Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-10-31T11:27:19.492Z Has data issue: false hasContentIssue false

Association between cardiopulmonary function, health-related quality of life and cognitive impairment among the older nursing home residents in Shanghai, China

Published online by Cambridge University Press:  15 March 2023

Zhitong Zhou
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China Institute of Clinical Epidemiology and Evidence-based medicine, Tongji University School of Medicine, Shanghai 200092, China
Longbing Ren
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China Institute of Clinical Epidemiology and Evidence-based medicine, Tongji University School of Medicine, Shanghai 200092, China
Ziyan Zhang
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China Institute of Clinical Epidemiology and Evidence-based medicine, Tongji University School of Medicine, Shanghai 200092, China
Xiaoting Sun
Affiliation:
Tenth People’s Hospital Affiliated to Tongji University, Shanghai 200072, China
Yongtao Zheng
Affiliation:
Xiaoshan Center for Disease Control and Prevention, Hangzhou 311200, China
Yijun Gu
Affiliation:
The Second Affiliated Hospital of Zhejiang University, Hangzhou 310009, China
Hengjing Wu
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China
Jue Li*
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China Department of Epidemiology, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
Lijuan Zhang*
Affiliation:
Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China Institute of Clinical Epidemiology and Evidence-based medicine, Tongji University School of Medicine, Shanghai 200092, China
*
Author for correspondence: Jue Li and Lijuan Zhang, Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China. E-mails: jueli@tongji.edu.cn; zhangxiaoyi@tongji.edu.cn
Author for correspondence: Jue Li and Lijuan Zhang, Clinical Center for Intelligent Rehabilitation Research, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University School of Medicine, Tongji University, Shanghai 201613, China. E-mails: jueli@tongji.edu.cn; zhangxiaoyi@tongji.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

Background:

This study aimed to examine the association between cardiopulmonary function, health-related quality of life (HRQOL) and cognitive function among nursing home residents aged 80 years and over.

Methods:

A nursing home-based, cross-sectional study was implemented among 677 aged over 80 years in Shanghai, China. A total of 197 participants underwent effective cardiopulmonary function examinations. Mini-Mental Status Examination (MMSE) and Short Form-36 scales (SF-36) were used to assess cognitive function and HRQOL, respectively.

Results:

Decline in left ventricular ejection fractions (LVEF) [adjusted odds ratio (AOR), 1.98; 95% confidential interval (CI), 1.03–3.81)] and vital capacity (VC) (AOR, 2.08; 95%CI, 1.07–4.04) was associated with cognitive impairment. After adjusting confounding factors, relationships between cognitive function and physical functioning (PF) (AOR, 0.98; 95%CI, 0.97–0.99) still existed.

Conclusions:

Healthcare professionals should pay more attention to cardiopulmonary health and HRQOL in the nursing home residents. Actions of public health strategies focus on the improvement of cardiopulmonary function, and PF among older nursing home residents with cognitive impairment is required.

Type
Research
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Introduction

Approximately 9% of the global population is aged 65 or over in 2019, predicted to increase to 16% by 2050 (Qiu et al., Reference Qiu, Li, Wang, Zheng, Wang, Wang and Chen2022). As the population ageing, age-associated diseases and disabilities worldwide become an important public health issue, which imposes heavy economic burden on society and families (Pérez Palmer et al., Reference Pérez Palmer, Trejo Ortega and Joshi2022; Su et al., Reference Su, Shang, Xu, Li, Chen, Zhang, Zhang and Hua2014). Cognitive impairment has tended to worsen to a great extent in recent years. The estimated prevalence of cognitive impairment is 10%–15% among population over 65 years and reaches 25% in those aged 80–84 (Anderson, Reference Anderson2019; Pérez Palmer et al., Reference Pérez Palmer, Trejo Ortega and Joshi2022). Population aged 80 years and over comprises the fastest-growing segment of Chinese population. The prevalence of cognitive impairment ranges between 6% and 16% among Chinese people aged 60 years and over (Jia et al., Reference Jia, Du, Chu, Zhang, Li, Lyu, Li, Li, Zhu, Jiao, Song, Shi, Zhang, Gong, Wei, Tang, Fang, Guo, Wang, Zhou, Chu, Zuo, Yu, Yuan, Wang, Li, Shi, Yang, Zhou, Liao, Lv, Li, Kan, Zhao, Wang, Yang, Li, Liu, Wang, Qin and Jia2020) and reaches around 48% in people aged more than 80 years old (An and Liu, Reference An and Liu2016). Primary health care plays a vital role in cognitive impairment (Mullins et al., Reference Mullins, Bynum, Judd and Clarke2021). Nevertheless, current primary health care faces serious challenge in preventing and managing cognitive impairment in the elderly population (Belmin et al., Reference Belmin, Min, Roth, Reuben and Wenger2012; Lu et al., Reference Lu, Liu, Wells and Yu2022).

