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DOSE-RESPONSIVE IMPACTS OF SOCIAL FRAILTY ON INTRINSIC CAPACITY AND HEALTHY AGING AMONG COMMUNITY-DWELLING MIDDLE-AGED AND OLDER ADULTS: STRONGER ROLES OF SOCIAL DETERMINANTS OVER BIOMARKERS

 

S.-T. Huang1,2, W.-H. Lu3, W.-J. Lee2,4,5, L.-N. Peng2,5,6, L.-K. Chen2,6,7, F.-Y. Hsiao8,9,10

 

1. Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, Taiwan; 2. Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; 3. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 4. Department of Geriatric Medicine, National Yang Ming Chiao Tung University, School of Medicine, Taipei, Taiwan; 5. Department of Family Medicine, Taipei Veterans General Hospital Yuanshan Branch, Yi-Lan County, Taiwan; 6. Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan; 7. Taipei Municipal Gan-Dau Hospital, Taipei, Taiwan; 8. Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; 9. School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan; 10. Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan

Corresponding Author: Fei-Yuan Hsiao, Ph.D., Professor and Director, Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Room 220, 33, Linsen S. Rd, Taipei, Taiwan 10050, TEL: +886-2-33668787, FAX: +886-2-33668780, Email: fyshsiao@ntu.edu.tw, Or Professor Liang-Kung Chen, Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, No. 201, Sec 2, Shih-Pai Road, Taipei, Taiwan 11217, TEL: +886-2-28757830, FAX: +886-2-28757711, Email: lkchen2@vghtpe.gov.tw

J Frailty Aging 2024;in press
Published online January 30, 2024, http://dx.doi.org/10.14283/jfa.2024.8

 


Abstract

OBJECTIVE: The intricate relationship between social determinants, e.g., social frailty, biomarkers and healthy aging remains largely unexplored, despite the potential for social frailty to impact both intrinsic capacity (IC) and functional ability in the aging process.
DESIGN: Retrospective longitudinal cohort study.
SETTING AND PARTICIPANTS: Participants aged 50+ years from the Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan, stratified into three age groups: 50-64, 65-74 and 75+.
MEASUREMENTS: Social frailty was defined based on a score derived from four domains: exclusion from general resources, social resources, social activity, and fulfillment of basic social needs. The scores were categorized as score=0 (no social frailty), 1 (social pre-frailty), and 2+ (social frailty). Multivariable logistic regression and Cox proportional hazard models were employed to examine the dose-responsive relationship between social frailty, low IC, functional and psychological health, and mortality.
RESULTS: Of 1015 study participants, 24.9% and 7.9% were classified as social pre-frailty and social frailty, respectively. No significant differences were observed in most biomarkers between those with social frailty and those without. A dose-responsive relationship was found between social frailty and increased risk of low IC (social pre-frailty: aOR 2.20 [95% CI 1.59-3.04]; social frailty: 5.73 [3.39-9.69]). Similar results were found for functional and psychological health. However, no significant association between social frailty and all-cause mortality was found at the 4-year follow-up (social pre-frailty: aHR 1.52 [95% CI 0.94-2.43]; social frailty: 1.59 [0.81-3.09]).
CONCLUSIONS: The significant association between social frailty and low IC, functional limitations, cognitive declines, and depressive symptoms underscores the pressing need for research on intervention strategies to enhance healthy aging in the lifespan course.

Key words: Social frailty, biomarkers, intrinsic capacity, healthy aging, functional ability.


 

