Healthy Aging Index Research Articles

The Association of Healthy Aging with Multimorbidity: IKARIA Study.

The aim of this study was to evaluate several sociodemographic, lifestyle, and clinical characteristics of the IKARIA study participants and to find healthy aging trajectories of multimorbidity of Ikarian islanders. During 2009, 1410 people (aged 30+) from Ikaria Island, Greece, were voluntarily enrolled in the IKARIA study. Multimorbidity was defined as the combination of at least two of the following chronic diseases: hypertension; hypercholesterolemia; diabetes; obesity; cancer; CVD; osteoporosis; thyroid, renal, and chronic obstructive pulmonary disease. A healthy aging index (HAI) ranging from 0 to 100 was constructed using 4 attributes, i.e., depression symptomatology, cognitive function, mobility, and socializing. The prevalence of multimorbidity was 51% among men and 65.5% among women, while the average number of comorbidities was 1.7 ± 1.4 for men and 2.2 ± 1.4 for women. The most prevalent chronic diseases among men with multimorbidity were hypertension, hypercholesterolemia, and obesity while among women they were hypertension, hypercholesterolemia, and thyroid disease. Multimorbidity was correlated with HAI (Spearman’s rho = −0.127, p < 0.001) and for every 10-unit increase in HAI, participants had 20% lower odds of being multimorbid. Multimorbidity in relation to HAI revealed a different trend across aging among men and women, coinciding only in the seventh decade of life. Aging is usually accompanied by chronic diseases, but multimorbidity seems to also be common among younger adults. However, healthy aging is a lifelong process that may lead to limited co-morbidities across the lifespan.

Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts.

In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2-7 of the English Longitudinal Study of Ageing (ELSA). Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a 'high stable' group (76% in ATHLOS, 61% in ELSA), 2) a 'low stable' group (22% in ATHLOS, 36% in ELSA) and 3) a 'rapid decline' group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4-2.1) more likely to be in the 'rapid decline' group and 11.7 times (OR = 11.7 95% CI: 10.9-12.6) more likely to be in the 'low stable' group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the 'rapid decline' and the 'low stable' groups (OR = 2.1, 95% CI: 1.2-3.8 and OR = 9.8, 95% CI: 7.5-12.7 respectively). Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

Multimorbidity patterns, all-cause mortality and healthy aging in older English adults: Results from the English Longitudinal Study of Aging.

This study aimed to investigate the relationships between multimorbidity, healthy aging and mortality. Using data from 9171 individuals aged ≥50 years at wave 2 and mortality data at wave 5 of the English Longitudinal Study of Aging, a multiple linear regression model and a Cox proportional hazards model were used to investigate how multimorbidity patterns (identified as cardiorespiratory/arthritis/cataracts, metabolic and relatively healthy) were associated with a composite index of healthy aging (derived from 41 intrinsic capacity and functional ability items) and with mortality. A total of 60% of the sample with multimorbidity had a moderate or high level of healthy aging. Both the cardiorespiratory/arthritis/cataracts group (n = 1826) and the metabolic group (n = 844) were negatively associated with healthy aging. The expected healthy aging index score decreased by 5.81 points (95% CI -6.69, -4.92) for the first group, and by 2.39 points (95% CI -3.54, -1.24) for the latter group. Only the cardiorespiratory/arthritis/cataracts group was positively associated with mortality. The risk of death for this group was 1.27-fold (95% CI: 1.14, 1.43) than the relatively healthy group. The relationship between multimorbidity patterns and mortality did not differ when considering levels of healthy aging. Although it is not impossible for people with multimorbidity to age healthily, those with the most complex combination of diseases are at higher risk of death and have lower levels of healthy aging. Geriatr Gerontol Int 2020; 20: 1126-1132.

