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The Journal of Gene Medicine Jan 2024Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies...
BACKGROUND
Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women.
METHODS
The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios.
RESULTS
High PRS was significantly associated with increased RA risk in individuals aged 60-70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%-42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%-64.9%) for those in the top 5% of the PRS distribution.
CONCLUSIONS
The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.
Topics: Humans; Female; Aged; Risk Factors; Genetic Risk Score; Genome-Wide Association Study; Postmenopause; Genetic Predisposition to Disease; Arthritis, Rheumatoid
PubMed: 38282146
DOI: 10.1002/jgm.3659 -
Acta Diabetologica May 2024Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic...
BACKGROUND
Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic complications and is typically associated with obesity. A group of common variants at eleven loci are associated with insulin resistance and risk of both type 2 diabetes and coronary artery disease. These variants describe a polygenic correlate of lipodystrophy, with a high metabolic disease risk despite a low BMI.
OBJECTIVES
In this cross-sectional study, we sought to investigate the association of a polygenic risk score composed of eleven lipodystrophy variants with anthropometric, glycaemic and metabolic traits in an island population characterised by a high prevalence of both obesity and type 2 diabetes.
METHODS
814 unrelated adults (n = 477 controls and n = 337 T2DM cases) of Maltese-Caucasian ethnicity were genotyped and associations with phenotypes explored.
RESULTS
A higher polygenic lipodystrophy risk score was correlated with lower adiposity indices (lower waist circumference and body mass index measurements) and higher HOMA-IR, atherogenic dyslipidaemia and visceral fat dysfunction as assessed by the visceral adiposity index in the DM group. In crude and covariate-adjusted models, individuals in the top quartile of polygenic risk had a higher T2DM risk relative to individuals in the first quartile of the risk score distribution.
CONCLUSION
This study consolidates the association between polygenic lipodystrophy risk alleles, metabolic syndrome parameters and T2DM risk particularly in normal-weight individuals. Our findings demonstrate that polygenic lipodystrophy risk alleles drive insulin resistance and diabetes risk independent of an increased BMI.
Topics: Humans; Male; Female; Middle Aged; Diabetes Mellitus, Type 2; Cross-Sectional Studies; Lipodystrophy; Adult; Malta; Multifactorial Inheritance; Genetic Predisposition to Disease; Prevalence; Insulin Resistance; Risk Factors; Aged; Obesity; Body Mass Index; Genetic Risk Score
PubMed: 38280973
DOI: 10.1007/s00592-023-02230-9 -
Metabolites Jan 2024Obesity-resistant (non-responder, NR) phenotypes that exhibit reduced susceptibility to developing obesity despite being exposed to high dietary fat are crucial in...
Obesity-resistant (non-responder, NR) phenotypes that exhibit reduced susceptibility to developing obesity despite being exposed to high dietary fat are crucial in exploring the metabolic responses that protect against obesity. Although several efforts have been made to study them in mice and humans, the individual protective mechanisms are poorly understood. In this exploratory study, we used a polygenic C57BL/6J mouse model of diet-induced obesity to show that NR mice developed healthier fat/lean body mass ratios (0.43 ± 0.05) versus the obesity-prone (super-responder, SR) phenotypes (0.69 ± 0.07, < 0.0001) by upregulating gene expression networks that promote the accumulation of type 2a, fast-twitch, oxidative muscle tissues. This was achieved in part by a metabolic adaptation in the form of blood glucose sparing, thus aggravating glucose tolerance. Resistance to obesity in NR mice was associated with 4.9-fold upregulated mitoferrin 1 (), an essential mitochondrial iron importer. SR mice also showed fecal volatile metabolite signatures of enhanced short-chain fatty acid metabolism, including increases in detrimental methyl formate and ethyl propionate, and these effects were reversed in NR mice. Continued research into obesity-resistant phenotypes can offer valuable insights into the underlying mechanisms of obesity and metabolic health, potentially leading to more personalized and effective approaches for managing weight and related health issues.
PubMed: 38276304
DOI: 10.3390/metabo14010069 -
Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis.International Journal of Obesity (2005) May 2024While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic...