Due to lack of family companionship, cognitive function of nursing home residents is more worthy of attention. Notably, the prevalence of CI in the older nursing home residents is higher than the community elderly (Wu et al., Reference Wu, Ke and Su1998; Miranda et al., Reference Miranda, Smets, Van Den Noortgate, Deliens and Van den Block2021; Yuan et al., Reference Yuan, Lapane, Tjia, Baek, Liu and Ulbricht2021; Qiu et al., Reference Qiu, Li, Wang, Zheng, Wang, Wang and Chen2022). Previous studies report age, married status, male, diabetes, low income, low education, unfavourable cardiovascular health and carrying apolipoprotein E (APOE) e4 allele were risk factors of cognitive impairment (Peloso et al., Reference Peloso, Beiser, Satizabal, Xanthakis, Vasan, Pase, Destefano and Seshadri2020; Frison et al., Reference Frison, Proust-Lima, Mangin, Habert, Bombois, Ousset, Pasquier, Hanon, Paquet, Gabelle, Ceccaldi, Annweiler, Krolak-Salmon, Béjot, Belin, Wallon, Sauvee, Beaufils, Bourdel-Marchasson, Jalenques, Chupin, Chêne and Dufouil2021; Najar et al., Reference Najar, Aakre, Vassilaki, Wetterberg, Rydén, Zettergren, Skoog, Jack, Knopman, Petersen, Kern and Mielke2021). Cognitive impairment is always accompanied by declines in health-related quality of life (HRQOL), disability, death and cardiopulmonary dysfunction. About 18% of CI patients experience respiratory dysfunction (Martinez et al., Reference Martinez, Richardson, Han and Cigolle2014), and 43% of cognitive decline patients have heart failure (HF) (Cannon et al., Reference Cannon, Moffitt, Perez-Moreno, Walters, Broomfield, McMurray and Quinn2017). Accumulating evidence from different countries not only indicates abnormal cardiopulmonary function increased the risk of cognitive impairment (Emery et al., Reference Emery, Finkel and Pedersen2012; Cermakova et al., Reference Cermakova, Eriksdotter, Lund, Winblad, Religa and Religa2015; Jefferson et al., Reference Jefferson, Beiser, Himali, Seshadri, O’Donnell, Manning, Wolf, Au and Benjamin2015; Zhang et al., Reference Zhang, Gao, Han, Xiang and Liu2020; Duong et al., Reference Duong, Usman, Ma, Xie, Huang, Zaman, Dragoman, Jiatong Chen, Farooqi and Raina2022) but also reveals the complex interactions between cognitive impairment and HRQOL (Ezzati et al., Reference Ezzati, Zammit, Katz, Derby, Zimmerman and Lipton2019; Liu et al., Reference Liu, Tsai, Chiu, Tang, Lee, Shyu and Wang2019; Phyo et al., Reference Phyo, Gonzalez-Chica, Stocks, Storey, Woods, Murray, Orchard, Shah, Gasevic, Freak-Poli and Ryan2021). Currently, the mechanisms of abnormal cardiac function and cognitive impairment mainly involve cerebral hypoperfusion, hypoxia, systemic inflammation, endothelial injury and neurohormonal activation (Cermakova et al., Reference Cermakova, Eriksdotter, Lund, Winblad, Religa and Religa2015; Diener et al., Reference Diener, Hart, Koudstaal, Lane and Lip2019) and of abnormal pulmonary function and cognitive impairment mainly involved hypoxia, systemic inflammation and cerebral vascular dysfunction (Wang et al., Reference Wang, Mao, Wang, Li, Liu and Wu2020). Related studies mainly focus on community populations under the age of 80. The cognitive function of order nursing home residents received less attention. However, the association between abnormal cardiopulmonary function, HRQOL and cognitive impairment remains to be clarified in the nursing home residents aged over 80 years old.

In this study, we aimed to assess the association between cognitive impairment and HRQOL and cardiopulmonary function, so as to explore the notable indicators of cognitive impairment, therapy providing evidence for the prevention and control of cardiovascular-related cognitive dysfunction and reduction of the disease burden of cognitive impairment.

Methods

Study participants

Strategy of participants’ selection using a multi-stage stratified cluster sampling method in this study is shown in Supplementary material 1. Briefly, a randomized district in the urban and suburban areas of Shanghai from 2016 to 2017 was selected, respectively, and then randomly selected four nursing homes from each district, and a total of 677 participants who met the criterion were recruited in the study.

Participants enrolled in the study met the following conditions: (1) participants were at least 80 years old; (2) locally residents lived in nursing homes in Shanghai; (3) had normal hearing, vision and speech.

Exclusion criteria included (1) absence of cognitive function or in a vegetative state; (2) diagnoses of schizophrenia or serious mental retardation; (3) suffered a traumatic brain injury; and (4) inability to cooperate with the following inspections or assessment.

Each participant received physical and chemical inspection, face-to-face interview and cognitive assessment.

For effective assessment in cardiopulmonary function and HRQOL, the additional exclusion criteria were formulated: (1) unable to follow the precautions for cardiopulmonary function tests as directed by the researchers; (2) patients who could not complete the questions about daily activities in HRQOL test. Finally, a total of 197 participants who met the criteria were included in the analysis.

Cognitive assessment

Chinese version of the Mini-Mental State Examination (MMSE) was used to evaluate cognitive function because of the high proportion of low-educated subjects in this study (Wu et al., Reference Wu, Zhang, Yuan, Guo and Gao2021). The MMSE is a 30-point questionnaire assessing five areas of cognitive function including orientation, registration, language and praxis, attention and calculation, and recall, and education attainments were adjusted when calculating the raw total score. Cognitive impairment was classified if MMSE ≤ 17 for illiterates; MMSE ≤ 20 for elementary school graduates; MMSE ≤ 24 for junior school graduates or above (Li et al., Reference Li, Jia and Yang2016). Cronbach’s alpha value of the MMSE scale was tested to examine the reliability of the MMSE questionnaire, and the result was 0.899, indicating that it can be utilized to assess cognitive impairment of subjects in this study.

Assessment of HRQOL

Chinese version of the Short Form-36 scales (SF-36) was used to measure HRQOL of participants, which not only has a better split-half reliability (r = 0.91, P < 0.001) (Zhou et al., 2018) but also is a valid and reliable tool for assessing HRQOL among Chinese population (Wang et al., Reference Wang, Wu, Zhao, Yan, Ma, Wu, Liu, Gu, Zhao and He2008). SF-36 includes eight scales, evaluating the following areas: physical functioning (PF), role limitations due to physical health (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitations due to emotional problems (RE) and mental health (MH). Cronbach’s alpha value of Chinese version SF-36 scale was 0.821 in this study, suggesting good internal consistency of the scale.

Assessment of heart function

Heart function was measured by trained technicians via the Vscan V1.2 (GE Healthcare, Milwaukee, WI, USA). The sensitivity and specificity of palm ultrasound to detect left ventricular dilatation range from 71% to 94% and 97% to 100%, respectively (Liebo et al., Reference Liebo, Israel, Lillie, Smith, Rubenson and Topol2011). According to the American Society of Echocardiography (ASE) (Lang et al., Reference Lang, Bierig, Devereux, Flachskampf, Foster, Pellikka, Picard, Roman, Seward, Shanewise, Solomon, Spencer, Sutton and Stewart2005), heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), end-diastolic dimension of left ventricle (LVD), end-systolic dimension of left ventricle (LVS), the thickness of the basal interventricular septum (IVS), left ventricular posterior wall (LVPW), LVEF and calculated left ventricle fractional shortening (LVFS) were determined. The measured values were divided into normal and abnormal groups according to the standard (Supplementary material 2) (Yancy et al., Reference Yancy, Jessup, Bozkurt, Butler, Casey, Colvin, Drazner, Filippatos, Fonarow, Givertz, Hollenberg, Lindenfeld, Masoudi, McBride, Peterson, Stevenson and Westlake2017).

