Five Distinct Intrinsic Capacity Aging Patterns Identified in Chinese Older Adults: A Nationwide Study

Five Distinct Intrinsic Capacity Aging Patterns Identified in Chinese Older Adults: A Nationwide Study | 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 Five Distinct Intrinsic Capacity Aging Patterns Identified in Chinese Older Adults: A Nationwide Study Fangda Leng, Lina Ma, Zhiyuan Yang, Wenhua Yu, Jie Zhao, Chenchen Meng, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293825/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 Background Intrinsic capacity (IC), encompassing locomotive, cognitive, vitality, sensory and psychological domains, is central to defining healthy aging. Establishing norms for IC is crucial for understanding the aging process in Chinese population. Distinct aging patterns could be further captured to guide future studies and clinical practice. Methods In a nationwide cross-sectional community-based study, a total of 6025 elderly participants were recruited from 24 provincial administrative regions in China. IC was assessed over the 5 domains for each participant. Descriptive statistics and generalised additive models were employed to construct norms of IC as function of age, stratified by education or biological sex. A cosine similarity matrix of participants was further computed, upon which graph-based Louvain community detection algorithm (CDA) was applied to capture distinct aging patterns in the population. Results Population norms for the 5 IC domains were established. Data-driven CDA captured 5 distinct aging patterns: 1. Healthy aging (N = 2315), where all IC domains were relatively preserved; 2. Sensory dominant aging (N = 534), where sensory function showed the most profound impairment in aging; 3. Vitality dominant aging (N = 397), where vitality was the single most impaired domain; 4. Locomotion dominant aging (N = 608), in which motor function was persistently below average; and 5. Global accelerated aging (N = 2171), where all the 5 IC domains profoundly declined with age. Conclusions This study provided the norms of IC in aging Chinese population. More importantly, 5 distinct aging patterns were identified, which is of both clinical and scientific interest. Intrinsic capacity aging pattern Chinese population centile reference Figures Figure 1 Figure 2 Figure 3 Background The global population is aging at an unprecedented rate. By 2050, the number of individuals above 60 is projected to reach 22%, presenting significant challenges worldwide 1 . Traditional health paradigms have focused on disease management; however, there is a growing recognition to shift towards promoting functional capacity and well-being in aging 2 . In response, the World Health Organization (WHO) introduced the concept of Healthy Aging , defined as “the process of developing and maintaining the functional ability that enables well-being in older age” 3 . Central to this framework is the concept of Intrinsic Capacity (IC), which refers to “the composite of all the physical and mental capacities that an individual can draw on at any point in time” 1 . IC encompasses five key domains: cognition, psychological well-being, sensory functions, vitality, and locomotion. Since the proposal of the framework, numerous studies have proven IC as a vital predicative and interventional tool for public health 4 . The fundamental premise behind monitoring IC is its potential as an early warning system, enabling proactive interventions rather than reacting to established disease 5 . Previous studies have reported reference centiles for IC factors scores in French and Brazil cohorts 6 – 8 . However, with a diverse population in China, there is a pressing need to establish a stratified norm for IC assessment instruments in Chinese population at first hand. While the IC framework provides a comprehensive model for aging, most existing studies have compressed these domains into a single index to determine functional impairment 9 – 11 . Further studies have attempted identifying different trajectories of IC decline with this simplified approach, highlighting different rates of IC decline 12 , 13 . The heterogeneity in trajectories suggests underlying distinct aging patterns, but they might not have been well characterized by traditional models. In this study, we aim to: (1) Provide age-stratified normative data for each IC domain. (2) Identify distinct patterns of aging based on the integrated assessment of IC domains. By leveraging a community-based diverse cohort and employing advanced analytical methods, we seek to enhance our understanding of the multifaceted nature of aging and inform health-promoting strategies. Methods Study design and population Intrinsic capacity data were drawn from the HEALTHY study (CompreHensive EvALuation of InTrinsic Capacity in CHina StudY), a cross-sectional, community-based observational investigation on older adults in China. Participants were recruited between December 2023 and December 2024 in 24 provincial administrative regions across China, representing both residential communities and care facilities. Communities and care facilities were randomly selected using a stratified sampling approach to ensure broad geographical and demographic representation. Within each selected site, residents aged 60 years or older were invited to volunteer. Outreach efforts included local health authorities and institutions to maximize community engagement and participation. Inclusion criteria were: (1) aged 60 years or older; (2) provided written informed consent, either personally or through legal guardian; and (3) were able to complete the standardized IC assessments. Individuals were excluded if they were experiencing acute medical illnesses, at terminal stage of diseases, or were unable to complete the assessments due to cognitive or physical limitations. 2. Intrinsic capacity assessment and information collection Intrinsic capacity was assessed across five domains as outlined by the WHO: cognition, psychological well-being, sensory function, vitality, and locomotion. The following standardized tools were employed to ensure consistency and comparability across study sites. Cognition was evaluated using the Mini-Mental State Examination (MMSE), a widely used screening tool for cognitive impairment. Psychological well-being was measured using the 15-item Geriatric Depression Scale (GDS-15), with higher scores indicating higher likelihood of depression. Vitality was assessed using the Mini Nutritional Assessment – Short Form (MNA-SF), which screens for nutritional risk. Scores range from 0 to 14, with lower scores indicating poorer nutritional status 14 . Locomotion was evaluated using the Short Physical Performance Battery (SPPB), which includes gait speed, chair stand, and balance tests. The total score ranges from 0 to 12, with higher scores reflecting better motor function 15 . Sensory function included both vision and hearing assessments: (1) Distance vision was tested using a simplified E chart protocol. Scores ranged from 0 to 3 based on the participant’s ability to identify a small or large E at varying distances (each eye scored separately). 3 points were given if the participant could answer 3 out of 4 small Es’ directions correctly at 3 meters; 2 points were given for identifying ¾ large Es correctly at 3 meters; and 1 point was given for identifying ¾ large Es correctly at 1.5 meters; Near vision was scored (0 ~ 3) using a near vision chart (3 rows with decremental font sizes). (2) Hearing was assessed using whisper test. The assessor stood at arm's length on one side of the subject, asked the subject to press the opposite tragus, and whispers four common and unrelated words. The subject is asked to repeat the above words. If the subject can repeat ≥ 3 words, the hearing on this side is normal; different words are used to assess the opposite side. 2 points were assigned to each ear (possible scores were 0, 2, 4 points for hearing). This protocol yielded a 0–10 points scale for sensory function. Additional demographic information including age, biological sex, geographic location, education. In addition, a subset of participants (4511 out of 6025) underwent a systemic review of medical conditions by doctors on site. 3. Statistical analysis and modelling All statistical analyses were performed using R (4.4.2, R Foundation for Statistical Computing, Vienna, Austria). Firstly, descriptive analyses of demographics, clinical characteristics, and IC were performed. For each of the five intrinsic capacity domains, descriptive statistics including mean, standard deviation, and selected percentiles. Analyses were stratified by age in 10-year bins from 60 to 100 years. To provide a more fine-grained understanding of intrinsic capacity decline in Chinese population, generalized additive models (GAMs) were fitted with age as a continuous independent variable and domain scores as dependent variables. GAMs were employed for their accuracy in characterizing the non-linear relationship between age and IC. For cognitive function (MMSE) and psychological well-being (GDS-15), models were stratified by education level due to observed interaction effects. For sensory, vitality, and locomotion domains, analyses were stratified by sex. Lastly, to uncover latent patterns of aging in the cohort, a graph-based community detection algorithm was applied. Domain