1
|
Chen H, Yin J, Xiang Y, Zhang N, Huang Z, Zhang Y, Tang D, Wang Z, Baimayangji, Chen L, Jiang X, Xiao X, Zhao X. Alcohol consumption and accelerated biological ageing in middle-aged and older people: A longitudinal study from two cohorts. Addiction 2024; 119:1387-1399. [PMID: 38679855 DOI: 10.1111/add.16501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/13/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND AIMS The relationship between alcohol consumption and age-related diseases is inconsistent. Biological age (BA) serves as both a precursor and a predictor of age-related diseases; however, longitudinal associations between alcohol consumption and BA in middle-aged and older people remain unclear. We measured whether there was a longitudinal association between drinking frequency and pure alcohol intake with BA among middle-aged and older people. DESIGN AND SETTING AND PARTICIPANTS This study involved two prospective cohort studies, set in Southwestern China and the United Kingdom. A total of 8046 participants from the China Multi-Ethnic Cohort study (CMEC) and 5412 participants from the UK Biobank (UKB), aged 30-79 years, took part, with complete data from two waves of clinical biomarkers. MEASUREMENTS BA was calculated by the Klemera Doubal's method. Accelerated BA equalled BA minus chronological age. Drinking frequency and pure alcohol intake were obtained through self-reported questionnaires. Drinking frequency in the past year was classified as current non-drinking, occasional (monthly drinking) and regular (weekly drinking). FINDINGS Compared with consistent current non-drinkers, more frequent drinkers [CMEC: β = 0.46, 95% confidence interval (CI) = 0.13-0.80; UKB: β = 0.65, 95% CI = 0.01-1.29)], less frequent drinkers (CMEC: β = 0.62, 95% CI = 0.37-0.87; UKB: β = 0.54, 95% CI = -0.01-1.09), consistent occasional drinkers (CMEC: β = 0.51, 95% CI = 0.23-0.79; UKB: β = 0.63, 95% CI = 0.13-1.13) and consistent regular drinkers (CMEC: β = 0.56, 95% CI = 0.17-0.95; UKB: β = 0.46, 95% CI = 0.00-0.91) exhibited increased accelerated BA. A non-linear relationship between pure alcohol intake and accelerated BA was observed among consistent regular drinkers. CONCLUSIONS In middle-aged and older people, any change in drinking frequency and any amount of pure alcohol intake seem to be positively associated with acceleration of biological ageing, compared with maintaining abstinence.
Collapse
Affiliation(s)
- Hongxiang Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jianzhong Yin
- School of Public Health, Kunming Medical University, Kunming, China
- Baoshan College of Traditional Chinese Medicine, Baoshan, China
| | - Yi Xiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ning Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zitong Huang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuan Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Dan Tang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ziyun Wang
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China
| | - Baimayangji
- School of Medicine, Tibet University, Lhasa, China
| | - Liling Chen
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Institute of Chronic Non-Communicable Disease Control and Prevention, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Xiaoman Jiang
- Chengdu Center for Disease Control and Prevention, Chengdu, China
| | - Xiong Xiao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
2
|
Mesa R, Llabre M, Lee D, Rundek T, Kezios K, Hazzouri AZA, Elfassy T. Social Determinants of Health and Biological Age among Diverse U.S. Adults, NHANES 2011-2018. RESEARCH SQUARE 2024:rs.3.rs-4540892. [PMID: 38978574 PMCID: PMC11230476 DOI: 10.21203/rs.3.rs-4540892/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
We examined the sex-specific association between education and income with biological age (BA) and by race/ethnicity. The Klemera-Doubal method was used to calculate BA among 6,213 females and 5,938 males aged 30-75 years who were Hispanic, non-Hispanic (NH) White, NH Black (NHB), or NH Asian (NHA). Compared with a college education, less than a high school education was associated with greater BA by 3.06 years (95% CI: 1.58, 4.54) among females only; associations were strongest among NHB, Hispanic, and NHA females. Compared with an annual income of ≥$75,000, an income <$25,000 was associated with greater BA by 4.95 years (95% CI: 3.42, 6.48) among males and 2.76 years among females (95% CI: 1.51, 4.01); associations were strongest among NHW and NHA adults, and Hispanic males. Targeting upstream sources of structural disadvantage among racial/ethnic minority groups, in conjunction with improvements in income and education, may promote healthy aging in these populations.
