1
|
Luo YX, Zhou XH, Heng T, Yang LL, Zhu YH, Hu P, Yao XQ. Bidirectional transitions of sarcopenia states in older adults: The longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle 2024; 15:1915-1929. [PMID: 39001569 PMCID: PMC11446714 DOI: 10.1002/jcsm.13541] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/05/2024] [Accepted: 06/15/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND Sarcopenia, the age-related loss of muscle mass and function, brings multiple adverse outcomes including disability and death. Several sarcopenia consensuses have newly introduced the premorbid concept of possible sarcopenia and recommended early lifestyle interventions. Bidirectional transitions of premorbid states have been revealed in several chronic diseases yet not clarified in sarcopenia. This study aims to investigate the underlying transition patterns of sarcopenia states. METHODS The study utilized three waves of data from a nationally representative survey, the China Health and Retirement Longitudinal Study (CHARLS), and included community-dwelling individuals aged 60 years and older with at least two sarcopenia states assessments based on the Asian Working Group for Sarcopenia criteria 2019 (AWGS2019) between 2011 and 2015. The estimated transition intensity and probability between non-sarcopenia, possible sarcopenia, sarcopenia, and death were investigated using multi-stage Markov (MSM) models. RESULTS The study comprised 4395 individuals (49.2% female, median age 67 years) with a total of 10 778 records of sarcopenia state assessment, and the mean follow-up period was 3.29 years. A total of 24.5% of individuals with a current state of possible sarcopenia returned to non-sarcopenia, 60.3% remained possible sarcopenia, 6.7% progressed to sarcopenia, and 8.5% died by the next follow-up. The transition intensity of recovery to non-sarcopenia (0.252, 95% CI 0.231-0.275) was 2.8 times greater than the deterioration to sarcopenia (0.090, 95% CI 0.080-0.100) for individuals with possible sarcopenia. For individuals with possible sarcopenia, the estimated probabilities of recovering to non-sarcopenia, progressing to sarcopenia, and transitioning to death within a 1-year observation were 0.181, 0.066, and 0.035, respectively. For individuals with sarcopenia, the estimated probabilities of recovering to non-sarcopenia, recovering to possible sarcopenia, and transitioning to death within 1-year observation were 0.016, 0.125, and 0.075, respectively. In covariables analysis, age, sex, body mass index, physical function impairment, smoking, hypertension, and diabetes are important factors influencing bidirectional transitions. CONCLUSIONS The findings highlight the bidirectional transitions of sarcopenia states among older adults and reveal a notable proportion of possible sarcopenia show potential for recovery in the natural course. Screening and intensifying interventions based on risk factors may facilitate a recovery transition.
Collapse
Affiliation(s)
- Ya-Xi Luo
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Han Zhou
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tian Heng
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling-Ling Yang
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying-Hai Zhu
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peng Hu
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiu-Qing Yao
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Municipality Clinical Research Center for Geriatric Medicine, Chongqing, China
- Department of Rehabilitation Therapy, Chongqing Medical University, Chongqing, China
| |
Collapse
|
2
|
Chen Y, Xu L, Cheng Z, Zhang D, Yang J, Yin C, Li S, Li J, Hu Y, Wang Y, Liu Y, Wang Z, Zhang L, Chen R, Dou Q, Bai Y. Progression from different blood glucose states to cardiovascular diseases: a prospective study based on multi-state model. Eur J Prev Cardiol 2023; 30:1482-1491. [PMID: 37315161 DOI: 10.1093/eurjpc/zwad196] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/17/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
AIMS To quantify the trajectories from normoglycaemia to pre-diabetes, subsequently to type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cardiovascular death, and the effects of risk factors on the rates of transition. METHODS AND RESULTS We used data from the Jinchang Cohort of 42 585 adults aged 20-88 free of coronary heart disease (CHD) and stroke at baseline. A multistate model was applied for analysing the progression of CVD and its relation to various risk factors. During a median follow-up of 7 years, 7498 participants developed pre-diabetes, 2307 developed T2DM, 2499 developed CVD, and 324 died from CVD. Among 15 postulated transitions, transition from comorbid CHD and stroke to cardiovascular death had the highest rate (157.21/1000 person-years), followed by transition from stroke alone to cardiovascular death (69.31/1000 person-years) and transition from pre-diabetes to normoglycaemia (46.51/1000 person-years). Pre-diabetes had a sojourn time of 6.77 years, and controlling weight, blood lipids, blood pressure, and uric acid within normal limits may promote reversion to normoglycaemia. Among transitions to CHD alone and stroke alone, transition from T2DM had the highest rate (12.21/1000 and 12.16/1000 person-years), followed by transition from pre-diabetes (6.81/1000 and 4.93/1000 person-years) and normoglycaemia (3.28/1000 and 2.39/1000 person-years). Age and hypertension were associated with an accelerated rate for most transitions. Overweight/obesity, smoking, dyslipidaemia, and hyperuricaemia played crucial but different roles in transitions. CONCLUSION Pre-diabetes was the optimal intervention stage in the disease trajectory. The derived transition rates, sojourn time, and influence factors could provide scientific support for the primary prevention of both T2DM and CVD.
