Association of healthy aging with parental longevity
Various measures incorporated in geriatric assessment have found their way into frailty indices (FIs), which have been used as indicators of survival/mortality and longevity. Our goal is to understand the genetic basis of healthy aging to enhance its evidence base and utility. We constructed a FI as a quantitative measure of healthy aging and examined its characteristics and potential for genetic analyses. Two groups were selected from two separate studies. One group (OLLP for offspring of long-lived parents) consisted of unrelated participants at least one of whose parents was age 90 or older, and the other group of unrelated participants (OSLP for offspring of short-lived parents), both of whose parents died before age 76. FI34 scores were computed from 34 common health variables and compared between the two groups. The FI34 was better correlated than chronological age with mortality. The mean FI34 value of the OSLP was 31 % higher than that of the OLLP (P = 0.0034). The FI34 increased exponentially, at an instantaneous rate that accelerated 2.0 % annually in the OLLP (P = 0.024) and 2.7 % in the OSLP (P < < 0.0001) consequently yielding a 63 % larger accumulation in the latter group (P = 0.0002). The results suggest that accumulation of health deficiencies over the life course is not the same in the two groups, likely due to inheritance related to parental longevity. Consistent with this, sib pairs were significantly correlated regarding FI34 scores, and heritability of the FI34 was estimated to be 0.39. Finally, hierarchical clustering suggests that the OLLP and OSLP differ in their aging patterns. Variation in the FI34 is, in part, due to genetic variation; thus, the FI34 can be a phenotypic measure suitable for genetic analyses of healthy aging.
KeywordsFrailty Deficits Longevity Aging Heritability Age
Aging can be defined as the progressive decline in the ability to withstand damage and stress, which is associated with an increase in the incidence of disease and degenerative disorders (Finch 1990). This definition separates biological aging from a strict relationship with chronological age. Aging processes involve many factors, both genetic and nongenetic. The complexity of human aging is further increased by various interactions that occur among these factors in the development and progression of aging-related changes.
In an attempt to systematically approach human aging, Rowe and Kahn (1997) defined the concept of successful aging as: (1) relatively low risk of disease and disease-related disability, (2) relatively high mental and physical function, and (3) active and productive engagement with life. A quantitative approach to successful aging was developed by estimating the amount of physical and functional loss that occurs during the life course and incorporating these losses into a condition termed “frailty” (Rockwood et al. 1994, 1999). Fried et al. (2001) defined frailty as a clinical syndrome involving five features: weight loss, exhaustion, muscle weakness, slow gait speed, and low physical activity. They found that the prevalence of frailty increases with age. Mitnitski et al. (2001) developed an expanded approach to frailty by introducing a frailty index (FI), as the proportion of accumulated deficits in a set of 92 health variables surveyed for an individual at a given age. Their health variables included symptoms, signs, laboratory measurements, diseases, and disabilities. The purpose of the FI was to enumerate a broad spectrum of changes that occur in multiple domains of the human body. Since then, different FIs with different numbers of health variables have been studied (Rockwood and Mitnitski 2007; Rockwood et al. 2007), and others taking the FI approach have used the term deficit index (DI) (Kulminski et al. 2007b, 2008).
The FI appears to be a promising tool for studying human aging as an indicator of biological age and predictor of survival (Mitnitski et al. 2001, 2002a, b; Kulminski et al. 2007a, b). The FI seems to be relatively robust and consistent between studies, as long as the number of health variables is reasonably large (≥20) and sufficiently diverse to represent multiple domains of body function (Mitnitski et al. 2006; Rockwood et al. 2007; Searle et al. 2008).
Despite the potentially useful features of the FI in human aging research, studies addressing its utility in genetic analyses are extremely limited. In a twin study, Kulminski et al. (2009) found geriatric diseases can be used as cumulative indices to predict lifespan among family members. Matteini et al. (2010), in a family study, estimated the heritability of 28 health-related variables to range from 0.01 to 0.45, individually or in statistical combinations.
Here, we have constructed a FI based on 34 health variables (FI34) and studied its properties as a composite phenotype. Our 34 variables include diseases and symptoms throughout the body, deficiencies in physical and cognitive functioning, and self-rated health status. We validate the FI34 as a predictor of survival and mortality. We also describe its behavior across age groups in a family-based sample and determine its heritability. Finally, we replicate these features of the FI34 in subjects drawn from a sample of unrelated individuals, which, together with its heritability, suggests the utility of the FI34 in genetic studies of aging.
