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Li J, Wu Z, Xin S, Xu Y, Wang F, Liu Y, Wang S, Dong Y, Guo Y, Han Y, Zhao J, Gao Y, Sun M, Li B. Body mass index mediates the association between four dietary indices and phenotypic age acceleration in adults: a cross-sectional study. Food Funct 2024. [PMID: 38916856 DOI: 10.1039/d4fo01088d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background: Diet and body mass index (BMI) are widely recognized as being closely associated with aging. However, it remains unclear which dietary indices are associated with aging, and the extent to which BMI mediates the relationship between diet and aging. Therefore, this study investigates the mediating role of BMI in the association between various dietary indices and phenotypic age acceleration (PhenoAgeAccel). Methods: Data were sourced from the National Health and Nutrition Examination Survey (NHANES), using two 24 hour recall interviews to compute four dietary indices: the Dietary Inflammatory Index (DII), Healthy Eating Index-2020 (HEI-2020), Alternative Healthy Eating Index-2010 (AHEI-2010), and Composite Dietary Antioxidant Index (CDAI). Linear regression analyses and mediation analyses assessed the associations between dietary indices and PhenoAgeAccel and the mediating effects of BMI. Z-score transformations (zDII, zHEI-2020, zAHEI-2010, and zCDAI) were used to ensure comparability between different dietary indices. Results: After adjusting for covariates, the zHEI-2020, zAHEI-2010, and zCDAI were negatively associated with PhenoAgeAccel (P < 0.05), with β values being -0.36, -0.40, and -0.41, respectively. The zDII was positively associated with PhenoAgeAccel (P < 0.001) with a β value of 0.70. Mediation analyses suggested that BMI significantly mediated the relationships between these dietary indices and PhenoAgeAccel. The mediation proportions were 23.7% for zDII, 43.3% for zHEI-2020, 24.5% for zAHEI-2010, and 23.6% for zCDAI. Conclusions: This study indicates that all dietary indices and BMI were significantly associated with PhenoAgeAccel. In addition, BMI exhibited the highest mediation proportion in the relationship between HEI-2020 and PhenoAgeAccel.
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Affiliation(s)
- Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Zibo Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Sitong Xin
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yang Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Fengdan Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yan Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Sizhe Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yibo Dong
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yuangang Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yu Han
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Jing Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Yuqi Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
| | - Mengzi Sun
- The First Affiliated Hospital of Xi'an Jiaotong University, No. 69, Xiaozhai West Road, Xi'an, 710061, P. R. China
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710100, P. R. China
| | - Bo Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Xinmin Street No.1163, Changchun, 130021, P. R. China.
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2
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Föhr T, Hendrix A, Kankaanpää A, Laakkonen EK, Kujala U, Pietiläinen KH, Lehtimäki T, Kähönen M, Raitakari O, Wang X, Kaprio J, Ollikainen M, Sillanpää E. Metabolic syndrome and epigenetic aging: a twin study. Int J Obes (Lond) 2024; 48:778-787. [PMID: 38273034 PMCID: PMC11129944 DOI: 10.1038/s41366-024-01466-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/13/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND Metabolic syndrome (MetS) is associated with premature aging, but whether this association is driven by genetic or lifestyle factors remains unclear. METHODS Two independent discovery cohorts, consisting of twins and unrelated individuals, were examined (N = 268, aged 23-69 years). The findings were replicated in two cohorts from the same base population. One consisted of unrelated individuals (N = 1 564), and the other of twins (N = 293). Participants' epigenetic age, estimated using blood DNA methylation data, was determined using the epigenetic clocks GrimAge and DunedinPACE. The individual-level linear regression models for investigating the associations of MetS and its components with epigenetic aging were followed by within-twin-pair analyses using fixed-effects regression models to account for genetic factors. RESULTS In individual-level analyses, GrimAge age acceleration was higher among participants with MetS (N = 56) compared to participants without MetS (N = 212) (mean 2.078 [95% CI = 0.996,3.160] years vs. -0.549 [-1.053,-0.045] years, between-group p = 3.5E-5). Likewise, the DunedinPACE estimate was higher among the participants with MetS compared to the participants without MetS (1.032 [1.002,1.063] years/calendar year vs. 0.911 [0.896,0.927] years/calendar year, p = 4.8E-11). An adverse profile in terms of specific MetS components was associated with accelerated aging. However, adjustments for lifestyle attenuated these associations; nevertheless, for DunedinPACE, they remained statistically significant. The within-twin-pair analyses suggested that genetics explains these associations fully for GrimAge and partly for DunedinPACE. The replication analyses provided additional evidence that the association between MetS components and accelerated aging is independent of the lifestyle factors considered in this study, however, suggesting that genetics is a significant confounder in this association. CONCLUSIONS The results of this study suggests that MetS is associated with accelerated epigenetic aging, independent of physical activity, smoking or alcohol consumption, and that the association may be explained by genetics.