Collapse
Affiliation(s)
- Robert Mesa
- University of Miami Miller School of Medicine
| | | | - David Lee
- University of Miami Miller School of Medicine
| | | | | | | | | |
Collapse
|
3
|
Hou J, Sun H, Lu B, Yue Y, Li X, Ban K, Fu M, Zhang B, Luo X. Accelerated biological aging mediated associations of ammonium, sulfate in fine particulate matter with liver cirrhosis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172638. [PMID: 38643869 DOI: 10.1016/j.scitotenv.2024.172638] [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: 01/02/2024] [Revised: 04/02/2024] [Accepted: 04/18/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND Although both air pollution and aging are related to the development of liver cirrhosis, the role of biological aging in association of the mixture of fine particulate matter (PM2.5) and its constituents with liver cirrhosis was unknown. METHODS This case-control retrospective study included 100 liver cirrhosis patients and 100 control subjects matched by age and sex. The concentrations of PM2.5 and its constituents were estimated for patients using machine-learning methods. The clinical biomarkers were used to calculate biological age using the Klemera-Doubalmethod (KDM) algorithms. Individual associations of PM2.5 and its constituents or biological age with liver cirrhosis were analyzed by generalized linear models. WQS and BKMR were applied to analyze association of mixture of PM2.5 and its constituents with liver cirrhosis. The mediation effect of biological age on associations of PM2.5 and its constituents with liver cirrhosis was further explored. RESULTS we found that each 1-unit increment in NH4+, NO3-, SO42- and biological age were related to 3.618-fold (95%CI: 1.896, 6.904), 1.880-fold (95%CI: 1.319, 2.680), 2.955-fold (95%CI: 1.656, 5.272) and 1.244-fold (95%CI: 1.093, 1.414) increased liver cirrhosis. Both WQS and BKMR models showed that the mixture of PM2.5 and its constituents was related to increased liver cirrhosis. Furthermore, the mediated proportion of biological age on associations of NH4+ and SO42- with liver cirrhosis were 14.7 % and 14.6 %, respectively. CONCLUSIONS Biological aging may partly explain the exposure to PM2.5 and its constituents in association with increased risk for liver cirrhosis, implying that delaying the aging process may be a key step for preventing PM2.5-related liver cirrhosis risk.
Collapse
Affiliation(s)
- Jian Hou
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China; Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Huizhen Sun
- Hubei Provincial Center for Disease Control and Prevention, Hubei, Wuhan, PR China
| | - Bingxin Lu
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China
| | - Yanqin Yue
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China
| | - Xianxi Li
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China
| | - Kangjia Ban
- School of Architecture, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Mengze Fu
- School of Architecture, Zhengzhou University, Zhengzhou, Henan, PR China.
| | - Bingyong Zhang
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China.
| | - Xiaoying Luo
- Department of Gastroenterology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, PR China.
| |
Collapse
|
4
|
Jia Q, Chen C, Xu A, Wang S, He X, Shen G, Luo Y, Tu H, Sun T, Wu X. A biological age model based on physical examination data to predict mortality in a Chinese population. iScience 2024; 27:108891. [PMID: 38384842 PMCID: PMC10879664 DOI: 10.1016/j.isci.2024.108891] [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: 03/23/2023] [Revised: 09/02/2023] [Accepted: 01/09/2024] [Indexed: 02/23/2024] Open
Abstract
Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.
Collapse
Affiliation(s)
- Qingqing Jia
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Chen Chen
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Andi Xu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Sicong Wang
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xiaojie He
- Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Guoli Shen
- Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yihong Luo
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Huakang Tu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Ting Sun
- Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
- School of Medicine and Health Science, George Washington University, Washington, DC, USA
| |
Collapse
|
5
|
Li K, Wu J, Zhou Q, Zhao J, Li Y, Yang M, Yang Y, Hu Y, Xu J, Zhao M, Xu Q. The mediating role of accelerated biological aging in the association between blood metals and cognitive function. JOURNAL OF HAZARDOUS MATERIALS 2024; 462:132779. [PMID: 37879277 DOI: 10.1016/j.jhazmat.2023.132779] [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: 07/23/2023] [Revised: 09/28/2023] [Accepted: 10/12/2023] [Indexed: 10/27/2023]
Abstract
Aging is a key risk factor in cognitive diseases. Recently, metal exposures were found associated with both biological aging and cognitive function. Here, we aim to evaluate the associations of blood metals with cognitive function and the mediated effect of biological aging. Fourteen metals were detected and biological age was calculated through Klemera and Doubal method among 514 adults in Beijing, China. The generalized linear models indicated that the copper (Cu), molybdenum (Mo), and strontium (Sr) were positively associated with biological aging [βCu (95% CI): 12.76 (9.26, 16.27); βMo (95% CI): 1.50 (0.15, 2.85)], and βSr (95% CI): 1.86 (0.68, 3.03)], while vanadium (V) was inversely related to biological aging [βV (95% CI): -0.76 (-1.48, -0.05)]. Subsequently, Cu, lead (Pb), selenium (Se), and biological aging were associated with cognitive function and further mediation analyses confirmed that biological aging partially mediated (33.98%, P = 0.019) the association of Cu and cognitive function. Additionally, we constructed a lifestyle index that implied the modifiable healthy lifestyle could slow aging to attenuate the detrimental effect of metals on cognition. Our findings provide insights into the potential pathways linking multiple metals exposure to aging and cognition and underscore the importance of adopting healthy lifestyles.