Assessment of respiratory function

Respiratory function was measured using CHESTHI-101 (COSMED S.r.l. Japan). Vital capacity (VC) is the amount of air that people can try to exhale after maximal inspiration. Maximal ventilatory volume (MVV) was collected to measure elasticity of thoracic lung tissue, airway resistance and respiratory muscle strength. However, forced expiratory volume in 1 s, which is a commonly used indicator to determine asthma and chronic obstructive pulmonary disease, was not collected because it is difficult for ultra-aged people to exhale in a second.

Other baseline measurements

Sociodemographic information and health-related data were collected for all participants by using an interviewer-administered questionnaire, including age, gender, education, marital status, smoking status, body mass index (BMI, weight, in kilograms/height2, in metres), physical activity, disease histories and medication histories. Education attainment was divided into 3 groups: illiteracy, elementary school (<6 years of education) and junior school or above (≥6 years of education). Marital status included unmarried, married, widowed and divorced, and we further defined it as a dichotomous variable (with and without partners). Physical activity was defined as standing, walking and outdoor time of participants (≤1 h/>1 h). Information of disease history including previous hypertension, diabetes, dyslipidemia, coronary heart disease, stroke and tumour were collected by self-reported physican's diagnosis or medical records. Medication histories were recorded as antihypertensive, antidiabetic, anticoagulation and lipid-lowering drugs usage.

Quality control

Experienced professionals were invited to review and give amendments to the survey questionnaires, and their professional guidance was required. Standardized protocols and statistical methods were used, and all investigators (doctors and research technicians) underwent joint training sessions before implementation of the study.

The quality control of echocardiographic data collection followed the ‘Echocardiography laboratory standards and accreditation guidelines for adults’ recommended by ASE (Restrepo et al., Reference Restrepo, Gutiérrez Fajardo, Lowenstein, Paz-Ardaya, Vieira, Spina, Córdova-Alvéstegui, Beltrán, Pizzano and Revilla-Alcocer2011), and the quality control of pulmonary function examination data collection followed the ‘Pulmonary Function Examination Guidelines’ recommended by the Chinese Medical Association.

Statistical analysis

EpiData3.1 (Odense, Denmark) was used for double data input. SPSS20.0 (SPSS Inc., Chicago, IL, USA) was used for data management and analysis. Continuous variables were presented as means [95% confidence intervals (95%CI)]. Cronbach’s alpha value was calculated to assess internal reliability of MMSE and SF-36 scales. Crude differences in the proportions according to cognitive state were analysed by chi-square test. Two independent samples t test and the Mann-Whitney test were performed for the comparison of two independent samples. Multivariate logistic regression analysis was performed to evaluate the association between cardiopulmonary function, health-related quality of life and cognitive impairment adjusted for potential confounders. Adjusted odds ratio (AOR) and their 95%CI were reported. Variance inflation factor (VIF) was used to detect multicollinearity of variables in the model, and VIF > 10 was diagnosed as multicollinearity (Athavale et al., Reference Athavale, Kumar, Clark, Mondal and Sur2021). Two-sided P-value <0.05 was considered statistically significant.

Results

Population analysis

The baseline information of 677 participants is shown in Supplementary material 3. The prevalence of cognitive impairment was 30.7% among 677 nursing home-based older population aged 80 years and over. The average age was 84.99 years (95%CI, 84.61–85.38 years). Compared to subjects with normal cognition, patients with cognitive impairment were more likely to be older, women, non-partnered, less physical activity, no history of smoking, lower BMI, no history of hypertension, dyslipidemia and coronary heart disease history (All P < 0.05).

One hundred and ninety-seven subjects who have effective assessment in cardiopulmonary function and HRQOL were included in the present investigation, the average age was 86.1 years (95%CI, 85.6–86.7 years), and 75% were women, average BMI was 23.91 kg/m2 (95%CI, 23.35–24.47 kg/m2). About 32.5% of the subjects were diagnosed as cognitive impairment. Demographic characteristics stratified by cognitive status are presented in Table 1. Compared to subjects with normal cognition, patients with cognitive impairment were more likely to be older, women, non-partnered, less physical activity, lower BMI, hypertensive history and no history of antihypertensive drugs (All P < 0.05).

Table 1. Demographic and health characteristics of the older nursing home residents in Shanghai, China (N =  197)

Continuous parameters were presented as means (95% confidence intervals). Categorical variables were presented as number of participants (column percentage). BMI = body mass index; BP = body pain; DBP = diastolic blood pressure; GH = general health; IVS = the thickness of the basal interventricular septum; LVD = end-diastolic dimension of left ventricle; LVEF = left ventricular ejection fractions; LVFS = left ventricle fractional shortening; LVPW = left ventricular posterior wall; LVS = end-systolic dimension of left ventricle; MH = mental health; MMSE = Mini-Mental State Examination; MVV = maximal ventilatory volume; PF = physical functioning; RE = role limitations due to emotional problems; RP = role limitations due to physical health; SBP = systolic blood pressure; SF = social functioning; SF-36 = short form-36 scales; VC = vital capacity; VT = vitality.

Abnormal LVEF, VC and MVV detected in patients with cognitive impairment were 42.2%, 79.7% and 98.4%, respectively. When comparing to subjects with normal cognitive function, subjects with cognitive impairment showed significantly crude decline in LVEF, LVFS, VC, MVV, PF and VT (All P < 0.05) (Table 1).

Sub-Domain analysis of cognitive function

The transformed MMSE scores of domains stratified by gender and the classification of cognitive function were displayed (Figure 1 ). In the older Chinese nursing home residents, MMSE scores were clearly the lowest in attention and calculation (43.1%) and recall (40.0%). Men had higher scores in all MMSE dimensions than women. Besides, participants classified as cognitive impairment performed worse than those with normal cognition in all domains, and cognitive scores of the two groups differed most in attention and calculation (57.7% vs. 10.7%) and recall (52.7% vs. 11.7%).

Figure 1. Transformed MMSE scores of five domains stratified by gender and cognitive function.

The logistic regression model adjusted age and gender. The logistic regression model adjusted age and gender, marital status, physical activity and BMI. HRQOL: health-related quality of life; LVEF: left ventricular ejection fraction; LVFS: left ventricle fractional shortening; VC: vital capacity; MVV: maximal ventilator volume; PF: physical functioning; VT: vitality; BMI: body mass index; AOR, adjusted odds ratio. MMSE: Mini-Mental State Examination.

