
==== Front
J Gerontol A Biol Sci Med Sci
J Gerontol A Biol Sci Med Sci
gerona
The Journals of Gerontology Series A: Biological Sciences and Medical Sciences
1079-5006
1758-535X
Oxford University Press US

38366153
10.1093/gerona/glae048
glae048
THE JOURNAL OF GERONTOLOGY: Medical Sciences
Special Issue: Complex Systems Dynamics and the Aging Process
Special Article
AcademicSubjects/MED00280
AcademicSubjects/SCI00960
Investigating Resilience Through Intrinsic Capacity Networks in Older Adults
https://orcid.org/0000-0002-0710-0069
Koivunen Kaisa PhD Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

https://orcid.org/0000-0002-0863-3916
Lindeman Katja MSc Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

Välimaa Maija MSc Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

https://orcid.org/0000-0002-1604-1945
Rantanen Taina PhD Faculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland

Lipsitz Lewis A MD, FGSA (Medical Sciences Section) Decision Editor
Address correspondence to: Kaisa Koivunen, PhD. E-mail: kaisa.m.koivunen@jyu.fi
10 2024
15 2 2024
15 2 2024
79 10 glae04826 11 2023
02 2 2024
04 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.
2024
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Abstract

Background

The network approach may provide a framework for understanding intrinsic capacity (IC) as a system’s underlying functioning. The system’s resilience to resist functional decline may arise from the interrelationships among system components, that is, body functions or capacities. We applied network analysis to investigate whether the interplay between different intrinsic capacities differs according to age and self-rated health (SRH) in older adults.

Methods

The study sample consisted of a population-based cohort of community-dwelling older adults aged 75, 80, and 85 years (men n = 356 and women n = 469). We quantified 5 IC domains: vitality, locomotion, cognition, psychology, and sensory, using performance-based measurements and questionnaires, and estimated IC networks for 2 age (75 vs 80 and 85 years) and SRH (higher vs lower) groups separately for sexes. Differences in global network properties (eg, density, overall connectivity) and centrality indices were compared between the groups.

Results

Intrinsic capacity network density (ie, the number of edges) was higher in the 80- and 85-year-olds compared to the 75-year-olds, and in the worse compared to the better SRH group in both sexes. However, the differences in edge weights and global strength of the networks were statistically nonsignificant. Walking speed was the most central node in the estimated networks.

Conclusions

With increasing age and health decline, the IC network seems to become denser, which may indicate a loss of system resilience. Walking is a more complex activity than the others requiring the functioning of many subsystems, which may explain why it connects multiple domains in the IC network.

Complex systems approach
Emergent property
Functional reserves
Healthy aging
JYU.Well Wellbeing Research Community of the University of Jyväskylä Juho Vainio Foundation 10.13039/501100004037 Research Council of Finland 10.13039/501100002341 310526 European Research Council 10.13039/501100000781 ERC AdvG 693045
==== Body
pmcIn aging research, the term intrinsic capacity (IC) has long been used to describe a person’s inherent, mainly physical resources (eg, muscle strength) that can be harnessed for functioning and activity in specific contexts. More recently, the term has taken a broader meaning as part of the World Health Organization’s (WHO) Healthy Aging model covering both the physical and mental capacities of an individual (1). Five domains, vitality, locomotion, sensory, cognition, and psychology, have been adopted as key domains of IC to cover the most important aspects of functional aging (2). The objective of the IC concept is to provide means to monitor the multidimensional functional reserves over the life course, but especially in later life, when many capacities tend to decline, which could guide preventive actions even before significant functional limitations.

The operationalization of the IC concept usually relies on forming IC summary scores (3,4). This is problematic, as summary scores lump together complex measures requiring voluntary effort and use of several physiological systems, and more simple functions with fewer underlying organ systems. Such scores do not capture the complex interplay between the 5 domains of IC that together form the elements of functional capacity (2). Neither do sum scores capture the potential changes in the interplay with increasing age and declining health. Earlier studies have shown, for example, that with declining strength reserves, walking capacity becomes more dependent on muscle strength (5), and that sensory impairments increase the risk for cognitive decline and depressive symptoms (6).