scores were first transformed to a uniform 0–1 scale to harmonize directionality of impairment: For GDS-15, scores were scaled as \(\:\frac{Score}{Max\left(Scores\right)}\) . For the remaining domains, scores were transformed as \(\:1-\frac{Score}{Max\left(Scores\right)}\) . Such scaling also ensures better angular estimation accuracy for the participants at more advanced stage of aging, which aids the cosine similarity calculation as described below. A participant similarity matrix (supplementary Fig. 1) was then constructed using cosine similarity function, chosen for its robustness to scale (vector norm) and its sensitivity to pattern directionality in multidimensional functional space. In other words, we care about whether two participants are on the same track of aging, rather than the stages of their aging process: $$\:Cosine\:similarity(A,B)=\:\frac{A\bullet\:B}{‖A‖\bullet\:‖B‖}$$ Where A and B are vectors representations of participant’s position in intrinsic function space, and \(\:‖A‖\) is the L2 norm of vector A. A threshold of 0.4 was applied to remove spurious low-similarity edges and construct a weighted undirected graph of 6,025 participants. Community detection was then performed using the weighted Louvain algorithm, which partitions nodes into communities by optimizing modularity. To ensure the stability of community detection, bootstrapping was performed over 1,000 resampling iterations. The mean Adjusted Rand Index (ARI) between bootstrapped and original assignments was calculated to assess the stability of assignment. Moreover, the number of communities was validated using two approaches: (1) Hierarchical clustering on the raw similarity matrix yielded an optimal solution of 5 communities based on the highest average silhouette width (supplementary Fig. 2). (2) Additionally, hierarchical clustering on the co-assignment matrix also supported a five-cluster solution. Final consensus community assignments (from bootstrapping) were extracted for all participants. Principal component analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE) were performed with the original dataset and participants were projected into the PCA and t-SNE space for illustration. Demographic characteristics were then summarized across the five identified communities. Analysis of variance (Anova), Chi-square test and Student’s t-test were performed where appropriate. Finally, domain-wise GAMs were applied within each community to model age-related trajectories, revealing distinct aging profiles across clusters. Results 1. Characteristics of study sample A total of 6025 participants were recruited, of which 3209 were female (53%). 11% of the participants had no formal education, 25% had primary education, 29% had lower secondary education, 24% had upper secondary education, and 11% had tertiary education. The prevalence of hypertension, diabetes mellites, coronary artery diseases, cerebrovascular diseases were 66%, 28%, 11% and 11% respectively. The demographic, geographic and medical characteristics stratified by biological sex are summarised in Table 1 . Table 1 Characteristics of study cohort Characteristics Female Male Number N = 3,209(53%) N = 2,816(47%) Ethnicity Han 2963(92%) 2543(90%) Other 246(8%) 273(10%) Education No formal education 490 (15%) 174 (6.2%) Primary education 817 (25%) 678 (24%) Lower secondary education 865 (27%) 876 (31%) Upper secondary education 736 (23%) 681 (24%) Tertiary education 301 (9.4%) 407 (14.5%) Medical conditions N 2346 2165 Hypertension 1,573 (67%) 1,400 (65%) Coronary artery disease 358 (15%) 273 (13%) Myocardial infarction 30 (1.3%) 59 (2.7%) Other cardiovascular diseases 152 (6.5%) 124 (5.7%) COPD 29 (1.2%) 56 (2.6%) Other pulmonary diseases 65 (2.8%) 49 (2.3%) gastrointestinal diseases 128 (5.5%) 88 (4.1%) Chronic hepatic diseases 25 (1.1%) 13 (0.6%) Chronic renal diseases 50 (2.1%) 47 (2.2%) Intracranial haemorrhage 20 (0.9%) 40 (1.8%) Ischaemic cerebral vascular diseases 100 (4.3%) 121 (5.6%) Dementia 18 (0.8%) 9 (0.4%) Parkinson's disease 16 (0.7%) 19 (0.9%) Epilepsy 3 (0.1%) 10 (0.5%) Other neurological diseases 20 (0.9%) 12 (0.6%) Diabetes mellitus 647 (28%) 627 (29%) Depression or anxiety 36 (1.5%) 10 (0.5%) Osteoarthritis 211 (9.0%) 84 (3.9%) Osteoporosis 116 (4.9%) 30 (1.4%) Malignancy 41 (1.7%) 27 (1.2%) Cataract 243 (10%) 122 (5.6%) Glaucoma 24 (1.0%) 9 (0.4%) Other chronic diseases 18 (0.8%) 7 (0.3%) Intrinsic capacity assessment MMSE 26.0 (22.0, 28.0) 26.0 (23.0, 28.0) GDS-15 2.0 (1.0, 4.0) 2.0 (1.0, 4.0) Sensory score 9.0 (7.0, 10.0) 9.00 (7.0, 10.0) MNA-SF 13.0 (12.0, 14.0) 13.0 (12.0, 14.0) SPPB 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) Hearing loss 368 (11%) 356 (13%) Vision loss 362 (11%) 278 (9.9%) Gait abnormality 536 (17%) 579 (21%) BMI 23.8 (21.8, 26.2) 24.2 (22.0, 26.0) 2. Stratified norm of intrinsic capacity assessments across age The descriptive statistics of intrinsic capacity assessments, including the mean, standard deviation, median and 10–90 percentiles, stratified by age are summarised in Table 2 for quick reference. Table 2 Age Stratified reference for intrinsic capacity assessments Median (Q1, Q3) or N (%) are presented as appropriate Age Variable Mean ± SD Median (10 ~ 90%) [60,70) BMI 24.4 ± 3.5 24.2 (20.3 ~ 28.5) MMSE 25.6 ± 4.2 27.0 (20.0 ~ 30.0) SPPB 8.6 ± 3.2 10.0 (4.0 ~ 12.0) GDS-15 2.8 ± 2.6 2.0 (0.0 ~ 6.0) Sensory Score 8.7 ± 1.9 10.0 (6.0 ~ 10.0) MNA-SF 12.7 ± 1.7 13.0 (11.0 ~ 14.0) [70,80) BMI 24.1 ± 3.7 23.8 (20.0 ~ 28.3) MMSE 24.4 ± 5.0 26.0 (17.0 ~ 29.0) SPPB 7.9 ± 3.5 9.0 (2.0 ~ 12.0) GDS-15 3.2 ± 2.8 2.0 (1.0 ~ 7.0) Sensory Score 8.2 ± 2.3 9.0 (5.0 ~ 10.0) MNA-SF 12.4 ± 1.8 13.0 (10.0 ~ 14.0) [80,90) BMI 23.2 ± 4.1 23.1 (18.7 ~ 28.0) MMSE 21.2 ± 6.6 22.0 (12.0 ~ 29.0) SPPB 6.1 ± 3.4 6.0 (1.0 ~ 11.0) GDS-15 4.1 ± 3.3 3.0 (1.0 ~ 9.0) Sensory Score 6.5 ± 2.9 6.0 (2.0 ~ 10.0) MNA-SF 11.8 ± 2.1 12.0 (9.0 ~ 14.0) [90,100] BMI 23.1 ± 3.5 23.2 (18.6 ~ 27.3) MMSE 18.3 ± 6.9 19.0 (9.0 ~ 27.1) SPPB 4.0 ± 3.4 3.5 (0.0 ~ 8.0) GDS-15 4.6 ± 3.4 4.0 (1.0 ~ 10.0) Sensory Score 5.0 ± 3.4 5.0 (0.0 ~ 10.0) MNA-SF 11.2 ± 1.9 11.5 (8.0 ~ 13.0) GAMs showed that education had a significant influence on both cognitive and psychological assessment education (terms’ P values < 0.001, Fig. 1 ). While for vitality, BMI, sensory and locomotor assessments, education and biological sex did not show significant contribution in the model. For illustration, the fitted models were shown in male and female facets (Fig. 1 ). Notably, most of the internal capacities seemed to demonstrate a threshold of age, after which the ICs decline rapidly, manifesting as a steep slope of their decline trajectory. Regarding cognition (MMSE), psychological wellbeing (GDS-15), locomotive and sensory functions, this ‘rapid-declining’ threshold occurred in the early 70s (70–75 years of age). Whereas for vitality, it was in the late 70s, and the slope change appeared more gradual. Reference ranges given by the general additive models at 5-year increments are summarised in supplementary table 1 –5. 3. Graph-based community detection based on intrinsic capacity Community detection indicated 5 communities in the participants (N = 2315, 534, 397, 608 and 2171). Two-dimensional projection of participants into PCA and t-SNE space demonstrated reasonable separation between the clusters with some overlapping in marginal cases, as expected in the real-world aging population (Fig. 2 A, B). Hierarchical clustering on co-assignment matrix strongly supported the 5-cluster aggregation pattern (Fig. 2 C), and consensus community assignment based on the co-assignment matrix had > 98% agreement with original Louvain-derived communities. The mean Adjusted Rand Index (ARI) between bootstrapped and original assignments was 0.83 (SD = 0.09, Fig. 2 D), indicating strong concordance. 4. Characteristics of individual aging patterns To gain better understanding of the 5 distinct aging patterns, GAM models were fitted with community included as both interaction term and intercept. Participants in community 1 followed an optimal pattern, as their ICs were relatively retained along the aging trajectory. Community 2 showed predominant sensory impairment, with lower baseline at age 60 (T=-53, P < 1.0*10 − 10 ) and steeper decline with age (F = 5.1, P = 0.003). Community 3 featured significantly lower baseline vitality (T=-41, P < P < 1.0*10 − 10 ) and higher baseline depression level (T = 6.5, P = 1.2*10 − 10 ). Participants in community 4 had significantly lower baseline locomotive capacity (T=-55, P < 1.0*10 − 10 ). Notably, a considerable portion (36%) of participants were assigned to community 5, where intrinsic capacities among all 5 domains are profoundly impaired at baseline, and had significantly steeper slope of decline except for psychological symptoms (Fig. 3 ). Demographic characteristics were further compared across the 5 communities (supplementary table 6). The median ages were 69, 71, 70, 70 and 72 years respectively (F = 411, P < 0.001). Distribution of education, hypertension, coronary artery disease (CAD), gastrointestinal disease, dementia, Parkinson’s disease and cataract differed significantly across the 5 communities (P < 0.002, Bonferroni corrected). Post-hoc tests revealed that healthy aging community had better education attainment compared to the rest. Compared with healthy aging community, sensory aging community had higher