Abstract
Objective
Frailty is recognised as an emerging public health priority. However, there is still a lack of large-sample, well-designed clinical observational studies investigating frailty status among multidistrict community-dwelling older adults in Shanghai. Therefore, this study aims to establish a large-sample prospective cohort in Shanghai, investigate the frailty status of multidistrict community-dwelling older adults, analyse the characteristics of the frail population and explore potential risk factors for frailty in older adults.
Design
The present study was a cross-sectional analysis embedded within an ongoing prospective population-based cohort study.
Setting and participants
A total of 2950 residents (≥65 years) from 9 subdistricts were recruited.
Measures
We used a stratified cluster random sampling method to obtain a representative sample of older adults in Shanghai. All participants completed paper questionnaires during face-to-face interviews and underwent physical examinations conducted by the investigation team.
Results
Our results showed that the overall age-standardised prevalence of frailty was 9.91% (95% CI 9.50% to 10.31%). Prevalence was 8.30% (95% CI 6.84% to 9.76%) in men and 9.93% (95% CI 8.85% to 11.01%) in women. Advancing age, female sex, lower education level and comorbidities were significantly associated with frailty among community-dwelling older adults.
Conclusions
Clinical and public health efforts to reduce the burden of frailty in China should devote greater attention to older women, particularly those with multiple comorbidities, and prioritise community-based frailty screening and prevention initiatives.
Trial registration number
ChiCTR2000039491.
Keywords: Risk Factors, Frailty, Health, EPIDEMIOLOGIC STUDIES
STRENGTHS AND LIMITATIONS OF THIS STUDY.
This study represents the largest investigation of community-dwelling older adults in this population, using a stratified cluster random sampling method.
Adopting criteria analogous to Fried et al, we established a frailty assessment standard validated for the Shanghai population.
The survey results provide a more accurate representation of frailty prevalence within the Shanghai population.
As a cross-sectional study, this research identifies factors associated with frailty but cannot establish causal relationships.
The inclusion of only a limited number of demographic factors and comorbidities may introduce omitted variable bias into the results.
Introduction
With the ageing population in China and worldwide, the incidence of degenerative, senile and hereditary chronic diseases is increasing, which is placing greater pressures on medical and health resources and on the social economy. According to the National Bureau of Statistics, data from the seventh Chinese national census show that the proportion of the population aged 65 and above has reached 13.50%, and the degree of population ageing has exceeded the world average. The period from 2021 to 2050 will be a period of accelerated ageing. It is expected that by 2050, the total number of older people will exceed 400 million, and the ageing level will reach more than 30%. As a result, the prevention and control of the occurrence and development of chronic diseases has become a major strategic problem that urgently needs to be solved in China and all countries worldwide.
Frailty is recognised as an emerging public health priority.1 2 A validated phenotype of frailty has been shown to predict the incidence of worsening disability, hospitalisation, falls, fractures and mortality.3 4 Some studies have shown that risk factors for frailty include ageing, being female, diagnosis with chronic diseases, lacking physical exercise, excessive comorbidities and being overweight or underweight.5 6 To date, a large sample of community-dwelling older adults’ surveys on the prevalence of and risk factors for frailty are still in the initial stages of exploration. Therefore, the aims of this study are as follows: to establish a large sample prospective cohort study in Shanghai, to investigate the frailty status of the multidistrict older population in the community, to analyse the characteristics and rules of the frail population, and to explore the possible risk factors for frailty in the older population.
Methods and analysis
Recruitment of the participants
This cross-sectional study was a part of the registered protocol at Chinese Clinical Trial Registry (ChiCTR2000039491).
We used a stratified cluster random sampling method to enrol a sample of people who would be representative of older adults in Shanghai. The residents of this study were from nine communities (Huangdu, Daqiao, Jiangpu, Nanxiang, Pengpu, Quyang, Situan, Taopu and Yinhang) in the Yangpu, Jing'an, Hongkou, Putuo, Fengxian and Jiading districts of Shanghai.
The investigation team, composed of community workers and clinicians, informed residents in advance and determined the investigation time according to the sampling list. Eligible participants were permanent residents (more than 5 years), including those aged ≥65 years. Residents whose physical function tests were affected by schizophrenia, dementia or other diseases and who could not cooperate with the investigations were excluded. In total, 2950 residents responded and signed informed consent forms from September 2018 to June 2021.
