Individuals aged 70 and older frequently experience an increased risk of deficits in both physical and cognitive functions. However, the natural progression and interrelationship of these deficits, as well as their neurologic correlates, remain unclear. We aimed to classify the data-driven physical-cognitive phenotypes and then investigate their associations with neuroimaging markers. This cross-sectional study included 70-year-old participants from the Gothenburg H70 Birth Cohort (2014-2016). Based on physical performance (grip strength, balance, walking speed, and chair stand) and cognitive measures (episodic memory, perceptual speed, executive function, verbal fluency, and visuospatial abilities), we applied latent class analysis to identify physical-cognitive phenotypes. Based on the brain MRI measurements, 3 groups of neuroimaging markers were involved-neurodegeneration, cerebral small vessel disease (cSVD), and microstructural white matter (WM) integrity. We performed multinomial logistic regressions to examine the differences between the physical-cognitive phenotypes. In total, 1,140 participants (female: 53.3%) without dementia and disability were included in the study, with 721 (female: 52.2%) undergoing MRI scans. Three physical-cognitive phenotypes were identified: an "optimal" group characterized by high performance in both physical and cognitive functions, an "intermediate" group showing a slight reduction in both domains, and a "physical deficit" group marked by a significant reduction in physical performance. Compared with the optimal group, the other 2 groups were more likely to present with vascular risk factors. The physical deficit group had higher odds of experiencing depression compared with the intermediate group (adjusted odds ratio [aOR] 2.9, 95% CI 1.4-5.9). Compared with the optimal group, the odds of presenting all 3 severe neuroimaging markers were higher in both the intermediate (aOR 3.4, 95% CI 1.5-7.9) and physical deficit (aOR 10.3, 95% CI 2.4-45.0) groups. This study highlights the variability in physical and cognitive performance among older adults and suggests that neuroimaging markers of neurodegeneration, cSVD, and microstructural WM integrity may account for these variations. Our findings indicate the potential for developing group-based strategies to prevent and manage age-related functional decline. Further research with larger sample sizes is needed to deepen our understanding of physical-cognitive decline patterns.
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