Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Differentiating monogenic and syndromic obesities from polygenic obesity: Assessment, diagnosis, and management

Background

Obesity is a multifactorial neurohormonal disease that results from dysfunction within energy regulation pathways and is associated with increased morbidity, mortality, and reduced quality of life. The most common form is polygenic obesity, which results from interactions between multiple gene variants and environmental factors. Highly penetrant monogenic and syndromic obesities result from rare genetic variants with minimal environmental influence and can be differentiated from polygenic obesity depending on key symptoms, including hyperphagia; early-onset, severe obesity; and suboptimal responses to nontargeted therapies. Timely diagnosis of monogenic or syndromic obesity is critical to inform management strategies and reduce disease burden. We outline the physiology of weight regulation, role of genetics in obesity, and differentiating characteristics between polygenic and rare genetic obesity to facilitate diagnosis and transition toward targeted therapies.

Methods

In this narrative review, we focused on case reports, case studies, and natural history studies of patients with monogenic and syndromic obesities and clinical trials examining the efficacy, safety, and quality of life impact of nontargeted and targeted therapies in these populations. We also provide comprehensive algorithms for diagnosis of patients with suspected rare genetic causes of obesity.

Results

Patients with monogenic and syndromic obesities commonly present with hyperphagia (ie, pathologic, insatiable hunger) and early-onset, severe obesity, and the presence of hallmark characteristics can inform genetic testing and diagnostic approach. Following diagnosis, specialized care teams can address complex symptoms, and hyperphagia is managed behaviorally. Various pharmacotherapies show promise in these patient populations, including setmelanotide and glucagon-like peptide-1 receptor agonists.

Conclusion

Understanding the pathophysiology and differentiating characteristics of monogenic and syndromic obesities can facilitate diagnosis and management and has led to development of targeted pharmacotherapies with demonstrated efficacy for reducing body weight and hunger in the affected populations.

Evidence of gene-nutrient interaction association with waist circumference, cross-sectional analysis

BACKGROUND AND AIMS

Waist circumference (WC) is a significant indicator of body adiposity and is associated with increased mortality and morbidity of cardiovascular diseases. Although, single nutrient intake and candidate genes were previously associated with WC. Little is known about WC association with overall diet quality, genetic risk score and gene-nutrient interaction. This study aims to investigate the influence of overall diet quality and multiple WC-associated single nucleotide polymorphisms on WC. In addition to investigating gene-nutrient interaction association with WC.

METHODS

This study explored cross-sectional data from two large sample-size studies, to provide reproducible results. As a representation of the UK population, the Airwave Health Monitoring Study (n = 6,502) and the UK-Biobank Cohort Study (n = 171,129) were explored for factors associated with WC. Diet quality was evaluated based on the Mellen Index for Dietary Approaches to Stop Hypertension (Mellen-DASH). The genetic risk score for WC (GRS-Waist) was calculated by screening the population genotype for WC-associated single nucleotide polymorphisms. Multivariate linear regression models were built to explore WC association with diet quality and genetic risk score. Gene-nutrient interaction was explored by introducing the interaction term (GRS-Waist X Mellen-DASH score) to multivariate linear regression analysis.

RESULTS

The prevalence of high WC (Female > 80 cm, Male > 94 cm) was 46.5% and 51.7% in both populations. Diet quality and genetic risk score of WC were significantly associated with WC. There was no evidence of interaction between GRS-Waist, DASH diet scores and nutrient intake on WC.

CONCLUSION

This study's findings provided reproducible results on waist circumference association with diet and genetics and tested the possibility of gene-nutrient interaction. These reproducible results are successful in building the foundation for using diet and genetics for early identification of those at risk of having high WC and WC-associated diseases. In addition, evidence on gene-diet interactions on WC is limited and lacks replication, therefore our findings may guide future research in investigating this interaction and investigating its application in precision nutrition.

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study

Genetic factors, lifestyle, and diet have been shown to play important roles in the development of hypertension. Increased salt intake is an important risk factor for hypertension. However, research on the involvement of genetic factors in the relationship between salt intake and hypertension in Asians is lacking. We aimed to investigate the risk of hypertension in relation to sodium and potassium intake and the effects of genetic factors on their interactions. We used Korean Genome and Epidemiology Study data and calculated the polygenic risk score (PRS) for the effect of systolic and diastolic blood pressure (SBP and DBP). We also conducted multivariable logistic modeling to evaluate associations among incident hypertension, PRSSBP, PRSDBP, and sodium and potassium intake. In total, 41,351 subjects were included in the test set. The top 10% PRSSBP group was the youngest of the three groups (bottom 10%, middle, top 10%), had the highest proportion of women, and had the highest body mass index, baseline BP, red meat intake, and alcohol consumption. The multivariable logistic regression model revealed the risk of hypertension was significantly associated with higher PRSSBP, higher sodium intake, and lower potassium intake. There was significant interaction between sodium intake and PRSSBP for incident hypertension especially in sodium intake ≥2.0 g/day and PRSSBP top 10% group (OR 1.27 (1.07-1.51), P = 0.007). Among patients at a high risk of incident hypertension due to sodium intake, lifestyle modifications and sodium restriction were especially important to prevent hypertension.

Obesity-enriched gut microbe degrades myo-inositol and promotes lipid absorption

Numerous studies have reported critical roles for the gut microbiota in obesity. However, the specific microbes that causally contribute to obesity and the underlying mechanisms remain undetermined. Here, we conducted shotgun metagenomic sequencing in a Chinese cohort of 631 obese subjects and 374 normal-weight controls and identified a Megamonas-dominated, enterotype-like cluster enriched in obese subjects. Among this cohort, the presence of Megamonas and polygenic risk exhibited an additive impact on obesity. Megamonas rupellensis possessed genes for myo-inositol degradation, as demonstrated in vitro and in vivo, and the addition of myo-inositol effectively inhibited fatty acid absorption in intestinal organoids. Furthermore, mice colonized with M. rupellensis or E. coli heterologously expressing the myo-inositol-degrading iolG gene exhibited enhanced intestinal lipid absorption, thereby leading to obesity. Altogether, our findings uncover roles for M. rupellensis as a myo-inositol degrader that enhances lipid absorption and obesity, suggesting potential strategies for future obesity management.