Systems theory, specifically the network approach, may provide a framework for understanding the fabric and functioning of the elements of IC in a way that provides more information than the sum of its parts (7,8). According to the network approach, IC can be understood as a complex system and an emergent property arising from the interrelationships among different system components, that is, body functions or capacities. These interactions can be studied through statistical relationships that form edges between the IC components in the network. A better understanding of the structure and interplay between the systems’ elements may allow us to find novel ways to predict and intervene with IC during aging.

In the context of IC research, interesting questions to explore through the network approach are, for example, how different physical and mental capacities interconnect with each other, do certain capacities play a more central role in the IC network, and whether the overall structure of the IC network varies according to age, health, and functional ability status. Previously, body functions have been modeled as a network in the context of health deficit accumulation (ie, frailty) during aging. García-Peña et al. (9) found that self-rated health (SRH) and difficulty walking a block were the most connected and relevant factors in defining the risk of death, and with increasing frailty levels, the number of connections in the health deficit network progressively increased. Also, Gijzel et al. (10) reported higher cross-correlations between time series of self-rated physical, mental, and social health in frail compared to nonfrail older adults. These findings, according to the dynamical systems theory, may signal a loss of resilience in the multidimensional functional capacity system during aging (11). It is, therefore, possible that as physiological resilience mechanisms and health deteriorate, different intrinsic capacities become increasingly mutually dependent and connected. To test this hypothesis, we applied network analysis to study whether the structure of IC networks differs according to age and SRH. We also investigated whether some specific intrinsic capacities play a more central role in the network connecting different IC domains.

Method

Study Design and Participants

We present cross-sectional analyses using baseline data of the observational Active Aging—Resilience and external support as modifiers of the disablement outcome (AGNES) cohort collected in 2017–2018. A detailed description of the study protocol, recruitment, and participation has been presented elsewhere (12,13). In brief, a population-based sample of 1 021 75-, 80-, and 85-year-old community-dwelling men and women living in the city of Jyväskylä, Central Finland, participated in an interview, and of them, 910 attended the assessments in the research center, where a health examination and functional capacity measurements were carried out. Inclusion criteria were willingness to participate and ability to communicate. The analyses of the present study comprise all the participants for whom complete data on subjective age and IC variables were available (men, n = 356 and women, n = 469).

Study Variables

Intrinsic capacity was measured with variables that fit with the WHO’s conceptualization of IC consisting of 5 domains:

We measured vitality with muscle strength. Maximal isometric hand grip strength and knee extension strength were measured using an adjustable dynamometer chair (Good Strength; Metitur Oy, Palokka, Finland), and the results were expressed in Newtons (N). Both muscle strength measurements were performed on the side of the dominant hand in a sitting position with the lower back supported. Hand grip strength was measured using a dynamometer attached to the arm of the chair. Knee extension strength was measured at an angle of 60° from the fully extended leg toward flexion. After a practice trial, the test was performed at least 3 times with a 1-minute rest period between trials until there was no further improvement, and the highest value was recorded (14).

Locomotion was measured with walking speed and chair rises. Ten-meter maximal walking speed was assessed in the laboratory corridor using photocells. Five meters were allowed for acceleration, and the participants were instructed to continue a few meters past the finish line. Chair rises were assessed at the participants’ homes during the interview. The participants were instructed to perform 5 chair stands as quickly as possible. The performance time (in seconds) was recorded and used in the analyses.

The sensory domain was measured with hearing and visual acuity. Hearing acuity was assessed with a pure-tone audiometric air conduction test for frequencies 0.125, 0.25, 0.5, 1, 2, 4, and, 8 kHz for both ears separately using a screening audiometer (Oscilla USB-330, Inmedico A/S, Hovedstaden, Denmark). The hearing assessment was conducted in a quiet office room at the research center by a trained research assistant. Peltor noise-reducing headphones with a noise reduction rating of 21 dB and the automatic Hughson-Westlake protocol with intensity limits of −10 to 95 dB were used. If the pure-tone threshold could not be measured at a given frequency, a value of 130 dB was given according to the recommendations of the British Society of Audiology (15). In the present analyses, we used the better ear hearing threshold level (in dB) over the speech frequencies of 0.5–4 kHz with lower results indicating better hearing acuity. Distance visual acuity was assessed at the research center with an illuminated Landolt ring chart (Oculus 4512) at a 5-m distance binocularly with and without participants’ spectacles. Visual acuity values are presented in decimals ranging from 0.125 to 2, with higher results indicating better vision. In the present analyses, we used the best result as a measure of vision.

