Associations of vision impairment and eye diseases with frailty in community-dwelling older adults: a nationwide longitudinal study in China

Shang, Xianwen; Wu, Guanrong; Wang, Wei; Zhu, Zhuoting; Zhang, Xueli; Huang, Yu; Hu, Yijun; He, Mingguang; Yu, Honghua

doi:10.1136/bjo-2022-322048

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…We found that the best predictors of VI included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, haemoglobin, high-density lipoprotein cholesterol, and arthritis or rheumatism. This finding aligns with those documented in prior research [24,25,[33][34][35]. Significantly, our study newly highlighted the crucial role of hearing impairment, self-expectation of health status, and pain in predicting VI.…”

Section: Comparison With Prior Worksupporting

confidence: 92%

“…The self-reported questionnaire included (1) demographic factors (gender, age, region); [24] (2) lifestyle (night sleep duration, smoking, drinking); (3) health status factors (pain, weight change, health status during childhood, self-expectations of health status ); [25] (4) disease factors (depression, hearing impairment, hypertension, dyslipidaemia, diabetes, liver disease, heart disease, stroke, kidney disease, stomach or other digestive disease, memory-related disease, arthritis or rheumatism, menopause, prostatic diseases); (5) living environment factors (house structure, heating energy, cooking energy, room temperature); (6) socioeconomic factors (standard of living, education level). Measurement parameters included (1) physical examination data ( [26] hand grip strength, waist, body mass index (BMI)) and ( 2) blood biomarker data (white blood cell, platelets, glycated haemoglobin, haemoglobin, glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol). The characteristics of the predictor distribution in the study are shown in (Multimedia Appendix 1).…”

Section: Predictors Of VImentioning

confidence: 99%

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Prediction and Determinants of Visual Impairment among Chinese Middle-aged and Elderly Adults: Machine Learning Algorithms (Preprint)

Mao,

Yu,

Lin

et al. 2024

Preprint

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BACKGROUND Visual impairment (VI) is a prevalent global health issue, affecting over 2.2 billion people worldwide, with nearly half of the Chinese population aged 60 and above being affected. Early detection of high-risk VI is essential for preventing irreversible vision loss among Chinese middle-aged and elderly adults. While machine learning (ML) algorithms exhibit significant predictive advantages, their application in predicting VI risk among the general middle-aged and elderly population in China remains limited. OBJECTIVE We aimed to predict VI and identify its determinants using ML algorithms. METHODS We used 19,047 participants from four waves of the China Health and Retirement Longitudinal Study (CHARLS) that were conducted between 2011 and 2018. To envisage the prevalence of VI, we generated a geographical distribution map. Additionally, we constructed a model using indicators of self-reported questionnaire, physical examination, and blood biomarkers as predictors. Multiple ML algorithms, including gradient boosting machine (GBM), dynamic random forest (DRF), generalised linear model (GLM), deep learning (DL), and stacked ensemble, were used for prediction. We plotted receiver operating characteristics (ROC) and calibration curves to assess the predictive performance. Variable importance analysis was used to identify key predictors. RESULTS Among 19,047 participants, 33.9% suffered from VI. Qinghai, Chongqing, Anhui, and Sichuan showed the highest VI rates, while Beijing and Xinjiang had the lowest. GLM, GBM, and stacked ensemble achieved acceptable area under curve values of 0.706, 0.710, and 0.715, respectively, with the stacked ensemble performing best. Key predictors included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, haemoglobin, high-density lipoprotein cholesterol, and arthritis or rheumatism. CONCLUSIONS Nearly one-third of middle-aged and elderly adults in China had VI. The prevalence of VI shows regional variations, but no distinct east-west or north-south distribution differences. ML algorithms demonstrate accurate predictive capabilities for VI. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of VI among Chinese middle-aged and elderly adults.

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Section: Comparison With Prior Worksupporting

confidence: 92%

Section: Predictors Of VImentioning

confidence: 99%

Prediction and Determinants of Visual Impairment among Chinese Middle-aged and Elderly Adults: Machine Learning Algorithms (Preprint)

Mao,

Yu,

Lin

et al. 2024

Preprint

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“…The current study also revealed associations between frailty in patients with cardiovascular disease and vision and daily living activities (ADL). A previous large cohort study of Chinese older adults found that those with visual problems, distance vision impairment, near vision impairment, or glaucoma were more likely to develop frailty during follow-up [51]. More recent longitudinal studies demonstrated that vision impairment was associated with a higher incidence of frailty in older adults [52][53][54].…”