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) 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/m higher BMI at week five of treatment (P = 3.9 × 10). In the ONTIME study, we also identified two genome-wide significant (P < 5×10) 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.
Topics: Adult; Female; Humans; Male; Middle Aged; Body Mass Index; Genome-Wide Association Study; Multifactorial Inheritance; Obesity; Polymorphism, Single Nucleotide; Weight Loss
PubMed: 38267484
DOI: 10.1038/s41366-024-01470-1 -
BMC Public Health Jan 2024The association between Metabolic Syndrome (MetS), its components, and the risk of osteoarthritis (OA) has been a topic of conflicting evidence in different studies. The...
OBJECTIVE
The association between Metabolic Syndrome (MetS), its components, and the risk of osteoarthritis (OA) has been a topic of conflicting evidence in different studies. The aim of this present study is to investigate the association between MetS, its components, and the risk of OA using data from the UK Biobank.
METHODS
A prospective cohort study was conducted in the UK Biobank to assess the risk of osteoarthritis (OA) related to MetS. MetS was defined according to the criteria set by the International Diabetes Federation (IDF). Additionally, lifestyle factors, medications, and the inflammatory marker C-reactive protein (CRP) were included in the model. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI). The cumulative risk of OA was analyzed using Kaplan-Meier curves and log-rank tests. To explore potential nonlinear associations between MetS components and OA risk, a restricted cubic splines (RCS) model was employed. In addition, the polygenic risk score (PRS) of OA was calculated to characterize individual genetic risk.
RESULTS
A total of 45,581 cases of OA were identified among 370,311 participants, with a median follow-up time of 12.48 years. The study found that individuals with MetS had a 15% higher risk of developing OA (HR = 1.15, 95%CI:1.12-1.19). Additionally, central obesity was associated with a 58% increased risk of OA (HR = 1.58, 95%CI:1.5-1.66), while hyperglycemia was linked to a 13% higher risk (HR = 1.13, 95%CI:1.1-1.15). Dyslipidemia, specifically in triglycerides (HR = 1.07, 95%CI:1.05-1.09) and high-density lipoprotein (HR = 1.05, 95%CI:1.02-1.07), was also found to be slightly associated with OA risk. When stratified by PRS, those in the high PRS group had a significantly higher risk of OA compared to those with a low PRS, whereas no interaction was found between MetS and PRS on OA risks. Furthermore, the presence of MetS significantly increased the risk of OA by up to 35% in individuals with elevated CRP levels (HR = 1.35, 95% CI:1.3-1.4).
CONCLUSION
MetS and its components have been found to be associated with an increased risk of OA, particularly in individuals with elevated levels of CRP. These findings highlight the significance of managing MetS as a preventive and intervention measure for OA.
Topics: Humans; Metabolic Syndrome; Prospective Studies; Biological Specimen Banks; UK Biobank; Osteoarthritis; Risk Factors; C-Reactive Protein
PubMed: 38243159
DOI: 10.1186/s12889-024-17682-z -
Translational Psychiatry Jan 2024Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we... (Meta-Analysis)
Meta-Analysis
Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we evaluated the predictive performance, risk stratification, and potential systemic health effects of tobacco use disorder (TUD) predisposing germline variants using a European- ancestry-derived polygenic score (PGS) in 24,202 participants from the multi-ancestry, hospital-based UCLA ATLAS biobank. Among genetically inferred ancestry groups (GIAs), TUD-PGS was significantly associated with TUD in European American (EA) (OR: 1.20, CI: [1.16, 1.24]), Hispanic/Latin American (HL) (OR:1.19, CI: [1.11, 1.28]), and East Asian American (EAA) (OR: 1.18, CI: [1.06, 1.31]) GIAs but not in African American (AA) GIA (OR: 1.04, CI: [0.93, 1.17]). Similarly, TUD-PGS offered strong risk stratification across PGS quantiles in EA and HL GIAs and inconsistently in EAA and AA GIAs. In a cross-ancestry phenome-wide association meta-analysis, TUD-PGS was associated with cardiometabolic, respiratory, and psychiatric phecodes (17 phecodes at P < 2.7E-05). In individuals with no history of smoking, the top TUD-PGS associations with obesity and alcohol-related disorders (P = 3.54E-07, 1.61E-06) persist. Mendelian Randomization (MR) analysis provides evidence of a causal association between adiposity measures and tobacco use. Inconsistent predictive performance of the TUD-PGS across GIAs motivates the inclusion of multiple ancestry populations at all levels of genetic research of tobacco use for equitable clinical translation of TUD-PGS. Phenome associations suggest that TUD-predisposed individuals may require comprehensive tobacco use prevention and management approaches to address underlying addictive tendencies.
