Multimorbidity, Depressive Symptoms, and Their Associations with Frailty and Quality of Life in Community-Dwelling Older Adults

Multimorbidity, Depressive Symptoms, and Their Associations with Frailty and Quality of Life in Community-Dwelling Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multimorbidity, Depressive Symptoms, and Their Associations with Frailty and Quality of Life in Community-Dwelling Older Adults Bader A Alqahtani, Norah A Alhwoaimel, Ali Alattas, Mohamed Gamal Elsehrawy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003843/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Multimorbidity and frailty are common among older adults, and they have been linked with negative physical and psychological consequences. This study looked at the direct and indirect relationships between multiple long-term conditions (MLTC), frailty, and quality of life (QoL), as well as the impact of depressive symptoms as a mediator among Saudi community-dwelling adults. Methods: This cross-sectional study included 182 community-dwelling people aged ≥60 years from the Riyadh region. Multimorbidity was defined as the presence of two or more chronic illnesses. Frailty was measured using the Tilburg Frailty Indicator (TFI), depressive symptoms with the Geriatric Depression Scale (GDS-15), and quality of life with the SF-12 (Physical [PCS] and Mental [MCS] Component Summary scores). Path analysis with robust maximum likelihood estimation was used to investigate direct and indirect relationships. Sex and age-stratified analyses were carried out. Results: Having more chronic illnesses was linked to increased depressive symptoms (β = 0.29, p < 0.001) and frailty (β = 0.30, p < 0.001). Depressive symptoms were significantly associated with frailty (β = 0.56, p < 0.001) and largely moderated the association between multimorbidity and frailty (indirect β = 0.16, 95% CI: 0.11-0.36). Multimorbidity was also related to lower physical QoL, both directly and indirectly via depressive symptoms, but no significant relationships were found for mental QoL. The structural connections were consistent across sex and age groups. Higher frailty ratings independently predicted falls in the past 12 months. Conclusions: Multimorbidity is related to higher frailty and worse physical quality of life among community-dwelling older individuals, with depressive symptoms serving as a partial mediator. These findings emphasize the need to include mental health assessments in chronic illness and frailty care to maintain physical function and avoid negative outcomes in aging populations. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The aging population is rapidly growing worldwide, with the number of individuals aged 60 or older expected to reach more than 2 billion in 2050. 1 This transition will lead to a significant increase in multimorbidity, worsening the burden of chronic illnesses, functional decline, and psychological disorders, putting strain on healthcare systems. Multimorbidity is defined as the presence of two or more chronic diseases, leading to multiple negative health outcomes that include functional decline and disability, and decreased health-related quality of life (HRQoL). 2 The pooled prevalence of multimorbidity among community-dwelling individuals worldwide is around 37%, which is more than 50% in those 60 years of age and older and frequently higher than 70% in high-income nations. 3 The estimates in the Middle East, including Saudi Arabia, range from 7% to 78%. 4 Multimorbidity has been reported to be independently associated with increased risk of falls, recurrent falls, and fear of falling among community-dwelling older adults in Saudi Arabia. 5 The incidence of frailty also increases with the occurrence of multimorbidity. A recent study among 5,232 adults showed that individuals with multimorbidity of cardiovascular, respiratory, and hepatic disease had 1.66 times higher odds of being physically frail. 6 Frailty affects roughly 13.6% of older persons living in the community worldwide, with prefrailty prevalence rates estimated at 30.9%. 7 A recent systematic review and meta-analysis reported pooled prevalence estimates of pre-frailty and frailty in Middle Eastern countries of 39% and 35%, respectively. 8 Traditionally, frailty assessment focused primarily on physical function decline and reduced mobility. However, several studies suggested that frailty includes both physical impairment and psychological vulnerability. 9 – 11 A recent longitudinal study among older adults aged ≥ 65 showed that psychological factors, including anxiety and depression, contribute to frailty over time. 9 Specifically, a higher score on the depression subscale of the Hospital Anxiety and Depression Scale (HADS) was significantly associated with the risk of being pre-frail or frail (OR = 1.28, 95% CI: 1.21–1.36, p < 0.001). 9 Depressive symptoms are also highly prevalent among older adults with multimorbidity. 12 A recent prospective 10-year cohort study showed that the risk of experiencing depressive symptoms rises correspondingly with an increasing number of chronic illnesses. 12 Quality of life (QoL) is a key indicator for comprehensively understanding how older adults with frailty and multimorbidity experience daily life. A large population-based study among 1497 community-dwelling older adults in China showed that multimorbidity was present in 83.8% of the participants and associated with poor HRQL, in which functional dependence and depressive symptoms partly mediate this association. 13 Further, a recent cross-sectional study among community-dwelling older adults reported that coexisting frailty and depression were significantly associated with lower physical QoL. 14 – 16 Several logical explanations may clarify how frailty and multimorbidity lead to reduced quality of life. First, the increased burden of multimorbidity and increased symptoms of frailty, including decreased strength, impaired balance, and reduced mobility, will lead to limitations in daily functioning. 15 – 17 Second, the deterioration of mental health, including cognitive decline and depressive symptoms, is accompanied by frailty. 18 , 19 Taken together, the limited daily functioning and impaired mental health can lead to social isolation and loneliness and thereby contribute to poorer QoL. While earlier studies have reported an association between multimorbidity and depression, and between frailty and depression, few have examined the association between multimorbidity and frailty while considering the potential mediating role of depressive symptoms. 6 Furthermore, the influence of multimorbidity and frailty on QoL has not been investigated in terms of the mediating effect of the psychological aspect. Identifying the interrelationship between multimorbidity, frailty, and depressive symptoms and their impact on QoL will provide deeper insights into the clinical needs of ageing populations and inform clinical practice for developing a multidimensional assessment and intervention plan. Therefore, the aims of this study were: 1) to investigate the association between multiple long-term conditions (MLTC) and frailty, as well as between MLTC and QoL, and the potential mediating role of depressive symptoms in these associations. 2) to examine the direct and indirect associations between MLTC, frailty, and QoL through depressive symptoms moderated by age and sex. Methods Study design and participants This study employed a cross-sectional observational design and was conducted in the Riyadh region of Saudi Arabia among community-dwelling individuals aged 60 years and older. Participants were recruited between February 2024 and May 2024 from the King Salman Social Center (KSSC), which is usually visited by the elderly in their spare time. After obtaining permission from the center’s management and the necessary regulatory approval, we recruited participants. Eligible participants were individuals aged 60 years or older, fluent in Arabic, and able to walk independently. Participants were excluded if they scored < 26 on the Montreal Cognitive Assessment (MoCA), had any acute conditions that would prevent them from participating in the study, or had a history of disabling disorders such as stroke, multiple sclerosis, Parkinson's disease, or any other neurological conditions. Before participating in this study, all participants provided written informed consent in accordance with the Declaration of Helsinki. Ethical approval for the study was obtained from the Research Ethics Committee at Prince Sattam bin Abdulaziz University, IRB approval (NO. RHPT/024/003). Additionally, data confidentiality and security were ensured through participant data anonymization, secure storage in encrypted files, restricted access to only authorized individuals, and adherence to data retention standards that allowed for secure deletion after the appropriate period. Measures Frailty was assessed using the Arabic Tilburg Frailty Indicator (TFI), a self-reported frailty assessment tool comprising 15 items. This multidimensional scale consists of eight physically related items, four covering the psychological aspect and three the social area. The overall scores ranged from 0 to 15, with the highest score indicating a significant degree of frailty. To diagnose frailty, a cutoff score of at least 5 is utilized.17 A study conducted in Saudi Arabia revealed that the Saudi version of the TFI is reliable and valid in assessing frailty among the Saudi older population who live in the community. 