- Research article
- Open Access
- Open Peer Review
Indicators of "Healthy Aging" in older women (65-69 years of age). A data-mining approach based on prediction of long-term survival
- William R Swindell1, 2Email author,
- Kristine E Ensrud†3,
- Peggy M Cawthon†4,
- Jane A Cauley†5,
- Steve R Cummings4,
- Richard A Miller1, 2, 6 and
- Study Of Osteoporotic Fractures Research Group
© Swindell et al; licensee BioMed Central Ltd. 2010
- Received: 25 January 2010
- Accepted: 17 August 2010
- Published: 17 August 2010
Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF).
We considered only the youngest subjects (n = 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a "frail" phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics.
Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR ≥ 0.879 or RH ≤ 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03).
The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the "healthy aging" concept.
- Risk Score
- Contrast Sensitivity
- Healthy Aging
- Frailty Index
- Physical Performance Measure
Prediction of long-term survivorship and health outcomes among older individuals depends upon the recognition of factors that contribute to healthy aging. Individuals that exhibit healthy aging patterns maintain high quality of life into late stages of the life span, with few daily living impairments and a near absence of age-related disease [1, 2]. On the other hand, an unhealthy aging trajectory is associated with the onset of comorbid diseases, diminished quality of life, and increased mortality risk [1, 2]. From the standpoint of geriatric care, the ability to predict future changes in health is of great importance, since this can improve the efficiency and quality of care by identifying at-risk individuals, helping to target preventative measures most effectively . Development of prognostic models can also provide new tools for investigations into aging mechanisms. One view, for instance, suggests that variation in aging outcomes is attributable to different rates of aging, with increased healthspan in some individuals due to a reduced "biological" relative to chronological age [4–9]. If this notion is correct, quantitative characterization of the "biological age" construct should provide a powerful prognostic device, and statistical models optimized to predict long-term survival should, by direct design or not, be based upon measures that most closely track the progression of aging . This reasoning suggests that construction of predictive models can point towards aging biomarkers, which are essential for investigating aging mechanisms in long-lived organisms, for which survival is not a practical endpoint for scientific study. In humans, progress along these lines would be of great value, since clinical trials have been initiated or completed for evaluation of several anti-aging interventions [11–13], but there is no consensus on which experimental measures are in fact most suitable for detection of decelerated aging.
Healthy aging should be recognizable based upon a well-chosen set of measurements from a broad range of domains, which collectively reflect the multifaceted effects of aging, and the interdependence of these effects among organ systems . Currently, however, while multiple sets of measurements have been proposed as indices of "frailty" in the elderly [14–22], there is no analogous set of measures to define a healthy aging phenotype in middle-aged or otherwise healthy adults. The absence of an objective and well-validated summary measure of health status has placed limits on insights derived from well-designed studies. Caloric restriction, for example, may slow the rate of intrinsic aging in middle-aged adults, and clinical investigations have shown that this intervention induces favorable shifts of individual variables, including body mass index, lipid profiles, body fat, inflammatory markers and insulin sensitivity . It is unclear, however, which of these measures are most robustly associated with long-term survival, which are linked to one or instead to multiple disease processes, which provide independent versus redundant information, and which might ultimately constitute a combination of variables that best characterizes the "healthy aging" concept. To some degree, measures that best characterize "healthy aging" are likely to overlap with those that characterize "frailty", such that indices that identify "frail" individuals may also serve to detect healthy aging as well . At the same time, however, some pre-frail and apparently healthy adults may not yet exhibit age-related disease traits and the advanced deficit accumulation to which many frailty measures are particularly sensitive. Potentially, therefore, standard measures of frailty may not provide optimal tools for characterization of "healthy aging" in younger cohorts that exclude the oldest old. A well-known frailty index, for example, did not include a measure that directly relates to individual smoking or diabetes status , but these are certainly key factors underlying aging trajectory and long-term survivorship.