prevalence of cataract (χ 2 = 13.1, P = 0.0003), vitality aging community had higher prevalence of hypertension and gastrointestinal diseases (χ 2 = 11.8 and 57.9, P = 0.0006 and < 0.0001),global aging community had higher prevalence of hypertension, CAD, dementia and Parkinson’s disease (χ 2 = 11.2, 8.7, 11.1 and 13.9, P = 0.0008, 0.003, 0.0009 and 0.0002). Discussion In this nationwide study of over 6,000 older Chinese adults, we established stratified normative data for the five domains of Intrinsic Capacity (IC) and applied graph-based community detection to uncover five distinct patterns of functional aging. These data-driven phenotypes—characterized by either domain-specific or global declines—offer a more granular framework than prior trajectory models and hold significant implications for personalized aging care and public health strategies. Our age and education stratified MMSE scores were in good concordance with the previous population based normative study 16 . In addition, we implemented generalized additive model to capture the non-linear pattern of cognitive decline in our sample, and demonstrated the existence of a ‘transition’ period in the early 70s when accelerated cognitive decline began. This observation coincides with recent functional MRI study that also suggested non-linear brain aging pattern, though the transition seems to come earlier in midlife 17 . This offset between brain network aging and cognitive aging comes as no surprise, as it has been established in neurodegenerative diseases that brain functional and structural changes precede cognitive decline by decades 18 . The current study implemented GDS-15 to assess psychological wellbeing. The geriatric depression scale is a well-established screening tool for depression, and a cut-off of ≥ 5 points is commonly used 19 . However, it should be noted that a score above 3 is well above the study population’s 95 percentile at age 60, and above 4 for those aged 80+. While the participants with slightly higher GDS-15 score might not meet the criteria of major depressive disorder, concerns should be raised regarding the psychological wellbeing. Further, in the current analysis, we observed that participants with lower education attainment had higher GDS scores. Previous studies had inconsistent opinions on the effect of education on the GDS instrument, suggesting possible cultural and socioeconomical influences 20 – 22 . Further investigations are needed to full entangle the effect of education on GDS items in Chinese population, while considering of potential mediators such as socioeconomical status. The MNA-SF assesses the risk of malnutrition and was used as a proxy of vitality. The established cut-off point was ≤ 11, which can be considered as a tool with high specificity, as the 5 percentiles of MNA-SF in our sample were above 11 until age reaches the 90s 23 . Regarding the SPPB for motor function, however, the commonly used cut-offs of ≤8–9 24,25 seemed to be overly stringent for our sample: the mean value start at 8.9 at age 60, and reduced to below 8 at early 70s. Further investigations on the optimal cut-off of SPPB in Chinese population are needed, and our normative model might serve a temporary reference as of now. There has not been a consensus instrument for vision and hearing as a combined intrinsic sensory function indicator, and we used a customised scoring system for study purpose, based on which future standardized sensory scoring systems can be developed. The concept of "healthy aging" as defined by the WHO fundamentally acknowledges that there is "no typical older person" 3 . This principle underscores the vast diversity in health and functional states among older adults. Prior research, including studies conducted within China, have often compressed the five dimensions in a single IC metric with cut-offs for dichotomous classification of whether IC impairment existed 11 , 26 , 27 . Later studies have attempted to map this heterogeneity by identifying 3 or 4 broad intrinsic capacity (IC) trajectories, such as stable high capacity, medium-level (decreasing and increasing), and low-level capacity groups 28 , 29 . These studies have highlighted that factors like age, gender, exercise, drinking habits, social activity, and chronic diseases significantly influence these trajectories. The current study advances these findings, by identifying fine-grained features of IC decline patterns which were overlooked by previous studies combining multiple domains into a single score. In the current study, graph-based community detection analysis was applied on comprehensive assessments of all five WHO intrinsic capacity domains. The identified patterns—'Healthy aging' (38.4%), 'Sensory dominant aging' (8.9%), 'Vitality dominant aging' (6.6%), 'Locomotion dominant aging' (10.1%), and 'Global accelerated aging' (36.0%)—provide a granular and actionable classification that has not been previously established. Likewise, evidence from the French ICOPE-Care cohort also supported distinct patterns of IC decline 30 . Both studies observed the ‘low impairment’ and ‘all IC impaired’ patterns, while the compositions of other phenotypes were slightly different, possibly due to the usage of IC assessment instruments or population difference. The current findings suggest that while the overall trend of IC decline might be similar across population, further studies should be aware of subtle differences in diverse population 31 . The dominant fraction of globally preservation or impairment pattern could explain the previous observation of a single dominant factor accounting for IC in pervious analyses using multivariate tools 12 , 27 . On the other hand, the graph-based community detection algorithms are designed to find groups of nodes that are more densely connected within themselves than with the rest of the network, making them well-suited for identifying small, distinct communities 32 . This sensitivity is crucial because it shifts the paradigm from generalized observations of decline to a precise understanding of how and where functional impairments manifest in different subgroups of older adults. Numerous studies have already demonstrated the prognostic value of intrinsic capacity assessments 10 , 33 . Understanding more fine-grained patterns can enhance the prognostic value of these assessments. Future longitudinal studies can investigate whether individuals within these specific patterns exhibit differential risks for adverse health outcomes or transitions between patterns over time. The identification of these distinct patterns may also be helpful for personalized approach to healthy aging. For example, participants who fall within the single domain predominant aging communities may benefit from targeted functional rehabilitation programmes 34 . The 'Global accelerated aging' group, representing a substantial portion of aging population, highlights the urgent need for comprehensive, multidisciplinary care models 34 . This fine-grained model could support more person-centred and function-centred care, aligning with the WHO's Integrated Care for Older People (ICOPE) framework 35 , 36 . One of the limitations of current study is its cross-sectional nature, thus within subject stability was not tested across time. However, we believe that the identified patterns provide a foundation for future longitudinal investigations. Tracking individuals within these communities over time will be crucial to understand the dynamic nature of functional aging, including factors that promote resilience, slow decline, or trigger transitions between patterns. Secondly, while over 6000 participants were adequate from modelling perspective, the sample size is limited relative to the Chinese population. Further, given the higher prevalence of comorbidities in different aging communities, more comprehensive investigations are needed to fully elucidate the environmental, physiological and pathological substrates of different aging patterns. Moreover, the clinical assessments could be related to a more objective epigenetic clock to fully elucidate the biological mechanism of aging 37 . Conclusions This work firstly established stratified normative reference for the five domains of Intrinsic Capacity (IC) in Chinese older adults. Further, it proposed a novel, data-driven framework to classify aging patterns based on IC, grounded in a large, diverse population. It opened avenues for translational research, early risk stratification, and personalized healthy aging interventions. Declarations 1. Ethics approval and consent to participate The study protocol was reviewed and approved by the Ethics Committee of Peking University First Hospital (Ethics Approval No: 2023-Y-564-001). All procedures adhered to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from each participant. 2. Consent for publication Not applicable 5. Funding The study was supported by a research grant from National Key R&D Program of China (2023YFC3605200, 2023YFC3605202). 6. Clinical trial number Not applicable Author Contribution Leng F performed formal analysis, interpreted results and wrote the manuscript. Ma L interpreted results and revised the manuscript. Yang Z conducted data analysis and visualization. Yu W, Zhao J, Meng C, Song W, Li S, Wang X, Liu M, Wang H contributed to data collection and data curation. Chang H, Zhong L, Wang Z conceptualized the study, obtained funding and oversaw the study process. All authors critically reviewed and approved the manuscript. 