Materials and methods
Louisiana residents from the New Orleans Greater Metropolitan Area who were at least 90 years old and their offspring (N = 320) were recruited to the Healthy Aging Family Study (HAFS) (Online Resource Table 1). Ethnicity was self-declared. Eighty-nine offspring were randomly sampled each from a different family, and this group was named “offspring of long-lived parents” (OLLP).
Age of participants sampled from the Healthy Aging Family Study (HAFS) and the Louisiana Healthy Aging Study (LHAS). Numbers are the mean age ± standard deviation (sample number)
64 ± 6 (132)
64 ± 6 (69)
64 ± 6 (201)
64 ± 7 (57)
63 ± 5 (32)
64 ± 6 (89)
66 ± 24 (415)
69 ± 22 (258)
67 ± 23 (673)
60 ± 13 (28)
60 ± 12 (20)
60 ± 12 (48)
Ages of participants were verified using both documentary evidence (birth certificates, passports, and driver's licenses) and demographic questionnaires. All participants provided informed consent according to protocols approved by the Institutional Review Boards.
The variables used to count health deficits in both HAFS and LHAS are listed in Online Resource Table 2. Collected data are quantitative measures, either continuous or discrete, or categorical responses from medical history questionnaires. Binary categorical responses were numerically coded: 0 for the absence of deficit and 1 for the presence of deficit. Quantitative data and multicategorical responses were recoded essentially in the same way as reported previously (Searle et al. 2008) or with modifications as shown in Online Resource Table 2. Mortality data were collected using Social Security Death Index search. For the analyses of FI34 in mortality and survival, we calculated the follow-up period (in months) for each individual as follows: for those who died, the follow-up period is the time elapsed from the date of deficit data collection to the recorded date of death (82.5 ± 20.6). For the survivors, the follow-up period is the time passed between the date of deficit data collection and the date of mortality data collection (38.3 ± 24.1).
All statistical analyses were performed in R (R 2008). Only Caucasian participants were included in the analyses to avoid confounding by population admixture and because of sample size considerations. The FI34, with positive skewness, was considered not normally distributed in both study samples (Online Resource Fig. 1). Therefore, in statistical tests that assume a normal distribution, we applied both parametric and nonparametric tests and compared the outcomes. In all instances, both outcomes were very similar, and we present only those from the parametric tests. Fitting of the exponential function a · e(b · age), where a and b are parameters, and weighted least squares estimation of the parameters were performed using the nls function in the R stats package. The integrate utility was used to calculate the definite integral of this function with the lower and upper limits of age set at 40 and 90 years, and permutation analysis (10,000 random samples) was used to test significance of differences between OSLP and OLLP. For hierarchical clustering of 34 variables and age, which are binary or quantitative, we used the general dissimilarity coefficient of Gower (Gower 1971), which is available in the function daisy in the cluster library with standardization (Kaufman and Rousseeuw 2005). This metric is known to be capable of handling different types of variables at the same time. Hierarchical clustering analyses based on the dissimilarity matrices were performed using the hclust function in the base package stats and plotted using the plot function in R. In addition to the “complete” method that we used to generate Fig. 2, we used different agglomeration methods, such as “ward” and “average,” all of which gave essentially the same clustering patterns. All reported P values are two-tailed.
Heritability in the narrow sense (h2), the ratio of the additive genotypic variance to the total phenotypic variance (σa2/σ2), was estimated for 86 full sib pairs with an equal sibship size (k = 2), as described by Hartl (1980) and its standard error as described by Roff (1997) (Online Resource Table 3).
FI34 as a predictor of mortality
Correlation between FI34, age, and time to death in the Louisiana Healthy Aging Study (LHAS)
95 % CI
Time to death
−0.339 to −0.067
Time to death
−0.282 to −0.004
Cox regression for time to death as a function of FI34 or age in the Louisiana Healthy Aging Study (LHAS)
Wald test P
Differences in FI34 accumulation between OLLP and OSLP
Our goal was to test whether part of the variation in the FI34 is attributable to genetic differences and, if so, to estimate the extent of the genetic effect. To achieve this goal, we formed two study groups, OLLP from the HAFS and OSLP from the LHAS. These two groups differed in parental longevity. Differences in the FI34 between the two groups can be ascribed to differences in parental longevity, upon matching for other demographic parameters.