Collapse
Affiliation(s)
- Yarong Chen
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Lulu Xu
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Zhiyuan Cheng
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 xueyuan Street, Shenzhen, Guangdong 518055, China
| | - Desheng Zhang
- Workers' Hospital of Jinchuan Corporation, Jinchuan Group CO., LTD, 53 Beijing Road, Jinchang, Gansu 737100, China
| | - Jingli Yang
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Chun Yin
- Workers' Hospital of Jinchuan Corporation, Jinchuan Group CO., LTD, 53 Beijing Road, Jinchang, Gansu 737100, China
| | - Siyu Li
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Jing Li
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Yujia Hu
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Yufeng Wang
- Workers' Hospital of Jinchuan Corporation, Jinchuan Group CO., LTD, 53 Beijing Road, Jinchang, Gansu 737100, China
| | - Yanyan Liu
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Zhongge Wang
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Lizhen Zhang
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Ruirui Chen
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Qian Dou
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| | - Yana Bai
- Institution of Epidemiology and Statistics, School of Public Health, Lanzhou University, 199 Donggang West Road, Lanzhou, Gansu 730000, China
| |
Collapse
|
3
|
Shi H, Chen L, Zhang S, Li R, Wu Y, Zou H, Wang C, Cai M, Lin H. Dynamic association of ambient air pollution with incidence and mortality of pulmonary hypertension: A multistate trajectory analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 262:115126. [PMID: 37315366 PMCID: PMC10443233 DOI: 10.1016/j.ecoenv.2023.115126] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND There is little evidence regarding the association between ambient air pollution and incidence and the mortality of pulmonary hypertension (PH). METHODS We included 494,750 participants at baseline in the UK Biobank study. Exposures to PM2.5, PM10, NO2, and NOx were estimated at geocoded participants' residential addresses, utilizing pollution data provided by UK Department for Environment, Food and Rural Affairs (DEFRA). The outcomes were the incidence and mortality of PH. We used multivariate multistate models to investigate the impacts of various ambient air pollutants on both incidence and mortality of PH. RESULTS During a median follow-up of 11.75 years, 2517 participants developed incident PH, and 696 died. We observed that all ambient air pollutants were associated with increased incidence of PH with different magnitudes, with adjusted hazard ratios (HRs) [95% confidence intervals (95% CIs)] for each interquartile range (IQR) increase of 1.73 (1.65, 1.81) for PM2.5, 1.70 (1.63, 1.78) for PM10, 1.42 (1.37, 1.48) for NO2, and 1.35 (1.31, 1.40) for NOx. Furthermore, PM2.5, PM10, NO2 and NO2 influenced the transition from PH to death, and the corresponding HRs (95% CIs) were 1.35 (1.25, 1.45), 1.31 (1.21, 1.41), 1.28 (1.20, 1.37) and 1.24 (1.17, 1.32), respectively. CONCLUSION The results of our study indicate that exposure to various ambient air pollutants might play key but differential roles in both the incidence and mortality of PH.