The Healthy Aging Index analyzed over 15 years in the general population: The Doetinchem Cohort Study

The Healthy Aging Index (HAI), an index of physiological aging, has been demonstrated to predicts mortality, morbidity and disability. We studied the longitudinal development of the HAI to identify aging trajectories and evaluated the role of baseline sociodemographic characteristics and lifestyle factors of the trajectories. Four measurements with intervals of 5 years were included from the Doetinchem Cohort Study. The HAI reflects levels of systolic blood pressure, non-fasting plasma glucose levels, global cognitive functioning, plasma creatinine levels and lung functioning. The HAI score ranges from 0 to 10: higher scores indicate a better health profile. Latent class mixture modelling was used to model within-person change and to identify aging trajectories. Area under the curve was calculated per trajectory to estimate total healthy years. In total, 2324 women and 2013 men were included. One HAI trajectory was identified for women, and two trajectories for men, labelled ‘gradual’ aging (76%) and ‘early’ aging (24%). Men who were medium/high educated, below 36 years at baseline, complied with guidelines on physical activity and were not obese in any round were associated with increased odds to ‘gradual’ aging of 1.46 (CI: 1.18–1.81), 1.93 (CI: 1.42–2.62), 1.26 (1.02–1.57) and 1.76 (1.32–2.35), respectively. Between 30 and 70 years of age, men in the ‘early’ aging trajectory had the least healthy years (29.6 years), followed by women (30.1 years), and ‘gradual’ aging men (34.7 years). This study emphasizes that ‘physiological aging’ is not only an issue of older ages. Between 30 and 70 years of age, ‘early’ aging men and women had approximately five healthy years less compared to ‘gradual’ aging men. Lifestyle factors (e.g. nutrition and physical activity) seem to play an important role in optimal aging.

The modified Healthy Aging Index is associated with mobility limitations and falls in a community-based sample of oldest old.

The Healthy Aging Index (HAI) is useful in capturing the health status of multiple organ systems in older adults. Previous studies have mainly focused on the association of HAI with mortality and disability. We constructed a modified HAI (mHAI) to examine its association with mobility limitations and falls in a community-based sampling of older Chinese adults. We investigated 399 community-dwelling older adults aged 80years or older, and constructed the mHAI with five non-invasive tests (systolic blood pressure, the Montreal Cognitive Assessment test, glucose concentrations, cystatin C levels, and self-reported respiratory problems). The mean mHAI score for the participants in our study was 3.6. After multivariate adjustment, per unit increase in mHAI score was associated with self-reported difficulty in stooping, kneeling, or crouching (odds ratio [OR] = 1.16, 95% confidence interval [CI] 1.00-1.34), and walking 400m (OR = 1.21, 95% CI 1.03-1.42). Per unit increase in mHAI score was also associated with poor balance (OR = 1.29, 95% CI 1.07-1.55), lower extremity strength limitation (OR = 1.30, 95% CI 1.10-1.52), low handgrip strength (OR = 1.25, 95% CI 1.08-1.46), and slow gait speed (OR = 1.21, 95% CI 1.02-1.42). The association between mHAI and falls was also significant (per unit of mHAI OR = 1.21, 95% CI 1.04-1.40). The mHAI can be used as a simple assessment tool to determine mobility status in older adults and identify those at high risk for falls.

Comparing socio-economic inequalities in healthy ageing in the United States of America, England, China and Japan: evidence from four longitudinal studies of ageing

AbstractHealthy ageing has become a popular topic worldwide. So far, a consensus measure of healthy ageing has not been reached; and no studies have compared the magnitude of socio-economic inequality in healthy ageing outside Europe. This study aims to create a universal measure of healthy ageing and compare socio-economic inequalities in healthy ageing in the United States of America (USA), England, China and Japan. We included 10,305 American, 6,590 English, 5,930 Chinese and 1,935 Japanese participants for longitudinal analysis. A harmonised healthy ageing index (HAI) was developed to measure healthy ageing multi-dimensionally. Educational, income and wealth rank scores were derived accounting for the entire socio-economic distribution and the sample size of each category of socio-economic indicator. Associations between socio-economic rank scores and HAIs were assessed using multi-level modelling to calculate the Slope Indices of Inequality. Healthy ageing trajectories were predicted based on the full-adjusted age-cohort models. We found that education was a universally influential socio-economic predictor of healthy ageing. Moving from the highest to the lowest educational groups was associated with a 6.7 (5.2–8.2), 8.2 (6.0–10.4), 13.9 (11.4–16.3) and 6.1 per cent (3.9–8.2%) decrease in average HAI at 60 years in the USA, England, China and Japan, respectively. After 60 years, the educational inequality in healthy ageing kept increasing in the USA and China. The educational inequality in healthy ageing in China was also greater than any other socio-economic inequality in the four countries. Wealth was more influential in predicting healthy ageing inequality among American, English and Japanese participants, while income was more influential among Chinese participants. The socio-economic inequality in healthy ageing in Japan was relatively small. Chinese and American participants had worse healthy ageing profiles than Japanese and English participants.