Introduction

Population aging is a global phenomenon that poses unprecedented challenges to health and social care systems, wherein the interaction between multifaceted care needs and the influence of social determinants becomes increasingly prominent as individuals age (1). Both the Healthy Aging initiative by the World Health Organization (WHO)(2) and the Healthy Longevity framework by the US National Academy of Medicine (3) underscore the significance of social determinants in facilitating well-being during the later stages of life. For example, social isolation or disconnection, has garnered extensive attention as they have been suggested to have detrimental impacts on the health of older adults, particularly during the COVID-19 pandemic (4-6). A recent meta-analysis provides a global estimate of prevalence in social isolation among community-dwelling older adults as 25%, highlighting the need for targeted interventions among these vulnerable groups (7).
On the other hand, “social frailty,” a more comprehensive concept to capture multi-domains of social determinants, including absence of social behaviors, social activities, self-management abilities, and social resources to fulfill basic social needs based on the Bunt’s social frailty theory has been proposed (8). Several previous studies have revealed that social frailty may be linked to unfavorable clinical outcomes among older adults, such as physical frailty or cognitive deficits (6, 9-11). However, the connection between social frailty and long-term outcomes, such as mortality, remains a subject warranting further investigation. Additionally, a more in-depth exploration of the role of biomarkers is imperative for a comprehensive understanding of the mechanisms that underlie the association between social frailty and clinical outcomes. This association is frequently attributed to stress and may encompass immune and endocrine dysregulation (12).
Furthermore, the majority of extant studies examining the effects of social frailty have predominantly concentrated on specific outcomes, neglecting a more comprehensive appraisal of healthy aging. Notably, there is a scarcity of research investigating the link between social frailty and intrinsic capacity, which encompasses the physical and mental capacities essential for promoting healthy aging, as outlined in the World Health Organization’s 2015 World Report on Aging and Health (2). This may hinder the development of effective interventions to mitigate the negative consequences of social frailty in the aging process. Hence, this study aims to bridge the existing knowledge gaps by investigating the potential dose-response relationships between social frailty, healthy aging, and mortality across different age groups (50-64 years vs. 65-74 years vs. 75+ years), while comprehensively exploring the roles of biomarkers in elucidating the underlying mechanisms.

 

Methods

Data source

This retrospective study used data from the Social Environment and Biomarkers of Aging Study (SEBAS), a survey based on the Taiwan Longitudinal Study of Aging (TLSA), Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW). In response to the potential impacts of the economic, medical, familial, and social aspects of population aging in Taiwan, the Health Promotion Administration (HPA), with collaborative efforts with the University of Michigan, has been conducting TLSA surveys via face-to-face household interviews with middle-aged and older adults since 1989 to gather data on demographics, health status, and well-being. Sufficient national representation was accomplished because the TLSA sample was randomly selected from adult residents aged 50 years and older in all nonaboriginal townships of Taiwan.
The first SEBAS cohort was established in 2000 (wave I), for which 1023 participants were randomly selected for interviews from the 1999 TLSA survey. The cohort was then expanded to 1284 participants in 2006 (wave II) and included 757 participants who had been interviewed in wave I and 527 new participants. The details of SEBAS and TLSA are described on the HPA website and in previous studies (13, 14).

Ethical statement

Ethical approval for this study was obtained from the Institutional Review Board of the National Taiwan University Hospital, with the registration number 202307176W.

Study subjects

In this study, a cohort of research subjects aged 50 years or older was identified from the SEBAS survey. The subjects had successfully undergone interviews in 2006, resulting in a total of 1,284 participants initially. To accurately determine the status of social frailty, participants with incomplete exams (n=248) or missing data on the variable of social frailty (n=21) were excluded. Finally, a final sample of 1,015 study subjects was included in the analysis (Figure S1). Moreover, these participants were categorized into three distinct age groups, namely «aged 50 to 64 years», «aged 65 to 74 years» and «aged 75 years and above,» based on their age at the time of the aforementioned survey.

Definition of social frailty

The operational definition of social frailty in this study was based on a previous Japanese study that utilized a 4-item scale to measure four domains of social frailty, which is grounded in the theory of social frailty proposed by Bunt S et al (8, 9). The four domains of social frailty and their corresponding measurements were as follows: 1) Exclusion from general resources (“financial difficulties”), 2) social resources (“domestic isolation” or “living alone”), 3) social activity, and 4) the fulfillment of basic social needs (“social contact”).
(1) The absence of general resources was assessed as the severity of financial difficulties. Participants who answered “having severe difficulties” meeting living expenses were defined as having financial difficulties (score=1) vs. others (score=0).
(2) The absence of social resources was assessed by the question of living alone. Participants who currently lived alone without any companions or caregivers were defined as living alone (score=1) vs. living with one or more people (score=0).
(3) The absence of social activities was assessed by the question “Have you reduced leisure or outdoor activities due to health in past year?” Those who answered “yes” or that they “never” participate in leisure or outdoor activities were defined as having a lack of social activities (score=1) vs. others (score=0).
(4) The absence of fulfillment of basic social needs (“social contact”) was assessed by the question “How many friends and neighbors do you visit or speak with (in person or by phone) at least once a week?” Those who answered “no friends/neighbors” were defined as having a lack of contact with friends/neighbors to fulfill their basic social needs (score=1) vs. others (score=0).
The sum of all scores yielded a “social frailty score” ranked from 0 to 4 in this study, as defined based on a previous study (9). A higher social frailty score indicating a higher level of social frailty. Each participant was categorized based on their social frailty score, as score=0 (social non-frailty), 1 (social pre-frailty), and 2+ (social frailty) to test the hypothesis that there might be a dose-response association between social frailty and mortality (15).

