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Annual Review of Nutrition Oct 2021Considerable recent advancements in elucidating the genetic architecture of sleep traits and sleep disorders may provide insight into the relationship between sleep and...
Considerable recent advancements in elucidating the genetic architecture of sleep traits and sleep disorders may provide insight into the relationship between sleep and obesity. Despite the involvement of the circadian clock in sleep and metabolism, few shared genes, including , were implicated in genome-wide association studies (GWASs) of sleep and obesity. Polygenic scores composed of signals from GWASs of sleep traits show largely null associations with obesity, suggesting lead variants are unique to sleep. Modest genome-wide genetic correlations are observed between many sleep traits and obesity and are largest for snoring. Notably, U-shaped positive genetic correlations with body mass index (BMI) exist for both short and long sleep durations. Findings from Mendelian randomization suggest robust causal effects of insomnia on higher BMI and, conversely, of higher BMI on snoring and daytime sleepiness. In addition, bidirectional effects between sleep duration and daytime napping with obesity may also exist. Limited gene-sleep interaction studies suggest that achieving favorable sleep, as part of a healthy lifestyle, may attenuate genetic predisposition to obesity,but whether these improvements produce clinically meaningful reductions in obesity risk remains unclear. Investigations of the genetic link between sleep and obesity for sleep disorders other than insomnia and in populations of non-European ancestry are currently limited.
Topics: Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Body Mass Index; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Obesity; Sleep
PubMed: 34102077
DOI: 10.1146/annurev-nutr-082018-124258 -
Nutrients Aug 2023Obesity is a metabolic state generated by the expansion of adipose tissue. Adipose tissue expansion depends on the interplay between hyperplasia and hypertrophy, and is... (Review)
Review
Obesity is a metabolic state generated by the expansion of adipose tissue. Adipose tissue expansion depends on the interplay between hyperplasia and hypertrophy, and is mainly regulated by a complex interaction between genetics and excess energy intake. However, the genetic regulation of adipose tissue expansion is yet to be fully understood. Obesity can be divided into common multifactorial/polygenic obesity and monogenic obesity, non-syndromic and syndromic. Several genes related to obesity were found through studies of monogenic non-syndromic obesity models. However, syndromic obesity, characterized by additional features other than obesity, suggesting a more global role of the mutant genes related to the syndrome and, thus, an additional peripheral influence on the development of obesity, were hardly studied to date in this regard. This review summarizes present knowledge regarding the hyperplasia and hypertrophy of adipocytes in common obesity. Additionally, we highlight the scarce research on syndromic obesity as a model for studying adipocyte hyperplasia and hypertrophy, focusing on Bardet-Biedl syndrome (BBS). BBS obesity involves central and peripheral mechanisms, with molecular and mechanistic alternation in adipocyte hyperplasia and hypertrophy. Thus, we argue that using syndromic obesity models, such as BBS, can further advance our knowledge regarding peripheral adipocyte regulation in obesity.
PubMed: 37571382
DOI: 10.3390/nu15153445 -
Clinical Therapeutics Mar 2021The prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing over the years and is now as high in Asia as in the Western world, so much so that it... (Review)
Review
The prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing over the years and is now as high in Asia as in the Western world, so much so that it should no longer be considered a Western disease. In fact, China is expected to have the largest increase in the number of NAFLD cases in the coming years. The increase in prevalence of NAFLD in Asia lags behind that of the Western world; thus, there will be a lag in more severe liver disease in Asia despite a similar prevalence of the disease. NAFLD is more prevalent among patients with diabetes mellitus, which is also an important risk factor for more severe liver disease. Patients with diabetes mellitus thus represent an important target for screening for NAFLD and more severe liver disease. Although the PNPLA3 gene polymorphism is the most studied in NAFLD, it is increasingly clear that the cumulative effect of multiple genes likely predisposes to NAFLD and more severe liver disease in the different ethnic groups, and polygenic risk scores are emerging. Lean NAFLD has been largely reported in Asia but is increasingly recognized worldwide. Multiple risk factors have been identified for the disease that manifests in metabolically unhealthy normal weight individuals; however, it responds to lifestyle intervention, similar to the disease in obese individuals. Lastly, the newer term "metabolic dysfunction-associated fatty liver disease" provides a more accurate reflection of the disease, giving more focus to clinicians and researchers in tackling this increasingly common and challenging disease.
Topics: Diabetes Mellitus; Humans; Liver; Non-alcoholic Fatty Liver Disease; Obesity; Prevalence; Risk Factors
PubMed: 33526312
DOI: 10.1016/j.clinthera.2021.01.007 -
Genetics of Pulmonary Pressure and Right Ventricle Stress Identify Diabetes as a Causal Risk Factor.Journal of the American Heart... Aug 2023Background Epidemiologic studies have identified risk factors associated with pulmonary hypertension and right heart failure, but causative drivers of pulmonary...