Questionnaires and physical examinations
All the participants completed paper-based questionnaires during face-to-face interviews. The questionnaires included information about age, sex, marital status, floor of residence, education level, smoking and drinking status, comorbid diseases that had ever been diagnosed or were currently being suffered from, including infectious pneumonia, cerebral infarction, cerebral haemorrhage, coronary heart disease (CHD) and lower limb fracture, chronic obstructive pulmonary disease (COPD), hypertension, Parkinson’s disease, chronic liver disease, diabetes, chronic kidney disease, hyperlipidaemia, osteoporosis and cancer. Marital status was categorised as follows: married, never married, separated/divorced or widowed. The floor of residence was divided into three categories: first floor, walk-up high floor and elevator high floor. Education level was categorised into the following six categories: no formal education, primary school, middle school, high school, college and university or higher education. Smoking status was classified into the following three categories: never smoked, ex-regular smoker and current regular smoker. Drinking status was classified into the following three categories: never drinker, ex-regular drinker or occasional/seasonal drinker. Information about chronic diseases was obtained from the outpatient and emergency treatment records provided by the participants. Body mass index (BMI) was calculated as weight (kg)/height squared (m2).
Assessment and diagnosis of frailty
Frailty status at baseline was defined using criteria similar to those of Fried et al3:
Shrinking: weight loss, unintentional, of >5 kg in the prior year or, at follow-up, of >5% reduction in body weight in the prior year (by direct measurement of weight).
Weakness: grip strength in the lowest 20% at baseline, adjusted for sex and BMI.
Slowness: The slowest 20% of the population was defined on the basis of time to walk 4 m, adjusting for sex and standing height.
Low physical activity level: The physical activity status of the subjects in the past month was recorded in the following four aspects: (1) Very little physical activity: mainly sitting and lying, very little activity, unable to walk independently for 10 min; (2) little physical activity: no exercise, few outdoor activities, unable to walk independently for 30 min; (3) less physical activity: some exercise but not fixed, less housework, slower movement; (4) normal physical activity: regular exercise such as exercise, walking, etc, often do housework. Any one of the items (1), (2) or (3) is recognised as low physical activity.
Poor endurance and energy: Subjects were asked to record their fatigue status in the past month. (1) Almost every day, ≥5 days a week; (2) Often, 3–4 days a week; (3) Occasionally, 1 or 2 days a week; (4) This is not the case. Item (1) or item (2) is recognised as having poor endurance and energy.
Participants who met none of the above criteria were considered robust; those meeting one or two criteria were considered prefrail; and those meeting three or more criteria were considered frail.
Statistical analysis
For baseline characteristics analysis, missing values were recorded as N/A and excluded from the final analysis using listwise deletion if the participants refused the assessment, physical examinations or part of the questionnaire. Sensitivity analysis was performed in case of missing values over 10% of the total values.
The characteristics of the participants are presented as the means and SD, medians and IQRs, or numbers and proportions in the overall population and in subgroups stratified by sex.
The age-standardised prevalence and 95% CIs of frailty were estimated by sex on the basis of data from the China 2022 census. The relationships between frailty and sociodemographic variables or other factors were examined separately using univariate linear models. A multivariable multinomial logit analysis was used to examine the associations of variables with the odds of frailty. All p values were two-tailed and not adjusted for multiple testing. All p values <0.05 were considered statistically significant. All statistical analyses were conducted using SPSS V.26.0 (SPSS).
Patient and public involvement
None.
Results
Demographic characteristics of the participants
A total of 2950 subjects were included in this survey, including 1374 males (46.6%) and 1576 females (53.4%), with an average age of 71.9±5.3 years. There were significant differences in marital history, education, smoking history, drinking history, dietary habits, physical activity and history of present illness between males and females. Men currently smoking and occasionally drinking accounted for 33.6% and 37.2% of the study group, respectively, as compared with few women who smoked. Among the 15 chronic diseases, hypertension (56.6%), diabetes (19.3%) and hyperlipidaemia (13.6%) were the most prevalent. The demographic characteristics of the participants are presented in table 1.