Development of a healthy ageing index in Latin American countries - a 10/66 dementia research group population-based study

BackgroundOur population is ageing and in 2050 more than one out of five people will be 60 years or older; 80% of whom will be living in a low-and-middle income country. Living longer does not entail living healthier; however, there is not a widely accepted measure of healthy ageing hampering policy and research. The World Health Organization defines healthy ageing as the process of developing and maintaining functional ability that will enable well-being in older age. We aimed to create a healthy ageing index (HAI) in a subset of six low-and-middle income countries, part of the 10/66 study, by using items of functional ability and intrinsic capacity.MethodsThe study sample included residents 65-years old and over (n = 12,865) from catchment area sites in Cuba, Dominican Republic, Peru, Venezuela, Mexico and Puerto Rico. Items were collected by interviewing participants or key informants between 2003 and 2010. Two-stage factor analysis was employed and we compared one-factor, second-order and bifactor models. The psychometric properties of the index, including reliability, replicability, unidimensionality and concurrent convergent validity as well as measurement invariance per ethnic group and gender were further examined in the best fit model.ResultsThe bifactor model displayed superior model fit statistics supporting that a general factor underlies the various items but other subdomain factors are also needed. The HAI indicated excellent reliability (ω = 0.96, ωΗ = 0.84), replicability (H = 0.96), some support for unidimensionality (Explained Common Variance = 0.65) and some concurrent convergent validity with self-rated health. Scalar measurement invariance per ethnic group and gender was supported.ConclusionsA HAI with excellent psychometric properties was created by using items of functional ability and intrinsic capacity in a subset of six low-and-middle income countries. Further research is needed to explore sub-population differences and to validate this index to other cultural settings.

Healthy ageing and the prediction of mortality and incidence dependence in low- and middle- income countries: a 10/66 population-based cohort study

BackgroundIn the absence of a consensus on definition and measurement of healthy ageing, we created a healthy ageing index tallying with the functional ability framework provided by the World Health Organization. To create this index, we employed items of functional ability and intrinsic capacity. The current study aims to establish the predictive validity and discrimination properties of this healthy ageing index in settings in Latin American, part of the 10/66 cohort.MethodsPopulation-based cohort studies including 12,865 people ≥65 years old in catchment areas of Cuba, Dominican Republic, Venezuela, Mexico and Peru. We employed latent variable modelling to estimate the healthy ageing scores of each participant. We grouped participants according to the quintiles of the healthy ageing score distribution. Cox’s proportional hazard models for mortality and sub-hazard (competing risks) models for incident dependence (i.e. needing care) were calculated per area after a median of 3.9 years and 3.7 years, respectively. Results were pooled together via fixed-effects meta-analysis. Our findings were compared with those obtained from self-rated health.ResultsParticipants with lowest levels, compared to participants with highest level of healthy ageing, had increased risk of mortality and incident dependence, even after adjusting for sociodemographic and health conditions (HR: 3.25, 95%CI: 2.63–4.02; sub-HR: 5.21, 95%CI: 4.02–6.75). Healthy ageing scores compared to self-rated health had higher population attributable fractions (PAFs) for mortality (43.6% vs 19.3%) and incident dependence (58.6% vs 17.0%), and better discriminative power (Harrell’s c-statistic: mortality 0.74 vs 0.72; incident dependence 0.76 vs 0.70).ConclusionThese results provide evidence that our healthy ageing index could be a valuable tool for prevention strategies as it demonstrated predictive and discriminative properties. Further research in other cultural settings will assist moving from a theoretical conceptualisation of healthy ageing to a more practical one.