Cognition was measured with 3 tests: A modified version of the Word Fluency Test was used to test phonemic verbal fluency reflecting semantic memory and verbal ability (16,17). The participants named as many Finnish words as possible starting with the letter K for 3 minutes (instead of the original 5 minutes). The total score is the number of acceptable words. The Digit Symbol coding task was used to measure processing speed and short-term visual memory (Wechsler Adult Intelligence Scale—Revised18). In the test, the participant drew the correct symbols below their corresponding numbers by using a number-to-symbol coding key. The test time limit was 90 seconds and the total score is the number of correct symbols in the correct order (maximum score of 65). Reaction time, which measures motor and mental response speeds as well as response impulsivity and accuracy, was assessed with a complex finger movement task using an apparatus constructed in-house at the University of Jyväskylä (19). The participant was seated with the index finger of the dominant hand on the rest button in the middle of a row of buttons. The task was to react and move the finger as soon as possible onto the button closest to the light when it was switched on. Reaction time and movement time were measured in milliseconds. The test was repeated 12 times. The average reaction and movement times of the last 5 correctly performed tasks were summed and included in the analyses.

The Psychology domain measures consisted of depressive symptoms and self-rated stress-coping ability assessments. We assessed depressive symptoms with the 20-item Center for Epidemiologic Studies Depression scale (CES-D) scale (20), which focuses on symptoms experienced in the past week. The total score ranges from 0 to 60, with higher scores indicating more severe symptoms. Self-rated stress-coping ability was assessed using the Connor–Davidson Resilience Scale (CD-RISC) consisting of 10 items. The items measure the perceived ability to adapt positively to changes in life (21,22). The response categories of each item range from 0 (not true at all) to 4 (true nearly all the time) and the sum score ranges from 0 to 40 with higher scores indicating higher self-rated stress-coping ability. The Finnish version of the scale has shown good measurement properties in most of the psychometric domains (23). We included only participants with at most 3 missing answers on the scale. The scores for missing items were imputed for 12 participants based on the mean of their responses to the other items.

The age variable had 3 categories: 75, 80, and 85 years. However, for the network group comparisons, the variable was coded as dichotomous, combining the 2 oldest age groups due to small group sizes of 85-year-olds in both sexes.

Self-rated health was assessed with a single question on current general health with a 5-point rating scale from 1 (very good) to 5 (very poor). Due to low frequencies in the poor and very poor categories, the responses were dichotomized as (1) higher (very good/good) and (2) lower (average/poor/very poor) SRH.

Background variables included sex, height and weight, number of chronic conditions, and years of full-time education as an indicator of socioeconomic status.

Statistical Analyses

Descriptive statistics

We compared baseline characteristics between 3 age groups using analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Descriptive group comparisons were conducted using IBM SPSS Statistics Version 28 and a p value of less than <.05 was used to indicate statistical significance between the groups (IBM Corp., Armonk, NY).

Network analysis

Network analysis provides a data-based approach to explore the relationships between the proposed variables of the system (24). In the network models, nodes represent the variables and edges the association between 2 variables. Networks were estimated using the EstimateNetwork function of the bootnet R-package (25) separately for men and women. The edges in the network were undirected and the strength of the cross-sectional associations (positive or negative) between 2 nodes was estimated while controlling for all other nodes (ie, partial correlations) (24). Thus, the absence of a relationship between 2 variables indicates that those 2 variables are conditionally independent given all other variables. The blue (solid) edges indicate positive, and the red (dashed) edges indicate negative associations. The thickness of an edge represents the magnitude of the connection. The ‘least absolute shrinkage and selection operator’ (LASSO) (26) was applied to obtain sparse networks by limiting the estimation of false positive node associations. The LASSO utilizes an Extended Bayesian Information Criterion (EBICglasso) selection parameter to control the degree to which regularization is performed, which was set at 0.5 in the current analyses as suggested in the literature (24). The qgraph statistical package (27) was used to visualize the networks (R version 4.2.1).

First, we analyzed global network properties to study whether the IC networks are different according to age and SRH. We compared the networks visually, calculated the network density (ie, the number of edges divided by the possible number of edges), and, compared the overall connectivity of the network by using the NetworkComparisonTest package (NCT) (28) between the age and SRH groups. With NCT, we investigated network invariance (possible edge weight differences) and global strength invariance (possible difference in the absolute sum of network edge weights). We further analyzed the stability of the edge estimates with nonparametric bootstrapping (ie, resampling data with replacement, nboots = 1 000) using bootnet R-package (25). Edge stability plots visualize how accurate the estimated edges are and how stable the order of relative edge magnitudes is.

Second, we analyzed the local properties of the networks to study whether some intrinsic capacities have a more central role in the IC network among younger versus older people and among people with better versus worse subjective health. To investigate the importance of the nodes (ie, IC indicators) in the network, the 3 most commonly used node centrality metrics were studied: “strength,” which sums the absolute edge weights per node; “closeness,” which quantifies how well a node is indirectly connected to other nodes; and “betweenness,” which quantifies how important a node is in the average path between 2 other nodes (29). We also tested the stability of the centrality indices with case-dropping subset bootstrap analysis (nboots = 1 000) (25). A correlation of less than 0.5 between the estimated and bootstrapped samples’ centrality indices is in the 30% sampled cases indicating only a weak to moderate correlation between the original sample’s centrality indices and the bootstrapped samples’ values. Due to unconnected nodes in the networks, bootstrapped stability analyses did not contain any variance for closeness statistics, and we limited our analysis of centrality statistics to strength and betweenness centrality indices.

Finally, as additional analyses, we run the network models for both age and SRH groups with samples combining men and women to increase statistical power in the group comparisons. In these models, we included sex as a covariate in the networks due to sex differences in IC variables that were especially prominent in the muscle strength levels.

Results

The characteristics of the study sample are shown according to age groups in Table 1. In general, the younger participants had better scores in IC measurements and better health, were taller, and had longer education than the older participants. However, stress-coping ability and weight did not differ between age groups in either sex, nor did verbal fluency in men or depressive symptoms in women.