Table 1. General characteristics of the participants.
| Total N=2950 | Female N=1576 | Male N=1374 | P value | |
|---|---|---|---|---|
| Age, year | 71.0 (68.0; 75.0) | 70.0 (68.0; 74.0) | 71.0 (68.0; 75.0) | 0.002 |
| BMI, kg/m2 | 24.5 (22.4; 26.6) | 24.2 (22.1; 26.4) | 24.7 (22.8; 26.8) | 0.001 |
| Marital status (N=2939), n(%) | 0.001 | |||
| Married | 2591 (88.2%) | 1301 (82.9%) | 1290 (94.2%) | |
| Never married | 11 (0.37%) | 8 (0.51%) | 3 (0.22%) | |
| Separated/divorced | 18 (0.61%) | 10 (0.64%) | 8 (0.58%) | |
| Widowed | 319 (10.9%) | 251 (16.0%) | 68 (4.97%) | |
| Floor of residence, n(%) | 0.161 | |||
| Walk-up high floor | 2087 (70.7%) | 1092 (69.3%) | 912 (66.4%) | |
| Elevator high floor | 353 (12.0%) | 191 (12.1%) | 197 (14.4%) | |
| First floor | 510 (17.3%) | 293 (18.6%) | 251 (18.3%) | |
| Education (N=2942), n(%) | <0.001 | |||
| No formal school | 419 (14.2%) | 345 (21.9%) | 74 (5.40%) | |
| Primary school | 721 (24.5%) | 401 (25.5%) | 320 (23.4%) | |
| Middle school | 1036 (35.2%) | 504 (32.1%) | 532 (38.8%) | |
| High school | 518 (17.6%) | 238 (15.1%) | 280 (20.4%) | |
| University | 248 (8.43%) | 84 (5.34%) | 164 (12.0%) | |
| Smoking status (N=2947), n(%) | <0.001 | |||
| Never smoker | 2138 (72.5%) | 1556 (98.9%) | 582 (42.4%) | |
| Ex-regular smoker | 333 (11.3%) | 4 (0.25%) | 329 (24.0%) | |
| Current regular | 476 (16.2%) | 14 (0.89%) | 462 (33.6%) | |
| Drinking status (N=2935), n(%) | <0.001 | |||
| Never drinker | 2221 (75.5%) | 1538 (97.8%) | 683 (49.8%) | |
| Ex-regular drinker | 174 (5.91%) | 3 (0.19%) | 171 (12.5%) | |
| Occasional or seasonal drinker | 540 (18.3%) | 30 (1.91%) | 510 (37.2%) | |
| Regular drinker | 8 (0.27%) | 1 (0.06%) | 7 (0.51%) | |
| Diet (N=2926), n(%) | <0.001 | |||
| Meat-eater | 152 (5.19%) | 41 (2.62%) | 111 (8.15%) | |
| More vegetarian food | 1200 (41.01%) | 743 (47.5%) | 457 (33.6%) | |
| Meat and vegetable balance | 1503 (51.37%) | 730 (46.7%) | 773 (56.8%) | |
| Vegetarians | 71 (2.43%) | 50 (3.20%) | 21 (1.54%) | |
| Frequency of physical activity (N=2947), n(%) | 0.046 | |||
| Daily or almost every day | 1718 (58.5%) | 886 (56.4%) | 832 (61.0%) | |
| 1–2 times/week | 304 (10.4%) | 172 (11.0%) | 132 (9.67%) | |
| Never or almost never | 913 (31.1%) | 512 (32.6%) | 401 (29.4%) | |
| Comorbid diseases, n(%) | ||||
| COPD | 63 (2.14%) | 28 (1.78%) | 35 (2.55%) | 0.188 |
| Infectious pneumonia | 21 (0.71%) | 14 (0.89%) | 7 (0.51%) | 0.317 |
| CHD | 484 (16.4%) | 268 (17.0%) | 216 (15.7%) | 0.374 |
| Hypertension | 1671 (56.6%) | 857 (54.4%) | 814 (59.2%) | 0.009 |
| Cerebral infarction | 412 (14.0%) | 240 (15.2%) | 172 (12.5%) | 0.039 |
| Cerebral haemorrhage | 17 (0.58%) | 5 (0.32%) | 12 (0.87%) | 0.081 |
| Parkinson's disease | 14 (0.47%) | 6 (0.38%) | 8 (0.58%) | 0.599 |
| Chronic liver disease | 45 (1.53%) | 20 (1.27%) | 25 (1.82%) | 0.286 |
| Diabetes | 570 (19.3%) | 299 (19.0%) | 271 (19.7%) | 0.639 |
| Chronic kidney disease | 46 (1.56%) | 19 (1.21%) | 27 (1.97%) | 0.131 |
| Hyperlipidaemia | 401 (13.6%) | 254 (16.1%) | 147 (10.7%) | <0.001 |
| Gout | 109 (3.69%) | 39 (2.47%) | 70 (5.09%) | <0.001 |
| Osteoporosis | 224 (7.59%) | 177 (11.2%) | 47 (3.42%) | <0.001 |
| Lower limb fracture | 95 (3.22%) | 61 (3.87%) | 34 (2.47%) | 0.042 |
| Cancer | 66 (2.24%) | 43 (2.73%) | 23 (1.67%) | 0.071 |
Number of values for every single analysis was listed if there were missing values.