20 Depressive symptoms were measured using the Geriatric Depression Scale (GDS). The GDS-15 is a short version of the GDS developed to assess depression in the elderly, composed of 15 questions regarding a person's mood. The answers depend on feelings in the last week, and the answers are “yes” or "no". One point for either the answer “yes” or the answer “no” based on the question. The Arabic version of GDS-15 is a valid and reliable measure in older adults. 21 Quality of life was assessed using the Short Form-12 Health Survey (SF-12). Two summary scores were derived: the Physical Component Summary (PCS) and the Mental Component Summary (MCS), with higher scores indicating better perceived quality of life. The SF-12 is a self-report questionnaire comprising 12 items across eight health domains, generating composite scores for the PCS and the MCS. 22 Demographics and clinical variables Data collection was conducted in person by an independent, trained physiotherapy researcher, beginning with demographic and clinical data, including age, gender, body mass index (BMI), and chronic diseases. Major chronic diseases were recorded via self-reported diagnoses, including hypertension, diabetes, cardiovascular disease, lung disease, neurological diseases, cancer, and arthritis. Falls were assessed via self-report. Participants indicated whether they had experienced any falls in the previous 12 months. Statistical analysis Path analysis and mediation models Path analysis Models were estimated using maximum likelihood with robust standard errors (MLR) to account for potential non-normality. Direct and indirect effects were evaluated using robust confidence intervals derived from MLR. This approach is considered appropriate for mediation analyses involving continuous observed variables and moderate sample sizes. Sensitivity analyses using standard ML with bias-corrected bootstrap confidence intervals yielded substantively similar conclusions (results not shown). Mediation was considered statistically significant when the 95% confidence interval for the indirect effect did not include zero. Because the analyses were conducted using observed-variable path models that were near-saturated, global fit indices such as the Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI) were not reported. In such models, these indices provide limited or misleading information due to the minimal degrees of freedom. Accordingly, the interpretation focused on the magnitude and statistical significance of direct and indirect path estimates, which was consistent with the study’s primary objectives. Multiple-group analysis Multiple-group path analysis was conducted to examine whether the structural relationships among disease burden, depressive symptoms, frailty, quality of life, and falls differed across key participant subgroups. Because all constructs were modelled using observed total scores rather than latent variables, measurement invariance testing was not applicable. Instead, the equivalence of structural paths was evaluated by comparing models in which key regression paths were freely estimated across groups with models in which these paths were constrained to be equal. Differences were interpreted as evidence of effect heterogeneity across groups. Logistic regression analysis To examine criterion validity, binary logistic regression was used to assess whether frailty (TFI) predicted the occurrence of any falls in the previous 12 months. Models were estimated with frailty, physical quality of life, and mental quality of life entered simultaneously. Results are reported as regression coefficients and odds ratios with corresponding 95% confidence intervals. All analyses were conducted using R (version 5.4.0), primarily employing the lavaan package for path analysis and mediation models and the base glm function for logistic regression. Results Table 1 presents the sociodemographic and clinical characteristics of the study sample stratified by sex. Females were slightly younger, had higher BMI, higher levels of multimorbidity, frailty, and depressive symptoms, and lower physical quality of life compared with males. Table 1 sociodemographic and clinical characteristics of the study sample stratified by sex N = 182 Level Female Male p 82 100 Age 65.58 64.60 (5.05) 66.40 (5.28) 0.021 BMI 32.41 (5.58) 27.61 (4.15) < 0.001 Marital Status divorced 3 (3.7) 0 (0.0) < 0.001 married 62 (75.6) 97 (97.0) single 0 (0.0) 3 (3.0) widowed 17 (20.7) 0 (0.0) Falls in last 12 months 0 60 (73.2) 78 (78.0) 0.171 1 18 (22.0) 22 (22.0) 2 2 (2.4) 0 (0.0) 4 2 (2.4) 0 (0.0) Multimorbidity 0 or 1 20 (24.4) 55 (55.0) < 0.001 2+ 62 (75.6) 45 (45.0) Total TILBURG FRAILTY INDEX 4.95 (2.86) 2.32 (1.68) < 0.001 Total GERIATERIC DEPRESSION SCALE 2.55 (2.36) 1.33 (1.56) < 0.001 PHYSCIAL SP12 40.01 (7.21) 37.76 (5.78) 0.02 MENTAL_SP12 38.20 (4.70) 38.75 (3.81) 0.384 Disease burden, depression, and frailty The number of chronic diseases was positively associated with depressive symptoms (standardized β = 0.29, p < 0.001). Depressive symptoms, in turn, were strongly associated with higher TFI scores (standardized β = 0.56, p < 0.001). Mediation analysis indicated a significant indirect effect of disease burden on frailty through depressive symptoms (standardized indirect effect = 0.16, 95% CI: 0.11–0.36, p = 0.001), indicating partial mediation. The direct association between the number of diseases and frailty remained statistically significant after accounting for depression (standardized β = 0.30, p < 0.001), suggesting that disease burden influences frailty through both psychological and physical pathways. As shown in Fig. 1 , the model explained a substantial proportion of variance in frailty (R² = 0.51), whereas disease burden explained a smaller proportion of variance in depressive symptoms (R² = 0.08), indicating that frailty was more strongly accounted for by the combined effects of multimorbidity and depressive symptoms. Disease burden, depression, and quality of life A higher number of chronic diseases was associated with poorer physical quality of life both directly and indirectly through depressive symptoms. Depressive symptoms were significantly associated with physical quality of life (standardized β = 0.22, p = 0.020), and mediation analysis demonstrated a small but significant indirect effect of disease burden on PCS through depression (standardized indirect effect = 0.06, 95% CI: 0.04–0.44, p = 0.035), consistent with partial mediation. The direct association between number of diseases and PCS also remained significant (p = 0.013). In contrast, no direct or indirect associations were observed between disease burden, depressive symptoms, and mental quality of life (MCS). These associations are illustrated in Fig. 2 , which shows that depressive symptoms were linked to physical but not mental quality of life, and that the indirect pathway through depression was specific to the physical health domain. Multiple-group analysis Sex differences in structural associations Sex-stratified path analysis was conducted to examine whether the associations linking disease burden, depressive symptoms, and frailty differed between males and females (Fig. 3 ). In both groups, a higher number of long-term conditions was significantly associated with greater depressive symptoms, and depressive symptoms were strongly associated with higher frailty scores. A direct association between disease burden and frailty remained statistically significant after accounting for depression in both males and females, indicating partial mediation. Although females exhibited higher average levels of depressive symptoms and frailty, the pattern and magnitude of structural associations were comparable across sex. The indirect effect of disease burden on frailty through depressive symptoms was small and marginally significant in sex-stratified analyses, likely reflecting reduced statistical power within groups. Age differences in structural associations Age-stratified path analysis was performed to assess whether the structural relationships among disease burden, depressive symptoms, and frailty differed between younger and older adults (Fig. 4 ). In both age groups, multimorbidity was significantly associated with higher depressive symptoms, and depressive symptoms showed a strong positive association with frailty. Disease burden also retained a significant direct association with frailty after adjustment for depressive symptoms. In contrast to the sex-stratified analyses, the indirect effect of disease burden on frailty through depressive symptoms was statistically significant in both younger and older adults. The overall pattern of associations was highly consistent across age groups, indicating that age influenced levels of frailty and depressive symptoms rather than the underlying pathways linking multimorbidity to frailty. Sex- and age-stratified analyses of quality of life Sex- and age-stratified path models were additionally estimated to examine whether associations between disease burden, depressive symptoms, and quality of life differed across subgroups (Figs. 5 and 6 ). Across all models, a higher number of long-term conditions were associated with poorer physical quality of life both directly and indirectly through depressive symptoms. Depressive symptoms were consistently associated with physical quality of life in males and females, as well as in younger and older adults. In contrast, no significant direct or indirect associations were observed between disease burden, depressive symptoms, and mental quality of life in any subgroup. The similarity of findings across sex- and age-stratified models indicates that the relationship between multimorbidity, depression, and physical quality of life is robust and not modified by demographic subgroup. Discussion This study revealed the presence of more long-term conditions associated with higher frailty and lower physical health-related quality of life among community-dwelling older adults in KSA, with depressive symptoms serving as a partial mediator in the relationships mentioned. MLTC had direct and indirect paths to frailty (TFI) and physical quality of life (PCS) through depressive symptoms, but not for mental quality of life (MCS). Such structural relationships remained stable across age and sex subgroups. Additionally, higher TFI scores are associated with a higher risk of falls in the past 12 months. The current study found that higher TFI scores and lower PCS were associated with increased multimorbidity, and depressive symptoms partially mediated both associations. This pattern is consistent with longitudinal evidence showing that higher frailty index scores predict the emergence of physical–psychological multimorbidity, with adjacent risk gradients in pre-frail and frail status, highlighting the need for early mitigation. 