Measures that best characterize "frailty" have often been validated based on their ability to predict survival with respect to heterogeneous populations that include healthy younger subjects in combination with frailer older subjects [16, 21, 22]. In the study of Fried et al. , for example, a frailty index was validated based upon its ability to predict short-term (8-year) survival among subjects that varied between 65 and 101 years of age. For characterization of healthy aging, we suggest that greater emphasis be placed on prediction of more long-term survival outcomes with respect to reasonably healthy cohorts in which few subjects suffer from advanced age-related disease [4, 25]. Given this criteria, there are numerous variables that, based upon prior epidemiological studies, might conceivably be included within a "healthy aging" index. Simple body composition measures, such as body mass index and waist circumference, are easily obtained during a clinical interview, and are widely used as informal indicators of mortality risk from cardiovascular disease and cancer [26–32]. The most useful variables, however, may in principle not be directly linked to any single disease process, but might instead reflect a subject's overall health and fitness [33–35]. These measures, which have generally not been evaluated in human studies of anti-aging interventions, include tests of mental function , hand-grip strength , visual acuity , walking ability , or indices based upon total accumulation of "deficits" or health disorders [19, 34, 35, 40–43]. A wide range of measures at cellular and physiological levels, such as blood glucose , telomere length , inflammation markers , and bone turnover rates , could potentially provide further information regarding future health status of subjects. Moreover, in humans, feedback generated from standardized questionnaires can provide inexpensive, easily obtained, and potentially valuable prognostic data for prediction of both lifespan and healthspan [48, 49]. Given all the variables previously shown to predict survival in certain cohorts, a key challenge is to establish the relative value of individual variables, and to isolate those that can be used to form a parsimonious predictive model. For this purpose, data mining methods applied to large datasets are especially useful , and such approaches can be used to derive evidence-based indices that will ultimately bolster efficiency in both clinical and research settings [50–54].
The goal of this study was to identify variables useful for prediction of long-term survival within a cohort of women between the ages of 65 and 69, and to develop a multivariate index of healthy aging that can potentially provide a clinical and research tool. We analyze data from 4,097 women (65 to 69 years of age) enrolled between 1986 and 1988 in the Study of Osteoporotic Fractures (SOF) and followed prospectively over an average of 16.1 years for mortality. This cohort was well-suited for our investigation, since most subjects were healthy at baseline examinations, with low incidence of advanced age-related disease. Additionally, all subjects in the SOF study had been evaluated with respect to hundreds of variables relating to body composition, visual acuity, physical performance and function, demographic measures, mental health, lifestyle practices, current and previous medication use, and family history. The data we consider thus represent a valuable resource for establishing the relative prognostic value of numerous phenotypic traits, and for identifying indices of variables that best predict long-term survival, which may lead to standardized criteria to characterize the phenotype that precedes successful aging.
The Study of Osteoporotic Fractures (SOF) is a prospective cohort study of community-dwelling Caucasian women over the age of 65. Participants were recruited between September 1986 and October 1988 based upon community-based listings (e.g., health plan membership lists, voter registration lists, and Department of Motor Vehicle tapes) from four separate regions in the United States (The Kaiser Foundation Research Institute and Center for Health Research, Portland, Oregon; The Epidemiology Clinical Research Center at the University of Minnesota, Minneapolis, Minnesota; Monongahela Valley Clinic of the University of Pittsburgh, Monessen, Pennsylvania; and The University of Maryland Osteoporosis Clinic, Baltimore, Maryland). The SOF was originally designed to identify risk factors for osteoporotic fractures, and therefore excluded women unable to walk without assistance and women with bilateral hip replacements. Black women were also excluded from the baseline exam due to their low incidence of hip fracture. All subjects provided written consent at entry into the study and at each clinical examination. The protocol and consent form were approved by the institutional review boards (IRB) at all participating institutions (i.e., Kaiser Permanente Northwest IRB; University of Pittsburgh IRB; University of Minnesota IRB; California Pacific Medical Center IRB; University of Maryland, School of Medicine Human Research Protections Office; University of California, San Francisco Committee on Human Research).
The complete SOF cohort included 9704 women between the ages of 65 and 89, with a median age of 70 years (Additional File 1). Since the main purpose of our analysis was to identify factors associated with healthy aging and long-term survivorship, it was desirable to exclude older subjects, since this group was much more likely to be short-lived and more likely to exhibit symptoms associated with frailty . Our analysis therefore focuses on the 4097 SOF participants less than 70 years of age at the baseline examination (Additional File 1). Mortality assessments were completed in this cohort at 4-month intervals by mail or phone contact of participants (or family member proxies). For each death, an official death certificate was obtained and, when possible, a hospital discharge summary as well. This information was used by one or more epidemiologists to assign cause of death, in accordance with International Classification of Disease, Ninth Revision, Clinical Modification (ICD9) codes. It should be noted that, in general, some level of classification error is likely in the assignment of ICD9 codes, particularly among older individuals in whom comorbidities are frequent. Nevertheless, atherosclerosis was the most frequently assigned cause of death, and was assigned in 467 of 1523 (30.7%) documented cases (ICD9 = 425; ICD9 = 429.2; 440 ≤ ICD9 < 445; ICD9 = 428; 401 ≤ ICD9 < 405; 410 ≤ ICD9 < 415; ICD9 = 798; 430 ≤ ICD9 < 439. Cancer deaths were also common, and cancer was assigned as the cause in 426 of 1523 (27.8%) of documented deaths (140 ≤ ICD9 < 240). More than one-third (38.5%) of all deaths, however, were attributable to neither to atherosclerosis nor cancer (586 of 1523 deaths). There were 25 deaths due to injury and poisoning (ICD9 > = 800) and an additional 19 deaths due to other external causes of injury (ICD9 E800-E990), and in these cases, the survival times of subjects were right-censored (Additional File 1).