FI34 scores of subjects in different study groups
Mean ± SD
0.124 ± 0.069a
0.128 ± 0.068
0.116 ± 0.071
0.163 ± 0.077a
0.159 ± 0.085
0.168 ± 0.065
Comparison of FI34 between OLLP and OSLP by multiple linear regression (FI34 = β0 + β1 · sex + β2 · age + β3 · group, where group is coded 0 for OLLP and 1 for OSLP and sex is coded 0 for female and 1 for male)
R2 (P value)
df = 133
Test for association of FI34 with parental longevity in LHAS using multiple linear regression (FI34 = β0 + β1 · sex + β2 · age + β3 · parental longevity). Parental longevity was coded 0 for those (n = 90) with either or both parents long-lived (age ≥ 90) and 1 for OSLP (n = 48) and. Sex was coded 0 for female and 1 for male
R2 (P for model)
df = 134
Difference in FI34 between OLLP and OSLP at later ages
The two study groups were each dichotomized using age 60 as a cutoff for comparison (Online Resource Fig. 2). The mean FI34 was 34 % greater in ‘young’ (age < 60) OSLP compared to ‘young’ (age < 60) OLLP and 57 % greater in ‘old’ (age ≥ 60) OSLP compared to ‘old’ (age ≥ 60) OLLP (Online Resource Table 4). These differences between two age groups imply that the rate of deficit accumulation may be larger at later age in the OSLP than in the OLLP.
Sib correlation and heritability of FI34
We next determined the extent of familial aggregation and heritability of the FI34. For this purpose, each OLLP individual was paired with his/her sib (or a sib was randomly selected in case of multisib (≥3) families), and using the 86 full sib pairs only, sib correlation and narrow-sense heritability were estimated. The correlation coefficient was 0.459 (df = 84, 95 % CI = 0.273–0.611, P < < 0.0001) and the estimated heritability was 0.391 (standard error = 0.209).
Group-specific profiles of healthy aging
The main conclusion of our work is that parental longevity has a significant impact on healthy aging because FI34 scores of offspring significantly differed depending on their parents' longevity. This finding was made by comparing two different study groups and was confirmed by an analysis of a within-study sample. Using the sib pair data, we found siblings within sibships significantly correlated with each other with regard to their FI34 scores and estimated the heritability to be 0.39.
Our variables cover cardiovascular-related diseases and symptoms (10), deficiencies in physical functioning (6), respiratory functioning (4), cognitive functioning (3), and other diseases and symptoms throughout the body. Our FI34 performed as well or better than other FIs. For example, the FI34 was better correlated with time to death than age was, which replicates the previous finding by Mitnitski et al. (2001) in which their FI was based on 92 variables. The effect of our FI34 on the hazard of death, as shown by a Cox regression, is also consonant with the finding by Mitnitski et al. (2002a) based on 20 variables, though the effect we observe is stronger. They used biological age derived from their FI, but these are not independent variables. Matteini et al. (2010) performed principal component analyses on 28 variables and found no single component responsible for more than 14 % of the variance. Thus, the robustness of this type of index is likely to reflect interrelationships between biological systems at many different levels (Mitnitski et al. 2001). In this context, it is very interesting to note the differences in the clustering patterns of variables between the two groups characterized by different parental longevity. Perhaps, the difference reflects differing interactions of biological systems depending on the genetic backgrounds transmitted from previous generations.
In examining the rate of increase in FI34 with age, we rely on cross-sectional data. The trend of deficit accumulation with age may differ among different birth cohorts (Yang and Lee 2010). A few reports based on a longitudinal study and other cross-sectional data available to date suggest that accumulation of deficits increases in a nonlinear fashion (Mitnitski et al. 2001; Kulminski et al. 2007a, b; Yang and Lee 2010; Kulminski et al. 2011). Our data also fit an exponential model of deficit accumulation. The instantaneous rate of increase in deficits at a given age can be obtained by differentiating Eqs. 1 and 2. Thus, for example, the instantaneous rate of FI34 increase is 12 % for OLLP and 20 % for OSLP, respectively, at age 70.