Cardiopulmonary dysfunction, HRQOL and cognitive impairment

The variables of P < 0.05 in the Table 1 were integrated into the multivariable logistic regression model. Multicollinearity analysis did not detect severe collinearity among these variables in the model (Supplementary material 4). Abnormal LVEF (AOR, 1.983; 95%CI, 1.034–3.805) and VC (AOR, 2.079; 95%CI, 1.069–4.041) were significantly associated with cognitive impairment after adjusting confounders including age and gender, while history of antihypertensive drugs (AOR, 0.397; 95%CI, 0.208–0.759) and higher PF (AOR, 0.982; 95%CI, 0.970–0.994) was associated with the decrease in cognitive impairment risk and this association still existed after adjusting age and gender, marital status, physical activity, and BMI (Table 2).

Table 2. Estimating the risk of cognitive impairment associated with factors of interest among the older nursing home residents in Shanghai, China (N = 197)

Model 1 adjusted age and gender. Model 2 adjusted age, gender, marital status, physical activity and BMI. AOR = adjusted odds ratio; BMI = body mass index; CI = confidence intervals; LVEF = left ventricular ejection fraction; LVFS = left ventricle fractional shortening; MVV = maximal ventilatory volume; PF = physical functioning; VC = vital capacity; VT = vitality.

Analysis of five domains of cognitive function

After adjusting age and gender, marital status, physical activity and BMI, decreasing in LVEF was related to the abnormal orientation (AOR, 2.337; 95%CI, 1.204–4.538), registration (AOR, 2.707; 95%CI, 1.237–5.924) and language and praxis (AOR, 2.085; 95%CI, 1.055–4.119). Besides, PF (AOR, 0.982; 95%CI, 0.970–0.995) and VT (AOR, 0.978; 95%CI, 0.961–0.995) were protective factors of language and praxis. Moreover, PF was associated with better attention and calculation (AOR, 0.970; 95%CI, 0.953–0.987) (Figure 2).

Figure 2. The association of cardiopulmonary function, HRQOL and five domains of cognitive function.

The logistic regression model adjusted age and gender, marital status, physical activity and BMI. HRQOL: health-related quality of life; LVEF: left ventricular ejection fraction; LVFS: left ventricle fractional shortening; VC: vital capacity; MVV: maximal ventilator volume; PF: physical functioning; VT: vitality; BMI: body mass index. A&C: attention and calculation; L&P: language and praxis. *P < 0.05, P < 0.01. Size of the point represents the weight.

Discussion

As one of the major public health concerns, cognitive impairment is not only highly prevalent, but has a negative impact on HRQOL, thus imposing a substantial socioeconomic burden (Sanford, Reference Sanford2017). Besides those confirmed risk factors including age, gender, marital status, personal income, physical activity, BMI and history of stroke, impaired cardiopulmonary function had already been identified as medical co-morbidity with cognitive impairment (Jia et al., Reference Jia, Du, Chu, Zhang, Li, Lyu, Li, Li, Zhu, Jiao, Song, Shi, Zhang, Gong, Wei, Tang, Fang, Guo, Wang, Zhou, Chu, Zuo, Yu, Yuan, Wang, Li, Shi, Yang, Zhou, Liao, Lv, Li, Kan, Zhao, Wang, Yang, Li, Liu, Wang, Qin and Jia2020; Duong et al., Reference Duong, Usman, Ma, Xie, Huang, Zaman, Dragoman, Jiatong Chen, Farooqi and Raina2022; Huang et al., Reference Huang, Chen, Leng, Kuo, Wang, Cui, Tan, Wang, Dong and Yu2022). Thus, ascertain relationships between HRQOL score, cardiopulmonary function and cognitive performance are fundamental for providing evidence for the prevention and control of cognitive decline-associated disease burden.

In this study, we found that 30.7% of the older nursing home residents aged over 80 years in Shanghai were classified as cognitive impairment, which was higher than the community elderly aged over 80 years (Lv et al., Reference Lv, Li, Ma, Chen, Zeng, Yu, Hofman and Wang2019; Jia et al., Reference Jia, Du, Chu, Zhang, Li, Lyu, Li, Li, Zhu, Jiao, Song, Shi, Zhang, Gong, Wei, Tang, Fang, Guo, Wang, Zhou, Chu, Zuo, Yu, Yuan, Wang, Li, Shi, Yang, Zhou, Liao, Lv, Li, Kan, Zhao, Wang, Yang, Li, Liu, Wang, Qin and Jia2020), suggesting it was necessary to pay close attention to the cognitive function of the elderly over 80 years old. Antihypertensive drug was found to be a protective factor of cognitive function in this study, which was consistent with some previous researches (Levi Marpillat et al., Reference Levi Marpillat, Macquin-Mavier, Tropeano, Bachoud-Levi and Maison2013; Peters et al., Reference Peters, Collerton, Granic, Davies, Kirkwood and Jagger2015; Ou et al., Reference Ou, Tan, Shen, Xu, Hou, Dong, Tan and Yu2020), which might be due to the fact that antihypertensive drug ameliorates or mitigates white matter lesions caused by high blood pressure (Iadecola and Gottesman, Reference Iadecola and Gottesman2019).