Collapse
Affiliation(s)
- Kai Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
| | - Jingtao Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
| | - Quan Zhou
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jiaxin Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Yanbing Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Ming Yang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Yisen Yang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Yaoyu Hu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Jing Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Meiduo Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Qun Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China; Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
| |
Collapse
|
6
|
Li Q, Legault V, Hermann Honfo S, Milot E, Jia Q, Wang F, Ferrucci L, Bandinelli S, Cohen AA. Physiological Dysregulation Proceeds and Predicts Health Outcomes Similarly in Chinese and Western Populations. J Gerontol A Biol Sci Med Sci 2024; 79:glad146. [PMID: 37313838 DOI: 10.1093/gerona/glad146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND A decade ago, we proposed an index of physiological dysregulation based on Mahalanobis distance (DM) that measures how far from the norm an individual biomarker profile is. While extensive validation has been performed, focus was mostly on Western populations with little comparison to developing countries, particularly at a physiological system level. The degree to which the approach would work in other sociocultural contexts and the similarity of dysregulation signatures across diverse populations are still open questions. METHODS Using 2 data sets from China and 3 from Western countries (United States, United Kingdom, and Italy), we calculated DM globally and per physiological system. We assessed pairwise correlations among systems, difference with age, prediction of mortality and age-related diseases, and sensitivity to interchanging data sets with one another as the reference in DM calculation. RESULTS Overall, results were comparable across all data sets. Different physiological systems showed distinct dysregulation processes. Association with age was moderate and often nonlinear, similarly for all populations. Mahalanobis distance predicted most health outcomes, although differently by physiological system. Using a Chinese population as the reference when calculating DM for Western populations, or vice versa, led to similar associations with health outcomes, with a few exceptions. CONCLUSIONS While small differences were noticeable, they did not systematically emerge between Chinese and Western populations, but rather diffusively across all data sets. These findings suggest that DM presents similar properties, notwithstanding sociocultural backgrounds, and that it is equally effective in capturing the loss of homeostasis that occurs during aging in diverse industrial human populations.
Collapse
Affiliation(s)
- Qing Li
- Center for Innovation Management Research of Xinjiang, Urumqi 830046, China
- School of Economics and Management, Xinjiang University, Urumqi 830046, China
| | - Véronique Legault
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Sewanou Hermann Honfo
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Emmanuel Milot
- Department of Chemistry, Biochemistry, and Physics, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec G9A 5H7, Canada
| | - Qingzhou Jia
- School of Economics and Management, Xinjiang University, Urumqi 830046, China
| | - Fuqing Wang
- School of Economics and Management, Xinjiang University, Urumqi 830046, China
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, Baltimore, Maryland 21225, USA
| | | | - Alan A Cohen
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Gao S, Deng H, Wen S, Wang Y. Effects of accelerated biological age on depressive symptoms in a causal reasoning framework. J Affect Disord 2023; 339:732-741. [PMID: 37442448 DOI: 10.1016/j.jad.2023.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 06/07/2023] [Accepted: 07/08/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND Depression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation. METHODS We utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly. RESULTS In the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives. LIMITATIONS Depressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population. CONCLUSIONS This study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.
Collapse
Affiliation(s)
- Sunan Gao
- School of Statistics, Renmin University of China, Beijing, China
| | - Heming Deng
- School of Statistics, Renmin University of China, Beijing, China
| | - Shaobo Wen
- School of Statistics, Renmin University of China, Beijing, China
| | - Yu Wang
- Center for Applied Statistics, Renmin University of China, Beijing, China; School of Statistics, Renmin University of China, Beijing, China.
| |
Collapse
|
9
|
Bafei SEC, Shen C. Biomarkers selection and mathematical modeling in biological age estimation. NPJ AGING 2023; 9:13. [PMID: 37393295 DOI: 10.1038/s41514-023-00110-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/08/2023] [Indexed: 07/03/2023]
Abstract
Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.