BMI, body mass index; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease.
Estimated prevalence of frailty in older adults in Shanghai
The estimated prevalence of frailty is listed in figure 1 and table 2. The overall age-standardised prevalence of frailty was estimated to be 9.91% (95% CI 9.50% to 10.31%) in older Shanghai permanent residents, 8.30% (95% CI 6.84% to 9.76%) in men and 9.93% (95% CI 8.85% to 11.01%) in women. The overall prevalence of prefrailty was 41.51% (95% CI 41.19% to 41.83%), 44.16% (95% CI, 41.71% to 46.61%) in men and 38.57% (95% CI 36.00% to 41.15%) in women. The prevalence of frailty increased with age in women and men (figure 1). For men, the prevalence of frailty was 5.9%, 5.3%, 8.8% and 25.2% for those aged 65–69 years, 70–74 years, 75–79 years and 80 s and older, respectively. For women, the prevalence of frailty was 7.7%, 7.9%, 16.1% and 32.6% for those aged 65–69 years, 70–74 years, 75–79 years and 80 years and older, respectively. The prevalence of frailty in men and women also increased significantly in older individuals over 75 years of age.
Figure 1. Prevalence of frailty in older people in Shanghai. (A) Estimated prevalence of frailty in older people by age and gender. (B) Prevalence of frailty and prefrailty in the older people by gender. (C) Age-standardised prevalence of frailty in older people.
Table 2. Age-standardised prevalence of frailty in each group.
| Age, years | Total | Men | Women |
|---|---|---|---|
| 65–69 | 6.85 (6.18, 7.52) |
5.90 (3.88, 7.92) | 7.70 (5.68, 9.72) |
| 70–74 | 6.65 (5.87, 7.43) | 5.30 (3.28, 7.32) | 7.90 (5.58, 10.22) |
| 75–79 | 12.65 (11.68, 13.62) | 8.80 (5.12, 12.48) |
16.10 (11.47, 20.73) |
| ≥80 | 29.42 (28.60, 30.24) | 25.20 (18.28, 32.13) |
32.60 (24.94, 40.26) |
Data are expressed as age-standardised prevalence (95% CI).
Risk factors for frailty in older adults in Shanghai
According to the multivariable multinomial logit models, compared with men, women were more susceptible to frailty (OR=1.60, 95% CI (1.01 to 2.55)). In addition, ageing (OR=1.15, 95% CI (1.11 to 1.18)), a low education level (including middle school (OR=2.41, 95% CI (1.19 to 4.89)), primary school (OR=2.73, 95% CI (1.34 to 5.57)) or no formal education (OR=4.20, 95% CI (1.97 to 8.95))) and a history of CHD (OR=1.81, 95% CI (1.19 to 2.74)), cerebral infarction (OR=2.78, 95% CI (1.83 to 4.23)), diabetes (OR=2.06, 95% CI (1.42 to 3.00)) and osteoporosis (OR=2.49, 95% CI (1.42 to 4.36)) were significantly associated with a greater risk of frailty in older adults. However, being married and performing daily or almost daily activities was associated with a lower risk of frailty. The risk factors for frailty according to univariate and multivariate logistic regression analyses are listed in table 3.
