Abstract
Age trajectories of physiological indices contain important information about aging-related changes in the human organism and therefore may help us understand human longevity. The goal of this study is to investigate whether shapes of such trajectories earlier in life affect the residual life span distribution. We used longitudinal limited access data from seven physiological indices and life spans of respective individuals collected in the Framingham Heart Study (FHS). These include: diastolic blood pressure (DBP), pulse pressure (PP), body mass index (BMI), serum cholesterol (SCH), blood glucose (BG), hematocrit (HC), and pulse rate (PR). We developed a method for assigning individuals to groups of potentially long-lived (PLL) and potentially medium-lived (PML) groups using age trajectories of physiological indices at the age interval between 40 and 60 years. The analysis shows that the longevity of individuals who survived to age of 65 depends on the behavior of the physiological indices between 40 and 60 years of age.
Similar content being viewed by others
Abbreviations
- BG:
-
blood glucose
- BMI:
-
body mass index
- CVD:
-
cardiovascular disease
- DBP:
-
diastolic blood pressure
- FHS:
-
the Framingham Heart Study
- HC:
-
hematocrit
- LL:
-
long-lived
- ML:
-
medium-lived
- PLL:
-
potentially long-lived
- PML:
-
potentially medium-lived
- PP:
-
pulse pressure
- PR:
-
pulse rate
- SCH:
-
serum cholesterol
- SL:
-
short-lived
References
Coady SA, Jaquish CE, Fabsitz RR, Larson MG, Cupples A, Myers RM (2002) Genetic variability of adult body mass index: a longitudinal assessment in Framingham families. Obes Res 10:675–681
Cox DR (1972) Regression models and life-tables. J R Stat Soc B 34(2):187–220
D’Agostino RB, Kannel WB (1989) Epidemiological background and design: the Framingham Study. In: Gail MH, Johnson NJ (eds) Proceedings of the American Statistical Association Sesquicentennial 1988–89. Am Stat Assoc, Alexandria, VA, pp707–718
Dawber TR (1980) The Framingham Study: the epidemiology of atherosclerotic disease. Harvard University Press, Cambridge, MA
Dawber TR, Meadors GF, Moore FE (1951) Epidemiologic approaches to heart disease: the Framingham Study. Am J Public Health 41:279–286
Dawber TR, Kannel WB, Lyell LP (1963) An approach to longitudinal studies in a community: the Framingham Study. Ann N Y Acad Sci 107:539–556
Franco OH, Peeters A, Bonneux L, de Laet C (2005) Blood pressure in adulthood and life expectancy with cardiovascular disease in men and women: life course analysis. Hypertension 46:280–286
Franklin SS, Pio JR, Wong ND, Larson MG, Leip EP, Vasan RS, Levy D (2005) Predictors of new-onset diastolic and systolic hypertension: the Framingham Heart Study. Circulation 111:1121–1127
Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB, Wilson PW (2003) Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA 290:891–897
Kannel WB, Vasan RS, Levy D (2003) Is the relation of systolic blood pressure to risk of cardiovascular disease continuous and graded, or are there critical values? Hypertension 42(4):453–456
Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481
Natarajan S, Liao Y, Cao G, Lipsitz SR, McGee DL (2003) Sex differences in risk for coronary heart disease mortality associated with diabetes and established coronary heart disease. Arch Intern Med 163(14):1735–1740
Port SC, Boyle NG, Hsueh WA, Quinones MJ, Jennrich RI, Goodarzi MO (2006) The predictive role of blood glucose for mortality in subjects with cardiovascular disease. Am J Epidemiol 163(4):342–351
Singh JP, Larson MG, O’Donnell CJ, Tsuji H, Evans JC, Levy D (1999) Heritability of heart rate variability: the Framingham Heart Study. Circulation 99(17):2251–2254
Terry DF, Pencina MJ, Vasan RS, Murabito JM, Wolf PA, Hayes MK, Levy D, D’Agostino RB, Benjamin EJ (2005) Cardiovascular risk factors predictive for survival and morbidity-free survival in the oldest-old Framingham Heart Study participants. J Am Geriatr Soc 53(11):1944–1950
Tsai W-Y (1988) Estimation of the survival function with increasing failure rate based on left truncated and right censored data. Biometrika 75(2):319–324
Tsai W-Y, Jewell NP, Wang M-C (1987) A note on the product-limit estimator under right censoring and left truncation. Biometrika 74(4):883–886
Vlagopoulos PT, Tighiouart H, Weiner DE, Griffith J, Pettitt D, Salem DN, Levey AS, Sarnak MJ (2005) Anemia as a risk factor for cardiovascular disease and all-cause mortality in diabetes: the impact of chronic kidney disease. J Am Soc Nephrol 16(11):3403–3410
Yashin AI, Manton KG (1997) Effects of unobserved and partially observed covariate processes on system failure: a review of models and estimation strategies. Stat Sci 12(1):20–34
Acknowledgements
The Framingham Heart Study (FHS) is conducted and supported by the NHLBI in collaboration with FHS Investigators. This manuscript was prepared using a limited access dataset obtained by the NHLBI and does not necessarily reflect the opinions or views of the FHS or the NHLBI. The work on this project was partly supported by the NIH/NIA grants 1R01 AG028259-01, 1RO1-AG-027019-01 and 5PO1-AG-008761-16.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Yashin, A.I., Akushevich, I.V., Arbeev, K.G. et al. Insights on aging and exceptional longevity from longitudinal data: novel findings from the Framingham Heart Study. AGE 28, 363–374 (2006). https://doi.org/10.1007/s11357-006-9023-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11357-006-9023-7