Collapse
Affiliation(s)
- Hui Shi
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Rui Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yinglin Wu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| |
Collapse
|
4
|
Qin Y, Han H, Li Y, Cui J, Jia H, Ge X, Ma Y, Bai W, Zhang R, Chen D, Yi F, Yu H. Estimating Bidirectional Transitions and Identifying Predictors of Mild Cognitive Impairment. Neurology 2023; 100:e297-e307. [PMID: 36220593 PMCID: PMC9869761 DOI: 10.1212/wnl.0000000000201386] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/26/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Various resources exist for treating mild cognitive impairment (MCI) or dementia separately as terminal events or for focusing solely on a 1-way path from MCI to dementia without taking into account heterogeneous transitions. Little is known about the trajectory of reversion from MCI to normal cognition (NC) or near-NC and patterns of postreversion, which refers to cognitive trajectories of patients who have reversed from MCI to NC. Our objectives were to (1) quantitatively predict bidirectional transitions of MCI (reversion and progression), (2) explore patterns of future cognitive trajectories for postreversion, and (3) estimate the effects of demographic characteristics, APOE, cognition, daily activity ability, depression, and neuropsychiatric symptoms on transition probabilities. METHODS We constructed a retrospective cohort by reviewing patients with an MCI diagnosis at study entry and at least 2 follow-up visits between June 2005 and February 2021. Defining NC or near-NC and MCI as transient states and dementia as an absorbing state, we used continuous-time multistate Markov models to estimate instantaneous transition intensity between states, transition probabilities from one state to another at any given time during follow-up, and hazard ratios of reversion-related variables. RESULTS Among 24,220 observations from 6,651 participants, there were 2,729 transitions to dementia and 1,785 reversions. As for postreversion, there were 630 and 73 transitions of progression to MCI and dementia, respectively. The transition intensity of progression to MCI for postreversion was 0.317 (2.48-fold greater than that for MCI progression or reversion). For postreversion participants, the probability of progressing to dementia increased by 2% yearly. Participants who progressed to MCI were likely to reverse again (probability of 40% over 15 years). Age, independence level, APOE, cognition, daily activity ability, depression, and neuropsychiatric symptoms were significant predictors of bidirectional transitions. DISCUSSION The nature of bidirectional transitions cannot be ignored in multidimensional MCI research. We found that postreversion participants remained at an increased risk of progression to MCI or dementia over the longer term and experienced recurrent reversions. Our findings may serve as a valuable reference for future research and enable health care professionals to better develop proactive management plans and targeted interventions.
Collapse
Affiliation(s)
- Yao Qin
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Hongjuan Han
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Yang Li
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Jing Cui
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Haixia Jia
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Xiaoyan Ge
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Yifei Ma
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Wenlin Bai
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Rong Zhang
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Durong Chen
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Fuliang Yi
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China
| | - Hongmei Yu
- From the Department of Health Statistics (Y.Q., H.H., J.C., X.G., Y.M., W.B., R.Z., D.C., F.Y., H.Y.), School of Public Health, Shanxi Medical University, Taiyuan; Department of Medical Device Ethics (Y.L.), Shanxi Province Cancer Hospital, Taiyuan; Department of Neurology (H.J.), First Hospital of Shanxi Medical University, Taiyuan; and Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment (H.Y.), Taiyuan, China.
| |
Collapse
|
5
|
Hong X, Miao K, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association Between DNA Methylation and Blood Pressure: A 5-Year Longitudinal Twin Study. Hypertension 2023; 80:169-181. [PMID: 36345830 DOI: 10.1161/hypertensionaha.122.19953] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous EWASs (Epigenome-Wide Association Studies) have reported hundreds of blood pressure (BP) associated 5'-cytosine-phosphate-guanine-3' (CpG) sites. However, their results were inconsistent. Longitudinal observations on the temporal relationship between DNA methylation and BP are lacking. METHODS A candidate CpG site association study for BP was conducted on 1072 twins in the Chinese National Twin Registry. PubMed and EMBASE were searched for candidate CpG sites. Cross-lagged models were used to assess the temporal relationship between BP and DNA methylation in 308 twins who completed 2 surveys in 2013 and 2018. Then, the significant cross-lagged associations were validated by adopting the Inference About Causation From Examination of Familial Confounding approach. Finally, to evaluate the cumulative effects of DNA methylation on the progression of hypertension, we established methylation risk scores based on BP-associated CpG sites and performed Markov multistate models. RESULTS 16 and 20 CpG sites were validated to be associated with systolic BP and diastolic BP, respectively. In the cross-lagged analysis, we detected that methylation of 2 CpG sites could predict subsequent systolic BP, and systolic BP predicted methylation at another 3 CpG sites. For diastolic BP, methylation at 3 CpG sites had significant cross-lagged effects for predicting diastolic BP levels, while the prediction from the opposite direction was observed at one site. Among these, 3 associations were validated in the Inference About Causation From Examination of Familial Confounding analysis. Using the Markov multistate model, we observed that methylation risk scores were associated with the development of hypertension. CONCLUSIONS Our findings suggest the significance of DNA methylation in the development of hypertension.
Collapse
Affiliation(s)
- Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, China (Z.P.)
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China (M.Y.)
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China (H.W.)
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China (X.W.)
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China (Y.L.)