Table 3. Risk factors for frailty by univariate and multivariate logistic regression analyses.
| Risk factors | OR (95% CI)* | P value* | Adjusted OR (95% CI)† | P value† |
|---|---|---|---|---|
| Gender | ||||
| Male | 1.00 | 1.00 | ||
| Female | 0.61 (0.47 to 0.79) | <0.001 | 1.60 (1.01 to 2.55) | 0.047 |
| Age, years | 1.16 (1.13 to 1.19) | <0.001 | 1.15 (1.11 to 1.18) | <0.001 |
| BMI, kg/m2 | 1.00 (0.96 to 1.04) | 0.984 | ||
| Marital status | ||||
| Widowed | 1.00 | 1.00 | ||
| Married | 0.28 (0.20 to 0.40) | <0.001 | 0.59 (0.35 to 0.98) | 0.042 |
| Never married | 0.65 (0.12 to 3.46) | 0.615 | 2.05 (0.22 to 18.96) | 0.528 |
| Separated/divorced | 0.00 (0.00 to 0.00) | 0.999 | 0.00 (0.00 to 0.00) | 0.999 |
| Floor of residence | ||||
| First floor | 1.00 | |||
| Walk-up high floor | 0.47 (0.34 to 0.65) | <0.001 | ||
| Elevator high | 0.74 (0.48 to 1.13) | 0.161 | ||
| Education | ||||
| University | ||||
| No formal school | 5.77 (3.12 to 10.65) | <0.001 | 4.20 (1.97 to 8.95) | <0.001 |
| Primary school | 2.12 (1.16 to 3.88) | 0.014 | 2.73 (1.34 to 5.57) | 0.008 |
| Middle school | 1.48 (0.81 to 2.68) | 0.201 | 2.41 (1.19 to 4.89) | 0.015 |
| High school | 1.04 (0.53 to 2.04) | 0.901 | 1.45 (0.67 to 3.13) | 0.349 |
| Smoking status | ||||
| Current regular | 1.00 | 1.00 | ||
| Never smoker | 1.51 (1.03 to 2.20) | 0.034 | 0.68 (0.38 to 1.20) | 0.185 |
| Ex-regular smoker | 1.76 (1.08 to 2.87) | 0.023 | 1.48 (0.83 to 2.65) | 0.189 |
| Drinking status | ||||
| Regular drinker | 1.00 | |||
| Never drinker | 1.14 (0.13 to 9.84) | 0.903 | ||
| Ex-regular drinker | 1.18 (0.13 to 10.64) | 0.883 | ||
| Occasional or seasonal drinker | 0.55 (0.06 to 4.85) | 0.590 | ||
| Diet | ||||
| Vegetarians | 1.00 | |||
| Meat-eater | 0.81 (0.35 to 1.88) | 0.627 | ||
| More vegetarian food | 0.70 (0.36 to 1.37) | 0.297 | ||
| Meat and vegetable balance | 0.60 (0.31 to 1.18) | 0.139 | ||
| Frequency of physical activity | ||||
| Never or almost never | 1.00 | 1.00 | ||
| Daily or almost every day | 0.40 (0.30 to 0.53) | <0.001 | 0.48 (0.34 to 0.69) | <0.001 |
| 1–2 times/week | 0.90 (0.61 to 1.34) | 0.603 | 1.03 (0.63 to 1.68) | 0.919 |
| Combined diseases | ||||
| COPD | 5.13 (2.59 to 10.17) | <0.001 | ||
| Infectious pneumonia | 0.98 (0.21 to 4.48) | 0.976 | ||
| CHD | 2.86 (2.11 to 3.88) | <0.001 | 1.81 (1.19 to 2.74) | 0.005 |
| Hypertension | 1.63 (1.25 to 2.12) | <0.001 | ||
| Cerebral infarction | 4.51 (3.32 to 6.13) | <0.001 | 2.78 (1.83 to 4.23) | <0.001 |
| Cerebral haemorrhage | 8.74 (2.54 to 30.04) | 0.001 | ||
| Parkinson’s disease | 9.96 (2.48 to 40.05) | 0.001 | ||
| Chronic liver disease | 2.62 (1.25 to 5.50) | 0.011 | ||
| Chronic kidney disease | 1.29 (0.48 to 3.49) | 0.615 | ||
| Diabetes | 1.93 (1.44 to 2.58) | <0.001 | 2.06 (1.42 to 3.00) | <0.001 |
| Hyperlipidaemia | 1.35 (0.96 to 1.92) | 0.087 | ||
| Gout | 1.89 (1.08 to 3.29) | 0.026 | ||
| Osteoporosis | 3.16 (2.11 to 4.73) | <0.001 | 2.49 (1.42 to 4.36) | 0.001 |
| Lower limb fracture | 2.69 (1.50 to 4.84) | 0.001 | ||
| Cancer | 2.17 (1.02 to 4.60) | 0.045 |
Univariate linear regression model without adjustment.