Predictor Variables and Data Pre-processing
After recruitment of the SOF cohort, study participants have periodically returned to clinical centers for as many as nine visits. In our analyses, however, we have utilized only baseline variable measurements recorded in the first visit of each SOF participant (occurring between September 1986 and October 1988). All variables included in our analysis, therefore, are obtainable during a single clinical examination and interview. These variables can roughly be categorized as anthropomorphic (e.g., height), demographic (e.g., age and race), blood pressure and pulse measures, cognitive function (i.e., short mini mental status exam), family history, female history (e.g., prior pregnancies, hysterectomy), physical function and performance (e.g., number of seconds to complete 5 stands), medical history, medication inventory, lifestyle survey (e.g., caffeine use) and evaluation of vision (e.g., contrast sensitivity scores). Altogether, the complete dataset included measurements of 465 variables on the 4097 subjects. We screened these variables, and excluded from further analysis those with more than 5% missing values. Following this pre-processing, there remained 300 variables (146 continuous, 28 ordinal and 126 categorical) upon which further analyses were based. We applied Grubb's test to each continuous variable to check for the presence of outliers , and log-transformed 77 continuous variables for which there was strong evidence indicating that at least one outlying observation was present (Grubb's test; P < 10-3). All categorical variables with n = 2 classes were coded as a single 0-1 indicator variable. For categorical variables with n > 2 classes, we recoded the variable as a set n of 0-1 indicator variables, with a single 0-1 indicator variable representing each of the n classes. We do not use n - 1 indicator variables in such cases, because prior to variable selection, it was uncertain which indicators would be selected in a final model, and it was expected that our variable selection strategy would ultimately identify an appropriate (and non-redundant) subset of the n indicators for any categorical variable with n > 2 classes. This recoding of categorical variables yielded a total of 203 categorical variables (each with 0-1 coding). The complete analysis, therefore, was based upon a total of 377 variables (146 continuous, 28 ordinal and 203 categorical). Further pre-processing of variables to eliminate those highly correlated with other variables was unnecessary at this stage, since this would be accomplished as part of model-building procedures. A complete list of the 377 variables, along with 88 excluded from the analysis, is provided as supplementary data (see Additional File 2).
All variables included in the analysis contained fewer than 5% missing data, and most variables included much less than 5% missing data. Among all variables we considered, missing values accounted for, on average, only 0.40% of data entries. For these missing data, it was not feasible to perform multiple imputations, given that our analyses were computationally expensive (see below). We therefore employed a k-nearest-neighbor imputation approach , in which missing values were replaced based upon the k = 20 nearest neighbors of a given subject for which data was missing. The nearest neighbors of each subject were identified based upon the Euclidean distance across all variables. These variables included both continuous and categorical variables, of which the Euclidean distance is meaningful only for the former. For the purpose of nearest-neighbor computations, therefore, categorical variables were expressed as 8 standard coordinates obtained by multiple correspondence analysis (with retention of 70% of the variance associated with the entire set of 126 categorical variables). Once nearest neighbors were identified for a subject with missing data, we imputed missing data using the average value among the k = 20 neighbors for continuous variables, the median value among the k = 20 neighbors for ordinal variables, and the modal value among the k = 20 neighbors for categorical variables.
Statistical Assessment of Prognostic Value
One criterion for evaluating an index for healthy aging is that the index should discriminate among subjects based upon their observed survival times. For an index generated from a Cox regression model, this output corresponds to a "risk score" (i.e., linear predictor), which is proportional to the predicted mortality risk of a subject relative to all other subjects. This risk score is a useful diagnostic index if subjects assigned high scores tend to be short-lived, and if subjects assigned low scores are usually long-lived. Along these lines, we evaluated predictive capacity of models based upon a cross-validation criterion, in which a model is first constructed using one set of subjects (i.e., a training set), and then used to generate risk scores for a distinct set of subjects (i.e., a testing set). The predictive capacity of the model is measured according to how well risk scores discriminate among short and long-lived subjects within the testing set. In our analyses, training data consisted of randomly selected sets of 3687 subjects (90% of cohort), while testing data consisted of the remaining 410 subjects (10% of cohort). Prognostic capacity is measured according to a concordance index (based on time-specific area under the curve (AUC) statistics; see below), which measures the degree to which high risk scores are assigned to short-lived subjects, and the degree to which low scores are assigned to long-lived subjects. For a given model, this process is repeated 10,000 times, and the average concordance score among all simulation trials is computed. This cross-validation approach, involving random splitting of subjects into "training" and "testing" sets, provides a good indication of how well the model characterizes meaningful patterns in the data that are useful for prediction, rather than random noise .
