Abstract
Aging is characterized by deleterious immune and metabolic changes, but the onset of these changes is unknown. We measured immune and metabolic biomarkers in adults beginning at age 30. To our knowledge, this is the first study to evaluate these biomarkers in adults aged 30 to over 80. Biomarkers were quantified in 961 adults. Tumor necrosis factor alpha (TNF-α), tumor necrosis factor receptor I (TNFR-I), tumor necrosis factor receptor II (TNFR-II), interleukin (IL)-2, IL-6, VCAM-I, D-Dimer, G-CSF, regulated on activation, normal T cell expressed and secreted (RANTES), matrix metalloproteinase-3 (MMP-3), adiponectin, and paraoxonase activity were measured by ELISA. Acylcarnitines and amino acids (AAs) were measured by mass spectrometry and reduced to a single factor using principal components analysis (PCA). Glycine was analyzed separately. The relationship between age and biomarkers was analyzed by linear regression with sex, race, and body mass index (BMI) as covariates. Age was positively correlated with TNF-α, TNFR-I, TNFR-II, IL-6, IL-2, VCAM-1, D-Dimer, MMP-3, adiponectin, acylcarnitines, and AAs. Age was negative correlated with G-CSF, RANTES, and paraoxonase activity. BMI was significant for all biomarkers except IL-2, VCAM-1, RANTES, paraoxonase activity, and the AA factor. Excluding MMP-3, greater BMI was associated with potentially adverse changes in biomarker concentrations. Age-related changes in immune and metabolic biomarkers, known to be associated with poor outcomes in older adults, begin as early as the thirties.
Keywords: Metabolism, Functional impairment, Biomarker, Life span
Biological aging is characterized by dysregulated immune and metabolic homeostasis (1). These changes comprise two of the nine so-called hallmarks of aging as described by López-Otín; they manifest clinically as an age-related increase in the incidence of diabetes, serious infections, autoimmunity, cardiovascular disease, and cancer (1). Even in the absence of associated clinical comorbidity, these changes are associated with increased risk of functional impairment, frailty, and mortality (2–6). The age of onset for immune and metabolic dysregulation is unknown; but, it is increasingly apparent that biological aging begins—and is measurable—in early adulthood (6). This study is the first—to our knowledge—to characterize these biomarkers in adults across the life span.
A central component of aging is increased basal inflammation in the absence of infection, or inflamm-aging, that is reflected by changes in circulating immune markers including C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α) and its soluble receptors (tumor necrosis factor receptor I [TNFR-I] and tumor necrosis factor receptor II [TNFR-II]), vascular cell adhesion molecule I (VCAM-I), and d-dimer among others (7,8). These changes manifest clinically in the decreased responsiveness to vaccination, delayed wound healing, and increased incidence of sepsis observed in older adults (9). Hypothesized mechanisms for inflamm-aging are reviewed in detail elsewhere, but potential pathways include chronically activated immune cells, senescent nonimmune cells that acquire the pro-inflammatory senescence-associated secretory phenotype (SASP), and alterations in the coupling of anabolic and inflammatory signaling (7).
Age-related metabolic dysregulation occurs in tandem with inflamm-aging, though the relationship between these phenomena is poorly understood. Previous analyses of circulating acylcarnitines—intermediate metabolites derived from mitochondrial oxidation of fatty acids, carbohydrates, and amino acids (AAs)—have revealed conserved phenotypes associated with age, excess body mass index (BMI), and insulin resistance (2,10–13). Variation in the relative proportions of circulating long neutral AAs and medium chain acylcarnitines can be used as markers of metabolic health (10). Greater plasma concentrations of adiponectin, an abundant adipokine—a peptide hormone released from adipose tissue—and glycine—the structurally simplest, nonessential AA—are positive markers of metabolic health (10,14).
Methods
The Physical Performance Across the LifeSpan (PALS) Study is a nested longitudinal cohort study within the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) registry (15–17). The overall objective of the PALS study is to examine patterns of age and sex-related differences in physical function and biological markers across the adult life span, and to longitudinally characterize changes in biological markers on functional aging (18). The design of PALS—in particular, the inclusion of young adults—was motivated by the recognition that biological aging occurs across the life span. The data presented here were obtained from the baseline assessment.