23 Cohort data reveal that some multimorbidity clusters, especially psychiatric, cardiovascular, or those combinations with anemia and dementia, are highly predictive of physical frailty over 6–12 years, indicating older adults with neuropsychiatric–cardiovascular combinations face heightened risk compared to nonspecific patterns and need targeted prevention. 24 The current findings align with those of Sun et al. (2024), which demonstrate that depressive symptoms independently predict incident and worsening frailty, even after controlling for the central phenotypic components of weakness, slowness, and exhaustion. 25 For example, in analyses of multimorbidity patterns, the association of clusters for psychiatric and cerebrovascular disease with physical frailty is partially mediated by depressive symptoms. 6 Based on the current findings, this study also supports the relationship between multimorbidity and frailty, proposing a self-perpetuating cycle in which multimorbidity and frailty mutually accelerate each other. The mediating effect of depression, likewise, implies that timely management of the symptoms of depression may be of critical importance for preventing the development of both frailty and multimorbidity. 26 In the same context, systematic reviews corroborate with higher impacts of multimorbidity on physical HRQoL, with sharper declines in physical versus mental quality-of-life domains as shown in various HRQoL measures, suggesting that while the presence of comorbidities can affect mental health, the decline in physical scales is more pronounced and is driven by functional limitations rather than psychological adaptation. 27 Using cross-sectional data from urban and rural Chinese older adults reveals that highly pronounced multimorbidity effects on physical outcomes such as mobility, self-care, and usual activities, especially for psycho-cognitive and organic disease clusters, showed that there are significant associations with need for both physical and psychological care, highlighting the importance of integrated care. 28 , 29 Longitudinal and path-analytic studies confirm that multimorbidity impacts QoL largely through physical mediators, e.g., impact on Activities of Daily Living (ADLs) and depressive symptoms, the latter having the largest indirect effects and indicating the need to make depression management as essential as functional rehabilitation to preserve them among populations. 30 , 31 Age-stratified models suggested that the pathways from multimorbidity to depressive symptoms and frailty were not different by age group, with age appearing to mainly affect levels of TFI and depressive symptoms rather than the underlying structure of associations. The lack of age effect likely relates to our mean age of ~ 65 years, which is considered younger older adults, where age mainly affected symptom severity rather than pathway structure. This pattern reflects findings that multimorbidity and frailty intersect dynamically in later life, jointly contributing to adverse outcomes and mortality. 32 This longitudinal study demonstrates that multimorbidity substantially affects multiple health-related quality-of-life dimensions in community-dwelling older adults, and that certain patterns of multimorbidity (e.g., digestive/respiratory and cardiovascular/metabolic disorders) exert particularly strong negative effects on emotional well-being and self-care. Age-related progression exacerbates these declines over time, as general health, emotion, and social adaptability deteriorate progressively each month, underscoring the need for age-tailored interventions to mitigate cumulative impacts. 33 In agreement with this, age is a major multiplier of multimorbidity, for older groups impaired by multimorbidity, physical limitations, and mental health problems would progressively worse, and functional dependence and depressive symptoms explain much of the association between increased multimorbidity burden and poor HRQoL. 13 , 34 Sex-stratified analyses in this study revealed that women had higher mean frailty and depressive symptoms than men, but the strength and pattern of structural paths among multimorbidity, depression, and frailty were similar across sexes. There was a partial contrast with large population-based analyses results, which showed that, compared to men, female subjects have more significant impairment in physical and mental QoL related to multimorbidity and appear to be more vulnerable to the impact of depressive symptoms and functional dependence. 35 Women show greater multimorbidity-related impairments in physical and mental HRQoL than men, driven by higher prevalence and heightened vulnerability to depressive symptoms and functional dependency, necessitating sex-specific interventions. 13 , 34 However, in the present study, these sex differences were not statistically significant, which may be partly explained by the sample characteristics, including a relatively younger older-adult group and limited power to detect sex-specific effects in subgroup analyses. Targeted clinical implications were highlighted for the early detection and management of frailty, as well as relevant outcomes, in community-dwelling older adults from Saudi Arabia or similar communities. The integrated approach needs to be applied in the primary care model, focusing on screening for multimorbidity, depressive symptoms, and frailty, provided that these three health problems can have synergistic effects and should be co-managed. This approach should utilize brief screening instruments, such as the Tilburg Frailty Indicator and the Geriatric Depression Scale, which will enable health professionals to immediately identify vulnerable individuals and provide targeted interventions. Depressive symptoms as partial mediators of the associations between disease burden, frailty, and physical quality of life suggest that mental health care should be embedded in chronic disease and geriatric care services. This cycle can be broken by multimodal interventions of optimal pharmacological therapy together with exercise and psychosocial support; by targeting physical limitations (e.g., mobility, pain, impairment), this approach is able to optimize QoL by maximizing improvements in physical function. Our findings show that greater TFI consistently predicts falls, independent of the more traditional predictors of falls. This supports a case for considering TFI in risk screening, with a higher emphasis on risk prioritization in multifactorial prevention (strength/balance training, home modifications, medication/vision review) rather than QoL measures alone. The practical community-based self-reporting approaches adopted in our study result in the growth of early screening for frailty and depression among primary care centers in Saudi Arabia. They suggest that several investments are worthwhile considering, including investments in the training of healthcare providers and the integration of mental health assessments into the care of chronic diseases, as well as the development of specific guidelines for slowing the progression of frailty, maintaining independence, and preventing fall-related problems in an aging population. The current study has some limitations; the cross-sectional design limits the establishment of a causal relationship in the results. Nevertheless, this approach was considered appropriate given the study’s sample size, the use of well-validated instruments, and the primary focus on examining structural associations and indirect effects rather than latent construct measurement. Another limitation is that the single geographical area from which the sample has been drawn might li it the generalizability of our findings; future studies may include a broader sample from different areas. Conclusion Multimorbidity was shown to be substantially linked with increased frailty and a worse physical quality of life among Saudi older individuals living in the community. Depressive symptoms influenced the association between multimorbidity and frailty, indicating an essential psychological mechanism connecting chronic illness load to physical frailty. These findings highlight the necessity of integrated care methods that treat both chronic illnesses and mental health to avoid frailty development and preserve physical function in aging populations. Longitudinal investigations are required to establish links of causality. Declarations Ethics approval and consent to participate This study has been approved by the ethical committee at Prince Sattam Bin Abdulaziz University. Participants provided written informed consent. Consent for publication Not applicable Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests On behalf of all authors, the corresponding author states that there is no conflict of interest. Clinical trial number Not applicable. Funding The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/ 2025/03/38710). Authors' contributions BA conceived and designed the study. BA obtained funding. BA and AMA helped with data extraction. MGE and MMA helped with data interpretation. AA helped with data analysis. All authors have drafted, read, and approved the final version of the manuscript. Acknowledgements The authors would like to thank the Prince Sattam bin Abdulaziz University for their support throughout this project. References das nações Unidas O. 