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Affiliation(s)
- Tiina Föhr
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland.
| | - Arne Hendrix
- Physical Activity, Sport & Health Research Group, Department of Movement Sciences, KU Leuven - University of Leuven, Leuven, Belgium
| | - Anna Kankaanpää
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland
| | - Eija K Laakkonen
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland
| | - Urho Kujala
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Healthy Weight Hub, Endocrinology, Abdominal Center, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Xiaoling Wang
- Georgia Prevention Institute (GPI), Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Elina Sillanpää
- Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland
- The Wellbeing Services County of Central Finland, Jyväskylä, Finland
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3
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Biemans Y, Bach D, Behrouzi P, Horvath S, Kramer CS, Liu S, Manson JE, Shadyab AH, Stewart J, Whitsel EA, Yang B, de Groot L, Grootswagers P. Identifying the relation between food groups and biological ageing: a data-driven approach. Age Ageing 2024; 53:ii20-ii29. [PMID: 38745494 PMCID: PMC11094402 DOI: 10.1093/ageing/afae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Heterogeneity in ageing rates drives the need for research into lifestyle secrets of successful agers. Biological age, predicted by epigenetic clocks, has been shown to be a more reliable measure of ageing than chronological age. Dietary habits are known to affect the ageing process. However, much remains to be learnt about specific dietary habits that may directly affect the biological process of ageing. OBJECTIVE To identify food groups that are directly related to biological ageing, using Copula Graphical Models. METHODS We performed a preregistered analysis of 3,990 postmenopausal women from the Women's Health Initiative, based in North America. Biological age acceleration was calculated by the epigenetic clock PhenoAge using whole-blood DNA methylation. Copula Graphical Modelling, a powerful data-driven exploratory tool, was used to examine relations between food groups and biological ageing whilst adjusting for an extensive amount of confounders. Two food group-age acceleration networks were established: one based on the MyPyramid food grouping system and another based on item-level food group data. RESULTS Intake of eggs, organ meat, sausages, cheese, legumes, starchy vegetables, added sugar and lunch meat was associated with biological age acceleration, whereas intake of peaches/nectarines/plums, poultry, nuts, discretionary oil and solid fat was associated with decelerated ageing. CONCLUSION We identified several associations between specific food groups and biological ageing. These findings pave the way for subsequent studies to ascertain causality and magnitude of these relationships, thereby improving the understanding of biological mechanisms underlying the interplay between food groups and biological ageing.
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Affiliation(s)
- Ynte Biemans
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Daimy Bach
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Pariya Behrouzi
- Biometrics, Mathematical and Statistical Methods, Wageningen University and Research, Wageningen, The Netherlands
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Charlotte S Kramer
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Simin Liu
- Departments of Medicine and Surgery, Alpert School of Medicine, Brown University, Providence, RI, USA
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - James Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Bo Yang
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI, USA
| | - Lisette de Groot
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Pol Grootswagers
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
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4
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Wang Y, Wang X, Chen Y, Zhang Y, Zhen X, Tao S, Dou J, Li P, Jiang G. Perivascular fat tissue and vascular aging: A sword and a shield. Pharmacol Res 2024; 203:107140. [PMID: 38513826 DOI: 10.1016/j.phrs.2024.107140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/16/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024]
Abstract
The understanding of the function of perivascular adipose tissue (PVAT) in vascular aging has significantly changed due to the increasing amount of information regarding its biology. Adipose tissue surrounding blood vessels is increasingly recognized as a key regulator of vascular disorders. It has significant endocrine and paracrine effects on the vasculature and is mediated by the production of a variety of bioactive chemicals. It also participates in a number of pathological regulatory processes, including oxidative stress, immunological inflammation, lipid metabolism, vasoconstriction, and dilation. Mechanisms of homeostasis and interactions between cells at the local level tightly regulate the function and secretory repertoire of PVAT, which can become dysregulated during vascular aging. The PVAT secretion group changes from being reducing inflammation and lowering cholesterol to increasing inflammation and increasing cholesterol in response to systemic or local inflammation and insulin resistance. In addition, the interaction between the PVAT and the vasculature is reciprocal, and the biological processes of PVAT are directly influenced by the pertinent indicators of vascular aging. The architectural and biological traits of PVAT, the molecular mechanism of crosstalk between PVAT and vascular aging, and the clinical correlation of vascular age-related disorders are all summarized in this review. In addition, this paper aims to elucidate and evaluate the potential benefits of therapeutically targeting PVAT in the context of mitigating vascular aging. Furthermore, it will discuss the latest advancements in technology used for targeting PVAT.
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Affiliation(s)
- Yan Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xianmin Wang
- Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine, Xinjiang 830000, China
| | - Yang Chen
- School of Traditional Chinese Medicine, Xinjiang Medical University, Xinjiang 830011, China
| | - Yuelin Zhang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xianjie Zhen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Siyu Tao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jinfang Dou
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Peng Li
- Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine, Xinjiang 830000, China
| | - Guangjian Jiang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China; School of Traditional Chinese Medicine, Xinjiang Medical University, Xinjiang 830011, China.