Study participants were eligible for PALS if they were (a) at least 30 years of age; (b) residents of Cabarrus County, the city of Kannapolis or certain portions of Rowan, Mecklenburg, or Stanly counties, as well as other surrounding areas in North Carolina for at least 6 months; and (c) provided written informed consent. All potential participants were scheduled for an in-person baseline study visit, during which time informed consent, HIPAA authorization, and study measures were administered. PALS was approved by the Institutional Review Boards of Duke University Medical Center and the Carolinas Healthcare System. Study data were collected and managed using REDCap electronic data capture tools hosted at Duke University. Demographic variables (education, race, and ethnicity) were collected by self-report. Other measures included surveys of self-reported health and behaviors, physical performance measures, 1 week of accelerometry, and collection of blood in a nonfasting state for storage for future investigations. Enrolled participants (n = 1000) were stratified by age and sex in equal numbers by decade of life with a target of 100 participants per decade divided equally between men and women from age 30 to 59, 200 per decade from age 60 to 79, and all interested participants over age 80.
Plasma samples were used for quantitative determination of TNF-α, TNFR-I, TNFR-II, IL-2, IL-6, VCAM-1, D-Dimer, G-CSF, regulated on activation, normal T cell expressed and secreted (RANTES), matrix metalloproteinase-3 (MMP-3), paraoxonase activity, adiponectin, 45 acylcarnitines, and 15 AAs. TNF-α, TNFR-I, TNFR-II, IL-2, IL-6, G-CSF, RANTES, MMP-3, and adiponectin were quantified by electrochemiluminescence (Mesoscale Discovery Systems, Rockville, MD). VCAM-1 and d-dimer were quantified by ELISA (R&D Systems, Minneapolis, MN and Sekisui Diagnostics, Lexington, MA, respectively). Paraoxonase activity was quantified by measuring its organophosphatase activity using a fluorometric assay (Invitrogen Molecular Probes, Waltham, MA). Acylcarnitines and AAs were quantified using mass spectrometry by the Sarah W. Stedman Nutrition and Metabolism metabolomics core laboratory of the Duke Molecular Physiology Institute, as described previously (19).
An acylcarnitine (AC) factor and AA factor were generated using principal components analysis (PCA) according to previously published methods (2). The AC factor was weighted on medium to long-chain acylcarnitines with side chains of 10–18 carbons; there was a small contribution from odd chain species likely generated by AA metabolism. The AA factor was weighted on large neutral AAs including essential branched-chain AAs (isoleucine, leucine, and valine), methionine, and the aromatic AAs phenylalanine and tyrosine. Glycine was the primary component of a second AA factor and was treated separately. As described in previously published work, these factors are reliably correlated with age and BMI and reflect age- and obesity-related metabolic dysregulation (10).
Physical performance measures that were collected included rapid and usual gait speed, timed single leg stance test (20), number of completed chair stands in 30 s (19), and 6-min walk test (21). Each of these physical performance measures has demonstrated excellent validity and reliability in healthy and diseased populations covering the adult age span (22–25). Physical activity was measured by a waist-mounted triaxial Actigraph accelerometer (models GT3X and GT3X+; Pensacola, FL) over a period of 7 consecutive days. PA was examined in three ways: (a) average number of daily steps taken, (b) time spent in moderate vigorous PA (MVPA; minutes), and (c) time spent in sedentary-light intensity activities (%) (26). The Actigraph triaxial accelerometer has been widely validated and is a reliable instrument (27).
Self-reported clinical data, including medical diagnoses and medications were also collected as part of the baseline visit and updated once yearly thereafter via survey. Other data collected at the baseline visit included Montreal Cognitive Assessment (MOCA) scores and the Optum SF-8™ Health Survey, which assesses participants’ impressions of functional health and well-being.