Measures

Demographic and socioeconomic characteristics

The following demographic characteristics for each study participant were collected in the SEBAS: age, sex, educational levels (illiterate, primary school, junior school, senior high school, college/graduate school), employment status (yes or no), lifestyle behaviors (smoking status; yes or no). Socioeconomic characteristics including marital status (married, never married or others (divorced/separated/widowed)), socioeconomic ladder (ranked from 1 (worst) to 10 (best)), general health status (at current stage: below average (yes/no); versus last year: become worse (yes/no)), current living situation or neighborhood (felt dissatisfied: yes/no).

Mitimorbidity and healthcare utilizations

Self-reported comorbidities, including fall/injuries, hip fracture, osteoporosis, arthritis/rheumatism, heart disease, diabetes mellitus, renal disease, gastroenterological disease and respiratory disease, were collected. The number of self-reported conditions above was further calculated as the multimorbidity. Self-reported healthcare utilization, including emergency department visit and hospitalization, in the prior year was also collected.

Biomarkers

Blood samples collected from SEBAS participants were analyzed for a range of biomarkers. Based on previous studies, we specified cardiometabolic, neuroendocrine and immune biomarkers. Cardiometabolic biomarkers included systolic/diastolic blood pressure (mmHg), total cholesterol (mg/dl), HDL cholesterol (mg/dl), triglyceride (mg/dl), glycosylated hemoglobin (%), fasting glucose (mg/dl), and body mass index (kg/m2), albumin (mg/dL) and creatinine (mg/dL). Neuroendocrine biomarkers included dehydroepiandrosterone sulfate (DHEA-S; ug/dL), insulin-like growth factor-1 (IGF-1; mmol/L)), 12-hour urine cortisol (ug/L), 12-hour urine epinephrine (ug/L), 12-hour urine norepinephrine (mol/mol Cr), and 12-hour urine dopamine (mol/mol Cr). Immune biomarkers (white blood cell count (WBC; as continuous counts per 109/L), percentage of neutrophils (%), percentage of lymphocytes (%), and interleukin-6 (IL-6; pg/mL).

Outcome: intrinsic capacity

The assessment of intrinsic capacity was conducted using a 12-point scoring system, which was established based on the SEBAS cohort (12). This scoring system has been validated for predicting mortality and identifying the early loss of functional ability and common pathophysiology throughout the aging process, as demonstrated in the previous study (12). The 12-score IC scoring system comprises five subdomains: locomotion, sensory, vitality, psychological, and cognition. Each subdomain is further divided into two categories, resulting in a total of 10 categories with the weights assigned to each category are based on their associations with impairments in instrumental activities of daily living (IADL). The details of 12-score IC scoring system are described elsewhere (12).

Outcome: functional health

The functional health of the study participants was assessed by using the 6-item activities of daily living (ADL) and the 6-item instrumental ADL (IADL) in the SEBAS survey. Each item was scored as 0 for no difficulty, 1 for some difficulty, 2 for great difficulty, or 3 if the participant was unable to do it. The items were then summed to create an overall score for physical and functional performance.

Outcome: Psychological and cognitive health

Mental health status was measured by the 10-item Center for Epidemiologic Studies Depression (CES-D) scale comprising eight negative-affect items and two positive-affect items. The items were scored based on the frequency of symptoms in the previous week (for negative-affect items: 0: none; 1: only 1 day, 2: 2 or 3 days, 3: 4 days or more; for positive-affect items: 3: none; 2: only 1 day, 1: 2 or 3 days, 0: 4 days or more). The cutoff point of the overall CES-D score for defined depressive symptoms was 10 or more, according to previous studies (16, 17).
We used the Chinese version of the Short Portable Mental Status Questionnaire (SPMSQ), a well-established tool to evaluate cognitive performance of our participants (18). The SPMSQ included 10 questions and scored the participants from 0 to 10 based on whether they could answer each question independently and correctly. In addition, those with scores of 3 to 4 were defined as “mild cognitive impairment”, and those with scores of 5 or more error were defined as “moderate to severe cognitive impairment”, as suggested by previous studies (18).