Multivariate logistic regression analyses adjustment for sex, age, marital status, education, smoking status, activities, history of COPD, CHD, hypertension, cerebral infarction, Cerebral haemorrhage, Parkinson’s disease, chronic liver disease, diabetes, gout, osteoporosis, lower limb fracture and cancer as independent variables.
BMI, body mass index; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease.
Discussion
This cross-sectional study is a multidistrict, community-based study of frailty phenotypes and the prevalence of frailty in Shanghai. In a recent meta-analysis of frailty among Chinese community-dwelling older adults,7 the overall prevalence rates of frailty and prefrailty were 10.1% (95% CI 8.5% to 11.7%) and 43.9% (95% CI 40.1% to 47.8%), respectively. In Shanghai, the prevalence rates of frailty and prefrailty were 7.7% (95% CI 3.1% to 12.5%) and 40.5% (95% CI 32.7% to 48.4%), respectively. One article from 62 countries8 reported that for adults aged ≥50 years, the overall prevalence of frailty measured using physical frailty was 12% (95% CI 11% to 13%). These data were similar to ours. Our data revealed that the standardised prevalence of frailty was 9.91% (95% CI 9.50% to 10.31%), and the standardised prevalence of prefrailty was 41.51% (95% CI 41.19% to 41.83%). Considering the differences in regional, ethnic and other factors, we used our own database to establish evaluation criteria for frailty phenotypes in Shanghai. Our results are close to the national prevalence of frailty, indicating that our established frailty assessment criteria can better distinguish frailty groups.
Moreover, we found that the prevalence of frailty was 8.30% (95% CI 6.84% to 9.76%) in men and 9.93% (95% CI 8.85% to 11.01%) in women. We also found that the prevalence of frailty increased with age, especially among those older than 80 years. Accelerated frailty in people over 80 years of age was similar to previous findings. This phenomenon was detected in both male and female older participants. Both domestic and overseas studies have also revealed that ageing and female sex are associated with increased odds of being frail.7 8
In addition to age and sex, other factors that are closely related to frailty of older adults in this community include a lower education level and a chronic history of CHD, cerebral infarction, diabetes and osteoporosis. However, being married and performing daily or almost daily activities was associated with a lower risk of frailty. Some conclusions are consistent with those of the Beijing survey.9 They reported that the degree of frailty was related to sex, age, occupation type, living alone, allergy history, polypharmacy, long-term pain, self-care degree, traditional Chinese medicine intervention and common diseases of older individuals.
Education synergistically acts in the incidence of frailty. The risk of frailty in those with a middle school education was 2.41 times greater than that in those with a university education, whereas the risk in those with a primary school education was 2.73 times greater and the risk in those with no formal education was 4.2 times greater. A survey10 that included 911 community-dwelling older adults (mean age 79.5 years) who underwent a comprehensive geriatric assessment in Milano revealed that education and age interact in the incidence of frailty in an exponential-type relationship. Whereas age is a non-modifiable risk factor, much can be done to address the social component of frailty represented here by education. The same is true even in the developed city of Shanghai, China. An imbalance in education can aggravate the degree of frailty.
According to the latest data released by the National Bureau of Statistics, there were 230 million people aged 60 years and above in China in 2016, accounting for 16.7% of the total population, and 150 million people aged 65 years and above, accounting for 10.8%. It is expected that by 2050, the total number of older adults will exceed 400 million, and the ageing level will reach more than 30%. According to a 2019 survey in China, the prevalence of hypertension, diabetes and hypercholesterolaemia was 58.3%, 19.4%, 10.5% and 75.8%, respectively, among residents who had one or more chronic diseases, with a higher incidence in females than males, and in cities than in rural areas.11
Frailty is strongly associated with multimorbidity, and the presence of comorbidities contributes to both frailty and cardiovascular disease (CVD) risk.12,15 Our findings indicate that frailty is associated with a chronic history of CHD, cerebral infarction, diabetes and osteoporosis. A previous study revealed that frailty was associated with the quality of life of patients with coronary artery disease and could also affect the prognosis of CHD.16 A large longitudinal cohort study17 also revealed that physical frailty was associated with CVD incidence. The article also noted that inflammation, insulin resistance, endocrine and immune disorders and other pathologic reasons, as well as lifestyle and metabolic dysregulation, can also lead to a high incidence of cardiovascular events in the frail population.