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5
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Miao K, Hong X, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association between epigenetic age and type 2 diabetes mellitus or glycemic traits: A longitudinal twin study. Aging Cell 2024:e14175. [PMID: 38660768 DOI: 10.1111/acel.14175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024] Open
Abstract
Epigenetic clocks based on DNA methylation have been known as biomarkers of aging, including principal component (PC) clocks representing the degree of aging and DunedinPACE representing the pace of aging. Prior studies have shown the associations between epigenetic aging and T2DM, but the results vary by epigenetic age metrics and people. This study explored the associations between epigenetic age metrics and T2DM or glycemic traits, based on 1070 twins (535 twin pairs) from the Chinese National Twin Registry. It also explored the temporal relationships of epigenetic age metrics and glycemic traits in 314 twins (157 twin pairs) who participated in baseline and follow-up visits after a mean of 4.6 years. DNA methylation data were used to calculate epigenetic age metrics, including PCGrimAge acceleration (PCGrimAA), PCPhenoAge acceleration (PCPhenoAA), DunedinPACE, and the longitudinal change rate of PCGrimAge/PCPhenoAge. Mixed-effects and cross-lagged modelling assessed the cross-sectional and temporal relationships between epigenetic age metrics and T2DM or glycemic traits, respectively. In the cross-sectional analysis, positive associations were identified between DunedinPACE and glycemic traits, as well as between PCPhenoAA and fasting plasma glucose, which may be not confounded by shared genetic factors. Cross-lagged models revealed that glycemic traits (fasting plasma glucose, HbA1c, and TyG index) preceded DunedinPACE increases, and TyG index preceded PCGrimAA increases. Glycemic traits are positively associated with epigenetic age metrics, especially DunedinPACE. Glycemic traits preceded the increases in DunedinPACE and PCGrimAA. Lowering the levels of glycemic traits may reduce DunedinPACE and PCGrimAA, thereby mitigating age-related comorbidities.
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Affiliation(s)
- Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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Wu D, Qu C, Huang P, Geng X, Zhang J, Shen Y, Rao Z, Zhao J. Better Life's Essential 8 contributes to slowing the biological aging process: a cross-sectional study based on NHANES 2007-2010 data. Front Public Health 2024; 12:1295477. [PMID: 38544722 PMCID: PMC10965682 DOI: 10.3389/fpubh.2024.1295477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 02/07/2024] [Indexed: 05/03/2024] Open
Abstract
Objective To investigate the relationship between Life's Essential 8 (LE8) and Phenotypic Age Acceleration (PhenoAgeAccel) in United States adults and to explore the impact of LE8 on phenotypic biological aging, thereby providing references for public health policies and health education. Methods Utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2010, this cross-sectional study analyzed 7,339 adults aged 20 and above. Comprehensive assessments of LE8, PhenoAgeAccel, and research covariates were achieved through the integration of Demographics Data, Dietary Data, Laboratory Data, and Questionnaire Data derived from NHANES. Weighted generalized linear regression models and restricted cubic spline plots were employed to analyze the linear and non-linear associations between LE8 and PhenoAgeAccel, along with gender subgroup analysis and interaction effect testing. Results (1) Dividing the 2007-2010 NHANES cohort into quartiles based on LE8 unveiled significant disparities in age, gender, race, body mass index, education level, marital status, poverty-income ratio, smoking and drinking statuses, diabetes, hypertension, hyperlipidemia, phenotypic age, PhenoAgeAccel, and various biological markers (p < 0.05). Mean cell volume demonstrated no intergroup differences (p > 0.05). (2) The generalized linear regression weighted models revealed a more pronounced negative correlation between higher quartiles of LE8 (Q2, Q3, and Q4) and PhenoAgeAccel compared to the lowest LE8 quartile in both crude and fully adjusted models (p < 0.05). This trend was statistically significant (p < 0.001) in the full adjustment model. Gender subgroup analysis within the fully adjusted models exhibited a significant negative relationship between LE8 and PhenoAgeAccel in both male and female participants, with trend tests demonstrating significant results (p < 0.001 for males and p = 0.001 for females). (3) Restricted cubic spline (RCS) plots elucidated no significant non-linear trends between LE8 and PhenoAgeAccel overall and in gender subgroups (p for non-linear > 0.05). (4) Interaction effect tests denoted no interaction effects between the studied stratified variables such as age, gender, race, education level, and marital status on the relationship between LE8 and PhenoAgeAccel (p for interaction > 0.05). However, body mass index and diabetes manifested interaction effects (p for interaction < 0.05), suggesting that the influence of LE8 on PhenoAgeAccel might vary depending on an individual's BMI and diabetes status. Conclusion This study, based on NHANES data from 2007-2010, has revealed a significant negative correlation between LE8 and PhenoAgeAccel, emphasizing the importance of maintaining a healthy lifestyle in slowing down the biological aging process. Despite the limitations posed by the study's design and geographical constraints, these findings provide a scientific basis for the development of public health policies focused on healthy lifestyle practices. Future research should further investigate the causal mechanisms underlying the relationship between LE8 and PhenoAgeAccel and consider cross-cultural comparisons to enhance our understanding of healthy aging.
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Affiliation(s)
- Dongzhe Wu
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
| | - Chaoyi Qu
- Physical Education College, Hebei Normal University, Shijiazhuang, China
| | - Peng Huang
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
| | - Xue Geng
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
| | | | - Yulin Shen
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
| | - Zhijian Rao
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
- College of Physical Education, Shanghai Normal University, Shanghai, China
| | - Jiexiu Zhao
- Exercise Biological Center, China Institute of Sport Science, Beijing, China
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7
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Drouard G, Wang Z, Heikkinen A, Foraster M, Julvez J, Kanninen KM, van Kamp I, Pirinen M, Ollikainen M, Kaprio J. Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.12.23299868. [PMID: 38168348 PMCID: PMC10760270 DOI: 10.1101/2023.12.12.23299868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Zhiyang Wang
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aino Heikkinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Maria Foraster
- PHAGEX Research Group, Blanquerna School of Health Science, Universitat Ramon Llull (URL), Barcelona, Spain
| | - Jordi Julvez
- Clinical and epidemiological Neuroscience (NeuroÈpia), Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- ISGlobal, Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Spain
| | - Katja M. Kanninen
- A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Irene van Kamp
- National Institute for Public Health and the Environment, centre for Sustainability, Environment and Health, Netherlands
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Kuiper LM, Polinder-Bos HA, Bizzarri D, Vojinovic D, Vallerga CL, Beekman M, Dollé MET, Ghanbari M, Voortman T, Reinders MJT, Verschuren WMM, Slagboom PE, van den Akker EB, van Meurs JBJ. Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk. J Gerontol A Biol Sci Med Sci 2023; 78:1753-1762. [PMID: 37303208 PMCID: PMC10562890 DOI: 10.1093/gerona/glad137] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Indexed: 06/13/2023] Open
Abstract
Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.