Mortality

The survival status of study subjects was retrieved from the mortality database of TLSA, which was linked to the Household Registration and Conscription Information System of the Ministry of Interior (MOI) and the death certificate report system of MOHW. All TLSA participants were recorded as 1) alive, 2) deceased, and 3) survival status unknown or emigration in the database and updated concurrently in each follow-up survey. If any subject had an unknown survival status in 2010, the latest record of alive was deemed to the censored time.

Statistical analysis

Comparisons of demographics and subjective ranked status across the three social frailty groups were performed by X2/Fisher exact test for categorical variables and analysis of variance (ANOVA) for continuous variables. Descriptive statistics were also calculated to examine the association between different severities of social frailty and healthy aging outcomes (intrinsic capacity, physical, functional, cognitive, and psychological outcomes). A multivariable logistic regression model was used to further examine the level of social frailty on each health outcome measurement by adjusting for age, sex and number of comorbidities.
For the association between social frailty and mortality, we first used the Kaplan-Meier method and log-rank test to examine the differences in mortality risk across different groups. Furthermore, we performed the Cox proportional hazard model to explore the association between social frailty and the risk of mortality, stratified by age. Both univariate and multivariable models adjusted for various variables (age, sex and number of comorbidities) were conducted. Statistical significance was indicated by a P value less than 0.05. All data were analyzed using SAS, version 9.4 (SAS Institute, Inc., Cary, NC).

 

Results

Demographics of study subjects

The baseline characteristics of the participants and their differences across the social frailty groups are described in Table 1. A total of 1015 study subjects were included in the study. Among them, 253 (24.9%) were classified in the “social pre-frailty” group, and 80 (7.9%) were classified as in the “social frailty” group at the baseline assessment. The prevalence of social frailty increased with age, particularly in men, with prevalence of 1.1%, 6.0%, 5.6%, 9.2%, 11.7%, 9.7%, 12.8%, and 13.5% in those aged 50-54, 55-59, 60-64, 65–69, 70–74, 75–79, 80–84 and 85–89 years, respectively (Figure S1). Compared to women, men have higher proportion in the social frailty across all age group. Lack of “social contact” was the most common factor contributing to social frailty, particularly in the oldest old population (aged 85–89 years; male 45.5% vs. female 46.7%). For those aged 85–89 years, lack of social activity was also more prevalent in men (42.9%) than in women (9.5%) (Figure S2).

Table 1. Baseline characteristics of study population, stratified by severity of social frailty

Compared with participants who were not socially frail, those classified in the «social pre-frailty» and «social frailty» groups were more likely to be older, have a low education level, without employment, not be married, poor health status, feel dissatisfied about their current living situation, and have more comorbidities. However, there were no significant differences in healthcare utilization, and most biomarkers (except for systolic blood pressure, DHEA-S, and IGF-1, neutrophils) across different social frailty groups (Table 1). Furthermore, across all age groups, individuals classified as social pre-frailty and social frailty consistently exhibit characteristics such as low education level, unmarried status, poor health status, and dissatisfaction with their current living situation, in comparison to those who are not socially frail (Table S1-S3).

Social frailty and intrinsic capacity, functional health and psychological health

The demographic characteristics of intrinsic capacity, functional health and psychological health across different social frailty groups stratified by age (50-64, 65–74 and 75+ years old) is shown in Table 2 and Table S4-S6. The score in intrinsic capacity decreases with the level of social frailty and age. The prevalence of low intrinsic capacity were 24.2%, 44.7% and 67.5% for participants in the social non-frailty, social pre-frailty, and social frailty groups, respectively. Furthermore, for those aged 50-64 years, the prevalence of low intrinsic capacity were 17.3%, 39.0% and 57.7% for participants in the social non-frailty, social pre-frailty, and social frailty groups, respectively; for those aged 75+ years, the prevalence of low intrinsic capacity were 38.5%, 57.9% and 75.0% for participants in the social non-frailty, social pre-frailty, and social frailty groups, respectively. Similar results were found for functional health (i.e., having at least one difficulty in ADL and iADL) and psychological health (i.e., SPMSQ and CES-D scores).

