Lifestyle Genomics最新文献

{"title":"Proteomics and Recurrence of Atrial Fibrillation: A Pilot Study Nested in the PREDIMAR Trial.","authors":"Cristina Razquin, Joaquín Fernandez-Irigoyen, María Teresa Barrio-López, Begoña López, Susana Ravassa, Pablo Ramos, Rosa Macías-Ruíz, Alicia Ibañez Criado, Enrique Santamaría, Leticia Goni, Eduardo Castellanos, Jose Luis Ibañez Criado, Luis Tercedor, Ignacio García-Bolao, Miguel A Martínez-González, Jesús Almendral, Miguel Ruiz-Canela","doi":"10.1159/000543639","DOIUrl":"10.1159/000543639","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>This pilot study identifies ten differentially expressed serum proteins associated with AF recurrence, offering potential biomarkers for improved prediction and management.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"52-58"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047143","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":"Uncoupling Proteins Variants Are Linked to Hypercholesterolemia and Abdominal Obesity in Metabolically Unhealthy Women.","authors":"Erika Sierra-Ruelas, Nathaly Torres-Castillo, Barbara Vizmanos, Wendy Campos Pérez, Erika Martínez-López","doi":"10.1159/000543484","DOIUrl":"10.1159/000543484","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;It has been reported that even with the same body mass index (BMI); there are subjects with metabolically healthy (MH) or unhealthy (MUH) phenotype. The main determinants of the unhealthy phenotype are the type and distribution of fat, ectopic fat accumulation, genetics, and lifestyle factors. Uncoupling proteins (UCPs) disengage mitochondrial respiration from ATP synthesis and result in heat production, which in turn is related to energy expenditure and, thus, to fat mass accumulation. The association of the UCP1 3826A/G (rs1800592), UCP2 Ala55Val (rs660339), and UCP3 55C/T (rs1800849) variants with metabolic variables was evaluated according to metabolic phenotype in Mexican women.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Women aged 18-65 years classified as normal weight (NW) or excessive weight (EW) according to their BMI (from 18.5 to &lt;25 kg/m2 for NW, and from 25 to &lt;40 kg/m2 for EW), were included. Participants were classified into two metabolic phenotypes: MH or MUH, respectively, based on ATP-III criteria and the homeostasis model assessment of insulin resistance (HOMA-IR). The genetic variants were determined by allelic discrimination using TaqMan® probes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In participants with the UCP1 3826A/G variant, an increased risk of hypercholesterolemia was observed in those with the NW-MUH phenotype (OR = 5.09, 95% CI = 1.03-25.12, p = 0.017). The UCP2 Ala55Val variant in EW-MUH subjects was associated with higher abdominal obesity risk (OR = 3.23, 95% CI = 1.21-8.60, p = 0.019), while no associations were found with the UCP3 55C/T variant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;UCP1 and UCP2 variants are related with hypercholesterolemia and visceral fat accumulation in women with MUH phenotype.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;It has been reported that even with the same body mass index (BMI); there are subjects with metabolically healthy (MH) or unhealthy (MUH) phenotype. The main determinants of the unhealthy phenotype are the type and distribution of fat, ectopic fat accumulation, genetics, and lifestyle factors. Uncoupling proteins (UCPs) disengage mitochondrial respiration from ATP synthesis and result in heat production, which in turn is related to energy expenditure and, thus, to fat mass accumulation. The association of the UCP1 3826A/G (rs1800592), UCP2 Ala55Val (rs660339), and UCP3 55C/T (rs1800849) variants with metabolic variables was evaluated according to metabolic phenotype in Mexican women.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Women aged 18-65 years classified as normal weight (NW) or excessive weight (EW) according to their BMI (from 18.5 to &lt;25 kg/m2 for NW, and from 25 to &lt;40 kg/m2 for EW), were included. Participants were classified into two metabolic phenotypes: MH or MUH, respectively, based on ATP-III criteria and the homeostasis model assessment of insulin resistance (HOMA-IR). The genetic variants were determined by allelic discrimination using TaqMan® probes.