Herskind et al. (1996) reported the heritability of human longevity ranging 0.23–0.26 with no evidence for an impact of shared (family) environment. All subsequent estimates of heritability of longevity fall between 0.15 and 0.35 (Gudmundsson et al. 2000; Kerber et al. 2001). In addition, a number of studies reported heritability during aging of physical and cognitive functions (McClearn et al. 1997; Carmelli et al. 2000; Frederiksen et al. 2002), and even a measure of health-related quality of life (Romeis et al. 2005). Our estimate of heritability of FI34, 0.39, falls within the range that Matteini et al. (2010) estimated for 28 different variables, alone and in combinations. It is also within the 95 % CI of heritability, 0.31–0.53, recently reported by Dato et al. (2012) for their “frailty phenotype,” which is based on survival, age, MMSE, Katz's index of ADL, BMI, and self-reported health rating. The inclusion of survival and age lessens the utility of this frailty phenotype as a predictive tool, however.
In genetic analyses of a complex trait such as aging, the selection of an appropriate phenotype is paramount. Ho et al. (2011) investigated association of women’s frailty with single-nucleotide polymorphisms (SNPs) in candidate genes. In that study, the FI was based on a five-point scale from measurements of muscle weakness, slow gait speed, weight loss, fatigue, and low physical activity. The candidate genes were selected based on their roles in skeletal muscle function and inflammation. However, no SNPs passed statistical significance. Edwards et al. (2011) collected data from 214 Amish subjects over age 80 for 13 variables. These variables belonged to the three domains of successful aging, as described earlier (Rowe and Kahn 1997). Linkage analysis of the binary trait of successful aging (yes/no) led to identification of three genomic regions. Although the numbers and selections of health variables were limited in these studies and the results await replication and validation, these studies suggest that a multidimensional phenotype like FI34 will be useful for genetic analyses.
In sum, we showed that (1) our FI changes with age at different rates, depending on the longevity of parents; (2) this index is heritable; and (3) it discriminates between different patterns of aging. Unraveling of these additional properties of the FI was possible due to the collected information on familial longevity.
This study was supported by grants from the National Institute on Aging of the National Institutes of Health (K01AG027905 to SK and P01AG022064 to SMJ), by the Louisiana Board of Regents through the Millennium Trust Health Excellence Fund [HEF(2001–06)-02] (to SMJ), and by the Louisiana Board of Regents RC/EEP Fund through the Tulane-LSU CTRC at LSU Interim University Hospital. We thank the CTRC for nursing services, subject testing, and blood draw, and the core lab support for blood sample processing. We also thank the people of Louisiana for participation in our studies. The corresponding authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
- Finch CE (1990) Longevity, senescence, and the genome. The John D. and Catherine T. MacArthur Foundation series on mental health and development. University of Chicago Press, ChicagoGoogle Scholar
- Hartl DL (1980) Principles of population genetics. Sinauer Associates, Inc., SunderlandGoogle Scholar
- Jazwinski SM, Kim S, Dai J, Li L, Bi X, Jiang JC, Arnold J, Batzer MA, Walker JA, Welsh DA, Lefante CM, Volaufova J, Myers L, Su LJ, Hausman DB, Miceli MV, Ravussin E, Poon LW, Cherry KE, Welsch MA (2010) HRAS1 and LASS1 with APOE are associated with human longevity and healthy aging. Aging Cell 9(5):698–708PubMedCrossRefGoogle Scholar
- Kaufman L, Rousseeuw PJ (2005) Finding groups in data: an introduction to cluster analysis. Wiley series in probability and mathematical statistics. Wiley, HobokenGoogle Scholar
- Kulminski AM, Arbeev KG, Christensen K, Mayeux R, Newman AB, Province MA, Hadley EC, Rossi W, Perls TT, Elo IT, Yashin AI (2011) Do gender, disability, and morbidity affect aging rate in the LLFS? Application of indices of cumulative deficits. Mech Ageing Dev 132(4):195–201PubMedCrossRefGoogle Scholar
- R (2008) Version R 2.11.1; R Development Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar