WC displaying stronger associations among women. Blood pressure, plasma lipids and dysglycaemia accounted for much of the observed excess risk. MR results were generally consistent with the observational, implying causality. Body fat distribution measures displayed similar associations with CHD risk as BMI except for HC, which was inversely associated with CHD risk (given WC and BMI). These findings suggest that different measures of body fat distribution likely influence CHD risk through both overlapping and independent mechanisms.

How do sex-specific BMI trajectories shape diabetes risk? A longitudinal analysis of Indonesian adults

IntroductionSex is a critical predictor of body mass index (BMI) trajectory over the life span, playing a role in disparities in the risk of diabetes. While there is some evidence...

Mexican Biobank advances population and medical genomics of diverse ancestries.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico ata resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

The role of obesity severity in Minnesota multiphasic personality inventory-2-restructured form prediction of 12-month bariatric surgery outcomes

BackgroundThe Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) has been widely used in bariatric surgery samples. However, questions remain regarding its utility when predicting changes in body mass index over time following bariatric surgery. ObjectivesExamine whether MMPI-2-RF scales differentially predict 12-month changes in body mass index (BMI) following bariatric surgery when comparing patients with Class III or higher versus Class II or lower obesity. SettingMilitary hospital in the Northwestern United States. MethodsThis retrospective study evaluated data from 193 bariatric surgery patients who completed the MMPI-2-RF as part of presurgical evaluation requirements. Hierarchical linear modeling was used to predict body mass index over a 12-month postsurgical period based on MMPI-2-RF scales. Loss to follow-up rate was 30% during this period. ResultsAmong patients with Class II or lower obesity, the Symptom Validity, Adjustment Validity, Response Bias, Cynicism, Aggression, Stress/Worry, and Anger Proneness scales showed a significant relationship to BMI after bariatric surgery. Among patients with Class III or higher obesity, the Infrequent Psychopathology Responses, Emotional / Internalizing Dysfunction, Ideas of Persecution, Multiple Specific Fears, and Inefficacy scales showed a significant relationship to body mass index after bariatric surgery. ConclusionsCertain MMPI-2-RF scales may have better utility in predicting bariatric surgery outcomes based on the patient’s obesity severity. The interaction of metabolic and personality factors may play a significant role in weight change following bariatric surgery.