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China (X.H., K.M., W.C., J.L., C.Y., T.H., D.S., C.L., Y.P., W.G., L.L.)
| |
Collapse
|
6
|
Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
|
7
|
Zheng X, Xiong J, Zhang Y, Xu L, Zhou L, Zhao B, Wang Y. Multistate Markov model application for blood pressure transition among the Chinese elderly population: a quantitative longitudinal study. BMJ Open 2022; 12:e059805. [PMID: 35835530 PMCID: PMC9289040 DOI: 10.1136/bmjopen-2021-059805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE To explore the transitions of different blood pressure states based on a multistate Markov model among the Chinese elderly population. SETTING A community health centre in Xiamen, China. PARTICIPANTS 1833 elderly Chinese people. METHODS A multistate Markov model was built based on 5001 blood pressure measurements from 2015 to 2020. Research was conducted to explore the process of hypertension progression, providing information on the transition probability, HR and the mean sojourn time in three blood pressure states, namely normal state, elevated state and hypertensive state. RESULTS Probabilities of moving from the normal state to the hypertensive state in the first year were 16.97% (female) and 21.73% (male); they increased dramatically to 47.31% (female) and 51.70% (male) within a 3-year follow-up period. The sojourn time in the normal state was 1.5±0.08 years. Elderly women in the normal state had a 16.97%, 33.30% and 47.31% chance of progressing to hypertension within 1, 2 and 3 years, respectively. The corresponding probabilities for elderly men were 21.73%, 38.56% and 51.70%, respectively. For elderly women starting in the elevated state, the probabilities of developing hypertension were 25.07%, 43.03% and 56.32% in the next 1, 2 and 3 years, respectively; while the corresponding changes for elderly men were 20.96%, 37.65% and 50.86%. Increasing age, body mass index (BMI) and glucose were associated with the probability of developing hypertension from the normal state or elevated state. CONCLUSIONS Preventive actions against progression to hypertension should be conducted at an early stage. More awareness should be paid to elderly women with elevated state and elderly men with normal state. Increasing age, BMI and glucose were critical risk factors for developing hypertension. The derived transition probabilities and sojourn time can serve as a significant reference for making targeted interventions for hypertension progression among the Chinese elderly population.
Collapse
Affiliation(s)
- Xujuan Zheng
- Health Science Center, Shenzhen University, Shenzhen, China
| | - Juan Xiong
- Health Science Center, Shenzhen University, Shenzhen, China
| | - Yiqin Zhang
- Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Liping Xu
- Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Lina Zhou
- Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Bin Zhao
- Department of Medical Laboratory, Affiliated Xiang'an Hospital of Xiamen University, Xiamen, Fujian, China
| | - Yuxin Wang
- Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| |
Collapse
|
8
|
Wu Y, Wu W, Lin Y, Xiong J, Zheng X. Blood pressure states transitions among bus drivers: the application of multi-state Markov model. Int Arch Occup Environ Health 2022; 95:1995-2003. [DOI: 10.1007/s00420-022-01903-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/14/2022] [Indexed: 11/27/2022]
|
9
|
Artificial Intelligence and Hypertension Management. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
10
|
Yang J, Ju X, Liu F, Asan O, Church TS, Smith JO. Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2021; 2:291-298. [PMID: 35402965 PMCID: PMC8940207 DOI: 10.1109/ojemb.2021.3117872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/22/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022] Open
Abstract
Objective: Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. Results: Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC. The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. Our result shows that all proposed models achieved satisfactory performance, with the AUC values ranging from 0.826 to 0.850. Among the seven predictive models, the gradient boosting tree model outperformed other models, achieving an AUC of 0.850. Conclusions: Our risk prediction model shows great promise in automating real-time diagnosis, supporting healthcare practitioners to target high-risk individuals efficiently, and helping healthcare practitioners tailor proactive strategies to prevent the onset or delay the progression of the chronic diseases.
Collapse
Affiliation(s)
- Jingmei Yang
- Division of System EngineeringBoston UniversityBostonMA02246USA
| | - Xinglong Ju
- Price College of BusinessUniversity of OklahomaNormanOK73019USA
- School of Civil and Environmental EngineeringCornell UniversityIthacaNY14853USA
| | - Feng Liu
- School of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA
| | - Onur Asan
- School of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA
| | | | | |
Collapse
|
11
|
Xiong J, Fang Q, Chen J, Li Y, Li H, Li W, Zheng X. States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:7449. [PMID: 34299899 PMCID: PMC8304364 DOI: 10.3390/ijerph18147449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/08/2021] [Accepted: 07/10/2021] [Indexed: 01/10/2023]
Abstract
Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42-0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46-2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Xujuan Zheng
- Health Science Center, Shenzhen University, Shenzhen 518060, China; (J.X.); (Q.F.); (J.C.); (Y.L.); (H.L.); (W.L.)
| |
Collapse
|
12
|
Artificial Intelligence and Hypertension Management. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_263-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|