Gestational organophosphate esters (OPEs) exposure in association with placental DNA methylation levels of peroxisome proliferator-activated receptors (PPARs) signaling pathway-related genes

BACKGROUND

Organophosphate esters (OPEs) exposure could affect offspring health. However, the underlying mechanisms are not well documented.

OBJECTIVES

Based on a birth cohort study, we aimed to investigate the associations among gestational OPEs exposure, placental DNA methylation levels of peroxisome proliferator-activated receptor (PPAR) signaling pathway-related genes, and fetal growth.

METHODS

We measured the concentrations of eight OPE metabolites in maternal urine samples and neonatal anthropometric measurements in 733 mother-child pairs. In 327 placental samples, we assessed the DNA methylation levels of 14 genes which were involved in the PPARs signaling pathway and expressed in placenta. Multiple linear regression models were used to examine the associations of OPEs exposure with placental DNA methylation, and of OPEs and placental DNA methylation with neonatal anthropometric measurements. Causal mediation analyses were conducted to examine the potential mediating role of placental DNA methylation in the pathway between OPEs exposure and fetal growth.

RESULTS

We observed a general pattern of OPEs exposure being associated with hypermethylation of candidate genes, with statistically significant associations identified for several OPEs with RXRA, ACAA1, ACADL, ACADM, PLTP, and NR1H3 methylation. Further, gestational exposure to BCIPP, DPP, BBOEP, ∑NCl-OPEs, and ∑OPEs tended to be associated with lower anthropometric measurements, with more significant associations observed on arm circumference, and abdominal and back skinfold thickness. Notably, RXRA, ACAA1, ACOX1, CPT2, ACADM, and NR1H3 methylation tended to be associated with lower neonatal anthropometric measurements, especially for abdominal and back skinfold thickness. Moreover, mediation analyses showed that 19.42 % of the total effect of DPP on the back skinfold thickness was mediated by changes in RXRA methylation, and there was a significant indirect effect of RXRA methylation.

CONCLUSIONS

Gestational OPEs exposure could disrupt the placental DNA methylation levels of PPAR signaling pathway-related genes, which might contribute to the effect of OPEs on fetal growth.

Engineering an energy-dissipating hybrid tissue in vivo for obesity treatment

Obesity is a global health challenge with limited therapeutic solutions. Here, we demonstrate the engineering of an energy-dissipating hybrid tissue (EDHT) in the body for weight control. EDHT is constructed by implanting a synthetic gel matrix comprising immunomodulatory signals and functional cells into the recipient mouse. The immunomodulatory signals induce the host stromal cells to create an immunosuppressive niche that protects the functional cells, which are overexpressing the uncoupling protein 1 (UCP1), from immune rejection. Consequently, these endogenous and exogenous cells co-develop a hybrid tissue that sustainedly produces UCP1 to accelerate the host's energy expenditure. Systematic experiments in high-fat diet (HFD) and transgenic (ob/ob) mice show that EDHT efficiently reduces body weight and relieves obesity-associated pathological conditions. Importantly, an 18-month observation for safety assessment excludes cell leakage from EDHT and reports no adverse physiological responses. Overall, EDHT demonstrates convincing efficacy and safety in controlling body weight.

Trapped fat: Obesity pathogenesis as an intrinsic disorder in metabolic fuel partitioning

Our understanding of the pathophysiology of obesity remains at best incomplete despite a century of research. During this time, two alternative perspectives have helped shape thinking about the etiology of the disorder. The currently prevailing view holds that excessive fat accumulation results because energy intake exceeds energy expenditure, with excessive food consumption being the primary cause of the imbalance. The other perspective attributes the initiating cause of obesity to intrinsic metabolic defects that shift fuel partitioning from pathways for mobilization and oxidation to those for synthesis and storage. The resulting reduction in fuel oxidation and trapping of energy in adipose tissue drives a compensatory increase in energy intake and, under some conditions, a decrease in expenditure. This theory of obesity pathogenesis has historically garnered relatively less attention despite its pedigree. Here, we present an updated comprehensive formulation of the fuel partitioning theory, focused on evidence gathered over the last 80 years from major animal models of obesity showing a redirection of fuel fluxes from oxidation to storage and accumulation of excess body fat with energy intake equal to or even less than that of lean animals. The aim is to inform current discussions about the etiology of obesity and by so doing, help lay new foundations for the design of more efficacious approaches to obesity research, treatment and prevention.

Increased BMI associated with decreased breastfeeding initiation in Million Veteran Program participants

Background

Breastfeeding has been associated with maternal and infant health benefits but has been inversely associated with body mass index (BMI) prepartum. Breastfeeding and BMI are both linked to socioeconomic factors.

Methods

Data from parous female participants with available breastfeeding information from the Million Veteran Program cohort was included. BMI at enrollment and earliest BMI available were extracted, and polygenic scores (PGS) for BMI were calculated. We modeled breastfeeding for one month or more as a function of BMI at enrollment; earliest BMI where available pre-pregnancy; and PGS for BMI. We conducted Mendelian randomization for breastfeeding initiation using PGS as an instrumental variable.

Results

A higher BMI predicted a lower likelihood of breastfeeding for one month or more in all analyses. A +5 kg/m 2 BMI pre-pregnancy was associated with a 24% reduced odds of breastfeeding, and a +5 kg/m 2 genetically predicted BMI was associated with a 17% reduced odds of breastfeeding.

Conclusions

BMI predicts a lower likelihood of breastfeeding for one month or longer. Given the high success of breastfeeding initiation regardless of BMI in supportive environments as well as potential health benefits, patients with elevated BMI may benefit from additional postpartum breastfeeding support.

Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities

The extent to which modifiable lifestyle factors offset the determined genetic risk of obesity and obesity-related morbidities remains unknown. We explored how the interaction between genetic and lifestyle factors influences the risk of obesity and obesity-related morbidities. The polygenic score for body mass index was calculated to quantify inherited susceptibility to obesity in 338,645 UK Biobank European participants, and a composite lifestyle score was derived from five obesogenic factors (physical activity, diet, sedentary behavior, alcohol consumption, and sleep duration). We observed significant interaction between high genetic risk and poor lifestyles (pinteraction < 0.001). Absolute differences in obesity risk between those who adhere to healthy lifestyles and those who do not had gradually expanded with an increase in polygenic score. Despite a high genetic risk for obesity, individuals can prevent obesity-related morbidities by adhering to a healthy lifestyle and maintaining a normal body weight. Healthy lifestyles should be promoted irrespective of genetic background.

Obesity risk in young adults from the Jerusalem Perinatal Study (JPS): the contribution of polygenic risk and early life exposure

BACKGROUND/OBJECTIVES

The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk.

METHODS

We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

One standard deviation (SD) change in PRS was associated with a significant increase in BMI (β = 1.40) and WC (β = 2.45). These associations were slightly attenuated (13.7-14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (β = 0.39) and WC (β = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068-0.225).

CONCLUSIONS

Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Adiposity, type 2 diabetes and atherosclerotic cardiovascular disease risk: Use and abuse of the body mass index

The worldwide prevalence of individuals with an elevated body weight has increased steadily over the past five decades. Billions of research dollars have been invested to improve our understanding of the causes and consequences of having an elevated body weight. All this knowledge has, however, failed to influence populational body weight trajectories of most countries around the world. Research on the definition of "obesity" has also evolved. Body mass index (BMI), the most commonly used tool to make its diagnosis, has major limitations. In this review article, we will highlight evidence from observational studies, genetic association studies and randomized clinical trials that have shown the remarkable inter-individual differences in the way humans store energy as body fat. Increasing evidence also suggests that, as opposed to weight inclusive, lifestyle-based approaches, weight-centric approaches advising people to simply eat less and move more are not sustainable for most people for long-term weight loss and maintenance. It is time to recognize that this outdated approach may have produced more harm than good. On the basis of pathophysiological, genetic and clinical evidence presented in this review, we propose that it may be time to shift away from the traditional clinical approach, which is BMI-centric. Rather, emphasis should be placed on actionable lifestyle-related risk factors aiming at improving overall diet quality and increasing physical activity level in the general population.

Influence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia

BACKGROUND

Heterozygous familial hypercholesterolemia (FH) is among the most common genetic conditions worldwide that affects ≈ 1 in 300 individuals. FH is characterized by increased levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of coronary artery disease (CAD), but there is a wide spectrum of severity within the FH population. This variability in expression is incompletely explained by known risk factors. We hypothesized that genome-wide genetic influences, as represented by polygenic risk scores (PRSs) for cardiometabolic traits, would influence the phenotypic severity of FH.

METHODS

We studied individuals with clinically diagnosed FH (n=1123) from the FH Canada National Registry, as well as individuals with genetically identified FH from the UK Biobank (n=723). For all individuals, we used genome-wide gene array data to calculate PRSs for CAD, LDL-C, lipoprotein(a), and other cardiometabolic traits. We compared the distribution of PRSs in individuals with clinically diagnosed FH, genetically diagnosed FH, and non-FH controls and examined the association of the PRSs with the risk of atherosclerotic cardiovascular disease.

RESULTS

Individuals with clinically diagnosed FH had higher levels of LDL-C, and the incidence of atherosclerotic cardiovascular disease was higher in individuals with clinically diagnosed compared with genetically identified FH. Individuals with clinically diagnosed FH displayed enrichment for higher PRSs for CAD, LDL-C, and lipoprotein(a) but not for other cardiometabolic risk factors. The CAD PRS was associated with a risk of atherosclerotic cardiovascular disease among individuals with an FH-causing genetic variant.

CONCLUSIONS

Genetic background, as expressed by genome-wide PRSs for CAD, LDL-C, and lipoprotein(a), influences the phenotypic severity of FH, expanding our understanding of the determinants that contribute to the variable expressivity of FH. A PRS for CAD may aid in risk prediction among individuals with FH.

Polygenic Risk Scores and the Risk of Childhood Overweight/Obesity in Association With the Consumption of Sweetened Beverages: A Population-Based Cohort Study

Background: Sugar-sweetened beverage (SSB) and non-nutritive sweetened beverage (NNSB) consumption is associated with obesity and are targets for population-level dietary interventions. In children (<16 years), we evaluate whether SSB or NNSB consumption is associated with subsequent (2 years later) overweight and/or obesity, and the effect of consumption on subsequent overweight/obesity differs by BMI polygenic risk score (BMI-PRS). Methods: The nationally representative Longitudinal-Study-of-Australian-Children had biennial data collection from birth (n = 5107) until age 14/15 years (n = 3127). At age 11/12 years, a comprehensive biomedical assessment, including PRS assessment, was undertaken (n = 1422). Parent- or self-reported beverage consumption (SSBs: soft drinks, energy drinks, and/or juice; NNSBs: diet drinks) was measured as any/none over previous 24 hours. BMI-PRS was derived using published results (high PRS ≥75th percentile). At ages 4/5-14/15 children were classified as having obesity, overweight/obesity, or not having overweight/obesity using BMI z-score (CDC cut points). Results: SSB consumption had limited association with subsequent overweight/obesity. NNSB consumption was associated with ∼8% more children with subsequent overweight/obesity at most ages. In older children with high BMI-PRS, associations between NNSB consumption and subsequent overweight/obesity strengthened with age [at age 14-15 for high BMI-PRS, difference in proportion with overweight/obesity among NNSB consumers vs. nonconsumers = 0.38 (95% confidence interval: 0.22 to 0.55, p ≤ 0.001)]. There was limited association between SSB consumption and BMI-PRS. Conclusion: NNSB consumption was associated with increased risk of overweight/obesity for children with greater genetic risk at older ages (12-15 years). Focused intervention among children with high genetic risk could target NNSB consumption; however, reverse causality (children with genetic risk and/or high BMI consume more NNSBs) cannot be excluded.