The estimated (incident) sensitivity rate forms the vertical axis of the time-specific ROC curve and reflects the ability of risk scores to accurately identify subjects that die at time t (i.e., true-positive identification rate). The estimated (dynamic) specificity rate is used to calculate false-positive rates, which forms the horizontal axis of the time-specific ROC curve, reflecting the degree to which high risk scores are incorrectly assigned to subjects living beyond time t (i.e., 1 - Specificity).
Values of AUC(t) and C are used in our analyses to evaluate the ability of models, developed on a randomly selected training set, to discriminate among the survival outcomes of subjects within a testing data set. In our analyses, data are randomly split 10,000 times into training and testing sets, and values of AUC(t) and C are estimated with each iteration. Discrimination ability of models over all iterations is measured by averaging values of AUC(t) and C across all 10,000 simulations.
Best Univariate Predictors of Long-term Survival
Variables with greatest predictive value in univariate Cox PH models
Mean Concordance (C)
Number of step-ups completed in 10 seconds
Response to Question: How is your health compared to others your age? (categories: excellent, good, fair, poor, very poor)
Average step length, Usual Pace (m)
Walking speed, Usual Pace (m/s)
Contrast sensitivity score, average of high and low spatial frequencies
Contrast sensitivity score, average of low spatial frequencies
Impairments associated with daily living, index of 6 tasks
Contrast sensitivity score, average of low spatial frequencies, normalized
Impairments associated with daily living, index of 5 tasks (walking 2 or 3 blocks, climbing up 10 steps, climbing down 10 steps, preparing own means and doing housework).
Contrast sensitivity score, average of low and high spatial frequencies, normalized
We considered whether the associations between Table 1 variables and survival might be indirect, and due primarily to correlations with, for example, smoking or diabetes history. We thus evaluated whether hazard ratio estimates of Table 1 variables remained significant in bivariate Cox PH models that included a second measure of potential importance. For each Table 1 variable, we estimated a hazard ratio in 376 separate bivariate models, representing all possible bivariate models that include the Table 1 variable, given the total number of variables included in the analysis (i.e., 377 variables). For each Table 1 variable, significance of the estimated hazard ratio was not altered within any bivariate Cox PH model, with only two exceptions. First, contrast sensitivity measures became non-significant (P > 0.05) if another contrast sensitivity measure was included in the same model, and second, the 5- and 6-variable functional status indices were non-significant if both were included within the same model. Otherwise, each variable shown in Table 1 remained significant after adjustment for any of the other variables included in the analysis, including smoking history, diabetes history and baseline age.
Data mining strategy for building a predictive model
We searched for less complex models that might have performed equally well, by dropping each of the 13 terms one-at-a-time, and evaluating the mean concordance associated with each resulting 12-variable model. However, we were unable to identify a smaller model that yielded a mean concordance value equal to or greater than 0.673. Overall, based on the process described above, the 13-variable model we identified is likely to be a high-quality model, but our partly heuristic variable selection process does not exclude the possibility that another of 4.05 × 1023 possible 13-variable models might exhibit better performance. Lastly, we note that AUC values associated with our model are small (Figure 7) relative to some that have been reported in the literature for prediction of survival . In part, this may reflect characteristics of the cohort we evaluated, which did not include shorter-lived older subjects for which it is usually easier to generate accurate mortality forecasts (e.g., see Figure 2B). Additionally, AUC values reported in our study may differ from those of some previous investigations due to differences in overall cohort size, the set of predictor variables used to build prognostic models, the type of cross-validation procedure implemented (e.g., ten-fold, five-fold, leave-one-out, etc.), or the method used to calculate AUC values.
A 13-variable Index that Predicts Long-Term Survival in Women 65-69 Years of Age
Multivariate index for prediction of long-term survival
Number of step-ups completed in 10 seconds1
0.832 (< 0.001)
Smoking: indicator with value 1 if subject is a current smoker
2.354 (< 0.001)
Diabetes: indicator with value 1 if a subject is not diabetic
0.443 (< 0.001)
Age at baseline examination (65 - 69 for all subjects)
1.146 (< 0.001)
Response to Question: How is your health compared to others your age? (categories: excellent, good, fair, poor, very poor)
1.205 (< 0.001)
Smoking: indicator with value 1 if subject is a past smoker
1.390 (< 0.001)
Contrast sensitivity score, average of high and low spatial frequencies2
0.879 (< 0.001)
Pulse Lying Down (beats/60 seconds)3
1.131 (< 0.001)
Hypertension: indicator with value 1 if systolic blood pressure exceeds 160, diastolic blood pressure exceeds 90, or if subject used thiazide4
1.324 (< 0.001)
Past thiazide use: indicator variable with value 1 if the subject has previously used thiazide
1.785 (< 0.001)
Height change since the age of 25 (self-reported at baseline exam)
1.137 (< 0.001)
Participant's clinic throughout the study: indicator with value 1 if subject has attended clinic B
1.357 (< 0.001)
Marriage: indicator with value 1 if subject was married at the time of the baseline examination
0.822 (< 0.001)
The 13 variables did not include biochemical measures, but we evaluated whether risk scores generated from the model were associated with blood serum measures obtained from a small SOF cohort during the baseline visit (n ≤ 400 for each of 37 measures considered). We found that model risk scores were positively associated with C-reactive protein (r s = 0.20; P = 0.013, n = 151), chorionic gonadotropin (r s = 0.18; P = 0.026; n = 150), and 1,25-hydroxyvitamin D (r s = 0.16; P = 0.048; n = 151), but negatively associated with 25-hydroxyvitamin D (r s = -0.21; P = 0.010; n = 152). In each case, the observed correlations, though significant, were modest in magnitude (|r| ≤ 0.21.