Table 2. Demographic characteristics of intrinsic capacity, functional health and psychological health, stratified by severity of social frailty

 

The associations between social frailty and low intrinsic capacity, disabilities of physical and functional activities, depression, and cognitive decline are presented in Figure 1 and Table S7. After adjusting for age, sex and number of comorbidities, we found that those with social pre-frailty and social frailty had a higher risk of low intrinsic capacity, having difficulties in physical movements, ADL, IADL, depression and cognitive decline. Moreover, these risks were found to increase in a dose-response manner across all age group, especially in those aged 75+ years. Compared with those who were not socially frail, those who were socially frail had a higher risk of low intrinsic capacity (social pre-frailty: adjusted odds ratio (aOR) 1.94 [95% confidence interval (95% CI) 1.06-3.53]; social frailty: 5.09 [1.95-13.31]), difficulties with physical movements (social pre-frailty: 2.15 [0.85-5.44]; social frailty: 8.40 [1.04-67.7]), IADLs (social pre-frailty: 1.87 [0.98-3.59]; social frailty: 6.03 [2.00-18.22]), ADLs (social pre-frailty: 2.20 [0.92-4.45]; social frailty: 6.06 [2.28-16.11]), depression (social pre-frailty: 2.08 [1.02-4.27]; social frailty: 4.94 [1.92-12.67]), and cognitive decline (social pre-frailty: 1.18 [0.59-2.37]; social frailty: 5.93 [2.34-15.06]) in those aged 75+ years.

Figure 1. Results of adjusted multivariable logistic regression models for study outcomes in different status of social frailty among community-dwelling older adults, overall and stratified by age

a) Low intrinsic capacity; b) 1+Difficulties of physical movements; c) 1+Difficulties of IADL; d) 1+Difficulties of ADLs; e) Depression; f) Cognitive decline

 

Social frailty and all-cause mortality

Figure 2, Figure3, and Table S8 summarize the impacts of social frailty on all-cause mortality according to the results of Kaplan-Meier curves and Cox proportional hazard models without and with adjustment. At the 4-year follow-up, a significant association between social frailty and all-cause mortality was observed among all participants based on the log-rank test and unadjusted Cox proportional hazard models. However, this association as presented hazard ratio (aHR) [95% CI] did not reach statistical significance after adjusting for age, gender and the number of diseases, with 1.52 [0.94-2.43] in the social pre-frailty group and 1.59 [0.81-3.09] in the social frailty group. Similar results were observed across all age groups.

Figure 2. Kaplan-Meier curves for all-cause mortality in different status of social frailty among community-dwelling older adults, overall and stratified by age

a) Overall; b) Aged 50-64; c) Aged 65-74; d) Aged 75+

Figure 3. Results of for Cox proportional hazard models for all-cause mortality in different status of social frailty among community-dwelling older adults

a) Unadjusted model; b) Adjusted model

 

