It is well known from epidemiology that values of indices describing physiological state in a given age may influence human morbidity and mortality risks. Studies of connection between aging and life span suggest a possibility that dynamic properties of age trajectories of the physiological indices could also be important contributors to morbidity and mortality risks. In this paper we use data on longitudinal changes in body mass index, diastolic blood pressure, pulse pressure, pulse rate, blood glucose, hematocrit, and serum cholesterol in the Framingham Heart Study participants, to investigate this possibility in depth. We found that some of the variables describing individual dynamics of the age-associated changes in physiological indices influence human longevity and exceptional health more substantially than the variables describing physiological state. These newly identified variables are promising targets for prevention aiming to postpone onsets of common elderly diseases and increase longevity.
Individual age trajectories of physiological indices result from complicated interplay among genetic and environmental (including behavioral) factors taking place during the aging process and so, they may differ substantially among individuals in cohort. Despite this fact the average age trajectories for the same index follow remarkable regularities. Figure
Mean values (± s.e.) of physiological indices in participants of the original cohort of the Framingham Heart Study (pooled data of available measurements from exams 1–25).
One can see from this figure that some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; the pulse pressure (PP) increases from age 40 till age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are nonmonotonic: they tend to increase first and then decline. Physiological average body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases till age 55–60 and then declines, serum cholesterol (SCH) increases till age 50 in males, and till age 70 in females and then declines, pulse rate (PR) increases till age 55 in males and till age 45 in females and then declines, hematocrit (HC) declines after age 70 in both sexes. With small variations, these general patterns are similar in males and females.
The effects of these indices on mortality risk were studied in [
Researchers continue the debates about determinants of the aging rate and about possible contribution of this rate to life span and healthy life span [
A number of studies available in the literature support the view about the importance of using dynamic properties of individual age associated changes in physiological indices as the characteristics of aging process that predict morbidity and mortality risks, in addition to the use of the age-specific baseline measurements [
In this paper we investigate the effects of selected parameters describing the dynamic properties of the age trajectories of seven physiological indices on consequent morbidity and mortality risks in participants of the FHS original cohort.
The FHS Original cohort was launched in 1948 (Exam 1), with 5,209 respondents (55% females) aged 28–62 years residing in Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease, and continued to the present with biennial examinations (29 exams to date, data from exams 1–25 were used in this study) that include detailed medical history, physical exams, and laboratory tests.
Phenotypic traits collected in the FHS cohorts over 60 years and relevant to our analyses include life span, ages at onset of diseases (with the emphasis on cardiovascular diseases (CVD), cancer, and diabetes mellitus), as well as indices characterizing physiological state. The occurrence of diseases (CVD and cancer) and death has been followed through continuous surveillance of hospital admissions, death registries, clinical exams, and other sources, so that all the respective events are included in the study. We used data on onset of CVD, cancer (calculated from the followup data) and diabetes (defined as the age at the first exam when an individual has a value of BG exceeding 140 mg/dl and/or takes insulin and/or oral hypoglycemic agent) to define the age at onset of “unhealthy life” as the minimum of ages at onset of these three diseases. If an individual did not contract any of these diseases during the observation period than the individual was considered censored at the age of the last followup or death. Individuals who had any of the diseases before the first FHS exam were excluded from the analyses of “unhealthy life.” Data on physiological indices include random blood glucose (BG, exams 1–4, 6, 8–10, 13–23), body mass index (BMI, exams 1–25), diastolic blood pressure (DBP, exams 1–25), hematocrit (HC, exams 4–21), pulse pressure (PP, exams 1–25), pulse rate (PR, exams 1, 4–25), and serum cholesterol (SCH, exams 1–11, 13–15, 20, 22–25).
We investigated dynamic properties of individual age trajectories of seven physiological indices mentioned above to select factors (referred to as “dynamic” risk factors) capable of affecting mortality risk and risk of onset of “unhealthy life.” BG was excluded from the list of indices for analyses of onset of “unhealthy life” because in the FHS data the onset of diabetes is specifically defined from the values of BG.
First, we evaluated the effect of the rate of changes in physiological indices at ages 40–60 on mortality risk and risk of onset of “unhealthy life” at ages 60+. For this purpose, we approximated the individual trajectories of those physiological indices that have a nearly linear dynamics (both for females and males) at ages 40–60 (BG, BMI, HC, and PP) by a linear function of the form
Effect of “dynamic” risk factors calculated from individual trajectories of physiological indices at ages 40–60 on mortality risk at ages 60+ in the Framingham Heart Study (original cohort) estimated by the Cox proportional hazards model.
