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Tian G, Zhou R, Guo X, Li R. Causal effects of blood pressure and the risk of frailty: a bi-directional two-sample Mendelian randomization study. J Hum Hypertens 2024; 38:329-335. [PMID: 38361027 DOI: 10.1038/s41371-024-00901-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/17/2024]
Abstract
Observational studies have indicated that high blood pressure (BP) may be a risk factor to frailty. However, the causal association between BP and frailty remains not well determined. The purpose of this bi-directional two-sample Mendelian randomization (MR) study was to investigate the causal relationship between BP and frailty. Independent single nucleotide polymorphisms (SNPs) strongly (P < 5E-08) associated with systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) were selected as instrumental variables. Two different published genome-wide association studies (GWAS) on BP from the CHARGE (n = 810,865) and ICBP (n = 757,601) consortia were included. Summary-level data on frailty index (FI) were obtained from the latest GWAS based on UK Biobank and Swedish TwinGene cohorts (n = 175,226). Inverse variance weighted (IVW) approach with other sensitivity analyses were used to calculate the causal estimate. Using the CHARGE dataset, genetic predisposition to increased SBP (β = 0.135, 95% CI = 0.093 to 0.176, P = 1.73E-10), DBP (β = 0.145, 95% CI = 0.104 to 0.186, P = 3.14E-12), and PP (β = 0.114, 95% CI = 0.070 to 0.157, p = 2.87E-07) contributed to a higher FI, which was validated in the ICBP dataset. There was no significant causal effect of FI on SBP, DBP, and PP. Similar results were obtained from different MR methods, indicating good stability. There was potential heterogeneity detected by Cochran's Q test, but no horizontal pleiotropy was observed in MR-Egger intercept test (P > 0.05). These findings evinced that higher BP and PP were causally associated with an increased risk of frailty, suggesting that controlling hypertension could reduce the risk of frailty.
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Affiliation(s)
- Ge Tian
- Xi'an Medical University, Xi'an, 710021, Shaanxi, China
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Rong Zhou
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China
| | - Xingzhi Guo
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China.
| | - Rui Li
- Xi'an Medical University, Xi'an, 710021, Shaanxi, China.
- Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China.
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Guo Z, Xue H, Fan L, Wu D, Wang Y, Chung Y, Liao Y, Ruan Z, Du W. Differential effects of size-specific particulate matter on frailty transitions among middle-aged and older adults in China: findings from the China Health and Retirement Longitudinal Study (CHARLS), 2015-2018. Int Health 2024; 16:182-193. [PMID: 37161970 PMCID: PMC10939306 DOI: 10.1093/inthealth/ihad033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/07/2023] [Accepted: 05/07/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND This study aimed to assess the long-term effects of size-specific particulate matter (PM) on frailty transitions in middle-aged and older Chinese adults. METHODS We included 13 910 participants ≥45 y of age from the China Health and Retirement Longitudinal Study (CHARLS) for 2015 and 2018 who were classified into three categories in 2015 according to their frailty states: robust, prefrail and frail. Air quality data were obtained from the National Urban Air Quality Real-time Publishing Platform. A two-level logistic regression model was used to examine the association between concentrations of PM and frailty transitions. RESULTS At baseline, the total number of robust, prefrail and frail participants were 7516 (54.0%), 4324 (31.1%) and 2070 (14.9%), respectively. Significant associations were found between PM concentrations and frailty transitions. For each 10 μg/m3 increase in the 3-y averaged 2.5-μm PM (PM2.5) concentrations, the risk of worsening in frailty increased in robust (odds ratio [OR] 1.06 [95% confidence interval {CI} 1.01 to 1.12]) and prefrail (OR 1.07 [95% CI 1.01 to 1.13]) participants, while the probability of improvement in frailty in prefrail (OR 0.91 [95% CI 0.84 to 0.98]) participants decreased. In addition, the associations of PM10 and coarse fraction of PM with frailty transitions showed similar patterns. CONCLUSIONS Long-term exposure to PM was associated with higher risks of worsening and lower risks of improvement in frailty among middle-aged and older adults in China.