We evaluated whether variables included in our model were age-sensitive and whether the prognostic value of the model and component variables extended to older subjects in the SOF cohort (i.e., 70 - 89 years of age; Additional Files 3 and 4). First, to evaluate the age-sensitivity of variables within the model, we carried out a cross-sectional analysis based upon all 9704 subjects within the SOF cohort (i.e., ages 65 - 89; Additional File 3). This analysis demonstrated that certain continuous variables within the model exhibited monotonic or near-monotonic trends across age groups (e.g., contrast sensitivity scores, number of step-ups completed in 10 seconds, pulse lying down, self-reported height loss since age 25; see Additional File 3), although we note that inferences concerning age-associated trends based upon cross-sectional data should be treated with caution. We next used cross-validation methods to determine whether the model accurately forecasted survival patterns among the older SOF subjects excluded from the above analyses (i.e., subjects 70 - 89 years of age; Additional File 4). Among older SOF subjects, the model predicted survival patterns with greater accuracy than expected on the basis of chance alone, with a mean concordance estimates of 0.630 among those aged 70-74 (n = 3033), 0.616 among those aged 75-79 (n = 1538), 0.586 among those aged 80-84 (n = 765), and 0.584 among those aged 85-89 (n = 228) (see Additional File 4; each estimate based on 10,000 cross-validation simulations). The progressive decline of predictive performance among increasingly older SOF cohorts may be attributed to the fact that the index was developed for prediction of (long-term) survival with respect to a relatively young cohort (age 65-69), and thus includes variables for which prognostic value declines with age (i.e., smoking and diabetes status at baseline; see Figures C and D from Additional File 4).
Previous studies have shown that random choice of variables can often provide high-quality models that predict short or long-term survival, suggesting that prediction accuracy may in some cases not be sensitive to an exact specification of model variables [40, 61]. To evaluate this possibility, we compared the performance of our 13-varible model to models formed by choosing variables randomly from the 377 variables evaluated on the cohort we studied (Additional File 5). This showed that random selection of variables can indeed lead to models that perform moderately well (0.583 ≤ mean C ≤ 0.633, depending upon how the pool of eligible variables is filtered), but in each case performance was poorer and less consistent relative to that of the 13-variable model described in Table 2 (see Additional File 5). We note, however, that our model includes only 13 variables, and that exact variable specification is likely to be less important for larger models that include more variables . Lastly, we compared performance of our 13-variable model to that of models in which an index quantifying accumulation of many deficits served as the predictor variable  (Additional File 6). This analysis showed that a deficit index generated survival forecasts more accurate than expected on the basis of chance alone (0.584 < mean C < 0.630) (Additional File 6, Figures C - F). Among the youngest SOF subjects (age 65-69), our 13-variable index outperformed the deficit index (mean C of 0.673 versus 0.617), but this advantage progressively declined as we examined increasingly older SOF cohorts (Additional File 6, Figures C - F).
Evaluation of the Index as a Measure of "Healthy Aging"
An index that characterizes "healthy aging" should not primarily reflect a single major disease process, but should instead be sensitive to multiple forms of age-related disease, and should indeed predict multiple age-associated outcomes besides survival alone. The index developed above was thus further evaluated along these lines in order to judge its value as a comprehensive measure of healthy aging. We first evaluated whether the index was able to characterize multiple forms of age-related disease, which appeared plausible, since some variables included in the index (e.g., number of step-ups completed and contrast sensitivity) had a similar association with mortality regardless of the assigned cause of death (see Figure 4). We therefore hypothesized that model output would be informative with respect to sub-populations of subjects that appeared to develop different types of age-related pathology.