Discussion

The study revealed a significant association between social frailty, age, and indicators pertinent to healthy aging, encompassing intrinsic capacity, physical and functional limitations, depressive symptoms, and cognitive performance. The findings demonstrate that social frailty and advanced age are associated with an elevated prevalence of these health concerns. This study also unveiled a substantial dose-response association between social frailty and the risk of low intrinsic capacity, impairments in physical mobility, ADL and IADL, depressive symptoms, and cognitive decline, highlighting the robust prognostic significance of social frailty in healthy aging and underscoring the importance of addressing social frailty as a public health concern for older population. Moreover, our study has demonstrated no significant association between social frailty and an increased risk of all-cause mortality at 4-year follow-up, indicating that social frailty may have a limited impact on short-term mortality. However, the long-term impact of social frailty on mortality remains uncertain, and additional research with longer follow-up is needed for clarification. Despite previous reports suggesting that biomarkers related to nutrition, inflammation, or stress may play a role in the association between social frailty and healthy aging, our study did not find any significant associations between these biomarkers and social frailty with robust result across all age group. Overall, this study underscores the vital contribution of social frailty to healthy aging across multiple domains, highlighting its robust relationship with intrinsic capacity and functional ability, surpassing the commonly postulated biological pathways, thereby bridging the existing gap between the two concepts.
The WHO defines healthy aging as a process that involves developing and maintaining functional ability, integrating an individual’s intrinsic capacity with their functional ability, and emphasizing the vital role of social and environmental factors in enhancing well-being in later life (2, 19). Sex-specific patterns have been documented in prior studies exploring social determinants, as exemplified by a meta-analysis (7), and a study conducted among a cohort of Finnish older adults (20). Our previous research aligns with these findings, indicating that social frailty may exhibit sex-specific patterns. Other social determinants, such as marital status, may contribute differently to social frailty among men and women (6). Among all domains of social frailty examined in this study, social activity and lack of social contact were the main contributor to social frailty. Previous studies have consistently demonstrated that insufficient social contact and social activity were a crucial factor contributing to social frailty in older adults (21), and our investigation extends these insights by examining age-stratified patterns of social frailty across various domains. In our investigation, a mere 4.4% of participants in the 50-54 age group reported a reduction in leisure or outdoor activities, while a significant proportion of participants in the 85-89 age group, nearly half, reported a reduction in such activities. Likewise, a mere 6% of participants in the 50-54 age group reported a lack of social contact, whether in-person or via phone, with friends or neighbors at least once per week. In contrast, a significant proportion of older individuals in the 85-89 age group, a quarter of them, reported a lack of social contact. Our research has demonstrated a negative correlation between social activity, social contact and age, with advancing age increasing the likelihood of experiencing social frailty and loneliness among older adults.
In our earlier investigations, utilizing the identical dataset, we discovered that intrinsic capacity was a robust predictor of mortality and exhibited a profound correlation with various biomarkers, encompassing inflammation, nutrition, stress, and apolipoprotein E genotype (12). Nevertheless, upon incorporating social frailty into the broader perspective of healthy aging, wherein cognition, psychology, and mobility served as principal components of intrinsic capacity, the previously notable prognostic value of those biomarkers diminished considerably. Another study investigating the association between social vulnerability, as measured by the social vulnerability index, and all-cause mortality, and how this relationship may be moderated by ApoE and Serotonin transporter genotyping (5-HTTLPR), found that a higher social vulnerability was associated with an increased risk of all-cause mortality in middle-aged and older adults, and that this association was moderated by ApoE genotypes but not 5-HTTLPR (22). The current study’s findings suggest that social frailty may serve as a potent indicator of overall social vulnerability, and as a key social determinant for healthy aging. At present, the WHO has put forth operational definitions of intrinsic capacity, but the holistic concept of functional ability, which incorporates personal, social, and environmental factors, still lacks clarity. While intrinsic capacity is a key component of functional ability, functional ability is also affected by factors such as the social and physical environment, and the availability of supportive services and assistive technologies. Closing the gap between intrinsic capacity and functional ability is an important goal of healthy aging initiatives, as it can help older adults maintain their independence, quality of life, and social participation. Hence, the current study has provided further insight into the impact of social determinants, specifically social frailty, on the various facets of intrinsic capacity over the course of time, which subsequently affects the outcomes of healthy aging.
Social frailty among older adults was found to be associated with reduced physical activity and limitations in performing ADLs and IADLs in our study. These findings are consistent with previous research, which has shown that social frailty is linked to decreased physical activity levels, mobility, and functional independence among older adults. Overall, these findings indicate that social frailty has detrimental effects on the physical activity levels and functional independence of older individuals. These effects are in line with the interaction between social determinants and intrinsic capacity, as social frailty can negatively impact mobility, cognition, psychological well-being, and potentially vitality. Consequently, social frailty may contribute to changes in intrinsic capacity during the aging process.
Although this study made significant strides towards advancing our understanding of intrinsic capacity and functional ability in relation to healthy aging, it is important to acknowledge that there are still limitations that need to be addressed. First, it should be noted that the sample size of this study limited the statistical power of subgroup analyses focused on specific components of social frailty or sex differences among individuals aged 85 years and older. Notwithstanding, the established relationship between social frailty and the determinants of healthy aging persisted in a dose-responsive fashion. Second, the observation period of this study was relatively short, which may have limited both the examination of the impact on social frailty on long-term mortality and reversibility of social frailty over the course of an individual’s life. Nevertheless, it may be challenging to reverse the status of social frailty without targeted interventions in observational studies. Third, the social activities question was measured by the question «Have you reduced leisure or outdoor activities due to health in past year?» in this study. This might be a self-guided question as the question is asked if the activity reduction is directly linked to health. However, as SEBAS is not originally conducted for the assessment of “social frailty”, this is the most relevant question we could use to assess the “social activity” domain. Future studies are thus warranted to examine whether a general question to ask the reduction in social activity is more appropriate to capture the concept of “social activity” in measuring social frailty. Fourth, 248 individuals were excluded due to missing some examination data within the SEBAS cohort. However, our additional analysis found that most of the characteristics were comparable between the study participants included in this study (n=1015) and those excluded (n=248), except for gastroenterological disease. Potential bias due to this exclusion might be small. Fifth, the observational design of this study limited the ability to draw causal inferences, and further interventional research is needed to explore the potential benefits of health promotion programs in reducing social frailty and associated outcomes.

