Subgroups in Late Adulthood Are Associated With Cognition and Wellbeing Later in Life

Abstract

Objectives: In this study, we aim to discover whether there are valid subgroups in aging that are defined by modifiable factors and are determinant of clinically relevant outcomes regarding healthy aging.Method: Data from interviews were collected in the Longitudinal Aging Study Amsterdam at two measurement occasions with a 3-year interval. Input for the analyses were seven well-known vulnerability and protective factors of healthy aging. By means of community detection, we tested whether we could distinguish subgroups in a sample of 1478 participants (T1-sample, aged 61–101 years). We tested both the external validity (T1) and predictive validity (T2) for wellbeing and subjective cognitive decline. Moreover, replicability and long-term stability were determined in 1186 participants (T2-sample, aged 61–101 years).Results: Three similar subgroups were identified at T1 and T2. Subgroup A was characterized by high levels of education with personal vulnerabilities, subgroup B by being physically active with low support and low levels of education, and subgroup C by high levels of support with low levels of education. Subgroup C showed the lowest wellbeing and memory profile, both at T1 and T2. On most measures of wellbeing and memory, subgroups A and B did not differ from each other. At T2, the same number of subgroups was identified and subgroup profiles at T1 and T2 were practically identical. Per T1 subgroup 47–62% retained their membership at T2.Discussion: We identified valid subgroups that replicate over time and differ on external variables at current and later measurement occasions. Individual change in subgroup membership over time shows that transitions to subgroups with better outcomes are possible.

Clinical subtyping using community detection: Limited utility?

“…In the ADHD literature, there is a growing number of papers that have established the existence of three temperament subtypes using community detection (Blanken et al., 2021 ; Goh et al., 2020 ; Karalunas et al., 2014 , 2019 ; Nigg et al., 2020 ). In general, community detection is often used for subtyping analyses of ADHD and autism samples (Bathelt et al., 2018 ; Cordova et al., 2020 ; Deserno et al., 2022 ; Fair et al., 2014 ; Feczko et al., 2018 ; Groenman, et al., 2019 ; Mostert et al., 2018 ; Radhoe, Agelink van Rentergem, Torenvliet, et al., 2021 ), although there are also applications outside these disorders (e.g., Radhoe, Agelink van Rentergem, Kok, et al., 2021 ; Saliasi et al., 2015 ). Since this literature using community detection seems to keep growing, and researchers make use of many untested variations, it is important to examine this method closely.…”