Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging – Nature.com

Do aging rates differ across the adult age span?

Of the 968 participants included in this analysis, 512 (52.9%) were women and baseline age ranged between 24.9 and 93.7 years, with a median follow-up of around 7–9 years (Fig. 2, Supplementary Table 1 and Supplementary Fig. 1). We characterized sex-specific, population-based, longitudinal trajectories of several aging phenotypic traits grouped into four domains—body composition, energetics, homeostatic mechanisms and neurodegeneration/neuroplasticity. By fitting a family of polynomial regressions, we previously demonstrated that some of these age trajectories are linear (interleukin-6, c-reactive protein, albumin, red blood cell distribution width) while others are nonlinear (waist circumference, waist/height ratio, body mass index, lean mass, appendicular lean mass, fat mass, mid-thigh area, resting metabolic rate, peak oxygen consumption during treadmill testing, peak oxygen consumption during 400-m walk, cost/capacity ratio, forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), FEV1/FVC, hemoglobin, absolute neutrophil count, fasting glucose, pulse pressure, systolic blood pressure, diastolic blood pressure, carotid/femoral pulse wave velocity, creatinine clearance, total cholesterol, low-density lipoproteins, high-density lipoproteins, triglyceride, total brain volume, white matter volume, gray matter volume, ventricular volume, nerve conduction velocity), and trajectories in men and women generally differ (Fig. 3 and Supplementary Figs. 2 and 3). Specifically, we calculated for each phenotype the difference between an individual’s rate of change and the sex-specific average rate of change in the population at the baseline age of that participant (Fig. 3). Of note, because some phenotypes show nonlinear trajectories over the lifespan, the population rate of change used for comparison may differ depending on the participant’s age.

Further, we standardized these values and averaged them within each domain. For each domain, higher scores indicate faster longitudinal age-phenotypic changes (that is, accelerated aging) while lower scores represent slower longitudinal age-phenotypic changes compared with the overall population at the same age of the participant. Longitudinal scores in four domain-specific phenotypes—body composition, energetics, homeostatic mechanisms and neuroplasticity/neurodegeneration—were symmetrically distributed across the age range (Supplementary Fig. 4). These findings suggest that, even in a cohort of healthy individuals, there exists wide heterogeneity of phenotypic changes from adulthood to late life. Correlations between the four domain-specific longitudinal phenotypic scores are modest (|correlation| ranging between 0.03 and 0.10), suggesting that substantial intra-individual heterogeneity in the rate of aging exists across phenotypic domains and justifying the need to combine information across domains. Finally, the standardized scores were averaged into a global, longitudinal phenotypic score that represents the rate of phenotypic aging compared with that of the general population.

Several lines of research indicate that health and quality of life in older persons are best assessed through measures of physical and cognitive function, in addition to disease diagnoses and clinical signs or symptoms22,23. Both physical and cognitive function strongly predict ‘hard’ health outcomes including loss of autonomy and death22,24. Based on these considerations, we validated our global longitudinal phenotypic score by evaluating its correlations with rates of change in physical and cognitive functions.

Longitudinal phenotypic aging and change in physical function

In the BLSA, physical function was measured using usual gait speed over 6 m, time to finish a 400-m walk (measured by time to walking 400 m as quickly as possible25) and the Health, Aging and Body Composition short physical performance battery (HABC SPPB; a continuous score derived from gait speed over 6 m unrestricted and over a 20-m-wide course of repeated chair stands, and sequential balance testing, with higher scores indicating better function)26. As shown in Fig. 4, lower global longitudinal phenotypic scores (that is, decelerated decline in aging phenotypes) were associated with slower physical function decline across all measures. Adjusting for sex, baseline age, height, weight, interactions between sex and time, and baseline age and time, a global longitudinal phenotypic score (that is, decelerated decline in aging phenotypes) one point lower was associated with (1) slower (0.0032 m s–1) annual decline in gait speed (95% confidence interval (CI): 0.0004, 0.0059), (2) 2.57 s less annual increase in time to finish a 400-m walk (95% CI: 1.51, 3.64) and (3) 0.0177 less annual decline in HABC SPPB score (95% CI: 0.0113, 0.0240) (Supplementary Table 2). In our study population, gait speed declined at an average annual rate of 0.014 m s–1, and 1 year older in chronological age was associated with faster decline in gait speed with an annual increment of 0.0008 m s–1 (Supplementary Tables 3 and 4). The associations between one point ‘lower’ in global longitudinal phenotypic score and physical function measures were equivalent to 4.09, 6.83 and 6.73 years ‘younger’ gait speed, time to finish a 400-m walk and HABC SPPB score, respectively (Fig. 5). Interestingly, associations between domain-specific longitudinal phenotypic scores and physical function measures were all weaker than that between global longitudinal phenotypic score and physical function measures (Supplementary Fig. 6). This finding further confirms the notion that building a robust longitudinal phenotypic score of aging requires the combination of information across different domains of measurement.

