Lifestyle Genomics最新文献

{"title":"Investigation of the Association between High Arachidonic Acid Synthesis and Colorectal Polyp Incidence within a Generally Healthy UK Population: A Mendelian Randomization Approach.","authors":"Rachel Moon, J Bernadette Moore, Mark A Hull, Michael A Zulyniak","doi":"10.1159/000526447","DOIUrl":"10.1159/000526447","url":null,"abstract":"<p><strong>Background: </strong>Arachidonic acid (ARA) is associated with colorectal cancer (CRC), a major public health concern. However, it is uncertain if ARA contributes to the development of colorectal polyps which are pre-malignant precursors of CRC.</p><p><strong>Objective: </strong>The study aimed to investigate the association between lifelong exposure to elevated ARA and colorectal polyp incidence.</p><p><strong>Methods: </strong>Summary-level GWAS data from European, Singaporean, and Chinese cohorts (n = 10,171) identified 4 single-nucleotide polymorphisms (SNPs) associated with blood ARA levels (p < 5 × 10-8). After pruning, 1 SNP was retained (rs174547; p = 3.0 × 10-971) for 2-stage Mendelian randomization.</p><p><strong>Results: </strong>No association between ARA and colorectal polyp incidence was observed (OR = 1.00; 95% CI: 0.99, 1.00; p value = 0.50) within the UK Biobank (1,391 cases; 462,933 total).</p><p><strong>Conclusions: </strong>Blood levels of ARA do not associate with colorectal polyp incidence in a general healthy population. Although not providing direct evidence, this work supports the contention that downstream lipid mediators, such as PGE2 rather than ARA itself, are key for polyp formation during early-stage colorectal carcinogenesis.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 4","pages":"107-110"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10852998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans.","authors":"Won-Jun Lee,&nbsp;Ji Eun Lim,&nbsp;Ji-One Kang,&nbsp;Tae-Woong Ha,&nbsp;Hae-Un Jung,&nbsp;Dong Jun Kim,&nbsp;Eun Ju Baek,&nbsp;Han Kyul Kim,&nbsp;Ju Yeon Chung,&nbsp;Bermseok Oh","doi":"10.1159/000525749","DOIUrl":"https://doi.org/10.1159/000525749","url":null,"abstract":"<p><strong>Introduction: </strong>Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples.</p><p><strong>Methods: </strong>We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group.</p><p><strong>Results: </strong>We identified four genome-wide significant loci in interactions with the smoking status (pstratified < 5 × 10-8): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity.</p><p><strong>Conclusions: </strong>Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 3","pages":"87-97"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9905158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Age-Related Cataract Risk with High Polygenetic Risk Scores Involved in Galactose-Related Metabolism and Dietary Interactions.","authors":"Donghyun Jee,&nbsp;Suna Kang,&nbsp;Sunmin Park","doi":"10.1159/000521548","DOIUrl":"https://doi.org/10.1159/000521548","url":null,"abstract":"<p><strong>Introduction: </strong>Cataracts are associated with the accumulation of galactose and galactitol in the lens. We determined the polygenetic risk scores for the best model (PRSBM) associated with age-related cataract (ARC) risk and their interaction with diets and lifestyles in 40,262 Korean adults aged over 50 years belonging to a hospital-based city cohort.</p><p><strong>Methods: </strong>The genetic variants for ARC risk were selected in lactose and galactose metabolism-related genes with multivariate logistic regression using PLINK 1.9 version. PRSBM from the selected genetic variants were estimated by generalized multifactor dimensionality reduction (GMDR) after adjusting for covariates. The interactions between the PRSBM and each lifestyle factor were determined to modulate ARC risk.</p><p><strong>Results: </strong>The genetic variants for ARC risk related to lactose and galactose metabolism were SLC2A1_rs3729548, ST3GAL3_rs3791047, LCT_rs2304371, GALNT5_rs6728956, ST6GAL1_rs2268536, GALNT17_rs17058752, CSGALNACT1_rs1994788, GALNTL4_rs10831608, B4GALT6_rs1667288, and A4GALT_ rs9623659. In GMDR, the best model included all ten genetic variants. The highest odds ratio for a single SNP in the PRSBM was 1.26. However, subjects with a high-PRSBM had a higher ARC risk by 2.1-fold than a low-PRSBM after adjusting for covariates. Carbohydrate, dairy products, kimchi, and alcohol intake interacted with PRSBM for ARC risk, where participants with high-PRSBM had a much higher ARC risk than those with low-PRSBM when consuming diets with high carbohydrate and low dairy product and kimchi intake. However, only with low alcohol intake, the participants with high-PRSBM had a higher ARC risk than those with low-PRSBM.</p><p><strong>Conclusion: </strong>Adults aged >50 years having high-PRSBM may modulate dietary habits to reduce ARC risk.