About 42.2% of subjects with cognitive impairment had abnormal LVEF value, suggesting these residents were at a high risk of HF; 79.7% of them had abnormal VC value and might have restricted ventilation disorders; furthermore, 98.4% of cognitive impairment subjects had abnormal MVV value, indicating reduced ventilation reserve capacity. Thus, the co-prevalence of cognitive dysfunction and cardiopulmonary diseases was worthy of attention. Association between LVEF and cognitive function still remains unclear nowadays. A nonlinear U-shaped correlation between LVEF and measures of accelerated cognitive ageing was reported in one of the Framingham Heart Studies (Jefferson et al., Reference Jefferson, Himali, Au, Seshadri, Decarli, O’Donnell, Wolf, Manning, Beiser and Benjamin2011). Abnormal LVEF was found to be related with cognitive impairment in our study, which was agreement with previous studies (Bossola et al., Reference Bossola, Laudisio, Antocicco, Tazza, Colloca, Tosato and Zuccalà2014; Xing et al., Reference Xing, Chen, Sun, Neradilek, Wu, Zhang, Huang, Cui, Yang, Li and Zhao2020; Shang et al., Reference Shang, Liu, Gao, Wang, Lu, Fei, Zhang, Mi, Li, Ma, Jiang, Chen, Dang, Liu and Qu2022). Lower LVEF always elevates levels of catecholamine and endothelin 1 (Macrae et al., Reference Macrae, Robinson, Graham, Reid and McCulloch1993), and decreased cerebral blood flow then causes the damage of nerve cells, which affects cognitive function. Animal experiments also demonstrated that abnormal cardiac function can cause cognitive impairment by mediating oxidative stress (Jinawong et al., Reference Jinawong, Apaijai, Piamsiri, Maneechote, Arunsak, Chunchai, Pintana, Nawara, Chattipakorn and Chattipakorn2022). Additionally, our study also found that abnormal LVEF was associated with three domains of cognitive function, including orientation, registration and language and praxis. Abnormal VC could increase the risk of cognitive impairment in older participants in this study. An explanation was that poor breathing was associated with a reduction in the lung tissues available for gas exchange; thus, it may increase the chance of hypoventilation and chronic hypoxia and hypercapnia, which may affect neurocognitive function (Giltay et al., Reference Giltay, Nissinen, Giampaoli and Kromhout2009); besides, elevated levels of systemic inflammation markers in patients with respiratory diseases, such as C-reactive protein, interleukin 6 and fibrinogen, might increase the risk of cognitive impairment (Su et al., Reference Su, Liu, Fan, Chen, Ding, Wu, Wang and Hou2016).

Higher PF and VT scores showed significant relation with better cognitive function, which mainly shown in the orientation, attention and calculation and language and praxis domains. Physical functioning mainly assessed whether health status affects daily physiological activities. Previous studies also demonstrated that appropriate physical activities can improve cognitive function (Song and Yu, Reference Song and Yu2019; Alsubaie et al., Reference Alsubaie, Alkathiry, Abdelbasset and Nambi2020), which indicated that nursing homes should focus on physical exercise of the elderly, such as setting up some exercise equipment in nursing homes. Vitality mainly evaluated individual subjective feelings about their own energy and fatigue. Prior studies also indicated that long-term fatigue and low energy had negative effect on cognitive function (Lin et al., Reference Lin, Chen, Vance, Ball and Mapstone2013; Zhang et al., Reference Zhang, Murphy, Fang and Caban-Martinez2015). Physical exercise, nutrition supplementation, yoga and meditation can be effective methods to decrease fatigue and increase energy (Bower et al., Reference Bower, Garet, Sternlieb, Ganz, Irwin, Olmstead and Greendale2012; Ng et al., Reference Ng, Feng, Nyunt, Feng, Niti, Tan, Chan, Khoo, Chan, Yap and Yap2015). Thus, more intervention trials should be performed to explore effective measures to improve PF and VT among nursing home residents.

Our study has some implications for the formulation of primary healthcare policy. Current primary health care still faces serious challenges in detection and management of cognitive impairment (Lu et al., Reference Lu, Liu, Wells and Yu2022). The cognitive impairment of nursing home residents over 80 is a problem that cannot be ignored. That requires healthcare providers to pay more attention to the health of nursing home residents, especially cardiopulmonary function and HRQOL. Thus, there is an urgent need to provide a range of support to facilitate healthcare providers to offer accessible, affordable and appropriate cognitive impairment-related primary healthcare services for nursing home residents, such as clinical practice guidelines, policy and financial support, and information infrastructures (Lu et al., Reference Lu, Liu, Wells and Yu2022).

The present study has several limitations. Firstly, cognitive impairment was identified using MMSE rather than the gold standard measures (eg, DSM-111, NINCDS-ADRDA and clinical record) and other international scales (McKhann et al., Reference McKhann, Drachman, Folstein, Katzman, Price and Stadlan1984). Secondly, a cross-sectional study may not provide evidence of causal relationship between risk factors and cognitive impairment. A longitudinal follow-up study should be conducted to fully assess the causality. Some information such as marital status and smoking status was collected based on self-report, which could be affected by recall bias. Finally, only 197 of 677 people had qualified cardiopulmonary function tests, so selection bias may have existed.

Conclusions

Cognitive impairment is still one of the major problems in elderly nursing home residents. Previous antihypertensive drug usage, abnormal LVEF, VC, PF and VT were associated with cognitive dysfunction. We recommend that healthcare professionals and nursing staff should increase attention to the improvement of cardiopulmonary function, PF and VT. Public health policy formulation should give more consideration to the health of the nursing home residents. Further prospective studies are needed to evaluate HRQOL and cardiopulmonary risk predictors of patients with cognitive impairment.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S1463423623000075

Availability of data and materials

The data of this study are available from the corresponding author upon reasonable request.

Acknowledgments

Not applicable.

Authors’ contributions

Conception: ZTZ, LBR, JL and LJZ; Data acquisition and formal analysis: TZ, LBR, ZYZ, XTS, YTZ, YJG and WHJ; Validation: ZTZ, LBR and ZYZ; Writing – Original Draft: ZTZ, LBR and ZYZ; Writing – Review & Editing: ZTZ, JL and LJZ; Funding Acquisition: JL and LJZ. All authors read and approved it for publication.

Financial support

This work was supported by the National Natural Science Foundation of China (Grant No. 81872720) and Shanghai Municipal Health Commission (Grant No. 201840066) to Lijuan Zhang. The Shanghai Committee of Science and Technology, China (Grant No. 22ZR1463700), the Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities to Jue Li.

Conflicts of interest

None.

Ethics approval and consent to participate

This study was approved by medical ethical review committee of Tongji University. Informed consent was obtained from all participants.

Footnotes

The authors contribute equally.