Table 1. The Characteristics of the Participants by Sex and Age Group

	Men		Women		
	75 y (n = 171)	80 y (n = 122)	85 y (n = 63)	p Value*	75 y (n = 236)	80 y (n = 146)	85 y (n = 87)	p Value*	
	Mean (SD)	Mean (SD)	Mean (SD)		Mean (SD)	Mean (SD)	Mean (SD)		
Grip strength, N	407.7 (72.0)a	370.0 (73.3)b	318.9 (72.3)c	<.001	238.9 (52.2)a	216.5 (43.5)a	203.6 (48.9)b	<.001	
Knee extension strength, N	455.7 (100.4)a	403.2 (98.7)b	368.4 (89.9)c	<.001	303.1 (81.6)a	280.7 (81.5)b	252.9 (73.9)c	<.001	
Walking speed, m/s	2.0 (0.4)a	1.9 (0.4)b	1.7 (0.4)c	<.001	1.8 (0.3)a	1.6 (0.3)b	1.5 (0.4)c	<.001	
Chair rise, s	12.1 (3.2)a	12.2 (3.5)a	14.6 (4.8)b	<.001	12.5 (3.6)a	13.3 (4.4)a	14.3 (4.2)b	.002	
Stress-coping ability	31.8 (4.7)	31.0 (4.5)	31.8 (5.1)	.295	31.1 (5.3)	30.8 (5.5)	30.2 (6.0)	.427	
Depressive symptoms	4.1 (3.6)a	5.0 (4.5)a	5.5 (4.8)b	.030	5.2 (3.9)	5.3 (4.1)	6.0 (4.5)	.241	
Digit symbol coding task	32.8 (10.4)a	28.5 (8.1)a	26.9 (10.0)b	<.001	35.8 (9.8)a	32.6 (8.6)b	28.1 (8.7)c	<.001	
Verbal fluency	33.6 (12.4)	33.6 (11.3)	31.6 (12.0)	.496	37.8 (11.5)a	38.6 (11.7)b	34.5 (12.3)b	.032	
Reaction time, ms	901.9 (287.5)a	1 020.5 (376.1)b	1 113.4 (306.7)b	<.001	1 020.9 (313.3)a	1 098.62 (314.5)a	1 314.5 (573.5)b	<.001	
Hearing acuity, dB	31.9 (11.8)a	38.6 (15.9)b	46.2 (14.0)c	<.001	30.3 (11.3)a	36.2 (12.1)b	43.1 (13.2)c	<.001	
Visual acuity	1.1 (0.4)a	1.0 (0.4)b	0.8 (0.4)b	<.001	1.0 (0.4)a	0.9 (0.3)b	0.8 (0.3)b	<.001	
Education, y	12.3 (4.4)a	11.9 (4.5)b	10.3 (4.2)b	.012	12.0 (4.1)a	12.0 (6.4)b	9.8 (3.4)b	<.001	
No. of chronic conditions	2.9 (1.8)a	3.3 (1.9)a	3.6 (1.9)b	.016	3.2 (2.0)a	3.5 (2.0)a	4.1 (2.0)b	.004	
Weight, kg	80.3 (13.1)	80.2 (12.4)	77.2 (11.1)	.196	71.1 (12.5)	69.8 (12.0)	68.1 (10.1)	.134	
Height, cm	172.9 (6.0)a	172.8 (5.9)a	169.3 (6.1)b	<.001	159.4 (5.0)a	158.0 (5.4)a	155.5 (5.6)b	<.001	
Self-rated health, higher, n (%)	102 (61)	58 (48)	30 (48)	.053	135 (61)	61 (28)	25 (11)	<.001	
Notes: m/s = meters per second; ms = milliseconds; N = newtons; s = second; SD = standard deviation. Bold typeface indicates statistically significant at the significance level of .05.

*ANOVA for continuous variables and chi-square test for categorical variables. Means sharing the same the same sub-index letter (a–c) are not significantly different from each other (Tukey’s HSD, p <.05).

Supplementary Figure 1 and Supplementary Tables 1 and 2 show the associations between the IC variables and age conditioning on other variables in the network in men and women. In both sexes, the most strongly connected IC variables with age were hearing, grip strength, and reaction time. Also walking speed was associated with age independent of other variables in both sexes but more strongly in women. Weak independent associations with age were also found in both sexes for vision and in men, for knee extension strength. The most strongly connected IC variables with SRH were walking speed, chair rise, depressive symptoms, and reaction time in both sexes (Supplementary Figure 2, Supplementary Tables 3 and 4).

The IC Network Comparisons According to Age

Figure 1 shows the estimated IC networks for men and women stratified by age groups. In both sexes, fewer edges between the IC variables were present in the network of 75-year-olds than in the 80- and 85-year-olds. In men, the number of nonzero edges among 75-year-olds was 13 (density 0.24, ie, 24% of all possible connections), and in 80- and 85-year-olds was 17 (density 0.31). In women, the corresponding number of nonzero edges for the age groups was 10 (density 0.18) and 17 (density 0.31). In both sexes, among the younger age group, the IC variables of different domains were mainly linked through walking speed, whereas the older age group showed more cross-correlations between different domains. Only sensory capacities, that is, hearing and vision, remained disconnected from the network. Although the network connectivity was denser among the older age group in both sexes, the NCT analyses showed that the differences in edge weights and global strength of the network were statistically nonsignificant (in men, edge weight difference [M] = 0.154, p = .793; global strength difference [S] = 0.277, p = .874; in women, M = 0.161, p = .378, S = 0.776, p = .392). The bootstrap analyses showed somewhat inaccuracy in the estimated edges (Supplementary Figures 3–6) most likely due to lack of statistical power in estimating the networks. However, the stability plots also indicated that the relative order of the strength of the estimated edges was very similar to that in the bootstrapped samples. Differences in the edge weights or global strength in the networks did not emerge in the additional analyses where the sexes were combined (Supplementary Figure 19). Compared to the sex-stratified analyses, the total number of connections in the networks was higher, and both age groups had similar network densities (0.52 vs 0.47).

Figure 1. The IC networks for men and women according to age. Nodes: (1) grip strength, (2) knee extension strength, (3) walking speed (m/s), (4) chair rise (s), (5) stress-coping ability, (6) depressive symptoms, (7) reaction time (ms), (8) word test, (9) digit symbol coding test, (10) hearing, and (11) vision. The blue (solid) edges indicate positive and the red (dashed) edges indicate negative associations.

Between age and sex groups, there was much similarity in the pattern of centrality indices (Figure 2). In all groups, walking speed showed the highest centrality based on the strength and betweenness indices connecting different IC domains with each other, which was also visible in the additional analyses combining sexes (Supplementary Figure 20). In addition, the digit symbol coding test and knee extension strength had high strength centrality but not betweenness in the networks, which is most likely the result of their strong connections with the other variables of their own IC domain in addition to connections with walking speed. According to the bootstrap analyses, the centrality measures showed stability in women, while in men, the estimated centrality indicators were relatively unstable (Supplementary Figures 7–10).

Figure 2. Centrality indices for men and women according to age groups.

The IC Network Comparisons According to Self-Rated Health

In both sexes, fewer edges were observed in the networks of higher compared to lower SRH (Figure 3). In men, we observed 8 (density 0.14) versus 12 (density 0.22), and in women, 12 (density 0.22) versus 17 (density 0.30) nonzero edges. In men, among those who had higher SRH, cross-correlations between intrinsic capacities of different domains were observed only between muscle strength measurements and walking speed and chair rise. These connections were also visible in the lower SRH group but somewhat stronger. In addition, the lower SRH group had cross-correlations between depressive symptoms and reaction time, coding test and walking speed, and knee extension strength and reaction time. In the higher SRH group among women, most cross-connections were observed between muscle strength measurements, walking speed, chair rise, and cognitive tests. In the lower SRH group, more cross-correlations were observed and all IC domains were directly linked to at least one other domain, including the sensory capacities, which remained basically unconnected from other IC domains in the networks of other groups. The NCT comparisons between the SRH groups showed nonsignificant differences in the edge weight and global strength differences between the groups (in men, M = 0.153, p = .815, S = 0.553, p = .749; in women, M = 0.150, p = .5, S = 0.785, p = .401). As in the networks based on age, the strength of the edge estimations showed variability across bootstrapped samples, while their relative order showed to be stable (Supplementary Figures 11–14). In the additional analyses with larger samples combining sexes, statistically significant differences in the edge weights or global strength in the networks did not emerge either (Supplementary Figure 21). However, the number of connections in the networks was higher compared to sex-stratified analyses and the network was denser among those with lower compared to higher SRH (0.53 vs 0.71).

Figure 3. The IC networks for men and women according to self-rated health (SRH). Nodes: (1) grip strength, (2) knee extension strength, (3) walking speed (m/s), (4) chair rise (s), (5) stress-coping ability, (6) depressive symptoms, (7) reaction time (ms), (8) word test, (9) digit symbol coding test, (10) hearing, and (11) vision. The blue (solid) edges indicate positive and the red (dashed) edges indicate negative associations.

