Predictors Of Body Mass Index Research Articles

54592 Genetically predicted body mass index is associated with statistically significant higher odds of hidradenitis suppurativa

54592 Genetically predicted body mass index is associated with statistically significant higher odds of hidradenitis suppurativa

Comparative analysis of body mass index and obesity-related anthropometric indices for mortality prediction: a study of the Namwon and Dong-gu cohorts.

This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices' predictive ability with that of the body mass index (BMI). We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell c-index. Adding anthropometric indices to the basic mortality model (c-index 0.7723; 95% CI, 0.7647-0.7799) significantly increased the predictive power of BMI (c-index 0.7735; 95% CI, 0.7659-0.7811), a body shape index (ABSI, c-index 0.7735; 95% CI, 0.7659-0.7810), weight-adjusted waist index (WWI, c-index 0.7731; 95% CI, 0.7656-0.7807), and waist to hip index (WHI, c-index 0.7733; 95% CI, 0.7657-0.7809). The differences between the BMI model and the other 3 models were not statistically significant. In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

Development and validation of a deep learning-based model for diabetes diagnosis

Diabetes is a systemic disease caused by hyperglycemia, and the number of people with diabetes worldwide may reach 1.31 billion by 2050. The traditional diagnosis of early diabetes is difficult and inaccurate. Computer-aided automatic method has been widely applied in diabetes diagnosis at early stage. In order to realize automatic diagnosis of diabetes, this study proposed a deep neural network-based model for diabetes diagnosis, and its performance was compared with those of other classical machine leaning models. After data cleaning, Synthetic Minority Over-sampling Technique (SMOTE) sampling and feature selecting of the diabetes dataset, multiple models were applied to the features such as Body Mass Index (BMI), Age and other features for prediction. The results showed that Deep Neural Network (DNN) is the best diagnostic solution for diabetes with excellent performance with an accuracy of 99.5%. More specific features will be considered to ensure the accuracy and credibility of clinical diagnosis of diabetes.

Sex-Moderated Divergence between Adult Child and Parental Dietary Behavior Patterns in Relation to Body Mass Condition-Evaluating the Mediating Role of Physical Activity: A Cross-Sectional Study.

The main objective of this study was to explore the dietary behaviors of parents and their adult children, focusing on patterns, potential intrinsic and extrinsic predictors of body mass, and determinants of becoming overweight. Non-probability, cross-sectional sampling was used to select participants from a university student population. Young adults (19-21 years of age, n = 144) and their parents were examined. The data of those family pairs with complete sets of results were used. Dietary patterns and physical activity were assessed with questionnaires (QEB and IPAQ), and body height, weight measurements, and body mass indexes were calculated. A cophylogenetic approach with tanglegrams and heatmaps was used to study patterns, while predictors of body mass index were identified using multiple linear regression, stepwise logistic regression, and mediation analysis procedures. Cophenetic statistics confirmed significant incongruence between fathers and sons, confirmed by Baker's Gamma correlation (rBG = 0.23, p = 0.021), and mothers and daughters (rBG = 0.26, p = 0.030). The relationships between the dietary patterns of the fathers and daughters, as well as mothers and sons, were of medium strength (rBG = 0.33, p = 0.032, rBG = 0.43, p = 0.031; respectively). Most of the patterns were mixed. Fast food, fried meals, alcoholic drinks, energy drinks, and sweetened beverages were associated significantly with being overweight. Significant intrinsic predictors of excessive weight in young adults were sex (b = 2.31, p < 0.001), PA (b = -0.02, p < 0.001), and eating fermented milk and curd cheese (b = -0.55, p = 0.024), while extrinsic (parental) predictors included eating fast food and fried meals (b = -0.44, p = 0.049). Both physical activity and dietary behaviors independently determined the sons' overweight status (b = -1.25, p = 0.008; b = -0.04, p < 0.001; respectively); while only PA did in daughters (b = -0.04, p < 0.001). No mediating effects of physical activity were observed. Adult children and parental dietary patterns were divergent, reflecting the influence of multiple factors on a child's dietary habits. However, this divergence is moderated by sex. Reciprocal interactions between dietary intake-particularly positive dyads such as fruits and vegetables, fermented milk, and curd cheese-and physical activity significantly impacted children's body mass index (BMI). The study of dietary patterns in conjunction with physical activity (both as independent determinants), particularly in relation to the link between overweight/obese children and overweight/obese parents, presents a separate challenge.

Obesity in childhood: associations with parental neglect, nutritional habits, and obesity awareness.

The relationships underlying the dynamic between obesity and parental neglect in terms of nutritional habits and obesity awareness are unclear. Parental neglect remains a significant subject of concern that needs to be examined in the context of obesity. The aim was to examine the relationships between childhood obesity, parental neglect, children's eating habits and obesity. The study group consisted of 404 children and their parents from Ankara, Turkiye. As data collection tools, an Individual Information Form, Obesity Awareness Scale, the Parents Form of the Multidimensional Neglectful Behaviors Scale were administered. In addition, information on the children's body mass indexes was obtained by anthropometric measurements and the findings were recorded on the questionnaires of each child. It was found that 98 (24.3%) of the children included in the study were overweight and 63 (15.6%) were obese. The results of the multinomial logistic regression analysis indicated that in the underweight and overweight group, the parents' perception of their child's weight predicted body mass index in children, and in the obese group, along with the parents' perception of their child's weight, the age and gender of the child, eating fast, obesity in the family and parental neglect were also predictors. Practitioners such as nurses, dietitians and child developmentalists working in schools should consider weight problems in children as one of the indicators of parental neglect and should implement interventive efforts to enhance parental supervision of children at risk.

Relationship between Recurrent Laryngeal Nerve Diameter and Body Mass Index for Preoperative Prediction of Nerve Size in Thyroidectomy

Relationship between Recurrent Laryngeal Nerve Diameter and Body Mass Index for Preoperative Prediction of Nerve Size in Thyroidectomy

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Assessing biomarkers of remission in female patients with anorexia nervosa (REMANO): a protocol for a prospective cohort study with a nested case–control study using clinical, neurocognitive, biological, genetic, epigenetic and neuroimaging markers in a French specialised inpatient unit

BackgroundAnorexia nervosa (AN) is a severe psychiatric disorder associated with frequent relapses and variability in treatment responses. Previous literature suggested that such variability is influenced by premorbid vulnerabilities such as...

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

Obesity is a multifactorial pathophysiological condition with an imbalance in biochemical, immunochemical, redox status and genetic parameters values. We aimed to estimate the connection between relative leucocyte telomere lengths (rLTL) - biomarker of cellular ageing 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 sexes. The results suggested that rLTL are significantly shorter in obese, compared with 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 BMI > 30 kg/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 that oxidative stress importance in biological ageing is caused by obesity and its multiple consequences related to prooxidants augmentation and antioxidants exhaustion and gave us clear signs of disturbed cellular homoeostasis 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.