Background Epidemiologic studies have identified risk factors associated with pulmonary hypertension and right heart failure, but causative drivers of pulmonary hypertension and right heart adaptation are not well known. We sought to leverage unbiased genetic approaches to determine clinical conditions that share genetic architecture with pulmonary pressure and right ventricular dysfunction. Methods and Results We leveraged Vanderbilt University's deidentified electronic health records and DNA biobank to identify 14 861 subjects of European ancestry who underwent at least 1 echocardiogram with available estimates of pulmonary pressure and right ventricular function. Analyses of the study were performed between 2020 and 2022. The final analytical sample included 14 861 participants (mean [SD] age, 63 [15] years and mean [SD] body mass index, 29 [7] kg/m). An unbiased phenome-wide association study identified diabetes as the most statistically significant clinical , () code associated with polygenic risk for increased pulmonary pressure. We validated this finding further by finding significant associations between genetic risk for diabetes and a related condition, obesity, with pulmonary pressure estimate. We then used 2-sample univariable Mendelian randomization and multivariable Mendelian randomization to show that diabetes, but not obesity, was independently associated with genetic risk for increased pulmonary pressure and decreased right ventricle load stress. Conclusions Our findings show that genetic risk for diabetes is the only significant independent causative driver of genetic risk for increased pulmonary pressure and decreased right ventricle load stress. These findings suggest that therapies targeting genetic risk for diabetes may also potentially be beneficial in treating pulmonary hypertension and right heart dysfunction.
Topics: Humans; Middle Aged; Diabetes Mellitus, Type 2; Genome-Wide Association Study; Heart Ventricles; Hypertension, Pulmonary; Obesity; Risk Factors; Aged
PubMed: 37522172
DOI: 10.1161/JAHA.122.029190 -
Briefings in Bioinformatics Nov 2021Mendelian randomization (MR) is a common analytic tool for exploring the causal relationship among complex traits. Existing MR methods require selecting a small set of...
Mendelian randomization (MR) is a common analytic tool for exploring the causal relationship among complex traits. Existing MR methods require selecting a small set of single nucleotide polymorphisms (SNPs) to serve as instrument variables. However, selecting a small set of SNPs may not be ideal, as most complex traits have a polygenic or omnigenic architecture and are each influenced by thousands of SNPs. Here, motivated by the recent omnigenic hypothesis, we present an MR method that uses all genome-wide SNPs for causal inference. Our method uses summary statistics from genome-wide association studies as input, accommodates the commonly encountered horizontal pleiotropy effects and relies on a composite likelihood framework for scalable computation. We refer to our method as the omnigenic Mendelian randomization, or OMR. We examine the power and robustness of OMR through extensive simulations including those under various modeling misspecifications. We apply OMR to several real data applications, where we identify multiple complex traits that potentially causally influence coronary artery disease (CAD) and asthma. The identified new associations reveal important roles of blood lipids, blood pressure and immunity underlying CAD as well as important roles of immunity and obesity underlying asthma.
Topics: Algorithms; Diagnosis, Computer-Assisted; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Mendelian Randomization Analysis; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Software
PubMed: 34379090
DOI: 10.1093/bib/bbab322 -
Reviews in Endocrine & Metabolic... Oct 2023Obesity continues to increase in prevalence globally, driven by changes in environmental factors which have accelerated the development of obesity in individuals with an... (Review)
Review
Obesity continues to increase in prevalence globally, driven by changes in environmental factors which have accelerated the development of obesity in individuals with an underlying predisposition to weight gain. The adverse health effects and increased risk for chronic disease associated with obesity are ameliorated by weight loss, with greater benefits from larger amounts of weight reduction. Obesity is a heterogeneous condition, with the drivers, phenotype and complications differing substantially between individuals. This raises the question of whether treatments for obesity, specifically pharmacotherapy, can be targeted based on individual characteristics. This review examines the rationale and the clinical data evaluating this strategy in adults. Individualised prescribing of obesity medication has been successful in rare cases of monogenic obesity where medications have been developed to target dysfunctions in leptin/melanocortin signalling pathways but has been unsuccessful in polygenic obesity due to a lack of understanding of how the gene variants associated with body mass index affect phenotype. At present, the only factor consistently associated with longer-term efficacy of obesity pharmacotherapy is early weight loss outcome, which cannot inform choice of therapy at the time of medication initiation. The concept of matching a therapy for obesity to the characteristics of the individual is appealing but as yet unproven in randomised clinical trials. With increasing technology allowing deeper phenotyping of individuals, increased sophistication in the analysis of big data and the emergence of new treatments, it is possible that precision medicine for obesity will eventuate. For now, a personalised approach that takes into account the person's context, preferences, comorbidities and contraindications is recommended.