References

Alsubaie, SF, Alkathiry, AA, Abdelbasset, WK and Nambi, G (2020) The physical activity type most related to cognitive function and quality of life. BioMed Research International 2020, 8856284.CrossRefGoogle ScholarPubMed
An, R and Liu, GG (2016) Cognitive impairment and mortality among the oldest-old Chinese. International Journal of Geriatric Psychiatry 31, 13451353.CrossRefGoogle ScholarPubMed
Anderson, ND (2019) State of the science on mild cognitive impairment (MCI). CNS Spectrums 24, 7887.CrossRefGoogle ScholarPubMed
Athavale, P, Kumar, V, Clark, J, Mondal, S and Sur, S (2021) Differential Impact of COVID-19 risk factors on ethnicities in the United States. Frontiers in Public Health 9, 743003.CrossRefGoogle ScholarPubMed
Belmin, J, Min, L, Roth, C, Reuben, D and Wenger, N (2012) Assessment and management of patients with cognitive impairment and dementia in primary care. The Journal of Nutrition, Health & Aging 16, 462467.CrossRefGoogle ScholarPubMed
Bossola, M, Laudisio, A, Antocicco, M, Tazza, L, Colloca, G, Tosato, M and Zuccalà, G (2014) Cognitive performance is associated with left ventricular function in older chronic hemodialysis patients: result of a pilot study. Aging Clinical and Experimental Research 26, 445451.CrossRefGoogle ScholarPubMed
Bower, JE, Garet, D, Sternlieb, B, Ganz, PA, Irwin, MR, Olmstead, R and Greendale, G (2012) Yoga for persistent fatigue in breast cancer survivors: a randomized controlled trial. Cancer 118, 37663775.CrossRefGoogle ScholarPubMed
Cannon, JA, Moffitt, P, Perez-Moreno, AC, Walters, MR, Broomfield, NM, McMurray, JJV and Quinn, TJ (2017) Cognitive impairment and heart failure: systematic review and meta-analysis. Journal of Cardiac Failure 23, 464475.CrossRefGoogle ScholarPubMed
Cermakova, P, Eriksdotter, M, Lund, LH, Winblad, B, Religa, P and Religa, D (2015) Heart failure and Alzheimer’s disease. Journal of Internal Medicine 277, 406425.CrossRefGoogle ScholarPubMed
Diener, HC, Hart, RG, Koudstaal, PJ, Lane, DA and Lip, GYH (2019) Atrial fibrillation and cognitive function: JACC review topic of the week. Journal of the American College of Cardiology 73, 612619.CrossRefGoogle ScholarPubMed
Duong, M, Usman, A, Ma, J, Xie, Y, Huang, J, Zaman, M, Dragoman, A, Jiatong Chen, S, Farooqi, M and Raina, P (2022) Associations between lung function and physical and cognitive health in the Canadian Longitudinal Study on Aging (CLSA): a cross-sectional study from a multicenter national cohort. PLoS Medicine 19, e1003909.CrossRefGoogle ScholarPubMed
Emery, CF, Finkel, D and Pedersen, NL (2012) Pulmonary function as a cause of cognitive aging. Psychological Science 23, 10241032.CrossRefGoogle ScholarPubMed
Ezzati, A, Zammit, AR, Katz, MJ, Derby, CA, Zimmerman, ME and Lipton, RB (2019) Health-related quality of life, cognitive performance, and incident dementia in a community-based elderly cohort. Alzheimer Disease and Associated Disorders 33, 240245.CrossRefGoogle Scholar
Frison, E, Proust-Lima, C, Mangin, JF, Habert, MO, Bombois, S, Ousset, PJ, Pasquier, F, Hanon, O, Paquet, C, Gabelle, A, Ceccaldi, M, Annweiler, C, Krolak-Salmon, P, Béjot, Y, Belin, C, Wallon, D, Sauvee, M, Beaufils, E, Bourdel-Marchasson, I, Jalenques, I, Chupin, M, Chêne, G and Dufouil, C (2021) Diabetes mellitus and cognition: pathway analysis in the MEMENTO cohort. Neurology 97, e836e848.CrossRefGoogle ScholarPubMed
Giltay, EJ, Nissinen, A, Giampaoli, S and Kromhout, D (2009) Apolipoprotein E genotype modifies the association between midlife lung function and cognitive function in old age. Dementia and Geriatric Cognitive Disorders 28, 433441.CrossRefGoogle ScholarPubMed
Huang, YY, Chen, SD, Leng, XY, Kuo, K, Wang, ZT, Cui, M, Tan, L, Wang, K, Dong, Q and Yu, JT (2022) Post-stroke cognitive impairment: epidemiology, risk factors, and management. Journal of Alzheimer’s Disease: JAD 86, 983999.CrossRefGoogle ScholarPubMed
Iadecola, C and Gottesman, RF (2019) Neurovascular and cognitive dysfunction in hypertension. Circulation Research 124, 10251044.CrossRefGoogle ScholarPubMed
Jefferson, AL, Beiser, AS, Himali, JJ, Seshadri, S, O’Donnell, CJ, Manning, WJ, Wolf, PA, Au, R and Benjamin, EJ (2015) Low cardiac index is associated with incident dementia and Alzheimer disease: the Framingham heart study. Circulation 131, 13331339.CrossRefGoogle ScholarPubMed
Jefferson, AL, Himali, JJ, Au, R, Seshadri, S, Decarli, C, O’Donnell, CJ, Wolf, PA, Manning, WJ, Beiser, AS and Benjamin, EJ (2011) Relation of left ventricular ejection fraction to cognitive aging (from the Framingham heart study). American Journal of Cardiology 108, 13461351.CrossRefGoogle ScholarPubMed
Jia, L, Du, Y, Chu, L, Zhang, Z, Li, F, Lyu, D, Li, Y, Li, Y, Zhu, M, Jiao, H, Song, Y, Shi, Y, Zhang, H, Gong, M, Wei, C, Tang, Y, Fang, B, Guo, D, Wang, F, Zhou, A, Chu, C, Zuo, X, Yu, Y, Yuan, Q, Wang, W, Li, F, Shi, S, Yang, H, Zhou, C, Liao, Z, Lv, Y, Li, Y, Kan, M, Zhao, H, Wang, S, Yang, S, Li, H, Liu, Z, Wang, Q, Qin, W and Jia, J (2020) Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. The Lancet. Public Health 5, e661e671.CrossRefGoogle ScholarPubMed
Jinawong, K, Apaijai, N, Piamsiri, C, Maneechote, C, Arunsak, B, Chunchai, T, Pintana, H, Nawara, W, Chattipakorn, N and Chattipakorn, SC (2022) Mild Cognitive impairment occurs in rats during the early remodeling phase of myocardial infarction. Neuroscience 493, 3140.CrossRefGoogle ScholarPubMed
Lang, RM, Bierig, M, Devereux, RB, Flachskampf, FA, Foster, E, Pellikka, PA, Picard, MH, Roman, MJ, Seward, J, Shanewise, JS, Solomon, SD, Spencer, KT, Sutton, MSJ, Stewart, WJ, Chamber Quantification Writing G, American Society of Echocardiography’s G, Standards C, and European Association of E (2005) Recommendations for chamber quantification: a report from the American Society of Echocardiography’s Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. Journal of the American Society of Echocardiography 18, 14401463.CrossRefGoogle Scholar
Levi Marpillat, N, Macquin-Mavier, I, Tropeano, AI, Bachoud-Levi, AC and Maison, P (2013) Antihypertensive classes, cognitive decline and incidence of dementia: a network meta-analysis. Journal of Hypertension 31, 10731082.CrossRefGoogle ScholarPubMed
Li, H, Jia, J and Yang, Z (2016) Mini-mental state examination in elderly Chinese: a population-based normative study. Journal of Alzheimer’s Disease: JAD 53, 487496.CrossRefGoogle ScholarPubMed
Liebo, MJ, Israel, RL, Lillie, EO, Smith, MR, Rubenson, DS and Topol, EJ (2011) Is pocket mobile echocardiography the next-generation stethoscope? A cross-sectional comparison of rapidly acquired images with standard transthoracic echocardiography. Annals of Internal Medicine 155, 3338.CrossRefGoogle ScholarPubMed
Lin, F, Chen, DG, Vance, DE, Ball, KK and Mapstone, M (2013) Longitudinal relationships between subjective fatigue, cognitive function, and everyday functioning in old age. International Psychogeriatrics 25, 275285.CrossRefGoogle ScholarPubMed
Liu, HY, Tsai, WC, Chiu, MJ, Tang, LY, Lee, HJ, Shyu, YL and Wang, WS (2019) Relationships between cognitive dysfunction and health-related quality of life among older persons in Taiwan: a nationwide population-based survey. American Journal of Alzheimer’s Disease and Other Dementias 34, 4148.CrossRefGoogle ScholarPubMed
Lu, Y, Liu, C, Wells, Y and Yu, D (2022) Challenges in detecting and managing mild cognitive impairment in primary care: a focus group study in Shanghai, China. BMJ Open 12, e062240.CrossRefGoogle ScholarPubMed
Lv, X, Li, W, Ma, Y, Chen, H, Zeng, Y, Yu, X, Hofman, A and Wang, H (2019) Cognitive decline and mortality among community-dwelling Chinese older people. BMC Medicine 17, 63.CrossRefGoogle ScholarPubMed