Fig. 4: Relationship between global longitudinal phenotypic score and change in physical functions for three mobility tests.
figure 4

ac, Higher global longitudinal phenotypic scores indicate accelerated phenotypic aging trajectories. Higher annual decrease in gait speed (a) and HABC SPPB scores (c), along with higher annual increase in the time to walk 400 m (b), indicate faster decline of physical function. Two-sided tests were used, and the displayed P values were not adjusted for multiple comparisons. Participants with at least two visits, n = 951; usual gait speed, n = 904; time to finish 400-m walk, n = 950 (HABC SPPB); also see Supplementary Table 1b for more details.

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Fig. 5: Age equivalence of a one-point difference in global longitudinal phenotypic score for different functional outcomes.
figure 5

Estimated age equivalence of a one-point difference in global longitudinal phenotypic score for different functional outcomes, including cognitive function, physical function and multimorbidity. Age equivalence presented here is a scaled regression coefficient (point estimates and 95% CIs) relating longitudinal phenotypic score and rate of change in functions and illustrates how many years older in functional age are individuals with one point higher in longitudinal phenotypic score. Participants: DSST, n = 921; memory, n = 922; executive function, n = 929; attention, n = 929; language, n = 929; visuospatial ability, n = 919; usual gait speed, n = 968; time to finish 400-m walk, n = 943; HABC SPPB, n = 968; multimorbidity index, n = 828); also see Supplementary Table 1b for more details.

Longitudinal phenotypic aging and change in cognitive function

Measures of cognition used for this analysis cover memory, executive function, attention, language and visuospatial ability. The digital symbol substitution test (DSST) score was kept alone because this test covers more than one domain and has been clinically considered sensitive to change in cognition globally. As shown in Fig. 6, a higher global longitudinal phenotypic score (that is, accelerated decline in aging phenotypes) was associated with faster cognitive decline. Adjusting for baseline age, sex, race, years of education and interactions between sex and time, and baseline age and time, one point higher in global longitudinal phenotypic score (that is, accelerated decline in aging phenotypes) was associated with 0.236 faster annual decline in DSST score (95% CI: 0.138, 0.334) and with significantly faster annual decline in executive function, attention, memory, language and visuospatial ability (Supplementary Table 2). In our study population, DSST declined at an average annual rate of 1.159, while 1 year older in chronological age was associated with faster decline in DSST, with an annual increment of 0.021 (Supplementary Tables 3 and 4). One point ‘lower’ in global longitudinal phenotypic score was equivalent to 6.75–12.95 years ‘younger’ chronological age in terms of rate of cognitive decline across different measures of cognition (Fig. 5). Like the physical function analyses, associations between domain-specific longitudinal phenotypic scores and cognitive functions were all weaker than that between global longitudinal phenotypic score and cognitive test performance (Supplementary Fig. 6).

Fig. 6: Relationship between global longitudinal phenotypic score and changes in cognitive function.
figure 6

ac, Higher global longitudinal phenotypic score indicates accelerated phenotypic aging trajectories. Higher annual decreases in DSST (a), memory (b), executive function (c), attention (d), language (e) and visuospatial ability (f) indicate faster decline of cognitive function. Memory score is constructed by the average of standardized immediate recall and long-delay-free recall from the CVL test. Language score is constructed as the average of standardized letter fluency and standardized category fluency. Attention score is constructed as the average standardized log-transformed trail-making tests part A and digit span forward. Executive function is constructed by the average of standardized log-transformed trail-making tests part B and digit span backward. Visuospatial ability is calculated by standardized cart rotations test. Two-sided tests were used, and the displayed P values were not adjusted for multiple comparisons. Participants with at least two visits: DSST, n = 867; memory, n = 877; executive function, n = 894; attention, n = 894; language, n = 892; visuospatial ability, n = 878; also, see Supplementary Table 1b for more details.

