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Qian T, Zhang J, Liu J, Wu J, Ruan Z, Shi W, Fan Y, Ye D, Fang X. Associations of phthalates with accelerated aging and the mitigating role of physical activity. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 278:116438. [PMID: 38744065 DOI: 10.1016/j.ecoenv.2024.116438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/06/2024] [Accepted: 05/03/2024] [Indexed: 05/16/2024]
Abstract
Phthalates are positioned as potential risk factors for health-related diseases. However, the effects of exposure to phthalates on accelerated aging and the potential modifications of physical activity remain unclear. A total of 2317 participants containing complete study-related information from the National Health and Nutrition Examination Survey 2007-2010 were included in the current study. We used two indicators, the Klemera-Doubal method biological age acceleration (BioAgeAccel) and phenotypic age acceleration (PhenoAgeAccel), to assess the accelerated aging status of the subjects. Multiple linear regression (single pollutant models), weighted quantile sum (WQS) regression, Quantile g-computation, and Bayesian kernel machine regression (BKMR) models were utilized to explore the associations between urinary phthalate metabolites and accelerated aging. Three groups of physical activity with different intensities were used to evaluate the modifying effects on the above associations. Results indicated that most phthalate metabolites were significantly associated with BioAgeAccel and PhenoAgeAccel, with effect values (β) ranging from 0.16 to 0.21 and 0.16-0.37, respectively. The WQS indices were positively associated with BioAgeAccel (0.33, 95% CI: 0.11, 0.54) and PhenoAgeAccel (0.50, 95% CI: 0.19, 0.82). Quantile g-computation indicated that phthalate mixtures were associated with accelerated aging, with effect values of 0.15 (95% CI: 0.02, 0.28) for BioAgeAccel and 0.39 (95% CI: 0.12, 0.67) for PhenoAgeAccel respectively. The BKMR models indicated a significant positive association between the concentrations of urinary phthalate mixtures with the two indicators. In addition, we found that most phthalate metabolites showed the strongest effects on accelerated aging in the no physical activity group and that the effects decreased gradually with increasing levels of physical activity (P < 0.05 for trend). Similar results were also observed in the mixed exposure models (WQS and Quantile g-computation). This study indicates that phthalates exposure is associated with accelerated aging, while physical activity may be a crucial barrier against phthalates exposure-related aging.
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Affiliation(s)
- Tingting Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China
| | - Jie Zhang
- School of Public Health, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Key Laboratory of Industrial Dust Prevention and Control, Occupational Health and Safety, Ministry of Education, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Anhui Institute of Occupational Safety and Health, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Joint Research Center of Occupational Medicine and Health, Institute of Grand Health, Hefei Comprehensive National Science Center, Anhui University of Science and Technology, Hefei, Anhui 231131, China
| | - Jintao Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China
| | - Jingwei Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China
| | - Zhaohui Ruan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China
| | - Wenru Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China
| | - Yinguang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China.
| | - Dongqing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; School of Public Health, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China; Key Laboratory of Industrial Dust Prevention and Control, Occupational Health and Safety, Ministry of Education, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Anhui Institute of Occupational Safety and Health, Anhui University of Science and Technology, Hefei, Anhui 231131, China; Joint Research Center of Occupational Medicine and Health, Institute of Grand Health, Hefei Comprehensive National Science Center, Anhui University of Science and Technology, Hefei, Anhui 231131, China.
| | - Xinyu Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230032, China.
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Zhao C, Yang Y, Wang Y, Jia X, Fan J, Wang N, Bo Y, Shi X. Combined effects of genetic predisposition and sleep quality on acceleration of biological ageing: Findings from the UK biobank cohort. Arch Gerontol Geriatr 2024; 126:105525. [PMID: 38896974 DOI: 10.1016/j.archger.2024.105525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE Genetic risks can accelerate ageing, yet better quality sleep may slow down it. We thus examined the interaction and combined effects of genetic predisposition and sleep quality on the risk of accelerate aging. METHODS This study included 407,027 participants from the UK Biobank. Sleep index of each participant was retrieved from the following seven sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, nap and difficulties in getting up. The biological age (PhenoAge) were estimated by corresponding algorithms based on clinical traits, and their residual discrepancies with chronological age were defined as the age accelerations (PhenoAgeaccel). We explored the interaction and combined effects of genetic risk and sleep quality on accelerated ageing by constructing a linear model. RESULTS Compared with participants in low sleep quality group, those in medium and high sleep quality group decreased 0.727 (95%CI, 0.653 to 0.801) and 1.056 (95%CI, 0.982 to 1.130) years of PhenoAgeaccel, respectively. Compared with participants in low genetic risk group, those in medium and high genetic risk group increased 0.833 (95%CI, 0.792 to 0.874) and 1.543 (95%CI, 1.494 to 1.592) years of PhenoAgeaccel, respectively. There was interaction between the genetic risk and sleep quality (P-interaction<0.001). For combined effect, compared to the group with high sleep quality and lower genetic risk, people with low sleep quality and high genetic risk had 2.747 (95%CI, 2.602 to 2.892) years higher PhenoAgeaccel. CONCLUSION Our findings elucidate that better sleep quality could lessen accelerated biological ageing especially among population with high genetic risk.
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Affiliation(s)
- Chenyu Zhao
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Yongli Yang
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Yuping Wang
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Xiaocan Jia
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Jingwen Fan
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Nana Wang
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China
| | - Yacong Bo
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China.
| | - Xuezhong Shi
- College of Public Health, Zhengzhou University, No.100 Science Avenue, Zhengzhou, Henan, 450001, China.
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Sehgal R, Markov Y, Qin C, Meer M, Hadley C, Shadyab AH, Casanova R, Manson JE, Bhatti P, Crimmins EM, Hägg S, Assimes TL, Whitsel EA, Higgins-Chen AT, Levine M. Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.13.548904. [PMID: 37503069 PMCID: PMC10370047 DOI: 10.1101/2023.07.13.548904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.
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Liang R, Fan L, Lai X, Shi D, Wang H, Shi W, Liu W, Yu L, Song J, Wang B. Air pollution exposure, accelerated biological aging, and increased thyroid dysfunction risk: Evidence from a nationwide prospective study. ENVIRONMENT INTERNATIONAL 2024; 188:108773. [PMID: 38810493 DOI: 10.1016/j.envint.2024.108773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Long-term air pollution exposure is a major health concern, yet its associations with thyroid dysfunction (hyperthyroidism and hypothyroidism) and biological aging remain unclear. We aimed to determine the association of long-term air pollution exposure with thyroid dysfunction and to investigate the potential roles of biological aging. METHODS A prospective cohort study was conducted on 432,340 participants with available data on air pollutants including particulate matter (PM2.5, PM10, and PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) from the UK Biobank. An air pollution score was calculated using principal component analysis to reflect joint exposure to these pollutants. Biological aging was assessed using the Klemera-Doubal method biological age and the phenotypic age algorithms. The associations of individual and joint air pollutants with thyroid dysfunction were estimated using the Cox proportional hazards regression model. The roles of biological aging were explored using interaction and mediation analyses. RESULTS During a median follow-up of 12.41 years, 1,721 (0.40 %) and 9,296 (2.15 %) participants developed hyperthyroidism and hypothyroidism, respectively. All air pollutants were observed to be significantly associated with an increased risk of incident hypothyroidism, while PM2.5, PM10, and NO2 were observed to be significantly associated with an increased risk of incident hyperthyroidism. The hazard ratios (HRs) for hyperthyroidism and hypothyroidism were 1.15 (95 % confidence interval: 1.00-1.32) and 1.15 (1.08-1.22) for individuals in the highest quartile compared with those in the lowest quartile of air pollution score, respectively. Additionally, we noticed that individuals with higher pollutant levels and biologically older generally had a higher risk of incident thyroid dysfunction. Moreover, accelerated biological aging partially mediated 1.9 %-9.4 % of air pollution-associated thyroid dysfunction. CONCLUSIONS Despite the possible underestimation of incident thyroid dysfunction, long-term air pollution exposure may increase the risk of incident thyroid dysfunction, particularly in biologically older participants, with biological aging potentially involved in the mechanisms.