Physiological Index | Risk Factor (RF) | Mean RF (St. Dev.) | Cox Regression Model | |
Parameter (S.E.) | Hazard Ratio (95% C.I.) | |||
BG | 77.468 (20.370) | 0.003 (0.002) | 1.056 (0.978, 1.140) | |
0.553 (1.932) | 1.088 (1.002, 1.182) | |||
8.518 (6.798) | 1.086 (1.033, 1.141) | |||
Sex | 1.789 (1.611, 1.985) | |||
BMI | 25.867 (4.215) | 1.086 (1.020, 1.157) | ||
0.050 (0.171) | 0.945 (0.897, 0.995) | |||
0.697 (0.392) | 1.074 (1.024, 1.126) | |||
Sex | 1.757 (1.610, 1.918) | |||
HC | 45.341 (4.664) | 1.622 (1.430, 1.839) | ||
−0.026 (0.272) | 1.311 (1.189, 1.446) | |||
1.548 (0.633) | 1.073 (1.002, 1.148) | |||
Sex | 1.291 (1.123, 1.484) | |||
PP | 44.112 (13.095) | 1.349 (1.273, 1.428) | ||
0.506 (0.846) | 1.360 (1.277, 1.448) | |||
4.815 (2.032) | 1.090 (1.033, 1.150) | |||
Sex | 1.842 (1.691, 2.007) |
Effect of “dynamic” risk factors calculated from individual trajectories of physiological indices at ages 40–60 on risk of onset of “unhealthy life” at ages 60+ in the Framingham Heart Study (original cohort) estimated by the Cox proportional hazards model.
Physiological Index | Risk Factor (RF) | Mean RF (St. Dev.) | Cox Regression Model | |
Parameter (S.E.) | Hazard Ratio (95% C.I.) | |||
BMI | 25.587 (3.954) | 1.198 (1.119, 1.283) | ||
0.057 (0.162) | 1.116 (1.055, 1.180) | |||
0.679 (0.381) | 0.013 (0.070) | 1.005 (0.953, 1.060) | ||
Sex | 1.668 (1.513, 1.837) | |||
HC | 45.044 (4.641) | 1.556 (1.365, 1.774) | ||
−0.021 (0.269) | 1.384 (1.250, 1.533) | |||
1.547 (0.635) | 0.082 (0.046) | 1.069 (0.993, 1.150) | ||
Sex | 1.332 (1.155, 1.536) | |||
PP | 43.612 (12.635) | 1.249 (1.170, 1.334) | ||
0.480 (0.814) | 1.325 (1.237, 1.420) | |||
4.667 (1.944) | 1.111 (1.046, 1.181) | |||
Sex | 1.781 (1.622, 1.957) |
Second, we evaluated the effect of dynamic characteristics of physiological indices with nonmonotonic age trajectories on mortality risk and risk of onset of “unhealthy life.” For this purpose, we approximated the age trajectories of such indices (BMI, DBP, HC, PR, and SCH) by two linear functions. The first one approximates the increase in the trajectory at the initial interval [
Effect of “dynamic” risk factors calculated from individual trajectories of physiological indices with nonmonotonic patterns on mortality risk in the Framingham Heart Study (original cohort) estimated by the Cox proportional hazards model.
