Trajectories of social health, cognitive, and daily functioning in community-dwelling older adults

Trajectories of social health, cognitive, and daily functioning in community-dwelling older adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trajectories of social health, cognitive, and daily functioning in community-dwelling older adults Anna Marseglia, PhD, Eline Verspoor, Marieke Perry, Myrra Vernooij-Dassen, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528919/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract INTRODUCTION: Cognitive and functional impairments can both influence and stem from deteriorating social health). However, the interplay between these dimensions while aging remains poorly understood. This study investigated the concordance and discordance of SH, cognitive, and daily functioning trajectories. METHODS: We analyzed 15-year follow-up data (2001-2015) from 2848 initially dementia-free older adults in the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Cognition and daily functioning were assessed with the MMSE and ADL/IADLs. Social health encompassed indices of social participation, connections, and support. Longitudinal trajectories across these five dimensions were identified using latent growth curve analysis, latent class growth analysis, and growth mixture models. RESULTS: Two cognitive trajectories—relatively preserved (91%) and fast decline (9%)—and two daily functioning trajectories—stable (95%) and declining (5%)—were identified. For SH, alongside the stable groups, further subgroups included gradually declining social participation (70%) and low initial social connections (29%). Social support showed stable (95%), declining (2%), and increasing (3%) trajectories. Females were more likely to be in the initially low-stable social connections group, whereas higher education was linked to favorable trajectories across almost all dimensions but support. Membership in the lowest class for cognition, daily functioning, social connections and participation showed strong concordance. Yet, increasing social support was associated with low cognition but high daily functioning (pseudo-class’s OR 4.2, 95%CI 2.3–7.6). DISCUSSION: Our findings highlight the crucial role of social health in influencing cognitive and daily functioning, offering new insights into the dynamic interplay between social participation, connections, and support in aging. Geriatrics & Gerontology Figures Figure 1 INTRODUCTION Individuals do not age in isolation but interact with their social environments. The concept of Social Health (SH) allows to structure these interactions in relation to health and disease, capturing variation in social health at the personal level, as well as the level of one’s social environment ( 1 ). At the individual level, SH involves the fulfillment of social roles, compliance with social norms, autonomy, and social engagement in activities that are physically and cognitively stimulating. At the social environment level, SH encompasses the frequency of social relationships, size/density/type of one’s social network (structure), the roles of the social network, i.e., emotional support and instrumental aid (function), and the appraisal of relationship quality, i.e., satisfaction and loneliness ( 1 ). Previous studies have linked various indicators of poor SH to age-related conditions, including cognitive disorders ( 2 , 3 ), chronic medical conditions ( 4 – 6 ), depression ( 7 , 8 ), and mortality ( 9 , 10 ). While these studies cover indicators like social disengagement, isolation, lack of social support, and loneliness, they typically examine SH unidimensionally, focusing on isolated SH factors, without considering their interconnectedness. SH is multidimensional, characterized by a complex interplay of intertwined and co-dependent social factors that collectively influence health. Moreover, most studies overlook changes in SH over the lifespan, despite its dynamic nature and the potential interplay between mid-to-late life SH changes and critical shifts in cognitive and physical functioning ( 11 ). SH plays a crucial role, especially when cognitive disorders emerge, as these conditions can disrupt an individual’s relationship with their social environment, just as the social environment can influence the individual’s health, including cognitive health. Reduced social engagement, fewer social contacts, and weaker social networks have been linked to faster rates of cognitive decline and increased dementia risk ( 12 ). The Lancet Commission attributed 5% of global dementia cases to late-life social isolation ( 13 ). Conversely, strategies targeting SH factors (e.g., encouraging social participation) may help reduce dementia risk and slow progression by supporting individuals in leveraging their remaining capacities ( 14 , 15 ). When cognition becomes impaired, individuals cannot perform as well in their daily lives. Functional impairment may both stem from and contribute to deterioration in SH. Few studies, especially longitudinal ones, have explored the bidirectional links between SH factors and functional impairment. For instance, community-based studies have found strong associations of factors like separation/divorce, social isolation, reduced engagement in community activities, and feelings of loneliness with functional impairment, disability, or physical frailty ( 16 – 18 ). Despite these findings, our understanding of how different dimensions of SH evolve over time along with cognitive and functional changes remains limited. Understanding simultaneous patterns of change across different social, cognitive and daily functioning indicators may allow us to identify factors linked to concordant versus discordant trajectories over time, which may in turn inform and promote successful aging strategies. This study aimed to identify homogeneous subgroups (i.e., classes) of individuals based on changes in SH indicators, cognitive and daily functioning over time. We also intended to explore concordant versus discordant trajectories of cognitive and daily functioning, and the sociodemographic and SH factors linked to such (in)congruences. METHODS 2.1 Setting and study population. Data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a community-based longitudinal cohort study in Stockholm, were used. Initiated in 2001, SNAC-K involved stratified sampling by eleven age groups. A random sample of 4590 older adults, aged ≥ 60 years, living at home or institutions, were asked to participate, with 3363 individuals participating in the baseline survey (March 2001 to June 2024; 73% participation rate). The younger age-cohorts (60, 66, and 72 years) were followed every six years and the older age-cohorts (≥ 78 years) every three years. At each wave, participants underwent comprehensive clinical and behavioral assessments with trained physicians, nurses, and neuropsychologists. Data on sociodemographic factors (chronological age, sex, education), SH, and cognitive and daily functioning were collected through structured interviews, self-administered questionnaires, and neuropsychological testing. In the current study, we included data from the first five data-collection waves of SNAC-K, up to end of 2015. We included participants without dementia at baseline who completed the Mini-Mental State Examination (MMSE) (n = 3039). Additionally, participants with neuro-psychiatric disorders (n = 40), living in a nursing home (n = 36), or missing data on all SH variables (n = 115) were excluded. This resulted in a final sample of 2848 participants, followed over 15 years. Each wave of SNAC-K data collection was approved by the Regional Ethical Review Board in Stockholm or the Swedish Ethical Review Authority, and written informed consent was obtained from participants or their next of kin. 2.2 Assessment of social health indicators Detailed interview protocols, questionnaires, and operationalizations of SH individual- (social participation) and environment-related (connections and support) indices have been previously published. Please, refer to Marseglia et al (Appendices A and B) ( 19 ) and Marseglia et al. (Appendix B) ( 14 ) in Supplementary materials online. 2.2.1 Social participation At each examination wave, participants reported the frequency (0 = weekly, 1 = monthly, 2 = less frequently, 3 = never) of their engagement in various leisure activities over the past 12 months. Activities were predominantly mental (reading, painting/drawing/working with clay/pottery, playing chess/card games, musical instrument, listening to music, using the internet/playing computer games), physical (gardening, hiking in the forest/pick berries or mushrooms, hunting/fishing, doing car/mechanical or home repairs, regular light-to-intense physical exercise), and social (attending cinema/theater/concerts, sport events, museums/art exhibitions, restaurants/bar/cafés, bingo, dancing, church service, travelling, volunteering, study circles/courses, and other social meetings). Given that each activity often covers varying degrees of the other dimensions, we used a procedure developed earlier to rate each activity’s proportion of social, mental, and physical components ( 20 ). A total of 78 raters (mean age = 77 ± 7 years; 67% female; mean education = 14.0 ± 4.7 years) scored all leisure activities on their social, mental, and physical components using a 4-point scale (i.e., 0 = not at all to 3 = to a big extent). We created a social participation index in three sequential steps: (a) multiplying the frequency of the activity by the average social component weight for each activity), (b) summing the obtained scores for all activities, and (c) Z-standardizing the summed scores using the baseline mean and SD of the cognitively unimpaired SNAC-K participants. Higher scores indicated greater engagement in socially demanding activities. Follow-up scores were calculated using baseline means and standard deviations. The z-scores were rescaled to an index with mean of 100 and standard deviation of 15, for easier interpretation of the growth model estimates. 2.2.2 Social connections and support A social connection index was derived from the following items: relationship status, living arrangement, the number of living children, frequency of direct contact, frequency of remote contact, and social network size ( 19 ). A social support index was generated through items including satisfaction with contacts, perceived material support, perceived psychological support, and sense of affinity ( 19 ). Raw scores were z-standardized and averaged into the respective indices, with scores above 0 indicating more social connections or higher perceived social support compared to the study population average. The same procedure was repeated at follow-ups, using baseline means and standard deviations for Z-standardization. The z-scores were rescaled to an index with mean of 100 and standard deviation of 15. 2.3 Assessment of cognition The MMSE was used as a measure of global cognitive functioning. MMSE scores range from 0 to 30, with higher scores indicating better cognitive performance. 2.5 Assessment of daily functioning Daily functioning was assessed at all waves using the basic (ADL) and instrumental (IADL) activities of daily living scales ( 21 ). For ADL (score range 0–6), a point was assigned when the person was unable to manage basic personal needs independently, including bathing, dressing, toileting, continence, transferring/ambulating, and eating. For IADL (score range 0–8), a point was assigned when the person was unable to perform more complex tasks independently such as using the telephone, grocery shopping, food preparation, housekeeping, laundry, using public transportation, handling finances, and taking medications ( 22 ). Items from both scales were combined into a single index, reflecting the number of ADL and IADL tasks on which an individual was dependent (range 0–14). For easier interpretation, this total index was reverse coded so that higher scores indicated better daily functioning. 2.6 Other study variables Information on chronological age (years), biological sex (female versus male), and educational attainment (elementary, high school, or university) was obtained at baseline through structured nurse interview. The operationalization of the dementia clinical diagnosis within SNAC-K was thoroughly detailed earlier ( 19 ). Briefly, at each examination wave, two independent diagnoses were made by an examining physician and a reviewing physician, following the Diagnostic and Statistical Manual of Mental Disorders-4th Edition criteria. In cases of disagreement, an external neurologist made the final decision. For participants who died between visits without a dementia diagnosis, additional information was obtained from clinical charts, medical records, and the Swedish National Cause of Death Register. Dementia at baseline was used as an exclusion criterion in the present study. 