Topics: Humans; Biological Specimen Banks; Los Angeles; Tobacco Use; Tobacco Use Disorder; Risk Factors; Obesity; Genome-Wide Association Study
PubMed: 38238290
DOI: 10.1038/s41398-024-02743-z -
European Journal of Pediatrics Apr 2024The prevalence of obesity in children and adolescents is increasing, and it is recognised as a complex disorder that often begins in early childhood and persists... (Review)
Review
The prevalence of obesity in children and adolescents is increasing, and it is recognised as a complex disorder that often begins in early childhood and persists throughout life. Both polygenic and monogenic obesity are influenced by a combination of genetic predisposition and environmental factors. Rare genetic obesity forms are caused by specific pathogenic variants in single genes that have a significant impact on weight regulation, particularly genes involved in the leptin-melanocortin pathway. Genetic testing is recommended for patients who exhibit rapid weight gain in infancy and show additional clinical features suggestive of monogenic obesity as an early identification allows for appropriate treatment, preventing the development of obesity-related complications, avoiding the failure of traditional treatment approaches. In the past, the primary recommendations for managing obesity in children and teenagers have been focused on making multiple lifestyle changes that address diet, physical activity, and behaviour, with the goal of maintaining these changes long-term. However, achieving substantial and lasting weight loss and improvements in body mass index (BMI) through lifestyle interventions alone is rare. Recently the progress made in genetic analysis has paved the way for innovative pharmacological treatments for different forms of genetic obesity. By understanding the molecular pathways that contribute to the development of obesity, it is now feasible to identify specific patients who can benefit from targeted treatments based on their unique genetic mechanisms. Conclusion: However, additional preclinical research and studies in the paediatric population are required, both to develop more personalised prevention and therapeutic programs, particularly for the early implementation of innovative and beneficial management options, and to enable the translation of these novel therapy approaches into clinical practice. What is Known: • The prevalence of obesity in the paediatric population is increasing, and it is considered as a multifaceted condition that often begins in early childhood and persists in the adult life. Particularly, rare genetic forms of obesity are influenced by a combination of genetic predisposition and environmental factors and are caused by specific pathogenic variants in single genes showing a remarkable impact on weight regulation, particularly genes involved in the leptin-melanocortin pathway. • Patients who present with rapid weight gain in infancy and show additional clinical characteristics indicative of monogenic obesity should undergo genetic testing, which, by enabling a correct diagnosis, can prevent the development of obesity-related consequences through the identification for appropriate treatment. What is New: • In recent years, advances made in genetic analysis has made it possible to develop innovative pharmacological treatments for various forms of genetic obesity. In fact, it is now achievable to identify specific patients who can benefit from targeted treatments based on their unique genetic mechanisms by understanding the molecular pathways involved in the development of obesity. • As demonstrated over the last years, two drugs, setmelanotide and metreleptin, have been identified as potentially effective interventions in the treatment of certain rare forms of monogenic obesity caused by loss-of-function mutations in genes involved in the leptin-melanocortin pathway. Recent advancements have led to the development of novel treatments, including liraglutide, semaglutide and retatrutide, that have the potential to prevent the progression of metabolic abnormalities and improve the prognosis of individuals with these rare and severe forms of obesity. However, extensive preclinical research and, specifically, additional studies in the paediatric population are necessary to facilitate the translation of these innovative treatment techniques into clinical practice.
Topics: Child; Adult; Adolescent; Humans; Child, Preschool; Pediatric Obesity; Leptin; Genetic Predisposition to Disease; alpha-MSH; Weight Gain
PubMed: 38227053
DOI: 10.1007/s00431-024-05427-4 -
Genome Medicine Jan 2024Type 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition...