Physiological Index | Risk Factor (RF) | Mean RF (St. Dev.) | Cox Regression Model | |
Parameter (S.E.) | Hazard Ratio (95% C.I.) | |||
BMI | Age Max | 62.063 (8.762) | −0.001 (0.004) | 0.983 (0.887, 1.089) |
Max Index | 27.869 (4.392) | −0.001 (0.012) | 0.997 (0.884, 1.124) | |
26.171 (4.187) | 0.007 (0.012) | 1.034 (0.925, 1.156) | ||
Left Slope | 0.220 (0.505) | −0.017 (0.049) | 0.996 (0.975, 1.018) | |
Right Slope | −0.224 (0.576) | 0.959 (0.948, 0.970) | ||
0.729 (0.371) | 1.153 (1.088, 1.221) | |||
Sex | 1.753 (1.545, 1.989) | |||
DBP | Age Max | 55.165 (6.973) | 0.903 (0.822, 0.992) | |
Max Index | 86.804 (10.465) | 1.245 (1.141, 1.358) | ||
80.471 (12.962) | 0.001 (0.003) | 1.008 (0.942, 1.079) | ||
Left Slope | 0.842 (1.439) | 0.006 (0.021) | 1.006 (0.966, 1.047) | |
Right Slope | −0.988 (1.976) | 0.939 (0.918, 0.961) | ||
3.984 (1.383) | 1.172 (1.114, 1.233) | |||
Sex | 1.671 (1.536, 1.818) | |||
HC | Age Max | 66.061 (7.020) | 0.882 (0.795, 0.978) | |
Max Index | 46.567 (3.265) | 1.108 (1.003, 1.224) | ||
43.756 (4.848) | 0.007 (0.008) | 1.031 (0.960, 1.108) | ||
Left Slope | 0.390 (0.733) | −0.011 (0.054) | 0.996 (0.956, 1.037) | |
Right Slope | −0.856 (3.533) | 0.988 (0.979, 0.997) | ||
1.472 (0.551) | 1.066 (1.006, 1.129) | |||
Sex | 1.488 (1.323, 1.675) | |||
PR | Age Max | 47.279 (7.676) | 0.851 (0.742, 0.977) | |
Max Index | 81.206 (10.689) | 1.247 (1.126, 1.381) | ||
71.554 (15.226) | −0.002 (0.003) | 0.979 (0.904, 1.059) | ||
Left Slope | 1.535 (3.445) | 0.007 (0.013) | 1.011 (0.972, 1.052) | |
Right Slope | −0.898 (1.980) | 0.927 (0.886, 0.970) | ||
5.057 (1.978) | 0.028 (0.017) | 1.070 (0.988, 1.159) | ||
Sex | 2.069 (1.804, 2.374) | |||
SCH | Age Max | 55.574 (8.298) | 0.002 (0.005) | 1.023 (0.923, 1.134) |
Max Index | 261.965 (42.429) | 0.001 (0.001) | 1.059 (0.958, 1.170) | |
225.428 (61.457) | −0.0003 (0.001) | 0.981 (0.903, 1.066) | ||
Left Slope | 5.517 (8.442) | −0.005 (0.005) | 0.975 (0.921, 1.032) | |
Right Slope | −4.121 (8.689) | 0.969 (0.946, 0.993) | ||
13.484 (6.237) | 1.101 (1.039, 1.166) | |||
Sex | 1.761 (1.526, 2.031) |
Effect of “dynamic” risk factors calculated from individual trajectories of physiological indices with nonmonotonic patterns on risk of onset of “unhealthy life” in the Framingham Heart Study (original cohort) estimated by the Cox proportional hazards model.
Physiological Index | Risk Factor (RF) | Mean RF (St. Dev.) | iCox Regression Model | |
Parameter (S.E.) | Hazard Ratio (95% C.I.) | |||
BMI | Age Max | 63.199 (8.535) | 0.012 (0.006) | 1.189 (0.997, 1.417) |
Max Index | 27.383 (4.016) | −0.011 (0.024) | 0.947 (0.757, 1.186) | |
25.696 (3.875) | 0.037 (0.025) | 1.184 (0.954, 1.469) | ||
Left Slope | 0.194 (0.350) | 1.073 (1.007, 1.143) | ||
Right Slope | −0.235 (0.801) | −0.041 (0.044) | 0.991 (0.972, 1.011) | |
0.703 (0.359) | −0.007 (0.121) | 0.997 (0.910, 1.092) | ||
Sex | 1.644 (1.346, 2.009) | |||
DBP | Age Max | 55.325 (7.112) | −0.006 (0.004) | 0.926 (0.827, 1.037) |
Max Index | 85.806 (9.966) | 1.239 (1.129, 1.360) | ||
79.309 (12.066) | 0.001 (0.003) | 1.013 (0.940, 1.093) | ||
Left Slope | 0.912 (1.808) | 0.009 (0.017) | 1.008 (0.978, 1.040) | |
Right Slope | −0.980 (2.390) | −0.014 (0.009) | 0.986 (0.968, 1.004) | |
3.850 (1.311) | 0.039 (0.020) | 1.064 (0.999, 1.133) | ||
Sex | 1.629 (1.470, 1.806) | |||
HC | Age Max | 66.094 (7.125) | −0.011 (0.006) | 0.863 (0.732, 1.017) |