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Affiliation(s)
- Ruyi Liang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lieyang Fan
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xuefeng Lai
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Da Shi
- Agricultural, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, Alberta T6G 2P5, Canada
| | - Hao Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wendi Shi
- Lucy Cavendish College, University of Cambridge, Cambridge CB3 0BU, UK
| | - Wei Liu
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Linling Yu
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jiahao Song
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Bin Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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Kuo CL, Chen Z, Liu P, Pilling LC, Atkins JL, Fortinsky RH, Kuchel GA, Diniz BS. Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults. Aging Cell 2024:e14195. [PMID: 38747160 DOI: 10.1111/acel.14195] [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: 01/20/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/28/2024] Open
Abstract
Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
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Affiliation(s)
- Chia-Ling Kuo
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, Connecticut, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Zhiduo Chen
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Peiran Liu
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Luke C Pilling
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Janice L Atkins
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Richard H Fortinsky
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Breno S Diniz
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
- Department of Psychiatry, University of Connecticut Health Center, Farmington, Connecticut, USA
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Cui F, Tang L, Li D, Ma Y, Wang J, Xie J, Su B, Tian Y, Zheng X. Early-life exposure to tobacco, genetic susceptibility, and accelerated biological aging in adulthood. SCIENCE ADVANCES 2024; 10:eadl3747. [PMID: 38701212 PMCID: PMC11068008 DOI: 10.1126/sciadv.adl3747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 04/03/2024] [Indexed: 05/05/2024]
Abstract
Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high-polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.
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Affiliation(s)
- Feipeng Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Linxi Tang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Dankang Li
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Yudiyang Ma
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Jianing Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Junqing Xie
- Center for Statistics in Medicine, NDORMS, University of Oxford, The Botnar Research Centre, Oxford, UK
| | - Binbin Su
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, No. 31, Beijige-3, Dongcheng District, Beijing 100730, PR China
| | - Yaohua Tian
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, Hubei, PR China
| | - Xiaoying Zheng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, No. 31, Beijige-3, Dongcheng District, Beijing 100730, PR China
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Li S, Wen C, Bai X, Yang D. Association between biological aging and periodontitis using NHANES 2009-2014 and mendelian randomization. Sci Rep 2024; 14:10089. [PMID: 38698209 PMCID: PMC11065868 DOI: 10.1038/s41598-024-61002-9] [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: 11/10/2023] [Accepted: 04/30/2024] [Indexed: 05/05/2024] Open
Abstract
Aging is a recognized risk factor for periodontitis, while biological aging could provide more accurate insights into an individual's functional status. This study aimed to investigate the potential association between biological aging and periodontitis. Epidemiological data from 9803 participants in the 2009-2014 National Health and Nutrition Examination Survey were analyzed at a cross-sectional level to assess this link. Three biological ages [Klemera-Doubal method (KDM), PhenoAge, and homeostatic dysregulation (HD)] and two measures of accelerated biological aging (BioAgeAccel and PhenoAgeAccel) were set as primary exposure and were calculated. Logistic regression and restricted cubic spline regression were employed to examine the relationship between biological aging and periodontitis. Additionally, Mendelian randomization analysis was conducted to explore the causal connection between accelerated biological aging and periodontitis. After adjusting for age, gender, race, educational level, marital status, ratio of family income, and disease conditions, this study, found a significant association between subjects with older higher biological ages, accelerated biological aging, and periodontitis. Specifically, for a per year increase in the three biological ages (HD, KDM, and PhenoAge), the risk of periodontitis increases by 15%, 3%, and 4% respectively. Individuals who had positive BioAgeAccel or PhenoAgeAccel were 20% or 37% more likely to develop periodontitis compared with those who had negative BioAgeAccel or PhenoAgeAccel. Furthermore, a significant non-linear positive relationship was observed between the three biological ages, accelerated biological aging, and periodontitis. However, the Mendelian randomization analysis indicated no causal effect of accelerated biological aging on periodontitis. Our findings suggest that biological aging may contribute to the risk of periodontitis, highlighting the potential utility of preventive strategies targeting aging-related pathways in reducing periodontitis risk among older adults.
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Affiliation(s)
- Sihong Li
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Chang Wen
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xueying Bai
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Dong Yang
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
- Department of Periodontology, School and Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Hongshan District, Wuhan, 430079, China.
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Bian L, Ma Z, Fu X, Ji C, Wang T, Yan C, Dai J, Ma H, Hu Z, Shen H, Wang L, Zhu M, Jin G. Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study. eLife 2024; 13:RP91101. [PMID: 38687190 PMCID: PMC11060710 DOI: 10.7554/elife.91101] [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] [Indexed: 05/02/2024] Open
Abstract
Background Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer. Methods Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs). Results Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001). Conclusions Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle. Funding This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
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Affiliation(s)
- Lijun Bian
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Xiangjin Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Chen Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Tianpei Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical SciencesBeijingChina
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
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Kuo CL, Chen Z, Liu P, Pilling LC, Atkins JL, Fortinsky RH, Kuchel GA, Diniz BS. Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.19.23300228. [PMID: 38196645 PMCID: PMC10775323 DOI: 10.1101/2023.12.19.23300228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
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Affiliation(s)
- Chia-Ling Kuo
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington CT, USA
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, CT, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - Zhiduo Chen
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - Peiran Liu
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, CT, USA
| | - Luke C Pilling
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Janice L Atkins
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Richard H Fortinsky
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
| | - Breno S Diniz
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington CT, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, CT, USA
- Department of Psychiatry, University of Connecticut Health Center, Farmington CT, USA
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10
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Lau CHE, Manou M, Markozannes G, Ala-Korpela M, Ben-Shlomo Y, Chaturvedi N, Engmann J, Gentry-Maharaj A, Herzig KH, Hingorani A, Järvelin MR, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt AF, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modeling of age and lifespan: A multicohort analysis. Aging Cell 2024:e14164. [PMID: 38637937 DOI: 10.1111/acel.14164] [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: 11/03/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung-Ho E Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Maria Manou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics, London, UK
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
- Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
| | - Karl-Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center Oulu, Oulu University Hospital, Faculty of Medicine, Oulu University, Oulu, Finland
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics, London, UK
| | - Marjo-Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kivimäki
- Brain Sciences, University College London, London, UK
| | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
- Department of Clinical Chemistry Fimlab Laboratories, Tampere, Finland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Gerontology Research Center (GEREC), Tampere University, Tampere, Finland
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - 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
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- UCL BHF Research Accelerator Centre, London, UK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Biomedical Research Foundation, Academy of Athens, Athens, Greece
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK
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11
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Duan S, Wu Y, Zhu J, Wang X, Fang Y. Associations of polycyclic aromatic hydrocarbons mixtures with cardiovascular diseases mortality and all-cause mortality and the mediation role of phenotypic ageing: A time-to-event analysis. ENVIRONMENT INTERNATIONAL 2024; 186:108616. [PMID: 38593687 DOI: 10.1016/j.envint.2024.108616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/11/2024] [Accepted: 03/28/2024] [Indexed: 04/11/2024]
Abstract
The associations of polycyclic aromatic hydrocarbons (PAHs) with cardiovascular diseases (CVDs) and all-cause mortality are unclear, especially the joint effects of PAHs exposure. Meanwhile, no studies have examined the effect of phenotypic ageing on the relationship between PAHs and mortality. Therefore, this study aimed to investigate the independent and joint associations between PAHs and CVDs, all-cause mortality, and assess whether phenotypic age acceleration (PhenoAgeAccel) mediate this relationship. We retrospectively collected data of 11,983 adults from the National Health and Nutrition Examination Survey database. Firstly, Cox proportional hazards regression and restricted cubic splines were applied to evaluate the independent association of single PAH on mortality. Further, time-dependent Probit extension of Bayesian Kernel Machine Regression and quantile-based g-computation models were conducted to test the joint effect of PAHs on mortality. Then, difference method was used to calculate the mediation proportion of PhenoAgeAccel in the association between PAHs and mortality. Our results revealed that joint exposure to PAHs showed positive association with CVDs and all-cause mortality. By controlling potential confounders, 1-Hydroxynapthalene (1-NAP) (HR = 1.24, P = 0.035) and 2-Hydroxyfluorene (2-FLU) (HR = 1.25, P < 0.001) showed positive association with CVDs mortality, and they were the top 2 predictors (weight: 0.82 for 1-NAP, 0.14 for 2-FLU) of CVDs mortality. 1-NAP (HR = 1.15, P < 0.001) and 2-FLU (HR = 1.13, P < 0.001) also showed positive association with all-cause mortality, and they were also the top 2 predictors of all-cause mortality (weight: 0.66 for 1-NAP, 0.34 for 2-FLU). PhenoAgeAccel mediated the relationship between 1-NAP, 2-FLU and CVDs, all-cause mortality, with a mediation proportion of 10.00 % to 24.90 % (P < 0.05). Specifically, the components of PhenoAgeAccel including C-reactive protein, lymphocyte percent, white blood cell count, red cell distribution width, and mean cell volume were the main contributors of mediation effects. Our study highlights the hazards of joint exposure of PAHs and the importance of phenotypic ageing on the relationship between PAHs and mortality.