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Affiliation(s)
- Lieke M Kuiper
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
| | | | - Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Dina Vojinovic
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Martijn E T Dollé
- Center for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Marcel J T Reinders
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - W M Monique Verschuren
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care Utrecht, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Erik B van den Akker
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Orthopaedics and Sports Medicine, Erasmus MC, Rotterdam, The Netherlands
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Freilich CD. How does loneliness "get under the skin" to become biologically embedded? BIODEMOGRAPHY AND SOCIAL BIOLOGY 2023; 68:115-148. [PMID: 37800557 PMCID: PMC10843517 DOI: 10.1080/19485565.2023.2260742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Loneliness is linked to declining physical health across cardiovascular, inflammatory, metabolic, and cognitive domains. As a result, loneliness is increasingly being recognized as a public health threat, though the mechanisms that have been studied do not yet explain all loneliness-related health risk. Potential mechanisms include loneliness having 1.) direct, causal impacts on health, possibly maintained by epigenetic modification, 2.) indirect effects mediated through health-limiting behaviors, and 3.) artifactual associations perhaps related to genetic overlap and reverse causation. In this scoping review, we examine the evidence surrounding each of these pathways, with a particular emphasis on emerging research on epigenetic effects, in order to evaluate how loneliness becomes biologically embedded. We conclude that there are significant gaps in our knowledge of how psychosocial stress may lead to physiological changes, so more work is needed to understand if, how, and when loneliness has a direct influence on health. Hypothalamic-pituitary adrenocortical axis disruptions that lead to changes in gene expression through methylation and the activity of transcription factor proteins are one promising area of research but are confounded by a number of unmeasured factors. Therefore, wok is needed using causally informative designs, such as twin and family studies and intensively longitudinal diary studies.
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Zhang B, Yuan Q, Luan Y, Xia J. Effect of women's fertility and sexual development on epigenetic clock: Mendelian randomization study. Clin Epigenetics 2023; 15:154. [PMID: 37770973 PMCID: PMC10540426 DOI: 10.1186/s13148-023-01572-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/25/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND AND OBJECTIVES In observational studies, women's fertility and sexual development traits may have implications for DNA methylation patterns, and pregnancy-related risk factors can also affect maternal DNA methylation patterns. The aim of our study is to disentangle any potential causal associations between women's fertility and sexual development traits and epigenetic clocks, as well as to search for probable mediators by using the Mendelian randomization (MR) method. METHODS Instrumental variables for exposures, mediators, and outcomes were adopted from genome-wide association studies data of European ancestry individuals. The potential causal relationship between women's fertility and sexual development traits and four epigenetic clocks were evaluated by inverse variance weighted method and verified by other two methods. Furthermore, we employed multivariable MR (MVMR) adjusting for hypertension, hyperglycemia, BMI changes, and insomnia. Then, combining the MVMR results and previous research, we performed two-step MR to explore the mediating effects of BMI, AFS, and AFB. Multiple sensitivity analyses were further performed to verify the robustness of our findings. RESULTS Leveraging two-sample MR analysis, we observed statistically significant associations between earlier age at first birth (AFB) with a higher HannumAge, PhenoAge and GrimAge acceleration(β = - 0.429, 95% CI [- 0.781 to - 0.077], p = 0.017 for HannumAge; β = - 0.571, 95% CI [- 1.006 to - 0.136], p = 0.010 for PhenoAge, and β = - 1.136, 95% CI [- 1.508 to - 0.765], p = 2.03E-09 for GrimAge respectively) and age at first sexual intercourse (AFS) with a higher HannumAge and GrimAge acceleration(β = - 0.175, 95% CI [- 0.336 to - 0.014], p = 0.033 for HannumAge; β = - 0.210, 95% CI [- 0.350 to - 0.070], p = 0.003 for GrimAge, respectively). Further analyses indicated that BMI, AFB and AFS played mediator roles in the path from women's fertility and sexual development traits to epigenetic aging. CONCLUSIONS Our study suggested that AFS and AFB are associated with epigenetic aging. These findings may prove valuable in informing the development of prevention strategies and interventions targeted towards women's fertility and sexual development experiences and their relationship with epigenetic aging-related diseases.
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Affiliation(s)
- Boxin Zhang
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road of Kaifu District, Changsha, 410008, China
- Hunan Clinical Research Center for Cerebrovascular Disease, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qizhi Yuan
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road of Kaifu District, Changsha, 410008, China
- Hunan Clinical Research Center for Cerebrovascular Disease, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yining Luan
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road of Kaifu District, Changsha, 410008, China
- Hunan Clinical Research Center for Cerebrovascular Disease, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Xia
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road of Kaifu District, Changsha, 410008, China.