 

Conclusions

In conclusion, social frailty was found to be significantly associated with functional limitations, cognitive declines, and depressive symptoms in community-dwelling middle-aged and older adults, and may impact both intrinsic capacity and functional ability, both of which play pivotal roles in healthy aging. As such, there is a need for further research to examine the reversibility of social frailty and to investigate potential intervention strategies that could yield favorable effects on healthy aging outcomes.

 

Contributions: All authors drafted the article, revised it critically for important intellectual content, and approved the final version for publication. Huang ST, Lu WH, Chen LK, and Hsiao FY designed the research. Huang ST and Lu WH performed the research and analyzed the data. Huang ST, Chen LK, and Hsiao FY drafted and prepared the manuscript. Peng LN, Lee WJ, Chen LK and Hsiao FY contributed to the clinical interpretation.
Acknowledgments: The authors thank Tiffaney Wei for her support of literature review and data management. The authors also thank the hard work and dedication of the staff at the Health Promotion Administration, Ministry of Health and Welfare, who were instrumental in the design and implementation of SEBAS and supervised all aspects of the fieldwork and data processing of SEBAS. We thank the Health and Welfare Data Science Center (HWDC) for making the databases used in this study available. The content of this article, however, in no way represents any official position of the HWDC. The author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest: All authors declared no financial conflict of interest.

Funding: This research was funded by the Taiwan Ministry of Science and Technology (MOST 110-2634-F-010-001), the Taiwan National Science and Technology Council (NSTC111-2622-8-A49-019-IE, NSTC112-2923-B-A49-002-MY2), and the Interdisciplinary Research Center for Healthy Longevity of National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The funding source had no role in conducting this study, including study design, data collection and analysis, manuscript preparation and review, and the decision to submit the manuscript for publication.

 

SUPPLEMENTARY MATERIAL

 