Clinical findings also revealed that frailty in older individuals can lead to adverse events such as falls, disability and hospitalisation. The musculoskeletal system is considered the target organ of frailty in older individuals. Frailty in older adults may be associated with hormones and cytokines that influence bone metabolism. For instance, with advancing age, there is a decline in the levels of sex hormones (oestrogens and androgens),18 19 as well as a reduced secretion of calcium-regulating hormones (parathyroid hormone and vitamin D).20 21 This leads to a decreased secretion of cytokines (such as insulin-like growth factor and inflammatory factors).22 23
In addition, in the older population undergoing physical examination in Shanghai, frailty is significantly related to diabetes, which is considered related to diabetes causing a reduction in muscle mass, resulting in symptoms such as slow gait speed and reduced grip strength.24 Further, studies25 have suggested that frailty and diabetes have a common pathological basis, because both affect the body in the context of high levels of insulin resistance and chronic inflammation, intestinal flora disturbance, increased oxidative stress levels and bone metabolic abnormalities. Therefore, active assessment of frailty in older patients is helpful for assessing the risk and prognosis of patients and is highly important for the rehabilitation of patients, the improvement of quality of life, the reduction of mortality and hospitalisation rates, and the reversal of frailty.
This study is based on a cross-sectional survey design and therefore has inherent limitations. First, while we identified several risk factors associated with frailty, we cannot establish causal relationships. Second, the inclusion of only a limited set of demographic factors and comorbidities may introduce omitted variable bias. Third, although comorbidities showed associations with frailty, more specific elements such as the quantity of comorbidities and concomitant medications were not incorporated into the analysis.
However, this study also has many innovations. First, this study has the largest sample size and uses a stratified cluster random sampling method based on community-dwelling older individuals for investigation. Second, we found a high prevalence of frailty and prefrailty in the 75-year-old group in Shanghai. Third, this study is informative and contains demographic information, economic and cultural factors, living habits, awareness and understanding of history and chronic debilitating disease patients, which can be obtained from different dimensions of population characteristics. Fourth, the high prevalence of frailty and prefrailty in 75-year-old older groups in Shanghai, especially for females, prompts governments to focus mainly on screening key populations. Finally, this project will include long-term tracking and follow-up to provide more accurate evidence-based evidence to examine the associations between risk factors and frailty in older individuals.
Overall, the assessment of frailty serves as a valuable indicator for identifying risks associated with adverse health outcomes among older adults. Furthermore, it enables the stratification of the elderly population to facilitate targeted medical interventions and enhanced healthcare provision. Particularly in subgroups such as women aged over 75 years, individuals with low educational attainment and those with multiple comorbidities, the implementation of focused protective strategies may help slow the progression of frailty.
Conclusions
In this cross-sectional study, we used a stratified cluster random sampling method to investigate the prevalence of frailty. Age, female sex, a lower education level and comorbidities are closely related to the frailty of older adults in the community. Clinical and public health efforts to reduce the burden of frailty in China should devote more attention to older women, especially those with multiple diseases, and should focus on screening and preventing frailty in the community.
Acknowledgements
All authors contributed to the study’s conception and design, as well as the acquisition, analysis, interpretation of data, drafting and revising of the article, and agreed to be accountable for all aspects of the work.
Footnotes
Funding: This paper was supported by the Inheritance and Innovation Team Project of National Traditional Chinese Medicine (ZYYCXTD-C-202202).
Prepublication history for this paper is available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2024-095371).
Patient consent for publication: Not applicable.
Ethics approval: The protocol was approved by the Institutional Review Board at Longhua Hospital, which is affiliated with the Shanghai University of Traditional Chinese Medicine (2018LCSY035), and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All participants provided written informed consent before participation.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available on reasonable request.
References
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