Max Index | 46.088 (3.141) | 0.028 (0.018) | 1.129 (0.967, 1.319) | |
43.417 (3.810) | −0.002 (0.016) | 0.990 (0.868, 1.130) | ||
Left Slope | 0.372 (0.713) | −0.041 (0.078) | 0.985 (0.933, 1.041) | |
Right Slope | −0.775 (2.966) | 0.0002 (0.013) | 1.000 (0.985, 1.016) | |
1.442 (0.534) | 0.033 (0.074) | 1.021 (0.930, 1.122) | ||
Sex | 1.438 (1.203, 1.718) | |||
PR | Age Max | 47.167 (7.589) | 0.004 (0.005) | 1.057 (0.921, 1.213) |
Max Index | 80.449 (10.535) | 1.129 (1.011, 1.262) | ||
71.162 (13.289) | −0.001 (0.004) | 0.987 (0.893, 1.091) | ||
Left Slope | 1.443 (3.034) | 0.007 (0.013) | 1.010 (0.973, 1.049) | |
Right Slope | −0.850 (1.650) | 0.013 (0.023) | 1.014 (0.968, 1.062) | |
4.954 (1.936) | −0.002 (0.018) | 0.994 (0.913, 1.082) | ||
Sex | 1.766 (1.539, 2.025) | |||
SCH | Age Max | 56.821 (8.200) | 0.005 (0.006) | 1.063 (0.908, 1.245) |
Max Index | 261.843 (42.286) | 0.002 (0.001) | 1.120 (0.978, 1.282) | |
225.919 (62.186) | −0.001 (0.001) | 0.943 (0.854, 1.040) | ||
Left Slope | 5.147 (8.013) | 0.002 (0.007) | 1.012 (0.936, 1.095) | |
Right Slope | −4.329 (9.443) | −0.004 (0.004) | 0.984 (0.952, 1.017) | |
13.452 (6.246) | 0.010 (0.006) | 1.070 (0.986, 1.161) | ||
Sex | 1.813 (1.499, 2.192) |
Dynamic characteristics of a hypothetical non-monotonically changing physiological index (denoted here “DBP”) considered as potential risk factors: 1) Maximum value; 2) Age at which the maximum has been reached; 3) Average rate of decline after reaching the maximum. The figure illustrates evaluation of average rates of decline in two individuals having the same pattern of increase until reaching the maximum and different patterns of decline after reaching the maximum: a) the solid line for a rapidly declining index and its approximation by a straight line; b) the dotted line for a slowly declining index and its linear approximation. The slopes of respective straight lines are considered as risk factors for mortality and onset of “unhealthy life.”
We also evaluated the empirical (Kaplan-Meier) estimates of survival functions (and probabilities of staying free of the diseases defining the onset of “unhealthy life”) for individuals with different values of the dynamic risk factors based on the indices with nonmonotonic trajectories (separately for females and males). For each physiological index and each dynamic risk factor (“
Kaplan-Meier estimates of survival functions for females (a) and males (b) having “variability” of different physiological indices (the mean of absolute values of residuals, i.e., deviations of observed values of an index from those approximated by two linear functions at respective age intervals, see Section
Kaplan-Meier estimates of survival functions for females (a) and males (b) having ages at reaching the maximum and the estimated maximal value (see Section
Kaplan-Meier estimates of probabilities of staying free of the diseases defining the onset of “unhealthy life” for females (a) and males (b) having initial values of diastolic blood pressure (DBP) at age 65, the estimated maximal values of DBP, and the average rates of decline of DBP after reaching the maximum (“intercept,” “maximum,” and “right slope,” respectively, see Section
Statistical analyses and graphic output were performed with SAS/STAT (SAS Institute Inc.) and MATLAB (MathWorks Inc.) software packages.
As described in Section
The effect of these dynamic characteristics on incidence of “unhealthy life” is similar (see Table
For indices with nonmonotonic age trajectories, we evaluated the maximum value of respective index, age at which this maximum is reached, the intercept, and the left and right slopes of the linear functions approximating the increase and decline of respective indices as described in Section
The effect of these dynamic characteristics on risk of onset of “unhealthy life” is less pronounced than that on mortality risks. Table
We also evaluated the Kaplan-Meier estimates of survival functions for individuals with different values of the dynamic risk factors based on the indices with nonmonotonic trajectories dividing the entire sex-specific samples into strata representing individuals with the values of the index in the lower and upper halves of the empirical distribution of respective index (see Section
Figure
Figure
Later ages at reaching the maximal value of DBP and PR in females from the upper half of the distribution are associated with better survival (Figure
Similar calculations for probabilities of staying free of the diseases defining the onset of “unhealthy life” revealed a more mosaic picture. The most consistent results were observed for DBP (Figure
The higher initial values of DBP at age 45 and the higher estimated values of DBP reached in individuals from the upper halves of respective distributions are associated with worse chances of staying free of the “unhealthy life,” for both sexes. The lower rates of the postmaximum decline of DBP in females, but not males, from the upper half of the distribution correspond to better chances of staying free of the “unhealthy life” (Figure
We should note that the question about the effect of the quality of estimates is important given that at most 11 observations for the monotone indices or 15 observations for non-monotone indices were used (note that for non-monotone indices data from 30-year intervals [
An increase in mortality rate with age is traditionally associated with progressing aging. This influence is mediated by the aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of such variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of respective indices affect the life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors to both life span and healthy life span (as show the estimates of
Table
The fact that the effect of the studied dynamic characteristics on risks of “unhealthy life” onset (Table
The review of the literature (below) supports our findings with respect to importance of taking into account longitudinal changes in physiological indices when evaluating/predicting morbidity and mortality risks. One should note, however, that the impact and comparative contributions of dynamic parameters (left and right slopes, variability, intercept) on mortality risks were evaluated in our study for the first time. In our two recent publications we demonstrated that individuals who have different rates of aging related changes in BG levels also differ in longevity [
The effects of aging associated changes in serum cholesterol on coronary and all-cause mortality were evaluated in Finnish Cohorts of the Seven Countries study [
Similar to SCH, high blood pressure (BP) is a major risk factor for CVD. A study of two independent French male cohorts suggested that longitudinal changes in systolic and diastolic BP may be more accurate determinants of cardiovascular risks than baseline BP measures. In both cohorts, the group with a long-term increase in systolic and a decrease in diastolic BP (i.e., with increase in pulse pressure) had the highest relative risk of mortality from CVD compared to the group with no changes in either systolic or diastolic BP, independently of absolute values of BP or other risk factors [
The heart rate (HR) is one more index characterizing functioning of cardiovascular system. Prognostic importance of its baseline values as well as variability during 24-hour HR monitoring in patients with heart disease and in general population is recognized [
The body mass index (BMI) is, probably, the most intensively studied index in connection with health and survival. Over recent decades, many studies addressed the effect of BMI dynamics on morbidity and mortality, especially the effect of losing body weight in overweight/obese individuals on risk factors for CVD and diabetes. It was shown that overweight adults who lost weight over 9 years had more favorable (lower) total and LDL cholesterol levels compared to normal-weight control, but less favorable BG levels [
It was also shown that the weight stability was associated with a lower mortality risk as compared with weight change (gain or loss) [
Note that the seven physiological indices used in this paper do not exhaust the list of all possible physiological risk factors for mortality and morbidity. Therefore, the dynamic characteristics calculated from these seven indices cannot explain the entire variability in human life span and healthy life span. Other indices and risk factors can be explored on their association with mortality/morbidity risk if measurements of such indices are available in a longitudinal study for a substantially long-time period. See for example [
In sum, our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in the aging individuals. Previously published epidemiological findings are generally in concert with our results, which clearly indicates the need for further detailed studies of the dynamic parameters of aging related changes in human body with further application of these principles to the prevention strategies. We showed that the rate of changes in physiological state at the age interval between 40 and 60 years may serve as a good predictor of morbidity and mortality risks later in life. For nonmonotonically changing indices, the rates of decline after reaching the maximum, the maximal values, and the age at the maximum are important predictors of morbidity and mortality risks.
Senescence is likely to be the key player in physiological and biological changes observed in aging humans. The dynamic properties of these changes contain important information about the individual aging processes. This information, however, can be masked by the effects of compensatory adaptation and remodeling developing in response to the primary aging process. Studying mechanisms of such adaptation and its connection to morbidity and mortality risks is important for better understanding factors and mechanisms affecting long and healthy life.
The research reported in this paper was supported by the National Institute on Aging Grants R01AG027019 and R01AG028259. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health. The Framingham Heart Study (FHS) is conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with the FHS Investigators. This paper was prepared using a limited access dataset obtained from the NHLBI and does not necessarily reflect the opinions or views of the FHS or the NHLBI.