Statistical Analysis
Clinical and demographic measures were described by means ± SDs for continuous variables. The ages of participants 90 years and older were recoded to 80 when presented by age decade to minimize the risk of identifying individual participants. Biomarker values below the lower limit of detection (LLOD) were imputed as one-half the LLOD. For IL-2, 525 participants (55% of the total sample) had values below the LLOD. Biomarker values were not normally distributed and were log-transformed. To standardize values for direct comparison among biomarkers, biomarker concentrations were scaled (converted to z values) by subtracting the sample mean for each biomarker from individual concentrations and dividing by the standard deviation. The AC and AA measures were log-transformed prior to PCA. To determine the relationship of age with concentrations of circulating biomarkers, ordinary least squares linear regressions were conducted, adjusting for sex, race, and BMI. These covariates were selected for inclusion in the analysis based on previously published reports of associations with the biomarkers of interest. Age was included as a continuous variable. Race was included as a three-level factor: Caucasian, African-American, and other. Statistical significance was declared at p < .05 (two-tailed). All statistical analyses were conducted in R.
Results
A total of 961 participants with complete data sets were included in the analysis (Table 1). There were approximately one hundred participants per decade between the ages of 30 and 59, two hundred between the ages of 60 and 79, and 278 aged 80 or above. Average BMI was 27.6 ± 4.9 kg/m2, placing most participants in the overweight range (BMI 25.0–30.0 kg/m2). The sample was skewed female above age 80 and was mostly Caucasian, particularly above age 60.
Table 1.
PALS Sample Descriptive Statistics
| BMI | |||||
|---|---|---|---|---|---|
| Age | n | Female (%) | White (%) | BMI Mean | BMI SD |
| 30–39 | 96 | 48 | 69 | 28.3 | 5.6 |
| 40–49 | 98 | 50 | 74 | 28.5 | 6.1 |
| 50–59 | 98 | 50 | 81 | 28.5 | 4.8 |
| 60–69 | 196 | 48 | 92 | 28.1 | 4.9 |
| 70–79 | 195 | 50 | 93 | 27.6 | 4.6 |
| 80+ | 278 | 65 | 94 | 26.3 | 4.1 |
| Total | 961 | ||||
Scatter plots, depicting the distribution of log-transformed and scaled biomarker concentrations with participant age, are shown in Figure 1. Starting as early as the thirties, the biomarkers are linearly associated with age. Greater age was associated with statistically significant elevations in biomarker concentrations, except for G-CSF, RANTES, and paraoxonase activity, which were lower with greater age. Additional statistics for the age-only linear regression model are given in Table 2.
Figure 1.
Biomarker concentrations by age. Log-transformed and scaled biomarker concentrations are plotted by age. Regression lines were plotted using least squares linear regression. The shaded area represents the 95% confidence interval for the fitted values. The r2 and p-value for the correlation between age and each biomarker are shown in the lower right-hand corner.
Table 2.
Age-Only Model
| TNF-α | TNFR-I | TNFR-II | IL-6 | IL-2 | |
|---|---|---|---|---|---|
| Age | 0.02*** (0.002) |
0.04*** (0.002) |
0.04*** (0.002) |
0.02*** (0.002) |
0.01*** (0.002) |
| Constant | −1.35*** (0.12) |
−2.33*** (0.11) |
−2.31*** (0.11) |
−1.23*** (0.13) |
−0.98*** (0.13) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.13 | 0.34 | 0.33 | 0.09 | 0.06 |
| F Statistic | 145.00*** | 486.56*** | 475.45*** | 98.05*** | 59.61*** |
| VCAM-I | D-Dimer | G-CSF | RANTES | MMP-3 | |
| Age | 0.01** (0.002) |
0.03*** (0.002) |
−0.01*** (0.002) |
−0.01** (0.002) |
0.01*** (0.002) |
| Constant | −0.38** (0.13) |
−2.29*** (0.11) |
0.60*** (0.13) |
0.41** (0.13) |
−0.88*** (0.13) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.01 | 0.32 | 0.02 | 0.01 | 0.05 |
| F Statistic | 8.28** | 454.91*** | 22.97*** | 10.23** | 48.14*** |
| Paraoxonase | Adiponectin | AC Factor | AA Factor | Glycine | |
| Age | −0.01*** (0.002) |
0.02*** (0.002) |
0.02*** (0.002) |
0.01** (0.002) |
0.01*** (0.002) |
| Constant | 0.46*** (0.11) |
−1.53*** (0.12) |
−1.37*** (0.13) |
−0.42** (0.13) |
−0.66*** (0.13) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.02 | 0.14 | 0.12 | 0.01 | 0.03 |
| F Statistic | 17.50*** | 161.76*** | 126.33*** | 10.84** | 26.56*** |
Notes: Results of least squares linear regression using log-transformed and scaled biomarker concentrations as the dependent variable. Age is included as a continuous variable. AC factor = Acylcarnitine factor; AA Factor = Amino acid factor. The standard error is given in parentheses.
