Lifestyle Genomics最新文献

Introduction: Smoking cessation is influenced by genetic and environmental factors, particularly genetic polymorphisms influencing nicotine metabolism. This study investigated the association between specific nicotine metabolism-related genetic variants and smoking cessation among Korean men.

Methods: A candidate gene association study was performed targeting single nucleotide polymorphisms (SNPs) within nicotine metabolism-related genes. Participants were categorized as never, former, or current smokers. A Genetic Risk Score (GRS) was computed using significant SNPs to evaluate cumulative genetic influence.

Results: Six SNPs showed significant association with smoking cessation in a Korean cohort. A higher GRS was associated with increased odds of current smoking compared to former smoking (OR = 1.18, 95% CI: 1.12-1.25, p < 0.001).

Conclusion: This study indicates a substantial genetic component in smoking cessation, highlighting the importance of population-specific approaches, and may aid personalized smoking cessation strategies based on genetic predisposition among Koreans.

Background: Diet-induced obesity (DIO) leads to insulin resistance (IR) and alters gene expression through epigenetic mechanisms, including DNA methylation. Here, we aimed to investigate whether experimental environment is an important variable in determining DNA methylation and one-carbon metabolism in DIO mice fed a multi-vitamin-mineral mixture (MVM).

Methods: Three experiments with identical design were conducted in three independent animal facilities (i.e., experimental environments or locations). In each location, 12-week-old male C57BL/6J mice were randomly assigned to two dietary groups: high-fat (HF) and HF-MVM for an average of 10 weeks. Global and gene-specific methylation of adipose function related genes in epididymal white adipose tissue (eWAT), and insulin signaling genes in the liver were analyzed using bisulfite pyrosequencing. Hepatic 1-C metabolites were measured and the ratio of S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) was used as an indicator of methylation potential.

Results: Experimental location affected global methylation patterns in the eWAT, but not in the liver. At the gene-specific level, experimental location, MVM, and their interaction altered the methylation of genes related to adipose function (Srebf1, Acaca, Fasn, Pparg, and Rbp4) in the eWAT and insulin signaling (Pi3kr1 and Akt1) in the liver (p < 0.05). The SAM/SAH ratio was correlated with gene-specific methylation at some CpG sites of Srebf1, Pi3kr1, Acaca, Fasn, Pparg, Rbp4, and Akt1) genes (p < 0.05).

Conclusion: The experimental environment is a significant determinant of the effects of micronutrient supplement on 1-C metabolism and the methylation of genes associated with IR in tissues of DIO adult male mice.

Introduction: The prevalence of gout, a chronic metabolic disease, has recently increased. Polygenic risk scores (PRSs) represent a useful tool for predicting patient outcomes of this condition. However, the clinical utility of PRS in disease prediction remains controversial.

Methods: Using data from the Korean Genome and Epidemiology Study, machine learning (ML) models were developed to predict gout based on PRS and clinical variables such as uric acid, lifestyle habits, and metabolic syndrome (MetS) profiles. Five supervised learning algorithms were applied: logistic regression (a traditional statistical model often used in ML contexts), random forest (RF), decision tree, extreme gradient boosting, and light gradient boosting.

Results: Among the models, the RF model incorporating PRS, age, sex, MetS, and uric acid levels achieved the highest area under the curve (0.7204, 95% CI = 0.7124-0.7284). Feature importance analysis highlighted uric acid levels as the most important predictor of gout, followed by PRS and age. Although PRS enhanced the predictive power of the ML models, its effect was modest, suggesting that traditional risk factors remain important for gout prediction.

Conclusion: This study demonstrated that integrating genetic data with clinical variables improves gout prediction. Further research is necessary to optimize the utility of PRS in diverse populations.

Introduction: Preterm birth (PTB) is a major contributor to neonatal morbidity and mortality. DNA methylation plays a critical role in fetal development and may serve as an epigenetic biomarker for PTB. However, few epigenetic studies have investigated PTB-specific DNA methylation changes. This study aimed to identify epigenetic differences between PTB and term birth (TB) infants.

Methods: A total of 218 cord blood samples from three independent PTB studies were analyzed to identify epigenetic differences between PTB and TB infants. Differential methylation analysis was conducted while adjusting for key covariates, including gestational age, sex, and disease status. Differentially methylated regions (DMRs) (genes and promoters) and differentially methylated sites (DMSs) (CpG sites) were assessed for significant methylation differences between the two groups.

Results: In PTB infants, several genes, including RNASE3, HGF, CLEC5A, LIPN, NXF1, and CCDC12 showed significant hypermethylation (p < 0.05), while the MUC20 and IFNL4 genes showed significant hypomethylation (p < 0.05). The eForge analysis revealed that hypermethylated (p < 0.05) CpG sites were significantly enriched in different fetal tissues such as the small and large intestines, adrenal gland, fetal heart, lungs, and kidney, whereas hypomethylated CpGs showed no significant enrichment. Gene ontology analysis indicated that differentially methylated genes were primarily involved in immune response regulation. Notably, S100A9 and S100A8 genes, which play crucial roles in neonatal immune function and sepsis risk, were hypermethylated (p < 0.05) in PTB infants.