2.7 Data analysis The trajectories for social participation, social connections, social support, cognitive, and daily functioning were described accounting for age. For each of the five health domains, a stepwise approach was used to determine the best model fit and number of classes ( 23 ). First, we conducted 1-class latent growth curve analyses (LGCA), one for each separate domain as a function of SNAC-K wave (0 = baseline, 3-, 6-, 9-, and 12-year follow-up) to assess whether domains changed linearly or quadratically over time and to identify necessary random effects. Interaction terms between baseline age centered at 60 years (continuous variable) and study wave were included in the models to account for baseline age in the growth curves. We also examined the influence of freely estimating the residual variance per wave versus constraining it to be equal across time points. Next, using the 1-class latent growth curve identified model, we performed latent class growth analysis (LCGA) where models with 2, 3, and 4 classes were fitted for each health domain. The determination of the number of growth classes was done twice: once constraining random effects to zero and next allowing for an overall random intercept. Finally, we fitted growth mixture models (GMM) with the identified number of classes through LCGA, adding random effects to allow variance and covariance to vary within each class. The following random effects were added stepwise: (a) overall random intercept, (b) overall random intercept, linear slope and covariance, (c) class-specific random intercept, and ( 4 ) class-specific random intercept, linear slope and covariance. Model fit was evaluated using Akaike information criteria (AIC), Bayesian information criteria (BIC), Lo-Mendel-Rubin test, entropy score, class size, and class probability. Visual checks ensured selection of the most parsimonious model. Data were assumed to be missing at random. For LCGA and GMM models, we used 500 random-starts and 20 iterations. Associations between sociodemographic factors (age, sex, and education) and class membership were examined using a three-step method for proximal variables to account for the fact that class membership is a latent rather than an observed variable ( 24 ). To examine concordance across the five health domain trajectories, we used a multiple pseudo-class drawing method to compute pairwise odds ratios (OR) between assigned growth classes. This method was also used to study differences in sociodemographic and SH indicators between concordant and discordant trajectories for cognitive and daily functioning. This method accounts for latent class membership by multiple imputing the latent growth class using class membership probabilities. We imputed 20 times, performed logistic regressions for each imputation set that we combined using Rubin’s rule 25). The LGCA, LCGA, and GMM analyses were conducted using Mplus Version 8.4 and the MplusAutomation package in R ( 26 ). Data preparation and processing were done in R. We followed the Guidelines for Reporting on Latent Trajectory Studies to describe data analysis and results ( 27 ). RESULTS 3.1 Baseline characteristics of the study participants The sample included 2848 participants, with a mean age of 73 (SD = 10) (Table 1 ). The majority were female (63%) and highly educated (84% completed high school or university). Baseline cognitive and daily functioning were relatively preserved, with a median MMSE score of 29 (IQR = 2.0) and a daily functioning score of 14 (IQR = 0). Index scores for SH indicators were higher than average in sexagenarians, around average in septuagenarians, and lower than average in the octogenarians. Participants’ characteristics for all five SNAC-K waves can be found in Appendix A . Table 1 Baseline characteristics of the study participants by age decade. Characteristics a Overall N = 2,848 Sexagenarians N = 1,240 Septuagenarians N = 864 Octagenarians N = 744 p-value b Age (years) 73 ( 10 ) 63 ( 3 ) 75 ( 3 ) 87 ( 5 ) < 0.001 Female sex, n (%) 1,781 (63%) 698 (56%) 552 (64%) 531 (71%) < 0.001 Education, n (%) Elementary 1,006 (35%) 620 (50%) 254 (29%) 132 (18%) < 0.001 High school 1,409 (49%) 530 (43%) 474 (55%) 405 (54%) University 433 (15%) 90 (7.3%) 136 (16%) 207 (28%) MMSE score, median (IQR) 29 (2.0) 30 ( 1 ) 29 ( 2 ) 28 ( 2 ) < 0.001 Daily functioning, median (IQR) 14 (0) 14 (0) 14 (0) 14 ( 2 ) < 0.001 Social participation index, mean (SD) 100 ( 15 ) 107 ( 12 ) 101 ( 14 ) 88 ( 15 ) < 0.001 Social connections index, mean (SD) 100 ( 10 ) 102 ( 10 ) 100 ( 10 ) 96 ( 9 ) < 0.001 Social support index, mean (SD) 100 ( 9 ) 102 ( 7 ) 100 ( 9 ) 97 ( 10 ) < 0.001 3.2 Identification of trajectories of SH, cognitive, and daily functioning For all health domains, the best fit was a two-class quadratic growth model, except for social support, which required a three-class model (Fig. 1 ). Fit statistics for the selected growth mixture models, including the checks for local maxima (OPTSEED option in Mplus) are presented in Appendix B . Further details on LGCA, LCGA and GMM model selection are provided in Appendix C . Table 2 The parameter estimates and class size by class for the final growth models for the five outcomes. Cognitive functioning Daily functioning Social participation Social connections Social support Parameter Class estimate (SE) p-value estimate (SE) p-value estimate (SE) p-value estimate (SE) p-value estimate (SE) p-value Class 1 Fixed intercept 1 29.57 (0.032) < 0.001 14.063 (0.02) < 0.001 121.016 (0.846) < 0.001 107.606 (0.679) < 0.001 102.874 (0.212) < 0.001 Baseline age (+ 1 year) * Fixed intercept 1 -0.045 (0.003) < 0.001 -0.019 (0.002) < 0.001 -0.74 (0.027) < 0.001 -0.251 (0.018) < 0.001 -0.142 (0.014) < 0.001 Fixed linear slope 1 -0.109 (0.019) < 0.001 -0.018 (0.01) 0.108 0.455 (0.133) 0.001 0.136 (0.064) 0.033 0.213 (0.065) 0.001 Baseline age (+ 1 year) * Fixed linear slope 1 -0.007 (0.002) 0.001 -0.01 (0.002) < 0.001 -0.017 (0.008) 0.030 -0.005 (0.004) 0.194 -0.019 (0.006) 0.002 Fixed quadratic slope 1 0.006 (0.002) < 0.001 0.007 (0.001) < 0.001 -0.045 (0.011) < 0.001 -0.016 (0.005) 0.002 -0.017 (0.005) 0.002 Baseline age (+ 1 year) * Fixed quadratic slope 1 -0.001 (0.000) 0.016 -0.001 (0.000) < 0.001 -0.002 (0.001) 0.004 -0.002 (0.000) < 0.001 0.001 (0.001) 0.338 Random intercept variance 1 0.499 0.234 29.28 32.526 Covariance RI and RL 1 0.065 0.061 -0.603 0.17 Random linear slope variance 1 0.026 0.066 0.086 0.029 Random quadratic slope variance 1 Class size, n (%) 1 2599 (0.91) 2687 (0.95) 861 (0.3) 2020 (0.71) 2693 (0.95) Class 2 Fixed intercept 2 27.11 (0.206) < 0.001 8.865 (0.280) < 0.001 104.091 (0.594) < 0.001 93.213 (1.037) < 0.001 101.02 (1.506) < 0.001 Baseline age (+ 1 year) * Fixed intercept 2 -0.045 (0.003) < 0.001 -0.019 (0.002) < 0.001 -0.74 (0.027) < 0.001 -0.251 (0.018) < 0.001 -0.142 (0.014) < 0.001 Fixed linear slope 2 -0.883 (0.255) 0.001 -0.214 (0.215) 0.318 0.567 (0.115) < 0.001 -0.125 (0.112) 0.267 3.015 (0.532) < 0.001 Baseline age (+ 1 year) * Fixed linear slope 2 -0.007 (0.002) 0.001 -0.01 (0.002) < 0.001 -0.017 (0.008) 0.03 -0.005 (0.004) 0.194 -0.019 (0.006) 0.002 Fixed quadratic slope 2 -0.086 (0.021) < 0.001 0.005 (0.022) 0.829 -0.05 (0.009) < 0.001 0.002 (0.009) 0.803 -0.465 (0.044) < 0.001 Baseline age (+ 1 year) * Fixed quadratic slope 2 -0.001 (0.000) 0.016 -0.001 (0.000) < 0.001 -0.002 (0.001) 0.004 -0.002 (0.000) < 0.001 0.001 (0.001) 0.338 Random intercept variance 2 0.499 0.234 38.045 32.526 Covariance RI and RL 2 0.065 0.061 -0.603 0.17 Random linear slope variance 2 0.026 0.066 0.086 0.029 Random quadratic slope variance 2 Class size, n (%) 2 247 (0.09) 133 (0.05) 1987 (0.7) 828 (0.29) 58 (0.02) Class 3 Fixed intercept 3 75.425 (0.952) < 0.001 Baseline age (+ 1 year) * Fixed intercept 3 -0.142 (0.014) < 0.001 Fixed linear slope 3 7.985 (0.866) < 0.001 Baseline age (+ 1 year) * Fixed linear slope 3 -0.019 (0.006) 0.002 Fixed quadratic slope 3 -0.449 (0.067) < 0.001 Baseline age (+ 1 year) * Fixed quadratic slope 3 0.001 (0.001) 0.338 Random intercept variance 3 32.526 Covariance RI and RL 3 0.17 Random linear slope variance 3 0.029 Random quadratic slope variance 3 Class size, n (%) 3 97 (0.03) Residual error time point 1 variance any 0.922 0.208 102.292 13.007 16.566 Residual error time point 2 variance any 9.727 2.278 71.553 13.443 42.993 Residual error time point 3 variance any 3.05 1.084 67.804 9.698 26.3 Residual error time point 4 variance any 10.603 4.656 70.526 11.915 28.824 Residual error time point 5 variance any 2.917 1.421 86.983 16.05 13.954 For cognitive functioning, most participants (91%) were assigned to a class characterized by high baseline cognitive performance and relatively stable MMSE score over time (class 1; blue in Fig. 1 A). A minority (9%) were assigned to a class with an accelerated MMSE decline (class 2; red in Fig. 1 A). Within classes, participants with a higher baseline age had a lower initial MMSE score (β = -0.045 MMSE point per year increase in baseline age) and accelerated rate of decline (linear decline of -0.007/year and quadratic decline of -0.001/year 2 in baseline age) (Table 2 ). For daily functioning, 95% of participants were assigned to a class with relatively preserved daily functioning across all examinations (class 1; blue in Fig. 1 B). The remaining 5% were assigned to class 2 (red in Fig. 1 B) with considerably impaired average daily functioning at baseline (score of 8.9 [SE 0.28] for a 60-year-old individual) and a steeper decline (linear decline of -0.21/year [SE 0.22] for a 60-year-old person). Within classes, participants with a higher baseline age had a lower initial daily functioning (β = -0.019 point per year increase in baseline age) and accelerated rate of decline (linear decline of -0.01/year and quadratic decline of -0.001/year 2 in baseline age). For the SH indicators of social participation and connections (Fig. 1 , C and D ), both classes showed relatively stable scores over time: one class above average and one below. However, for social participation, the majority (70%) were in the class with the lowest scores (class 1; red in Fig. 1 C), indicating poor social engagement. In contrast, for social connections, the majority (71%) were in the class with higher scores (class 2; blue in Fig. 1 D), indicating larger social connections. Baseline age was inversely associated with baseline social participation and connection scores (β: -0.74 [SE 0.02] and − 0.25 [SE 0.03]) and—except for the linear slope in social connections—also with the linear and quadratic slope parameters (Table 2 ), indicating lower initial levels of SH indicators and faster declines with increasing participants’ age at baseline. For the SH indicator of social support, the largest class, comprising 95% of participants (class 1; blue in Fig. 1 E), exhibited stable social support over time. Class 2 (2% of participants; red in Fig. 1 E) showed declining scores in social support, whereas class 3 (3% of participants; yellow in Fig. 1 E) showed increasing scores. Appendix D (top row) shows that the estimated random intercepts—reflecting the extent to which participants assigned to the specific class deviate at the start from the class-average trajectories—are particularly large for social connections and support, and smaller for cognitive and daily functioning. In contrast, the random linear slope variance—indicating deviations from the average slope within a class—is greater for daily functioning than cognition, social connections, and support. 3.3 Associations between class membership and sociodemographic factors Higher baseline age was associated with a greater likelihood of belonging to class 2 (fast rate of decline) rather than class 1 (more stable) for both cognitive and daily functioning (Table 3 ). Similar trends were observed for social support, where higher age was associated with membership in both class 2 (decreasing support over time) and class 3 (increasing support over time). Female sex was associated with higher odds of belonging to class 2 of social connections (OR 1.36, p = 0.01), characterized by lower social connections that remained stable over time. No significant associations were found between sex and the other four health domains. Higher educational attainment was linked to a reduced likelihood of belonging to class 2 (faster decline) rather than class 1 (stable) for cognitive and daily functioning. Additionally, participants with higher education had lower likelihoods of belonging to the classes with lower scores for social participation and connections. Regarding social support, higher educational attainment decreased the likelihood of belonging to the increasing support trajectory (class 3; OR 0.38, p < 0.001). Table 3 Associations between class membership for each outcome and sociodemographic factors. Cognitive functioning Daily functioning Social participation Social connections Social support OR (95%-CI) p-value OR (95%-CI) p-value OR (95%-CI) p-value OR (95%-CI) p-value OR (95%-CI) p-value Class 2 vs Class 1 Baseline age (years, + 1) 1.11 (1.09–1.13) < 0.001 1.17 (1.13–1.20) < 0.001 0.99 (0.97-1.00) 0.010 0.99 (0.98–1.01) 0.330 1.03 (1.01–1.06) < 0.001 Female (vs male) 1.33 (0.9–1.96) 0.15 0.9 (0.59–1.37) 0.63 1.01 (0.8–1.26) 0.95 1.36 (1.08–1.72) 0.010 1.34 (0.72–2.49) 0.360 Education, Intermediate (vs Education, Lower) 0.54 (0.38–0.79) < 0.001 0.65 (0.43-1.00) 0.05 0.31 (0.20–0.50) < 0.001 0.64 (0.47–0.87) < 0.001 0.85 (0.4–1.78) 0.660 Education, High (vs Education, Lower) 0.3 (0.17–0.52) < 0.001 0.49 (0.27–0.89) 0.02 0.14 (0.09–0.23) < 0.001 0.31 (0.22–0.44) < 0.001 0.82 (0.34–1.98) 0.650 Class 3 vs Class 1 Baseline age (years, + 1) 1.08 (1.05–1.11) < 0.001 Female (vs male) 0.83 (0.51–1.34) 0.440 Education, Intermediate (vs Education, Lower) 1.35 (0.78–2.33) 0.290 Education, High (vs Education, Lower) 0.38 (0.16–0.93) 0.030 3.4 Concordance and discordance across SH, cognitive and daily functioning trajectories Being in the declining class for cognitive functioning was associated with being in the declining class for daily functioning (OR 7.3 [95%-CI 4.8–11.1)] (Table 4 ). Participants with lower baseline social participation or connections (classes 2) were more likely to follow fast cognitive (ORs 2.3 [95% CI 1.5–3.6] and 1.9 [95% CI 1.3–2.6]) and functional (ORs 7.0 [95% CI 2.9–17.0] and 2.0 [95% CI 1.3–3.1]) declines. Both decreasing and increasing social support classes were associated with higher odds of being in the declining cognitive class. However, only decreasing social support was related to the declining class for daily functioning (OR 4.5 [95% CI 2.4–8.4]). Increasing and decreasing classes of social support were also associated with the lower social connections and/or social participation classes. Table 4 Concordance across cognitive, physical functional, and SH trajectories. Cognitive functioning, class 2 Daily functioning, class 2 Social participation, class 2 Social connections, class 2 Parameter Odds ratio (95%-CI) Odds ratio (95%-CI) Odds ratio (95%-CI) Odds ratio (95%-CI) Cognitive functioning, class 2 Daily functioning, class 2 7.31 (4.8-11.14) Social participation, class 2 2.38 (1.56–3.62) 7.01 (2.89–17.03) Social connections, class 2 1.85 (1.34–2.55) 1.95 (1.27-3) 2.93 (2.32–3.7) Social support, class 2 2.31 (1.27–4.19) 1.32 (0.46–3.83) 1.99 (1.17–3.38) 1.71 (1.09–2.69) Social support, class 3 4.52 (2.75–7.42) 4.48 (2.38–8.42) 3.39 (1.61–7.12) 3.15 (1.96–5.07) Abbreviations: CI, confidence intervals; OR, odds ratio. 