*p < .05; ** p < .01; ***p < .001.
The age-, sex-, race-, and BMI-adjusted regression models for each biomarker are shown in Table 3. Of all the included covariates, age alone accounted for the substantial portion (>80%) of explained variance for TNF-α (r2 = 0.13), TNFR-I (r2 = 0.34), TNFR-II (r2 = 0.33), IL-2 (r2 = 0.06), D-Dimer (r2 = 0.32), and the AC factor (r2 = 0.12).
Table 3.
Full Model
| TNF-a | TNFR-I | TNFR-II | IL-6 | IL-2 | |
|---|---|---|---|---|---|
| Age | 0.02*** (0.002) |
0.04*** (0.002) |
0.04*** (0.002) |
0.02*** (0.002) |
0.02*** (0.002) |
| Sex—male | 0.02 (0.06) |
0.03 (0.05) |
0.02 (0.05) |
−0.15* (0.06) |
0.10 (0.06) |
| Race—AA | −0.11 (0.11) |
−0.21* (0.10) |
−0.01 −(0.10) |
0.20 (0.11) |
0.02 (0.12) |
| Race—other | 0.07 (0.14) |
−0.21 (0.13) |
−0.09 (0.13) |
−0.09 (0.15) |
0.43** (0.16) |
| BMI | 0.03*** (0.01) |
0.04*** (0.01) |
0.03*** (0.01) |
0.06*** (0.01) |
−0.01 (0.01) |
| Constant | −2.25*** (0.21) |
−3.49*** (0.20) |
−3.39*** (0.20) |
−3.06*** (0.22) |
−0.86*** (0.24) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.15 | 0.38 | 0.36 | 0.19 | 0.07 |
| F statistic | 34.77*** | 114.96*** | 107.91*** | 45.47*** | 14.31*** |
| VCAM-I | D-Dimer | G-CSF | RANTES | MMP-3 | |
| Age | 0.005* (0.002) |
0.04*** (0.002) |
−0.01*** (0.002) |
−0.01** (0.002) |
0.02*** (0.002) |
| Sex—male | 0.23*** (0.06) |
−0.34*** (0.05) |
−0.19** (0.06) |
−0.07 (0.06) |
1.06*** (0.05) |
| Race—AA | −0.57*** (0.12) |
0.34*** (0.10) |
0.59*** (0.12) |
−0.004 (0.12) |
0.11 (0.10) |
| Race—other | −0.13 (0.16) |
0.002 (0.13) |
−0.10 (0.15) |
−0.26 (0.16) |
0.01 (0.13) |
| BMI | 0.0002 (0.01) |
0.03*** (0.01) |
0.04*** (0.01) |
0.01 (0.01) |
−0.01** (0.01) |
| Constant | −0.37 (0.24) |
−2.98*** (0.20) |
−0.77** (0.23) |
0.25 (0.25) |
−1.15*** (0.20) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.05 | 0.38 | 0.12 | 0.02 | 0.33 |
| F statistic | 9.21*** | 115.37*** | 24.87*** | 3.09** | 92.13*** |
| Paraoxonase | Adiponectin | AC Factor | AA Factor | Glycine | |
| Age | −0.01*** (0.002) |
0.02*** (0.002) |
0.02*** (0.002) |
0.01*** (0.002) |
0.01*** 0.002) |
| Sex—male | −0.10 (0.05) |
−0.59*** (0.05) |
0.10 (0.06) |
0.24*** (0.06) |
−0.35*** (0.06) |
| Race—AA | −0.10 (0.10) |
−0.35*** (0.10) |
−0.05 (0.12) |
0.03 (0.12) |
0.08 (0.12) |
| Race—other | −0.02 (0.13) |
−0.18 (0.13) |
−0.16 (0.15) |
0.16 (0.16) |
0.06 (0.15) |
| BMI | 0.003 (0.01) |
−0.05*** (0.01) |
0.01* (0.01) |
0.004 (0.01) |
−0.04*** (0.01) |
| Constant | 0.47* (0.20) |
0.56** (0.21) |
−1.82*** (0.23) |
−0.74** (0.25) |
0.83*** (0.24) |
| Observations | 961 | 961 | 961 | 961 | 961 |
| R 2 | 0.02 | 0.32 | 0.13 | 0.03 | 0.1 |
| F statistic | 4.32*** | 88.90*** | 27.34*** | 5.34*** | 22.18*** |