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Affiliation(s)
- Siyu Duan
- Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China
| | - Yafei Wu
- Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China
| | - Junmin Zhu
- Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China
| | - Xing Wang
- Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
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12
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Dan YL, Yang YQ, Zhu DC, Bo L, Lei SF. Accelerated biological aging as a potential risk factor for rheumatoid arthritis. Int J Rheum Dis 2024; 27:e15156. [PMID: 38665050 DOI: 10.1111/1756-185x.15156] [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: 02/08/2024] [Revised: 03/15/2024] [Accepted: 04/05/2024] [Indexed: 05/31/2024]
Abstract
OBJECTS Previous studies have suggested a potential correlation between rheumatoid arthritis (RA) and biological aging, but the intricate connections and mechanisms remain elusive. METHODS In our study, we focused on two specific measures of biological age (PhenoAge and BioAge), which are derived from clinical biomarkers. The residuals of these measures, when compared to chronological age, are defined as biological age accelerations (BAAs). Utilizing the extensive UK Biobank dataset along with various genetic datasets, we conducted a thorough assessment of the relationship between BAAs and RA at both the individual and aggregate levels. RESULTS Our observational studies revealed positive correlations between the two BAAs and the risk of developing both RA and seropositive RA. Furthermore, the genetic risk score (GRS) for PhenoAgeAccel was associated with an increased risk of RA and seropositive RA. Linkage disequilibrium score regression (LDSC) analysis further supported these findings, revealing a positive genetic correlation between PhenoAgeAccel and RA. PLACO analysis identified 38 lead pleiotropic single nucleotide polymorphisms linked to 301 genes, providing valuable insights into the potential mechanisms connecting PhenoAgeAccel and RA. CONCLUSION In summary, our study has successfully revealed a positive correlation between accelerated biological aging, as measured by BAAs, and the susceptibility to RA.
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Affiliation(s)
- Yi-Lin Dan
- Collaborative Innovation Center for Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou, Jiangsu, China
| | - Yi-Qun Yang
- Collaborative Innovation Center for Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou, Jiangsu, China
| | - Dong-Cheng Zhu
- Department of Orthopedics, Collaborative Innovation Center for Bone and Immunology between Sihong Hospital and Soochow University, Suqian, Jiangsu, China
| | - Lin Bo
- Department of Rheumatology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Shu-Feng Lei
- Collaborative Innovation Center for Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou, Jiangsu, China
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13
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Wang X, Yan X, Li M, Cheng L, Qi X, Zhang J, Pan S, Xu X, Wei W, Li Y. U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study. Aging Cell 2024:e14159. [PMID: 38556842 DOI: 10.1111/acel.14159] [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: 11/30/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.
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Affiliation(s)
- Xuanyang Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xuemin Yan
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Mengdi Li
- Department of Endodontics, The First Hospital, Harbin Medical University, Harbin, China
| | - Licheng Cheng
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiang Qi
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Jia Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Sijia Pan
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaoqing Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wei Wei
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
- Department of Pharmacology, College of Pharmacy, Key Laboratory of Cardiovascular Research, Ministry of Education, Harbin Medical University, Harbin, China
| | - Ying Li
- Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision Nutrition and Health, Ministry of Education, Harbin Medical University, Harbin, Heilongjiang, China
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14
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Wang M, Yang M, Liang S, Wang N, Wang Y, Sambou ML, Qin N, Zhu M, Wang C, Jiang Y, Dai J. Association between sleep traits and biological aging risk: a Mendelian randomization study based on 157 227 cases and 179 332 controls. Sleep 2024; 47:zsad299. [PMID: 37982786 DOI: 10.1093/sleep/zsad299] [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: 05/18/2023] [Revised: 09/23/2023] [Indexed: 11/21/2023] Open
Abstract
STUDY OBJECTIVES To investigate whether sleep traits are associated with the risk of biological aging using a case-control design with Mendelian randomization (MR) analyses. METHODS We studied 336 559 participants in the UK Biobank cohort, including 157 227 cases of accelerated biological aging and 179 332 controls. PhenoAge, derived from clinical traits, estimated biological ages, and the discrepancies from chronological age were defined as age accelerations (PhenoAgeAccel). Sleep behaviors were assessed with a standardized questionnaire. propensity score matching matched control participants to age-accelerated participants, and a conditional multivariable logistic regression model estimated odds ratio (OR) and 95% confidence intervals (95% CI). Causal relationships between sleep traits and PhenoAgeAccel were explored using linear and nonlinear MR methods. RESULTS A U-shaped association was found between sleep duration and PhenoAgeAccel risk. Short sleepers had a 7% higher risk (OR = 1.07; 95% CI: 1.03 to 1.11), while long sleepers had an 18% higher risk (OR = 1.18; 95% CI: 1.15 to 1.22), compared to normal sleepers (6-8 hours/day). Evening chronotype was linked to higher PhenoAgeAccel risk than morning chronotype (OR = 1.14; 95% CI: 1.10 to 1.18), while no significant associations were found for insomnia or snoring. Morning chronotype had a protective effect on PhenoAgeAccel risk (OR = 0.87, 95% CI: 0.79 to 0.95) per linear MR analysis. Genetically predicted sleep duration showed a U-shaped relationship with PhenoAgeAccel, suggesting a nonlinear association (pnonlinear < 0.001). CONCLUSIONS The study suggests that improving sleep can slow biological aging, highlighting the importance of optimizing sleep as an intervention to mitigate aging's adverse effects.