THE HEALTHY AGING INDEX AND ITS ASSOCIATION WITH MORTALITY IN OLDER MEXICAN AND EUROPEAN AMERICANS

Mexican Americans (MA) have higher morbidity compared to European Americans (EA); however, the mortality rate remains lower (Hispanic Paradox). The Healthy Aging Index (HAI) captures clinical and subclinical morbidity in older adults and is useful in examining ethnic differences in mortality for a given disease burden. We assessed the association between baseline HAI and all-cause mortality over 12 years of follow-up among older MAs (n=394) and EAs (n=355) in the San Antonio Longitudinal Study of Aging (SALSA) and examined differences between ethnic groups. HAI incorporates non-invasive measures (systolic blood pressure, forced vital capacity, creatinine, fasting plasma glucose (FPG), Mini-Mental State Exam). Missing baseline data for HAI components and covariates were imputed using multiple imputations. Proteinuria was used instead of creatinine due to non-availability. Scores of 0, 1, 2 were given from lowest to highest tertile HAI categories; diagnosis of diabetes, hypertension, and renal failure were included in the highest tertiles. Cox proportional hazards models estimated the association between HAI and mortality, adjusting for confounders. After adjusting for age, gender, education, income, BMI, smoking and ethnicity, HAI was independently associated with mortality (HR 1.25 (1.16-1.35), p-value <0.0001). We found no interaction effect between HAI and ethnicity on mortality ((p-value for interaction = 0.78). In the SALSA sample, HAI is a predictor of mortality after adjusting for confounders in both MAs and EAs. The absence of a significant HAI*ethnicity interaction effect further demonstrated that HAI works equally well as a predictor of mortality in both MAs and EAs.

THE HIGH COST OF SOCIOECONOMICS ON RACIAL DISPARITIES IN HEALTHY AGING

Race-related differences in overall health across the age-span are well established; how differences manifest within a cohort selected for good functional status is unclear. Using data from Black and White older adults aged 70-79, in the Health, Aging and Body Composition (Health ABC) study, we created a healthy aging index (HAI) of mental health, fitness, lung capacity, bone mineral density, inflammation, and metabolic syndrome. We assessed if racial differences existed in HAI, the extent education, financial resources and stress attenuated any observed differences, and whether this varied by site (Memphis v. Pittsburgh). Blacks had lower HAI than whites, adjusted for age and site. Further adjustment for finances and education eliminated the effect of race in women and reduced the effect in men by 64%. A significant interaction between site and financial stress was observed. Future research should assess ways to reduce the harmful impact of low socioeconomic status on health.

Individual healthy aging indices, measurements and scores.

The positive gerontological approach to aging has resulted in successive terminologies to describe the process of aging, including successful aging, active aging, healthy aging, or healthy and active aging, amongst others. Each definition proposed by geriatricians, psychologists, sociologists or public health specialists has been based on specific aspects of aging that are most important to the authors' discipline, explaining the current difficulty in determining which is the best set of criteria to determine "good aging". Two successive analyses of the measurements used in longitudinal studies from 1989 to 2018 testify to this heterogeneity in the types of questions proposed to evaluate the quality of the individual aging process. To confront this complexity, new and integrated indices have successively been proposed to quantify and qualify the survival period of aging individuals. The present paper aims to describe and compare the value of the "healthy aging index", the "modified healthy aging index", the "healthy aging score" and the "selfie aging test". Attempts to date to identify the best individual measurement of "aging well" have been interesting, and certainly show promise, but their limitations to specific populations call for more concerted effort from the scientific community to obtain worldwide validation. Another option would be to identify the best self-assessment questionnaire and include it in a mobile device, enabling longer term personal follow-up of aging functions. There is a clear lack of data of this type at present, and an urgent need to obtain such information, to enable early and targeted interventions.

Socioeconomic determinants of healthy ageing: evidence from the English Longitudinal Study of Ageing