8. Acknowledgements The contributions of all participants in this clinical study are greatly appreciated. Special thanks are extended to the volunteers who supported the project's outreach, participant evaluation, and data collection. The participating communities and health providers are also acknowledged for their collaboration, sharing expertise and facilities. 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Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc 21, 300–307 e302. 10.1016/j.jamda.2019.12.012 (2020). Koivunen K, et al. Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach. Aging Clin Exp Res. 2023;35:815–25. 10.1007/s40520-023-02366-2 . Beard JR, Si Y, Liu Z, Chenoweth L, Hanewald K. Intrinsic Capacity: Validation of a New WHO Concept for Healthy Aging in a Longitudinal Chinese Study. J Gerontol Biol Sci Med Sci. 2022;77:94–100. 10.1093/gerona/glab226 . Li Y, et al. Trajectories of intrinsic capacity decline and related factors in old persons: A 15-year community-based cohort study in Beijing. J Nutr Health Aging. 2025;29:100526. 10.1016/j.jnha.2025.100526 . Zhou J, Chang H, Wang Z. Developmental Trajectories of Intrinsic Capacity Among Older Adults: Results from the China Longitudinal Study of Aging. Healthc (Basel). 2025;13. 10.3390/healthcare13050520 . de Barreto S. Real-life intrinsic capacity screening data from the ICOPE-Care program. Nat Aging. 2024;4:1279–89. 10.1038/s43587-024-00684-2 . Beard JR, Hanewald K, Si Y, Amuthavalli Thiyagarajan J, Moreno-Agostino D. Cohort trends in intrinsic capacity in England and China. Nat Aging. 2025;5:87–98. 10.1038/s43587-024-00741-w . Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci U S A. 2002;99:7821–6. 10.1073/pnas.122653799 . Campbell CL, Cadar D, McMunn A, Zaninotto P. Operationalization of Intrinsic Capacity in Older People and Its Association With Subsequent Disability, Hospital Admission and Mortality: Results From The English Longitudinal Study of Ageing. J Gerontol Biol Sci Med Sci. 2023;78:698–703. 10.1093/gerona/glac250 . Seijas V, et al. Rehabilitation delivery models to foster healthy ageing-a scoping review. Front Rehabil Sci. 2024;5:1307536. 10.3389/fresc.2024.1307536 . WHO. in. Integrated care for older people: guidelines on community-level interventions to manage declines in intrinsic capacity. World Health Organization; 2017. Tavassoli N, et al. Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: a prospective study. Lancet Healthy Longev. 2022;3:e394–404. 10.1016/S2666-7568(22)00097-6 . Fuentealba M, et al. A blood-based epigenetic clock for intrinsic capacity predicts mortality and is associated with clinical, immunological and lifestyle factors. Nat Aging. 2025. 10.1038/s43587-025-00883-5 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 Sep, 2025 Editor invited by journal 12 Aug, 2025 Editor assigned by journal 08 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 04 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7293825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511977723,"identity":"a9da208d-c20b-4789-9539-7aba57c72787","order_by":0,"name":"Fangda Leng","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fangda","middleName":"","lastName":"Leng","suffix":""},{"id":511977724,"identity":"cf666fce-c808-46e8-a675-92a82412d180","order_by":1,"name":"Lina Ma","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Ma","suffix":""},{"id":511977728,"identity":"cc286359-290c-4569-89c8-12ab202aa435","order_by":2,"name":"Zhiyuan Yang","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Yang","suffix":""},{"id":511977730,"identity":"eca12a81-0099-48fa-8454-60b0e3c6ccf5","order_by":3,"name":"Wenhua Yu","email":"","orcid":"","institution":"Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenhua","middleName":"","lastName":"Yu","suffix":""},{"id":511977732,"identity":"c08a6a0f-bedd-417c-81a5-b9ed907fb320","order_by":4,"name":"Jie Zhao","email":"","orcid":"","institution":"Capital Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Su’ai","middleName":"","lastName":"Li","suffix":""},{"id":511977739,"identity":"b679619b-4e98-48bb-829a-40856281b95c","order_by":8,"name":"Xuan Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Wang","suffix":""},{"id":511977741,"identity":"31a13ed0-90d6-4e8b-8751-3ddb9813d27c","order_by":9,"name":"Mengrao Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengrao","middleName":"","lastName":"Liu","suffix":""},{"id":511977745,"identity":"79bcb0ee-b166-47ad-a86f-9e07eba91a53","order_by":10,"name":"Hui Wang","email":"","orcid":"","institution":"Peking University First 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDCCA1Can5n58APStEi2s6UZkKbF4DyPggRROvhunzH8XPCrLnHzYR4GA4Yam2iCWiTP5RhLz+xjMzY7zHvgAcOxtNwGQloMzvBukObt4ZEzO8yXYMDYcJgoLZt/8/ZI8Bg38xhIEKtlmzTPDwM5A2ZitUie4f9mzduQYCxxGBjICcT4he8MW/Jtnj91if39hw8/+FBjQ1gLGDC2QRkJRCkHgz/EKx0Fo2AUjIIRCACfjjxHt5QHwQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhaoxia","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-04 18:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7293825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7293825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91074124,"identity":"a1d7b0a7-5f76-4ae5-b2f4-a7d53ba43a10","added_by":"auto","created_at":"2025-09-11 11:02:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257113,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralised additive models of intrinsic capacity assessment.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7293825/v1/102bc3189eeb96c949ff3ffa.jpeg"},{"id":91072382,"identity":"cbc4c0f7-63aa-46b5-bd7d-3e3b055add5b","added_by":"auto","created_at":"2025-09-11 10:54:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":667922,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity detection analysis in study participants\u003c/p\u003e\n\u003cp\u003eA. Projection of participants on principle axes 1 and 2 plane. B. Projection of individuals on t-SNE space. Reasonable separation of clusters can be observed. C. Co-assignment probability matrix obtained from 1000 bootstrapping iterations, a 5-community pattern can be readily visualised. D. Distribution of adjusted rand index (ARI) observed in bootstrapping. An average ARI of 0.82 indicated stable community estimation.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7293825/v1/dcf9a646c5745e66589ad4a5.jpeg"},{"id":91076473,"identity":"626718d3-e01d-4af0-b931-7d415f4dbaf3","added_by":"auto","created_at":"2025-09-11 11:10:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197350,"visible":true,"origin":"","legend":"\u003cp\u003ePatterns of intrinsic capacity change in different aging communities.\u003c/p\u003e\n\u003cp\u003eCommunity 1(healthy aging, N=2315): all IC domains were relatively preserved; Community 2 (sensory dominant aging, N=534): sensory function showed most profound impairment in aging; Community 3 (vitality dominant aging, N=397): vitality was the most impaired domain; Community 4 (locomotion dominant aging, N=608): motor function was persistently below average; Community 5 (global accelerated aging, N=2171): all 5 IC domains showed significantly baseline status and profound decline with age.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7293825/v1/adb89267fa2accccb1a0d481.jpeg"},{"id":91078178,"identity":"c8f2ac29-1c6b-4c27-a365-f9200261e6c4","added_by":"auto","created_at":"2025-09-11 11:18:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1911180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293825/v1/8ddbae76-5c01-4bca-bdc5-5045de949fec.pdf"},{"id":91072381,"identity":"7cc261cf-4780-476c-930d-92d1a10a5996","added_by":"auto","created_at":"2025-09-11 10:54:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":722244,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7293825/v1/77b77c1fc874a8db24b68971.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Five Distinct Intrinsic Capacity Aging Patterns Identified in Chinese Older Adults: A Nationwide Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global population is aging at an unprecedented rate. By 2050, the number of individuals above 60 is projected to reach 22%, presenting significant challenges worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Traditional health paradigms have focused on disease management; however, there is a growing recognition to shift towards promoting functional capacity and well-being in aging\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In response, the World Health Organization (WHO) introduced the concept of \u003cem\u003eHealthy Aging\u003c/em\u003e, defined as \u0026ldquo;the process of developing and maintaining the functional ability that enables well-being in older age\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCentral to this framework is the concept of \u003cem\u003eIntrinsic Capacity\u003c/em\u003e (IC), which refers to \u0026ldquo;the composite of all the physical and mental capacities that an individual can draw on at any point in time\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. IC encompasses five key domains: cognition, psychological well-being, sensory functions, vitality, and locomotion. Since the proposal of the framework, numerous studies have proven IC as a vital predicative and interventional tool for public health\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The fundamental premise behind monitoring IC is its potential as an early warning system, enabling proactive interventions rather than reacting to established disease\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Previous studies have reported reference centiles for IC factors scores in French and Brazil cohorts\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, with a diverse population in China, there is a pressing need to establish a stratified norm for IC assessment instruments in Chinese population at first hand.