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Affiliation(s)
- Mei Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Meiqi Yang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Shuang Liang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Nanxi Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yifan Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Muhammed Lamin Sambou
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health, Genomic Science and Precision Medicine Institute, Gusu School, Nanjing Medical University, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health, Genomic Science and Precision Medicine Institute, Gusu School, Nanjing Medical University, Nanjing 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health, Genomic Science and Precision Medicine Institute, Gusu School, Nanjing Medical University, Nanjing 211166, China
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Yue Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health, Genomic Science and Precision Medicine Institute, Gusu School, Nanjing Medical University, Nanjing 211166, China
- Nanjing Yike Population Health Research Institute, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health, Genomic Science and Precision Medicine Institute, Gusu School, Nanjing Medical University, Nanjing 211166, China
- Nanjing Yike Population Health Research Institute, Nanjing 211166, China
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Ye C, Li Z, Ye C, Yuan L, Wu K, Zhu C. Association between Gut Microbiota and Biological Aging: A Two-Sample Mendelian Randomization Study. Microorganisms 2024; 12:370. [PMID: 38399774 PMCID: PMC10891714 DOI: 10.3390/microorganisms12020370] [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: 01/11/2024] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Recent observational studies revealed an association between gut microbiota and aging, but whether gut microbiota are causally associated with the aging process remains unknown. We used a two-sample Mendelian randomization approach to investigate the causal association between gut microbiota and biological age acceleration using the largest available gut microbiota GWAS summary data from the MiBioGen consortium and GWAS data on biological age acceleration. We further conducted sensitivity analysis using MR-PRESSO, MR-Egger regression, Cochran Q test, and reverse MR analysis. Streptococcus (IVW, β = 0.16, p = 0.0001) was causally associated with Bioage acceleration. Eubacterium (rectale group) (IVW, β = 0.20, p = 0.0190), Sellimonas (IVW, β = 0.06, p = 0.019), and Lachnospira (IVW, β = -0.18, p = 0.01) were suggestive of causal associations with Bioage acceleration, with the latter being protective. Actinomyces (IVW, β = 0.26, p = 0.0083), Butyricimonas (IVW, β = 0.21, p = 0.0184), and Lachnospiraceae (FCS020 group) (IVW, β = 0.24, p = 0.0194) were suggestive of causal associations with Phenoage acceleration. This Mendelian randomization study found that Streptococcus was causally associated with Bioage acceleration. Further randomized controlled trials are needed to investigate its role in the aging process.
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Affiliation(s)
- Chenglin Ye
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China; (C.Y.)
| | - Zhiqiang Li
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China; (C.Y.)
| | - Chun Ye
- Department of General Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China
| | - Li Yuan
- Department of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan 430060, China
| | - Kailang Wu
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Chengliang Zhu
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China; (C.Y.)
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Wang T, Duan W, Jia X, Huang X, Liu Y, Meng F, Ni C. Associations of combined phenotypic ageing and genetic risk with incidence of chronic respiratory diseases in the UK Biobank: a prospective cohort study. Eur Respir J 2024; 63:2301720. [PMID: 38061785 PMCID: PMC10882326 DOI: 10.1183/13993003.01720-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/29/2023] [Indexed: 02/24/2024]
Abstract
BACKGROUND Accelerated biological ageing has been associated with an increased risk of several chronic respiratory diseases. However, the associations between phenotypic age, a new biological age indicator based on clinical chemistry biomarkers, and common chronic respiratory diseases have not been evaluated. METHODS We analysed data from 308 592 participants at baseline in the UK Biobank. The phenotypic age was calculated from chronological age and nine clinical chemistry biomarkers, including albumin, alkaline phosphatase, creatinine, glucose, C-reactive protein, lymphocyte percent, mean cell volume, red cell distribution width and white blood cell count. Furthermore, phenotypic age acceleration (PhenoAgeAccel) was calculated by regressing phenotypic age on chronological age. The associations of PhenoAgeAccel with incident common chronic respiratory diseases and cross-sectional lung function were investigated. Moreover, we constructed polygenic risk scores and evaluated whether PhenoAgeAccel modified the effect of genetic susceptibility on chronic respiratory diseases and lung function. RESULTS The results showed significant associations of PhenoAgeAccel with increased risk of idiopathic pulmonary fibrosis (IPF) (hazard ratio (HR) 1.52, 95% CI 1.45-1.59), COPD (HR 1.54, 95% CI 1.51-1.57) and asthma (HR 1.18, 95% CI 1.15-1.20) per 5-year increase and decreased lung function. There was an additive interaction between PhenoAgeAccel and the genetic risk for IPF and COPD. Participants with high genetic risk and who were biologically older had the highest risk of incident IPF (HR 5.24, 95% CI 3.91-7.02), COPD (HR 2.99, 95% CI 2.66-3.36) and asthma (HR 2.07, 95% CI 1.86-2.31). Mediation analysis indicated that PhenoAgeAccel could mediate 10∼20% of the associations between smoking and chronic respiratory diseases, while ∼10% of the associations between particulate matter with aerodynamic diameter <2.5 µm and the disorders were mediated by PhenoAgeAccel. CONCLUSION PhenoAgeAccel was significantly associated with incident risk of common chronic respiratory diseases and decreased lung function and could serve as a novel clinical biomarker.
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Affiliation(s)
- Ting Wang
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Department of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Public Health, Kangda College of Nanjing Medical University, Lianyungang, China
| | - Weiwei Duan
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
- Joint first authors
- Joint first authors
| | - Xinying Jia
- Department of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xinmei Huang
- Department of Respiratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yi Liu
- Department of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
- Contributed equally to this article as lead authors and supervised the work
| | - Fanqing Meng
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Contributed equally to this article as lead authors and supervised the work
| | - Chunhui Ni
- Department of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Public Health, Kangda College of Nanjing Medical University, Lianyungang, China
- Contributed equally to this article as lead authors and supervised the work
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Tao MH, Drake CL, Lin CH. Association of sleep duration, chronotype, social jetlag, and sleep disturbance with phenotypic age acceleration: A cross-sectional analysis. Sleep Health 2024; 10:122-128. [PMID: 38238123 DOI: 10.1016/j.sleh.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/22/2023] [Accepted: 11/30/2023] [Indexed: 03/01/2024]
Abstract
OBJECTIVE Sleep is a critical health-related behavior; research evidence has shown that sleep duration, poor sleep quality and insomnia are associated with aging and relevant age-related diseases. However, the associations between sleep duration, chronotype, sleep disturbance, and biological age have not been comprehensively assessed. This study aimed to examine sleep characteristics with biological age. METHODS The study included 6534 participants aged 20 years and older from the National Health and Nutrition Examination Survey between 2017 and March 2020. Sleep questionnaires were used to collect information on sleep duration and wake behavior on workdays and workfree days and sleep disturbance. Phenotypic age acceleration (PhenoAgeAccel) was estimated as a biological age measure using 9 blood chemistry biomarkers. RESULTS Long sleep (>9 hours) and extremely short sleep (≤4 hours) on workdays were positively associated with PhenoAgeAccel, compared with optimal sleep duration (7-8 hours). Similar positive associations with PhenoAgeAccel were observed for sleep duration on workfree days and across the whole week. Both slightly evening and evening chronotypes were associated with faster PhenoAgeAccel compared to morning chronotype. Social jetlag and sleep disturbance were not associated with PhenoAgeAccel, while long corrected social jetlag was associated with faster PhenoAgeAccel. The associations of sleep duration, chronotype, and corrected social jetlag with PhenoAgeAccel appeared stronger among females than among males. CONCLUSIONS Findings suggest a U-shape relationship between sleep duration and biological aging; slightly evening and evening chronotypes may be risk factors for aging. Further studies are needed to confirm these findings.
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Affiliation(s)
- Meng-Hua Tao
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA.
| | - Christopher L Drake
- Department of Medicine, Division of Sleep Medicine, Henry Ford Health System, Novi, Michigan, USA
| | - Chun-Hui Lin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA
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18
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He Y, Li Z, Niu Y, Duan Y, Wang Q, Liu X, Dong Z, Zheng Y, Chen Y, Wang Y, Zhao D, Sun X, Cai G, Feng Z, Zhang W, Chen X. Progress in the study of aging marker criteria in human populations. Front Public Health 2024; 12:1305303. [PMID: 38327568 PMCID: PMC10847233 DOI: 10.3389/fpubh.2024.1305303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.
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Affiliation(s)
- Yan He
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Li
- The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yuting Duan
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Qian Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiaomin Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Yizhi Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
- Department of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Hainan Province Academician Team Innovation Center, Sanya, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Delong Zhao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Zhe Feng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
| | - Xiangmei Chen
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, National Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, China
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Li X, Cao X, Zhang J, Fu J, Mohedaner M, Zhuogadanzeng, Sun X, Yang G, Yang Z, Kuo CL, Chen X, Cohen AA, Liu Z. Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality. J Am Geriatr Soc 2024; 72:181-193. [PMID: 37789775 PMCID: PMC11078652 DOI: 10.1111/jgs.18611] [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: 04/13/2023] [Revised: 08/16/2023] [Accepted: 08/24/2023] [Indexed: 10/05/2023]
Abstract
BACKGROUND With two well-validated aging measures capturing mortality and morbidity risk, this study examined whether and to what extent aging mediates the associations of unhealthy lifestyles with adverse health outcomes. METHODS Data were from 405,944 adults (40-69 years) from UK Biobank (UKB) and 9972 adults (20-84 years) from the US National Health and Nutrition Examination Survey (NHANES). An unhealthy lifestyles score (range: 0-5) was constructed based on five factors (smoking, drinking, physical inactivity, unhealthy body mass index, and unhealthy diet). Two aging measures, Phenotypic Age Acceleration (PhenoAgeAccel) and Biological Age Acceleration (BioAgeAccel) were calculated using nine and seven blood biomarkers, respectively, with a higher value indicating the acceleration of aging. The outcomes included incident cardiovascular disease (CVD), incident cancer, and all-cause mortality in UKB; CVD mortality, cancer mortality, and all-cause mortality in NHANES. A general linear regression model, Cox proportional hazards model, and formal mediation analysis were performed. RESULTS The unhealthy lifestyles score was positively associated with PhenoAgeAccel (UKB: β = 0.741; NHANES: β = 0.874, all p < 0.001). We further confirmed the respective associations of PhenoAgeAccel and unhealthy lifestyles with the outcomes in UKB and NHANES. The mediation proportion of PhenoAgeAccel in associations of unhealthy lifestyles with incident CVD, incident cancer, and all-cause mortality were 20.0%, 17.8%, and 26.6% (all p < 0.001) in UKB, respectively. Similar results were found in NHANES. The findings were robust when using another aging measure-BioAgeAccel. CONCLUSIONS Accelerated aging partially mediated the associations of lifestyles with CVD, cancer, and mortality in UK and US populations. The findings reveal a novel pathway and the potential of geroprotective programs in mitigating health inequality in late life beyond lifestyle interventions.
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Affiliation(s)
- Xueqin Li
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jinjing Fu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Mayila Mohedaner
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhuogadanzeng
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xiaoyi Sun
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhenqing Yang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chia-Ling Kuo
- Department of Community Medicine and Health Care, Connecticut Convergence Institute for Translation in Regenerative Engineering, Institute for Systems Genomics, University of Connecticut Health, Farmington, CT 06030, USA
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, USA
- Department of Economics, Yale University, New Haven, CT 06520, USA
| | - Alan A Cohen
- Department of Family Medicine, Research Centre on Aging, CHUS Research Centre, University of Sherbrooke, Sherbrooke, QC, Canada
- Butler Columbia Aging Center and Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
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Qiu W, Chen H, Kaeberlein M, Lee SI. ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age. THE LANCET. HEALTHY LONGEVITY 2023; 4:e711-e723. [PMID: 37944549 DOI: 10.1016/s2666-7568(23)00189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/10/2023] [Accepted: 08/30/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations. METHODS To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses. FINDINGS Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms. INTERPRETATION ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes. FUNDING National Science Foundation and National Institutes of Health.
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Affiliation(s)
- Wei Qiu
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Hugh Chen
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington, Washington, DC, USA
| | - Su-In Lee
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA.
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Li H, Miao S, Zhang M, Zhang P, Li YB, Duan RS. U-shaped association between serum Klotho and accelerated aging among the middle-aged and elderly US population: a cross-sectional study. BMC Geriatr 2023; 23:780. [PMID: 38017397 PMCID: PMC10685632 DOI: 10.1186/s12877-023-04479-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Phenotypic age acceleration, which reflects the difference between phenotypic age and chronological age, is an assessment to measure accelerated aging. Klotho is a protein related to slower aging, but its association with accelerated aging remains unclear. METHODS Based on data from the 2007-2010 National Health and Nutrition Examination Survey, phenotypic age was calculated using chronological age and 9 aging-related biomarkers. A total of 4388 participants aged 40 to 79 years with measured serum Klotho and calculated phenotypic age were enrolled. The association between serum Klotho and phenotypic age acceleration was estimated using multivariable linear regression models. The possible nonlinear relationship was examined with smooth curve fitting. We also conducted a segmented regression model to examine the threshold effect. RESULTS The association between serum Klotho and phenotypic age acceleration followed a U-shaped curve (p for nonlinearity < 0.001), with the inflection point at 870.7 pg/ml. The phenotypic age acceleration significantly decreased with the increment of serum Klotho (per SD increment: β -1.77; 95% CI, -2.57 ~ -0.98) in participants with serum Klotho < 870.7 pg/ml, and increased with the increment of serum Klotho (per SD increment:β, 1.03; 95% CI: 0.53 ~ 1.54) in participants with serum Klotho ≥ 870.7 pg/ml. CONCLUSION There was a U-shaped association between serum Klotho and accelerated aging among the middle-aged and elderly US population.
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Affiliation(s)
- Heng Li
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, People's Republic of China
- Shandong Institute of Neuroimmunology, Jinan, 250014, People's Republic of China
| | - Shuai Miao
- Medical School of Chinese People's Liberation Army (PLA), Beijing, 100853, People's Republic of China
- Department of Neurology, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China
| | - Min Zhang
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, People's Republic of China
- Shandong Institute of Neuroimmunology, Jinan, 250014, People's Republic of China
| | - Peng Zhang
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, People's Republic of China
- Shandong Institute of Neuroimmunology, Jinan, 250014, People's Republic of China
| | - Yan-Bin Li
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, People's Republic of China
- Shandong Institute of Neuroimmunology, Jinan, 250014, People's Republic of China
| | - Rui-Sheng Duan
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, People's Republic of China.
- Shandong Institute of Neuroimmunology, Jinan, 250014, People's Republic of China.
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22
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Wu H, Huang L, Zhang S, Zhang Y, Lan Y. Daytime napping, biological aging and cognitive function among middle-aged and older Chinese: insights from the China health and retirement longitudinal study. Front Public Health 2023; 11:1294948. [PMID: 38045976 PMCID: PMC10693455 DOI: 10.3389/fpubh.2023.1294948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023] Open
Abstract
Objective The complicated association of daytime napping, biological aging and cognitive function remains inconclusive. We aimed to evaluate the cross-sectional and longitudinal associations of daytime napping and two aging measures with cognition and to examine whether napping affects cognition through a more advanced state of aging. Methods Data was collected from the China Health and Retirement Longitudinal Study. Napping was self-reported. We calculated two published biological aging measures: Klemera and Doubal biological age (KDM-BA) and physiological dysregulation (PD), which derived information from clinical biomarkers. Cognitive z-scores were calculated at each wave. Linear mixed models were used to explore the longitudinal association between napping, two aging measures, and cognitive decline. Mediation analyses were performed to assess the mediating effects of biological age acceleration on the association between napping and cognition. Results Participants aged over 45 years were included in the analyses. Non-nappers had greater KDM-BA and PD [LS means (LSM) = 0.255, p = 0.007; LSM = 0.085, p = 0.011] and faster cognitive decline (LSM = -0.061, p = 0.005)compared to moderate nappers (30-90 min/nap). KDM-BA (β = -0.007, p = 0.018) and PD (β = -0.034, p < 0.001) showed a negative association with overall cognitive z scores. KDM-BA and PD partially mediated the effect of napping on cognition. Conclusion In middle-aged and older Chinese, compared to moderate nappers, non-nappers seem to experience a more advanced state of aging and increased rates of cognitive decline. The aging status possibly mediates the association between napping and cognition. Moderate napping shows promise in promoting healthy aging and reducing the burden of cognitive decline in Chinese middle-aged and older adults.