Ribosomal DNA copy number is associated with body mass in humans and other mammals

Body mass results from a complex interplay between genetics and environment. Previous studies of the genetic contribution to body mass have excluded repetitive regions due to the technical limitations of platforms used for population scale studies. Here we apply genome-wide approaches, identifying an association between adult body mass and the copy number (CN) of 47S-ribosomal DNA (rDNA). rDNA codes for the 18 S, 5.8 S and 28 S ribosomal RNA (rRNA) components of the ribosome. In mammals, there are hundreds of copies of these genes. Inter-individual variation in the rDNA CN has not previously been associated with a mammalian phenotype. Here, we show that rDNA CN variation associates with post-pubertal growth rate in rats and body mass index in adult humans. rDNA CN is not associated with rRNA transcription rates in adult tissues, suggesting the mechanistic link occurs earlier in development. This aligns with the observation that the association emerges by early adulthood.

Assessing the Risk of Normal Weight Obesity in Korean Women across Generations: A Study on Body Composition and Physical Fitness

Normal weight obesity (NWO) refers to a condition in which the body mass index falls within the normal range, but the percent of body fat is excessive. Although there are reports of a high prevalence of cardiovascular and metabolic diseases in NWO, analyses regarding physical fitness have been lacking. Therefore, the purpose of this study was to analyze the age-related prevalence of NWO and to examine physical fitness across generations. Our study utilized a dataset comprising 119,835 participants for analysis. The prevalence of NWO across ages was examined using cross-tabulation analysis. For body composition and physical fitness, medians and group differences were assessed by generation through Kruskal-Wallis and Bonferroni post hoc tests. Additionally, univariate logistic regression was adopted to analyze the odds ratio. The prevalence of NWO in Korean women was 18.3%. The fat-free mass of the NWO group was consistently lower than that of both the group with normal body mass indexes (Normal) and obese body mass indexes (Obesity) across all generations. Additionally, the waist circumference and blood pressure were greater in the now group than in the Normal group. When considering maximal strength, muscle endurance, power, balance, and coordination, the NWO group exhibited lower levels compared to the Normal group. The NWO group showed lower muscle mass than both the Normal and Obesity groups, resulting in significantly reduced physical fitness compared to that of the Normal group, similar to the Obesity group. This condition may increase not only the risk of posing a potentially more serious health concern than obesity but also the risk of falls in elderly people. Therefore, based on this study, it is crucial to not only define obesity using BMI criteria but also to diagnose NWO. Public health policies and preventive measures must be implemented accordingly.

The healthcare costs of increased body mass index-evidence from The Trøndelag Health Study

BACKGROUND

Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses.

METHODS

In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations.

RESULTS

There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations.

CONCLUSION

The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.

Macronutrient-differential dietary pattern impacts on body weight, hepatic inflammation, and metabolism

Introduction

Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber.

Methods

Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated.

Results

Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways.

Conclusion

Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.

Genetics, pharmacotherapy, and dietary interventions in childhood obesity

Childhood obesity has emerged as a major global health issue, contributing to the increased prevalence of chronic conditions and adversely affecting the quality of life and future prospects of affected individuals, thereby presenting a substantial societal challenge. This complex condition, influenced by the interplay of genetic predispositions and environmental factors, is characterized by excessive energy intake due to uncontrolled appetite regulation and a Westernized diet. Managing obesity in childhood requires specific considerations compared with adulthood, given the vulnerability of the critical juvenile-adolescent period to toxicity and developmental defects. Consequently, common treatment options for adult obesity may not directly apply to younger populations. Therefore, research on childhood obesity has focused on genetic defects in regulating energy intake, alongside pharmacotherapy and dietary interventions as management approaches, with an emphasis on safety concerns. This review aims to summarize canonical knowledge and recent findings on genetic factors contributing to childhood obesity. Additionally, it assesses the efficacy and safety of existing pharmacotherapies and dietary interventions and suggests future research directions. By providing a comprehensive understanding of the complex dynamics of childhood obesity, this review aims to offer insights into more targeted and effective strategies for addressing this condition, including personalized healthcare solutions.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Metabolic liability for weight gain in early adulthood

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes

Rationale

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives

Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods

We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

Measurements and Main Results

We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

Conclusions

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Teaching gene-environment interaction concepts with narrative vignettes: Effects on knowledge, stigma, and behavior motivation

Gene-environment interaction (GxE) concepts underlie a proper understanding of complex disease risk and risk-reducing behavior. Communicating GxE concepts is a challenge. This study designed an educational intervention that communicated GxE concepts in the context of eating behavior and its impact on weight, and tested its efficacy in changing knowledge, stigma, and behavior motivation. The study also explored whether different framings of GxE education and matching frames with individual eating tendencies would result in stronger intervention impact. The experiment included four GxE education conditions and a control condition unrelated to GxE concepts. In the education conditions, participants watched a video introducing GxE concepts then one of four narrative vignettes depicting how a character's experience with eating hyperpalatable or bitter tasting food (reward-based eating drive vs. bitter taste perception scenario) is influenced by genetic or environmental variations (genetic vs. environmental framings). The education intervention increased GxE knowledge, genetic causal attributions, and empathetic concern. Mediation analyses suggest that causal attributions, particularly to genetics and willpower, are key factors that drive downstream stigma and eating behavior outcomes and could be targeted in future interventions. Tailoring GxE education frames to individual traits may lead to more meaningful outcomes. For example, genetic (vs. environmental) framed GxE education may reduce stigma toward individuals with certain eating tendencies among individuals without such tendencies. GxE education interventions would be most likely to achieve desired outcomes such as reducing stigma if they target certain causal beliefs and are strategically tailored to individual attributes.

Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis

BACKGROUND

While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic score for BMI was associated with weight loss effectiveness and aimed to identify common genetic variants associated with weight loss.

METHODS

Participants in the ONTIME study (n = 1210) followed a uniform, multimodal behavioral weight-loss intervention. We first tested associations between a genome-wide polygenic score for higher BMI and weight loss effectiveness (total weight loss, rate of weight loss, and attrition). We then conducted a genome-wide association study (GWAS) for weight loss in the ONTIME study and performed the largest weight loss meta-analysis with earlier studies (n = 3056). Lastly, we ran exploratory GWAS in the ONTIME study for other weight loss outcomes and related factors.