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Multimorbidity patterns and health-related quality of life among community-dwelling older adults: evidence from a rural town in Suzhou, China. Qual Life Res . 2024;33(5):1335-1346. doi:10.1007/s11136-024-03608-0 Lee J, Kim SY, Lee KS. The Mediating Role of Depressive Symptoms and Treatment Burden on Health-Related Quality of Life Among Multimorbid Patients With Hypertension: A Multi-Group Analysis. Nurs Health Sci . 2024;26(4). doi:10.1111/nhs.13176 Sieber S, Roquet A, Lampraki C, Jopp DS. Multimorbidity and Quality of Life: The Mediating Role of ADL, IADL, Loneliness, and Depressive Symptoms. Innov Aging . 2023;7(4). doi:10.1093/geroni/igad047 She R, Vetrano DL, Leung MKW, Jiang H, Qiu C. Differential interplay between multimorbidity patterns and frailty and their mutual mediation effect on mortality in old age. Journal of Nutrition, Health and Aging . 2024;28(8). doi:10.1016/j.jnha.2024.100305 Gu J, Chao J, Chen W, et al. Multimorbidity and health-related quality of life among the community-dwelling elderly: A longitudinal study. Arch Gerontol Geriatr . 2018;74:133-140. doi:10.1016/j.archger.2017.10.019 Bao XY, Xie YX, Zhang XX, et al. The association between multimorbidity and health-related quality of life: a cross-sectional survey among community middle-aged and elderly residents in southern China. Health Qual Life Outcomes . 2019;17(1). doi:10.1186/s12955-019-1175-0 Ahamad V, Mohammad R, Pal AK, Chouhan KR. Multimorbidity and its association with health-related quality of life among older adults in India: a cross-sectional analysis of LASI wave-1. BMC Geriatr . 2025;25(1):740. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9003843","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619607216,"identity":"08a53b76-4589-4e33-b061-e3128d36b143","order_by":0,"name":"Bader A Alqahtani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3PMUvEMBTA8RcKd0t6XVMU/Qo5/DhOt+gUcOxQS0FIF71Z8Dy/gl06JzyoS9C1cDfU5aYOTnIOirk6iENbR8H8CS8Z3m8IgMv1F1NA1G4CEAkQ2dvz0iHSHvZFzI6QXxMYtcrafjJZYa2aeJ0ENxeyflueHweZJduo6CTh4wnXi3LD2Fpn06viQVwjScmlWXUSboAjHSGDaiaZX5QitcQjso+MX5B+IDu0JHxflOJumFCOvkTGLdnz01jcD5HQ0DN9O8cwr2bZ0X6pRG6J7vvLxIzzunnF4KA63Tw3cSKWT6jrbdRN2uj3E9up+vd/kmRw2eVyuf5fn+oGaIzQylD3AAAAAElFTkSuQmCC","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Bader","middleName":"A","lastName":"Alqahtani","suffix":""},{"id":619607217,"identity":"e3accd9b-b4ca-4a1b-be1b-c59e79d85e0b","order_by":1,"name":"Norah A Alhwoaimel","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Norah","middleName":"A","lastName":"Alhwoaimel","suffix":""},{"id":619607218,"identity":"2496f93b-c6c8-4d79-a0b7-48b362c17390","order_by":2,"name":"Ali Alattas","email":"","orcid":"","institution":"King Saud bin Abdulaziz for Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Alattas","suffix":""},{"id":619607219,"identity":"9c291fef-1269-46e4-aeaf-bfafbc36c58e","order_by":3,"name":"Mohamed Gamal Elsehrawy","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Gamal","lastName":"Elsehrawy","suffix":""},{"id":619607220,"identity":"ff9589a7-b67c-4858-a37f-88a3421ad395","order_by":4,"name":"Mahitab Mohamed Abdelrahman","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Mahitab","middleName":"Mohamed","lastName":"Abdelrahman","suffix":""},{"id":619607221,"identity":"96d66443-f036-4016-b639-e2868aab0a78","order_by":5,"name":"Aqeel M Alenazi","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Aqeel","middleName":"M","lastName":"Alenazi","suffix":""}],"badges":[],"createdAt":"2026-03-01 21:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003843/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106477756,"identity":"31b84e6a-c1ae-4b8b-973b-accc3217dc85","added_by":"auto","created_at":"2026-04-09 03:48:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28827,"visible":true,"origin":"","legend":"\u003cp\u003ePath analysis model examining the direct and indirect associations between number of long-term conditions (LTC), depressive symptoms (GDS), and frailty (TFI). Standardized coefficients are presented. All shown paths reached statistical significance (p \u0026lt; 0.05). The direct path from LTC to the omitted outcome was non-significant and therefore excluded from the figure.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/32ec0fa5a9ae9accd263793f.png"},{"id":106477757,"identity":"8d33d5aa-4187-41e3-a0fa-34971e57ae93","added_by":"auto","created_at":"2026-04-09 03:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38534,"visible":true,"origin":"","legend":"\u003cp\u003ePath analysis model examining the associations between number of long-term conditions (LTC), depressive symptoms measured by the Geriatric Depression Scale (GDS), and physical and mental quality of life assessed using the SF-12 (PCS and MCS). Standardized regression coefficients are shown. All displayed paths were statistically significant (p \u0026lt; 0.05). The non-significant path from depressive symptoms to mental quality of life (MCS) was omitted from the figure for clarity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/22ec590d06d317df271bf3cd.png"},{"id":106724408,"identity":"b9c9cf18-507c-4715-a3fb-ff703adb5579","added_by":"auto","created_at":"2026-04-12 18:27:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54148,"visible":true,"origin":"","legend":"\u003cp\u003eSex-stratified path analysis models illustrating the associations between number of long-term conditions (LTC), depressive symptoms measured by the Geriatric Depression Scale (GDS), and frailty measured by the Tilburg Frailty Indicator (TFI) in males (upper panel) and females (lower panel). Standardized regression coefficients are shown. All displayed paths were statistically significant (p \u0026lt; 0.05). Non-significant paths were omitted for clarity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/f40bc69b80f447c0303863cc.png"},{"id":106477759,"identity":"b95c2c23-acdf-44d7-b087-46416266783c","added_by":"auto","created_at":"2026-04-09 03:48:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55111,"visible":true,"origin":"","legend":"\u003cp\u003eAge-stratified path analysis models illustrating the associations between number of long-term conditions (LTC), depressive symptoms (GDS), and frailty (TFI) in younger adults (upper panel) and older adults (lower panel). Values represent standardized regression coefficients. All displayed paths reached statistical significance (p \u0026lt; 0.05). Non-significant paths were omitted from the figure.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/3c32b068e955696c98d1ce67.png"},{"id":106724416,"identity":"096e2309-2e9d-4a4f-b12c-21ab475f8341","added_by":"auto","created_at":"2026-04-12 18:27:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58369,"visible":true,"origin":"","legend":"\u003cp\u003eSex-stratified path analysis models illustrating the associations between number of long-term conditions (LTC), depressive symptoms measured by the Geriatric Depression Scale (GDS), and physical and mental quality of life assessed using the SF-12 (PCS and MCS) in males (upper panel) and females (lower panel). Standardised regression coefficients are shown. All displayed paths were statistically significant (p \u0026lt; 0.05). The non-significant path from depressive symptoms to mental quality of life (MCS) was omitted from the figure for clarity.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/f7c909c3d8e3b01876830b4d.png"},{"id":106477761,"identity":"ac6ff45f-58cf-4be4-881d-bb5d580e555c","added_by":"auto","created_at":"2026-04-09 03:48:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60777,"visible":true,"origin":"","legend":"\u003cp\u003eAge-stratified path analysis models illustrating the associations between number of long-term conditions (LTC), depressive symptoms (GDS), and physical and mental quality of life assessed using the SF-12 (PCS and MCS) in younger adults (upper panel) and older adults (lower panel). Values represent standardized regression coefficients. All displayed paths reached statistical significance (p \u0026lt; 0.05). Non-significant paths were omitted from the figure.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/8c2982a3da8a50b1374f1557.png"},{"id":106726286,"identity":"f719d07d-7f9c-4474-9115-6516c1f7e8fb","added_by":"auto","created_at":"2026-04-12 18:35:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9003843/v1/a3c9b2ce-7e30-4a2c-8764-d2f0ec46e764.