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Affiliation(s)
- Huiyi Wu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lei Huang
- West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Shushan Zhang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yajia Lan
- Department of Environmental Health and Occupational Medicine, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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23
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Lau CHE, Manou M, Markozannes G, Ala-Korpela M, Ben-Shlomo Y, Chaturvedi N, Engmann J, Gentry-Maharaj A, Herzig KH, Hingorani A, Järvelin MR, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt F, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modelling of age and lifespan: a multi-cohort analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298200. [PMID: 37986811 PMCID: PMC10659522 DOI: 10.1101/2023.11.07.23298200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Metabolomic age models have been proposed for the study of biological aging, however they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. 98 metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈ 31,000 individuals, age range 24-86 years). We used non-linear and penalised regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with ageing risk factors and phenotypes. Within the UK Biobank (N≈ 102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, chronic obstructive pulmonary disease) and all-cause mortality. Cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47-0.65 in the training set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with chronological age were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06 / metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung-Ho E. Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Maria Manou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Yoav Ben-Shlomo
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, UK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, UCL, London, UK
| | - Karl-Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Medical Research Center Oulu, Oulu University Hospital, Faculty of Medicine, Oulu University; Finland
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics
| | - Marjo-Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kivimäki
- Brain Sciences, University College London, London, UK
| | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
- Department of Clinical Chemistry Fimlab Laboratories, Tampere, Finland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health Technology, Tampere University, Finland
- Gerontology Research Center (GEREC), Tampere University, Finland
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, UCL, London, UK
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, UK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, UK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - 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
| | - Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- UCL BHF Research Accelerator Centre, London, UK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, UK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK
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24
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Bortz J, Guariglia A, Klaric L, Tang D, Ward P, Geer M, Chadeau-Hyam M, Vuckovic D, Joshi PK. Biological age estimation using circulating blood biomarkers. Commun Biol 2023; 6:1089. [PMID: 37884697 PMCID: PMC10603148 DOI: 10.1038/s42003-023-05456-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.
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Affiliation(s)
- Jordan Bortz
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA.
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK.
| | - Andrea Guariglia
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Lucija Klaric
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA
| | - David Tang
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Peter Ward
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA
| | - Michael Geer
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR-HPRU, Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Public Health England and Imperial College London, London, UK
| | - Dragana Vuckovic
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK.
- NIHR-HPRU, Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Public Health England and Imperial College London, London, UK.
| | - Peter K Joshi
- Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA.
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK.
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25
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Wang S, Prizment A, Moshele P, Vivek S, Blaes AH, Nelson HH, Thyagarajan B. Aging measures and cancer: Findings from the Health and Retirement Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295845. [PMID: 37790462 PMCID: PMC10543046 DOI: 10.1101/2023.09.20.23295845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Background Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study. Methods In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA. Results Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence. Conclusion Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.
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26
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Ahadi S, Wilson KA, Babenko B, McLean CY, Bryant D, Pritchard O, Kumar A, Carrera EM, Lamy R, Stewart JM, Varadarajan A, Berndl M, Kapahi P, Bashir A. Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock. eLife 2023; 12:e82364. [PMID: 36975205 PMCID: PMC10110236 DOI: 10.7554/elife.82364] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.
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Affiliation(s)
- Sara Ahadi
- Google ResearchMountain ViewUnited States
| | | | | | | | | | | | - Ajay Kumar
- Department of Biophysics, Post Graduate Institute of Medical Education and ResearchChandigarhIndia
| | | | - Ricardo Lamy
- Department of Ophthalmology, Zuckerberg San Francisco General Hospital and Trauma CenterSan FranciscoUnited States
| | - Jay M Stewart
- Department of Ophthalmology, University of California, San FranciscoSan FranciscoUnited States
| | | | | | - Pankaj Kapahi
- Buck Institute for Research on AgingNovatoUnited States
| | - Ali Bashir
- Google ResearchMountain ViewUnited States
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27
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Ma Z, Zhu C, Wang H, Ji M, Huang Y, Wei X, Zhang J, Wang Y, Yin R, Dai J, Xu L, Ma H, Hu Z, Jin G, Zhu M, Shen H. Association between biological aging and lung cancer risk: Cohort study and Mendelian randomization analysis. iScience 2023; 26:106018. [PMID: 36852276 PMCID: PMC9958377 DOI: 10.1016/j.isci.2023.106018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/14/2022] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Chronological age only represents the passage of time, whereas biological age reflects the physiology states and the susceptibility to morbidity and mortality. The association between biological age and lung cancer risk remains controversial. Hence, we conducted a prospective analysis in the UK Biobank study and two-sample Mendelian randomization analysis to investigate this association. Biological aging was evaluated by PhenoAgeAccel, derived from routine clinical biomarkers. Independent of chronological age, PhenoAgeAccel was positively associated with the risk of overall and histological subtypes of lung cancer. There was a joint effect of PhenoAgeAccel and genetics in lung cancer incidence. In Mendelian randomization analysis, the genetically predicted PhenoAgeAccel was associated with the increased risk of overall lung cancer, small cell, and squamous cell carcinoma. Our findings suggest PhenoAgeAccel is an independent risk factor for lung cancer, which could be incorporated with polygenic risk score to identify high-risk individuals for lung cancer.
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Affiliation(s)
- Zhimin Ma
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Hui Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Mengmeng Ji
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yanqian Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xiaoxia Wei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Rong Yin
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100000, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Corresponding author
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China,Corresponding author
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Southeast University, Nanjing 210009, China,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China,Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022, China,Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100000, China,Corresponding author
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28
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Li Z, Zhang W, Duan Y, Niu Y, Chen Y, Liu X, Dong Z, Zheng Y, Chen X, Feng Z, Wang Y, Zhao D, Sun X, Cai G, Jiang H, Chen X. Progress in biological age research. Front Public Health 2023; 11:1074274. [PMID: 37124811 PMCID: PMC10130645 DOI: 10.3389/fpubh.2023.1074274] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/16/2023] [Indexed: 05/02/2023] Open
Abstract
Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.
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Affiliation(s)
- Zhe Li
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Weiguang Zhang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yuting Duan
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yue Niu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yizhi Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
- Department of Nephrology, Hainan Hospital of Chinese PLA General Hospital, Hainan Academician Team Innovation Center, Sanya, China
| | - Xiaomin Liu
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Zheyi Dong
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Ying Zheng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Xizhao Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Zhe Feng
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Yong Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Delong Zhao
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
| | - Hongwei Jiang
- The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- *Correspondence: Hongwei Jiang,
| | - Xiangmei Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China
- Xiangmei Chen,
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29
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Fermín‐Martínez CA, Márquez‐Salinas A, Guerra EC, Zavala‐Romero L, Antonio‐Villa NE, Fernández‐Chirino L, Sandoval‐Colin E, Barquera‐Guevara DA, Campos Muñoz A, Vargas‐Vázquez A, Paz‐Cabrera CD, Ramírez‐García D, Gutiérrez‐Robledo L, Bello‐Chavolla OY. AnthropoAge, a novel approach to integrate body composition into the estimation of biological age. Aging Cell 2022; 22:e13756. [PMID: 36547004 PMCID: PMC9835580 DOI: 10.1111/acel.13756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022] Open
Abstract
Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex-dependent role of anthropometry for the prediction of 10-year all-cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data-driven approach pointed to sex-specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all-cause mortality (AUROC 0.876, 95%CI 0.864-0.887) and cause-specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25-2.62) and comorbidity profiles. A simplified version of AnthropoAge (S-AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause-specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.