RESULTS

We found that each standard deviation increment in the polygenic score was associated with a decrease in the rate of weight loss (Beta (95% CI) = -0.04 kg per week (-0.06, -0.01); P = 3.7 × 10-03) and with higher attrition after adjusting by treatment duration. No associations reached genome-wide significance in meta-analysis with previous GWAS studies for weight loss. However, associations in the ONTIME study showed effects consistent with published studies for rs545936 (MIR486/NKX6.3/ANK1), a previously noted weight loss locus. In the meta-analysis, each copy of the minor A allele was associated with 0.12 (0.03) kg/m2 higher BMI at week five of treatment (P = 3.9 × 10-06). In the ONTIME study, we also identified two genome-wide significant (P < 5×10-08) loci for the rate of weight loss near genes implicated in lipolysis, body weight, and metabolic regulation: rs146905606 near NFIP1/SPRY4/FGF1; and rs151313458 near LSAMP.

CONCLUSION

Our findings are expected to help in developing personalized weight loss approaches based on genetics.

CLINICAL TRIAL REGISTRATION

Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME; clinicaltrials.gov: NCT02829619) study.

The Effects of FABP4 on Cardiovascular Disease in the Aging Population

PURPOSE OF REVIEW

Fatty acid-binding protein 4 (FABP4) plays a role in lipid metabolism and cardiovascular health. In this paper, we cover FABP4 biology, its implications in atherosclerosis from observational studies, genetic factors affecting FABP4 serum levels, and ongoing drug development to target FABP4 and offer insights into future FABP4 research.

RECENT FINDINGS

FABP4 impacts cells through JAK2/STAT2 and c-kit pathways, increasing inflammatory and adhesion-related proteins. In addition, FABP4 induces angiogenesis and vascular smooth muscle cell proliferation and migration. FABP4 is established as a reliable predictive biomarker for cardiovascular disease in specific at-risk groups. Genetic studies robustly link PPARG and FABP4 variants to FABP4 serum levels. Considering the potential effects on atherosclerotic lesion development, drug discovery programs have been initiated in search for potent inhibitors of FABP4. Elevated FABP4 levels indicate an increased cardiovascular risk and is causally related to acceleration of atherosclerotic disease, However, clinical trials for FABP4 inhibition are lacking, possibly due to concerns about available compounds' side effects. Further research on FABP4 genetics and its putative causal role in cardiovascular disease is needed, particularly in aging subgroups.

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.

Methylation profiles at birth linked to early childhood obesity

Childhood obesity represents a significant global health concern and identifying its risk factors is crucial for developing intervention programs. Many "omics" factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

The melanocortin-4 receptor pathway and the emergence of precision medicine in obesity management

Over the past few decades, there has been a global surge in the prevalence of obesity, rendering it a globally recognized epidemic. Contrary to simply being a medical condition, obesity is an intricate disease with a multifactorial aetiology. Understanding the precise cause of obesity remains a challenge; nevertheless, there seems to be a complex interplay among biological, psychosocial and behavioural factors. Studies on the genetic factors of obesity have revealed several pathways in the brain that play a crucial role in food intake regulation. The best characterized pathway, thus far, is the leptin-melanocortin pathway, from which disruptions are responsible for the majority of monogenic obesity disorders. The effectiveness of conservative lifestyle interventions in addressing monogenic obesity has been limited. Therefore, it is crucial to complement the management strategy with pharmacological and surgical options. Emphasis has been placed on developing drugs aimed at replacing the absent signals, with the goal of restoring the pathway. In both monogenic and polygenic forms of obesity, outcomes differ across various interventions, likely due to the multifaceted nature of the disease. This underscores the need to explore alternative therapeutic strategies that can mitigate this heterogeneity. Precision medicine can be regarded as a powerful tool that can address this concern, as it values the understanding of the underlying abnormality triggering the disease and provides a tailored treatment accordingly. This would assist in optimizing outcomes of the current therapeutic approaches and even aid in the development of novel treatments capable of more effectively managing the global obesity epidemic.

A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure

AIMS

Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations.

METHODS AND RESULTS

Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF.

CONCLUSIONS

The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.

Understanding risk and causal mechanisms for developing obesity in infants and young children: A National Institutes of Health workshop

Obesity in children remains a major public health problem, with the current prevalence in youth ages 2-19 years estimated to be 19.7%. Despite progress in identifying risk factors, current models do not accurately predict development of obesity in early childhood. There is also substantial individual variability in response to a given intervention that is not well understood. On April 29-30, 2021, the National Institutes of Health convened a virtual workshop on "Understanding Risk and Causal Mechanisms for Developing Obesity in Infants and Young Children." The workshop brought together scientists from diverse disciplines to discuss (1) what is known regarding epidemiology and underlying biological and behavioral mechanisms for rapid weight gain and development of obesity and (2) what new approaches can improve risk prediction and gain novel insights into causes of obesity in early life. Participants identified gaps and opportunities for future research to advance understanding of risk and underlying mechanisms for development of obesity in early life. It was emphasized that future studies will require multi-disciplinary efforts across basic, behavioral, and clinical sciences. An exposome framework is needed to elucidate how behavioral, biological, and environmental risk factors interact. Use of novel statistical methods may provide greater insights into causal mechanisms.

Polygenic Risk Scores for Glaucoma Onset in the Ocular Hypertension Treatment Study

Importance

Primary open-angle glaucoma (POAG) is a highly heritable disease, with 127 identified risk loci to date. Polygenic risk score (PRS) may provide a clinically useful measure of aggregate genetic burden and improve patient risk stratification.

Objective

To assess whether a PRS improves prediction of POAG onset in patients with ocular hypertension.

Design, Setting, and Participants

This was a post hoc analysis of the Ocular Hypertension Treatment Study. Data were collected from 22 US sites with a mean (SD) follow-up of 14.0 (6.9) years. A total of 1636 participants were followed up from February 1994 to December 2008; 1077 participants were enrolled in an ancillary genetics study, of which 1009 met criteria for this analysis. PRS was calculated using summary statistics from the largest cross-ancestry POAG meta-analysis, with weights trained using 8 813 496 variants from 449 186 cross-ancestry participants in the UK Biobank. Data were analyzed from July 2022 to December 2023.