Collapse
Affiliation(s)
- Solim Essomandan Clémence Bafei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
| |
Collapse
|
10
|
Liao X, Shi K, Zhang Y, Huang X, Wang N, Zhang L, Zhao X. Contribution of CKD to mortality in middle-aged and elderly people with diabetes: the China Health and Retirement Longitudinal Study : CKD was a chronic stressor for diabetics. Diabetol Metab Syndr 2023; 15:122. [PMID: 37291588 DOI: 10.1186/s13098-023-01083-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 05/08/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND The contribution of chronic kidney disease (CKD) to mortality in diabetic patients is unclear. This study aimed to explore the association between diabetics with CKD and mortality in middle-aged and elderly people of different ages. METHODS Data were obtained from the China Health and Retirement Longitudinal Study, including 1,715 diabetic individuals, 13.1% of whom also had CKD. Diabetes and CKD were assessed by combining the physical measurements and self-reports. We fitted Cox proportional hazards regression models to examine the effect of diabetics with CKD on mortality in middle-aged and elderly people. The risk factors for death were further predicted based on age stratification. RESULTS The mortality rate of diabetic patients with CKD (29.3%) was increased as compared to that of diabetic patients without CKD (12.4%). Diabetics with CKD were at a higher risk of all-cause mortality than those without CKD, with a hazard ratio of 1.921 (95% CI: 1.438, 2.566). Additionally, for participants 45 to 67 years of age, the hazard ratio was 2.530 (95% CI: 1.624, 3.943). CONCLUSIONS Our findings suggested that, for diabetics, CKD was a chronic stressor that led to death in middle-aged and elderly people, especially among participants aged 45 to 67 years.
Collapse
Affiliation(s)
- Xihong Liao
- Department of Obstetrics and Gynecology, Shanghai Songjiang District Central Hospital, Shanghai, China
| | - Ke Shi
- Department of Ophthalmology, Shanghai General Hospital, Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China
| | - Yumeng Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China
| | - Xiaoxu Huang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China
| | - Ning Wang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China
| | - Ling Zhang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xiaohuan Zhao
- Department of Ophthalmology, Shanghai General Hospital, Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China.
| |
Collapse
|
11
|
Pan R, Wang J, Chang WW, Song J, Yi W, Zhao F, Zhang Y, Fang J, Du P, Cheng J, Li T, Su H, Shi X. Association of PM 2.5 Components with Acceleration of Aging: Moderating Role of Sex Hormones. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:3772-3782. [PMID: 36811885 DOI: 10.1021/acs.est.2c09005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Fine particulate matter (PM2.5) has been linked to aging risk, and a lack of knowledge about the relationships between PM2.5 components and aging risk impeded the development of healthy aging. Participants were recruited through a multicenter cross-sectional study in the Beijing-Tianjin-Hebei region in China. Middle-age and older males and menopausal women completed the collection of basic information, blood samples, and clinical examinations. The biological age was estimated by Klemera-Doubal method (KDM) algorithms based on clinical biomarkers. Multiple linear regression models were applied to quantify the associations and interactions while controlling for confounders, and a restricted cubic spline function estimated the corresponding dose-response curves of the relationships. Overall, KDM-biological age acceleration was associated with PM2.5 component exposure over the preceding year in both males and females, with calcium [females: 0.795 (95% CI: 0.451, 1.138); males: 0.712 (95% CI: 0.389, 1.034)], arsenic [females: 0.770 (95% CI: 0.641, 0.899); males: 0.661 (95% CI: 0.532, 0.791)], and copper [females: 0.401 (95% CI: 0.158, 0.644); males: 0.379 (95% CI: 0.122, 0.636)] having greater estimates of the effect than total PM2.5 mass. Additionally, we observed that the associations of specific PM2.5 components with aging were lower in the higher sex hormone scenario. Maintaining high levels of sex hormones may be a crucial barrier against PM2.5 component-related aging in the middle and older age groups.
