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The Journals of Gerontology. Series A,... Apr 2024Longevity and disease-free survival are influenced by a combination of genetics and lifestyle. Biological age (BioAge), a measure of aging based on composite biomarkers,...
Longevity and disease-free survival are influenced by a combination of genetics and lifestyle. Biological age (BioAge), a measure of aging based on composite biomarkers, may outperform chronological age in predicting health and longevity. This study investigated the relationship between genetic risks, lifestyle factors, and delta age (Δage), estimated as the difference between biological and chronological age. BioAge and Δage were calculated for 52 418 participants from the population-based Lifelines cohort. We computed 2 independent polygenic risk scores (PRS) for health span and DNA methylation-based aging clock to characterize genetic risks. The capacity of BioAge to predict all-cause mortality when adjusted for chronological age and genetic risks for aging, was assessed. Obesity, lifestyle, socioeconomic status, sex, and genetic variations in a population contributed to the differences in the rates of accelerated aging. The overall risk of death for a 1-year increase in BioAge for a given chronological age and sex among the genotyped participants was 11% (HR = 1.11; 95% CI: 1.09, 1.13). After adjusting for genetic factors, BioAge maintained its sensitivity for predicting mortality. Findings from this study ascertain that BioAge can be a useful tool for risk stratification in research and aging interventions.
Topics: Humans; Aging; Longevity; DNA Methylation; Risk Factors; Biomarkers; Epigenesis, Genetic
PubMed: 38305578
DOI: 10.1093/gerona/glae024 -
Frontiers in Nutrition 2023Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic...
Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.
PubMed: 38303904
DOI: 10.3389/fnut.2023.1308622 -
BMC Medicine Feb 2024This study aims to investigate potential interactions between maternal smoking around birth (MSAB) and type 2 diabetes (T2D) pathway-specific genetic risks in relation...
BACKGROUND
This study aims to investigate potential interactions between maternal smoking around birth (MSAB) and type 2 diabetes (T2D) pathway-specific genetic risks in relation to the development of T2D in offspring. Additionally, it seeks to determine whether and how nutritional factors during different life stages may modify the association between MSAB and risk of T2D.
METHODS
This study included 460,234 participants aged 40 to 69 years, who were initially free of T2D from the UK Biobank. MSAB and breastfeeding were collected by questionnaire. The Alternative health eating index(AHEI) and dietary inflammation index(DII) were calculated. The polygenic risk scores(PRS) of T2D and pathway-specific were established, including β-cell function, proinsulin, obesity, lipodystrophy, liver function and glycated haemoglobin(HbA1c). Cox proportion hazards models were performed to evaluate the gene/diet-MSAB interaction on T2D. The relative excess risk due to additive interaction (RERI) were calculated.
RESULTS
During a median follow-up period of 12.7 years, we identified 27,342 cases of incident T2D. After adjustment for potential confounders, participants exposed to MSAB had an increased risk of T2D (HR=1.11, 95%CI:1.08-1.14), and this association remained significant among the participants with breastfeeding (HR= HR=1.10, 95%CI: 1.06-1.14). Moreover, among the participants in the highest quartile of AHEI or in the lowest quartile of DII, the association between MSAB and the increased risk of T2D become non-significant (HR=0.94, 95%CI: 0.79-1.13 for AHEI; HR=1.09, 95%CI:0.99-1.20 for DII). Additionally, the association between MSAB and risk of T2D became non-significant among the participants with lower genetic risk of lipodystrophy (HR=1.06, 95%CI:0.99-1.14), and exposed to MSAB with a higher genetic risk for β-cell dysfunction or lipodystrophy additively elevated the risk of T2D(RERI=0.18, 95%CI:0.06-0.30 for β-cell function; RERI=0.16, 95%CI:0.04-0.28 for lipodystrophy).
CONCLUSIONS
This study indicates that maintaining a high dietary quality or lower dietary inflammation in diet may reduce the risk of T2D associated with MSAB, and the combination of higher genetic risk of β-cell dysfunction or lipodystrophy and MSAB significantly elevate the risk of T2D in offspring.
Topics: Humans; Diabetes Mellitus, Type 2; Prospective Studies; UK Biobank; Biological Specimen Banks; Risk Factors; Inflammation; Smoking; Lipodystrophy
PubMed: 38302923
DOI: 10.1186/s12916-024-03256-8 -
Genome Medicine Jan 2024Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying...
BACKGROUND
Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner.
METHODS
We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI).
RESULTS
We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level.
CONCLUSIONS
Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
Topics: Humans; Cell Differentiation; Obesity; Adipose Tissue; Adipocytes; Aging; Transcription Factors
PubMed: 38297378
DOI: 10.1186/s13073-024-01291-x -
Current Diabetes Reports Mar 2024Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis... (Review)
Review
PURPOSE OF REVIEW
Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.
RECENT FINDINGS
We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
Topics: Humans; Proteomics; Diabetes Mellitus; Genomics; Metabolomics; Precision Medicine
PubMed: 38294727
DOI: 10.1007/s11892-024-01533-7 -
Nature Communications Jan 2024To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in... (Meta-Analysis)
Meta-Analysis
To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.
Topics: Humans; Thyroid Gland; Thyroxine; Genome-Wide Association Study; Triiodothyronine; Thyrotropin
PubMed: 38291025
DOI: 10.1038/s41467-024-44701-9 -
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