Exposures

From February 1994 to June 2002, participants were randomized to either topical intraocular pressure-lowering medication or close observation. After June 2002, both groups received medication.

Main Outcomes and Measures

Outcome measures were hazard ratios for POAG onset. Concordance index and time-dependent areas under the receiver operating characteristic curve were used to compare the predictive performance of multivariable Cox proportional hazards models.

Results

Of 1009 included participants, 562 (55.7%) were female, and the mean (SD) age was 55.9 (9.3) years. The mean (SD) PRS was significantly higher for 350 POAG converters (0.24 [0.95]) compared with 659 nonconverters (-0.12 [1.00]) (P < .001). POAG risk increased 1.36% (95% CI, 1.08-1.64) with each higher PRS decile, with conversion ranging from 9.52% (95% CI, 7.09-11.95) in the lowest PRS decile to 21.81% (95% CI, 19.37-24.25) in the highest decile. Comparison of low-risk and high-risk PRS tertiles showed a 2.0-fold increase in 20-year POAG risk for participants of European and African ancestries. In the subgroup randomized to delayed treatment, each increase in PRS decile was associated with a 0.52-year (95% CI, 0.01-1.03) decrease in age at diagnosis (P = .047). No significant linear association between PRS and age at POAG diagnosis was present in the early treatment group. Prediction models significantly improved with the addition of PRS as a covariate (C index = 0.77) compared with the Ocular Hypertension Treatment Study baseline model (C index = 0.75) (P < .001). Each 1-SD higher PRS conferred a mean hazard ratio of 1.25 (95% CI, 1.13-1.44) for POAG onset.

Conclusions and Relevance

Higher PRS was associated with increased risk for POAG in patients with ocular hypertension. The inclusion of a PRS improved the prediction of POAG onset.

Trial Registration

ClinicalTrials.gov Identifier: NCT00000125.

From the reward network to whole-brain metrics: structural connectivity in adolescents and young adults according to body mass index and genetic risk of obesity

BACKGROUND

Obesity is a multifactorial condition. Genetic variants, such as the fat mass and obesity related gene (FTO) polymorphism, may increase the vulnerability of developing obesity by disrupting dopamine signaling within the reward network. Yet, the association of obesity, genetic risk of obesity, and structural connectivity of the reward network in adolescents and young adults remains unexplored. We investigate, in adolescents and young adults, the structural connectivity differences in the reward network and at the whole-brain level according to body mass index (BMI) and the FTO rs9939609 polymorphism.

METHODS

One hundred thirty-two adolescents and young adults (age range: [10, 21] years, BMI z-score range: [-1.76, 2.69]) were included. Genetic risk of obesity was determined by the presence of the FTO A allele. Whole-brain and reward network structural connectivity were analyzed using graph metrics. Hierarchical linear regression was applied to test the association between BMI-z, genetic risk of obesity, and structural connectivity.

RESULTS

Higher BMI-z was associated with higher (B = 0.76, 95% CI = [0.30, 1.21], P = 0.0015) and lower (B = -0.003, 95% CI = [-0.006, -0.00005], P = 0.048) connectivity strength for fractional anisotropy at the whole-brain level and of the reward network, respectively. The FTO polymorphism was not associated with structural connectivity nor with BMI-z.

CONCLUSIONS

We provide evidence that, in healthy adolescents and young adults, higher BMI-z is associated with higher connectivity at the whole-brain level and lower connectivity of the reward network. We did not find the FTO polymorphism to correlate with structural connectivity. Future longitudinal studies with larger sample sizes are needed to assess how genetic determinants of obesity change brain structural connectivity and behavior.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes

Importance

Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is a commonly used estimate of obesity, which is a complex trait affected by genetic and lifestyle factors. Marked weight gain and loss could be associated with adverse biological processes.

Objective

To evaluate the association between BMI variability and incident cardiovascular disease (CVD) events in 2 distinct cohorts.

Design, Setting, and Participants

This cohort study used data from the Million Veteran Program (MVP) between 2011 and 2018 and participants in the UK Biobank (UKB) enrolled between 2006 and 2010. Participants were followed up for a median of 3.8 (5th-95th percentile, 3.5) years. Participants with baseline CVD or cancer were excluded. Data were analyzed from September 2022 and September 2023.

Exposure

BMI variability was calculated by the retrospective SD and coefficient of variation (CV) using multiple clinical BMI measurements up to the baseline.

Main Outcomes and Measures

The main outcome was incident composite CVD events (incident nonfatal myocardial infarction, acute ischemic stroke, and cardiovascular death), assessed using Cox proportional hazards modeling after adjustment for CVD risk factors, including age, sex, mean BMI, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking status, diabetes status, and statin use. Secondary analysis assessed whether associations were dependent on the polygenic score of BMI.

Results

Among 92 363 US veterans in the MVP cohort (81 675 [88%] male; mean [SD] age, 56.7 [14.1] years), there were 9695 Hispanic participants, 22 488 non-Hispanic Black participants, and 60 180 non-Hispanic White participants. A total of 4811 composite CVD events were observed from 2011 to 2018. The CV of BMI was associated with 16% higher risk for composite CVD across all groups (hazard ratio [HR], 1.16; 95% CI, 1.13-1.19). These associations were unchanged among subgroups and after adjustment for the polygenic score of BMI. The UKB cohort included 65 047 individuals (mean [SD] age, 57.30 (7.77) years; 38 065 [59%] female) and had 6934 composite CVD events. Each 1-SD increase in BMI variability in the UKB cohort was associated with 8% increased risk of cardiovascular death (HR, 1.08; 95% CI, 1.04-1.11).

Conclusions and Relevance

This cohort study found that among US veterans, higher BMI variability was a significant risk marker associated with adverse cardiovascular events independent of mean BMI across major racial and ethnic groups. Results were consistent in the UKB for the cardiovascular death end point. Further studies should investigate the phenotype of high BMI variability.