&lt;/p&gt;&lt;p&gt;&lt;","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"27-35"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11838145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143007863","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":"New Insight into Mechanisms Underlying Genotype-Dependent Responses to the Mediterranean Diet: Implications for the Future of Precision Nutrition.","authors":"Patrick V McTavish, Mathieu J Clavet","doi":"10.1159/000547052","DOIUrl":"10.1159/000547052","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"98-101"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144475831","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":"Awareness of Genetic Testing and Its Impact on Changing Behavior among General Population of the USA: Health Information National Trends Survey (HINTS 2022).","authors":"Athar Memon, Hiba Hamid, Ayesha Mehboob, Muhammad Ovais, Zahid Wali, Emma Khayat-Mishne","doi":"10.1159/000546588","DOIUrl":"10.1159/000546588","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"102-115"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144637451","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":"Guidance and Position of RINN22 regarding Precision Nutrition and Nutriomics.","authors":"Omar Ramos-Lopez, Taís Silveira Assmann, Elcy Yaned Astudillo Muñoz, Luis Baquerizo-Sedano, Elisa Barrón-Cabrera, Claudio Adrián Bernal, Josefina Bressan, Amanda Cuevas-Sierra, Alberto Dávalos, Ulises De la Cruz-Mosso, Ana Laura De la Garza, Daniel A De Luis, Rocío I Díaz de la Garza, Karina Dos Santos, Roxana Carla Fernández-Condori, Alfredo Fernández-Quintela, Diego F Garcia Diaz, Karina Gonzalez-Becerra, Eliane Lopes Rosado, María-Carmen López de Las Hazas, Bertha Araceli Marín Alejandre, Alberto Angel Martin, Erika Martinez-Lopez, Diego Martínez-Urbistondo, Fermin I Milagro, Helen Hermana M Hermsdorff, Begoña Muguerza, Carolina F Nicoletti, Ana Maria Obregón Rivas, Isela Parra-Rojas, Maria Puy Portillo, José L Santos, Thais Steemburgo, Maria Elizabeth Tejero, Anny Cristina Terán, Victor Treviño, Bárbara Vizmanos, J Alfredo Martinez","doi":"10.1159/000542789","DOIUrl":"10.1159/000542789","url":null,"abstract":"<p><strong>Background: </strong>Precision nutrition is based on the integration of individual's phenotypical and biological characteristics including genetic variants, epigenetic marks, gut microbiota profiles, and metabolite fingerprints as well as medical history, lifestyle practices, and environmental and cultural factors. Thus, nutriomics areas including nutrigenomics, nutrigenetics, nutriepigenetics, nutrimetabolomics, and nutrimetagenomics have emerged to comprehensively understand the complex interactions between nutrients, diet, and the human body's molecular processes through precision nutrition.</p><p><strong>Summary: </strong>This document from the Ibero-American Network of Nutriomics and Precision Nutrition (RINN22; <ext-link ext-link-type=\"uri\" xlink:href=\"https://rinn22.com/\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://rinn22.com/</ext-link>) provides a comprehensive overview of the concepts of precision nutrition approaches to guide their application in clinical and public health as well as establish the position of RINN22 regarding the current and future state of precision nutrition.</p><p><strong>Key messages: </strong>The progress and participation of nutriomics to precision nutrition is an essential pillar for addressing diet-related diseases and developing innovative managing strategies, which will be promoted by advances in bioinformatics, machine learning, and integrative software, as well as the description of specific novel biomarkers. In this context, synthesizing and critically evaluating the latest developments, potential applications, and future needs in the field of nutrition is necessary with a holistic perspective, incorporating progress in omics technologies aimed at precision nutrition interventions. This approach must address and confront healthy, social, food security, physically active lifestyle, sanitation, and sustainability challenges with preventive, participatory, and predictive strategies of personalized, population, and planetary nutrition for a precision tailored health.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"1-19"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770311","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":"A Pilot Study to Evaluate the Role of Obesity and Genetic Variants in Serum C-Reactive Protein Response to an Acute Fructose Load.","authors":"Sai Sravani Vennam, Valentina Talevi, Geethika Venkataraman, Rayyan Ahmed Syed, Xinruo Zhang, Baba B Mass, Venkata Saroja Voruganti","doi":"10.1159/000544832","DOIUrl":"10.1159/000544832","url":null,"abstract":"<p><strong>Introduction: </strong>Excess fructose intake has been linked to increased risk of dyslipidemia, insulin resistance, hyperuricemia, inflammation, and obesity. In this human study, we investigated if serum C-reactive protein (CRP) concentrations change after fructose consumption, and whether genetic variants and obesity status influence this change.