3.5 Concordance and discordance in cognitive and daily functioning trajectories: distribution of sociodemographic and SH factors In our study, 2491 (88%) participants had stable cognitive and daily functioning over time (concordant “high cognitive & daily functioning”) and 47 (2%) had declining cognitive and daily functioning (concordant “low cognitive & daily functioning”). Additionally, 86 (3%) participants had stable cognition and declining daily functioning (discordant “high cognitive & low daily functioning”), whereas 194 (7%) had declining cognition and stable daily functioning (discordant “low cognitive & high daily functioning”). Participants in the concordant “high cognitive & daily functioning” group (most optimal) were the youngest, with an average age of 71 years, and with the lowest proportion of women along with the highest proportion of participants with university education (Table 5 ). Conversely, those in the concordant “low cognitive & daily functioning” group (least favorable) were the oldest (mean age of 87 years) and included the highest proportions of women and participants with elementary education. A higher proportion of participants within the concordant high functioning group were in the best functioning class (class 1) for social support, social participation and connections than in the other three groups. Table 5 Distribution of sociodemographic factors and SH indicators across four groups with concordance or discordance in class membership for cognitive and daily functioning trajectories. Concordant Discordant Characteristics High cognitive & daily functioning N = 2491 (88.4%) Low cognitive & daily functioning N = 47 (1.7%) High cognitive & Low daily functioning N = 86 (3.0%) Low cognitive & High daily functioning N = 194 (6.9%) p-value a Age (years) 71 ( 10 ) 87 ( 8 ) 86 ( 10 ) 81 ( 9 ) < 0.001 Sex, female, n(%) 1,531 (61%) 39 (83%) 57 (66%) 143 (74%) 0.003 Education, n(%) Elementary 314 (13%) 18 (38%) 29 (34%) 66 (34%) < 0.001 Intermediate 1,226 (49%) 23 (49%) 44 (51%) 100 (52%) University 951 (38%) 6 (13%) 13 (15%) 28 (14%) MMSE score, median (IQR) 29 ( 1 ) 25 ( 2 ) 28 ( 1 ) 26 ( 2 ) < 0.001 Daily functioning, score, median (IQR) 14 ( 1 ) 8 ( 2 ) 9 ( 2 ) 14 ( 1 ) < 0.001 Social participation index, mean (SD) 102 ( 13 ) 72 ( 10 ) 77 ( 11 ) 90 ( 14 ) < 0.001 Social connections index, mean (SD) 101 ( 10 ) 93 ( 9 ) 92 ( 9 ) 96 ( 10 ) < 0.001 Social support index, mean (SD) 101 ( 8 ) 92 ( 12 ) 95 ( 11 ) 96 ( 11 ) < 0.001 Growth class Social participation, n (%) < 0.001 1 (stable) 818 (33%) 1 (2.1%) 4 (4.7%) 36 (19%) 2 (gradual decline) 1,673 (67%) 46 (98%) 82 (95%) 158 (81%) Social connections, n (%) < 0.001 1 (stable) 1,815 (73%) 30 (64%) 48 (56%) 113 (58%) 2 (initially lower, stable slope) 676 (27%) 17 (36%) 38 (44%) 81 (42%) Social support, n (%) < 0.001 b 1 (stable) 2,386 (96%) 40 (85%) 75 (87%) 164 (85%) 2 (declining) 45 (1.8%) 0 (0%) 1 (1.2%) 12 (6.2%) 3 (increasing) 60 (2.4%) 7 (15%) 10 (12%) 18 (9.3%) a Class characteristics for cognitive and daily functioning concordance are described by assigning participants to the class with the highest individual class probability. The pseudoclass membership method with 20 imputations was used to derive P-values from one-way ANOVAs or Chi squares tests as appropriate which were combined using Rubin's Rule. b For cells with expected count below 5,, p-value were calculated using Monte Carlo simulation with 2000 replications via the “simulate.p.value()” function.. Almost all participants within declining daily functioning—either with stable or declining cognitive functioning—were in the group with the least favorable social participation pattern (class 2; 98% of concordant “low cognitive and daily functioning” and 95% of discordant “high cognitive & low daily functioning”). Among those with declining cognition, 19% of participants with simultaneous preserved daily functioning were assigned to the favorable class of social participation; this proportion dropped to 2.1% when coupled with declining daily functioning (the lowest across the 4 groups). For social support, the highest proportion of participants with declining cognition but relatively stable daily functioning was in the declining social support group (class 2, 6.2%). Nobody from the concordant “low cognitive & daily functioning” was assigned to the declining social support class. On the contrary, this group had the highest proportion of participants assigned to the increasing social support group (class 3, 15%), followed by those with declining daily functioning and relatively stable cognition (12%). p>Classes 2 for social participation (gradual decline) and connections (initially lower, stable decline) were associated with higher odds non-favorable concordant and discordant cognitive and daily functioning patterns ( Appendix E ). Similar results were observed for social support’s class 3 (increasing trajectory) and class 2 (declining) for the discordant groups. DISCUSSION In this large population-based cohort of Swedish older adults, we identified two distinct latent groups based on separate trajectories of cognitive functioning, daily functioning, and SH indicators (social participation, connections, and support). One group (class 1) showed relatively preserved function across all health domains over the 15-year follow-up period. The second group (class 2) exhibited a steeper decline over time for cognition, daily functioning, and social support, started lower at baseline and declined gradually for social participation, and had lower baseline social connections but remained stable over time. Social support also exhibited a third group (class 3) that started lower at baseline but increased over time, suggesting enhanced resources from their social environment as they aged. Most SNAC-K participants (70–95%) were assigned to class 1, the thriving subgroup, except for social participation, where the majority was in the least favorable group (class 2). Our findings highlight that cognitive and daily functioning are interrelated, concordantly but also often discordantly, in older adults. Notably, participants with increasing social support over time were also more likely to show discordant trajectories of low cognition and high daily functioning over time. The ability of GMM to capture changes in SH indicators over time, beyond average levels, is a key yet underexplored advantage. Existing research has primarily focused on single average trajectories of specific health indicators, limiting cross-study comparisons. Consistent with our findings, a Swedish study observed that social participation remains relatively stable over time, with a notable decline after age 70 ( 28 ). This stability may be attributed to older adults choosing fewer yet more meaningful social activities as they age, maintaining regular engagement ( 11 ). An American nationally representative panel study using GMM, identified five latent subgroups of social engagement over a decade ( 29 ). Four were very similar, showing higher social participation initially with relatively stable or slightly changing trajectories, while one subgroup had lower initial participation and a faster decline. Despite some differences, both Thomas’ ( 29 ) and we found relatively stable patterns in social participation, though we failed to detect a fast-declining pattern. Beyond methodological differences (e.g., sample size, study design, follow-up length, operationalization of social participation, statistical method used), discrepancies could be due, but not limited, to socio-cultural, lifestyle habits, and/or intergenerational influences. Moreover, few studies have examined trajectories of social connections or social support in older age, with most focusing on the frequency of social contacts and support at a single point in time ( 30 , 31 ). Previous findings from the SNAC-K cohort have shown that high social connectedness and support can protect older adults from accelerated health decline (in terms of morbidity burden, physical and cognitive function and disability) ( 32 – 35 ), cognitive disorders in at-risk individuals (e.g., those with genetic susceptibility or cardiometabolic risk factors) ( 14 , 36 ), emergency care visits ( 37 ), injurious falls ( 38 ), and unplanned hospital admissions ( 39 , 40 ). These results were consistently supported by others ( 12 , 41 , 42 ). Our findings also show that biological sex and education may influence trajectories of cognitive, daily functioning, and SH. Women were more likely to belong to the subgroup with initially low but stable social connections over time, suggesting a tendency to maintain ties without socially withdrawing. This partially aligns with studies showing that women form more stable, intimate social connections than men and derive greater satisfaction from close social networks as they age ( 28 , 43 ). Another explanation may relate to female social roles in past generations represented in this study. Educational attainment was associated with more favorable trajectories across cognition, daily functioning, social participation, and connections. Longer education may enhance brain adaptability, delaying cognitive disorders by compensating for neuropathological changes, a mechanisms known as cognitive reserve ( 44 ). Longer education can also facilitate access to societal resources (e.g., income, healthier lifestyle habits) that may protect against disability ( 45 ). In our study, cognition, daily functioning, and social participation were closely interconnected, with greater social connections often supporting better cognitive and daily functioning and social participation. Moreover, participants with low cognitive and daily functioning were more likely to belong to both declining as well as increasing social support classes. Prior research indicates that perceived social support, rather than received support, is closely intertwined with disability trajectories, emphasizing the dynamic nature of social support as a potential buffer against the negative impacts of disability ( 31 ). Social participation and connectedness can foster resilience, helping preserve cognitive and physical functioning as individuals age or experience diseases ( 44 , 46 , 47 ). Notably, 70% of our participants showed initially low and gradually decreasing engagement in leisure activities, highlighting the need for further investigation into the long-term health implications of such disengagement and whether and how reduced participation also affects social connectedness. Results of this study also indicate that individuals with gradually declining social participation, low initial connections, and decreasing or increasing support were more likely to exhibit discordant patterns in cognitive and daily functioning or lower functioning on both. As cognitive or daily functioning declines, social support may decrease due to reduced engagement with one’s network, ultimately leading to social withdrawal ( 48 ). However, some individuals may reach out more to their network, or the network may step in when they notice cognitive or daily functioning deteriorating. In this way, the network may compensate by providing additional support, which aligns with our observation of discordant patterns. Of note, although the group with low cognitive functioning and high daily functioning did not differ significantly in MMSE scores compared to the concordantly low group, they showed notable differences in daily functioning as well as in SH indicators. Specifically, social participation was considerably higher in the discordant group. This suggests that, despite cognitive challenges, some individuals can maintain high levels of both daily functioning and social participation (whatever the causal link between them may be) over time, highlighting potential resilience in this subgroup. Future research should focus on characterizing this specific discordant subgroup to better understand the mechanisms that enable older adults to maintain functional independence even in the presence of age-related cognitive decline. Strengths and limitations One of the major strengths of our study is its long follow-up period of up to 12 years, which enabled us to observe changes in a population of older adults over an extended time. Also, the study included detailed and comprehensive data on SH indicators, which allowed us to implement a novel framework that enables a better understanding of SH dynamics in aging populations. The use of GMM is another strength of our study. These models allow for the identification of distinct subgroups sharing similar trajectories, uncovering heterogeneous patterns that might be missed with more commonly used mixed-effects models. This data-driven approach to subject classification may lead to better predictions of functional outcomes than average levels or a-priori categorizations ( 49 ). A key limitation of our study is that we lacked the computational power to perform parallel growth mixture models with multiple health domains simultaneously. This constraint limited our ability to explore how different trajectories of cognitive, physical, and SH factors are interrelated, and whether distinct subgroups exhibit similar patterns across multiple domains. While theoretically feasible, implementing parallel process GMM with three or more outcomes remains a well-recognized computational challenge. Moreover, the majority of the SNAC-K participants included in our study (84%) were highly educated, which may have led to an underestimation of the observed associations, as higher education is typically linked to more favorable cognitive and functional profiles ( 50 , 51 ). Furthermore, our study did not examine the determinants (e.g., lifestyle habits, chronic diseases and multimorbidity, health care, genetics, physical environment) of these trajectories, nor did it assess the extent to which changes in SH indicators from mid- to late-life are related to cognitive impairment and/or disability. Addressing these gaps will be a crucial next step in future research as it could inform on the bidirectionality between SH and cognitive and daily functioning. Finally, while our findings are generalizable to older populations with socio-cultural backgrounds comparable to that of SNAC-K participants, replications of our findings in cohorts with more diverse backgrounds is necessary. CONCLUSIONS The novelty of this study lies in its core goal of understanding how changes in