In the groups based on self-rated health, the node centrality indices varied more between groups than in the age group comparison (Figure 4). In men with higher SRH, who had a very sparse IC network, the grip strength showed the highest strength centrality, and both, grip strength and walking speed, showed the highest betweenness. In men with lower SRH, walking speed and knee extension strength were the most strongly connected nodes, while reaction time, digit symbol coding task, and knee extension strength exhibited the highest betweenness. In women in both SRH groups, walking speed, knee extension strength, and digit symbol coding task showed high strength centrality but the highest betweenness was observed only in walking speed and digit symbol coding task (only lower SRH group). The stability tests of the sex-stratified analyses did not fully meet the set criteria in all groups (Supplementary Figures 15–18) and the estimated results of the centrality indices should be interpreted with caution. In the additional analyses combining sexes, in the higher SRH group, walking speed and digit symbol coding test showed the highest betweenness of the IC measurements, whereas in the lower SRH group, which had more connections, many IC indicators acted as important bridges between other nodes (Supplementary Figures 21 and 22).

Figure 4. Centrality indices for men and women according to self-rated health (SRH).

Discussion

Our analyses suggest that the IC network exhibits higher density among those with lower versus higher SRH. This finding is in line with our hypothesis proposing that with declining health, different intrinsic capacities become mutually more dependent on each other, which may indicate a loss of system resilience and increased vulnerability when facing perturbations (11). The sex-stratified analyses also suggested that the network density was higher in older than younger people, but the difference was not as clear as in the SRH categories and did not emerge in the analyses where men and women were pooled in the same model. Although the additional cross-correlations between the IC variables in the lower SRH and older age groups were relatively weak and did not result in differences in the overall global strength of the networks when compared to younger participants and those with higher SRH, we observed similar trends in both sexes and outcome variables supporting the finding. Another central result of our analyses was that walking speed appeared to be the most central IC measurement exhibiting a bridging role in the networks. This suggests, that walking speed may best capture information on different domains of IC and possibly, also mediate some associations between different intrinsic capacities.

To our knowledge, this is the first study that utilizes network analysis to model IC as an emergent property. Our findings are in parallel with a previous study showing that there is increased connectivity between health deficits among older adults with higher frailty levels, and health deficits related to mobility play a more important role in defining the frailty phenotype and other measures (9). Also, Hao et al. (30) showed that compared to robust and prefrail groups, the older adults with frail status showed increased cross-correlations between different physiological systems underlying frailty. A rising cross-correlation between subsystems may indicate an elevated risk for systemic failure (11) as a regime shift in one subsystem (eg, IC domain) may make another closely connected subsystem more susceptible to shifting its state (31). This may create pathways, along which unfavorable changes in functional capacity can spread more rapidly. This phenomenon is most likely related to a decline in functional reserves that occurs during aging in some IC domains. For example, a certain level of muscle strength is required for walking, above which muscle strength and walking speed correlate more strongly up to a strength reserve threshold level where strength does not limit walking anymore (5,32,33). Also, in the current analyses, we observed stronger associations of knee extension strength with walking speed and chair rise among lower compared to higher SRH groups in both sexes. Women of the lower SRH group also showed associations of walking speed and chair rise with depressive symptoms, which may indicate a cascading deterioration in the system. However, the cross-sectional and undirected networks do not allow us to make causal inferences, and future studies are needed to explore the temporal dynamics of the IC system.

Walking speed is a well-known key indicator of health and functioning in older people, as was also observed in this study. In the network models, walking speed was increasingly associated with other IC domains in older versus younger participants and those with poorer versus better health. Walking requires the functioning of many subsystems, such as neuromuscular, sensory, and cardiorespiratory systems (34), which may explain its central role in the IC network. Thus, walking, which is a more complex activity compared to other intrinsic capacities, could be used as an integrative and sensitive single indicator for monitoring IC decline during aging.

Our network analysis and findings, however, support the identification of locomotor, cognitive, sensory, and psychological domains as distinct dimensions of IC that cannot be assumed to conceptually stem from one general IC trait as, for example, factor analyses would suggest (35). However, measuring the vitality domain has proved challenging. In this study, we assessed vitality with muscle strength, which is one of several attributes proposed to indicate the domain underlying the other IC domains, defined as “a physiological state (due to normal or accelerated biological aging processes) resulting from the interaction between multiple physiological systems” (36). Muscle strength measures were constantly and most strongly connected with locomotion (walking speed, chair rise) and, to some extent, with cognitive functions, but not with psychology and sensory domains. This suggests that muscle strength is not sufficient, at least on its own, to describe the underlying physiological processes influencing all the IC domains. For such a broad concept as IC, defining a single indicator for underlying physiological processes may be difficult, as it is not likely that all the IC domains, such as sensory and psychological capacities, share a similar biological basis and follow similar trajectories with aging as physical and cognitive capacities. However, the network approach may provide opportunities to investigate the complex interactions between lower-level biological and physiological mechanisms across different scales, and also to integrate their connections with the other more overt IC domains at the functional level, as conceptualized by Beard et al. (3).