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimorbidity, Depressive Symptoms, and Their Associations with Frailty and Quality of Life in Community-Dwelling Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe aging population is rapidly growing worldwide, with the number of individuals aged 60 or older expected to reach more than 2\u0026nbsp;billion in 2050.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e This transition will lead to a significant increase in multimorbidity, worsening the burden of chronic illnesses, functional decline, and psychological disorders, putting strain on healthcare systems. Multimorbidity is defined as the presence of two or more chronic diseases, leading to multiple negative health outcomes that include functional decline and disability, and decreased health-related quality of life (HRQoL).\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The pooled prevalence of multimorbidity among community-dwelling individuals worldwide is around 37%, which is more than 50% in those 60 years of age and older and frequently higher than 70% in high-income nations.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The estimates in the Middle East, including Saudi Arabia, range from 7% to 78%.\u003csup\u003e4\u003c/sup\u003e Multimorbidity has been reported to be independently associated with increased risk of falls, recurrent falls, and fear of falling among community-dwelling older adults in Saudi Arabia.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The incidence of frailty also increases with the occurrence of multimorbidity. A recent study among 5,232 adults showed that individuals with multimorbidity of cardiovascular, respiratory, and hepatic disease had 1.66 times higher odds of being physically frail.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Frailty affects roughly 13.6% of older persons living in the community worldwide, with prefrailty prevalence rates estimated at 30.9%.\u003csup\u003e7\u003c/sup\u003e A recent systematic review and meta-analysis reported pooled prevalence estimates of pre-frailty and frailty in Middle Eastern countries of 39% and 35%, respectively.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTraditionally, frailty assessment focused primarily on physical function decline and reduced mobility. However, several studies suggested that frailty includes both physical impairment and psychological vulnerability.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e A recent longitudinal study among older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 showed that psychological factors, including anxiety and depression, contribute to frailty over time.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Specifically, a higher score on the depression subscale of the Hospital Anxiety and Depression Scale (HADS) was significantly associated with the risk of being pre-frail or frail (OR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.21\u0026ndash;1.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003csup\u003e9\u003c/sup\u003e Depressive symptoms are also highly prevalent among older adults with multimorbidity.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e A recent prospective 10-year cohort study showed that the risk of experiencing depressive symptoms rises correspondingly with an increasing number of chronic illnesses.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eQuality of life (QoL) is a key indicator for comprehensively understanding how older adults with frailty and multimorbidity experience daily life. A large population-based study among 1497 community-dwelling older adults in China showed that multimorbidity was present in 83.8% of the participants and associated with poor HRQL, in which functional dependence and depressive symptoms partly mediate this association.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Further, a recent cross-sectional study among community-dwelling older adults reported that coexisting frailty and depression were significantly associated with lower physical QoL.\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral logical explanations may clarify how frailty and multimorbidity lead to reduced quality of life. First, the increased burden of multimorbidity and increased symptoms of frailty, including decreased strength, impaired balance, and reduced mobility, will lead to limitations in daily functioning.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Second, the deterioration of mental health, including cognitive decline and depressive symptoms, is accompanied by frailty.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Taken together, the limited daily functioning and impaired mental health can lead to social isolation and loneliness and thereby contribute to poorer QoL.\u003c/p\u003e \u003cp\u003eWhile earlier studies have reported an association between multimorbidity and depression, and between frailty and depression, few have examined the association between multimorbidity and frailty while considering the potential mediating role of depressive symptoms.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Furthermore, the influence of multimorbidity and frailty on QoL has not been investigated in terms of the mediating effect of the psychological aspect.\u003c/p\u003e \u003cp\u003eIdentifying the interrelationship between multimorbidity, frailty, and depressive symptoms and their impact on QoL will provide deeper insights into the clinical needs of ageing populations and inform clinical practice for developing a multidimensional assessment and intervention plan. Therefore, the aims of this study were: 1) to investigate the association between multiple long-term conditions (MLTC) and frailty, as well as between MLTC and QoL, and the potential mediating role of depressive symptoms in these associations. 2) to examine the direct and indirect associations between MLTC, frailty, and QoL through depressive symptoms moderated by age and sex.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional observational design and was conducted in the Riyadh region of Saudi Arabia among community-dwelling individuals aged 60 years and older. Participants were recruited between February 2024 and May 2024 from the King Salman Social Center (KSSC), which is usually visited by the elderly in their spare time. After obtaining permission from the center\u0026rsquo;s management and the necessary regulatory approval, we recruited participants. Eligible participants were individuals aged 60 years or older, fluent in Arabic, and able to walk independently. Participants were excluded if they scored\u0026thinsp;\u0026lt;\u0026thinsp;26 on the Montreal Cognitive Assessment (MoCA), had any acute conditions that would prevent them from participating in the study, or had a history of disabling disorders such as stroke, multiple sclerosis, Parkinson's disease, or any other neurological conditions. Before participating in this study, all participants provided written informed consent in accordance with the Declaration of Helsinki. Ethical approval for the study was obtained from the Research Ethics Committee at Prince Sattam bin Abdulaziz University, IRB approval (NO. RHPT/024/003). Additionally, data confidentiality and security were ensured through participant data anonymization, secure storage in encrypted files, restricted access to only authorized individuals, and adherence to data retention standards that allowed for secure deletion after the appropriate period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eFrailty\u003c/b\u003e was assessed using the Arabic Tilburg Frailty Indicator (TFI), a self-reported frailty assessment tool comprising 15 items. This multidimensional scale consists of eight physically related items, four covering the psychological aspect and three the social area. The overall scores ranged from 0 to 15, with the highest score indicating a significant degree of frailty. To diagnose frailty, a cutoff score of at least 5 is utilized.17 A study conducted in Saudi Arabia revealed that the Saudi version of the TFI is reliable and valid in assessing frailty among the Saudi older population who live in the community. \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eDepressive\u003c/b\u003e symptoms were measured using the Geriatric Depression Scale (GDS). The GDS-15 is a short version of the GDS developed to assess depression in the elderly, composed of 15 questions regarding a person's mood. The answers depend on feelings in the last week, and the answers are \u0026ldquo;yes\u0026rdquo; or \"no\". One point for either the answer \u0026ldquo;yes\u0026rdquo; or the answer \u0026ldquo;no\u0026rdquo; based on the question. The Arabic version of GDS-15 is a valid and reliable measure in older adults.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality of life\u003c/b\u003e was assessed using the Short Form-12 Health Survey (SF-12). Two summary scores were derived: the Physical Component Summary (PCS) and the Mental Component Summary (MCS), with higher scores indicating better perceived quality of life. The SF-12 is a self-report questionnaire comprising 12 items across eight health domains, generating composite scores for the PCS and the MCS.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDemographics and clinical variables\u003c/h3\u003e\n\u003cp\u003eData collection was conducted in person by an independent, trained physiotherapy researcher, beginning with demographic and clinical data, including age, gender, body mass index (BMI), and chronic diseases. Major chronic diseases were recorded via self-reported diagnoses, including hypertension, diabetes, cardiovascular disease, lung disease, neurological diseases, cancer, and arthritis. Falls were assessed via self-report. Participants indicated whether they had experienced any falls in the previous 12 months.