- Hunan Clinical Research Center for Cerebrovascular Disease, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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11
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Zhang Y, Yang H, Yang Z, Li X, Liu Z, Bai Y, Qian G, Wu H, Li J, Guo Y, Yang S, Chen L, Yang J, Han J, Ma S, Yang J, Yu L, Shui R, Jin X, Wang H, Zhang F, Chen T, Li X, Zong X, Liu L, Fan J, Wang W, Zhang Y, Shi G, Wang D, Tao S. Could long-term dialysis vintage and abnormal calcium, phosphorus and iPTH control accelerate aging among the maintenance hemodialysis population? Ren Fail 2023; 45:2250457. [PMID: 37724516 PMCID: PMC10512754 DOI: 10.1080/0886022x.2023.2250457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/16/2023] [Indexed: 09/21/2023] Open
Abstract
OBJECTIVE Aging is a complex process of physiological dysregulation of the body system and is common in hemodialysis patients. However, limited studies have investigated the links between dialysis vintage, calcium, phosphorus, and iPTH control and aging. The purpose of the current study was to examine these associations. METHODS During 2020, a cross-sectional study was conducted in 3025 hemodialysis patients from 27 centers in Anhui Province, China. Biological age was calculated by a formula using chronological age and clinical indicators. The absence of the target range for serum phosphorus (0.87-1.45 mmol/L), corrected calcium (2.1-2.5 mmol/L) and iPTH (130-585 pg/mL) were identified as abnormal calcium, phosphorus, and iPTH control. RESULTS A total of 1131 hemodialysis patients were included, 59.2% of whom were males (669/1131). The mean (standard deviation) of actual age and biological age were 56.07 (12.79) years and 66.94 (25.88), respectively. The median of dialysis vintage was 4.3 years. After adjusting for the confounders, linear regression models showed patients with abnormal calcium, phosphorus, and iPTH control and on hemodialysis for less than 4.3 years (B = 0.211, p = .002) or on hemodialysis for 4.3 years or more (B = 0.302, p < .001), patients with normal calcium, phosphorus, and iPTH control and on hemodialysis for 4.3 years or more (B = 0.087, p = .013) had a higher biological age. CONCLUSION Our findings support the hypothesis that long-term hemodialysis and abnormal calcium, phosphorus, and iPTH control may accelerate aging in the hemodialysis population. Further studies are warrant to verify the significance of maintaining normal calcium-phosphorus metabolism in aging.
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Affiliation(s)
- Yingxin Zhang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Huan Yang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhengling Yang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiuyong Li
- Blood Purification Center, NO.2 People’s Hospital of Fuyang City, Fuyang, China
| | - Zhi Liu
- Department of Nephrology, The First Affiliated Hospital of Anhui University of Science & Technology, Hefei, Anhui, China
| | - Youwei Bai
- Department of Nephrology, The Second People’s Hospital of Lu’an City, Lu’an, China
| | - Guangrong Qian
- Department of Nephrology, Maanshan People’s Hospital, Maanshan, Anhui, China
| | - Han Wu
- Blood Purification Center, Bozhou People’s Hospital, Bozhou, Anhui, China
| | - Ji Li
- Department of Nephrology, Tongling People’s Hospital, Tongling, China
| | - Yuwen Guo
- Department of Nephrology, Lujiang County People’s Hospital, Lucheng, China
| | - Shanfei Yang
- Department of Nephrology, Shouxian County Hospital, Suzhou, China
| | - Lei Chen
- Department of Nephrology, Hefei Jinnan Kidney Hospital, Hefei, China
| | - Jian Yang
- Department of Nephrology, Funan County People’s Hospital, Funan County, China
| | - Jiuhuai Han
- Department of Nephrology, Anqing Municipal Hospital, Anqing, China
| | - Shengyin Ma
- Department of Nephrology, Anhui Wanbei Coal-Electricity Group General Hospital, Suzhou, China
| | - Jing Yang
- Department of Nephrology, The First People’s Hospital of Hefei, Hefei, China
| | - Linfei Yu
- Department of Nephrology, The People’s Hospital of Taihu, Taihu County, China
| | - Runzhi Shui
- Blood Purification Center, Huangshan City People’s Hospital, Fuyang, China
| | - Xiping Jin
- Department of Nephrology, Huainan Chao Yang Hospital, Huainan, China
| | - Hongyu Wang
- Department of Nephrology, Lixin County People’s Hospital, Lixin County, China
| | - Fan Zhang
- Department of Nephrology, Dongzhi County People’s Hospital, Dongzhi County, China
| | - Tianhao Chen
- Department of Nephrology, Tianchang City People’s Hospital, Tianchang, China
| | - Xinke Li
- Department of Nephrology, Xiaoxian People’s Hospital, Xiaoxian County, China
| | - Xiaoying Zong
- Department of Nephrology, The Second Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Li Liu
- Department of Nephrology, The Second People’s Hospital of Hefei, Hefei, China
| | - Jihui Fan
- Department of Nephrology, Huaibei People’s Hospital, Huaibei, China
| | - Wei Wang
- Department of Nephrology, The People’s Hospital of Xuancheng City, Xuancheng, China
| | - Yong Zhang
- Department of Nephrology, Lujiang County Hospital of TCM, Lujiang, China
| | - Guangcai Shi
- Department of Nephrology, The Fifth People’s Hospital of Hefei, Hefei, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shuman Tao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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12
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Pan X, Yue L, Ren L, Ban J, Chen S. Triglyceride-glucose index and cervical vascular function: outpatient-based cohort study. BMC Endocr Disord 2023; 23:191. [PMID: 37684683 PMCID: PMC10486014 DOI: 10.1186/s12902-023-01449-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/04/2023] [Indexed: 09/10/2023] Open
Abstract
OBJECTIVES The purpose of this study was to investigate the correlation between triglyceride-glucose (TyG) index and cervical vascular function parameters in the general population without cerebrovascular disease. MATERIALS AND METHODS This was a cross-sectional study that recruited a total of 1996 participants without cerebrovascular disease. TyG index was calculated based on fasting triglycerides and glucose. All patients were divided into two groups based on the median TyG index: the high TyG group and the low TyG group. The differences in basic clinical characteristics and neck vascular function parameters between the two groups of participants were compared, and then the correlation between TyG index and neck vascular function parameters was investigated. RESULTS Participants with a high TyG index had lower systolic, diastolic, and mean flow velocities in the basilar, vertebral, and internal carotid arteries compared with those with a low TyG index. Participants with a high TyG index had higher pulsatility index in the left vertebral artery and right internal carotid artery, but this difference was not observed in the basilar artery. In addition, TyG index was significantly negatively correlated with systolic, diastolic, and mean flow velocities in the basilar, vertebral, and internal carotid arteries, and the correlation remained after adjusting for confounding factors. CONCLUSION In the general population, there was a well-defined correlation between TyG index and cervical vascular function parameters, and increased TyG index was independently associated with reduced cervical vascular blood flow velocity.