Neurodevelopmental Programming of Adiposity: Contributions to Obesity Risk

This review analyzes the published evidence regarding maternal factors that influence the developmental programming of long-term adiposity in humans and animals via the central nervous system (CNS). We describe the physiological outcomes of perinatal underfeeding and overfeeding and explore potential mechanisms that may mediate the impact of such exposures on the development of feeding circuits within the CNS-including the influences of metabolic hormones and epigenetic changes. The perinatal environment, reflective of maternal nutritional status, contributes to the programming of offspring adiposity. The in utero and early postnatal periods represent critically sensitive developmental windows during which the hormonal and metabolic milieu affects the maturation of the hypothalamus. Maternal hyperglycemia is associated with increased transfer of glucose to the fetus driving fetal hyperinsulinemia. Elevated fetal insulin causes increased adiposity and consequently higher fetal circulating leptin concentration. Mechanistic studies in animal models indicate important roles of leptin and insulin in central and peripheral programming of adiposity, and suggest that optimal concentrations of these hormones are critical during early life. Additionally, the environmental milieu during development may be conveyed to progeny through epigenetic marks and these can potentially be vertically transmitted to subsequent generations. Thus, nutritional and metabolic/endocrine signals during perinatal development can have lifelong (and possibly multigenerational) impacts on offspring body weight regulation.

Hyperphagia and impulsivity: use of self-administered Dykens' and in-house impulsivity questionnaires to characterize eating behaviors in children with severe and early-onset obesity

BACKGROUND

The determinants of early-onset obesity (< 6 years) are not completely elucidated, however eating behavior has a central role. To date no study has explored eating behavior in children with severe, early-onset obesity. Self-administered questionnaire data from these children were examined to evaluate eating behavior and the etiology of early-onset obesity.

METHODS

Children with severe, early-onset obesity (body mass index [BMI] > International Obesity Task Force [IOTF] 30) of different etiologies (hypothalamic obesity [HO], intellectual disability with obesity [IDO], common polygenic obesity [CO]) were prospectively included. BMI history and responses from the Dykens' Hyperphagia Questionnaire and an in-house Impulsivity Questionnaire at first visit were compared between groups.

RESULTS

This cohort of 75 children (39 girls; mean age ± standard deviation [SD] 10.8 ± 4.4 years) had severe, early-onset obesity at an age of 3.8 ± 2.7 years, with a BMI Z-score of 4.9 ± 1.5. BMI history varied between the 3 groups, with earlier severe obesity in the HO group versus 2 other groups (BMI > IOTF40 at 3.4 ± 1.6 vs. 4.6 ± 1.6 and 8.4 ± 4.1 years for the IDO and CO groups, respectively [P < 0.01]). Absence of adiposity rebound was more prevalent in the HO group (87% vs. 63% and 33% for the IDO and CO groups, respectively [P < 0.01]). The Dykens' mean total score for the cohort was 22.1 ± 7.2 with no significant between-group differences. Hyperphagia (Dykens' score > 19) and impulsivity (score > 7) were found in 50 (67%) and 11 children (15%), respectively, with no difference between the HO, IDO and CO groups regarding the number of patients with hyperphagia (10 [67%], 14 [74%], and 26 [63%] children, respectively) or impulsivity (2 [13%], 1 [7%], and 8 [19%] children, respectively). Children with food impulsivity had significantly higher total and severity scores on the Dykens' Questionnaire versus those without impulsivity.

CONCLUSION

The Dykens' and Impulsivity questionnaires can help diagnose severe hyperphagia with/without food impulsivity in children with early-onset obesity, regardless of disease origin. Their systematic use can allow more targeted management of food access control in clinical practice and monitor the evolution of eating behavior in the case of innovative therapeutic targeting hyperphagia.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction

Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.

Phenotyping obesity: A focus on metabolically healthy obesity and metabolically unhealthy normal weight

Over the past 4 decades, research has shown that having a normal body weight does not automatically imply preserved metabolic health and a considerable number of lean individuals harbour metabolic abnormalities typically associated with obesity. Conversely, excess adiposity does not always equate with an abnormal metabolic profile. In fact, evidence exists for the presence of a metabolically unhealthy normal weight (MUHNW) and a metabolically healthy obese (MHO) phenotype. It has become increasingly recognised that different fat depots exert different effects on the metabolic profile of each individual by virtue of their location, structure and function, giving rise to these different body composition phenotypes. Furthermore, other factors have been implicated in the aetiopathogenesis of the body composition phenotypes, including genetics, ethnicity, age and lifestyle/behavioural factors. Even though to date both MHO and MUHNW have been widely investigated and documented in the literature, studies report different outcomes on long-term cardiometabolic morbidity and mortality. Future large-scale, observational and population-based studies are required for better profiling of these phenotypes as well as to further elucidate the pathophysiological role of the adipocyte in the onset of metabolic disorders to allow for better risk stratification and a personalised treatment paradigm.

Plant-based dietary patterns and genetic susceptibility to obesity in the CARTaGENE cohort

OBJECTIVE

The study's objective was to examine whether adherence to three plant-based dietary indices (PDIs) mediated or moderated genetic susceptibility to obesity.

METHODS

Baseline participants were 7037 adults (57% women, aged 55.6 ± 7.7 years) from the CARTaGENE cohort of Quebec adults. Two polygenic risk scores for BMI (PRS-BMI), 92 single-nucleotide polymorphisms and 2 million single-nucleotide polymorphisms, and three plant-based scores were calculated (overall, healthy, and unhealthy). Follow-up participants were 2258 adults with data on obesity outcomes, measured 6 years later. General linear models were used to examine the relationships between PRSs and PDI scores on obesity outcomes. Causal mediation analyses were conducted to assess mediation and interaction models.

RESULTS

The overall- and healthy-PDIs and PRSs were significantly associated with obesity outcomes. Adherence to PDIs did not mediate or moderate genetic susceptibility to obesity. Associations between PRSs and obesity outcomes were partly mediated by meat intake cross-sectionally and whole grains intake among males both cross-sectionally and longitudinally. Higher meat intake had a positive association with obesity outcomes, whereas higher whole grains intake had an inverse association.

CONCLUSIONS

These findings suggest that components of a plant-based diet and a shift away from animal products, specifically meat, might be beneficial for nutrition interventions, particularly among individuals with higher genetic risk of obesity.