distinct SH dimensions relate in time to changes in cognitive and physical functioning during late life. Our study demonstrates that social participation, connections, and support follow dynamic and distinct trajectories as people age, mirroring patterns seen in cognitive and daily functioning, both key components of cognitive aging. While social participation and connections tend to remain stable, social support levels can decrease or increase over time. For a subgroup of older adults with declining cognition, stable levels of daily functioning and sustained social participation were observed. Determining the drivers of this favorable course may steer sensitive clinical and public health strategies aimed at promoting cognitive health and functional independence among community-dwelling older individuals. Abbreviations MMSE Mini-Mental State Examination IQR interquartile range SD standard deviations. Declarations Acknowledgements We thank the participants and staff involved in data collection and management in the SNAC-K study, and all members of the SHARED Consortium. CONFLICT OF INTEREST Henry Brodaty is or has been an advisory board member or consultant to Biogen, Eisai, Eli Lilly, Medicines Australia, Roche and Skin2Neuron. He is a Medical Advisory Board member for Cranbrook Care. DATA AVAILABILITY Data are from the SNAC-K project, a population-based study that aims to increase our knowledge about the aging process from the physical, social, and mental perspectives (http://www.snac-k.se /). Access to this original data is available to the research community upon approval by the SNAC-K coordination group. Applications for accessing these data can be submitted via the website or through Maria Wahlberg ( [email protected] ) at the Aging Research Center, Karolinska Institutet. CODE AVAILABILITY Code for data analyses is available on request from the corresponding authors, Amaia Calderón-Larrañaga ( [email protected] ) and Anna Marseglia ( [email protected] ). Funding Sources . Data collection for the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was supported by the Swedish Research Council (ongoing/current grant: 2021-00178), the Swedish Ministry of Health and Social Affairs, and the participating County Councils and Municipalities. This project is part of the SHARED Consortium (https://www.shared-dementia.eu/), an EU Joint Program–Neurodegenerative Disease Research (JPND; www.jpnd.eu) project (grant no. 733051082), nationally financed by the Swedish Research Council for Health, Working Life and Welfare (FORTE grant no. 2018-01888) and ZonMw /The Netherlands Organisation for Health Research and Development (grant no. 733051082) and by the Alzheimer’s Society in the UK (grant no. 469). Anna Marseglia received funding from the Swedish Research Council for Health, Working Life and Welfare (FORTE) (grant no. 2024-00210), the Center for Innovative Medicine (CIMED) (grant no. FoUI-988254), Demensfonden (grant no. 4-1759/2024), the Loo och Hans Ostermans stiftelsen (grants no. 2024-02166, 2023-01645, 2022-01255), the Foundation for Geriatric Diseases at Karolinska Institutet (grants no. 2024-02114, 2023-01598, 2022-01268), Gamla Tjännarinor Foundation (grants no. 2023-085), and Karolinska Institutet’s Research Foundation (grants no. 2024-02580). Jean Stafford is supported by an Alzheimer’s Society postdoctoral fellowship (AS-PDF-22-013). Amaia Calderón-Larrañaga received funding from the Swedish Research Council (project number 2021-06398), the Swedish Research Council for Health, Working Life and Welfare (project number 2021-00256), and Karolinska Institutet’s Strategic Research Area in Epidemiology and Biostatistics SFOepi (consolidator bridging grant, 2023). References Vernooij-Dassen M, Verspoor E, Samtani S, Sachdev PS, Ikram MA, Vernooij MW et al (2022) Recognition of social health: A conceptual framework in the context of dementia research. 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Aging Ment Health 26(7):1368–1376 Teas E, Marceau K, Friedman E (2023) Life-course social connectedness: Comparing data-driven and theoretical classifications as predictors of functional limitations in adulthood. Adv Life Course Res 55:100529 Rehnberg J, Huisman M, Fors S, Marseglia A, Kok A (2024) The Association between Education and Cognitive Performance Varies at Different Levels of Cognitive Performance: A Quantile Regression Approach. Gerontology 70(3):318–326. 10.1159/000535717 Epub 2023 Dec 12. PMID: 38086341; PMCID: PMC10911170 Lövdén M, Fratiglioni L, Glymour MM, Lindenberger U, Tucker-Drob EM (2020) Education and Cognitive Functioning Across the Life Span. Psychol Sci Public Interest 21(1):6–41 PMID: 32772803; PMCID: PMC7425377 Additional Declarations The authors declare potential competing interests as follows: Henry Brodaty is or has been an advisory board member or consultant to Biogen, Eisai, Eli Lilly, Medicines Australia, Roche and Skin2Neuron. He is a Medical Advisory Board member for Cranbrook Care. Supplementary Files 20250128SupplementarymaterialAppendix.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6528919","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448007442,"identity":"3a0d3e2a-0260-4dab-9fc4-4c31b8fce81c","order_by":0,"name":"Anna Marseglia, PhD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJADxgMJDDYGbCTpAWpJI1ULA8NhA4Kq+Gf3HvzAmGOXL9/efuHAg5rzxnwM7A8f4NMicedcsgTjtmTLDWfOFBxIOHbbjI2Bxxi/VTdyDIBamA0MJHISDiSw3bYBamGTwKdD/kaO8Q/GbfUG8vPfALX8OwfUwv78Bz4tBjdyzIC2AH19g/3AgcS2A0CHMZjhdZchUItF4rbjBgZnchgOJPYlG7Mx8xjjdZgc0GE3Pm6rNpBvP/7w4Y9vdobz29sffsBrDQgkgEkeaDgxE1QPB+wPiFc7CkbBKBgFIwoAAO/0SeQgTzWCAAAAAElFTkSuQmCC","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Marseglia","suffix":"PhD"},{"id":448007443,"identity":"9c206b1d-0ff9-4b39-b78b-9fcb5be8602a","order_by":1,"name":"Eline Verspoor","email":"","orcid":"","institution":"Radboud university medical center","correspondingAuthor":false,"prefix":"","firstName":"Eline","middleName":"","lastName":"Verspoor","suffix":""},{"id":448007444,"identity":"a9ebbeda-aa82-476b-a913-0a966aefca34","order_by":2,"name":"Marieke Perry","email":"","orcid":"","institution":"Radboud university medical center","correspondingAuthor":false,"prefix":"","firstName":"Marieke","middleName":"","lastName":"Perry","suffix":""},{"id":448007445,"identity":"a371b79a-0c19-4170-9f46-ea449f78d99d","order_by":3,"name":"Myrra Vernooij-Dassen","email":"","orcid":"","institution":"Radboud university medical center","correspondingAuthor":false,"prefix":"","firstName":"Myrra","middleName":"","lastName":"Vernooij-Dassen","suffix":""},{"id":448007446,"identity":"0a8e3e55-af18-478d-8715-b6e3e0de4355","order_by":4,"name":"Jeannie-Marie S Leoutsakos","email":"","orcid":"","institution":"Johns University Hopkins School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeannie-Marie","middleName":"S","lastName":"Leoutsakos","suffix":""},{"id":448007447,"identity":"72730825-0d36-4152-b898-4371725e89f9","order_by":5,"name":"Henry Brodaty","email":"","orcid":"","institution":"Centre for Healthy Brain Ageing (CHeBA), UNSW Sydney","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Brodaty","suffix":""},{"id":448007448,"identity":"10816481-4776-4648-b759-eca8582a4c19","order_by":6,"name":"Jean Stafford","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"Stafford","suffix":""},{"id":448007449,"identity":"024ecc06-b7f3-4f35-ade2-88dab5b26ac4","order_by":7,"name":"Arfan Ikram","email":"","orcid":"","institution":"Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Arfan","middleName":"","lastName":"Ikram","suffix":""},{"id":448007450,"identity":"aa24de3a-15d5-4dc4-a290-6336283ebf5a","order_by":8,"name":"Joanna Rymaszewska","email":"","orcid":"","institution":"Wroclaw University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Rymaszewska","suffix":""},{"id":448007451,"identity":"5e0f396b-01fd-41d6-81de-665ff736478a","order_by":9,"name":"Anna-Karin Welmer","email":"","orcid":"","institution":"Aging Research Center, Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Anna-Karin","middleName":"","lastName":"Welmer","suffix":""},{"id":448007452,"identity":"456b0e8a-ebbe-4db7-b0ce-75536fb9f5db","order_by":10,"name":"Karin Wolf-Ostermann","email":"","orcid":"","institution":"University of Bremen","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Wolf-Ostermann","suffix":""},{"id":448007453,"identity":"5b3e9e45-416e-46a9-9222-285a6114216b","order_by":11,"name":"Frank J. Wolters","email":"","orcid":"","institution":"Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"J.","lastName":"Wolters","suffix":""},{"id":448007454,"identity":"31e23064-0f4e-4f73-a5e1-5240bc07949d","order_by":12,"name":"Amaia Calderón-Larrañaga","email":"","orcid":"","institution":"Aging Research Center, Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Amaia","middleName":"","lastName":"Calderón-Larrañaga","suffix":""},{"id":448007455,"identity":"288b5b7d-dce0-4e4d-9704-0ae100b89f02","order_by":13,"name":"René J F Melis","email":"","orcid":"","institution":"Radboud university medical center","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"J F","lastName":"Melis","suffix":""}],"badges":[],"createdAt":"2025-04-25 12:26:44","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6528919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6528919/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81503061,"identity":"b95c14db-cb6c-435b-b3a9-f887f7997b1c","added_by":"auto","created_at":"2025-04-28 04:44:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe class-specific average growth for the final growth models for the five outcomes as indicated on the y-axis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe solid lines show the average trajectories for class 1 (blue), class 2 (red), and 3 (yellow, only for social support) by age at baseline (60 - 70 “sexagenarians”, 71 - 80 “septuagenarians”, and ≥81 years “octogenarians”). The shaded band around the solid lines indicate the proportional size of the classes, when assigning participants to a class based on the most likely class: a wider shaded area indicates that a larger proportion of the population is assigned to that specific class.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6528919/v1/de97bc4747845003d539d57e.jpeg"},{"id":81503862,"identity":"97bd07df-797b-4302-a262-df84bd51307f","added_by":"auto","created_at":"2025-04-28 05:00:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2300907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6528919/v1/85d3af8f-88c2-44ee-8778-63715d089ce9.pdf"},{"id":81503063,"identity":"6205603a-02d9-4883-9c56-c314d1906012","added_by":"auto","created_at":"2025-04-28 04:44:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1184111,"visible":true,"origin":"","legend":"","description":"","filename":"20250128SupplementarymaterialAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6528919/v1/8dff8692e42e14ecd8f884d2.docx"}],"financialInterests":"The authors declare potential competing interests as follows: Henry Brodaty is or has been an advisory board member or consultant to Biogen, Eisai, Eli Lilly, Medicines Australia, Roche and Skin2Neuron. He is a Medical Advisory Board member for Cranbrook Care.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTrajectories of social health, cognitive, and daily functioning in community-dwelling older adults\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIndividuals do not age in isolation but interact with their social environments. The concept of Social Health (SH) allows to structure these interactions in relation to health and disease, capturing variation in social health at the personal level, as well as the level of one\u0026rsquo;s social environment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). At the individual level, SH involves the fulfillment of social roles, compliance with social norms, autonomy, and social engagement in activities that are physically and cognitively stimulating. At the social environment level, SH encompasses the frequency of social relationships, size/density/type of one\u0026rsquo;s social network (structure), the roles of the social network, i.e., emotional support and instrumental aid (function), and the appraisal of relationship quality, i.e., satisfaction and loneliness (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have linked various indicators of poor SH to age-related conditions, including cognitive disorders (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), chronic medical conditions (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), depression (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). While these studies cover indicators like social disengagement, isolation, lack of social support, and loneliness, they typically examine SH unidimensionally, focusing on isolated SH factors, without considering their interconnectedness. SH is multidimensional, characterized by a complex interplay of intertwined and co-dependent social factors that collectively influence health. Moreover, most studies overlook changes in SH over the lifespan, despite its dynamic nature and the potential interplay between mid-to-late life SH changes and critical shifts in cognitive and physical functioning (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSH plays a crucial role, especially when cognitive disorders emerge, as these conditions can disrupt an individual\u0026rsquo;s relationship with their social environment, just as the social environment can influence the individual\u0026rsquo;s health, including cognitive health. Reduced social engagement, fewer social contacts, and weaker social networks have been linked to faster rates of cognitive decline and increased dementia risk (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The Lancet Commission attributed 5% of global dementia cases to late-life social isolation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Conversely, strategies targeting SH factors (e.g., encouraging social participation) may help reduce dementia risk and slow progression by supporting individuals in leveraging their remaining capacities (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen cognition becomes impaired, individuals cannot perform as well in their daily lives. Functional impairment may both stem from and contribute to deterioration in SH. Few studies, especially longitudinal ones, have explored the bidirectional links between SH factors and functional impairment. For instance, community-based studies have found strong associations of factors like separation/divorce, social isolation, reduced engagement in community activities, and feelings of loneliness with functional impairment, disability, or physical frailty (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Despite these findings, our understanding of how different dimensions of SH evolve over time along with cognitive and functional changes remains limited. Understanding simultaneous patterns of change across different social, cognitive and daily functioning indicators may allow us to identify factors linked to concordant versus discordant trajectories over time, which may in turn inform and promote successful aging strategies.