Collapse
Affiliation(s)
- Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230031, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230031, Anhui, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Wei-Wei Chang
- Department of Epidemiology and Health Statistics, School of Public Health, Wannan Medical College, Wuhu 241002, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230031, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230031, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230031, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230031, Anhui, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230031, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230031, Anhui, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230031, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei 230031, Anhui, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| |
Collapse
|
12
|
Wang C, Hong S, Guan X, Xiao Y, Fu M, Meng H, Feng Y, Zhou Y, Cao Q, Yuan F, Liu C, Zhong G, You Y, Wu T, Yang H, Zhang X, He M, Wu T, Guo H. Associations between multiple metals exposure and biological aging: Evidence from the Dongfeng-Tongji cohort. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160596. [PMID: 36464054 DOI: 10.1016/j.scitotenv.2022.160596] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Aging is related to a progressive decline in physiological functions and is affected by environmental factors. Metal exposures are linked with many health effects, but have poorly understood associations with aging. In this study, a total of 33,916 participants from the Dongfeng-Tongji cohort were included to establish biological age (BA) predictors by using recent advanced algorithms, Klemera and Doubal method (KDM) and Mahalanobis distance. Two biological aging indexes (BAIs), recorded as KDM-accel [the residual from regressing KDM-BA on chronological age] and physiological dysregulation (PD), were separately defined and tested on their associations with mortality by using Cox proportional hazard models. Among 3320 subjects with laboratory determinations of 23 metals in plasma, the individual and overall associations between these metals and BAIs were evaluated by using multiple-linear regression and weighted quantile sum (WQS) models. Both BAIs were prospectively associated with all-cause mortality among the whole participants [KDM-accel: HR(95%CI) = 1.23(1.18, 1.29); PD: HR(95%CI) = 1.37(1.31, 1.42)]. Each 1-unit increment in ln-transformed strontium and molybdenum were cross-sectionally associated with a separate 0.71- and 0.34-year increase in KDM-accel, and each 1 % increment in copper, rubidium, strontium, cobalt was cross-sectionally associated with a separate 0.10 %, 0.10 %, 0.09 %, 0.02 % increase in PD (all FDR < 0.05). The WQS models observed mixture effects of multi-metals on aging, with a 0.20-year increase in KDM-accel and a 0.04 % increase in PD for each quartile increase in ln-transformed concentrations of all metals [KDM-accel: β(95%CI) = 0.20(0.08, 0.32); PD: β(95%CI) = 0.04(0.02, 0.06)]. Our findings revealed that plasma strontium, molybdenum, copper, rubidium and cobalt were associated with accelerated aging. Multi-metals exposure showed mixture effects on the aging process, which highlights potential preventative interventions.
Collapse
Affiliation(s)
- Chenming Wang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shiru Hong
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xin Guan
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yang Xiao
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ming Fu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hua Meng
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yue Feng
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuhan Zhou
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qiang Cao
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Fangfang Yuan
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chenliang Liu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guorong Zhong
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yingqian You
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tianhao Wu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Meian He
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huan Guo
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| |
Collapse
|
13
|
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.
Collapse
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,
| |
Collapse
|
14
|
Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality. GeroScience 2022; 45:901-913. [PMID: 36401109 PMCID: PMC9886744 DOI: 10.1007/s11357-022-00687-0] [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: 10/13/2022] [Accepted: 11/06/2022] [Indexed: 11/20/2022] Open
Abstract
Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (Pinteraction = 0.04) and for daughters as compared to sons (Pinteraction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.
Collapse
|
15
|
Yang Q, Gao S, Lin J, Lyu K, Wu Z, Chen Y, Qiu Y, Zhao Y, Wang W, Lin T, Pan H, Chen M. A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model. BMC Bioinformatics 2022; 23:411. [PMID: 36192681 PMCID: PMC9528174 DOI: 10.1186/s12859-022-04966-7] [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: 06/03/2022] [Accepted: 09/26/2022] [Indexed: 11/11/2022] Open
Abstract
Background Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results. Methods and results Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas’ associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease. Conclusion We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04966-7.
Collapse
Affiliation(s)
- Qing Yang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Sunan Gao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China
| | - Junfen Lin
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Ke Lyu
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zexu Wu
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yuhao Chen
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yinwei Qiu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Yanrong Zhao
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Wei Wang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Tianxiang Lin
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Huiyun Pan
- The First Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ming Chen
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China. .,The First Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, 310058, China.
| |
Collapse
|
16
|
Cao X, Ma C, Zheng Z, He L, Hao M, Chen X, Crimmins EM, Gill TM, Levine ME, Liu Z. Contribution of life course circumstances to the acceleration of phenotypic and functional aging: A retrospective study. EClinicalMedicine 2022; 51:101548. [PMID: 35844770 PMCID: PMC9284373 DOI: 10.1016/j.eclinm.2022.101548] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/27/2022] [Accepted: 06/20/2022] [Indexed: 10/28/2022] Open
Abstract
Background Accelerated aging leads to increasing burdens of chronic diseases in late life, posing a huge challenge to the society. With two well-developed aging measures (i.e., physiological dysregulation [PD] and frailty index [FI]), this study aimed to evaluate the relative contributions of life course circumstances (e.g., childhood and adulthood socioeconomic status) to variance in aging. Methods We assembled data for 6224 middle-aged and older adults in China from the 2014 life course survey (June to December 2014), the 2015 biomarker collection (July 2015 to January 2016), and the 2015 main survey (July 2015 to January 2016) of the China Health and Retirement Longitudinal Study. Two aging measures (PD and FI) were calculated, with a higher value indicating more accelerated aging. Life course circumstances included childhood (i.e., socioeconomic status, war, health, trauma, relationship, and parents' health) and adulthood circumstances (i.e., socioeconomic status, adversity, and social support), demographics, and behaviours. The Shapley value decomposition, hierarchical clustering, and general linear regression models were performed. Findings The Shapley value decomposition revealed that all included life course circumstances accounted for about 6·3% and 29·7% of variance in PD and FI, respectively. We identified six subpopulations who shared similar patterns in terms of childhood and adulthood circumstances. The most disadvantaged subpopulation (i.e., subpopulation 6 [more childhood trauma and adulthood adversity]) consistently exhibited accelerated aging indicated by the two aging measures. Relative to the most advantaged subpopulation (i.e., subpopulation 1 [less childhood trauma and adulthood adversity]), PD and FI in the most disadvantaged subpopulation were increased by an average of 0·14 (i.e., coefficient, by one-standard deviation, 95% confidence interval [CI] 0·06-0·21; p < 0·0001) and 0·10 (by one-point, 95% CI 0·09-0·11; p < 0·0001), respectively. Interpretation Our findings highlight the different contributions of life course circumstances to phenotypic and functional aging. Special attention should be given to promoting health for the disadvantaged subpopulation and narrowing their health gap with advantaged counterparts. Funding National Natural Science Foundation of China, Milstein Medical Asian American Partnership Foundation, Natural Science Foundation of Zhejiang Province, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Centre for Advancing Translational Sciences, and Yale Alzheimer's Disease Research Centre.