\u003c/p\u003e\u003cp\u003eWhile the IC framework provides a comprehensive model for aging, most existing studies have compressed these domains into a single index to determine functional impairment\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. Further studies have attempted identifying different trajectories of IC decline with this simplified approach, highlighting different rates of IC decline\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The heterogeneity in trajectories suggests underlying distinct aging patterns, but they might not have been well characterized by traditional models.\u003c/p\u003e\u003cp\u003eIn this study, we aim to: (1) Provide age-stratified normative data for each IC domain. (2) Identify distinct patterns of aging based on the integrated assessment of IC domains. By leveraging a community-based diverse cohort and employing advanced analytical methods, we seek to enhance our understanding of the multifaceted nature of aging and inform health-promoting strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eStudy design and population\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eIntrinsic capacity data were drawn from the HEALTHY study (CompreHensive EvALuation of InTrinsic Capacity in CHina StudY), a cross-sectional, community-based observational investigation on older adults in China. Participants were recruited between December 2023 and December 2024 in 24 provincial administrative regions across China, representing both residential communities and care facilities.\u003c/p\u003e\u003cp\u003eCommunities and care facilities were randomly selected using a stratified sampling approach to ensure broad geographical and demographic representation. Within each selected site, residents aged 60 years or older were invited to volunteer. Outreach efforts included local health authorities and institutions to maximize community engagement and participation.\u003c/p\u003e\u003cp\u003eInclusion criteria were: (1) aged 60 years or older; (2) provided written informed consent, either personally or through legal guardian; and (3) were able to complete the standardized IC assessments. Individuals were excluded if they were experiencing acute medical illnesses, at terminal stage of diseases, or were unable to complete the assessments due to cognitive or physical limitations.\u003c/p\u003e\n\u003ch3\u003e2. Intrinsic capacity assessment and information collection\u003c/h3\u003e\n\u003cp\u003eIntrinsic capacity was assessed across five domains as outlined by the WHO: cognition, psychological well-being, sensory function, vitality, and locomotion. The following standardized tools were employed to ensure consistency and comparability across study sites.\u003c/p\u003e\u003cp\u003eCognition was evaluated using the Mini-Mental State Examination (MMSE), a widely used screening tool for cognitive impairment. Psychological well-being was measured using the 15-item Geriatric Depression Scale (GDS-15), with higher scores indicating higher likelihood of depression. Vitality was assessed using the Mini Nutritional Assessment \u0026ndash; Short Form (MNA-SF), which screens for nutritional risk. Scores range from 0 to 14, with lower scores indicating poorer nutritional status\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Locomotion was evaluated using the Short Physical Performance Battery (SPPB), which includes gait speed, chair stand, and balance tests. The total score ranges from 0 to 12, with higher scores reflecting better motor function\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSensory function included both vision and hearing assessments: (1) Distance vision was tested using a simplified E chart protocol. Scores ranged from 0 to 3 based on the participant\u0026rsquo;s ability to identify a small or large E at varying distances (each eye scored separately). 3 points were given if the participant could answer 3 out of 4 small Es\u0026rsquo; directions correctly at 3 meters; 2 points were given for identifying \u0026frac34; large Es correctly at 3 meters; and 1 point was given for identifying \u0026frac34; large Es correctly at 1.5 meters; Near vision was scored (0\u0026thinsp;~\u0026thinsp;3) using a near vision chart (3 rows with decremental font sizes). (2) Hearing was assessed using whisper test. The assessor stood at arm's length on one side of the subject, asked the subject to press the opposite tragus, and whispers four common and unrelated words. The subject is asked to repeat the above words. If the subject can repeat\u0026thinsp;\u0026ge;\u0026thinsp;3 words, the hearing on this side is normal; different words are used to assess the opposite side. 2 points were assigned to each ear (possible scores were 0, 2, 4 points for hearing). This protocol yielded a 0\u0026ndash;10 points scale for sensory function.\u003c/p\u003e\u003cp\u003eAdditional demographic information including age, biological sex, geographic location, education. In addition, a subset of participants (4511 out of 6025) underwent a systemic review of medical conditions by doctors on site.\u003c/p\u003e\n\u003ch3\u003e3. Statistical analysis and modelling\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R (4.4.2, R Foundation for Statistical Computing, Vienna, Austria). Firstly, descriptive analyses of demographics, clinical characteristics, and IC were performed. For each of the five intrinsic capacity domains, descriptive statistics including mean, standard deviation, and selected percentiles. Analyses were stratified by age in 10-year bins from 60 to 100 years.\u003c/p\u003e\u003cp\u003eTo provide a more fine-grained understanding of intrinsic capacity decline in Chinese population, generalized additive models (GAMs) were fitted with age as a continuous independent variable and domain scores as dependent variables. GAMs were employed for their accuracy in characterizing the non-linear relationship between age and IC. For cognitive function (MMSE) and psychological well-being (GDS-15), models were stratified by education level due to observed interaction effects. For sensory, vitality, and locomotion domains, analyses were stratified by sex.\u003c/p\u003e\u003cp\u003eLastly, to uncover latent patterns of aging in the cohort, a graph-based community detection algorithm was applied. Domain scores were first transformed to a uniform 0\u0026ndash;1 scale to harmonize directionality of impairment: For GDS-15, scores were scaled as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Score}{Max\\left(Scores\\right)}\\)\u003c/span\u003e\u003c/span\u003e. For the remaining domains, scores were transformed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1-\\frac{Score}{Max\\left(Scores\\right)}\\)\u003c/span\u003e\u003c/span\u003e. Such scaling also ensures better angular estimation accuracy for the participants at more advanced stage of aging, which aids the cosine similarity calculation as described below.\u003c/p\u003e\u003cp\u003eA participant similarity matrix (supplementary Fig.\u0026nbsp;1) was then constructed using cosine similarity function, chosen for its robustness to scale (vector norm) and its sensitivity to pattern directionality in multidimensional functional space. In other words, we care about whether two participants are on the same track of aging, rather than the stages of their aging process:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Cosine\\:similarity(A,B)=\\:\\frac{A\\bullet\\:B}{‖A‖\\bullet\\:‖B‖}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere A and B are vectors representations of participant\u0026rsquo;s position in intrinsic function space, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:‖A‖\\)\u003c/span\u003e\u003c/span\u003e is the L2 norm of vector A.\u003c/p\u003e\u003cp\u003eA threshold of 0.4 was applied to remove spurious low-similarity edges and construct a weighted undirected graph of 6,025 participants. Community detection was then performed using the weighted Louvain algorithm, which partitions nodes into communities by optimizing modularity. To ensure the stability of community detection, bootstrapping was performed over 1,000 resampling iterations. The mean Adjusted Rand Index (ARI) between bootstrapped and original assignments was calculated to assess the stability of assignment. Moreover, the number of communities was validated using two approaches: (1) Hierarchical clustering on the raw similarity matrix yielded an optimal solution of 5 communities based on the highest average silhouette width (supplementary Fig.\u0026nbsp;2). (2) Additionally, hierarchical clustering on the co-assignment matrix also supported a five-cluster solution.