The main limitation of this study was that the cross-sectional study design did not allow us to study the dynamic interactions in the networks. In the future, investigating longitudinal changes in network structures and incorporating controlled interventions or stressors into the network analysis are needed to examine the dynamical behavior and resilience of the IC system when perturbed. Another main limitation of this study was the relatively small sample sizes for modeling several IC variables in a network. We wanted to analyze men and women separately due to their level of differences in physical capacity measurements and to include broadly IC variables to explore their importance in relation to other domains. This probably caused the statistical models to be somewhat underpowered, which was shown as a lack of stability in the estimations of edge strength and centrality indices in the bootstrapped analyses. In addition, to ensure that the network comparisons did not differ too much in the number of participants, we had to combine the groups of 80- and 85-year-olds. As there were more 80-year-olds in the combined group than 85-year-olds, this may have meant that the group was too similar when compared to 75-year-olds to observe differences in the IC network patterns. In addition, in future studies, it may be useful to select variables in each IC domain that do not reflect the same entity, for example, muscle strength, which is strongly connected and may hinder the edge strength comparison between the networks. However, the consistent findings between sexes and outcomes related to network connectivity and the importance of walking speed support our conclusions. Nevertheless, it is important to aim to replicate the findings with larger study samples as well as concerning other grouping variables than chronological age and SRH.

A major strength of our study is the novelty of the network approach in IC research. The importance of the interactions between intrinsic capacities has been addressed in previous studies at the conceptual level (2,3,37) and the current findings are the first step to advance empirically the understanding of the characteristic properties of the IC as a system. The complex systems approach may be a meaningful way to link 2 central concepts of aging research, IC and resilience, to move away from a reductionist perspective where different factors are studied in silos (38). Another strength of our study was the performance-based measures and validated questionnaires for IC domains, which are rarely available so extensively in larger data sets. The indicators with continuous scales allow us to detect variation in different capacity levels rather than the mere absence versus presence of deficits, and this is essential in IC research (39). Finally, we used a population-based and heterogeneous sample of older adults instead of a self-selected convenience sample. Although the participants may have represented a healthier section of the same-aged population (13), there was variation in many characteristics of the participants. In addition, investigating IC and resilience in older adults with good functioning is important to detect declines even before the onset of disabilities.

In conclusion, the IC network presents a potentially informative assessment of the patterns of functional capacity and system resilience. We found supporting evidence that the IC network may exhibit higher density with loss of system resilience that occurs during aging and declining health. Walking speed plays the most central role in the networks, possibly capturing changes in IC across domains.

Supplementary Material

glae048_suppl_Supplementary_Figures_S1-S22_Tables_S1-S4

Acknowledgments

The authors thank the whole AGNES research team for their work in the data collection. The authors also thank all participants of the AGNES study for their time and effort. The Gerontology Research Center is a joint effort between the University of Jyvaskyla and the University of Tampere.

Funding

This work was supported by the JYU.Well—Wellbeing Research Community of the University of Jyväskylä and Juho Vainio Foundation (K.K.); the Research Council of Finland (grant number 310526 to T.R.), and European Research Council (grant number ERC AdvG 693045 to T.R.).

Conflict of Interest

T.R. serves on The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences editorial board. The other authors declare no conflict of interest.

Author Contributions

K.K.: Conception and design of the study, drafting the original manuscript and revision, data preparation, analysis, and interpretation. K.L. and M.V.: Critical revision for important intellectual content and contribution to the drafting of the original manuscript and revision. T.R.: Acquiring the data, interpretation, critical revision for important intellectual content, and drafting the original manuscript and revision.
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