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Affiliation(s)
- Carlos A. Fermín‐Martínez
- Research DivisionInstituto Nacional de GeriatríaMexico CityMexico,MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | - Alejandro Márquez‐Salinas
- Research DivisionInstituto Nacional de GeriatríaMexico CityMexico,MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | - Enrique C. Guerra
- Research DivisionInstituto Nacional de GeriatríaMexico CityMexico,MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | | | - Neftali Eduardo Antonio‐Villa
- Research DivisionInstituto Nacional de GeriatríaMexico CityMexico,MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | - Luisa Fernández‐Chirino
- Research DivisionInstituto Nacional de GeriatríaMexico CityMexico,Facultad de QuímicaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | - Eduardo Sandoval‐Colin
- MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
| | | | | | - Arsenio Vargas‐Vázquez
- MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MexicoMexico CityMexico
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Hu Y, Wang X, Huan J, Zhang L, Lin L, Li Y, Li Y. Effect of dietary inflammatory potential on the aging acceleration for cardiometabolic disease: A population-based study. Front Nutr 2022; 9:1048448. [PMID: 36532557 PMCID: PMC9755741 DOI: 10.3389/fnut.2022.1048448] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND/AIM Optimized dietary patterns have been considered an important determinant of delaying aging in cardiometabolic disease (CMD). Dietary pattern with high-level dietary inflammatory potential is a key risk factor for cardiometabolic disease, and has drawn increasing attention. The aim of this study was to investigate whether dietary pattern with high dietary inflammatory potential was associated with aging acceleration in cardiometabolic disease. MATERIALS AND METHODS We analyzed the cross-sectional data from six survey cycles (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) of the National Health and Nutritional Examination Surveys (NHANES). A total of 16,681 non-institutionalized adults and non-pregnant females with CMD were included in this study. Dietary inflammatory index (DII) was used to assess the dietary inflammatory potential. The two age acceleration biomarkers were calculated by the residuals from regressing chronologic age on Klemera-Doubal method biological age (KDM BioAge) or Phenotypic Age (PhenoAge), termed "KDMAccel" and "PhenoAgeAccel." A multivariable linear regression accounting for multistage survey design and sampling weights was used in different models to investigate the association between DII and aging acceleration. Four sensitivity analyses were used to ensure the robustness of our results. Besides, we also analyzed the anti-aging effects of DASH-type dietary pattern and "Life's Simple 7". RESULTS For 16,681 participants with CMD, compared with the first tertile of DII after adjusting for all potential confounders, the patients with second tertile of DII showed a 1.02-years increase in KDMAccel and 0.63-years increase in PhenoAgeAccel (KDMAccel, β = 1.02, 95% CI = 0.64 to 1.41, P < 0.001; PhenoAgeAccel, β = 0.63, 95% CI = 0.44 to 0.82, P < 0.001), while the patients with the third tertile of DII showed a 1.48-years increase in KDMAccel and 1.22-years increase in PhenoAgeAccel (KDMAccel, β = 1.48, 95% CI = 1.02 to 1.94, P < 0.001; PhenoAgeAccel, β = 1.22, 95% CI = 1.01 to 1.43, P < 0.001). In addition, DASH-type dietary pattern was associated with a 0.57-years reduction in KDMAccel (β = -0.57, 95% CI = -1.08 to -0.06, P = 0.031) and a 0.54-years reduction in PhenoAgeAccel (β = -0.54, 95% CI = -0.80 to -0.28, P < 0.001). The each one-unit increase in CVH score was associated with a 1.58-years decrease in KDMAccel (β = -1.58, 95% CI = -1.68 to -1.49, P < 0.001) and a 0.36-years in PhenoAgeAccel (β = -0.36, 95% CI = -0.41 to -0.31, P < 0.001). CONCLUSION Among CMD, the dietary pattern with high dietary inflammatory potential was association with aging acceleration, and the anti-aging potential of DASH-type dietary pattern and "Life's Simple 7" should also be given attention, but these observations require future prospective validation.
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Affiliation(s)
- Yuanlong Hu
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Shandong Province Engineering Laboratory of Traditional Chinese Medicine Precise Diagnosis and Treatment of Cardiovascular Disease, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Xiaojie Wang
- Shandong Province Engineering Laboratory of Traditional Chinese Medicine Precise Diagnosis and Treatment of Cardiovascular Disease, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Faculty of Chinese Medicine, Macau University of Science and Technology, Taipa, Macau, China
| | - Jiaming Huan
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lei Zhang
- Shandong Province Engineering Laboratory of Traditional Chinese Medicine Precise Diagnosis and Treatment of Cardiovascular Disease, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Lin
- Shandong Province Engineering Laboratory of Traditional Chinese Medicine Precise Diagnosis and Treatment of Cardiovascular Disease, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuan Li
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Shandong Provincial Key Laboratory of Traditional Chinese Medicine for Basic Research, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunlun Li
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Shandong Province Engineering Laboratory of Traditional Chinese Medicine Precise Diagnosis and Treatment of Cardiovascular Disease, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- Department of Cardiovascular, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
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Drewelies J, Hueluer G, Duezel S, Vetter VM, Pawelec G, Steinhagen-Thiessen E, Wagner GG, Lindenberger U, Lill CM, Bertram L, Gerstorf D, Demuth I. Using blood test parameters to define biological age among older adults: association with morbidity and mortality independent of chronological age validated in two separate birth cohorts. GeroScience 2022; 44:2685-2699. [PMID: 36151431 PMCID: PMC9768057 DOI: 10.1007/s11357-022-00662-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/12/2022] [Indexed: 01/07/2023] Open
Abstract
Biomarkers defining biological age are typically laborious or expensive to assess. Instead, in the current study, we identified parameters based on standard laboratory blood tests across metabolic, cardiovascular, inflammatory, and kidney functioning that had been assessed in the Berlin Aging Study (BASE) (n = 384) and Berlin Aging Study II (BASE-II) (n = 1517). We calculated biological age using those 12 parameters that individually predicted mortality hazards over 26 years in BASE. In BASE, older biological age was associated with more physician-observed morbidity and higher mortality hazards, over and above the effects of chronological age, sex, and education. Similarly, in BASE-II, biological age was associated with physician-observed morbidity and subjective health, over and above the effects of chronological age, sex, and education as well as alternative biomarkers including telomere length, DNA methylation age, skin age, and subjective age but not PhenoAge. We discuss the importance of biological age as one indicator of aging.
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Affiliation(s)
- Johanna Drewelies
- Humboldt University of Berlin, Berlin, Germany.
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.