Notes: Results of least squares linear regression using log-transformed and scaled biomarker concentrations as the dependent variable. Age and BMI are included as continuous variables. Race was included as a three-level factor: Caucasian, African-American, and other. AC factor = Acylcarnitine factor; AA factor = Amino acid factor. The standard error is given in parentheses.
*p < .05; **p < .01; ***p < .001.
Greater BMI was significantly associated with higher concentrations of TNF-α, TNFR-I, TNFR-II, IL-6, D-Dimer, G-CSF, AC factor, and lower concentrations of MMP-3, adiponectin, and glycine. For TNF-α, IL-6, G-CSF, adiponectin, and glycine, scaled regression coefficients for BMI were greater than those for age, suggesting a greater impact on affected biomarker concentrations from a one unit increase in BMI than a 1-year increase in age. Male sex was associated with higher concentrations of VCAM-I, MMP-3, and AA factor and lower concentrations of IL-6, D-Dimer, G-CSF, adiponectin, and glycine. Race was a significant covariate for TNFR-I, IL-2, VCAM-I, D-Dimer, G-CSF, and adiponectin with African-American race associated with lower levels of TNFR-I, VCAM-I, and adiponectin and higher levels of D-Dimer and MMP-3. Race other than African-American or Caucasian was associated with higher levels of IL-2.
Minimums, maximums, means and standard deviations for all biomarkers are given by decade of age in Table 1 (scaled units) and Table 2 (original units) of the Supplementary Materials.
Discussion
Our findings demonstrate that abnormalities in immune and metabolic biomarkers, associated with increased morbidity, mortality, and functional impairment, emerge as early as the thirties. To our knowledge, this study is the first to characterize these biomarkers in adults across the life span. These findings provide a clear validation of PALS’ design and the importance of including young and middle-aged participants in studies of biological aging. Our results are consistent with previous work by our group and others that measurable, adverse age-related biological indicators emerge early in adulthood (6,28).
Previously published findings from the PALS cohort identified declines in functional measures as early as the fifth decade—much earlier than previously reported (18). Our results suggest that immune and metabolic dysregulation precede age-related functional impairment and morbidity, suggesting a possible mechanism for age-associated functional impairment. Our results also suggest that excess adiposity is associated with an “older” immune and metabolic biomarker profile, which may reflect accelerated biological aging.