\u003c/p\u003e\u003cp\u003eFinal consensus community assignments (from bootstrapping) were extracted for all participants. Principal component analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE) were performed with the original dataset and participants were projected into the PCA and t-SNE space for illustration. Demographic characteristics were then summarized across the five identified communities. Analysis of variance (Anova), Chi-square test and Student\u0026rsquo;s t-test were performed where appropriate. Finally, domain-wise GAMs were applied within each community to model age-related trajectories, revealing distinct aging profiles across clusters.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Characteristics of study sample\u003c/h3\u003e\n\u003cp\u003eA total of 6025 participants were recruited, of which 3209 were female (53%). 11% of the participants had no formal education, 25% had primary education, 29% had lower secondary education, 24% had upper secondary education, and 11% had tertiary education. The prevalence of hypertension, diabetes mellites, coronary artery diseases, cerebrovascular diseases were 66%, 28%, 11% and 11% respectively. The demographic, geographic and medical characteristics stratified by biological sex are summarised in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eCharacteristics of study cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;3,209(53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,816(47%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2963(92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2543(90%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246(8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e273(10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e490 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e817 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e678 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower secondary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e865 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e876 (31%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper secondary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e736 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e681 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e407 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e\u003cp\u003eMedical conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,573 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,400 (65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoronary artery disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e358 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e273 (13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMyocardial infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther cardiovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther pulmonary diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egastrointestinal diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic hepatic diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic renal diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntracranial haemorrhage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIschaemic cerebral vascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDementia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParkinson's disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEpilepsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther neurological diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e647 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e627 (29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression or anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteoarthritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCataract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e243 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlaucoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther chronic diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eIntrinsic capacity assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.0 (22.0, 28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.0 (23.0, 28.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0, 4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensory score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0 (7.0, 10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.00 (7.0, 10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMNA-SF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.0 (12.0, 14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.0 (12.0, 14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0 (6.0, 11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0 (6.0, 11.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e368 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e356 (13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVision loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGait abnormality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e536 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e579 (21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8 (21.8, 26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.2 (22.0, 26.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e2. Stratified norm of intrinsic capacity assessments across age\u003c/h3\u003e\n\u003cp\u003eThe descriptive statistics of intrinsic capacity assessments, including the mean, standard deviation, median and 10\u0026ndash;90 percentiles, stratified by age are summarised in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for quick reference.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAge Stratified reference for intrinsic capacity assessments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (Q1, Q3) or N (%) are presented as appropriate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian (10\u0026thinsp;~\u0026thinsp;90%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e[60,70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.2 (20.3\u0026thinsp;~\u0026thinsp;28.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.0 (20.0\u0026thinsp;~\u0026thinsp;30.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.0 (4.0\u0026thinsp;~\u0026thinsp;12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (0.0\u0026thinsp;~\u0026thinsp;6.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensory Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.0 (6.0\u0026thinsp;~\u0026thinsp;10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMNA-SF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.0 (11.0\u0026thinsp;~\u0026thinsp;14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e[70,80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.8 (20.0\u0026thinsp;~\u0026thinsp;28.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.0 (17.0\u0026thinsp;~\u0026thinsp;29.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.0 (2.0\u0026thinsp;~\u0026thinsp;12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0\u0026thinsp;~\u0026thinsp;7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensory Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.0 (5.0\u0026thinsp;~\u0026thinsp;10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMNA-SF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.0 (10.0\u0026thinsp;~\u0026thinsp;14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e[80,90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.1 (18.7\u0026thinsp;~\u0026thinsp;28.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.0 (12.0\u0026thinsp;~\u0026thinsp;29.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0 (1.0\u0026thinsp;~\u0026thinsp;11.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0\u0026thinsp;~\u0026thinsp;9.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensory Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0 (2.0\u0026thinsp;~\u0026thinsp;10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMNA-SF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.0 (9.0\u0026thinsp;~\u0026thinsp;14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e[90,100]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.2 (18.6\u0026thinsp;~\u0026thinsp;27.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.0 (9.0\u0026thinsp;~\u0026thinsp;27.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.5 (0.0\u0026thinsp;~\u0026thinsp;8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0 (1.0\u0026thinsp;~\u0026thinsp;10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensory Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0 (0.0\u0026thinsp;~\u0026thinsp;10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMNA-SF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.5 (8.0\u0026thinsp;~\u0026thinsp;13.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGAMs showed that education had a significant influence on both cognitive and psychological assessment education (terms\u0026rsquo; P values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While for vitality, BMI, sensory and locomotor assessments, education and biological sex did not show significant contribution in the model. For illustration, the fitted models were shown in male and female facets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, most of the internal capacities seemed to demonstrate a threshold of age, after which the ICs decline rapidly, manifesting as a steep slope of their decline trajectory. Regarding cognition (MMSE), psychological wellbeing (GDS-15), locomotive and sensory functions, this \u0026lsquo;rapid-declining\u0026rsquo; threshold occurred in the early 70s (70\u0026ndash;75 years of age). Whereas for vitality, it was in the late 70s, and the slope change appeared more gradual. Reference ranges given by the general additive models at 5-year increments are summarised in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;5.