BACKGROUND
Type 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these approaches by performing colocalization across GWAS traits while assessing the causality and directionality of genetic associations.
METHODS
We applied colocalization between T2D and 20 related metabolic traits, across 243 loci, to obtain inferences of shared casual variants. Network-based unsupervised hierarchical clustering was performed on variant-trait associations. Partitioned polygenic risk scores (PRSs) were generated for each cluster using T2D summary statistics and validated in 21,742 individuals with T2D from 3 cohorts. Inferences of directionality and causality were obtained by applying Mendelian randomization Steiger's Z-test and further validated in a pediatric cohort without diabetes (aged 9-12 years old, n = 3866).
RESULTS
We identified 146 T2D loci that colocalized with at least one metabolic trait locus. T2D variants within these loci were grouped into 5 clusters. The clusters corresponded to the following pathways: obesity, lipodystrophic insulin resistance, liver and lipid metabolism, hepatic glucose metabolism, and beta-cell dysfunction. We observed heterogeneity in associations between PRSs and metabolic measures across clusters. For instance, the lipodystrophic insulin resistance (Beta - 0.08 SD, 95% CI [- 0.10-0.07], p = 6.50 × 10) and beta-cell dysfunction (Beta - 0.10 SD, 95% CI [- 0.12, - 0.08], p = 1.46 × 10) PRSs were associated to lower BMI. Mendelian randomization Steiger analysis indicated that increased T2D risk in these pathways was causally associated to lower BMI. However, the obesity PRS was conversely associated with increased BMI (Beta 0.08 SD, 95% CI 0.06-0.10, p = 8.0 × 10). Analyses within a pediatric cohort supported this finding. Additionally, the lipodystrophic insulin resistance PRS was associated with a higher odds of chronic kidney disease (OR 1.29, 95% CI 1.02-1.62, p = 0.03).
CONCLUSIONS
We successfully partitioned T2D genetic variants into phenotypic pathways using a colocalization first approach. Partitioned PRSs were associated to unique metabolic and clinical outcomes indicating successful partitioning of disease heterogeneity. Our work expands on previous approaches by providing stronger inferences of shared causal variants, causality, and directionality of GWAS variant-trait associations.
Topics: Humans; Child; Diabetes Mellitus, Type 2; Genetic Risk Score; Insulin Resistance; Cluster Analysis; Obesity
PubMed: 38200577
DOI: 10.1186/s13073-023-01255-7 -
International Journal of Obesity (2005) May 2024Higher mean body mass index (BMI) among lower socioeconomic position (SEP) groups is well established in Western societies, but the influence of genetic factors on these...
BACKGROUND
Higher mean body mass index (BMI) among lower socioeconomic position (SEP) groups is well established in Western societies, but the influence of genetic factors on these differences is not well characterized.
METHODS
We analyzed these associations using Finnish health surveys conducted between 1992 and 2017 (N = 33 523; 53% women) with information on measured weight and height, polygenic risk scores of BMI (PGS-BMI) and linked data from administrative registers to measure educational attainment, occupation-based social class and personal income.
RESULTS
In linear regressions, largest adjusted BMI differences were found between basic and tertiary educated men (1.4 kg/m, 95% confidence interval [CI] 1.2; 1.6) and women (2.5 kg/m, 95% CI 2.3; 2.8), and inverse BMI gradients were also found for social class and income. These SEP differences arose partly because mean PGS-BMI was higher and partly because PGS-BMI predicted BMI more strongly in lower SEP groups. The inverse SEP gradients of BMI were steeper in women than in men, but sex differences were not found in the genetic contributions to these differences.
CONCLUSIONS
Better understanding of the interplay between genes and environment provides insight into the mechanisms explaining SEP differences in BMI.
Topics: Humans; Body Mass Index; Male; Female; Finland; Adult; Middle Aged; Socioeconomic Factors; Social Class; Obesity; Aged; Health Surveys
PubMed: 38200145
DOI: 10.1038/s41366-024-01459-w