BackgroundMany studies done in England have shown that, with better socioeconomic conditions, individual healthy ageing can be achieved and maintained. Only a few studies, however, have attempted to identify the most sensitive socioeconomic determinants of healthy ageing. We aimed to create a comprehensive measure of healthy ageing for the English older population, and compare educational, income, and wealth inequalities in healthy ageing to identify the sensitive determinants. Methods6590 participants (≥60 years) from the English Longitudinal Study of Ageing (ELSA) were included (3685 [55·9%] women). Individuals without healthy ageing outcomes and baseline weights were excluded. A healthy ageing index (HAI) (0 least healthy to 100 most healthy) with 33 indicators of physical, cognitive, and physiological functions, and psychological and social wellbeing, was created. Variables such as Instrumental Activities of Daily Living, date-naming tests, Centre for Epidemiological Studies Depression Scale scores, self-reported chronic diseases, and participation in social activities were included. Socioeconomic rank scores were derived (0 most advantaged, 1 least advantaged), and multilevel modelling was applied to estimate the longitudinal association between educational, income, and wealth rank scores and HAIs. The slope index of inequality (SII) was calculated to show the HAI gaps between the most and least advantaged educational, income, and wealth groups. The healthy ageing trajectory was predicted using results of multilevel modelling. FindingsIf participants moved from the highest to the lowest levels of education and wealth, their HAIs would decrease by, respectively, 8·2% (95% CI 6·0–10·4) and 6·2% (4·9–7·5) at 60 years. At 90 years, SIIs for HAIs by education and wealth were around 9·3% (95% CI 7·3–10·5) and 7·6% (6·2–13·0), respectively. Income rank scores were not significantly associated with HAIs. InterpretationOur HAI showed that English participants were generally healthy. However, socioeconomic inequalities in healthy ageing still existed. After 60 years of age, education and wealth were the two most sensitive determinants of healthy ageing, respectively. Identifying sensitive socioeconomic determinants could help policy makers make efficient strategies to minimise inequalities in healthy ageing. Also, HAIs can be applied as a preliminary screening of people over 60 years of age, which might help clinicians to identify patients' healthy ageing profiles in both prevention and intervention trials. FundingUniversity College London Overseas Research Scholarship and the China Scholarship Council (file no 201608060385) (for WL), Economic and Social Research Council (award no ES/R008930/1) (for AS).

Effect of Intentional Weight Loss on Mortality Biomarkers in Older Adults With Obesity.

Observational research has identified several mortality biomarkers; however, their responsiveness to change is unknown. We tested whether the Healthy Aging Index (HAI) and other mortality biomarkers were responsive to intentional weight loss (WL), which is associated with lower mortality risk in recent meta-analyses. Older adults (70.3 ± 3.7 years) with obesity were randomized into a 6-month WL (n = 47) or weight stability (WS: ±5% baseline weight; n = 48) program. Baseline and 6-month HAI score (0-10) was calculated from component sum (each 0-2: systolic blood pressure, forced vital capacity [FVC], creatinine, fasting blood glucose [FBG], Montreal Cognitive Assessment), and gait speed, grip strength, Digit Symbol Substitution Test, FEV1, Interleukin-6, C-Reactive Protein, and Cystatin-C were assessed at baseline and 6 months. Mean baseline HAI was 3.2 ± 1.6. By 6 months, WL participants lost 8.87 (95% CI: -10.40, -7.34) kg, whereas WS participants remained weight stable. WL group reduced HAI score (WL: -0.75 [95% CI: -1.11, -0.39] vs WS: -0.22 [95% CI: -0.60, 0.15]; p = .04), and components changing the most were FBG (WL: -3.89 [95% CI: -7.78, 0.00] mg/dL vs WS: 1.45 [95% CI: -2.61, 5.50] mg/dL; p = .047) and FVC (WL: 0.11 [95% CI: -0.01, 0.23] L vs WS: -0.05 [95% CI: -0.17, 0.08] L; p = .06). Among other biomarkers, only Cystatin-C significantly changed (WL: -2.53 [95% CI: -4.38, -0.68] ng/mL vs WS: 0.07 [95% CI: -1.85, 1.98] ng/mL; p = .04). Combining treatment groups, 1 kg WL was associated with a 0.07 (95% CI: 0.03, 0.12) HAI reduction (p < .01). Intentional WL via caloric restriction reduced HAI score by 0.53 points, largely attributable to metabolic and pulmonary improvements.

Mortality in Relation to Changes in a Healthy Aging Index: The Health, Aging, and Body Composition Study.

Baseline scores on a Healthy Aging Index (HAI), including five key physiologic domains, strongly predict health outcomes. This study aimed to characterize 9-year changes in a HAI and explore their relationship to subsequent mortality. Data are from the Health, Aging, and Body Composition study of well-functioning adults aged 70-79 years. A HAI, which ranges from 0 to 10, was constructed at years 1 and 10 of the study including systolic blood pressure, forced expiratory volume, digit symbol substitution test, cystatin C, and fasting glucose. The relationships between the HAI at years 1 and 10 and the change between years and subsequent mortality until year 17 were estimated from Cox proportional hazards models. Two thousand two hundred sixty-four participants had complete data on a HAI at year 1, of these 1,122 had complete data at year 10. HAI scores tended to increase (i.e. get worse) over 9-year follow-up, from (mean [SD]) 4.3 (2.1) to 5.7 (2.1); mean within-person change 1.5 (1.6). After multivariable adjustment, HAI score was related to mortality from year 1 (hazard ratio [95% confidence interval] = 1.17 [1.13-1.21] per unit) and year 10 (1.20 [1.14-1.27] per unit). The change between years was also related to mortality (1.08 [1.02-1.15] per unit change). HAI scores tended to increase with advancing age and stratified mortality rates among participants remaining at year 10. The HAI may prove useful to understand changes in health with aging.