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Longitudinal phenotypic aging, multimorbidity and lifespan

Under the assumption that the aging process is the major cause of chronic disease and functional impairment, the rate of accumulation of chronic disease or health/functional problems can be considered a biomarker of accelerated aging. Using a multimorbidity index previously operationalized in the BLSA as the total number of 15 candidate chronic conditions27, as shown in Fig. 7a, a lower global longitudinal phenotypic score (that is, decelerated decline in aging phenotypes) was associated with slower increase in the multimorbidity index. Adjusting for baseline age, sex and interactions between sex and time, and baseline age and time, one point lower in global longitudinal phenotypic score (that is, decelerated decline in aging phenotypes) was independently associated with 0.025 fewer morbidities accumulated per year (95% CI: 0.004, 0.046) (Supplementary Table 2). In our study population, the multimorbidity index increased at an average annual rate of 0.199, and 1 year older in chronological age was associated with faster increase in multimorbidity index, with an annual increment of 0.008 (Supplementary Tables 3 and 4). One point ‘lower’ in the global longitudinal phenotypic score was independently associated with change in multimorbidity at the rate equivalent to being 3.27 years ‘younger’ compared with the overall population (Fig. 5). We also examined the relationship between global longitudinal phenotypic score and mortality. Survival curves stratified by global longitudinal phenotypic score are shown in Fig. 7b. Adjusting for age, sex and education, among those surviving to at least 60 years, one point ‘higher’ in global longitudinal phenotypic score was associated with 12% shorter survival time (adjusted time ratio: 0.88, 95% CI: 0.81, 0.96) (Supplementary Fig. 7 and Supplementary Table 5).

Fig. 7: Relationship of global longitudinal phenotypic score with change of multimorbidity index and survival probability.
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a, Higher global longitudinal phenotypic score indicates accelerated phenotypic aging trajectories. Higher annual increase in multimorbidity indicates faster accumulation of chronic diseases. b, Among 893 participants aged >60 years during follow-up, the group with global longitudinal phenotypic score >0 (red) includes participants who experienced accelerated phenotypic aging trajectories, showing higher mortality compared with those with global longitudinal phenotypic score ≤0 (black; unadjusted time ratio (95% CI) = 0.87 (0.80, 0.95), P = 0.001, by fitting of Weibull distribution.) Two-sided tests were used, and the displayed P value was not adjusted for multiple comparisons.

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Metrics of the rate of aging may be particularly useful in relatively young and healthy individuals, at the time when chronic disease is infrequent and traditional measures of physical function may be less informative because of substantial reserve capacity and strong resilience. To address this issue, we analyzed relationships between the global longitudinal phenotypic score and measures of physical and cognitive function within three age strata: ≤50, 51–79 and ≥80 years for physical functions and 50–65, 66–79 and ≥80 years for cognitive functions (Supplementary Figs. 8 and 9). Findings were consistent across age strata although, not surprisingly, associations appear to be stronger at older ages. We tested whether the relationship between global longitudinal phenotypic score and change in physical and cognitive functions was stronger among older adults, and significant interactions were found for usual gait speed (P = 0.002), time to finish a 400-m walk (P < 0.001), HABC SPPB (P < 0.001), memory (P < 0.001) and attention (P = 0.001), but not for DSST, executive function, language or visuospatial ability (Supplementary Figs. 8 and 9).

Comparison with cross-sectional phenotypic and epigenetic summaries

To further understand the added value of using longitudinal data to estimate the rate of aging, we developed a global cross-sectional phenotypic score for the four domains using the same analytical approach for estimation of longitudinal score. Specifically, we summarized the differences between measures of phenotypes in individual participants and estimated average measures of the same phenotypes in the population of the same age and sex. The associations between global cross-sectional phenotypic score and changes in physical and cognitive functions were substantially weaker than those between global longitudinal phenotypic score and changes in physical and cognitive functions, except for multimorbidity which was comparable (Fig. 8).

Fig. 8: Age equivalence of a one-point difference in global cross-sectional phenotypic score for different functional outcomes.
figure 8

Estimated age equivalence of a one-point difference in global cross-sectional phenotypic score for different functional outcomes, including cognitive function, physical function and multimorbidity. Age equivalence is a scaled regression coefficient (point estimates and 95% CIs) relating cross-sectional phenotypic score and rate of changes in functions and illustrates how many years older in functional age are individuals with one point higher in cross-sectional phenotypic scores. Number of participants: DSST, n = 922; memory, n = 922; executive function, n = 929; attention, n = 929; language, n = 929; visuospatial ability, n = 919; usual gait speed, n = 968; time to finish 400-m walk, n = 943; HABC SPPB, n = 968; multimorbidity index, n = 828; also see Supplementary Table 1b for more details.

Epigenetic age acceleration metrics based on measures of DNA methylation are generally considered powerful biomarkers for tracking biological aging28. We also investigated the relationships between six popular epigenetic age acceleration measurements and changes in physical and cognitive functions in the BLSA population. In comparison with the global longitudinal phenotypic score, associations of epigenetic clocks with changes in physical and cognitive functions were much weaker, indicating that these epigenetic age clocks may provide some information on the biological age of an individual but are poor metrics of longitudinal rates of aging in a relatively healthy population (Supplementary Fig. 10).

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