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Communications Biology Oct 2020Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown....
Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown. Here we conducted a genome-wide association study (GWAS) with 450,243 UK Biobank participants to uncover its genetic architecture. A total of 1059 conditionally independent variants from 799 loci were identified at the genome-wide significance level (p < 5 × 10), all of which were also significant at p < 5 × 10 in both sexes. These variants explained ~15.5% of the phenotypic variance, accounting for more than one quarter of the total ~50% GWAS-attributable heritability. There was no difference in genetic effect between sexes or among different age strata. Heritability was enriched in certain functional categories, such as conserved and coding regions, and in tissues related to the musculoskeletal system. Polygenic risk score prediction well distinguished participants with high and low ALM. The findings are important not only for lean mass but also for other complex diseases, such as type 2 diabetes, as ALM is shown to be a protective factor for type 2 diabetes.
Topics: Adult; Aged; Biological Specimen Banks; Diabetes Mellitus, Type 2; Female; Genetic Variation; Genome, Human; Genome-Wide Association Study; Humans; Male; Middle Aged; Muscle Strength; Muscle, Skeletal; Mutation; Obesity; Sarcopenia; United Kingdom
PubMed: 33097823
DOI: 10.1038/s42003-020-01334-0 -
Nature Communications May 2023Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with...
Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.
Topics: Humans; Autoimmunity; Influenza, Human; Influenza A Virus, H1N1 Subtype; Autoimmune Diseases; Influenza Vaccines; Narcolepsy
PubMed: 37188663
DOI: 10.1038/s41467-023-36120-z -
Open Respiratory Archives 2023The circadian rhythm of sleep occurs in a cyclical 24-h pattern that is adjusted by the influence of several main synchronizers or "zeitgebers". The most powerful... (Review)
Review
The circadian rhythm of sleep occurs in a cyclical 24-h pattern that is adjusted by the influence of several main synchronizers or "zeitgebers". The most powerful synchronizer is the light-dark alternation, but also, socio-economic factors play a role, such as social and work relationships. Circadian rhythm regulation plays a crucial role in human health. This disruption of circadian rhythm can lead to increased incidence of diseases: diabetes, obesity, cancer, neurodegenerative diseases, increased risk of cardiovascular disease and stroke. Polygenic variations and environmental factors influence the circadian rhythm of each person. This is known as chronotype, which manifests itself as the degree of morning of evening preferences of each individual. There are indications to establish an association between individual chronotype preferences and the behavior of respiratory diseases.
PubMed: 37497245
DOI: 10.1016/j.opresp.2022.100228 -
Genes Jan 2024Hypertriglyceridemia is an exceptionally complex metabolic disorder characterized by elevated plasma triglycerides associated with an increased risk of acute... (Review)
Review
Hypertriglyceridemia is an exceptionally complex metabolic disorder characterized by elevated plasma triglycerides associated with an increased risk of acute pancreatitis and cardiovascular diseases such as coronary artery disease. Its phenotype expression is widely heterogeneous and heavily influenced by conditions as obesity, alcohol consumption, or metabolic syndromes. Looking into the genetic underpinnings of hypertriglyceridemia, this review focuses on the genetic variants in , , , and triglyceride-regulating genes reportedly associated with abnormal genetic transcription and the translation of proteins participating in triglyceride-rich lipoprotein metabolism. Hypertriglyceridemia resulting from such genetic abnormalities can be categorized as monogenic or polygenic. Monogenic hypertriglyceridemia, also known as familial chylomicronemia syndrome, is caused by homozygous or compound heterozygous pathogenic variants in the five canonical genes. Polygenic hypertriglyceridemia, also known as multifactorial chylomicronemia syndrome in extreme cases of hypertriglyceridemia, is caused by heterozygous pathogenic genetic variants with variable penetrance affecting the canonical genes, and a set of common non-pathogenic genetic variants (polymorphisms, using the former nomenclature) with well-established association with elevated triglyceride levels. We further address recent progress in triglyceride-lowering treatments. Understanding the genetic basis of hypertriglyceridemia opens new translational opportunities in the scope of genetic screening and the development of novel therapies.
Topics: Humans; Lipoprotein Lipase; Acute Disease; Pancreatitis; Hypertriglyceridemia; Triglycerides
PubMed: 38397180
DOI: 10.3390/genes15020190 -
The Lancet. Digital Health Jun 2023Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and...
BACKGROUND
Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data.
METHODS
In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12).
FINDINGS
We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness.
INTERPRETATION
Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials.
FUNDING
European Union Innovative Medicines Initiative-2.