Accumulating data from animal and human studies of interventions, designed to modulate inflammation, support a causal link between inflammation, metabolic dysregulation, and age-related pathology. Experiments in mice disrupting pro-inflammatory signal transduction lead to preserved skeletal muscle strength and mass; this suggests a direct link between chronic inflammation and impaired muscle function, one of the core components of frailty (29). Studies of long-term caloric restriction decrease adiposity and basal inflammation, which may explain its benefits on health span and longevity (30). Exercise is effective at prolonging health span, functional independence, and delaying the onset of multiple age-related diseases; it also is associated with a decrease in basal inflammation (31). In patients with preexisting coronary artery disease (CAD) and excess inflammation reflected by elevated high-sensitivity CRP, inhibition of IL-1ß—a potent pro-inflammatory cytokine—decreased the risk of cardiovascular events and lung cancer (32,33). Inhibition of IL-1ß also led to improvements in glycemic control in nondiabetic adults (34). These data suggest that inflammation may play a causal role in age-related metabolic dysregulation, functional impairment, and morbidity and that interventions targeting inflammation may provide clinical benefit.
Limitations
Participants were self-referred, community-dwelling, and able to provide consent. Compared to a randomly sampled same age cohort, older PALS participants likely had a lower prevalence of dementia and other health problems that would have precluded their participation in this study. Our sample was sex-balanced up until age 80; therefore, generalizability of our findings to men over 80 is limited. Non-white participants comprised at least 20% of our sample until age 60. For participants under 60, these proportions closely matched the U.S. adult population (35). As a cross-sectional analyses, the observed age-related trends do not reflect longitudinal change over time. It is possible that these age-related trends might, at least partially, be explained by the presence of unmeasured effect modifiers or cofounders. Further limiting generalizability, this sample was also drawn exclusively from a rural/semi-urban geographic region of the Southeastern United States.
Conclusions
Our work demonstrates that changes in immune and metabolic biomarkers associated with increased morbidity, mortality, and functional impairment in older adults emerge early in adulthood. The recently published results described above showing clinical benefit with anti-inflammatory therapy may herald the use of anti-inflammatory interventions to prevent chronic conditions of aging. Our findings underscore the need to determine how this early adult phenotype of immune and metabolic dysregulation relates to the development of functional impairment, morbidity, and mortality later in life. Understanding the temporal relationship between these observations and their associated health consequences will be critical in the development of future interventions targeting immune and metabolic health. Furthermore, our results reinforce the critical importance of including younger participants in future studies of biological aging.
Funding
This work was supported by a philanthropic gift to Duke University from the David H. Murdock Institute for Business and Culture; by the Claude D. Pepper Older Americans Independence Center supported by the National Institute on Aging at the National Institutes of Health (grant number P30AG028716 to H.J.C and M.C.M); and by the National Center for Research Resources (NCRR) at the NIH (grant number UL1TR001117).
Supplementary Material
Acknowledgments
The authors thank research team members Ashley Dunham, Leah B. Bouk, Carla Kingsbury, Christopher E. Lewis, Betty Hover, Alice E. Glines, Deborah Alicea, Micki Roseman, Abha Singh, Melissa Johnston, Asia Lattimore, Cammie Yarborough, Sam Densen, Douglas Wixted, Siobhan Brunson, Tonya Furr, Juan Gomez, David T. Steele, Enya Rentas-Sherman, Ellen Emerson; the Duke Stedman Metabolomic Laboratory; and the study participants for their gracious contribution to this research. The views expressed by the authors do not necessarily reflect the views of the United States Government, NCRR, or NIH. Trial Registration: Clinicaltrials.gov (NCT01720472; https://www.clinicaltrials.gov/ct2/show/NCT01720472).
Conflict of Interest Statement
None declared.