Index of Healthy Aging in Chinese Older Adults: China Health and Retirement Longitudinal Study.

To characterize the distribution of an index of healthy aging-the Chinese Healthy Aging Index (CHAI)-in Chinese adults aged 60 and older according to sociodemographic characteristics and geographic region and to examine the association between the CHAI and mortality, disability, and functional limitation over 4 years. Nationally representative cohort study. China Health and Retirement Longitudinal Study. Chinese adults aged 60 and older (N=3,740). Six CHAI components (systolic blood pressure, peak expiratory flow, Telephone Interview for Cognitive Status, estimated glomerular filtration rate, fasting glucose, C-reactive protein) were scored 0 (healthiest), 1, and 2 (unhealthiest) according to sex-specific tertiles or clinically relevant cut-points and summed to construct the CHAI (range 0-12). Mean CHAI score was 5.6; 5.7% had a score of 0 to 2 (healthiest), 23.0% a score of 3 or 4, 37.5% a score of 5 or 6, and 33.8% a score of 7 to 12 (unhealthiest). Participants who were younger, more educated, and married were much more likely to have an ideal CHAI profile (score 0-2). Age-adjusted prevalence of an ideal CHAI profile ranged from 1.7% in the south to 8.1% in the north. After multivariable adjustment, persons with a CHAI score of 3 to 12 had substantially higher odds of mortality, disability, and functional limitation than those with a score of 0 to 2. The CHAI further stratified outcomes for persons with no clinically recognizable comorbidities. Substantial variation exists in the CHAI according to sociodemographic characteristics and geographic regions. The CHAI could identify Chinese elderly adults with low risk of adverse outcomes and provide incremental value for risk prediction beyond clinically diagnosed comorbidities.

Determinants of healthy ageing and its relation to 10-year cardiovascular disease incidence: the ATTICA study.

The aim of the study was to understand determinants of ageing in relation to future cardiovascular disease (CVD) events at population level, and to explore determinants of healthy ageing and its relation to 10-year CVD incidence among apparently healthy individuals. In the context of the ATTICA study 453 apparently healthy men (45±13 years) and 400 women (44±18 years) with complete psychological evaluation were studied and followed during 2002-2012. Healthy Ageing Index (HAI) (range 0-7) was calculated based on socio-economical, bio-clinical, psychological, and lifestyle characteristics (i.e. dietary habits and physical activity) of the participants during the baseline examination. CVD incidence during the follow-up period was defined according to ICD-10 criteria. Healthy Ageing Index was inversely associated with higher 10-year CVD risk (OR per 1/7 (95% CI): 0.47 (0.28, 0.80). Age and sex-adjusted determinants of healthy ageing were abnormal waist to hip ratio (p<0.001), increased coffee consumption (p=0.04), reduced basic metabolic rhythm (p<0.001), increased triglycerides (p=0.003), and C-reactive protein levels (p=0.02), as aggravating factors, while moderate alcohol consumption (p=0.002) was identified as a positive influential parameter. Understanding healthy ageing, as a dominant factor of CVD development, provides a new direction for better prevention efforts focused on healthy ageing at both population and individual level.

Metabolite Profiles of Healthy Aging Index Are Associated With Cardiovascular Disease in African Americans: The Health, Aging, and Body Composition Study.

