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Medicine and Science in Sports and... May 2024This study estimated an individual's genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high...
PURPOSE
This study estimated an individual's genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high cardiorespiratory fitness (CRF) modifies the association between PRS and cardiometabolic risk factors.
METHODS
This cross-sectional study enrolled 1,296 Japanese adults aged ≥40 years. The PRS for each cardiometabolic trait (blood lipids, glucose, hypertension, and obesity) was calculated using the LDpred2 and clumping and thresholding methods. Participants were divided into low-, intermediate-, and high-PRS groups according to PRS tertiles for each trait. CRF was quantified as peak oxygen uptake (VO 2 peak) per kg body weight. Participants were divided into low-, intermediate-, and high-CRF groups according to the tertile VO 2 peak value.
RESULTS
Linear regression analysis revealed a significant interaction between PRS for triglyceride (PRS TG ) and CRF groups on serum TG levels regardless of the PRS calculation method, and attenuated the association between PRS TG and TG levels in the high-CRF group. Logistic regression analysis revealed a significant sub-additive interaction between LDpred2 PRS TG and CRF on the prevalence of high TG, indicating that high CRF attenuated the genetic predisposition to high TG. Furthermore, a significant sub-additive interaction between PRS for body mass index and CRF on obesity was detected regardless of the PRS calculation method. These significant interaction effects on high TG and obesity were diminished in the sensitivity analysis using VO 2 peak per kg fat-free mass as the CRF index. Effects of PRSs for other cardiometabolic traits were not significantly attenuated in the high-CRF group regardless of PRS calculation methods.
CONCLUSIONS
The findings of the present study suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.
PubMed: 38768052
DOI: 10.1249/MSS.0000000000003477 -
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 -
Lipids in Health and Disease May 2024Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to...
BACKGROUND
Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to investigate the association between MetS and its components and the risk of kidney cancer.
METHODS
UK Biobank data was used in this study. MetS was defined as having three or more metabolic abnormalities, while pre-MetS was defined as the presence of one or two metabolic abnormalities. Hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk by MetS category were calculated using multivariable Cox proportional hazards models. Subgroup analyses were conducted for age, sex, BMI, smoking status and drinking status. The joint effects of MetS and genetic factors on kidney cancer risk were also analyzed.
RESULTS
This study included 355,678 participants without cancer at recruitment. During a median follow-up of 11 years, 1203 participants developed kidney cancer. Compared to the metabolically healthy group, participants with pre-MetS (HR= 1.36, 95% CI: 1.06-1.74) or MetS (HR= 1. 70, 95% CI: 1.30-2.23) had a significantly greater risk of kidney cancer. This risk increased with the increasing number of MetS components (P for trend < 0.001). The combination of hypertension, dyslipidemia and central obesity contributed to the highest risk of kidney cancer (HR= 3.03, 95% CI: 1.91-4.80). Compared with participants with non-MetS and low genetic risk, those with MetS and high genetic risk had the highest risk of kidney cancer (HR= 1. 74, 95% CI: 1.41-2.14).
CONCLUSIONS
Both pre-MetS and MetS status were positively associated with kidney cancer risk. The risk associated with kidney cancer varied by combinations of MetS components. These findings may offer novel perspectives on the aetiology of kidney cancer and assist in designing primary prevention strategies.
Topics: Humans; Metabolic Syndrome; Kidney Neoplasms; Female; Male; Middle Aged; Risk Factors; Prospective Studies; Proportional Hazards Models; Adult; Aged; Hypertension; Obesity, Abdominal; Dyslipidemias
PubMed: 38760801
DOI: 10.1186/s12944-024-02138-5 -
Diabetes May 2024Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the influence of T2D pPS...
Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the influence of T2D pPS on diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin or placebo arms. Associations were tested using general linear models and Cox regression adjusted for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at one year follow-up adjusted for baseline measures (effect per pPS standard deviation (SD) -0.04, P=9.6 x 10-7; -8.45 uU/mg, P=5.6 x 10-6, respectively) and with increased diabetes incidence adjusted for BMI at nominal significance (HR 1.10 per SD, P=0.035). The liver/lipid pPS was associated with reduced one-year baseline-adjusted triglyceride levels (effect per SD -4.37, P=0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.
PubMed: 38758294
DOI: 10.2337/db23-0761 -
American Journal of Human Genetics Jun 2024Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of...
Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.