The prospects of long-term survival and successful aging can be evaluated in healthy adults based upon characteristics that reflect an individual's aging trajectory. In clinical settings, these characteristics can identify those at greatest risk of developing age-related disease, at a time prior to disease onset, when preventative measures can still be implemented effectively . Moreover, for research purposes, such characteristics provide useful standards for the evaluation of human anti-aging interventions . This study has identified individual factors that are most strongly associated with long-term survival within a healthy cohort of older women between 65 and 69 years of age. Surprisingly, visual contrast sensitivity was among the top 5 strongest predictors of survival relative to all 377 phenotypic measures evaluated in our study (mean AUC = 0.570) (Table 1). This measure may warrant increased attention in clinical evaluation, since our findings indicate that its prognostic significance is comparable to that of smoking and diabetes status. Our study has also derived an evidence-based index that ties together multiple dimensions of an aging adult (mean AUC = 0.673). This index is based upon the number of step-ups completed in 10 seconds, contrast sensitivity, blood pressure, pulse and several pieces of information easily obtained from a questionnaire or brief interview (Table 2). The prognostic capacity of this index did not appear to depend upon characterization of any one disease process, and among surviving subjects, scores generated from the index were associated with multiple long-term outcomes (e.g., mini-mental status exam score). These properties require further validation in independent cohorts, but suggest that the index could provide a marker of healthy aging patterns in older women (65-69 years of age), and that age-sensitive components of this index should be considered for possible use as endpoints for research centered on anti-aging interventions in humans.
The analytic approach used in our study differs in two main ways from previous investigations of data generated from the study of osteoporotic fractures (SOF). First, we have focused on the youngest subjects that enrolled in the study (i.e., ages 65 - 69 at baseline), while prior investigations have based their analyses on the complete SOF cohort (ages 65 - 89 at baseline). We have chosen to consider only younger subjects, since in these individuals, the burden of age-related disease was reduced at the time of baseline examination. This was desirable, since we expected that those already affected by age-related disease would exhibit a distinct signature set of phenotypic characteristics (i.e., a "frail" phenoytpe), and that this signal in the data would interfere with our ability to identify patterns associated with long-term survival, which we expected to be more informative in terms of aging mechanisms . Secondly, prior investigations have aimed to identify single factors associated with survival, and to determine which factors were independent predictors of survival after adjusting for other influential variables. Along these lines, previous analyses have identified many variables associated with all-cause mortality in the SOF cohort, including fracture incidence and rate of bone loss [63–68], markers of cardiovascular health and function [69, 70], biochemical measures [71–74], body composition traits [30, 75], physical activity , sleeping habits , depressive symptoms , marital status and social connectedness , as well as visual acuity and contrast sensitivity . In our investigation, we also identified statistically significant predictors of survival, which remained significant after adjustment for other variables. However, our approach was more stringent in some respects, since we aimed to identify variables that were both significantly related to survival and also the strongest predictors relative to other baseline variables evaluated in the SOF cohort. These variables were isolated by adopting a global data mining strategy that involved competition among a wide range of variables, and variable combinations, allowing the most useful variables to emerge in a data-driven fashion.
Visual contrast sensitivity was a strong independent predictor of survival as well as an influential component of the multivariate index we derived. This result is consistent with conclusions from a previous study of SOF data, which found that among all SOF participants (65 to 89 years of age), contrast sensitivity (and visual acuity) were significantly associated with mortality . Previous analyses have also identified poor contrast sensitivity as a risk-factor for deleterious aging outcomes, such Alzheimer's disease  and hip fracture . It is unlikely that the prognostic value of contrast sensitivity in our analysis is due to an association between contrast sensitivity and accidental death (e.g., car accidents), since in our analysis, cases of accidental death were treated as right-censored data. Our findings therefore complement those that have accumulated from studies of other populations, which have documented associations between all-cause mortality and indicators of visual status, such as lens changes , poor visual acuity , self-reported visual impairment , cataract or prior cataract removal , age-related macular degeneration , retinopathy , nuclear sclerotic cataract severity  and high intraocular pressure . Many of these associations have been present in both diabetic and non-diabetic populations , and it has been speculated that measures of visual function could serve as indicators of biological aging [81, 82, 84, 88]. Contrast sensitivity has proven to be an especially sensitive measure of visual function that correlates with real-world performance on vision-oriented tasks (e.g., driving, reading speed, face recognition) [89–91]. It is known that contrast sensitivity declines with age, although the functional basis of this decline is unclear and may involve multiple factors . To some degree, age-related contrast sensitivity decline could reflect choroidal neovascularization that can accompany development of age-related macular degeneration (AMD) in some older individuals, given that contrast sensitivity scores are lower in AMD patients , and that therapies inhibiting choroidal neovascularization in AMD patients improves contrast sensitivity . On the other hand, contrast sensitivity is also impaired by several other ocular diseases (e.g. cataract, glaucoma, diabetic retinopathy) [95–97], suggesting that multiple mechanisms contribute to diminished contrast sensitivity with age. It is interesting to note that contrast sensitivity may represent a general indicator of a subject's sensory perception, and that useful prognostic data might have been obtained from other sensory systems not evaluated during baseline exams (e.g., hearing test). For instance, among men and white women, one study found that associations between vision and hearing impairment with survival were additive, with concurrent vision and hearing loss more strongly associated with mortality than impairment of either sensory system individually .