\u003c/p\u003e \u003cp\u003eThis study aimed to identify homogeneous subgroups (i.e., classes) of individuals based on changes in SH indicators, cognitive and daily functioning over time. We also intended to explore concordant versus discordant trajectories of cognitive and daily functioning, and the sociodemographic and SH factors linked to such (in)congruences.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003e2.1 Setting and study population.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a community-based longitudinal cohort study in Stockholm, were used. Initiated in 2001, SNAC-K involved stratified sampling by eleven age groups. A random sample of 4590 older adults, aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, living at home or institutions, were asked to participate, with 3363 individuals participating in the baseline survey (March 2001 to June 2024; 73% participation rate). The younger age-cohorts (60, 66, and 72 years) were followed every six years and the older age-cohorts (\u0026ge;\u0026thinsp;78 years) every three years. At each wave, participants underwent comprehensive clinical and behavioral assessments with trained physicians, nurses, and neuropsychologists. Data on sociodemographic factors (chronological age, sex, education), SH, and cognitive and daily functioning were collected through structured interviews, self-administered questionnaires, and neuropsychological testing.\u003c/p\u003e \u003cp\u003eIn the current study, we included data from the first five data-collection waves of SNAC-K, up to end of 2015. We included participants without dementia at baseline who completed the Mini-Mental State Examination (MMSE) (n\u0026thinsp;=\u0026thinsp;3039). Additionally, participants with neuro-psychiatric disorders (n\u0026thinsp;=\u0026thinsp;40), living in a nursing home (n\u0026thinsp;=\u0026thinsp;36), or missing data on all SH variables (n\u0026thinsp;=\u0026thinsp;115) were excluded. This resulted in a final sample of 2848 participants, followed over 15 years.\u003c/p\u003e \u003cp\u003eEach wave of SNAC-K data collection was approved by the Regional Ethical Review Board in Stockholm or the Swedish Ethical Review Authority, and written informed consent was obtained from participants or their next of kin.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of social health indicators\u003c/h2\u003e \u003cp\u003eDetailed interview protocols, questionnaires, and operationalizations of SH individual- (social participation) and environment-related (connections and support) indices have been previously published. Please, refer to Marseglia et al (Appendices A and B) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and Marseglia et al. (Appendix B) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) in Supplementary materials online.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2.1 Social participation\u003c/h3\u003e\n\u003cp\u003e At each examination wave, participants reported the frequency (0\u0026thinsp;=\u0026thinsp;weekly, 1\u0026thinsp;=\u0026thinsp;monthly, 2\u0026thinsp;=\u0026thinsp;less frequently, 3\u0026thinsp;=\u0026thinsp;never) of their engagement in various leisure activities over the past 12 months. Activities were predominantly mental (reading, painting/drawing/working with clay/pottery, playing chess/card games, musical instrument, listening to music, using the internet/playing computer games), physical (gardening, hiking in the forest/pick berries or mushrooms, hunting/fishing, doing car/mechanical or home repairs, regular light-to-intense physical exercise), and social (attending cinema/theater/concerts, sport events, museums/art exhibitions, restaurants/bar/caf\u0026eacute;s, bingo, dancing, church service, travelling, volunteering, study circles/courses, and other social meetings). Given that each activity often covers varying degrees of the other dimensions, we used a procedure developed earlier to rate each activity\u0026rsquo;s proportion of social, mental, and physical components (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 78 raters (mean age\u0026thinsp;=\u0026thinsp;77\u0026thinsp;\u0026plusmn;\u0026thinsp;7 years; 67% female; mean education\u0026thinsp;=\u0026thinsp;14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 years) scored all leisure activities on their social, mental, and physical components using a 4-point scale (i.e., 0\u0026thinsp;=\u0026thinsp;not at all to 3\u0026thinsp;=\u0026thinsp;to a big extent). We created a social participation index in three sequential steps: (a) multiplying the frequency of the activity by the average social component weight for each activity), (b) summing the obtained scores for all activities, and (c) Z-standardizing the summed scores using the baseline mean and SD of the cognitively unimpaired SNAC-K participants. Higher scores indicated greater engagement in socially demanding activities. Follow-up scores were calculated using baseline means and standard deviations. The z-scores were rescaled to an index with mean of 100 and standard deviation of 15, for easier interpretation of the growth model estimates.\u003c/p\u003e\n\u003ch3\u003e2.2.2 Social connections and support\u003c/h3\u003e\n\u003cp\u003eA social connection index was derived from the following items: relationship status, living arrangement, the number of living children, frequency of direct contact, frequency of remote contact, and social network size (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A social support index was generated through items including satisfaction with contacts, perceived material support, perceived psychological support, and sense of affinity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Raw scores were z-standardized and averaged into the respective indices, with scores above 0 indicating more social connections or higher perceived social support compared to the study population average. The same procedure was repeated at follow-ups, using baseline means and standard deviations for Z-standardization. The z-scores were rescaled to an index with mean of 100 and standard deviation of 15.\u003c/p\u003e\n\u003ch3\u003e2.3 Assessment of cognition\u003c/h3\u003e\n\u003cp\u003eThe MMSE was used as a measure of global cognitive functioning. MMSE scores range from 0 to 30, with higher scores indicating better cognitive performance.\u003c/p\u003e\n\u003ch3\u003e2.5 Assessment of daily functioning\u003c/h3\u003e\n\u003cp\u003eDaily functioning was assessed at all waves using the basic (ADL) and instrumental (IADL) activities of daily living scales (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For ADL (score range 0\u0026ndash;6), a point was assigned when the person was unable to manage basic personal needs independently, including bathing, dressing, toileting, continence, transferring/ambulating, and eating. For IADL (score range 0\u0026ndash;8), a point was assigned when the person was unable to perform more complex tasks independently such as using the telephone, grocery shopping, food preparation, housekeeping, laundry, using public transportation, handling finances, and taking medications (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Items from both scales were combined into a single index, reflecting the number of ADL and IADL tasks on which an individual was dependent (range 0\u0026ndash;14). For easier interpretation, this total index was reverse coded so that higher scores indicated better daily functioning.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Other study variables\u003c/h2\u003e \u003cp\u003eInformation on chronological age (years), biological sex (female versus male), and educational attainment (elementary, high school, or university) was obtained at baseline through structured nurse interview. The operationalization of the dementia clinical diagnosis within SNAC-K was thoroughly detailed earlier (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Briefly, at each examination wave, two independent diagnoses were made by an examining physician and a reviewing physician, following the Diagnostic and Statistical Manual of Mental Disorders-4th Edition criteria. In cases of disagreement, an external neurologist made the final decision. For participants who died between visits without a dementia diagnosis, additional information was obtained from clinical charts, medical records, and the Swedish National Cause of Death Register. Dementia at baseline was used as an exclusion criterion in the present study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.7 Data analysis\u003c/h3\u003e\n\u003cp\u003eThe trajectories for social participation, social connections, social support, cognitive, and daily functioning were described accounting for age. For each of the five health domains, a stepwise approach was used to determine the best model fit and number of classes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst, we conducted 1-class latent growth curve analyses (LGCA), one for each separate domain as a function of SNAC-K wave (0\u0026thinsp;=\u0026thinsp;baseline, 3-, 6-, 9-, and 12-year follow-up) to assess whether domains changed linearly or quadratically over time and to identify necessary random effects. Interaction terms between baseline age centered at 60 years (continuous variable) and study wave were included in the models to account for baseline age in the growth curves. We also examined the influence of freely estimating the residual variance per wave versus constraining it to be equal across time points. Next, using the 1-class latent growth curve identified model, we performed latent class growth analysis (LCGA) where models with 2, 3, and 4 classes were fitted for each health domain. The determination of the number of growth classes was done twice: once constraining random effects to zero and next allowing for an overall random intercept. Finally, we fitted growth mixture models (GMM) with the identified number of classes through LCGA, adding random effects to allow variance and covariance to vary within each class. The following random effects were added stepwise: (a) overall random intercept, (b) overall random intercept, linear slope and covariance, (c) class-specific random intercept, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) class-specific random intercept, linear slope and covariance. Model fit was evaluated using Akaike information criteria (AIC), Bayesian information criteria (BIC), Lo-Mendel-Rubin test, entropy score, class size, and class probability. Visual checks ensured selection of the most parsimonious model. Data were assumed to be missing at random. For LCGA and GMM models, we used 500 random-starts and 20 iterations.\u003c/p\u003e \u003cp\u003eAssociations between sociodemographic factors (age, sex, and education) and class membership were examined using a three-step method for proximal variables to account for the fact that class membership is a latent rather than an observed variable (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo examine concordance across the five health domain trajectories, we used a multiple pseudo-class drawing method to compute pairwise odds ratios (OR) between assigned growth classes. This method was also used to study differences in sociodemographic and SH indicators between concordant and discordant trajectories for cognitive and daily functioning. This method accounts for latent class membership by multiple imputing the latent growth class using class membership probabilities. We imputed 20 times, performed logistic regressions for each imputation set that we combined using Rubin\u0026rsquo;s rule 25).\u003c/p\u003e \u003cp\u003eThe LGCA, LCGA, and GMM analyses were conducted using Mplus Version 8.4 and the MplusAutomation package in R (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Data preparation and processing were done in R. We followed the \u003cem\u003eGuidelines for Reporting on Latent Trajectory Studies\u003c/em\u003e to describe data analysis and results (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of the study participants\u003c/h2\u003e \u003cp\u003eThe sample included 2848 participants, with a mean age of 73 (SD\u0026thinsp;=\u0026thinsp;10) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority were female (63%) and highly educated (84% completed high school or university). Baseline cognitive and daily functioning were relatively preserved, with a median MMSE score of 29 (IQR\u0026thinsp;=\u0026thinsp;2.0) and a daily functioning score of 14 (IQR\u0026thinsp;=\u0026thinsp;0). Index scores for SH indicators were higher than average in sexagenarians, around average in septuagenarians, and lower than average in the octogenarians. Participants\u0026rsquo; characteristics for all five SNAC-K waves can be found in \u003cb\u003eAppendix A\u003c/b\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\u003eBaseline characteristics of the study participants by age decade.