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Affiliation(s)
- Xiaoyu Pan
- Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Lin Yue
- Department of Endocrinology, The Third Hospital of Shijiazhuang, Shijiazhuang, Hebei, China
| | - Lin Ren
- Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Jiangli Ban
- Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Shuchun Chen
- Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei, China.
- Department of Endocrinology, Hebei General Hospital, Shijiazhuang, Hebei, China.
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13
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Bourdon C, Etain B, Spano L, Belzeaux R, Leboyer M, Delahaye-Duriez A, Ibrahim EC, Lutz PE, Gard S, Schwan R, Polosan M, Courtet P, Passerieux C, Bellivier F, Marie-Claire C. Accelerated aging in bipolar disorders: An exploratory study of six epigenetic clocks. Psychiatry Res 2023; 327:115373. [PMID: 37542794 DOI: 10.1016/j.psychres.2023.115373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 08/07/2023]
Abstract
Bipolar disorder (BD) is a chronic and severe psychiatric disorder associated with significant medical morbidity and reduced life expectancy. In this study, we assessed accelerated epigenetic aging in individuals with BD using various DNA methylation (DNAm)-based markers. For this purpose, we used five epigenetic clocks (Horvath, Hannum, EN, PhenoAge, and GrimAge) and a DNAm-based telomere length clock (DNAmTL). DNAm profiles were obtained using Infinium MethylationEPIC Arrays from whole-blood samples of 184 individuals with BD. We also estimated blood cell counts based on DNAm levels for adjustment. Significant correlations between chronological age and each epigenetic age estimated using the six different clocks were observed. Following adjustment for blood cell counts, we found that the six epigenetic AgeAccels (age accelerations) were significantly associated with the body mass index. GrimAge AgeAccel was significantly associated with male sex, smoking status and childhood maltreatment. DNAmTL AgeAccel was significantly associated with smoking status. Overall, this study showed that distinct epigenetic clocks are sensitive to different aspects of aging process in BD. Further investigations with comprehensive epigenetic clock analyses and large samples are required to confirm our findings of potential determinants of an accelerated epigenetic aging in BD.
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Affiliation(s)
- Céline Bourdon
- Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, F-75006 Paris, France.
| | - Bruno Etain
- Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, F-75006 Paris, France; Département de Psychiatrie et de Médecine Addictologique, Hôpitaux Lariboisière-Fernand Widal, GHU APHP.Nord - Université de Paris, Paris, F-75010, France; Fondation Fondamental, F-94010, Créteil, France
| | - Luana Spano
- Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, F-75006 Paris, France
| | - Raoul Belzeaux
- Pôle Universitaire de Psychiatrie, CHU de Montpellier, France; Pôle de Psychiatrie, Assistance Publique Hôpitaux de Marseille, INT-UMR7289, CNRS Aix-Marseille Université, Marseille, France; Université Paris Est Créteil, INSERM U955, IMRB, Translational Neuro-Psychiatry, Créteil, France
| | - Marion Leboyer
- Fondation Fondamental, F-94010, Créteil, France; Université Paris Est Créteil, INSERM U955, IMRB, Translational Neuro-Psychiatry, Créteil, France; AP-HP, Hôpitaux Universitaires Henri Mondor, Département Médico-Universitaire de Psychiatrie et d'Addictologie (DMU IMPACT), Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), Créteil, France
| | | | - El Chérif Ibrahim
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, 13005 Marseille, France
| | - Pierre-Eric Lutz
- Centre National de la Recherche Scientifique, Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, F-67000 Strasbourg, France
| | - Sébastien Gard
- Fondation Fondamental, F-94010, Créteil, France; Pôle de Psychiatrie Générale et Universitaire, Centre Hospitalier Charles Perrens, Bordeaux, France
| | - Raymund Schwan
- Fondation Fondamental, F-94010, Créteil, France; Université de Lorraine, Centre Psychothérapique de Nancy, Inserm U1254, Nancy, France
| | - Mircea Polosan
- Fondation Fondamental, F-94010, Créteil, France; Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble, Institut Neurosciences, Grenoble, France
| | - Philippe Courtet
- Fondation Fondamental, F-94010, Créteil, France; IGF, Univ. Montpellier France, CNRS, INSERM, Montpellier, France; Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Christine Passerieux
- Fondation Fondamental, F-94010, Créteil, France; Centre Hospitalier de Versailles, Service Universitaire de Psychiatrie d'adulte et d'addictologie, Le Chesnay, France; DisAP-DevPsy-CESP, INSERM UMR1018, Université de Versailles Saint-Quentin-En-Yvelines, Université Paris-Saclay, Villejuif, France
| | - Frank Bellivier
- Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, F-75006 Paris, France; Département de Psychiatrie et de Médecine Addictologique, Hôpitaux Lariboisière-Fernand Widal, GHU APHP.Nord - Université de Paris, Paris, F-75010, France; Fondation Fondamental, F-94010, Créteil, France
| | - Cynthia Marie-Claire
- Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, F-75006 Paris, France