</p><p><strong>Methods: </strong>Blood was drawn before and at four time points after administration of a fructose load (n = 57). Serum concentrations of CRP were measured, and 11 single nucleotides polymorphisms (SNPs) (rs1205, rs1417938, rs1470515, rs3093068, rs6588158, rs16842568, rs2259820, rs157581, rs2794521, rs3093062, rs17700633), previously associated with serum CRP were genotyped and assessed for their association with CRP levels.</p><p><strong>Results: </strong>Participants identifying as White (n = 37) had higher mean CRP levels across all time points compared to those identifying as Black (n = 20). Participants with obesity (body mass index ≥30 kg/m2) (n = 25) were younger and had higher mean CRP levels throughout the study period compared to those without (n = 32). All SNPs were in Hardy-Weinberg equilibrium and their effect allele frequencies ranged between 11 and 96%. Baseline CRP was associated with CRP SNPs rs1417938 and rs2794521 (p < 0.005); rs2794521 was also associated with CRP response to fructose challenge (p < 0.005). The variability in response to fructose and genetic associations was mainly observed in individuals without obesity. Obesity status was associated with early changes in CRP (0-30 min and 30-60 min) whereas CRP SNPs were associated with later changes (60-120 min and 120-180 min).</p><p><strong>Conclusion: </strong>Changes in serum CRP were associated with obesity status or SNPs based on the time elapsed since fructose ingestion. Larger studies are needed to confirm and validate these associations.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"64-75"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523862","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":"UPV Austral Summer Course and RINN22 Meeting: Precision nutrition and nutriomics.","authors":"Begoña de Cuevillas, J Alfredo Martinez","doi":"10.1159/000546874","DOIUrl":"10.1159/000546874","url":null,"abstract":"<p><strong>Reviewers: </strong></p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"18 Suppl 1","pages":"1-14"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698949","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":"Digital Twins: The Future of Personalized Nutrition and Health?","authors":"Mariëtte Abrahams","doi":"10.1159/000543483","DOIUrl":"10.1159/000543483","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"59-63"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414558","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":"Type 2 Diabetes Polygenic Risk Score Interactions with Lifestyle Risk Factors in Black Americans.","authors":"Ashley W Scadden, Aastha Kakar, Elizabeth M Litkowski, Mariah C Meyer, Nicole D Armstrong, Steven Buyske, Yanwei Cai, Iona Cheng, Burcu F Darst, Myriam Fornage, Mariaelisa Graff, Boya Guo, Christopher A Haiman, Heather M Highland, Charles Kooperberg, Loïc Le Marchand, Kari North, Ulrike Peters, Stephen S Rich, Jerome I Rotter, Vinodh Srinivasasainagendra, Hemant K Tiwari, Stephanie Waldrop, Kristin Young, Sridharan Raghavan, Ethan M Lange, Leslie A Lange, Marguerite R Irvin, Maggie A Stanislawski","doi":"10.1159/000546100","DOIUrl":"10.1159/000546100","url":null,"abstract":"<p><strong>Introduction: </strong>Prior work in predominantly European ancestry populations has explained how the risk associated with demographic, lifestyle, and health factors differs with underlying genetic susceptibility to type 2 diabetes (T2D), but less is known about these relationships in Black Americans.</p><p><strong>Methods: </strong>We used covariate-adjusted logistic regression models of T2D to examine interactions between a published trans-ancestry derived T2D polygenic risk score (PRS) and various demographic, lifestyle, and health-related factors among 28,251 self-identified Black Americans from six cohort studies.</p><p><strong>Results: </strong>The results are generally consistent with prior work in White populations. The PRS showed a significant interaction with body mass index, with a greater effect on T2D risk in individuals who were leaner (pinteraction = 0.038).</p><p><strong>Conclusion: </strong>These results contribute to understanding the relationship between genetics and other T2D risk factors in Black Americans who have a high burden of T2D, potentially informing targeted prevention strategies.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":" ","pages":"90-97"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199465","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":"Erratum.","authors":"","doi":"10.1159/000539272","DOIUrl":"10.1159/000539272","url":null,"abstract":"","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"17 1","pages":"41"},"PeriodicalIF":2.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065843","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}