References
- 1. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–1217. doi: 10.1016/j.cell.2013.05.039 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Lum H, Sloane R, Huffman KM, et al. Plasma acylcarnitines are associated with physical performance in elderly men. J Gerontol A Biol Sci Med Sci. 2011;66:548–553. doi: 10.1093/gerona/glr006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Peterson MJ, Thompson DK, Pieper CF, et al. A novel analytic technique to measure associations between circulating biomarkers and physical performance across the adult life span. J Gerontol A Biol Sci Med Sci. 2016;71:196–202. doi: 10.1093/gerona/glv007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Puzianowska-Kuźnicka M, Owczarz M, Wieczorowska-Tobis K, et al. Interleukin-6 and C-reactive protein, successful aging, and mortality: the PolSenior study. Immun Ageing. 2016;13:21. doi: 10.1186/s12979-016-0076-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Newman AB, Sanders JL, Kizer JR, et al. Trajectories of function and biomarkers with age: the CHS All Stars Study. Int J Epidemiol. 2016;45(4):1135–1145. doi: 10.1093/ije/dyw092 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Belsky DW, Caspi A, Houts R, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015;112:E4104–E4110. doi: 10.1073/pnas.1506264112 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4–S9. doi: 10.1093/gerona/glu057 [DOI] [PubMed] [Google Scholar]
- 8. Richter V, Rassoul F, Purschwitz K, Hentschel B, Reuter W, Kuntze T. Circulating vascular cell adhesion molecules VCAM-1, ICAM-1, and E-selectin in dependence on aging. Gerontology. 2003;49:293–300. doi: 10.1159/000071710 [DOI] [PubMed] [Google Scholar]
- 9. Bandaranayake T, Shaw AC. Host resistance and immune aging. Clin Geriatr Med. 2016;32:415–432. doi: 10.1016/j.cger.2016.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Kraus WE, Pieper CF, Huffman KM, et al. Association of plasma small-molecule intermediate metabolites with age and body mass index across six diverse study populations. J Gerontol A Biol Sci Med Sci. 2016;71(11):1507–1513. doi: 10.1093/gerona/glw031 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Batch BC, Shah SH, Newgard CB, et al. Branched chain amino acids are novel biomarkers for discrimination of metabolic wellness. Metabolism. 2013;62:961–969. doi: 10.1016/j.metabol.2013.01.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Newgard CB, An J, Bain JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9:311–326. doi: 10.1016/j.cmet.2009.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Huffman KM, Koves TR, Hubal MJ, et al. Metabolite signatures of exercise training in human skeletal muscle relate to mitochondrial remodelling and cardiometabolic fitness. Diabetologia. 2014;57:2282–2295. doi: 10.1007/s00125-014-3343-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Kizer JR, Benkeser D, Arnold AM, et al. Associations of total and high-molecular-weight adiponectin with all-cause and cardiovascular mortality in older persons: the Cardiovascular Health Study. Circulation. 2012;126:2951–2961. doi: 10.1161/CIRCULATIONAHA.112.135202 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Bhattacharya S, Dunham AA, Cornish MA, et al. The Measurement to Understand Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) study community registry and biorepository. Am J Transl Res. 2012;4:458–470. [PMC free article] [PubMed] [Google Scholar]
- 16. Tenenbaum JD, Christian V, Cornish MA, et al. The MURDOCK Study: a long-term initiative for disease reclassification through advanced biomarker discovery and integration with electronic health records. Am J Transl Res. 2012;4:291–301. [PMC free article] [PubMed] [Google Scholar]
- 17. Strauss BW, Valentiner EM, Bhattacharya S, et al. Improving population representation through geographic health information systems: mapping the MURDOCK study. Am J Transl Res. 2014;6:402–412. [PMC free article] [PubMed] [Google Scholar]