Section: Discussionmentioning

confidence: 98%

Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease

Luo,

Jiang,

Wang

et al. 2024

Preprint

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Background Cardiovascular disease (CVD) and frailty frequently coexist in older populations, resulting in a synergistic impact on health outcomes. This study aims to develop a prediction model for the risk of frailty among patients with cardiovascular disease. Methods Using data from the China Health and Retirement Longitudinal Study (CHARLS), a total of 2,457 patients with cardiovascular disease (CVD) in 2011 (n = 1,470) and 2015 (n = 987) were randomly divided into training set (n = 1,719) and validation set (n = 738) at a ratio of 7:3. LASSO regression analysis was used conducted to determine identify the predictor variables with the most significant influence on the model. Stepwise regression analysis and logistic regression model were used to analyze the risk factors of frailty in patients with cardiovascular disease. The prediction model was established by constructing a nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by the concordance index (C-index) and calibration curve. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. Results A total of 360 patients (17.2%) had frailty symptoms. Among the 29 independent variables, it was found that gender, age, pain, grip strength, vision, activities of daily living (ADL), and depression were significantly associated with the risk of frailty in CVD patients. Using these factors to construct a nomogram model, the model has good consistency and accuracy. The AUC values of the prediction model and the internal validation set were 0.859 (95%CI 0.836–0.882) and 0.860 (95%CI 0.827–0.894), respectively. The C-index of the prediction model and the internal validation set were 0.859 (95%CI 0.836–0.882) and 0.887 (95%CI 0.855–0.919), respectively. The Hosmer-Lemeshow test showed that the model's predicted probabilities were in reasonably good agreement with the actual observations. The calibration curve showed that the Nomogram model was consistent with the observed values. The robust predictive performance of the nomogram was confirmed by Decision Curve analysis (DCA). Conclusions This study established and validated a nomogram model, combining gender, age, pain, grip strength, ADL, visual acuity, and depression for predicting physical frailty in patients with cardiovascular disease. Developing this predictive model would be valuable for screening cardiovascular disease patients with a high risk of frailty.

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“…Those reporting “excellent,” “very good,” “good,” or “fair” were defined as having no visual impairment (reference group). Visual impairment assessment and categorization were performed in accordance with previous studies [ 15 - 17 ].…”

Section: Methodsmentioning

confidence: 99%

The Association Between Solid Fuel Use and Visual Impairment Among Middle-Aged and Older Chinese Adults: Nationwide Population-Based Cohort Study

Jiang¹,

Wang²,

Zhang³

et al. 2023

JMIR Public Health Surveill

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Background Indoor air pollution has been reported to have adverse effects on the eye; however, the health effects of exposure to cooking with solid fuels on visual impairment remain unclear in China. Objective We aimed to examine the association between cooking with solid fuels and visual impairment, including distance visual impairment (DVI) and near visual impairment (NVI). Methods Data were obtained from the China Health and Retirement Longitudinal Study, a nationwide survey of adults aged over 45 years who were enrolled in 2011 (Wave 1) and followed up in Wave 2 (2013), Wave 3 (2015), and Wave 4 (2018). We used Cox proportional hazards models to determine the association between solid fuels use and visual impairment. Additionally, the impact of switching cooking fuel types on vision function were examined through wave-specific data analysis (Wave 1 and Wave 4). Interaction and subgroup analyses were performed to explore the potential effect modifiers. Data were collected using the stratified multistage random sampling method and further analyzed using SPSS 27.0 and R 4.2.1 statistical software packages. Results A total of 9559 middle-aged and older Chinese adults without visual impairment at baseline were included in the study, with 51.2% (n=4914) of the participants reporting that they cooked with solid fuels. During the follow-up period, 2644 (27.5%) and 3130 (32.6%) participants developed DVI and NVI, respectively. Compared with the clean fuel users, participants who cooked with solid fuels had a higher risk of DVI (hazards ratio [HR] 1.38, 95% CI 1.28-1.50) and NVI (HR 1.18, 95% CI 1.10-1.27). In addition, switching the cooking fuel type from clean to solid fuels was associated with an elevated risk of DVI (HR 1.51, 95% CI 1.15-1.98) and NVI (HR 1.39, 95% CI 1.06-1.82) compared to persistently using clean fuels during the follow-up period, although no protective effect of switching from solid to clean fuels on NVI was found (P=.52). In subgroup analysis, we found that cooking with solid fuels increased the risk of DVI in participants younger than 65 years (HR 1.41, 95% CI 1.28-1.55), men (HR 1.45, 95% CI 1.28-1.65), urban residents (HR 1.41, 95% CI 1.08-1.75), and smokers (HR 1.43, 95% CI 1.25-1.64). By contrast, negative effects of cooking with solid fuels on NVI were found in nonsmokers (HR 1.21, 95% CI 1.11-1.33) and urban residents (HR 1.20, 95% CI 1.10-1.37). Conclusions Cooking with solid fuels was associated with an increased risk of visual impairment among middle-aged and older Chinese adults. These findings indicate that promoting the utilization of clean fuels is conducive to reducing the burden of visual impairment for the public.

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Associations of vision impairment and eye diseases with frailty in community-dwelling older adults: a nationwide longitudinal study in China

Cited by 6 publications

References 38 publications

Prediction and Determinants of Visual Impairment among Chinese Middle-aged and Elderly Adults: Machine Learning Algorithms (Preprint)

Prediction and Determinants of Visual Impairment among Chinese Middle-aged and Elderly Adults: Machine Learning Algorithms (Preprint)

Development and validation of a risk prediction model for incident frailty in elderly patients with cardiovascular disease

The Association Between Solid Fuel Use and Visual Impairment Among Middle-Aged and Older Chinese Adults: Nationwide Population-Based Cohort Study

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