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePath analysis and mediation models\u003c/h2\u003e \u003cp\u003ePath analysis Models were estimated using maximum likelihood with robust standard errors (MLR) to account for potential non-normality. Direct and indirect effects were evaluated using robust confidence intervals derived from MLR. This approach is considered appropriate for mediation analyses involving continuous observed variables and moderate sample sizes. Sensitivity analyses using standard ML with bias-corrected bootstrap confidence intervals yielded substantively similar conclusions (results not shown). Mediation was considered statistically significant when the 95% confidence interval for the indirect effect did not include zero.\u003c/p\u003e \u003cp\u003eBecause the analyses were conducted using observed-variable path models that were near-saturated, global fit indices such as the Comparative Fit Index (CFI) and Tucker\u0026ndash;Lewis Index (TLI) were not reported. In such models, these indices provide limited or misleading information due to the minimal degrees of freedom. Accordingly, the interpretation focused on the magnitude and statistical significance of direct and indirect path estimates, which was consistent with the study\u0026rsquo;s primary objectives.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMultiple-group analysis\u003c/h2\u003e \u003cp\u003eMultiple-group path analysis was conducted to examine whether the structural relationships among disease burden, depressive symptoms, frailty, quality of life, and falls differed across key participant subgroups. Because all constructs were modelled using observed total scores rather than latent variables, measurement invariance testing was not applicable. Instead, the equivalence of structural paths was evaluated by comparing models in which key regression paths were freely estimated across groups with models in which these paths were constrained to be equal. Differences were interpreted as evidence of effect heterogeneity across groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLogistic regression analysis\u003c/h3\u003e\n\u003cp\u003eTo examine criterion validity, binary logistic regression was used to assess whether frailty (TFI) predicted the occurrence of any falls in the previous 12 months. Models were estimated with frailty, physical quality of life, and mental quality of life entered simultaneously. Results are reported as regression coefficients and odds ratios with corresponding 95% confidence intervals. All analyses were conducted using R (version 5.4.0), primarily employing the \u003cem\u003elavaan\u003c/em\u003e package for path analysis and mediation models and the base \u003cem\u003eglm\u003c/em\u003e function for logistic regression.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic and clinical characteristics of the study sample stratified by sex. Females were slightly younger, had higher BMI, higher levels of multimorbidity, frailty, and depressive symptoms, and lower physical quality of life compared with males.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esociodemographic and clinical characteristics of the study sample stratified by sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;182\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.60 (5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.40 (5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.41 (5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.61 (4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFalls in last 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 or 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal TILBURG FRAILTY INDEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.95 (2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.32 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal GERIATERIC DEPRESSION SCALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.55 (2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHYSCIAL SP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.01 (7.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.76 (5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMENTAL_SP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.20 (4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.75 (3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDisease burden, depression, and frailty\u003c/h2\u003e \u003cp\u003eThe number of chronic diseases was positively associated with depressive symptoms (standardized β\u0026thinsp;=\u0026thinsp;0.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Depressive symptoms, in turn, were strongly associated with higher TFI scores (standardized β\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mediation analysis indicated a significant indirect effect of disease burden on frailty through depressive symptoms (standardized indirect effect\u0026thinsp;=\u0026thinsp;0.16, 95% CI: 0.11\u0026ndash;0.36, p\u0026thinsp;=\u0026thinsp;0.001), indicating partial mediation. The direct association between the number of diseases and frailty remained statistically significant after accounting for depression (standardized β\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that disease burden influences frailty through both psychological and physical pathways. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the model explained a substantial proportion of variance in frailty (R\u0026sup2; = 0.51), whereas disease burden explained a smaller proportion of variance in depressive symptoms (R\u0026sup2; = 0.08), indicating that frailty was more strongly accounted for by the combined effects of multimorbidity and depressive symptoms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDisease burden, depression, and quality of life\u003c/h2\u003e \u003cp\u003eA higher number of chronic diseases was associated with poorer physical quality of life both directly and indirectly through depressive symptoms. Depressive symptoms were significantly associated with physical quality of life (standardized β\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.020), and mediation analysis demonstrated a small but significant indirect effect of disease burden on PCS through depression (standardized indirect effect\u0026thinsp;=\u0026thinsp;0.06, 95% CI: 0.04\u0026ndash;0.44, p\u0026thinsp;=\u0026thinsp;0.035), consistent with partial mediation. The direct association between number of diseases and PCS also remained significant (p\u0026thinsp;=\u0026thinsp;0.013). In contrast, no direct or indirect associations were observed between disease burden, depressive symptoms, and mental quality of life (MCS). These associations are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which shows that depressive symptoms were linked to physical but not mental quality of life, and that the indirect pathway through depression was specific to the physical health domain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultiple-group analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eSex differences in structural associations\u003c/h2\u003e \u003cp\u003eSex-stratified path analysis was conducted to examine whether the associations linking disease burden, depressive symptoms, and frailty differed between males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In both groups, a higher number of long-term conditions was significantly associated with greater depressive symptoms, and depressive symptoms were strongly associated with higher frailty scores. A direct association between disease burden and frailty remained statistically significant after accounting for depression in both males and females, indicating partial mediation.\u003c/p\u003e \u003cp\u003eAlthough females exhibited higher average levels of depressive symptoms and frailty, the pattern and magnitude of structural associations were comparable across sex. The indirect effect of disease burden on frailty through depressive symptoms was small and marginally significant in sex-stratified analyses, likely reflecting reduced statistical power within groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAge differences in structural associations\u003c/h2\u003e \u003cp\u003eAge-stratified path analysis was performed to assess whether the structural relationships among disease burden, depressive symptoms, and frailty differed between younger and older adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In both age groups, multimorbidity was significantly associated with higher depressive symptoms, and depressive symptoms showed a strong positive association with frailty. Disease burden also retained a significant direct association with frailty after adjustment for depressive symptoms.\u003c/p\u003e \u003cp\u003eIn contrast to the sex-stratified analyses, the indirect effect of disease burden on frailty through depressive symptoms was statistically significant in both younger and older adults. The overall pattern of associations was highly consistent across age groups, indicating that age influenced levels of frailty and depressive symptoms rather than the underlying pathways linking multimorbidity to frailty.