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Holloway TD, Harvanek ZM, Xu K, Gordon DM, Sinha R. Greater stress and trauma mediate race-related differences in epigenetic age between Black and White young adults in a community sample. Neurobiol Stress 2023; 26:100557. [PMID: 37501940 PMCID: PMC10369475 DOI: 10.1016/j.ynstr.2023.100557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/29/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023] Open
Abstract
Black Americans suffer lower life expectancy and show signs of accelerated aging compared to other Americans. While previous studies observe these differences in children and populations with chronic illness, whether these pathologic processes exist or how these pathologic processes progress has yet to be explored prior to the onset of significant chronic illness, within a young adult population. Therefore, we investigated race-related differences in epigenetic age in a cross-sectional sample of young putatively healthy adults and assessed whether lifetime stress and/or trauma mediate those differences. Biological and psychological data were collected from self-reported healthy adult volunteers within the local New Haven area (399 volunteers, 19.8% Black, mean age: 29.28). Stress and trauma data was collected using the Cumulative Adversity Inventory (CAI) interview, which assessed specific types of stressors, including major life events, traumatic events, work, financial, relationship and chronic stressors cumulatively over time. GrimAge Acceleration (GAA), determined from whole blood collected from participants, measured epigenetic age. In order to understand the impact of stress and trauma on GAA, exploratory mediation analyses were then used. We found cumulative stressors across all types of events (mean difference of 6.9 p = 2.14e-4) and GAA (β = 2.29 years [1.57-3.01, p = 9.70e-10] for race, partial η2 = 0.091, model adjusted R2 = 0.242) were significantly greater in Black compared to White participants. Critically, CAI total score (proportion mediated: 0.185 [0.073-0.34, p = 6e-4]) significantly mediated the relationship between race and GAA. Further analysis attributed this difference to more traumatic events, particularly assaultive traumas and death of loved ones. Our results suggest that, prior to development of significant chronic disease, Black individuals have increased epigenetic age compared to White participants and that increased cumulative stress and traumatic events may contribute significantly to this epigenetic aging difference.
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Affiliation(s)
| | - Zachary M. Harvanek
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Yale Stress Center, Yale University, New Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Psychiatry, Connecticut Veteran Healthcare System, West Haven, CT, USA
| | | | - Rajita Sinha
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Yale Stress Center, Yale University, New Haven, CT, USA
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15
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Valencia CI, Saunders D, Daw J, Vasquez A. DNA methylation accelerated age as captured by epigenetic clocks influences breast cancer risk. Front Oncol 2023; 13:1150731. [PMID: 37007096 PMCID: PMC10050548 DOI: 10.3389/fonc.2023.1150731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Breast cancer continues to be the leading form of cancer among women in the United States. Additionally, disparities across the breast cancer continuum continue to increase for women of historically marginalized populations. The mechanism driving these trends are unclear, however, accelerated biological age may provide key insights into better understanding these disease patterns. Accelerated age measured by DNA methylation using epigenetic clocks is to date the most robust method for estimating accelerated age. Here we synthesize the existing evidence on epigenetic clocks measurement of DNA methylation based accelerated age and breast cancer outcomes. Methods Our database searches were conducted from January 2022 to April 2022 and yielded a total of 2,908 articles for consideration. We implemented methods derived from guidance of the PROSPERO Scoping Review Protocol to assess articles in the PubMed database on epigenetic clocks and breast cancer risk. Results Five articles were deemed appropriate for inclusion in this review. Ten epigenetic clocks were used across the five articles demonstrating statistically significant results for breast cancer risk. DNA methylation accelerated age varied by sample type. The studies did not consider social factors or epidemiological risk factors. The studies lacked representation of ancestrally diverse populations. Discussion DNA methylation based accelerated age as captured by epigenetic clocks has a statistically significant associative relationship with breast cancer risk, however, important social factors that contribute to patterns of methylation were not comprehensively considered in the available literature. More research is needed on DNA methylation based accelerated age across the lifespan including during menopausal transition and in diverse populations. This review demonstrates that DNA methylation accelerated age may provide key insights for tackling increasing rates of U.S. breast cancer incidence and overall disease disparities experienced by women from minoritized backgrounds.