Conclusion: This study identified PTB-associated DNA methylation changes in immune-related genes, suggesting their potential epigenetic biomarkers for PTB. These findings enhance our understanding of PTB pathogenesis and may contribute to the development of novel diagnostic and therapeutic strategies.

Introduction: Previous studies identified genetic links between the TCF7L2 C/T variant rs7903146, type 2 diabetes (T2D), and obesity. We wished to deepen our understanding of how specific diets interact with this variant to affect blood metabolites, an aspect not previously investigated. Hence, we conducted a controlled study where individuals with different genotypes followed a Mediterranean (Med) or low-fat (LF) diet for 1 week.

Methods: Participants were recruited from the Boston, MA (USA) area. Anthropometric and clinical measures were taken. Genotypes at rs7903146 were ascertained, with homozygous carriers of the more common and protective CC or risk TT genotype invited to participate. Participants followed both diets (LF or Med) for 1 week with ∼10 days' washout between diets. Blood samples taken at the beginning and end of each diet period underwent metabolomics analysis using nuclear magnetic resonance spectroscopy. We evaluated how the diet affected different metabolites based on genetic profile.

Results: The cohort of 35 persons was 43% female, aged 18-70 y, with BMI between 26.4 and 33.9 kg/m2. Focusing on fatty acids (FAs) and other lipid metabolic factors (n = 23), we observed a greater number and stronger correlations among these factors in the CC genotype-Med diet group than in the other three genotype-diet combinations. An aggregate of 11 factors, each negatively correlating with delta-saturated fatty acids (SFA), showed a significant genotype-Med diet interaction on delta-SFA in CC individuals on the Med diet (p = 0.0046). A similar genotype-Med diet interaction was observed for delta-monounsaturated fatty acids (p = 0.0078). These interactions were not statistically significant at the end of the LF intervention.

Conclusion: Our findings suggest that the Med diet has a stronger influence on regulating lipid factors in individuals with the CC genotype at the TCF7L2 variant rs7903146. This diet-genotype interaction may have significant implications for understanding the inter-individual variation of metabolic response on specific dietary regimens.

Introduction: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide. Although catheter ablation is the most efficacious therapy, relapses occur frequently (30%) in the first year after ablation. Novel biomarkers of recurrence are needed for a better prediction of recurrence and management of AF. In this pilot study, we aimed to analyze the baseline proteome of subjects included in a case-control study to find differential proteins associated with AF recurrence.

Methods: Baseline serum proteomics (354 proteins) data from 16 cases (recurrent AF) and 17 controls (non-recurrent) were obtained using MS/MS analysis. A false discovery rate was performed using a nonlinear fitting method for the selection of proteins. Logistic regression models were used to further analyze the association between differentially expressed proteins and AF recurrence.

Results: Ten proteins were differentially represented in cases vs. controls. Two were upregulated in the cases compared to the controls: keratin type I cytoskeletal 17 (Fold-change [FC] = 2.14; p = 0.017) and endoplasmic bifunctional protein (FC = 1.65; p = 0.032). Eight were downregulated in the cases compared to the controls: C4bpA (FC = 0.64; p = 0.006), coagulation factor XI (FC = 0.83; p = 0.011), CLIC1 (FC = 0.62; p = 0.017), haptoglobin (FC = 0.37; p = 0.021), Ig alpha-2 chain C region (FC = 0.49; p = 0.022), C4bpB (FC = 0.73; p = 0.028), N-acetylglucosamine-1-phosphotransferase subunit gamma (FC = 0.61; p = 0.033), and platelet glycoprotein Ib alpha chain (FC = 0.84; p = 0.038).

Conclusion: This pilot study identifies ten differentially expressed serum proteins associated with AF recurrence, offering potential biomarkers for improved prediction and management.

Introduction: This study aimed to determine the frequency of genetic testing awareness, the number of individuals who have undergone genetic testing, and the subsequent behavior changes following testing.

Methods: The analysis utilized recent data from the Health Information National Trends Survey (HINTS) 6, collected between March and September 2022, from a diverse sample of adults aged 18 and older. Logistic regressions were applied to assess predictors of outcome variables. A p value of < 0.05 was considered statistically significant.

Results: Among the 4,631 respondents, 81.6% reported being aware of genetic testing, 28.7% (n = 1,327) had undergone some form of testing, and 16.3% of those tested reported making behavioral changes based on their results. Ancestry-related genetic testing was the most widely recognized and frequently utilized. However, behavioral changes were most commonly reported among individuals who underwent disease-specific genetic testing, especially those who perceived themselves to be at high risk, were motivated to take preventive measures, and received assistance in understanding their results. Within this subgroup, lifestyle modification was the most frequently cited change, followed by adjustments in dietary supplement use, increased health screenings, and changes to medications. Additionally, individuals from racial and ethnic minority groups were more likely than non-Hispanic white respondents to undergo specific types of genetic testing and to report behavior changes in response to the findings.

Conclusion: The study highlights an increasing awareness and involvement in genetic testing, though a smaller percentage of individuals have altered their behavior based on the test results. Additionally, the study identifies genetic literacy as a key factor in predicting behavior changes.