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSex- and age-stratified analyses of quality of life\u003c/h2\u003e \u003cp\u003eSex- and age-stratified path models were additionally estimated to examine whether associations between disease burden, depressive symptoms, and quality of life differed across subgroups (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Across all models, a higher number of long-term conditions were associated with poorer physical quality of life both directly and indirectly through depressive symptoms. Depressive symptoms were consistently associated with physical quality of life in males and females, as well as in younger and older adults.\u003c/p\u003e \u003cp\u003eIn contrast, no significant direct or indirect associations were observed between disease burden, depressive symptoms, and mental quality of life in any subgroup. The similarity of findings across sex- and age-stratified models indicates that the relationship between multimorbidity, depression, and physical quality of life is robust and not modified by demographic subgroup.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed the presence of more long-term conditions associated with higher frailty and lower physical health-related quality of life among community-dwelling older adults in KSA, with depressive symptoms serving as a partial mediator\u0026ensp;in the relationships mentioned. MLTC had direct and indirect paths to\u0026ensp;frailty (TFI) and physical quality of life (PCS) through depressive symptoms, but not for mental quality of life (MCS). Such structural relationships remained stable across age and sex subgroups. Additionally, higher TFI scores are associated with a higher risk of falls in the past 12 months.\u003c/p\u003e \u003cp\u003eThe current study found that higher TFI scores and lower PCS were associated with increased multimorbidity, and depressive symptoms partially mediated both associations. This pattern is consistent with longitudinal evidence showing that\u0026ensp;higher frailty index scores predict the emergence of physical\u0026ndash;psychological multimorbidity, with adjacent risk gradients in pre-frail and frail status, highlighting the need for early mitigation.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Cohort data reveal that some multimorbidity clusters, especially psychiatric, cardiovascular, or those combinations with anemia and dementia, are highly predictive of physical frailty over 6\u0026ndash;12 years, indicating older adults with neuropsychiatric\u0026ndash;cardiovascular combinations face heightened risk compared to nonspecific patterns and need targeted prevention.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe current findings align with those of Sun et al. (2024), which demonstrate that depressive symptoms independently predict incident and worsening frailty, even after controlling for the central phenotypic components of weakness, slowness, and exhaustion.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e For example, in analyses of multimorbidity patterns, the association of clusters for psychiatric and cerebrovascular disease with physical frailty is partially mediated by depressive symptoms.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Based on the current findings, this study also supports the relationship between multimorbidity and frailty, proposing a self-perpetuating cycle in which multimorbidity and frailty mutually accelerate each other. The mediating effect of depression, likewise, implies that timely management of the symptoms of depression may be of critical importance for preventing the development of both frailty and multimorbidity.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the same context, systematic reviews corroborate with higher impacts of multimorbidity on physical HRQoL, with sharper declines in physical versus mental quality-of-life domains as shown in various HRQoL measures, suggesting that while the presence of comorbidities can affect mental health,\u0026ensp;the decline in physical scales is more pronounced and is driven by functional limitations rather than psychological adaptation.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Using cross-sectional data from urban and rural Chinese older adults reveals that highly pronounced multimorbidity effects on physical outcomes such as mobility, self-care, and usual activities, especially for psycho-cognitive and organic disease clusters, showed that there are significant associations with need for both physical and psychological care, highlighting the importance of integrated care.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Longitudinal and path-analytic studies confirm that multimorbidity\u0026ensp;impacts QoL largely through physical mediators, e.g., impact on Activities of Daily Living (ADLs) and depressive symptoms, the latter having the largest indirect effects and indicating the need to make depression management as essential as functional rehabilitation to preserve them among populations.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAge-stratified models suggested that the pathways from multimorbidity\u0026ensp;to depressive symptoms and frailty were not different by age group, with age appearing to mainly affect levels of TFI and depressive symptoms rather than the underlying structure of associations. The lack of age effect likely relates to our mean age of ~\u0026thinsp;65 years, which is considered younger older adults, where age mainly affected symptom severity rather than pathway structure. This pattern reflects findings that multimorbidity and frailty intersect dynamically in \u0026ensp;later life, jointly contributing to adverse outcomes and mortality.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e This longitudinal study demonstrates that multimorbidity substantially affects multiple health-related quality-of-life dimensions in community-dwelling older adults, and that certain patterns of multimorbidity (e.g., digestive/respiratory and cardiovascular/metabolic disorders) exert particularly strong negative effects on emotional well-being and self-care. Age-related progression exacerbates these declines over time, as general health, emotion, and social adaptability deteriorate progressively each month, underscoring the need for age-tailored interventions to mitigate cumulative impacts.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e In agreement with this, age is a major multiplier of multimorbidity, for older groups impaired by multimorbidity, physical limitations, and mental health problems would progressively worse, and functional dependence and depressive symptoms explain much of the association\u0026ensp;between increased multimorbidity burden and poor HRQoL.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSex-stratified analyses in this study revealed that women had higher mean frailty and depressive symptoms than men, but the strength\u0026ensp;and pattern of structural paths among multimorbidity, depression, and frailty were similar across sexes. There was a partial contrast with large population-based analyses results, which showed that, compared to men, female subjects have more significant\u0026ensp;impairment in physical and mental QoL related to multimorbidity and appear to be more vulnerable to the impact of depressive symptoms and functional dependence.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Women show greater multimorbidity-related impairments in physical and mental HRQoL than men, driven by higher prevalence and heightened vulnerability to depressive symptoms and functional dependency, necessitating sex-specific interventions.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, in the present study, these sex differences were not statistically significant, which may be partly explained by the sample characteristics, including a relatively younger older-adult group and limited power to detect sex-specific effects in subgroup analyses.\u003c/p\u003e \u003cp\u003eTargeted clinical implications were highlighted for the early detection and management of frailty, as well as relevant outcomes, in community-dwelling older adults from Saudi Arabia or similar communities. The integrated approach needs to be applied in the primary care model, focusing on screening for multimorbidity, depressive symptoms, and frailty, provided that these three health problems can have synergistic effects and should be co-managed. This approach should utilize brief screening instruments, such as the Tilburg Frailty Indicator and the Geriatric Depression Scale, which will enable health professionals to immediately identify vulnerable individuals and provide targeted interventions.\u003c/p\u003e \u003cp\u003eDepressive symptoms as partial mediators of\u0026ensp;the associations between disease burden, frailty, and physical quality of life suggest that mental health care should be embedded in chronic disease and geriatric care services. This cycle\u0026ensp;can be broken by multimodal interventions of optimal pharmacological therapy together with exercise and psychosocial support; by targeting physical limitations (e.g., mobility, pain, impairment), this approach is able to optimize QoL by maximizing improvements in physical function.\u003c/p\u003e \u003cp\u003eOur findings show that greater TFI consistently predicts falls, independent of the more traditional predictors of falls. This supports a case for considering TFI in risk screening, with a higher emphasis on risk prioritization in\u0026ensp;multifactorial prevention (strength/balance training, home modifications, medication/vision review) rather than QoL measures alone. The practical community-based self-reporting approaches adopted in\u0026ensp;our study result in the growth of early screening for frailty and depression among primary care centers in Saudi Arabia. They suggest that several investments are worthwhile considering, including investments in the training of healthcare providers and the integration of mental health assessments into the care of chronic diseases, as well as the development of specific guidelines for slowing the progression of frailty, maintaining independence, and preventing fall-related problems in an aging population.