Our results provide a validated index of traits, which predicts survival regardless of the assigned cause of death, and is significantly associated with several other long-term outcomes in addition to survival per se. The principle that underlies our index is that favorable combinations of measurements can be identified using a data mining approach, which aims to identify a set of non-redundant variables that predict long-term survival within a cohort for which age-related disease burden is low. The model we have generated by this approach includes many variables known to predict survival and several might have been surmised in advance (e.g., smoking and diabetes status). However, the exact combination of 13-variables (out of 4.05 × 1023 possible 13 variable combinations) is less likely to have been surmised in advance of our study, and likewise, the ranking of variables according to importance as listed in Table 2 was not obvious at the outset. For the purpose of characterizing frailty, previous work has developed a 5-variable "frailty index" . By analogy, our work suggests that a multi-variable index of "healthy aging" might be based upon variables drawn from at least six domains of the aging adult, including (i) a measure of physical function, (ii) smoking status/history, (iii) diabetes status/history, (iv) self-rated health, (v) visual performance and (vi) an indicator of cardiovascular health. The 13-variable index we identified includes specific measures that fall into each of these categories, but we note that substitute variables can be used in some cases with little overall effect on predictive performance. For example, as a measure of physical function, our index suggests that the number of step-ups completed by a subject in 10 seconds was the most informative variable, but concordance estimates of the index decreased only slightly when walking speed or grip strength was substituted for this variable (Table 2). The multivariate index we developed, therefore, should not be interpreted in overly rigid terms, but should be viewed as indicative of certain classes of measurements that are likely to be informative when used in combination, with the precise choice of measurements dictated primarily by practicality of clinical evaluation.
There are both advantages and disadvantages of the methodology we have used to derive an index that predicts long-term survival and which appears to characterize "healthy aging". The main advantage of our approach is that it is data-driven and variables were chosen in an objective fashion using a cross-validation criterion that ensures generalization ability (at least with respect to the SOF cohort we studied). The index we have developed is thus validated in terms of its ability to predict long-term survival patterns, and as we have shown, it also predicts outcomes among survivors that are unrelated to mortality (see Figure 10). A disadvantage of our approach is that, while an index built using data-mining methods may perform well in terms of predictive ability, this does not guarantee that the index will be easy to implement in practice, and it also does not guarantee that index components will fit into a conceptual scheme useful for understanding what "healthy aging" means. To develop a rule-based scheme for characterization of "frailty", for instance, Fried et al.  first developed a conceptual framework, devised a rule-based system within this framework, and then validated the predictive validity of the rule-based system using survival data from the Cardiovascular Health Study. The concept-driven approach followed by Fried et al.  thus ensures that the index generated can be connected to a broader framework and that the index is sensible from a medical standpoint. None of these assurances can be claimed of the data mining methodology we have implemented in this study. However, it can be argued that an index that fits an elegant theory but is sub-optimal in terms of predictive capacity is, with good reason, less likely to be assigned preference in practical contexts. Moreover, an appealing aspect of data-driven indices is that they provide a suitable and well-supported foundation for building new conceptual schemes. Indeed, the index developed by our analysis is sensible in many respects, given that physical performance measures are frequently advanced as cumulative indicators of general health [33, 39, 98–102], that smoking has been viewed as accelerating many features of aging [103–105], that diabetes is known to re-enforce age-related declines in telomere length and peripheral blood flow [106–108], that visual indices have been viewed as suitable for measurement of biological age [81, 82, 84, 88], and that the chronological age of subjects was in fact selected as the fourth most-important component of our index (a reassuring "positive control" for our methodology).
