Predictors Of Healthy Aging Research Articles

Prevalence and Factors Associated With Healthy Aging in a Large Representative Community Sample of Older Ethiopians.

Objectives: To examine the prevalence and predictors of healthy aging among community-dwelling older adults living in Bahir Dar, Ethiopia. Methods: A community-based cross-sectional study included older adults aged 60 and above (n = 845; Mage = 71 years; 56.4% females). Poisson regression estimated the Relative Risk (RR) of factors associated with healthy aging status. Results: 36.7% of respondents were classified as healthy agers. Factors associated with an increased likelihood of healthy aging included reporting not being lonely, adequate nutrition, no multimorbidity, good self-rated health, financial independence, and engaging in at least 1hour of moderate physical activity per week. Discussion: The proportion of healthy agers in this study was substantially lower compared to developed countries but comparable to the prevalence reported in similar developing nations. Enhancing physical activity, ensuring proper nutrition, interventions to foster social participation engagement, and networking, and managing chronic diseases were identified as promising strategies to promote healthy aging.

Mechanisms of Healthy Aging: An Integrated Neurobiological Perspective on Mood and Cognitive Function

Neurobiological approaches to healthy aging can protect against declines in cognitive function and mood disturbances in older adults. This symposium will focus on health-based mechanisms associated with mood and cognition in late life using an integrated micro- and macro-level perspective. Mechanistic pathways and predictors of healthy aging will be discussed across multiple levels of analysis (genetic, molecular, systems-level neurocircuitry, and behavioral). We will discuss these mechanisms of aging and late-life depression cross-sectionally and in the context of non-pharmacological interventions to identify potential pathways for more personalized treatments.This symposium will take a unique approach to classifying the neurobiology of healthy aging. Dr. Swathi Gujral will present a large study of 108 community-dwelling older adults who completed an exercise intervention and discuss differences in stress and emotional health as a function of exercise condition and APOE e4 carrier status. Dr. Julie Dumas will discuss functioning of the nicotinic cholinergic system in older adults and will present effects of nicotinic stimulation and blockade on brain activation and working memory performance in older adults with and without subjective cognitive decline. Dr. Lauren Oberlin will present neuroimaging features of small vessel disease (SVD) in older adults with and without depression and discuss the association between white matter abnormalities and cognitive decline in late life. Dr. Lindsay Victoria will present neural correlates of negative cognitive bias in late-life depression and their impact on treatment response to psychotherapy in depressed older adults.The unifying goal of these presentations is to characterize mechanistic pathways to precisely target cognitive deficits, improve mood, and promote healthy aging. With an emphasis on individual differences, we will examine predictors of change across a range of domains impacted in aging and late-life depression, including stress, working memory, and executive function. This mechanistic approach has important implications for the development of personalized interventions that emphasize health-based approaches to mood and cognitive function in older adults.

Do Life Course Physical Activity Profiles Reduce the Effects of Childhood Exposures on Cognition?

Although physical activity throughout life is one of the most reliable predictors of healthy aging, can less consistent or favorable trajectories also improve cognition trajectories among older adults? Drawing from accumulation theories, we use longitudinal data from the Health and Retirement Study and Life History Mail Survey (N=9,309) to examine the early antecedents of cognitive decline and the extent to which different life course physical activity profiles can slow such a decline. Results from latent class analysis reveal seven distinct profiles: consistently low, consistently high, consistently average (reference), improvers, decliners, midlife motivators, and previously athletic “couch potatoes.” Growth curve modeling analyses show that membership in the consistently high class and midlife motivators were associated with better cognition initially and over time, with no difference between the two classes. Additionally, though poor health and learning problems in childhood were associated with worse initial cognition, physical activity does not mediate the relationship.