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Affiliation(s)
- Celina I. Valencia
- Department of Family and Community Medicine, College of Medicine—Tucson, University of Arizona, Tucson, AZ, United States
| | - Devin Saunders
- Department of Nutritional Sciences, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, United States
| | - Jennifer Daw
- Cancer Biology Program, College of Medicine, University of Arizona, Tucson, AZ, United States
| | - Adria Vasquez
- Department of Health Sciences, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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Kankaanpää A, Tolvanen A, Heikkinen A, Kaprio J, Ollikainen M, Sillanpää E. The role of adolescent lifestyle habits in biological aging: A prospective twin study. eLife 2022; 11:80729. [PMID: 36345722 PMCID: PMC9642990 DOI: 10.7554/elife.80729] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/01/2022] [Indexed: 11/09/2022] Open
Abstract
Background: Adolescence is a stage of fast growth and development. Exposures during puberty can have long-term effects on health in later life. This study aims to investigate the role of adolescent lifestyle in biological aging. Methods: The study participants originated from the longitudinal FinnTwin12 study (n = 5114). Adolescent lifestyle-related factors, including body mass index (BMI), leisure-time physical activity, smoking, and alcohol use, were based on self-reports and measured at ages 12, 14, and 17 years. For a subsample, blood-based DNA methylation (DNAm) was used to assess biological aging with six epigenetic aging measures in young adulthood (21–25 years, n = 824). A latent class analysis was conducted to identify patterns of lifestyle behaviors in adolescence, and differences between the subgroups in later biological aging were studied. Genetic and environmental influences on biological aging shared with lifestyle behavior patterns were estimated using quantitative genetic modeling. Results: We identified five subgroups of participants with different adolescent lifestyle behavior patterns. When DNAm GrimAge, DunedinPoAm, and DunedinPACE estimators were used, the class with the unhealthiest lifestyle and the class of participants with high BMI were biologically older than the classes with healthier lifestyle habits. The differences in lifestyle-related factors were maintained into young adulthood. Most of the variation in biological aging shared with adolescent lifestyle was explained by common genetic factors. Conclusions: These findings suggest that an unhealthy lifestyle during pubertal years is associated with accelerated biological aging in young adulthood. Genetic pleiotropy may largely explain the observed associations. Funding: This work was supported by the Academy of Finland (213506, 265240, 263278, 312073 to J.K., 297908 to M.O. and 341750, 346509 to E.S.), EC FP5 GenomEUtwin (J.K.), National Institutes of Health/National Heart, Lung, and Blood Institute (grant HL104125), EC MC ITN Project EPITRAIN (J.K. and M.O.), the University of Helsinki Research Funds (M.O.), Sigrid Juselius Foundation (J.K. and M.O.), Yrjö Jahnsson Foundation (6868), Juho Vainio Foundation (E.S.) and Päivikki and Sakari Sohlberg foundation (E.S.). For most animals, events that occur early in life can have a lasting impact on individuals’ health. In humans, adolescence is a particularly vulnerable time when rapid growth and development collide with growing independence and experimentation. An unhealthy lifestyle during this period of rapid cell growth can contribute to later health problems like heart disease, lung disease, and premature death. This is due partly to accelerated biological aging, where the body deteriorates faster than what would be expected for an individual’s chronological age. One way to track the effects of lifestyle on biological aging is by measuring epigenetic changes. Epigenetic changes consist on adding or removing chemical ‘tags’ on genes. These tags can switch the genes on or off without changing their sequences. Scientists can measure certain epigenetic changes by measuring the levels of methylated DNA – DNA with a chemical ‘tag’ known as a methyl group – in blood samples. Several algorithms – known as ‘epigenetic clocks’ – are available that estimate how fast an individual is aging biologically based on DNA methylation. Kankaanpää et al. show that unhealthy lifestyles during adolescence may lead to accelerated aging in early adulthood. For their analysis, Kankaanpää et al. used data on the levels of DNA methylation in blood samples from 824 twins between 21 and 25 years old. The twins were participants in the FinnTwin12 study and had completed a survey about their lifestyles at ages 12, 14, and 17. Kankaanpää et al. classified individuals into five groups depending on their lifestyles. The first three groups, which included most of the twins, contained individuals that led relatively healthy lives. The fourth group contained individuals with a higher body mass index based on their height and weight. Finally, the last group included individuals with unhealthy lifestyles who binge drank, smoked and did not exercise. After estimating the biological ages for all of the participants, Kankaanpää et al. found that both the individuals with higher body mass indices and those in the group with unhealthy lifestyles aged faster than those who reported healthier lifestyles. However, the results varied depending on which epigenetic clock Kankaanpää et al. used to measure biological aging: clocks that had been developed earlier showed fewer differences in aging between groups; while newer clocks consistently found that individuals in the higher body mass index and unhealthy groups were older. Kankaanpää et al. also showed that shared genetic factors explained both unhealthy lifestyles and accelerated biological aging. The experiments performed by Kankaanpää et al. provide new insights into the vital role of an individual’s genetics in unhealthy lifestyles and cellular aging. These insights might help scientists identify at risk individuals early in life and try to prevent accelerated aging.
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Affiliation(s)
- Anna Kankaanpää
- Gerontology Research Center (GEREC), Faculty of Sport and Health Sciences, University of Jyväskylä
| | - Asko Tolvanen
- Methodology Center for Human Sciences, University of Jyväskylä
| | - Aino Heikkinen
- Institute for Molecular Medicine Finland (FIMM), HiLife, University of Helsinki
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLife, University of Helsinki
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), HiLife, University of Helsinki
| | - Elina Sillanpää
- Gerontology Research Center (GEREC), Faculty of Sport and Health Sciences, University of Jyväskylä
- Institute for Molecular Medicine Finland (FIMM), HiLife, University of Helsinki
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