\u003c/p\u003e \u003cp\u003eThe current study has some limitations; the cross-sectional design limits the establishment of a causal relationship in the results. Nevertheless, this approach was considered appropriate given the study\u0026rsquo;s sample size, the use of well-validated instruments, and the primary focus on examining structural associations and indirect effects rather than latent construct measurement. Another limitation is that the single geographical area from which the sample has been drawn might li it the generalizability of our findings; future studies may include a broader sample from different areas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMultimorbidity was shown to be substantially linked with increased frailty and a worse physical quality of life among Saudi older individuals living in the community. Depressive symptoms influenced the association between multimorbidity and frailty, indicating an essential psychological mechanism connecting chronic illness load to physical frailty. These findings highlight the necessity of integrated care methods that treat both chronic illnesses and mental health to avoid frailty development and preserve physical function in aging populations. Longitudinal investigations are required to establish links of causality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the ethical committee at Prince Sattam Bin Abdulaziz University. Participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/ 2025/03/38710).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBA conceived and designed the study. BA obtained funding. BA and AMA helped with data extraction. MGE and MMA helped with data interpretation. AA helped with data analysis. \u0026nbsp;All authors have drafted, read, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Prince Sattam bin Abdulaziz University for their support throughout this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003edas na\u0026ccedil;\u0026otilde;es Unidas O. United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019; \u003cem\u003eHighlights (ST/ESA/SERA/423)\u003c/em\u003e. Published online 2019.\u003c/li\u003e\n \u003cli\u003eMarengoni A, Angleman S, Melis R, et al. 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Prevalence of multiple long-term chronic conditions and associated disabilities among community-dwelling adults in Riyadh. \u003cem\u003eFront Public Health\u003c/em\u003e. 2024;12. doi:10.3389/fpubh.2024.1275124\u003c/li\u003e\n \u003cli\u003eAlmutairi GR, Almegbas NR, Alosaimi RM, et al. Comorbidities, medications, depression, and physical performance measures associated with severe cognitive impairments in community-dwelling adults. \u003cem\u003ePLoS One\u003c/em\u003e. 2024;19(9). doi:10.1371/journal.pone.0309765\u003c/li\u003e\n \u003cli\u003eALQAHTANI BA, ALENAZI AM, ALSHEHRI MM, OSAILAN AM, ALSUBAIE SF, ALQAHTANI MA. Prevalence of frailty and associated factors among Saudi community-dwelling older adults: A cross-sectional study. \u003cem\u003eBMC Geriatr\u003c/em\u003e. 21:1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eAlqahtani B, Abdelbasset WK, Alenazi A. 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Bidirectional Association Between Multimorbidity and Frailty and the Role of Depression in Older Europeans. \u003cem\u003eThe Journals of Gerontology: Series A\u003c/em\u003e. 2023;78(11):2162-2169.\u003c/li\u003e\n \u003cli\u003eMakovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. \u003cem\u003eAgeing Res Rev\u003c/em\u003e. 2019;53. doi:10.1016/J.ARR.2019.04.005\u003c/li\u003e\n \u003cli\u003eLiang X, Wei H, Mo H, et al. Impacts of chronic diseases and multimorbidity on health-related quality of life among community-dwelling elderly individuals in economically developed China: evidence from cross-sectional survey across three urban centers. \u003cem\u003eHealth Qual Life Outcomes\u003c/em\u003e. 2024;22(1). doi:10.1186/s12955-024-02309-z\u003c/li\u003e\n \u003cli\u003eLu H, Dong XX, Li DL, Nie XY, Wang P, Pan CW. Multimorbidity patterns and health-related quality of life among community-dwelling older adults: evidence from a rural town in Suzhou, China. \u003cem\u003eQual Life Res\u003c/em\u003e. 2024;33(5):1335-1346. doi:10.1007/s11136-024-03608-0\u003c/li\u003e\n \u003cli\u003eLee J, Kim SY, Lee KS. The Mediating Role of Depressive Symptoms and Treatment Burden on Health-Related Quality of Life Among Multimorbid Patients With Hypertension: A Multi-Group Analysis. \u003cem\u003eNurs Health Sci\u003c/em\u003e. 2024;26(4). doi:10.1111/nhs.13176\u003c/li\u003e\n \u003cli\u003eSieber S, Roquet A, Lampraki C, Jopp DS. Multimorbidity and Quality of Life: The Mediating Role of ADL, IADL, Loneliness, and Depressive Symptoms. \u003cem\u003eInnov Aging\u003c/em\u003e. 2023;7(4). doi:10.1093/geroni/igad047\u003c/li\u003e\n \u003cli\u003eShe R, Vetrano DL, Leung MKW, Jiang H, Qiu C. Differential interplay between multimorbidity patterns and frailty and their mutual mediation effect on mortality in old age. \u003cem\u003eJournal of Nutrition, Health and Aging\u003c/em\u003e. 2024;28(8). doi:10.1016/j.jnha.2024.100305\u003c/li\u003e\n \u003cli\u003eGu J, Chao J, Chen W, et al. Multimorbidity and health-related quality of life among the community-dwelling elderly: A longitudinal study. \u003cem\u003eArch Gerontol Geriatr\u003c/em\u003e. 2018;74:133-140. doi:10.1016/j.archger.2017.10.019\u003c/li\u003e\n \u003cli\u003eBao XY, Xie YX, Zhang XX, et al. The association between multimorbidity and health-related quality of life: a cross-sectional survey among community middle-aged and elderly residents in southern China. \u003cem\u003eHealth Qual Life Outcomes\u003c/em\u003e. 2019;17(1). doi:10.1186/s12955-019-1175-0\u003c/li\u003e\n \u003cli\u003eAhamad V, Mohammad R, Pal AK, Chouhan KR. Multimorbidity and its association with health-related quality of life among older adults in India: a cross-sectional analysis of LASI wave-1. \u003cem\u003eBMC Geriatr\u003c/em\u003e. 2025;25(1):740.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9003843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultimorbidity and frailty are common among older adults, and they have been linked with negative physical and psychological consequences. This study looked at the direct and indirect relationships between multiple long-term conditions (MLTC), frailty, and quality of life (QoL), as well as the impact of depressive symptoms as a mediator among Saudi community-dwelling adults.\u003c/p\u003e\n\u003cp\u003eMethods: This cross-sectional study included 182 community-dwelling people aged ≥60 years from the Riyadh region. Multimorbidity was defined as the presence of two or more chronic illnesses. Frailty was measured using the Tilburg Frailty Indicator (TFI), depressive symptoms with the Geriatric Depression Scale (GDS-15), and quality of life with the SF-12 (Physical [PCS] and Mental [MCS] Component Summary scores). Path analysis with robust maximum likelihood estimation was used to investigate direct and indirect relationships. Sex and age-stratified analyses were carried out.\u003c/p\u003e\n\u003cp\u003eResults: Having more chronic illnesses was linked to increased depressive symptoms (β = 0.29, p \u0026lt; 0.001) and frailty (β = 0.30, p \u0026lt; 0.001). Depressive symptoms were significantly associated with frailty (β = 0.56, p \u0026lt; 0.001) and largely moderated the association between multimorbidity and frailty (indirect β = 0.16, 95% CI: 0.11-0.36). Multimorbidity was also related to lower physical QoL, both directly and indirectly via depressive symptoms, but no significant relationships were found for mental QoL. The structural connections were consistent across sex and age groups. Higher frailty ratings independently predicted falls in the past 12 months.\u003c/p\u003e\n\u003cp\u003eConclusions: Multimorbidity is related to higher frailty and worse physical quality of life among community-dwelling older individuals, with depressive symptoms serving as a partial mediator. These findings emphasize the need to include mental health assessments in chronic illness and frailty care to maintain physical function and avoid negative outcomes in aging populations.\u003c/p\u003e","manuscriptTitle":"Multimorbidity, Depressive Symptoms, and Their Associations with Frailty and Quality of Life in Community-Dwelling Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 03:48:20","doi":"10.21203/rs.3.rs-9003843/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-03T09:52:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T06:10:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T08:27:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T11:21:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-03-07T11:16:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ce2c845-63ff-46e3-a561-1f474d6ea72b","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T03:48:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 03:48:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003843","identity":"rs-9003843","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}