Collapse
Affiliation(s)
- Xingqi Cao
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing 211189, Jiangsu, China
| | - Zhoutao Zheng
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Liu He
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Meng Hao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - 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
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Thomas M. Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Zuyun Liu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| |
Collapse
|
17
|
Wei K, Peng S, Liu N, Li G, Wang J, Chen X, He L, Chen Q, Lv Y, Guo H, Lin Y. All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations. J Gerontol A Biol Sci Med Sci 2022; 77:2288-2297. [PMID: 35417546 PMCID: PMC9923798 DOI: 10.1093/gerona/glac081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA. METHODS Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts. RESULTS A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations. CONCLUSION All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.
Collapse
Affiliation(s)
- Kai Wei
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shanshan Peng
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Na Liu
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Guyanan Li
- Department of Clinical Laboratory Medicine, Fifth People’s Hospital of Shanghai Fudan University, Shanghai, China
| | - Jiangjing Wang
- Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaotong Chen
- Department of Clinical Laboratory, Central Laboratory, Jing’an District Central Hospital of Shanghai, Fudan University, Shanghai, China
| | - Leqi He
- Department of Clinical Laboratory Medicine, Fifth People’s Hospital of Shanghai Fudan University, Shanghai, China
| | - Qiudan Chen
- Department of Clinical Laboratory, Central Laboratory, Jing’an District Central Hospital of Shanghai, Fudan University, Shanghai, China
| | - Yuan Lv
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Huan Guo
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yong Lin
- Address correspondence to: Yong Lin, PhD, Department of Laboratory Medicine, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Jing’an District, Shanghai 200040, People’s Republic of China. E-mail:
| |
Collapse
|
18
|
Li Q, Legault V, Girard VD, Ferrucci L, Fried LP, Cohen AA. An objective metric of individual health and aging for population surveys. Popul Health Metr 2022; 20:11. [PMID: 35361249 PMCID: PMC8974028 DOI: 10.1186/s12963-022-00289-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/21/2022] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND We have previously developed and validated a biomarker-based metric of overall health status using Mahalanobis distance (DM) to measure how far from the norm of a reference population (RP) an individual's biomarker profile is. DM is not particularly sensitive to the choice of biomarkers; however, this makes comparison across studies difficult. Here we aimed to identify and validate a standard, optimized version of DM that would be highly stable across populations, while using fewer and more commonly measured biomarkers. METHODS Using three datasets (the Baltimore Longitudinal Study of Aging, Invecchiare in Chianti and the National Health and Nutrition Examination Survey), we selected the most stable sets of biomarkers in all three populations, notably when interchanging RPs across populations. We performed regression models, using a fourth dataset (the Women's Health and Aging Study), to compare the new DM sets to other well-known metrics [allostatic load (AL) and self-assessed health (SAH)] in their association with diverse health outcomes: mortality, frailty, cardiovascular disease (CVD), diabetes, and comorbidity number. RESULTS A nine- (DM9) and a seventeen-biomarker set (DM17) were identified as highly stable regardless of the chosen RP (e.g.: mean correlation among versions generated by interchanging RPs across dataset of r = 0.94 for both DM9 and DM17). In general, DM17 and DM9 were both competitive compared with AL and SAH in predicting aging correlates, with some exceptions for DM9. For example, DM9, DM17, AL, and SAH all predicted mortality to a similar extent (ranges of hazard ratios of 1.15-1.30, 1.21-1.36, 1.17-1.38, and 1.17-1.49, respectively). On the other hand, DM9 predicted CVD less well than DM17 (ranges of odds ratios of 0.97-1.08, 1.07-1.85, respectively). CONCLUSIONS The metrics we propose here are easy to measure with data that are already available in a wide array of panel, cohort, and clinical studies. The standardized versions here lose a small amount of predictive power compared to more complete versions, but are nonetheless competitive with existing metrics of overall health. DM17 performs slightly better than DM9 and should be preferred in most cases, but DM9 may still be used when a more limited number of biomarkers is available.