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,848\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSexagenarians N\u0026thinsp;=\u0026thinsp;1,240\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeptuagenarians\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;864\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOctagenarians\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;744\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,781 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e698 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e552 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e531 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,006 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e620 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,409 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405 (54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily functioning, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participation index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial connections index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of trajectories of SH, cognitive, and daily functioning\u003c/h2\u003e \u003cp\u003eFor all health domains, the best fit was a two-class quadratic growth model, except for social support, which required a three-class model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Fit statistics for the selected growth mixture models, including the checks for local maxima (OPTSEED option in Mplus) are presented in \u003cb\u003eAppendix B\u003c/b\u003e. Further details on LGCA, LCGA and GMM model selection are provided in \u003cb\u003eAppendix C\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\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\u003eThe parameter estimates and class size by class for the final growth models for the five outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCognitive functioning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDaily functioning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSocial participation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSocial connections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eestimate (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eestimate\u003c/p\u003e \u003cp\u003e(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eestimate (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eestimate (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eestimate (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed intercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.57 (0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.063 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121.016 (0.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e107.606 (0.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e102.874 (0.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.045 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.019 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.74 (0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.251 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.142 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed linear slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.109 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.018 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.455 (0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.136 (0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.213 (0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed linear slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.017 (0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.005 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.019 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed quadratic slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.045 (0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.016 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.017 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed quadratic slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.001 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.002 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.002 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom intercept variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariance RI and RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom linear slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom quadratic slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass size, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2599 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2687 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e861 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2020 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2693 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed intercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.11 (0.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.865 (0.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e104.091 (0.594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.213 (1.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e101.02 (1.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.045 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.019 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.74 (0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.251 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.142 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed linear slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.883 (0.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.214 (0.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567 (0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.125 (0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.015 (0.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed linear slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.017 (0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.005 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.019 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed quadratic slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.086 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005 (0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05 (0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002 (0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.465 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed quadratic slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.001 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.002 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.002 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom intercept variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariance RI and RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom linear slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom quadratic slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass size, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1987 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e828 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e58 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed intercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e75.425 (0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.142 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed linear slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.985 (0.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed linear slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.019 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed quadratic slope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.449 (0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (+\u0026thinsp;1 year) * Fixed quadratic slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom intercept variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariance RI and RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom linear slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom quadratic slope variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass size, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e97 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual error time point 1 variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e102.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual error time point 2 variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e42.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual error time point 3 variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual error time point 4 variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual error time point 5 variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor cognitive functioning, most participants (91%) were assigned to a class characterized by high baseline cognitive performance and relatively stable MMSE score over time (class 1; blue in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A minority (9%) were assigned to a class with an accelerated MMSE decline (class 2; red in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Within classes, participants with a higher baseline age had a lower initial MMSE score (β = -0.045 MMSE point per year increase in baseline age) and accelerated rate of decline (linear decline of -0.007/year and quadratic decline of -0.001/year\u003csup\u003e2\u003c/sup\u003e in baseline age) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor daily functioning, 95% of participants were assigned to a class with relatively preserved daily functioning across all examinations (class 1; blue in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The remaining 5% were assigned to class 2 (red in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) with considerably impaired average daily functioning at baseline (score of 8.9 [SE 0.28] for a 60-year-old individual) and a steeper decline (linear decline of -0.21/year [SE 0.22] for a 60-year-old person). Within classes, participants with a higher baseline age had a lower initial daily functioning (β = -0.019 point per year increase in baseline age) and accelerated rate of decline (linear decline of -0.01/year and quadratic decline of -0.001/year\u003csup\u003e2\u003c/sup\u003e in baseline age).\u003c/p\u003e \u003cp\u003eFor the SH indicators of social participation and connections (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, C \u003cb\u003eand D\u003c/b\u003e), both classes showed relatively stable scores over time: one class above average and one below. However, for social participation, the majority (70%) were in the class with the lowest scores (class 1; red in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), indicating poor social engagement. In contrast, for social connections, the majority (71%) were in the class with higher scores (class 2; blue in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating larger social connections. Baseline age was inversely associated with baseline social participation and connection scores (β: -0.74 [SE 0.02] and \u0026minus;\u0026thinsp;0.25 [SE 0.03]) and\u0026mdash;except for the linear slope in social connections\u0026mdash;also with the linear and quadratic slope parameters (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating lower initial levels of SH indicators and faster declines with increasing participants\u0026rsquo; age at baseline.\u003c/p\u003e \u003cp\u003eFor the SH indicator of social support, the largest class, comprising 95% of participants (class 1; blue in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), exhibited stable social support over time. Class 2 (2% of participants; red in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) showed declining scores in social support, whereas class 3 (3% of participants; yellow in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) showed increasing scores. Appendix D (top row) shows that the estimated random intercepts\u0026mdash;reflecting the extent to which participants assigned to the specific class deviate at the start from the class-average trajectories\u0026mdash;are particularly large for social connections and support, and smaller for cognitive and daily functioning. In contrast, the random linear slope variance\u0026mdash;indicating deviations from the average slope within a class\u0026mdash;is greater for daily functioning than cognition, social connections, and support.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Associations between class membership and sociodemographic factors\u003c/h2\u003e \u003cp\u003eHigher baseline age was associated with a greater likelihood of belonging to class 2 (fast rate of decline) rather than class 1 (more stable) for both cognitive and daily functioning (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar trends were observed for social support, where higher age was associated with membership in both class 2 (decreasing support over time) and class 3 (increasing support over time). Female sex was associated with higher odds of belonging to class 2 of social connections (OR 1.36, p\u0026thinsp;=\u0026thinsp;0.01), characterized by lower social connections that remained stable over time. No significant associations were found between sex and the other four health domains. Higher educational attainment was linked to a reduced likelihood of belonging to class 2 (faster decline) rather than class 1 (stable) for cognitive and daily functioning. Additionally, participants with higher education had lower likelihoods of belonging to the classes with lower scores for social participation and connections. Regarding social support, higher educational attainment decreased the likelihood of belonging to the increasing support trajectory (class 3; OR 0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between class membership for each outcome and sociodemographic factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCognitive functioning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDaily functioning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSocial participation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSocial connections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2 vs Class 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (years, +\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.09\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.13\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.97-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (0.9\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.59\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.8\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.36 (1.08\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.34 (0.72\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, Intermediate (vs Education, Lower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.38\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.43-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31 (0.20\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64 (0.47\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85 (0.4\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, High (vs Education, Lower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.17\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.27\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 (0.09\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31 (0.22\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.82 (0.34\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 3 vs Class 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline age (years, +\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.08 (1.05\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.83 (0.51\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, Intermediate (vs Education, Lower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.35 (0.78\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, High (vs Education, Lower)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.38 (0.16\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Concordance and discordance across SH, cognitive and daily functioning trajectories\u003c/h2\u003e \u003cp\u003eBeing in the declining class for cognitive functioning was associated with being in the declining class for daily functioning (OR 7.3 [95%-CI 4.8\u0026ndash;11.1)] (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Participants with lower baseline social participation or connections (classes 2) were more likely to follow fast cognitive (ORs 2.3 [95% CI 1.5\u0026ndash;3.6] and 1.9 [95% CI 1.3\u0026ndash;2.6]) and functional (ORs 7.0 [95% CI 2.9\u0026ndash;17.0] and 2.0 [95% CI 1.3\u0026ndash;3.1]) declines. Both decreasing and increasing social support classes were associated with higher odds of being in the declining cognitive class. However, only decreasing social support was related to the declining class for daily functioning (OR 4.5 [95% CI 2.4\u0026ndash;8.4]). Increasing and decreasing classes of social support were also associated with the lower social connections and/or social participation classes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcordance across cognitive, physical functional, and SH trajectories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive functioning, class 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily functioning, class 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial participation, class 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial connections, class 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds ratio (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds ratio (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds ratio (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds ratio (95%-CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive functioning, class 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily functioning, class 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.31 (4.8-11.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial participation, class 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38 (1.56\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.01 (2.89\u0026ndash;17.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial connections, class 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.34\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95 (1.27-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.93 (2.32\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial support, class 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31 (1.27\u0026ndash;4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.46\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.99 (1.17\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.71 (1.09\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial support, class 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.52 (2.75\u0026ndash;7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.48 (2.38\u0026ndash;8.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39 (1.61\u0026ndash;7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.15 (1.96\u0026ndash;5.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CI, confidence intervals; OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Concordance and discordance in cognitive and daily functioning trajectories: distribution of sociodemographic and SH factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn our study, 2491 (88%) participants had stable cognitive and daily functioning over time (concordant \u0026ldquo;high cognitive \u0026amp; daily functioning\u0026rdquo;) and 47 (2%) had declining cognitive and daily functioning (concordant \u0026ldquo;low cognitive \u0026amp; daily functioning\u0026rdquo;). Additionally, 86 (3%) participants had stable cognition and declining daily functioning (discordant \u0026ldquo;high cognitive \u0026amp; low daily functioning\u0026rdquo;), whereas 194 (7%) had declining cognition and stable daily functioning (discordant \u0026ldquo;low cognitive \u0026amp; high daily functioning\u0026rdquo;).\u003c/p\u003e \u003cp\u003eParticipants in the concordant \u0026ldquo;high cognitive \u0026amp; daily functioning\u0026rdquo; group (most optimal) were the youngest, with an average age of 71 years, and with the lowest proportion of women along with the highest proportion of participants with university education (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Conversely, those in the concordant \u0026ldquo;low cognitive \u0026amp; daily functioning\u0026rdquo; group (least favorable) were the oldest (mean age of 87 years) and included the highest proportions of women and participants with elementary education. A higher proportion of participants within the concordant high functioning group were in the best functioning class (class 1) for social support, social participation and connections than in the other three groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of sociodemographic factors and SH indicators across four groups with concordance or discordance in class membership for cognitive and daily functioning trajectories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConcordant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDiscordant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh cognitive \u0026amp; daily functioning\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2491 (88.4%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow cognitive \u0026amp; daily functioning\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;47 (1.7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh cognitive \u0026amp; Low daily functioning\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;86 (3.0%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow cognitive \u0026amp; High daily functioning\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;194 (6.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, female, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,531 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,226 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily functioning, score, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participation index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial connections index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrowth class\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e818 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (gradual decline)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,673 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial connections, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,815 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (initially lower, stable slope)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e676 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,386 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (declining)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (increasing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Class characteristics for cognitive and daily functioning concordance are described by assigning participants to the class with the highest individual class probability. The pseudoclass membership method with 20 imputations was used to derive P-values from one-way ANOVAs or Chi squares tests as appropriate which were combined using Rubin's Rule.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e For cells with expected count below 5,, p-value were calculated using Monte Carlo simulation with 2000 replications via the \u0026ldquo;simulate.p.value()\u0026rdquo; function..\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlmost all participants within declining daily functioning\u0026mdash;either with stable or declining cognitive functioning\u0026mdash;were in the group with the least favorable social participation pattern (class 2; 98% of concordant \u0026ldquo;low cognitive and daily functioning\u0026rdquo; and 95% of discordant \u0026ldquo;high cognitive \u0026amp; low daily functioning\u0026rdquo;). Among those with declining cognition, 19% of participants with simultaneous preserved daily functioning were assigned to the favorable class of social participation; this proportion dropped to 2.1% when coupled with declining daily functioning (the lowest across the 4 groups).\u003c/p\u003e \u003cp\u003eFor social support, the highest proportion of participants with declining cognition but relatively stable daily functioning was in the declining social support group (class 2, 6.2%). Nobody from the concordant \u0026ldquo;low cognitive \u0026amp; daily functioning\u0026rdquo; was assigned to the declining social support class. On the contrary, this group had the highest proportion of participants assigned to the increasing social support group (class 3, 15%), followed by those with declining daily functioning and relatively stable cognition (12%).\u003c/p\u003e p\u003eClasses 2 for social participation (gradual decline) and connections (initially lower, stable decline) were associated with higher odds non-favorable concordant and discordant cognitive and daily functioning patterns (\u003cb\u003eAppendix E\u003c/b\u003e). Similar results were observed for social support\u0026rsquo;s class 3 (increasing trajectory) and class 2 (declining) for the discordant groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this large population-based cohort of Swedish older adults, we identified two distinct latent groups based on separate trajectories of cognitive functioning, daily functioning, and SH indicators (social participation, connections, and support). One group (class 1) showed relatively preserved function across all health domains over the 15-year follow-up period. The second group (class 2) exhibited a steeper decline over time for cognition, daily functioning, and social support, started lower at baseline and declined gradually for social participation, and had lower baseline social connections but remained stable over time. Social support also exhibited a third group (class 3) that started lower at baseline but increased over time, suggesting enhanced resources from their social environment as they aged. Most SNAC-K participants (70\u0026ndash;95%) were assigned to class 1, the thriving subgroup, except for social participation, where the majority was in the least favorable group (class 2). Our findings highlight that cognitive and daily functioning are interrelated, concordantly but also often discordantly, in older adults. Notably, participants with increasing social support over time were also more likely to show discordant trajectories of low cognition and high daily functioning over time.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe ability of GMM to capture changes in SH indicators over time, beyond average levels, is a key yet underexplored advantage. Existing research has primarily focused on single average trajectories of specific health indicators, limiting cross-study comparisons. Consistent with our findings, a Swedish study observed that social participation remains relatively stable over time, with a notable decline after age 70 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This stability may be attributed to older adults choosing fewer yet more meaningful social activities as they age, maintaining regular engagement (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). An American nationally representative panel study using GMM, identified five latent subgroups of social engagement over a decade (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Four were very similar, showing higher social participation initially with relatively stable or slightly changing trajectories, while one subgroup had lower initial participation and a faster decline. Despite some differences, both Thomas\u0026rsquo; (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and we found relatively stable patterns in social participation, though we failed to detect a fast-declining pattern. Beyond methodological differences (e.g., sample size, study design, follow-up length, operationalization of social participation, statistical method used), discrepancies could be due, but not limited, to socio-cultural, lifestyle habits, and/or intergenerational influences. Moreover, few studies have examined trajectories of social connections or social support in older age, with most focusing on the frequency of social contacts and support at a single point in time (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Previous findings from the SNAC-K cohort have shown that high social connectedness and support can protect older adults from accelerated health decline (in terms of morbidity burden, physical and cognitive function and disability) (\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), cognitive disorders in at-risk individuals (e.g., those with genetic susceptibility or