Metabolic dysfunction is a hallmark of differential aging, specifically in African Americans. Investigation of systemic metabolic state, multiorgan aging, and long-term cardiovascular outcome in African Americans has not been reported. We studied 291 African American males in the Health, Aging, and Body Composition (Health ABC) study to identify circulating metabolites related to the Newman healthy aging index (HAI; a multiparametric score comprised of blood pressure, blood glucose, neurocognitive function, creatinine, and forced vital capacity). We examined the relationship of selected metabolites differential abundant at the extremes of HAI with long-term survival from cardiovascular mortality. The median age was 73 years. We identified 19 metabolites differentially expressed in blood in 86 study participants at the extremes of HAI (HAI 0-3: N = 30 vs 8-10: N = 56). At a median follow-up of 10 years, 78 participants (27 per cent) died from cardiovascular causes. After adjustment for age, body mass index, presence of prevalent cardiovascular disease, creatinine, and HAI, six of these 19 metabolites were associated with long-term cardiovascular mortality. Although several metabolites had been previously reported in Caucasians (eg, isocitrate), we identified several metabolites with unreported association with cardiac disease. Metabolites associated with HAI and cardiac death in African Americans specified pathways relevant to nitric oxide, oxidative stress, mitochondrial function, urea cycle, and gut microbial metabolism. Metabolite profiling in African Americans identified known and novel metabolic pathways linked to HAI and cardiovascular death. Further investigation in larger patient cohorts is required to uncover race-based signatures of cardiovascular disease with aging.

A Modified Healthy Aging Index and Its Association with Mortality: The National Health and Nutrition Examination Survey, 1999-2002.

Comorbidity indices that are based on clinically recognized disease do not capture the full spectrum of health. The Healthy Aging Index (HAI) was recently developed to describe a wider range of health and disease across multiple organ systems. We characterized the distribution of a modified HAI (mHAI) by sociodemographics in a representative sample of the U.S. population. We also examined the association of the mHAI with mortality across individuals with different levels of clinically recognizable comorbidities. Data are from the National Health and Nutrition Examination Survey (1999-2000, 2001-2002) on 2,451 adults aged 60 years or older. Five mHAI components (systolic blood pressure, Digit Symbol Substitution Test, cystatin C, glucose, and respiratory problems) were scored 0 (healthiest), 1, or 2 (unhealthiest) by sex-specific tertiles or clinically relevant cutoffs and summed to construct the mHAI. The mean mHAI score was 4.3; 20.6% had a score of 0-2. 33.2% had a score of 3-4, 31.0% had a score of 5-6, and 15.2% had a score of 7-10. Mean mHAI scores were lower in adults who were younger, non-Hispanic whites, more educated, and married/living with partner. After multivariate adjustment, per unit higher of the mHAI was associated with higher all-cause mortality (HR = 1.19, 95% CI = 1.11-1.27) and higher cardiovascular mortality (HR = 1.23, 95% CI = 1.11-1.35). Within each comorbidity category (0, 1, 2, 3, 4+), the mHAI was still widely distributed and further stratified mortality. Substantial variation exists in the mHAI across sociodemographic subgroups. The mHAI could provide incremental value for mortality risk prediction beyond clinically diagnosed chronic diseases among elders.

Association of an Index of Healthy Aging With Incident Cardiovascular Disease and Mortality in a Community-Based Sample of Older Adults.

The healthy aging index (HAI) was developed as a marker of health in multiple systems that can identify individuals who age most successfully. We calculated an HAI in 934 Framingham Offspring Study participants aged 60 or older at baseline. Heart rate and C-reactive protein (CRP) were added in modified versions of the HAI. Cox proportional hazard models were used to quantify the association of the HAI with mortality, cardiovascular disease (CVD), and cancer. We used fully conditional specification to multiply impute missing values for HAI components, increasing the sample size by 44%. Over 10 years of follow-up, there were 138 deaths, 103 incident cases of CVD, and 138 incident cases of cancer. In models adjusted for age, sex, and behavioral risk factors, the HAI was associated with mortality (hazard ratio [HR] per unit of HAI 1.24, 95% confidence interval [CI] 1.13-1.36) and with CVD (HR 1.27, 95% CI 1.13-1.42), but not with cancer (HR 1.01, 95% CI 0.91-1.11) in observed (non-missing) data. In multivariable models further adjusting for prevalent diseases, results were slightly attenuated. When including heart rate and CRP, a modified HAI gave stronger associations. Results with imputed data are similar to results from complete case analyses. In our large community-based sample, the HAI is a strong predictor of mortality and CVD. Other factors that are strongly associated with mortality, such as heart rate and CRP can improve the ability of the HAI to identify the healthiest older adults.

Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study.

BackgroundThe Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes.MethodsWe performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests.ResultsWe found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3’ UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL).ConclusionsSequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12877-016-0253-y) contains supplementary material, which is available to authorized users.