The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

Abstract

Background We have witnessed frailty, which characterized by a decline in physiological reserves, become a major public health issue in older adults. Understanding the influential factors associated with frailty may help prevent or if possible reverse frailty. The present study aimed to investigate factors associated with frailty status and frailty transition in a community-dwelling older population. Methods A prospective cohort study on community-dwelling subjects aged ≥ 60 years was conducted, which was registered beforehand (ChiCTR 2,000,032,949). Participants who had completed two visits during 2020–2021 were included. Frailty status was evaluated using the Fried frailty phenotype. The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. Bayesian network analysis with the max-min hill-climbing (MMHC) algorithm was used to identify factors related to frailty status and frailty transition. Results Of 1,981 subjects at baseline, 1,040 (52.5%) and 165 (8.33%) were classified as prefrailty and frailty. After one year, improved, stable, and worsening frailty status was observed in 460 (35.6%), 526 (40.7%), and 306 (23.7%) subjects, respectively. Based on the variables screened by LASSO regression, the Bayesian network structure suggested that age, nutritional status, instrumental activities of daily living (IADL), balance capacity, and social support were directly related to frailty status. The probability of developing frailty is 14.4% in an individual aged ≥ 71 years, which increases to 20.2% and 53.2% if the individual has balance impairment alone, or combined with IADL disability and malnutrition. At a longitudinal level, ADL/IADL decline was a direct predictor of worsening in frailty state, which further increased the risk of hospitalization. Low high-density lipoprotein cholesterol (HDL-C) and diastolic blood pressure (DBP) levels were related to malnutrition, and further had impacts on ADL/IADL decline, and ultimately led to the worsening of the frailty state. Knowing the status of any one or more of these factors can be used to infer the risk of frailty based on conditional probabilities. Conclusion Older age, malnutrition, IADL disability, and balance impairment are important factors for identifying frailty. Malnutrition and ADL/IADL decline further predict worsening of the frailty state.

Prevalence and factors associated with changes in frailty among community‐dwelling older adults in Japan during the COVID‐19 pandemic: A prospective cohort study from 2020 to 2022

“…Participants who were classified as robust or pre‐frailty in 2020 and whose frailty status worsened by at least one level in 2022, as well as those who were classified as frailty in both 2020 and 2022, were assigned to the remained frail/deteriorated group ( n = 133), whereas all other participants were assigned to the non‐deteriorated group ( n = 416). These groupings were similar to those in previous studies 18,19 …”

Frailty and risk of systemic atherosclerosis: A bidirectional Mendelian randomization study

“…Blood lipids, such as triglycerides and LDL-C, play a significant role in the development of atherosclerosis, and research indicates that lowering these lipid levels can reduce the risk of cardiovascular diseases [ 35 ]. A Bayesian network analysis conducted on community-dwelling older people suggested that HDL-C levels may be associated with malnutrition and can have an impact on frailty status [ 36 ]. This finding aligns with the results of our reverse MR analysis.…”

Effect of overweight/obesity and metabolic syndrome on frailty in middle‐aged and older Japanese adults

“…Low HDL‐C and high LDL‐C levels are well‐known risk factors for cardiovascular diseases, whereas low serum cholesterol levels also indicate malnutrition 27 which is associated with frailty. 28 Wu et al.…”

“…Low HDL-C and high LDL-C levels are well-known risk factors for cardiovascular diseases, whereas low serum cholesterol levels also indicate malnutrition 27 which is associated with frailty. 28 -9 of 13 demonstrated that low LDL-C level was a risk factor for frailty in older Japanese males with diabetes but not in females.…”