cardiometabolic risk factors) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), emergency care visits (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), injurious falls (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and unplanned hospital admissions (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These results were consistently supported by others (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur findings also show that biological sex and education may influence trajectories of cognitive, daily functioning, and SH. Women were more likely to belong to the subgroup with initially low but stable social connections over time, suggesting a tendency to maintain ties without socially withdrawing. This partially aligns with studies showing that women form more stable, intimate social connections than men and derive greater satisfaction from close social networks as they age (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Another explanation may relate to female social roles in past generations represented in this study. Educational attainment was associated with more favorable trajectories across cognition, daily functioning, social participation, and connections. Longer education may enhance brain adaptability, delaying cognitive disorders by compensating for neuropathological changes, a mechanisms known as cognitive reserve (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Longer education can also facilitate access to societal resources (e.g., income, healthier lifestyle habits) that may protect against disability (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, cognition, daily functioning, and social participation were closely interconnected, with greater social connections often supporting better cognitive and daily functioning and social participation. Moreover, participants with low cognitive and daily functioning were more likely to belong to both declining as well as increasing social support classes. Prior research indicates that perceived social support, rather than received support, is closely intertwined with disability trajectories, emphasizing the dynamic nature of social support as a potential buffer against the negative impacts of disability (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Social participation and connectedness can foster resilience, helping preserve cognitive and physical functioning as individuals age or experience diseases (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Notably, 70% of our participants showed initially low and gradually decreasing engagement in leisure activities, highlighting the need for further investigation into the long-term health implications of such disengagement and whether and how reduced participation also affects social connectedness.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eResults of this study also indicate that individuals with gradually declining social participation, low initial connections, and decreasing or increasing support were more likely to exhibit discordant patterns in cognitive and daily functioning or lower functioning on both. As cognitive or daily functioning declines, social support may decrease due to reduced engagement with one\u0026rsquo;s network, ultimately leading to social withdrawal (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). However, some individuals may reach out more to their network, or the network may step in when they notice cognitive or daily functioning deteriorating. In this way, the network may compensate by providing additional support, which aligns with our observation of discordant patterns. Of note, although the group with low cognitive functioning and high daily functioning did not differ significantly in MMSE scores compared to the concordantly low group, they showed notable differences in daily functioning as well as in SH indicators. Specifically, social participation was considerably higher in the discordant group. This suggests that, despite cognitive challenges, some individuals can maintain high levels of both daily functioning and social participation (whatever the causal link between them may be) over time, highlighting potential resilience in this subgroup. Future research should focus on characterizing this specific discordant subgroup to better understand the mechanisms that enable older adults to maintain functional independence even in the presence of age-related cognitive decline.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOne of the major strengths of our study is its long follow-up period of up to 12 years, which enabled us to observe changes in a population of older adults over an extended time. Also, the study included detailed and comprehensive data on SH indicators, which allowed us to implement a novel framework that enables a better understanding of SH dynamics in aging populations. The use of GMM is another strength of our study. These models allow for the identification of distinct subgroups sharing similar trajectories, uncovering heterogeneous patterns that might be missed with more commonly used mixed-effects models. This data-driven approach to subject classification may lead to better predictions of functional outcomes than average levels or a-priori categorizations (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). A key limitation of our study is that we lacked the computational power to perform parallel growth mixture models with multiple health domains simultaneously. This constraint limited our ability to explore how different trajectories of cognitive, physical, and SH factors are interrelated, and whether distinct subgroups exhibit similar patterns across multiple domains. While theoretically feasible, implementing parallel process GMM with three or more outcomes remains a well-recognized computational challenge. Moreover, the majority of the SNAC-K participants included in our study (84%) were highly educated, which may have led to an underestimation of the observed associations, as higher education is typically linked to more favorable cognitive and functional profiles (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Furthermore, our study did not examine the determinants (e.g., lifestyle habits, chronic diseases and multimorbidity, health care, genetics, physical environment) of these trajectories, nor did it assess the extent to which changes in SH indicators from mid- to late-life are related to cognitive impairment and/or disability. Addressing these gaps will be a crucial next step in future research as it could inform on the bidirectionality between SH and cognitive and daily functioning. Finally, while our findings are generalizable to older populations with socio-cultural backgrounds comparable to that of SNAC-K participants, replications of our findings in cohorts with more diverse backgrounds is necessary.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe novelty of this study lies in its core goal of understanding how changes in distinct SH dimensions relate in time to changes in cognitive and physical functioning during late life. Our study demonstrates that social participation, connections, and support follow dynamic and distinct trajectories as people age, mirroring patterns seen in cognitive and daily functioning, both key components of cognitive aging. While social participation and connections tend to remain stable, social support levels can decrease or increase over time. For a subgroup of older adults with declining cognition, stable levels of daily functioning and sustained social participation were observed. Determining the drivers of this favorable course may steer sensitive clinical and public health strategies aimed at promoting cognitive health and functional independence among community-dwelling older individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and staff involved in data collection and management in the SNAC-K study, and all members of the SHARED Consortium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHenry Brodaty is or has been an advisory board member or consultant to Biogen, Eisai, Eli Lilly, Medicines Australia, Roche and Skin2Neuron. He is a Medical Advisory Board member for Cranbrook Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are from the SNAC-K project, a population-based study that aims to increase our knowledge about the aging process from the physical, social, and mental perspectives (http://www.snac-k.se /). Access to this original data is available to the research community upon approval by the SNAC-K coordination group. Applications for accessing these data can be submitted via the website or through Maria Wahlberg ([email protected]) at the Aging Research Center, Karolinska Institutet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCODE AVAILABILITY \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode for data analyses is available on request from the corresponding authors, Amaia Calder\u0026oacute;n-Larra\u0026ntilde;aga ([email protected]) and Anna Marseglia ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection for the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) was supported by the Swedish Research Council (ongoing/current grant: 2021-00178), the Swedish Ministry of Health and Social Affairs, and the participating County Councils and Municipalities. This project is part of the SHARED Consortium (https://www.shared-dementia.eu/), an EU Joint Program\u0026ndash;Neurodegenerative Disease Research (JPND; www.jpnd.eu) project (grant no. 733051082), nationally financed by the Swedish Research Council for Health, Working Life and Welfare (FORTE grant no. 2018-01888) and ZonMw /The Netherlands Organisation for Health Research and Development (grant no. 733051082) and by the Alzheimer\u0026rsquo;s Society in the UK (grant no. 469).\u003c/p\u003e\n\u003cp\u003eAnna Marseglia received funding from the Swedish Research Council for Health, Working Life and Welfare (FORTE) (grant no. 2024-00210), the Center for Innovative Medicine (CIMED) (grant no. FoUI-988254), Demensfonden (grant no. 4-1759/2024), the Loo och Hans Ostermans stiftelsen (grants no. 2024-02166, 2023-01645, 2022-01255), the Foundation for Geriatric Diseases at Karolinska Institutet (grants no. 2024-02114, 2023-01598, 2022-01268), Gamla Tj\u0026auml;nnarinor Foundation (grants no. 2023-085), and Karolinska Institutet\u0026rsquo;s Research Foundation (grants no. 2024-02580).\u003c/p\u003e\n\u003cp\u003eJean Stafford is supported by an Alzheimer\u0026rsquo;s Society postdoctoral fellowship (AS-PDF-22-013).\u003c/p\u003e\n\u003cp\u003eAmaia Calder\u0026oacute;n-Larra\u0026ntilde;aga received funding from the Swedish Research Council (project number 2021-06398), the Swedish Research Council for Health, Working Life and Welfare (project number 2021-00256), and Karolinska Institutet\u0026rsquo;s Strategic Research Area in Epidemiology and Biostatistics SFOepi (consolidator bridging grant, 2023).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVernooij-Dassen M, Verspoor E, Samtani S, Sachdev PS, Ikram MA, Vernooij MW et al (2022) Recognition of social health: A conceptual framework in the context of dementia research. 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Psychol Sci Public Interest 21(1):6\u0026ndash;41 PMID: 32772803; PMCID: PMC7425377\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Karolinska Institute","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6528919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6528919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eINTRODUCTION:\u003c/strong\u003e Cognitive and functional impairments can both influence and stem from deteriorating social health). However, the interplay between these dimensions while aging remains poorly understood. This study investigated the concordance and discordance of SH, cognitive, and daily functioning trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eWe analyzed 15-year follow-up data (2001-2015) from 2848 initially dementia-free older adults in the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Cognition and daily functioning were assessed with the MMSE and ADL/IADLs. Social health encompassed indices of social participation, connections, and support. Longitudinal trajectories across these five dimensions were identified using latent growth curve analysis, latent class growth analysis, and growth mixture models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eTwo cognitive trajectories—relatively preserved (91%) and fast decline (9%)—and two daily functioning trajectories—stable (95%) and declining (5%)—were identified. For SH, alongside the stable groups, further subgroups included gradually declining social participation (70%) and low initial social connections (29%). Social support showed stable (95%), declining (2%), and increasing (3%) trajectories. Females were more likely to be in the initially low-stable social connections group, whereas higher education was linked to favorable trajectories across almost all dimensions but support. Membership in the lowest class for cognition, daily functioning, social connections and participation showed strong concordance. Yet, increasing social support was associated with low cognition but high daily functioning (pseudo-class’s OR 4.2, 95%CI 2.3–7.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISCUSSION:\u003c/strong\u003e Our findings highlight the crucial role of social health in influencing cognitive and daily functioning, offering new insights into the dynamic interplay between social participation, connections, and support in aging.\u003c/p\u003e","manuscriptTitle":"Trajectories of social health, cognitive, and daily functioning in community-dwelling older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 04:43:57","doi":"10.21203/rs.3.rs-6528919/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"660dd5b4-ca1e-42d6-91f5-24f58cc8b5b4","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47685794,"name":"Geriatrics \u0026 Gerontology"}],"tags":[],"updatedAt":"2025-04-28T04:43:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 04:43:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6528919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6528919","identity":"rs-6528919","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}