Macrae, IM, Robinson, MJ, Graham, DI, Reid, JL and McCulloch, J (1993) Endothelin-1-induced reductions in cerebral blood flow: dose dependency, time course, and neuropathological consequences. Journal of Cerebral Blood Flow & Metabolism 13, 276284.CrossRefGoogle ScholarPubMed
Martinez, CH, Richardson, CR, Han, MK and Cigolle, CT (2014) Chronic obstructive pulmonary disease, cognitive impairment, and development of disability: the health and retirement study. Annals of the American Thoracic Society 11, 13621370.CrossRefGoogle ScholarPubMed
McKhann, G, Drachman, D, Folstein, M, Katzman, R, Price, D and Stadlan, EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34, 939944.CrossRefGoogle ScholarPubMed
Miranda, R, Smets, T, Van Den Noortgate, N, Deliens, L and Van den Block, L (2021) Higher prevalence of dementia but no change in total comfort while dying among nursing home residents with dementia between 2010 and 2015: results from two retrospective epidemiological studies. International Journal of Environmental Research and Public Health 83(3), 1269–1279.Google Scholar
Mullins, MA, Bynum, JPW, Judd, SE and Clarke, PJ (2021) Access to primary care and cognitive impairment: results from a national community study of aging Americans. BMC Geriatrics 21, 580.CrossRefGoogle ScholarPubMed
Najar, J, Aakre, JA, Vassilaki, M, Wetterberg, H, Rydén, L, Zettergren, A, Skoog, I, Jack, CR, Knopman, DS, Petersen, RC, Kern, S and Mielke, MM (2021) Sex difference in the relation between marital status and dementia risk in two population-based cohorts. Journal of Alzheimer’s Disease: JAD 83(3), 1269–1279.Google ScholarPubMed
Ng, TP, Feng, L, Nyunt, MS, Feng, L, Niti, M, Tan, BY, Chan, G, Khoo, SA, Chan, SM, Yap, P and Yap, KB (2015) Nutritional, physical, cognitive, and combination interventions and frailty reversal among older adults: a randomized controlled trial. The American Journal of Medicine 128, 12251236.e1221.CrossRefGoogle ScholarPubMed
Ou, YN, Tan, CC, Shen, XN, Xu, W, Hou, XH, Dong, Q, Tan, L and Yu, JT (2020) Blood pressure and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 209 prospective studies. Hypertension (Dallas, Tex.: 1979) 76, 217225.CrossRefGoogle ScholarPubMed
Peloso, GM, Beiser, AS, Satizabal, CL, Xanthakis, V, Vasan, RS, Pase, MP, Destefano, AL and Seshadri, S (2020) Cardiovascular health, genetic risk, and risk of dementia in the Framingham heart study. Neurology 95, e1341e1350.CrossRefGoogle ScholarPubMed
Pérez Palmer, N, Trejo Ortega, B and Joshi, P (2022) Cognitive impairment in older adults: epidemiology, diagnosis, and treatment. The Psychiatric Clinics of North America 45, 639661.CrossRefGoogle ScholarPubMed
Peters, R, Collerton, J, Granic, A, Davies, K, Kirkwood, T and Jagger, C (2015) Antihypertensive drug use and risk of cognitive decline in the very old: an observational study – the Newcastle 85+ Study. Journal of Hypertension 33, 21562164.CrossRefGoogle ScholarPubMed
Phyo, AZZ, Gonzalez-Chica, DA, Stocks, NP, Storey, E, Woods, RL, Murray, AM, Orchard, SG, Shah, RC, Gasevic, D, Freak-Poli, R and Ryan, J (2021) The utility of assessing health-related quality of life to predict cognitive decline and dementia. Journal of Alzheimer’s Disease: JAD 80, 895904.CrossRefGoogle ScholarPubMed
Qiu, Y, Li, G, Wang, X, Zheng, L, Wang, C, Wang, C and Chen, L (2022) Prevalence of cognitive frailty among community-dwelling older adults: a systematic review and meta-analysis. International Journal of Nursing Studies 125, 104112.CrossRefGoogle ScholarPubMed
Restrepo, G, Gutiérrez Fajardo, P, Lowenstein, J, Paz-Ardaya, A, Vieira, MLC, Spina, S, Córdova-Alvéstegui, S, Beltrán, A, Pizzano, N, Revilla-Alcocer, H and Grupo de trabajo sobre Acreditación de la Asociación de Ecocardiografía de la Sociedad Interamericana de C (2011) Guidelines for the accreditation in adult echocardiography and of the echocardiography laboratory from the Echocardiography Association of the Inter-American Society of Cardiology (ECHOIASC). Archivos de Cardiología de México 81, 5365.Google ScholarPubMed
Sanford, AM (2017) Mild cognitive impairment. Clinics in Geriatric Medicine 33, 325337.CrossRefGoogle ScholarPubMed
Shang, S, Liu, Z, Gao, J, Wang, J, Lu, W, Fei, Y, Zhang, B, Mi, B, Li, P, Ma, L, Jiang, Y, Chen, C, Dang, L, Liu, J and Qu, Q (2022) The relationship between pre-existing coronary heart disease and cognitive impairment is partly explained by reduced left ventricular ejection fraction in the subjects without clinical heart failure: a cross-sectional study. Frontiers in Human Neuroscience 16, 835900.CrossRefGoogle ScholarPubMed
Song, D and Yu, DSF (2019) Effects of a moderate-intensity aerobic exercise programme on the cognitive function and quality of life of community-dwelling elderly people with mild cognitive impairment: a randomised controlled trial. International Journal of Nursing Studies 93, 97105.CrossRefGoogle ScholarPubMed
Su, B, Liu, T, Fan, H, Chen, F, Ding, H, Wu, Z, Wang, H and Hou, S (2016) Inflammatory markers and the risk of chronic obstructive pulmonary disease: a systematic review and meta-analysis. PloS One 11, e0150586.CrossRefGoogle ScholarPubMed
Su, X, Shang, L, Xu, Q, Li, N, Chen, J, Zhang, L, Zhang, L and Hua, Q (2014) Prevalence and predictors of mild cognitive impairment in Xi’an: a community-based study among the elders. PLoS One 9, e83217e83217.CrossRefGoogle ScholarPubMed
Wang, R, Wu, C, Zhao, Y, Yan, X, Ma, X, Wu, M, Liu, W, Gu, Z, Zhao, J and He, J (2008) Health related quality of life measured by SF-36: a population-based study in Shanghai, China. BMC Public Health 8, 292.CrossRefGoogle ScholarPubMed
Wang, T, Mao, L, Wang, J, Li, P, Liu, X and Wu, W (2020) Influencing factors and exercise intervention of cognitive impairment in elderly patients with chronic obstructive pulmonary disease. Clinical Interventions in Aging 15, 557566.CrossRefGoogle ScholarPubMed
Wu, SC, Ke, D and Su, TL (1998) The prevalence of cognitive impairment among nursing home residents in Taipei, Taiwan. Neuroepidemiology 17, 147153.CrossRefGoogle ScholarPubMed
Wu, Y, Zhang, Y, Yuan, X, Guo, J and Gao, X (2021) Influence of education level on MMSE and MoCA scores of elderly inpatients. Applied Neuropsychology. Adult, 15. https://doi.org/10.1080/23279095.2021.1952588 Google ScholarPubMed
Xing, YL, Chen, MA, Sun, Y, Neradilek, MB, Wu, XT, Zhang, D, Huang, W, Cui, Y, Yang, QQ, Li, HW and Zhao, XQ (2020) Atherosclerosis, its risk factors, and cognitive impairment in older adults. Journal of Geriatric Cardiology: JGC 17, 434440.Google ScholarPubMed
Yancy, CW, Jessup, M, Bozkurt, B, Butler, J, Casey, DE Jr, Colvin, MM, Drazner, MH, Filippatos, GS, Fonarow, GC, Givertz, MM, Hollenberg, SM, Lindenfeld, J, Masoudi, FA, McBride, PE, Peterson, PN, Stevenson, LW and Westlake, C (2017) 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 136, e137e161.CrossRefGoogle Scholar
Yuan, Y, Lapane, KL, Tjia, J, Baek, J, Liu, SH and Ulbricht, CM (2021) Physical frailty and cognitive impairment in older nursing home residents: a latent class analysis. BMC Geriatrics 21, 487.CrossRefGoogle ScholarPubMed
Zhang, M, Murphy, LA, Fang, D and Caban-Martinez, AJ (2015) Influence of fatigue on construction workers’ physical and cognitive function. Occupational Medicine (Oxford, England) 65, 245250.CrossRefGoogle ScholarPubMed
Zhang, XL, Gao, B, Han, T, Xiang, BY and Liu, X (2020) Moderate-to-severe obstructive sleep apnea and cognitive function impairment in patients with COPD. International Journal of Chronic Obstructive Pulmonary Disease 15, 18131822.CrossRefGoogle ScholarPubMed
Zhou, B, Chen, K, Wang, J-F, Wu, Y-Y, Zheng, W-J and Wang, H (2008) Reliability and validity of a Short-Form Health Survey Scale (SF-36), Chinese version used in an elderly population of Zhejiang province in China. Zhonghua Liu Xing Bing Xue Za Zhi 29, 11931198.Google Scholar
Figure 0