- 18. Hall KS, Cohen HJ, Pieper CF, et al. Physical performance across the adult life span: correlates with age and physical activity. J Gerontol A Biol Sci Med Sci. 2016;72(4):572–578. doi: 10.1093/gerona/glw120 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Rikli RE, Jones CJ. Development and validation of criterion-referenced clinically relevant fitness standards for maintaining physical independence in later years. Gerontologist. 2013;53:255–267. doi: 10.1093/geront/gns071 [DOI] [PubMed] [Google Scholar]
- 20. Yoshimura N, Oka H, Muraki S, et al. Reference values for hand grip strength, muscle mass, walking time, and one-leg standing time as indices for locomotive syndrome and associated disability: the second survey of the ROAD study. J Orthop Sci. 2011;16:768–777. doi: 10.1007/s00776-011-0160-1 [DOI] [PubMed] [Google Scholar]
- 21. Troosters T, Gosselink R, Decramer M. Six-minute walk test: a valuable test, when properly standardized. Phys Ther. 2002;82:826–7; author reply 827. [PubMed] [Google Scholar]
- 22. Rikli RE, Jones CJ. Functional fitness normative scores for community-residing older adults, ages 60–94. J Aging Phys Act. 1999;7:162–181. doi: 10.1123/japa.7.2.162 [DOI] [Google Scholar]
- 23. Springer BA, Marin R, Cyhan T, Roberts H, Gill NW. Normative values for the unipedal stance test with eyes open and closed. J Geriatr Phys Ther. 2007;30:8–15. [DOI] [PubMed] [Google Scholar]
- 24. Steffen TM, Hacker TA, Mollinger L. Age- and gender-related test performance in community-dwelling elderly people: six-minute walk test, berg balance scale, timed up & go test, and gait speeds. Phys Ther. 2002;82:128–137. [DOI] [PubMed] [Google Scholar]
- 25. Studenski S, Perera S, Wallace D, et al. Physical performance measures in the clinical setting. J Am Geriatr Soc. 2003;51:314–322. doi: 10.1046/j.1532-5415.2003.51104.x [DOI] [PubMed] [Google Scholar]
- 26. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14:411–416. doi: 10.1016/j.jsams.2011.04.003 [DOI] [PubMed] [Google Scholar]
- 27. Kelly LA, McMillan DG, Anderson A, Fippinger M, Fillerup G, Rider J. Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med Phys. 2013;13:5. doi: 10.1186/1756-6649-13-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Belsky DW, Huffman KM, Pieper CF, Shalev I, Kraus WE. Change in the rate of biological aging in response to caloric restriction: calerie biobank analysis. J Gerontol Ser A. 2017;73(1):4–10. doi: 10.1093/gerona/glx096 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Reed SA, Senf SM, Cornwell EW, Kandarian SC, Judge AR. Inhibition of IkappaB kinase alpha (IKKα) or IKKbeta (IKKβ) plus forkhead box O (Foxo) abolishes skeletal muscle atrophy. Biochem Biophys Res Commun. 2011;405:491–496. doi: 10.1016/j.bbrc.2011.01.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Ravussin E, Redman LM, Rochon J, et al. ; CALERIE Study Group A 2-year randomized controlled trial of human caloric restriction: feasibility and effects on predictors of health span and longevity. J Gerontol A Biol Sci Med Sci. 2015;70:1097–1104. doi: 10.1093/gerona/glv057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Petersen AMW, Pedersen BK. The anti-inflammatory effect of exercise. J Appl Physiol Bethesda Md 1985. 2005;98:1154–1162. doi: 10.1152/japplphysiol.00164.2004 [DOI] [PubMed] [Google Scholar]
- 32. Ridker PM, Everett BM, Thuren T, et al. ; CANTOS Trial Group Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377:1119–1131. doi: 10.1056/NEJMoa1707914 [DOI] [PubMed] [Google Scholar]
- 33. Ridker PM, MacFadyen JG, Thuren T, Everett BM, Libby P, Glynn RJ; CANTOS Trial Group Effect of interleukin-1β inhibition with canakinumab on incident lung cancer in patients with atherosclerosis: exploratory results from a randomised, double-blind, placebo-controlled trial. Lancet. 2017;390:1833–1842. doi: 10.1016/S0140-6736(17)32247-X [DOI] [PubMed] [Google Scholar]
- 34. Huang J, Yang Y, Hu R, Chen L. Anti-interleukin-1 therapy has mild hypoglycaemic effect in type 2 diabetes. Diabetes Obes Metab. 2017;20(4):1024–1028. doi: 10.1111/dom.13140 [DOI] [PubMed] [Google Scholar]
- 35. Annual Estimates of the Resident Population by Sex, Age, Race, and Hispanic Origin for the United States and States: April 1, 2010 to July 1, 2014 2015. https://www.census.gov/popest/data/national/asrh/2014/index.html. Accessed February 2, 2018.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.