\u003c/p\u003e\n\u003ch3\u003e3. Graph-based community detection based on intrinsic capacity\u003c/h3\u003e\n\u003cp\u003eCommunity detection indicated 5 communities in the participants (N\u0026thinsp;=\u0026thinsp;2315, 534, 397, 608 and 2171). Two-dimensional projection of participants into PCA and t-SNE space demonstrated reasonable separation between the clusters with some overlapping in marginal cases, as expected in the real-world aging population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHierarchical clustering on co-assignment matrix strongly supported the 5-cluster aggregation pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and consensus community assignment based on the co-assignment matrix had\u0026thinsp;\u0026gt;\u0026thinsp;98% agreement with original Louvain-derived communities. The mean Adjusted Rand Index (ARI) between bootstrapped and original assignments was 0.83 (SD\u0026thinsp;=\u0026thinsp;0.09, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating strong concordance.\u003c/p\u003e\n\u003ch3\u003e4. Characteristics of individual aging patterns\u003c/h3\u003e\n\u003cp\u003eTo gain better understanding of the 5 distinct aging patterns, GAM models were fitted with community included as both interaction term and intercept. Participants in community 1 followed an optimal pattern, as their ICs were relatively retained along the aging trajectory. Community 2 showed predominant sensory impairment, with lower baseline at age 60 (T=-53, P\u0026thinsp;\u0026lt;\u0026thinsp;1.0*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e) and steeper decline with age (F\u0026thinsp;=\u0026thinsp;5.1, P\u0026thinsp;=\u0026thinsp;0.003). Community 3 featured significantly lower baseline vitality (T=-41, P\u0026thinsp;\u0026lt;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;1.0*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e) and higher baseline depression level (T\u0026thinsp;=\u0026thinsp;6.5, P\u0026thinsp;=\u0026thinsp;1.2*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e). Participants in community 4 had significantly lower baseline locomotive capacity (T=-55, P\u0026thinsp;\u0026lt;\u0026thinsp;1.0*10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e). Notably, a considerable portion (36%) of participants were assigned to community 5, where intrinsic capacities among all 5 domains are profoundly impaired at baseline, and had significantly steeper slope of decline except for psychological symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDemographic characteristics were further compared across the 5 communities (supplementary table 6). The median ages were 69, 71, 70, 70 and 72 years respectively (F\u0026thinsp;=\u0026thinsp;411, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Distribution of education, hypertension, coronary artery disease (CAD), gastrointestinal disease, dementia, Parkinson\u0026rsquo;s disease and cataract differed significantly across the 5 communities (P\u0026thinsp;\u0026lt;\u0026thinsp;0.002, Bonferroni corrected). Post-hoc tests revealed that healthy aging community had better education attainment compared to the rest. Compared with healthy aging community, sensory aging community had higher prevalence of cataract (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.1, P\u0026thinsp;=\u0026thinsp;0.0003), vitality aging community had higher prevalence of hypertension and gastrointestinal diseases (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.8 and 57.9, P\u0026thinsp;=\u0026thinsp;0.0006 and \u0026lt;\u0026thinsp;0.0001),global aging community had higher prevalence of hypertension, CAD, dementia and Parkinson\u0026rsquo;s disease (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.2, 8.7, 11.1 and 13.9, P\u0026thinsp;=\u0026thinsp;0.0008, 0.003, 0.0009 and 0.0002).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide study of over 6,000 older Chinese adults, we established stratified normative data for the five domains of Intrinsic Capacity (IC) and applied graph-based community detection to uncover five distinct patterns of functional aging. These data-driven phenotypes\u0026mdash;characterized by either domain-specific or global declines\u0026mdash;offer a more granular framework than prior trajectory models and hold significant implications for personalized aging care and public health strategies.\u003c/p\u003e\u003cp\u003eOur age and education stratified MMSE scores were in good concordance with the previous population based normative study\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition, we implemented generalized additive model to capture the non-linear pattern of cognitive decline in our sample, and demonstrated the existence of a \u0026lsquo;transition\u0026rsquo; period in the early 70s when accelerated cognitive decline began. This observation coincides with recent functional MRI study that also suggested non-linear brain aging pattern, though the transition seems to come earlier in midlife\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This offset between brain network aging and cognitive aging comes as no surprise, as it has been established in neurodegenerative diseases that brain functional and structural changes precede cognitive decline by decades\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe current study implemented GDS-15 to assess psychological wellbeing. The geriatric depression scale is a well-established screening tool for depression, and a cut-off of \u0026ge; 5 points is commonly used\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, it should be noted that a score above 3 is well above the study population\u0026rsquo;s 95 percentile at age 60, and above 4 for those aged 80+. While the participants with slightly higher GDS-15 score might not meet the criteria of major depressive disorder, concerns should be raised regarding the psychological wellbeing. Further, in the current analysis, we observed that participants with lower education attainment had higher GDS scores. Previous studies had inconsistent opinions on the effect of education on the GDS instrument, suggesting possible cultural and socioeconomical influences\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Further investigations are needed to full entangle the effect of education on GDS items in Chinese population, while considering of potential mediators such as socioeconomical status.\u003c/p\u003e\u003cp\u003eThe MNA-SF assesses the risk of malnutrition and was used as a proxy of vitality. The established cut-off point was \u0026le; 11, which can be considered as a tool with high specificity, as the 5 percentiles of MNA-SF in our sample were above 11 until age reaches the 90s \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Regarding the SPPB for motor function, however, the commonly used cut-offs of \u0026le;8\u0026ndash;9 \u003csup\u003e24,25\u003c/sup\u003e seemed to be overly stringent for our sample: the mean value start at 8.9 at age 60, and reduced to below 8 at early 70s. Further investigations on the optimal cut-off of SPPB in Chinese population are needed, and our normative model might serve a temporary reference as of now. There has not been a consensus instrument for vision and hearing as a combined intrinsic sensory function indicator, and we used a customised scoring system for study purpose, based on which future standardized sensory scoring systems can be developed.\u003c/p\u003e\u003cp\u003eThe concept of \"healthy aging\" as defined by the WHO fundamentally acknowledges that there is \"no typical older person\" \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This principle underscores the vast diversity in health and functional states among older adults. Prior research, including studies conducted within China, have often compressed the five dimensions in a single IC metric with cut-offs for dichotomous classification of whether IC impairment existed\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Later studies have attempted to map this heterogeneity by identifying 3 or 4 broad intrinsic capacity (IC) trajectories, such as stable high capacity, medium-level (decreasing and increasing), and low-level capacity groups\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These studies have highlighted that factors like age, gender, exercise, drinking habits, social activity, and chronic diseases significantly influence these trajectories. The current study advances these findings, by identifying fine-grained features of IC decline patterns which were overlooked by previous studies combining multiple domains into a single score.