EXPLORING MECHANISMS OF AGING THROUGH LONGITUDINAL TRAJECTORIES: BIOLOGICAL, PHENOTYPIC, AND CLINICAL

Over the past several decades, researchers have searched for early and accurate predictors of healthy aging, establishing metrics to potentially quantify the aging process based on functional performance, disease diagnosis, or other clinical tests. Using these metrics, efforts have been made to disentangle the concepts of “normal” versus “accelerated” aging to identify individuals transitioning into states of disease and disability, allowing for more targeted and effective treatment(s). Yet, many of the variables included in these indices have been chosen based on their availability using cross-sectional associations with chronological age. Such variables may reflect birth cohort differences and selective attrition and fail to accurately represent the mechanisms most representative of the aging process. Using data longitudinal data from the Baltimore Longitudinal Study of Aging and beyond, this symposium will present a conceptual framework of phenotypic aging and its relationship with biological aging, and trajectories of aging phenotypes using state-of-the-art measures collected over the past 10 years. Specifically, we will discuss new insights into trajectories of molecular (inflammatory and metabolic markers), physiological (energetics, body composition, brain atrophy), and clinical measures (gait speed and executive function), and discuss methodological considerations for combining these phenotypes into a measure that can be linked with biological aging measured using a systems approach to improve and refine future understanding of mechanisms of “normal” versus “accelerated” aging. Further development and application of the methodology presented in this symposium will highlight the importance of using longitudinal data to improve understanding of physical and cognitive trajectories with aging.

Midlife predictors of active and healthy aging (AHA) among older businessmen

BackgroundActive and healthy aging (AHA) is an important phenomenon in aging societies.AimsOur aim was to investigate midlife predictors of AHA in a socioeconomically homogenous male cohort.MethodsIn 2010, AHA was defined in the Helsinki Businessmen Study (men born in 1919–1934) with six criteria: (1) being alive, (2) responding to the mailed survey, (3) no reported cognitive problems, (4) feeling of happiness, (5) no difficulties in activities of daily living (ADL), and (6) no significant chronic diseases. Midlife factors were assessed in 1974 (n = 1759, mean age 47 years). Of the survivors in 2010 (n = 839), 10.0% (n = 84) fulfilled all AHA criteria, whilst 13.7% (n = 115) had chronic diseases but fulfilled other five criteria. Midlife predictors of AHA were analyzed with logistic models.ResultsOf the midlife factors, smoking [Odds ratio (OR) 0.44, 95% confidence interval (CI) 0.25–0.77], higher body mass index (BMI) (OR 0.75, 0.59–0.96), and higher total cholesterol (OR 0.76, 0.60–0.97) prevented significantly full AHA criteria, whereas higher self-rated health (SRH) (OR 1.73, 1.07–2.80) predicted significantly of fulfilling all AHA criteria. Midlife smoking (OR 0.87, 0.84–0.91), higher BMI (OR 0.73, 0.61–0.86), and higher alcohol consumption (OR 0.73, 0.60–0.90) prevented significantly of fulfilling the five AHA criteria with chronic diseases, and higher SRH (OR 1.90, 1.37–2.63) predicted significantly the five AHA criteria (chronic diseases present).DiscussionOur study suggests that midlife factors, especially good SRH and low levels of cardiovascular risk factors, are associated with AHA.ConclusionsThe study emphasizes the importance of life-course predictors of healthy aging.

Predictors of healthy aging: prospective evidence from the Alameda County study.

Long-term predictors of high levels of physical functioning were examined in a representative sample of Alameda County, California residents followed from 1965 through 1984. The cohort investigated in this study was born between 1895 and 1919, with survivors being age 65 to 89 at the time of follow-up. A scale of physical functioning was developed from a comprehensive set of questionnaire items which assessed the full spectrum of physical functioning. Those scoring in the top 20 percent, defined as healthy aging, were compared to the remainder of the cohort, including those who died and those with lower levels of functioning at follow-up. After adjustment for age and functional status at baseline, the following variables were predictive of high functioning at follow-up 19 years later: race (those not Black), higher family income level, absence of hypertension, absence of arthritis, absence of back pain, being a non-smoker, having normal weight, and consuming moderate amounts of alcohol. Sex did not predict high function because of the counterbalancing effects of higher survival in females but greater likelihood of high functioning among surviving males.