Collapse
Affiliation(s)
- Qing Li
- School of Economics and Management, Xinjiang University, 666 Shengli Road, Urumqi, 830046, China
| | - Véronique Legault
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada
| | - Vincent-Daniel Girard
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, 21225, USA
| | - Linda P Fried
- Mailman School of Public Health, Columbia University, 722 W. 168th Street, New York, NY, R140810032, USA
| | - Alan A Cohen
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
- Research Center on Aging, 1036 Belvédère S, Sherbrooke, QC, J1H 4C4, Canada.
- Research Center of Centre Hospitalier Universitaire de Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.
| |
Collapse
|
19
|
Cao X, Yang G, Jin X, He L, Li X, Zheng Z, Liu Z, Wu C. A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study. Front Med (Lausanne) 2021; 8:698851. [PMID: 34926482 PMCID: PMC8671693 DOI: 10.3389/fmed.2021.698851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.
Collapse
Affiliation(s)
- Xingqi Cao
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guanglai Yang
- Global Health Research Center, Duke Kunshan University, Kunshan, China
| | - Xurui Jin
- Global Health Research Center, Duke Kunshan University, Kunshan, China.,MindRank AI ltd., Hangzhou, China
| | - Liu He
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhoutao Zheng
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zuyun Liu
- Department of Big Data in Health Science, School of Public Health and Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Kunshan, China
| |
Collapse
|
20
|
Association of lifestyle with mortality and the mediating role of aging among older adults in China. Arch Gerontol Geriatr 2021; 98:104559. [PMID: 34741896 DOI: 10.1016/j.archger.2021.104559] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/30/2021] [Accepted: 10/10/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVES 1) examine the association between lifestyle and mortality; 2) examine the association between two aging measures and mortality; 3) evaluate the mediating effect of the two aging measures on the association between lifestyle and mortality among older Chinese adults. METHODS We used data from 2039 older adults (≥ 65 years) from the 2011/2012 biomarker substudy of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We created a healthy lifestyle index based on 5 factors (exercise, smoking, drinking, diet, and BMI, range: 0-5). We calculated two aging measures, the Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD), based on 10 blood-based biomarkers using algorithms developed previously. A Cox proportional hazards model, general linear regression model, and formal mediation analysis were performed. RESULTS After adjustment for age and sex, compared to participants without any healthy lifestyle factors, those with 5 healthy lifestyle factors had an 85% lower risk of mortality (hazard ratio [HR] = 0.15, 95% confidence interval [CI]: 0.04, 0.60). PD, but not KDM-BA, was significantly associated with mortality (HR = 1.69, 95% CI: 1.25, 2.29). The healthy lifestyle index was negatively associated with PD (β = -0.021, P = 0.012). PD mediated 9% (95% CI: 1%, 52%, P = 0.043) of the total effect of the healthy lifestyle index on mortality. CONCLUSIONS In the older Chinese population, healthy lifestyle reduces mortality risk and aging partially mediates this association. The findings highlight the importance of adherence to a healthy lifestyle for promoting phenotypic aging even in late life.
Collapse
|
21
|
Wang C, Guan X, Bai Y, Feng Y, Wei W, Li H, Li G, Meng H, Li M, Jie J, Fu M, Wu X, He M, Zhang X, Yang H, Lu Y, Guo H. A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort. Ann N Y Acad Sci 2021; 1507:108-120. [PMID: 34480349 DOI: 10.1111/nyas.14685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/04/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023]
Abstract
This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used to estimate BA. We developed a composite BA predictor showing a high correlation with chronological age (CA) (r = 0.82) by using an extreme gradient boosting (XGBoost) algorithm. The average frequency hearing threshold, forced expiratory volume in 1 second (FEV1 ), gender, systolic blood pressure, and homocysteine ranked as the top five important features for the BA predictor. Two aging indexes, recorded as the AgingAccel (the residual from regressing predicted age on CA) and aging rate (the ratio of predicted age to CA), showed positive associations with the risks of all-cause (HR (95% CI) = 1.12 (1.10-1.14) and 1.08 (1.07-1.10), respectively) and cause-specific (HRs ranged from 1.06 to ∼1.15) mortality. Each 1-point increase in healthy lifestyle score (including normal body mass index, never smoking, moderate alcohol drinking, physically active, and sleep 7-9 h/night) was associated with a 0.21-year decrease in the AgingAccel (95% CI: -0.27 to -0.15) and a 0.4% decrease in the aging rate (95% CI: -0.5% to -0.3%). This study developed a machine learning-based BA predictor in a prospective Chinese cohort. Adherence to healthy lifestyles showed associations with delayed biological aging, which highlights potential preventive interventions.