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 2","pages":"55-66"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39639866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantile-Specific Heritability of Mean Platelet Volume, Leukocyte Count, and Other Blood Cell Phenotypes.","authors":"Paul T Williams","doi":"10.1159/000527048","DOIUrl":"https://doi.org/10.1159/000527048","url":null,"abstract":"<p><strong>Introduction: </strong>\"Quantile-dependent expressivity\" occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., mean platelet volume, MPV) is high or low relative to its distribution.</p><p><strong>Methods: </strong>Offspring-parent regression slopes (βOP) were estimated by quantile regression, from which quantile-specific heritabilities (h2) were calculated (h2 = 2βOP/[1 + rspouse]) for blood cell phenotypes in 3,929 parent-offspring pairs from the Framingham Heart Study.</p><p><strong>Results: </strong>Quantile-specific h2 (±SE) increased with increasing percentiles of the offspring's age- and sex-adjusted MPV distribution (plinear = 0.0001): 0.48 ± 0.09 at the 10th, 0.53 ± 0.04 at the 25th, 0.70 ± 0.06 at the 50th, 0.74 ± 0.06 at the 75th, and 0.90 ± 0.12 at the 90th percentile. Quantile-specific h2 also increased with increasing percentiles of the offspring's white blood cell (WBC, plinear = 0.002), monocyte (plinear = 0.01), and eosinophil distributions (plinear = 0.0005). In contrast, heritibilities of red blood cell (RBC) count, hematocrit (HCT), and hemoglobin (HGB) showed little evidence of quantile dependence. Quantile-dependent expressivity is consistent with gene-environment interactions reported by others, including (1) greater increases in WBC and PLT concentrations in subjects who are glutathione-S-transferase Mu1 (GSTM1) null homozygotes than GSTM1 sufficient when exposed to endotoxin; (2) significantly higher WBC count in AA homozygotes than carriers of the G-allele of the glutathione S-transferase P1 (GSTP1) rs1695 polymorphism at low but not high benzene exposure in shoe factory workers; (3) higher WBC counts in TT homozygotes than C-allele carriers of the interleukin-1β (IL1B) c.315C>T polymorphism after undergoing surgery for infective endocarditis but not before surgery.</p><p><strong>Discussion/conclusion: </strong>Quantile-dependent expressivity may explain several purported gene-environment interactions involving blood cell phenotypes.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 4","pages":"111-123"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10511418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APOE Genotypes, Lipid Profiles, and Associated Clinical Markers in a Finnish Population with Cardiovascular Disease Risk Factors.","authors":"Heidi Leskinen,&nbsp;Maaria Tringham,&nbsp;Heli Karjalainen,&nbsp;Terhi Iso-Touru,&nbsp;Hanna-Leena Hietaranta-Luoma,&nbsp;Pertti Marnila,&nbsp;Juha-Matti Pihlava,&nbsp;Timo Hurme,&nbsp;Hannu Puolijoki,&nbsp;Kari Åkerman,&nbsp;Sari Mäkinen,&nbsp;Mari Sandell,&nbsp;Kirsi Vähäkangas,&nbsp;Raija Tahvonen,&nbsp;Susanna Rokka,&nbsp;Anu Hopia","doi":"10.1159/000520864","DOIUrl":"https://doi.org/10.1159/000520864","url":null,"abstract":"<p><strong>Introduction: </strong>The APOE ε4 allele predisposes to high cholesterol and increases the risk for lifestyle-related diseases such as Alzheimer's disease and cardiovascular diseases (CVDs). The aim of this study was to analyse interrelationships of APOE genotypes with lipid metabolism and lifestyle factors in middle-aged Finns among whom the CVD risk factors are common.</p><p><strong>Methods: </strong>Participants (n = 211) were analysed for APOE ε genotypes, physiological parameters, and health- and diet-related plasma markers. Lifestyle choices were determined by a questionnaire.</p><p><strong>Results: </strong>APOE genotypes ε3/ε4 and ε4/ε4 (ε4 group) represented 34.1% of the participants. Genotype ε3/ε3 (ε3 group) frequency was 54.5%. Carriers of ε2 (ε2 group; ε2/ε2, ε2/ε3 and ε2/ε4) represented 11.4%; 1.9% were of the genotype ε2/ε4. LDL and total cholesterol levels were lower (p < 0.05) in the ε2 carriers than in the ε3 or ε4 groups, while the ε3 and ε4 groups did not differ. Proportions of plasma saturated fatty acids (SFAs) were higher (p < 0.01), and omega-6 fatty acids lower (p = 0.01) in the ε2 carriers compared with the ε4 group. The ε2 carriers had a higher (p < 0.05) percentage of 22:4n-6 and 22:5n-6 and a lower (p < 0.05) percentage of 24:5n-3 and 24:6n-3 than individuals without the ε2 allele.</p><p><strong>Conclusions: </strong>The plasma fatty-acid profiles in the ε2 group were characterized by higher SFA and lower omega-6 fatty-acid proportions. Their lower cholesterol values indicated a lower risk for CVD compared with the ε4 group. A novel finding was that the ε2 carriers had different proportions of 22:4n-6, 22:5n-6, 24:5n-3, and 24:6n-3 than individuals without the ε2 allele. The significance of the differences in fatty-acid composition remains to be studied.