\u003c/p\u003e\u003cp\u003eIn the current study, graph-based community detection analysis was applied on comprehensive assessments of all five WHO intrinsic capacity domains. The identified patterns\u0026mdash;'Healthy aging' (38.4%), 'Sensory dominant aging' (8.9%), 'Vitality dominant aging' (6.6%), 'Locomotion dominant aging' (10.1%), and 'Global accelerated aging' (36.0%)\u0026mdash;provide a granular and actionable classification that has not been previously established. Likewise, evidence from the French ICOPE-Care cohort also supported distinct patterns of IC decline\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Both studies observed the \u0026lsquo;low impairment\u0026rsquo; and \u0026lsquo;all IC impaired\u0026rsquo; patterns, while the compositions of other phenotypes were slightly different, possibly due to the usage of IC assessment instruments or population difference. The current findings suggest that while the overall trend of IC decline might be similar across population, further studies should be aware of subtle differences in diverse population\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe dominant fraction of globally preservation or impairment pattern could explain the previous observation of a single dominant factor accounting for IC in pervious analyses using multivariate tools \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. On the other hand, the graph-based community detection algorithms are designed to find groups of nodes that are more densely connected within themselves than with the rest of the network, making them well-suited for identifying small, distinct communities \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This sensitivity is crucial because it shifts the paradigm from generalized observations of decline to a precise understanding of how and where functional impairments manifest in different subgroups of older adults.\u003c/p\u003e\u003cp\u003eNumerous studies have already demonstrated the prognostic value of intrinsic capacity assessments \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Understanding more fine-grained patterns can enhance the prognostic value of these assessments. Future longitudinal studies can investigate whether individuals within these specific patterns exhibit differential risks for adverse health outcomes or transitions between patterns over time.\u003c/p\u003e\u003cp\u003eThe identification of these distinct patterns may also be helpful for personalized approach to healthy aging. For example, participants who fall within the single domain predominant aging communities may benefit from targeted functional rehabilitation programmes \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The 'Global accelerated aging' group, representing a substantial portion of aging population, highlights the urgent need for comprehensive, multidisciplinary care models \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This fine-grained model could support more person-centred and function-centred care, aligning with the WHO's Integrated Care for Older People (ICOPE) framework \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne of the limitations of current study is its cross-sectional nature, thus within subject stability was not tested across time. However, we believe that the identified patterns provide a foundation for future longitudinal investigations. Tracking individuals within these communities over time will be crucial to understand the dynamic nature of functional aging, including factors that promote resilience, slow decline, or trigger transitions between patterns. Secondly, while over 6000 participants were adequate from modelling perspective, the sample size is limited relative to the Chinese population. Further, given the higher prevalence of comorbidities in different aging communities, more comprehensive investigations are needed to fully elucidate the environmental, physiological and pathological substrates of different aging patterns. Moreover, the clinical assessments could be related to a more objective epigenetic clock to fully elucidate the biological mechanism of aging\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis work firstly established stratified normative reference for the five domains of Intrinsic Capacity (IC) in Chinese older adults. Further, it proposed a novel, data-driven framework to classify aging patterns based on IC, grounded in a large, diverse population. It opened avenues for translational research, early risk stratification, and personalized healthy aging interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Ethics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study protocol was reviewed and approved by the Ethics Committee of Peking University First Hospital (Ethics Approval No: 2023-Y-564-001). All procedures adhered to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from each participant.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2. Consent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e5. Funding\u003c/h2\u003e\u003cp\u003eThe study was supported by a research grant from National Key R\u0026amp;D Program of China (2023YFC3605200, 2023YFC3605202).\u003c/p\u003e\u003cp\u003e6. Clinical trial number\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLeng F performed formal analysis, interpreted results and wrote the manuscript. Ma L interpreted results and revised the manuscript. Yang Z conducted data analysis and visualization. Yu W, Zhao J, Meng C, Song W, Li S, Wang X, Liu M, Wang H contributed to data collection and data curation. Chang H, Zhong L, Wang Z conceptualized the study, obtained funding and oversaw the study process. All authors critically reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003e8. Acknowledgements\u003c/h2\u003e\u003cp\u003eThe contributions of all participants in this clinical study are greatly appreciated. Special thanks are extended to the volunteers who supported the project's outreach, participant evaluation, and data collection. The participating communities and health providers are also acknowledged for their collaboration, sharing expertise and facilities.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAnonymized data are available from the corresponding authors upon request and subject to approval and completion of a Material Transfer Agreement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. (World Health Organization, 2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeard JR, et al. The World report on ageing and health: a policy framework for healthy ageing. 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Nat Aging. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s43587-025-00883-5\u003c/span\u003e\u003cspan address=\"10.1038/s43587-025-00883-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Intrinsic capacity, aging pattern, Chinese population, centile reference","lastPublishedDoi":"10.21203/rs.3.rs-7293825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIntrinsic capacity (IC), encompassing locomotive, cognitive, vitality, sensory and psychological domains, is central to defining healthy aging. Establishing norms for IC is crucial for understanding the aging process in Chinese population. Distinct aging patterns could be further captured to guide future studies and clinical practice.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a nationwide cross-sectional community-based study, a total of 6025 elderly participants were recruited from 24 provincial administrative regions in China. IC was assessed over the 5 domains for each participant. Descriptive statistics and generalised additive models were employed to construct norms of IC as function of age, stratified by education or biological sex. A cosine similarity matrix of participants was further computed, upon which graph-based Louvain community detection algorithm (CDA) was applied to capture distinct aging patterns in the population.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePopulation norms for the 5 IC domains were established. Data-driven CDA captured 5 distinct aging patterns: 1. Healthy aging (N\u0026thinsp;=\u0026thinsp;2315), where all IC domains were relatively preserved; 2. Sensory dominant aging (N\u0026thinsp;=\u0026thinsp;534), where sensory function showed the most profound impairment in aging; 3. Vitality dominant aging (N\u0026thinsp;=\u0026thinsp;397), where vitality was the single most impaired domain; 4. Locomotion dominant aging (N\u0026thinsp;=\u0026thinsp;608), in which motor function was persistently below average; and 5. Global accelerated aging (N\u0026thinsp;=\u0026thinsp;2171), where all the 5 IC domains profoundly declined with age.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study provided the norms of IC in aging Chinese population. More importantly, 5 distinct aging patterns were identified, which is of both clinical and scientific interest.\u003c/p\u003e","manuscriptTitle":"Five Distinct Intrinsic Capacity Aging Patterns Identified in Chinese Older Adults: A Nationwide Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:54:19","doi":"10.21203/rs.3.rs-7293825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-03T18:50:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-12T18:30:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T11:27:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T11:27:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-08-04T18:20:45+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":"003cf95e-3665-4e28-90ac-10b2699cb444","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T10:54:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 10:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7293825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7293825","identity":"rs-7293825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}