Topics: Humans; Genome-Wide Association Study; Obesity; Body Mass Index; Genetic Predisposition to Disease; Polymorphism, Single Nucleotide; Multifactorial Inheritance; Genetic Loci; Mendelian Randomization Analysis
PubMed: 38754426
DOI: 10.1016/j.ajhg.2024.04.016 -
Journal of Bone and Mineral Research :... May 2024Osteoporosis, a condition defined by low bone mineral density (BMD) (typically < -2.5 SD), cause a higher fracture risk and lead to significant economic, social, and...
Osteoporosis, a condition defined by low bone mineral density (BMD) (typically < -2.5 SD), cause a higher fracture risk and lead to significant economic, social, and clinical impacts. Genome-wide studies mainly in Caucasians have found many genetic links to osteoporosis, fractures, and BMD, with limited research in East Asians. We investigated the genetic aspects of BMD in 86,716 individuals from the Taiwan Biobank and their causal links to health conditions within East Asians. A genome-wide association study (GWAS) was conducted, followed by observational studies, polygenic risk score assessments, and genetic correlation analyses to identify associated health conditions linked to BMD. GWAS and gene-based GWAS studies identified 78 significant SNPs and 75 genes related to BMD, highlighting pathways like Hedgehog, WNT-mediated, and TGF-β. Our cross-trait linkage disequilibrium score regression analyses for BMD and osteoporosis consistently validated their genetic correlations with body mass index (BMI) and type 2 diabetes (T2D) in East Asians. Higher BMD was linked to lower osteoporosis risk but increased BMI and T2D, whereas osteoporosis linked to lower BMI, waist circumference, HbA1c, and reduced T2D risk. Bidirectional Mendelian randomization (MR) analyses revealed that a higher BMI causally increases BMD in East Asians. However, no direct causal relationships were found between BMD and T2D, or between osteoporosis and either BMI or T2D. This study identified key genetic factors for bone health in Taiwan, and revealed significant health conditions in East Asians, particularly highlighting the genetic interplay between bone health and metabolic traits like T2D and BMI.
PubMed: 38753886
DOI: 10.1093/jbmr/zjae078 -
MedRxiv : the Preprint Server For... May 2024The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol...
BACKGROUND
The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol consumption and eating behavior, alcohol- and body mass index (BMI)-related phenotypes show no or minimal genetic correlation. We hypothesized that the lack of genetic correlation is due to mixed effect directions of variants shared by AUD and BMI.
METHODS
We applied MiXeR, to investigate shared genetic architecture between AUD and BMI in individuals of European ancestry. We used conjunctional false discovery rate (conjFDR) analysis to detect loci associated with both phenotypes and their directional effect, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) samples to examine gene expression enrichment across tissue types, and BrainXcan to evaluate the shared associations of AUD and BMI with brain image-derived phenotypes.
RESULTS
MiXeR analysis indicated polygenic overlap of 80.9% between AUD and BMI, despite a genetic correlation (r ) of -.03. ConjFDR analysis yielded 56 lead SNPs with the same effect direction and 76 with the opposite direction. Of the 132 shared lead SNPs, 53 were novel for both AUD and BMI. GTEx analyses identified significant overexpression in the frontal cortex (BA9), hypothalamus, cortex, anterior cingulate cortex (BA24), hippocampus, and amygdala. Amygdala and caudate nucleus gray matter volumes were significantly associated with both AUD and BMI in BrainXcan analyses.
CONCLUSIONS
More than half of variants significantly associated with AUD and BMI had opposite directions of effect for the traits, supporting our hypothesis that this is the basis for their lack of genetic correlation. Follow-up analyses identified brain regions implicated in executive functioning, reward, homeostasis, and food intake regulation. Together, these findings clarify the extensive polygenic overlap between AUD and BMI and elucidate several overlapping neurobiological mechanisms.
PubMed: 38746260
DOI: 10.1101/2024.05.03.24306773 -
Surgery For Obesity and Related... Apr 2024Obesity is a polygenic multifactorial disease. Recent genome-wide association studies have identified several common loci associated with obesity-related phenotypes....
BACKGROUND
Obesity is a polygenic multifactorial disease. Recent genome-wide association studies have identified several common loci associated with obesity-related phenotypes. Bariatric surgery (BS) is the most effective long-term treatment for patients with severe obesity. The huge variability in BS outcomes between patients suggests a moderating effect of several factors, including the genetic architecture of the patients.
OBJECTIVE
To examine the role of a genetic risk score (GRS) based on 7 polymorphisms in 5 obesity-candidate genes (FTO, MC4R, SIRT1, LEP, and LEPR) on weight loss after BS.
SETTING
University hospital in Spain.