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Affiliation(s)
- Zhen Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - Hui Xue
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - Lijun Fan
- Department of Medical Insurance, School of Public Health, Southeast University, Nanjing 210009, China
| | - Di Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - Yiming Wang
- Department of Medical Insurance, School of Public Health, Southeast University, Nanjing 210009, China
| | - Younjin Chung
- National Centre for Epidemiology and Population Health, College of Health and Medicine, Australian National University, Canberra, ACT, Australia
| | - Yilan Liao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Zengliang Ruan
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
| | - Wei Du
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
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Cao X, Li X, Zhang J, Sun X, Yang G, Zhao Y, Li S, Hoogendijk EO, Wang X, Zhu Y, Allore H, Gill TM, Liu Z. Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study. JMIR Public Health Surveill 2023; 9:e45502. [PMID: 37200070 PMCID: PMC10236284 DOI: 10.2196/45502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Compared with adults with normal glucose metabolism, those with prediabetes tend to be frail. However, it remains poorly understood whether frailty could identify adults who are most at risk of adverse outcomes related to prediabetes. OBJECTIVE We aimed to systematically evaluate the associations between frailty, a simple health indicator, and risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), diabetes-related microvascular disease, cardiovascular disease (CVD), chronic kidney disease (CKD), eye disease, dementia, depression, and all-cause mortality in late life among middle-aged adults with prediabetes. METHODS We evaluated 38,950 adults aged 40 years to 64 years with prediabetes using the baseline survey from the UK Biobank. Frailty was assessed using the frailty phenotype (FP; range 0-5), and participants were grouped into nonfrail (FP=0), prefrail (1≤FP≤2), and frail (FP≥3). Multiple adverse outcomes (ie, T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality) were ascertained during a median follow-up of 12 years. Cox proportional hazards regression models were used to estimate the associations. Several sensitivity analyses were performed to test the robustness of the results. RESULTS At baseline, 49.1% (19,122/38,950) and 5.9% (2289/38,950) of adults with prediabetes were identified as prefrail and frail, respectively. Both prefrailty and frailty were associated with higher risks of multiple adverse outcomes in adults with prediabetes (P for trend <.001). For instance, compared with their nonfrail counterparts, frail participants with prediabetes had a significantly higher risk (P<.001) of T2DM (hazard ratio [HR]=1.73, 95% CI 1.55-1.92), diabetes-related microvascular disease (HR=1.89, 95% CI 1.64-2.18), CVD (HR=1.66, 95% CI 1.44-1.91), CKD (HR=1.76, 95% CI 1.45-2.13), eye disease (HR=1.31, 95% CI 1.14-1.51), dementia (HR=2.03, 95% CI 1.33-3.09), depression (HR=3.01, 95% CI 2.47-3.67), and all-cause mortality (HR=1.81, 95% CI 1.51-2.16) in the multivariable-adjusted models. Furthermore, with each 1-point increase in FP score, the risk of these adverse outcomes increased by 10% to 42%. Robust results were generally observed in sensitivity analyses. CONCLUSIONS In UK Biobank participants with prediabetes, both prefrailty and frailty are significantly associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related diseases, and all-cause mortality. Our findings suggest that frailty assessment should be incorporated into routine care for middle-aged adults with prediabetes, to improve the allocation of health care resources and reduce diabetes-related burden.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyi Sun
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Yining Zhao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Shujuan Li
- Department of Neurology, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Emiel O Hoogendijk
- Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Xiaofeng Wang
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Heather Allore
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
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Liu H, Zhou W, Liu Q, Yu J, Wang C. Global Prevalence and Factors Associated with Frailty among Community-Dwelling Older Adults with Hypertension: A Systematic Review and Meta-Analysis. J Nutr Health Aging 2023; 27:1238-1247. [PMID: 38151875 DOI: 10.1007/s12603-023-2035-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/06/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Frailty may increase the risk of adverse outcomes and the presence of comorbidities in hypertension. Understanding the prevalence of frailty in older adults with hypertension is of great importance, whereas estimates of the prevalence of frailty in this population vary greatly. OBJECTIVES A systematic review and meta-analysis was conducted to estimate the pooled prevalence of frailty and prefrailty among community-dwelling older adults with hypertension, and to examine the risk factors associated with (pre)frailty in this population. METHODS PubMed, Web of Science, The Cochrane Library, EMBASE, and CINAHL were searched from the inception to May 10, 2023. Investigators assessed eligibility, extracted data, and evaluated methodological quality. The pooled prevalence of frailty and prefrailty was calculated using the random-effects model. Meta-regression analysis and subgroup analysis were conducted to explore sources of heterogeneity. Sensitivity analysis was undertaken by the leave-one-out method and by removing studies with moderate/high risk of bias. The Mantel-Haenszel or inverse variance method was used to estimate risk factors of frailty. RESULTS A total of 14 studies met the inclusion criteria, involving 185,249 participants. The pooled prevalence in older adults with hypertension was 23% (95% CI 0.09-0.36) for frailty and 46% (95% CI 0.38-0.54) for prefrailty. The pooled prevalence of frailty was greater in studies with a higher proportion of females (24%, 95% CI 0.05-0.50), using multidimensional tools to define frailty (30%, 95% CI 0.10-0.51) and conducted in Western Pacific (27%, 95% CI 0.17-0.39). Age, female sex, depression, and previous hospitalizations were risk factors of frailty among older adults with hypertension. CONCLUSION Frailty and prefrailty are prevalent in community-dwelling older adults with hypertension, and limited risk factors are identified. This implicates the importance of frailty assessment integrated into the routine primary care for older adults with hypertension in community settings as well as the understanding of potential factors.
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Affiliation(s)
- H Liu
- Cuili Wang, PhD, is a senior research scientist, School of Nursing, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China; (C. Wang)
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