References

1. Chen LK, Iijima K, Shimada H, Arai H. Community re-designs for healthy longevity: Japan and Taiwan examples. Arch Gerontol Geriatr. 2022 Nov 23:104875. doi: 10.1016/j.archger.2022.104875.
2. Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel JP, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet (London, England). 2016 May 21;387(10033):2145-54. doi: 10.1016/S0140-6736(15)00516-4.
3. Dzau VJ, Inouye SK, Rowe JW, Finkelman E, Yamada T. Enabling Healthful Aging for All – The National Academy of Medicine Grand Challenge in Healthy Longevity. N Engl J Med. 2019 Oct 31;381(18):1699-701. doi: 10.1056/NEJMp1912298.
4. Holt-Lunstad J, Perissinotto C. Social Isolation and Loneliness as Medical Issues. N Engl J Med. 2023 Jan 19;388(3):193-5. doi: 10.1056/NEJMp2208029.
5. Na PJ, Jeste DV, Pietrzak RH. Social Disconnection as a Global Behavioral Epidemic-A Call to Action About a Major Health Risk Factor. JAMA Psychiatry. 2023 Feb 1;80(2):101-2. doi: 10.1001/jamapsychiatry.2022.4162.
6. Hsiao FY, Peng LN, Lee WJ, Chen LK. Sex-specific impacts of social isolation on loneliness, depressive symptoms, cognitive impairment, and biomarkers: Results from the social environment and biomarker of aging study. Archives of gerontology and geriatrics. 2023 Mar;106:104872. doi: 10.1016/j.archger.2022.104872.
7. Teo RH, Cheng WH, Cheng LJ, Lau Y, Lau ST. Global prevalence of social isolation among community-dwelling older adults: A systematic review and meta-analysis. Archives of gerontology and geriatrics. 2023 Apr;107:104904. doi: 10.1016/j.archger.2022.104904.
8. Bunt S, Steverink N, Olthof J, van der Schans CP, Hobbelen JSM. Social frailty in older adults: a scoping review. European journal of ageing. 2017 Sep;14(3):323-34. doi: 10.1007/s10433-017-0414-7.
9. Yamada M, Arai H. Social Frailty Predicts Incident Disability and Mortality Among Community-Dwelling Japanese Older Adults. Journal of the American Medical Directors Association. 2018 Dec;19(12):1099-103. doi: 10.1016/j.jamda.2018.09.013.
10. Tsutsumimoto K, Doi T, Makizako H, Hotta R, Nakakubo S, Makino K, et al. Association of Social Frailty With Both Cognitive and Physical Deficits Among Older People. Journal of the American Medical Directors Association. 2017 Jul 1;18(7):603-7. doi: 10.1016/j.jamda.2017.02.004.
11. Makizako H, Shimada H, Doi T, Tsutsumimoto K, Hotta R, Nakakubo S, et al. Social Frailty Leads to the Development of Physical Frailty among Physically Non-Frail Adults: A Four-Year Follow-Up Longitudinal Cohort Study. International journal of environmental research and public health. 2018 Mar 10;15(3). doi: 10.3390/ijerph15030490.
12. Meng LC, Huang ST, Peng LN, Chen LK, Hsiao FY. Biological Features of the Outcome-Based Intrinsic Capacity Composite Scores From a Population-Based Cohort Study: Pas de Deux of Biological and Functional Aging. Front Med (Lausanne). 2022;9:851882. doi: 10.3389/fmed.2022.851882.
13. Cornman JC, Glei DA, Goldman N, Chang MC, Lin HS, Chuang YL, et al. Cohort Profile: The Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan. International journal of epidemiology. 2016 Feb;45(1):54-63. doi: 10.1093/ije/dyu179.
14. Boey KW. Cross-validation of a short form of the CES-D in Chinese elderly. International journal of geriatric psychiatry. 1999 Aug;14(8):608-17. doi: 10.1002/(sici)1099-1166(199908)14:8<608::aid-gps991>3.0.co;2-z.
15. Pek K, Chew J, Lim JP, Yew S, Tan CN, Yeo A, et al. Social Frailty Is Independently Associated with Mood, Nutrition, Physical Performance, and Physical Activity: Insights from a Theory-Guided Approach. International journal of environmental research and public health. 2020 Jun 14;17(12). doi: 10.3390/ijerph17124239.
16. Hsiao FY, Peng LN, Lee WJ, Chen LK. Higher dietary diversity and better healthy aging: A 4-year study of community-dwelling middle-aged and older adults from the Taiwan Longitudinal Study of Aging. Exp Gerontol. 2022 Oct 15;168:111929. doi: 10.1016/j.exger.2022.111929.
17. Lin MH, Chen LJ, Huang ST, Meng LC, Lee WJ, Peng LN, et al. Age and sex differences in associations between self-reported health, physical function, mental function and mortality. Archives of gerontology and geriatrics. 2022 Jan-Feb;98:104537. doi: 10.1016/j.archger.2021.104537.
18. Pfeiffer E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. Journal of the American Geriatrics Society. 1975 Oct;23(10):433-41. doi: 10.1111/j.1532-5415.1975.tb00927.x.
19. Lee WJ, Peng LN, Lin MH, Loh CH, Hsiao FY, Chen LK. Intrinsic capacity differs from functional ability in predicting 10-year mortality and biological features in healthy aging: results from the I-Lan longitudinal aging study. Aging (Albany NY). 2023 Feb 6;15(3):748-64. doi: 10.18632/aging.204508.
20. Röhr S, Wittmann F, Engel C, Enzenbach C, Witte AV, Villringer A, et al. Social factors and the prevalence of social isolation in a population-based adult cohort. Soc Psychiatry Psychiatr Epidemiol. 2022 Oct;57(10):1959-68. doi: 10.1007/s00127-021-02174-x.
21. Tilvis R, Routasalo P, Karppinen H, Strandberg T, Kautiainen H, Pitkala K. Social isolation, social activity and loneliness as survival indicators in old age; a nationwide survey with a 7-year follow-up. European geriatric medicine. 2012;3(1):18-22. doi: 10.1016/j.eurger.2011.08.004
22. Liu HY, Peng LN, Lee WJ, Chou MY, Liang CK, Hsiao FY, et al. Differential moderation effects of ApoE and 5-HTTLPR genotypes on social vulnerability in predicting mortality among community-dwelling middle-aged and older adults: a nationwide population-based study. Aging (Albany NY). 2021 Oct 14;13(19):23348-60. doi: 10.18632/aging.203629.

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