Table 1. Demographic and health characteristics of the older nursing home residents in Shanghai, China (N =  197)

Figure 1

Figure 1. Transformed MMSE scores of five domains stratified by gender and cognitive function.The logistic regression model adjusted age and gender. The logistic regression model adjusted age and gender, marital status, physical activity and BMI. HRQOL: health-related quality of life; LVEF: left ventricular ejection fraction; LVFS: left ventricle fractional shortening; VC: vital capacity; MVV: maximal ventilator volume; PF: physical functioning; VT: vitality; BMI: body mass index; AOR, adjusted odds ratio. MMSE: Mini-Mental State Examination.

Figure 2

Table 2. Estimating the risk of cognitive impairment associated with factors of interest among the older nursing home residents in Shanghai, China (N = 197)

Figure 3

Figure 2. The association of cardiopulmonary function, HRQOL and five domains of cognitive function.The logistic regression model adjusted age and gender, marital status, physical activity and BMI. HRQOL: health-related quality of life; LVEF: left ventricular ejection fraction; LVFS: left ventricle fractional shortening; VC: vital capacity; MVV: maximal ventilator volume; PF: physical functioning; VT: vitality; BMI: body mass index. A&C: attention and calculation; L&P: language and praxis. *P < 0.05, P < 0.01. Size of the point represents the weight.

Supplementary material: File

Zhou et al. supplementary material

Zhou et al. supplementary material

Download Zhou et al. supplementary material(File)
File 2.7 MB