We anticipate that, in several ways, further studies will improve and refine the index we have developed here. Our analysis evaluated a wide range of variables that relate to multiple aspects of an aging adult, including past medical history and measures that reflect health status at presentation. It is quite possible, however, that other variables, not represented in our analysis, might have been better predictors of survivorship than those available in the SOF dataset. For example, our investigation did not include biochemical measurements, such as indicators of systemic inflammation , or markers based upon gene expression in blood cells , or genotype information derived from single nucleotide polymorphisms . It would be valuable to determine how such measures compete with those identified in our analysis, and whether any of these measures would contribute useful information to the index that we have developed. A second avenue for improvement is further validation of our index as a measure of healthy aging that reflects multiple forms of age-related illness. The index we developed was able to discriminate survival times among subsets of subjects for which the assigned cause of death was cancer, cardiovascular disease or non-cancer/non-cardiovascular disease. It is likely, however, that there exists some degree of overlap among these categories, which is not reflected in available SOF data, as well as some variance in the degree of certainty associated with the assigned cause of death. We therefore anticipate that analytical methods used in our analysis can be profitably applied in other contexts, possibly with narrowed and more fine-scaled cause of death categorizations, which would serve to further evaluate the index as a measure of an individual's aging trajectory that is sensitive to multiple disease processes. Lastly, the index we have generated has been validated primarily among community-dwelling Caucasian women between the ages of 65 and 69. We have evaluated the performance of this index with respect to older subjects from the SOF cohort (i.e., ages 70-89; see Additional File 4), and have found that its prognostic value declines with age, suggesting that certain indicators of long-term survival in younger populations (e.g., smoking and diabetes status) may not provide ideal tools for predicting comparatively short-term outcomes in older cohorts. We therefore expect that the index we developed will be most useful when applied to subjects that fall within a specific age bracket (i.e., 65-69 years of age, approximately). Moreover, with respect to subjects of this age, further validation of the index is necessary to determine whether findings from this study generalize to other independent cohorts, particularly cohorts that include subjects of both genders and a broad range of ethnic backgrounds and environmental settings. The most useful index to clinicians, as well as to research investigators, will most likely consist of variables that consistently emerge as the strongest predictors in multiple epidemiological datasets that include a comprehensive range of measures.
The translation of findings from basic aging research to practical anti-aging interventions would benefit greatly from the identification of variables that best predict future health outcomes in rodent models and people. Direct tests of putative anti-aging interventions for effects on survival in human populations would be extremely, perhaps prohibitively, expensive. In contrast, surrogate endpoints that are robustly associated with both long-term survival and exceptionally good health might be used in research trials to infer whether an intervention has favorably altered the odds of long-term survival. Some of the variables highlighted in our analysis exhibit increasing or decreasing trends across age groups, and likewise, risk scores generated from the index we developed change monotonically with age (see Additional File 2). We therefore propose that certain age-sensitive variables identified in our study be considered for inclusion within a panel of "validation measures", that once developed, could serve as a standard set of traits to be evaluated in human studies of anti-aging interventions. So far, clinical studies of anti-aging interventions, such as caloric restriction without malnutrition , have not evaluated measures of physical performance, contrast sensitivity, or most other variables included in the index we have developed. Ultimately, however, we believe that the determination of whether an intervention alters the rate of aging in humans (or other species) should not be based upon idiosyncratic sets of measures chosen by particular research groups, but rather, upon validated sets of measures that have emerged from independent global analyses of large datasets, which are in this fashion shown to be dominant predictors of long-term health and survivorship outcomes. An additional benefit from the identification of variables robustly associated with long-term human survival is the potential that, by "reverse translation", such variables will suggest new endpoints for evaluation in basic aging research using model organisms [111, 112]. Indeed, there are already several examples in which analogues of human physical performance traits have been successfully modeled in mice, worms and flies [113–115]. We anticipate that further development and characterization of a robust healthy aging phenotype in humans, based upon global analyses of comprehensive datasets, will promote further work along these lines and enhance the synergy between basic and applied aging research.
The recognition of healthy aging patterns and prediction of long-term survival is an important problem in clinical contexts with implications for the design of intervention studies that aim to evaluate anti-aging treatments. This investigation has focused on the challenging task of predicting long-term (20-year) survival within a healthy cohort of 4,097 women (ages 65 - 69) enrolled in the Study of Osteoporotic Fractures (SOF). Surprisingly, among 377 predictor variables evaluated, contrast sensitivity scores were among the strongest predictors of survival (ranked 5th of 377 variables, mean AUC = 0.570). We have implemented a data mining approach to develop a multivariate index that predicts mortality patterns among all 4,097 subjects (mean AUC = 0.673), as well as within sub-cohorts for which the assigned cause of death was cancer, cardiovascular disease, or non-cancer/non-cardiovascular disease. Among subjects that survived an average follow-up time of 20 years, this index was associated with multiple outcomes, including tests of cognitive function, geriatric depression, number of daily living impairments and grip strength. The index we present requires further validation with respect to other cohorts. However, our results suggest that components of our index characterize the clinical presentation of "healthy aging" as it frequently occurs in older women between 65 and 69 years of age. We suggest that our data-guided approach to index development can be profitably applied to other comprehensive datasets to further develop standardized criteria for recognizing healthy aging in older adults.
This work was supported by NIA grant T32-AG000114 and NIH grants T32-AR007197 and AG023122. We thank Lily Lui for providing us with assistance in working with the SOF dataset, as well as two reviewers for their helpful comments on this manuscript. The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: AG05407, AR35582, AG05394, AR35584, AR35583, R01 AG005407, R01 AG027576-22, 2 R01 AG005394-22A1, and 2 R01 AG027574-22A.
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