METHODS
We evaluated a cohort of 104 patients with severe obesity submitted to BS (Roux-en-Y gastric bypass or sleeve gastrectomy) followed up for >60 months (lost to follow-up, 19.23%). A GRS was calculated for each patient, considering the number of carried risk alleles for the analyzed genes. During the postoperative period, the percentage of excess weight loss total weight loss and changes in body mass index were evaluated. Generalized estimating equation models were used for the prospective analysis of the variation of these variables in relation to the GRS.
RESULTS
The longitudinal model showed a significant effect of the GRS on the percentage of excess weight loss (P = 1.5 × 10), percentage of total weight loss (P = 3.1 × 10), and change in body mass index (P = 7.8 × 10) over time. Individuals with a low GRS seemed to experience better outcomes at 24 and 60 months after surgery than those with a higher GRS.
CONCLUSION
The use of the GRS in considering the polygenic nature of obesity seems to be a useful tool to better understand the outcome of patients with obesity after BS.
PubMed: 38744640
DOI: 10.1016/j.soard.2024.04.002 -
Nutrients Apr 2024Obesity's variability is significantly influenced by the interplay between genetic and environmental factors. We aimed to integrate the combined impact of genetic risk...
Obesity's variability is significantly influenced by the interplay between genetic and environmental factors. We aimed to integrate the combined impact of genetic risk score (GRS) with physical activity (PA), sugar-sweetened beverages (SSB), wine intake, and eating habits score (EHS) on obesity predisposition risk. Adults' ( = 5824) data were analyzed for common obesity-related single nucleotide polymorphisms and lifestyle habits. The weighted GRS was constructed and categorized into quartiles (Qs), and the adjusted multivariate logistic regression models examined the association of GRS with obesity (BMI ≥ 30) and lifestyle factors. GRS was significantly associated with obesity risk. Each GRS unit was associated with an increase of 3.06 BMI units ( ≤ 0.0001). PA markedly reduced obesity risk across GRS Qs. Inactive participants' (≥90 min/week) mean BMI was higher in GRS Q3-Q4 compared to Q1 ( = 0.003 and < 0.001, respectively). Scoring EHS ≥ median, SSBs (≥1 cup/day), and non-wine drinking were associated with higher BMI within all GRS Qs compared to EHS < median, non-SSBs, and non-wine drinkers. Mean BMI was higher in GRS Q4 compared to other quartiles ( < 0.0001) in non-wine drinkers and compared to Q1 for SSB's consumers ( = 0.07). A higher GRS augmented the impact of lifestyle factors on obesity. The interplay between GRS and modifiable lifestyle factors provides a tailored personalized prevention and treatment for obesity management.
Topics: Humans; Male; Obesity; Female; Life Style; Genetic Predisposition to Disease; Adult; Middle Aged; Polymorphism, Single Nucleotide; Exercise; Body Mass Index; Risk Factors; Feeding Behavior; Sugar-Sweetened Beverages; Alcohol Drinking; Genetic Risk Score
PubMed: 38732542
DOI: 10.3390/nu16091296 -
Kidney International Reports May 2024Thousands of pathogenic variants in more than 100 genes can cause kidney cysts with substantial variability in phenotype and risk of subsequent kidney failure. Despite... (Review)
Review
Thousands of pathogenic variants in more than 100 genes can cause kidney cysts with substantial variability in phenotype and risk of subsequent kidney failure. Despite an established genotype-phenotype correlation in cystic kidney diseases, incomplete penetrance and variable disease expressivity are present as is the case in all monogenic diseases. In family members with autosomal dominant polycystic kidney disease (ADPKD), the same causal variant is responsible in all affected family members; however, there can still be striking discordance in phenotype severity. This narrative review explores contributors to within-family discordance in ADPKD severity. Cases of biallelic and digenic inheritance, where 2 rare pathogenic variants in cystogenic genes are coexistent in one family, account for a small proportion of within-family discordance. Genetic background, including cis and trans factors and the polygenic propensity for comorbid disease, also plays a role but has not yet been exhaustively quantified. Environmental exposures, including diet; smoking; alcohol, salt, and protein intake, and comorbid diseases, including obesity, diabetes, hypertension, kidney stones, dyslipidemia, and additional coexistent kidney diseases all contribute to ADPKD phenotypic variability among family members. Given that many of the factors contributing to phenotype variability are preventable, modifiable, or treatable, health care providers and patients need to be aware of these factors and address them in the treatment of ADPKD.
PubMed: 38707833
DOI: 10.1016/j.ekir.2024.01.053