Topics: Humans; Prognosis; Electronic Health Records; Atrial Fibrillation; Heart Failure; Machine Learning
PubMed: 37236697
DOI: 10.1016/S2589-7500(23)00065-1 -
GeroScience Aug 2023Frailty is an aging-related clinical phenotype defined as a state in which there is an increase in a person's vulnerability for dependency and/or mortality when exposed...
Frailty is an aging-related clinical phenotype defined as a state in which there is an increase in a person's vulnerability for dependency and/or mortality when exposed to a stressor. While underlying mechanisms leading to the occurrence of frailty are complex, the importance of genetic factors has not been fully investigated. We conducted a large-scale genome-wide association study (GWAS) of frailty, as defined by the five criteria (weight loss, exhaustion, physical activity, walking speed, and grip strength) captured in the Fried Frailty Score (FFS), in 386,565 European descent participants enrolled in the UK Biobank (mean age 57 [SD 8] years, 208,481 [54%] females). We identified 37 independent, novel loci associated with the FFS (p < 5 × 10), including seven loci without prior described associations with other traits. The variants associated with FFS were significantly enriched in brain tissues as well as aging-related pathways. Our post-GWAS bioinformatic analyses revealed significant genetic correlations between FFS and cardiovascular-, neurological-, and inflammation-related diseases/traits, and subsequent Mendelian Randomization analyses identified causal associations with chronic pain, obesity, diabetes, education-related traits, joint disorders, and depressive/neurological, metabolic, and respiratory diseases. The GWAS signals were replicated in the Health and Retirement Study (HRS, n = 9,720, mean age 73 [SD 7], 5,582 [57%] females), where the polygenic risk score built from UKB GWAS was significantly associated with the FFS in HRS individuals (OR per SD of the score 1.27, 95% CI 1.22-1.31, p = 1.3 × 10). These results provide new insight into the biology of frailty by comprehensively evaluating its genetic architecture.
Topics: Female; Humans; Aged; Male; Genome-Wide Association Study; Frailty; Obesity; Phenotype; Inflammation
PubMed: 36928559
DOI: 10.1007/s11357-023-00771-z -
Journal of Clinical Medicine Feb 2024Obesity remains a common metabolic disorder and a threat to health as it is associated with numerous complications. Lifestyle modifications and caloric restriction can... (Review)
Review
Obesity remains a common metabolic disorder and a threat to health as it is associated with numerous complications. Lifestyle modifications and caloric restriction can achieve limited weight loss. Bariatric surgery is an effective way of achieving substantial weight loss as well as glycemic control secondary to weight-related type 2 diabetes mellitus. It has been suggested that an anorexigenic gut hormone response following bariatric surgery contributes to weight loss. Understanding the changes in gut hormones and their contribution to weight loss physiology can lead to new therapeutic treatments for weight loss. Two distinct types of neurons in the arcuate hypothalamic nuclei control food intake: proopiomelanocortin neurons activated by the anorexigenic (satiety) hormones and neurons activated by the orexigenic peptides that release neuropeptide Y and agouti-related peptide (hunger centre). The arcuate nucleus of the hypothalamus integrates hormonal inputs from the gut and adipose tissue (the anorexigenic hormones cholecystokinin, polypeptide YY, glucagon-like peptide-1, oxyntomodulin, leptin, and others) and orexigeneic peptides (ghrelin). Replicating the endocrine response to bariatric surgery through pharmacological mimicry holds promise for medical treatment. Obesity has genetic and environmental factors. New advances in genetic testing have identified both monogenic and polygenic obesity-related genes. Understanding the function of genes contributing to obesity will increase insights into the biology of obesity. This review includes the physiology of appetite control, the influence of genetics on obesity, and the changes that occur following bariatric surgery. This has the potential to lead to the development of more subtle, individualised, treatments for obesity.
PubMed: 38546831
DOI: 10.3390/jcm13051347 -
Nature Communications Jun 2023We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare...
We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
Topics: Male; Female; Humans; Blood Pressure; Population Health; Risk Factors; Multifactorial Inheritance; Ethnicity; Genome-Wide Association Study; Genetic Predisposition to Disease
PubMed: 37268629
DOI: 10.1038/s41467-023-38990-9 -
Obesity Pillars Sep 2024Obesity is a multifactorial neurohormonal disease that results from dysfunction within energy regulation pathways and is associated with increased morbidity, mortality,... (Review)
Review
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.
PubMed: 38766314
DOI: 10.1016/j.obpill.2024.100110 -
Genome Biology Mar 2023Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a...
BACKGROUND
Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.
RESULTS
We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.
CONCLUSIONS
Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
Topics: Mice; Animals; Phosphorylation; Diabetes Mellitus, Type 2; Multiomics; Quantitative Trait Loci; Peptides
PubMed: 36944993
DOI: 10.1186/s13059-023-02892-2