| | | | - Sandra Duezel
- Max Planck Institute for Human Development, Berlin, Germany
| | - Valentin Max Vetter
- Humboldt University of Berlin, Berlin, Germany
- Charite - Universitätsmedizin Berlin, Berlin, Germany
| | - Graham Pawelec
- University of Tübingen, Tübingen, Germany
- Health Sciences North Research Institute, Sudbury, ON, Canada
| | | | - Gert G Wagner
- Max Planck Institute for Human Development, Berlin, Germany
- German Institute for Economic Research (DIW Berlin), Berlin, Germany
| | - Ulman Lindenberger
- Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
- Ageing and Epidemiology Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Denis Gerstorf
- Humboldt University of Berlin, Berlin, Germany
- German Institute for Economic Research (DIW Berlin), Berlin, Germany
| | - Ilja Demuth
- Charite - Universitätsmedizin Berlin, Berlin, Germany
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Cao X, Zhang J, Ma C, Li X, Chia-Ling K, Levine ME, Hu G, Allore H, Chen X, Wu X, Liu Z. Life course traumas and cardiovascular disease-the mediating role of accelerated aging. Ann N Y Acad Sci 2022; 1515:208-218. [PMID: 35725988 PMCID: PMC10145586 DOI: 10.1111/nyas.14843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The complex relationship between life course traumas and cardiovascular disease (CVD) and the underpinning pathways are poorly understood. We aimed to (1) examine the associations of three separate assessments including childhood, adulthood (after 16 years of age), and lifetime traumas (childhood or adulthood) with CVD; (2) examine the associations between diverse life course traumatic profiles and CVD; and (3) examine the extent to which PhenoAge, a well-developed phenotypic aging measure, mediated these associations. Using data from 104,939 participants from the UK Biobank, we demonstrate that subgroups of childhood, adulthood, and lifetime traumas were associated with CVD. Furthermore, life course traumatic profiles were significantly associated with CVD. For instance, compared with the subgroup experiencing nonsevere traumas across life course, those who experienced nonsevere childhood and severe adulthood traumas, severe childhood and nonsevere adulthood traumas, or severe traumas across life course had significantly higher odds of CVD (odds ratios: 1.07-1.33). Formal mediation analyses suggested that phenotypic aging partially mediated the above associations. These findings suggest a potential pathway from life course traumas to CVD through phenotypic aging, and underscore the importance of policy programs targeting traumas over the life course in ameliorating inequalities in cardiovascular health.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kuo Chia-Ling
- Department of Public Health Sciences, Connecticut Convergence Institute for Translation in Regenerative Engineering, Institute for Systems Genomics, University of Connecticut Health, Farmington, Connecticut, USA
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Guoqing Hu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Heather Allore
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
- Department of Economics, Yale University, New Haven, Connecticut, USA
| | - Xifeng Wu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
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Gao X, Huang N, Guo X, Huang T. Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: Findings from the UK Biobank cohort. Aging Cell 2022; 21:e13610. [PMID: 35421261 PMCID: PMC9124313 DOI: 10.1111/acel.13610] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/03/2022] [Accepted: 03/25/2022] [Indexed: 12/16/2022] Open
Abstract
Sleep has been associated with aging and relevant health outcomes, but the causal relationship remains inconclusive. In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly adults from UK Biobank. Sleep index (0 [worst]-6 [best]) of each participant was retrieved from the following six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM-biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their residual discrepancies with chronological age were defined as the age accelerations (AAs). We first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, a one-unit increase in sleep index was associated with 0.104- and 0.119-year decreases in KDM-biological AA and PhenoAge acceleration, respectively. Air pollution is another key driver of aging. We further observed significant independent and joint effects of sleep and air pollution (PM2.5 and NO2 ) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated AAs. For instance, an interquartile range increase in PM2.5 level was associated with 0.009-, 0.044-, and 0.074-year increase in PhenoAge acceleration among people with high (5-6), medium (3-4), and low (0-2) sleep index, respectively. Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from air pollution.
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Affiliation(s)
- Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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Kassab A, Rizk N, Prakash S. The Role of Systemic Filtrating Organs in Aging and Their Potential in Rejuvenation Strategies. Int J Mol Sci 2022; 23:ijms23084338. [PMID: 35457154 PMCID: PMC9025381 DOI: 10.3390/ijms23084338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/05/2022] [Accepted: 04/08/2022] [Indexed: 11/26/2022] Open
Abstract
Advances in aging studies brought about by heterochronic parabiosis suggest that aging might be a reversable process that is affected by changes in the systemic milieu of organs and cells. Given the broadness of such a systemic approach, research to date has mainly questioned the involvement of “shared organs” versus “circulating factors”. However, in the absence of a clear understanding of the chronological development of aging and a unified platform to evaluate the successes claimed by specific rejuvenation methods, current literature on this topic remains scattered. Herein, aging is assessed from an engineering standpoint to isolate possible aging potentiators via a juxtaposition between biological and mechanical systems. Such a simplification provides a general framework for future research in the field and examines the involvement of various factors in aging. Based on this simplified overview, the kidney as a filtration organ is clearly implicated, for the first time, with the aging phenomenon, necessitating a re-evaluation of current rejuvenation studies to untangle the extent of its involvement and its possible role as a potentiator in aging. Based on these findings, the review concludes with potential translatable and long-term therapeutics for aging while offering a critical view of rejuvenation methods proposed to date.
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Affiliation(s)
- Amal Kassab
- Biomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering, Faculty of Medicine, McGill University, 3775 University Street, Montreal, QC H3A 2BA, Canada
| | - Nasser Rizk
- Department of Biomedical Sciences, College of Health Sciences-QU-Health, Qatar University, Doha 2713, Qatar
| | - Satya Prakash
- Biomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering, Faculty of Medicine, McGill University, 3775 University Street, Montreal, QC H3A 2BA, Canada
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Mozhui K, Lu AT, Li CZ, Haghani A, Sandoval-Sierra JV, Wu Y, Williams RW, Horvath S. Genetic loci and metabolic states associated with murine epigenetic aging. eLife 2022; 11:e75244. [PMID: 35389339 PMCID: PMC9049972 DOI: 10.7554/elife.75244] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/01/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a 'pan-mammalian' microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4, NKX2-3, and CUTC. Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.
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Affiliation(s)
- Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
| | - Caesar Z Li
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
| | - Amin Haghani
- Department of Biostatistics, Fielding School of Public Health, University of California Los AngelesLos AngelesUnited States
| | - Jose Vladimir Sandoval-Sierra
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Yibo Wu
- YCI Laboratory for Next-Generation Proteomics, RIKEN Center for Integrative Medical SciencesYokohamaJapan
- University of GenevaGenevaSwitzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of MedicineMemphisUnited States
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los AngelesLos AngelesUnited States
- Department of Biostatistics, Fielding School of Public Health, University of California Los AngelesLos AngelesUnited States
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Functional genomics data: privacy risk assessment and technological mitigation. Nat Rev Genet 2022; 23:245-258. [PMID: 34759381 DOI: 10.1038/s41576-021-00428-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/15/2022]
Abstract
The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.
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Lin WY. Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants. J Gerontol A Biol Sci Med Sci 2021; 77:1189-1198. [PMID: 34427645 DOI: 10.1093/gerona/glab251] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians. METHODS I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS). RESULTS A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively. CONCLUSIONS A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
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Kuo CL, Pilling LC, Atkins JL, Masoli JAH, Delgado J, Tignanelli C, Kuchel GA, Melzer D, Beckman KB, Levine ME. Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants. J Gerontol A Biol Sci Med Sci 2021; 76:e133-e141. [PMID: 33684206 PMCID: PMC7989601 DOI: 10.1093/gerona/glab060] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Indexed: 12/22/2022] Open
Abstract
Background Age and disease prevalence are the 2 biggest risk factors for Coronavirus disease 2019 (COVID-19) symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. Methods Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of 2 COVID-19 severity outcomes (inpatient test positivity and COVID-19-related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and preexisting diseases/conditions. Results Six hundred and thirteen participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19-related mortality (ORMortality = 1.63 per 5 years, 95% CI: 1.43–1.86, p = 4.7 × 10−13) adjusting for demographics including age at the pandemic. Further adjustment for preexisting diseases/conditions at baseline (ORM = 1.50, 95% CI: 1.30–1.73 per 5 years, p = 3.1 × 10−8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04–1.40 per 5 years, p = .011) decreased the association. Conclusions PhenoAge measured in 2006–2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.
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Affiliation(s)
- Chia-Ling Kuo
- Connecticut Convergence Institute for Translation in Regenerative Engineering, University of Connecticut Health, Farmington, USA.,University of Connecticut Center on Aging, School of Medicine, Farmington, USA
| | - Luke C Pilling
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA.,College of Medicine and Health, University of Exeter, UK
| | | | | | - João Delgado
- College of Medicine and Health, University of Exeter, UK
| | | | - George A Kuchel
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA
| | - David Melzer
- University of Connecticut Center on Aging, School of Medicine, Farmington, USA.,College of Medicine and Health, University of Exeter, UK
| | - Kenneth B Beckman
- Institute for Health Informatics, University of Minnesota, Minneapolis, USA
| | - Morgan E Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
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