GLP1R (glucagon-like-peptide-1 incretin receptor), diabetes and obesity phenotypes: An in silico approach revealed new pathogenic variants

OBJECTIVE

Glucagon-like peptide-1 receptor belongs to the B family of G protein-coupled receptors, serving as a binding protein in membranes and is widely expressed in human tissues. Upon stimulation by its agonist, the glucagon-like peptide-1, the receptor plays a role in glucose metabolism, enhancing insulin secretion, and regulating appetite in the hypothalamus. Mutations in the glucagon-like peptide-1 receptor gene can lead to physiological changes that may explain phenotypic variations in individuals with obesity and diabetes. Therefore, this study aimed to evaluate missense variants of the glucagon-like peptide-1 receptor gene.

METHODS

Data mining was performed on the single nucleotide polymorphism database, retrieving a total of 16,399 variants. Among them, 356 were missense. These 356 variants were analyzed using the PolyPhen-2 and filtered based on allele frequency, resulting in 6 pathogenic variants.

RESULTS

D344E, A239T, R310Q, R227H, R421P, and R176G were analyzed using four different prediction tools. The D344E and A239T resulted in larger amino acid residues compared to their wild-type counterparts. The D344E showed a slightly destabilized structure, while A239T affected the transmembrane helices. Conversely, the R310Q, R227H, R421P, and R176G resulted in smaller amino acid residues than the wild-type, leading to a loss of positive charge and increased hydrophobicity. Particularly, the R421P, due to the presence of proline, significantly destabilized the α-helix structure and caused severe damage to the receptor.

CONCLUSION

Elucidating the glucagon-like peptide-1 receptor variants and their potentially detrimental effects on receptor functionality can contribute to an understanding of metabolic diseases and the response to available pharmacological treatments.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

Methylation profiles at birth linked to early childhood obesity

Childhood obesity represents a significant global health concern and identifying risk factors is crucial for developing intervention programs. Many 'omics' factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

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.

Obesity-Resistant Mice on a High-Fat Diet Display a Distinct Phenotype Linked to Enhanced Lipid Metabolism

Individually, metabolic variations can significantly influence predisposition to obesity in the form of the obesity-prone (super-responders) and obesity-resistant (non-responders) phenotypes in response to modern calorie-dense diets. In this study, C57BL/6J mice (n = 76) were randomly assigned to either a low-fat diet (LFD) or a high-fat diet (HFD) for 6 weeks, followed by selection of the normally obese (HFD), non-responders (NR), super-responders (SR), or super-responders switched back to the low-fat diet (SR-LFD) for an additional 8 weeks. SR mice showed the highest gains in body weight, lean and fat body mass, and total and free water, in part due to increased feed efficiency, despite having a respiratory exchange ratio (RER) similar to that of NR mice. A switch to the LFD was sufficient to revert most of the observed physiological changes in the SR-LFD mice; however, voluntary physical activity and exercise capacity did not return to the basal level. NR mice showed the highest food intake, lowest feed efficiency, increased oxygen consumption during the light (rest) cycle, increased physical activity during the dark (active) cycle, and increased heat production during both cycles. These variations were observed in the absence of changes in food intake and fecal parameters; however, NR fecal lipid content was lower, and the NR fecal microbiome profile was characterized by reduced abundance of Actinobacteria. Taken together, our findings suggest that NR mice showed an increased ability to metabolize excessive dietary fats in skeletal muscle at the expense of reduced exercise capacity that persisted for the duration of the study. These findings underscore the need for further comprehensive investigations into the mechanisms of obesity resistance, as they hold potential implications for weight-loss strategies in human subjects.

FTO variation and early frontostriatal brain development in children

OBJECTIVE

Common obesity-associated genetic variants at the fat mass and obesity-associated (FTO) locus have been associated with appetitive behaviors and altered structure and function of frontostriatal brain regions. The authors aimed to investigate the influence of FTO variation on frontostriatal appetite circuits in early life.

METHODS

Data were drawn from RESONANCE, a longitudinal study of early brain development. Growth trajectories of nucleus accumbens and frontal lobe volumes, as well as total gray matter and white matter volume, by risk allele (AA) carrier status on FTO single-nucleotide polymorphism rs9939609 were examined in 228 children (102 female, 126 male) using magnetic resonance imaging assessments obtained from infancy through middle childhood. The authors fit functional concurrent regression models with brain volume outcomes over age as functional responses, and FTO genotype, sex, BMI z score, and maternal education were included as predictors.

RESULTS

Bootstrap pointwise 95% CI for regression coefficient functions in the functional concurrent regression models showed that the AA group versus the group with no risk allele (TT) had greater nucleus accumbens volume (adjusted for total brain volume) in the interval of 750 to 2250 days (2-6 years).

CONCLUSIONS

These findings suggest that common genetic risk for obesity is associated with differences in early development of brain reward circuitry and argue for investigating dynamic relationships among genotype, brain, behavior, and weight throughout development.

Induced Pluripotent Stem Cells as a Possible Approach for Exploring the Pathophysiology of Polycystic Ovary Syndrome (PCOS)

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine condition among women with pleiotropic sequelae possessing reproductive, metabolic, and psychological characteristics. Although the exact origin of PCOS is elusive, it is known to be a complex multigenic disorder with a genetic, epigenetic, and environmental background. However, the pathogenesis of PCOS, and the role of genetic variants in increasing the risk of the condition, are still unknown due to the lack of an appropriate study model. Since the debut of induced pluripotent stem cell (iPSC) technology, the ability of reprogrammed somatic cells to self-renew and their potential for multidirectional differentiation have made them excellent tools to study different disease mechanisms. Recently, researchers have succeeded in establishing human in vitro PCOS disease models utilizing iPSC lines from heterogeneous PCOS patient groups (iPSCPCOS). The current review sets out to summarize, for the first time, our current knowledge of the implications and challenges of iPSC technology in comprehending PCOS pathogenesis and tissue-specific disease mechanisms. Additionally, we suggest that the analysis of polygenic risk prediction based on genome-wide association studies (GWAS) could, theoretically, be utilized when creating iPSC lines as an additional research tool to identify women who are genetically susceptible to PCOS. Taken together, iPSCPCOS may provide a new paradigm for the exploration of PCOS tissue-specific disease mechanisms.