Collapse
Affiliation(s)
- Chenming Wang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xin Guan
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yansen Bai
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yue Feng
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Wei
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hang Li
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guyanan Li
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hua Meng
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mengying Li
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiali Jie
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ming Fu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiulong Wu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Meian He
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Yanjun Lu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huan Guo
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| |
Collapse
|
22
|
Muscari A, Bianchi G, Forti P, Magalotti D, Pandolfi P, Zoli M. The association of proBNPage with manifestations of age-related cardiovascular, physical, and psychological impairment in community-dwelling older adults. GeroScience 2021; 43:2087-2100. [PMID: 33987773 PMCID: PMC8492850 DOI: 10.1007/s11357-021-00381-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/28/2021] [Indexed: 11/29/2022] Open
Abstract
NT-proB-type natriuretic peptide (NT-proBNP) serum concentration can be transformed by simple formulas into proBNPage, a surrogate of biological age strongly associated with chronological age, all-cause mortality, and disease count. This cross-sectional study aimed to assess whether proBNPage is also associated with other manifestations of the aging process in comparison with other variables. The study included 1117 noninstitutionalized older adults (73.1 ± 5.6 years, 537 men). Baseline measurements of serum NT-proBNP, erythrocyte sedimentation rate, hemoglobin, lymphocytes, and creatinine, which have previously been shown to be highly associated with both age and all-cause mortality, were performed. These variables were compared between subjects with and without manifestations of cardiovascular impairment (myocardial infarction (MI), stroke, peripheral artery disease (PAD), arterial revascularizations (AR)), physical impairment (long step test duration (LSTD), walking problems, falls, deficit in one or more activities of daily living), and psychological impairment (poor self-rating of health (PSRH), anxiety/depression, Mini Mental State Examination (MMSE) score < 24). ProBNPage (years) was independently associated (OR, 95% CI) with MI (1.08, 1.07-1.10), stroke (1.02, 1.00-1.05), PAD (1.04, 1.01-1.06), AR (1.06, 1.04-1.08), LSTD (1.03, 1.02-1.04), walking problems (1.02, 1.01-1.03), and PSRH (1.02, 1.01-1.02). For 5 of these 7 associations, the relationship was stronger than that of chronological age. In addition, proBNPage was univariately associated with MMSE score < 24, anxiety/depression, and falls. None of the other variables provided comparable performances. Thus, in addition to the known associations with mortality and disease count, proBNPage is also associated with cardiovascular manifestations as well as noncardiovascular manifestations of the aging process.
Collapse
Affiliation(s)
- Antonio Muscari
- Department of Medical and Surgical Sciences, University of Bologna, Via Albertoni, 15 40138 Bologna, Italy
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Giampaolo Bianchi
- Department of Medical and Surgical Sciences, University of Bologna, Via Albertoni, 15 40138 Bologna, Italy
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Paola Forti
- Department of Medical and Surgical Sciences, University of Bologna, Via Albertoni, 15 40138 Bologna, Italy
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Donatella Magalotti
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Paolo Pandolfi
- Epidemiological and Health Promotion Unit, Department of Public Health, AUSL Bologna, Bologna, Italy
| | - Marco Zoli
- Department of Medical and Surgical Sciences, University of Bologna, Via Albertoni, 15 40138 Bologna, Italy
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - the Pianoro Study Group
- Department of Medical and Surgical Sciences, University of Bologna, Via Albertoni, 15 40138 Bologna, Italy
- Medical Department of Continuity of Care and Disability, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Epidemiological and Health Promotion Unit, Department of Public Health, AUSL Bologna, Bologna, Italy
| |
Collapse
|
23
|
Cohen AA, Leblanc S, Roucou X. Robust Physiological Metrics From Sparsely Sampled Networks. Front Physiol 2021; 12:624097. [PMID: 33643068 PMCID: PMC7902772 DOI: 10.3389/fphys.2021.624097] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.
Collapse
Affiliation(s)
- Alan A. Cohen
- Groupe de Recherche PRIMUS, Département de Médecine de Famille et de Médecine d’Urgence, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, QC, Canada
- Research Center on Aging, CIUSSS-de-l’Estrie-CHUS, Sherbrooke, QC, Canada
| | - Sebastien Leblanc
- Département de Biochimie et de Génomique Fonctionnelle, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Xavier Roucou
- Département de Biochimie et de Génomique Fonctionnelle, Université de Sherbrooke, Sherbrooke, QC, Canada
| |
Collapse
|