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 2","pages":"45-54"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39751573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantile-Dependent Heritability of Glucose, Insulin, Proinsulin, Insulin Resistance, and Glycated Hemoglobin.","authors":"Paul T Williams","doi":"10.1159/000519382","DOIUrl":"10.1159/000519382","url":null,"abstract":"<p><strong>Background: </strong>\"Quantile-dependent expressivity\" is a dependence of genetic effects on whether the phenotype (e.g., insulin resistance) is high or low relative to its distribution.</p><p><strong>Methods: </strong>Quantile-specific offspring-parent regression slopes (βOP) were estimated by quantile regression for fasting glucose concentrations in 6,453 offspring-parent pairs from the Framingham Heart Study.</p><p><strong>Results: </strong>Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), increased 0.0045 ± 0.0007 (p = 8.8 × 10-14) for each 1% increment in the fasting glucose distribution, that is, h2 ± SE were 0.057 ± 0.021, 0.095 ± 0.024, 0.146 ± 0.019, 0.293 ± 0.038, and 0.456 ± 0.061 at the 10th, 25th, 50th, 75th, and 90th percentiles of the fasting glucose distribution, respectively. Significant increases in quantile-specific heritability were also suggested for fasting insulin (p = 1.2 × 10-6), homeostatic model assessment of insulin resistance (HOMA-IR, p = 5.3 × 10-5), insulin/glucose ratio (p = 3.9 × 10-5), proinsulin (p = 1.4 × 10-6), proinsulin/insulin ratio (p = 2.7 × 10-5), and glucose concentrations during a glucose tolerance test (p = 0.001), and their logarithmically transformed values.</p><p><strong>Discussion/conclusion: </strong>These findings suggest alternative interpretations to precision medicine and gene-environment interactions, including alternative interpretation of reported synergisms between ACE, ADRB3, PPAR-γ2, and TNF-α polymorphisms and being born small for gestational age on adult insulin resistance (fetal origin theory), and gene-adiposity (APOE, ENPP1, GCKR, IGF2BP2, IL-6, IRS-1, KIAA0280, LEPR, MFHAS1, RETN, TCF7L2), gene-exercise (INS), gene-diet (ACSL1, ELOVL6, IRS-1, PLIN, S100A9), and gene-socioeconomic interactions.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 1","pages":"10-34"},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766916/pdf/nihms-1746619.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10449465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgement to Reviewers","authors":"","doi":"10.1159/000521289","DOIUrl":"https://doi.org/10.1159/000521289","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"14 1","pages":"153 - 153"},"PeriodicalIF":2.6,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44807386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum","authors":"","doi":"10.1159/000512164","DOIUrl":"https://doi.org/10.1159/000512164","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"14 1","pages":"62 - 62"},"PeriodicalIF":2.6,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45816331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the 4th European Summer School on Nutrigenomics (ESSN 2021), June 21-25, 2021.","authors":"","doi":"10.1159/000517609","DOIUrl":"https://doi.org/10.1159/000517609","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"14 3","pages":"91-116"},"PeriodicalIF":2.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000517609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39119288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene-Centric Database Reveals Environmental and Lifestyle Relationships for Potential Risk Modification and Prevention.","authors":"Ron L Martin","doi":"10.1159/000512690","DOIUrl":"https://doi.org/10.1159/000512690","url":null,"abstract":"<p><p>The database at Nutrigenetics.net has been under development since 2007 to facilitate the identification and classification of PubMed articles relevant to human genetics. A controlled vocabulary (i.e., standardized terminology) is used to index these records, with links back to PubMed for every article title. This enables the display of indexes (alphabetical subtopic listings) for any given topic, or for any given combination of topics, including for genes and specific genetic variants. Stepwise use of such indexes (first for one topic, then for combinations of topics) can reveal relationships that are otherwise easily overlooked. These relationships include environmental and lifestyle variables with potential relevance to risk modification (both beneficial and detrimental), and to prevention, or at least to the potential delay of symptom onset for health conditions like Alzheimer disease among many others. Thirty-four specific genetic variants have each been mentioned in at least ≥1,000 PubMed titles/abstracts, and these numbers are steadily increasing. The benefits of indexing with standardized terminology are illustrated for genetic variants like MTHFR 677C-T and its various synonyms (e.g., rs1801133 or Ala222Val). Such use of a controlled vocabulary is also helpful for numerous health conditions, and for potential risk modifiers (i.e., potential risk/effect modifiers).</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"14 1","pages":"30-36"},"PeriodicalIF":2.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38831667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}