Topics: Humans; Obesity; Comorbidity; Weight Loss
PubMed: 37202547
DOI: 10.1007/s11154-023-09808-2 -
International Journal of Molecular... Nov 2020Rare genetic obesity disorders are characterized by mutations of genes strongly involved in the central or peripheral regulation of energy balance. These mutations are... (Review)
Review
Rare genetic obesity disorders are characterized by mutations of genes strongly involved in the central or peripheral regulation of energy balance. These mutations are effective in causing the early onset of severe obesity and insatiable hunger (hyperphagia), suggesting that the genetic component can contribute to 40-70% of obesity. However, genes' roles in the processes leading to obesity are still unclear. This review is aimed to summarize the current knowledge of the genetic causes of obesity, especially monogenic obesity, describing the role of epigenetic mechanisms in obesity and metabolic diseases. A comprehensive understanding of the underlying genetic and epigenetic mechanisms, with the metabolic processes they control, will permit adequate management and prevention of obesity.
Topics: Body Weight; Epigenesis, Genetic; Genetic Predisposition to Disease; Genetic Variation; Humans; Obesity; Risk Factors
PubMed: 33261141
DOI: 10.3390/ijms21239035 -
International Journal of Obesity (2005) Jun 2021Childhood obesity is a complex multifaceted condition, which is influenced by genetics, environmental factors, and their interaction. However, these interactions have...
BACKGROUND
Childhood obesity is a complex multifaceted condition, which is influenced by genetics, environmental factors, and their interaction. However, these interactions have mainly been studied in twin studies and evidence from population-based cohorts is limited. Here, we analyze the interaction of an obesity-related genome-wide polygenic risk score (PRS) with sociodemographic and lifestyle factors for BMI and waist circumference (WC) in European children and adolescents.
METHODS
The analyses are based on 8609 repeated observations from 3098 participants aged 2-16 years from the IDEFICS/I.Family cohort. A genome-wide polygenic risk score (PRS) was calculated using summary statistics from independent genome-wide association studies of BMI. Associations were estimated using generalized linear mixed models adjusted for sex, age, region of residence, parental education, dietary intake, relatedness, and population stratification.
RESULTS
The PRS was associated with BMI (beta estimate [95% confidence interval (95%-CI)] = 0.33 [0.30, 0.37], r = 0.11, p value = 7.9 × 10) and WC (beta [95%-CI] = 0.36 [0.32, 0.40], r = 0.09, p value = 1.8 × 10). We observed significant interactions with demographic and lifestyle factors for BMI as well as WC. Children from Southern Europe showed increased genetic liability to obesity (BMI: beta [95%-CI] = 0.40 [0.34, 0.45]) in comparison to children from central Europe (beta [95%-CI] = 0.29 [0.23, 0.34]), p-interaction = 0.0066). Children of parents with a low level of education showed an increased genetic liability to obesity (BMI: beta [95%-CI] = 0.48 [0.38, 0.59]) in comparison to children of parents with a high level of education (beta [95%-CI] = 0.30 [0.26, 0.34]), p-interaction = 0.0012). Furthermore, the genetic liability to obesity was attenuated by a higher intake of fiber (BMI: beta [95%-CI] interaction = -0.02 [-0.04,-0.01]) and shorter screen times (beta [95%-CI] interaction = 0.02 [0.00, 0.03]).
CONCLUSIONS
Our results highlight that a healthy childhood environment might partly offset a genetic predisposition to obesity during childhood and adolescence.
Topics: Adolescent; Child; Child, Preschool; Cohort Studies; Europe; Female; Genome-Wide Association Study; Humans; Life Style; Male; Pediatric Obesity; Social Factors
PubMed: 33753884
DOI: 10.1038/s41366-021-00795-5 -
Inflammation and Regeneration Jun 2021The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk... (Review)
Review
The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future.
PubMed: 34140035
DOI: 10.1186/s41232-021-00172-9 -
Biomedicines Sep 2021Nonalcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease worldwide. It is closely related to obesity, insulin resistance (IR) and... (Review)
Review
Nonalcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease worldwide. It is closely related to obesity, insulin resistance (IR) and dyslipidemia so much so it is considered the hepatic manifestation of the Metabolic Syndrome. The NAFLD spectrum extends from simple steatosis to nonalcoholic steatohepatitis (NASH), a clinical condition which may progress up to fibrosis, cirrhosis and hepatocellular carcinoma (HCC). NAFLD is a complex disease whose pathogenesis is shaped by both environmental and genetic factors. In the last two decades, several heritable modifications in genes influencing hepatic lipid remodeling, and mitochondrial oxidative status have been emerged as predictors of progressive hepatic damage. Among them, the patatin-like phospholipase domain-containing 3 (PNPLA3) p.I148M, the Transmembrane 6 superfamily member 2 (TM6SF2) p.E167K and the rs641738 membrane bound-o-acyltransferase domain-containing 7 (MBOAT7) polymorphisms are considered the most robust modifiers of NAFLD. However, a forefront frontier in the study of NAFLD heritability is to postulate score-based strategy, building polygenic risk scores (PRS), which aggregate the most relevant genetic determinants of NAFLD and biochemical parameters, with the purpose to foresee patients with greater risk of severe NAFLD, guaranteeing the most highly predictive value, the best diagnostic accuracy and the more precise individualized therapy.
PubMed: 34680476
DOI: 10.3390/biomedicines9101359