Abstract
Advocating continued health into old age, so called successful aging, is a growing public health goal. However, the development of tools to measure aging is limited by the lack of appropriate outcome measures, and operational definitions of successful aging. Using exploratory factor analysis, we attempted to identify distinguishable health domains with representative variables of physical function, cognitive status, social interactions, psychological status, blood biomarkers, disease history, and socioeconomic status from the InCHIANTI study. We then used logistic and mixed effect regression models to determine whether the resulting domains predicted outcomes of successful aging over a nine-year follow-up. A four-domain health model was identified: neuro-sensory function, muscle function, cardio-metabolic function and adiposity. After adjustment for age and gender, all domains contributed to the prediction of walking speed (R2=0.73). Only the muscle function domain predicted dependency (R2=0.50). None of the domains were a strong, significant predictor of self-rated health (R2=0.18) and emotional vitality (R2=0.23). Cross-sectional findings were essentially replicated in the longitudinal analysis extended to nine-year follow-up. Our results suggest a multi-domain health model can predict objective but not subjective measures of successful aging.
Highlights
IntroductionThe number of old and very old adults (aged 65 and over, and 80 and over respectively) is rapidly rising in all European countries, and represents a progressively growing percentage of the general population [1]
The number of old and very old adults is rapidly rising in all European countries, and represents a progressively growing percentage of the general population [1]
Using the Eigen value criteria (Table 1) as well as visual inspection of the scree plot (Supplementary Figure S1) four factors were identified defined as neuro-sensory function, cardio-metabolic function, muscle function and adiposity and were retained in the model (Table 1, and Figure 2)
Summary
The number of old and very old adults (aged 65 and over, and 80 and over respectively) is rapidly rising in all European countries, and represents a progressively growing percentage of the general population [1]. The proportion of working aged individuals is declining [2] These changes in the population pyramid, as well as increasing life expectancy, is challenging the stability of health and social care systems [3]. The development of tools to measure successful aging, and to timely identify the early stages of health impairment, has become a research priority [6]. Developing such tools is a challenge, as aging is a complex process and it is unlikely that a single measure will be able to track the aging trajectory, early in life, when disease symptoms and functional limitations are still rare [6]. Testing the validity of tools to measure healthy aging is complicated due to the lack of an agreed upon definition of healthy aging [6], as well as discrepancies in the terminology describing this concept
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