Classification for longevity potential: The use of novel biomarkers

Frontiers in Public Health

Volumn 4, Issue OCT, 2016, Pages

  Beekman, Marian ^a   Uh, Hae Won ^a   van Heemst, Diana ^a   Wuhrer, Manfred ^a   Ruhaak, L Renee ^a,b   Gonzalez Covarrubias, Vanessa ^c,d   Hankemeier, Thomas ^c,e   Houwing Duistermaat, Jeanine J ^a   Slagboom, P Eline ^a  

^a LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^c LEIDEN UNIVERSITY   (Netherlands)

^d INSTITUTO NACIONAL DE MEDICINA GENÓMICA   (Mexico)

^e Netherlands Metabolomics Center   (Netherlands)

Author keywords

Biomarker; Classification and prediction; Framingham Risk Score; Human longevity potential; Sex specific analysis

Indexed keywords

EID: 85015328542     PISSN: None     EISSN: 22962565     Source Type: Journal    
DOI: 10.3389/FPUBH.2016.00233     Document Type: Article

References (39)

1. Cohen AA, Milot E, Yong J, Seplaki CL, Fülöp T, Bandeen-Roche K, et al. A novel statistical approach shows evidence for multi-system physiological dysregulation during aging. Mech Ageing Dev (2013) 134:110-7. doi:10.1016/j.mad.2013.01.004
2. Seplaki CL, Goldman N, Glei D, Weinstein M. A comparative analysis of measurement approaches for physiological dysregulation in an older population. Exp Gerontol (2005) 40:438-49. doi:10.1016/j.exger.2005.03.002
3. Arbeev KG, Cohen AA, Arbeeva LS, Milot E, Stallard E, Kulminski AM, et al. Optimal versus realized trajectories of physiological dysregulation in aging and their relation to sex-specific mortality risk. Front Public Health (2016) 4:3. doi:10.3389/fpubh.2016.00003
4. Belsky DW, Caspi A, Houts R, Cohen HJ, Corcoran DL, Danese A, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci U S A (2015) 112:E4104-10. doi:10.1073/pnas.1506264112
5. Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci (2013) 68:667-74. doi:10.1093/gerona/gls233
6. Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC Med (2015) 13:161. doi:10.1186/s12916-015-0400-x
7. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation (1998) 97:1837-47. doi:10.1161/01.CIR.97.18.1837
8. Schoenmaker M, de Craen AJ, de Meijer PH, Beekman M, Blauw GJ, Slagboom PE, et al. Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study. Eur J Hum Genet (2006) 14:79-84. doi:10.1038/sj.ejhg.5201508
9. Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, Bogan H, et al. Life-long sustained mortality advantage of siblings of centenarians. Proc Natl Acad Sci U S A (2002) 99:8442-7. doi:10.1073/pnas.122587599
10. Terry DF, Wilcox MA, McCormick MA, Perls TT. Cardiovascular disease delay in centenarian offspring. J Gerontol A Biol Sci Med Sci (2004) 59:385-9. doi:10.1093/gerona/59.4.M385
11. Terry DF, Wilcox MA, McCormick MA, Pennington JY, Schoenhofen EA, Andersen SL, et al. Lower all-cause, cardiovascular, and cancer mortality in centenarians' offspring. J Am Geriatr Soc (2004) 52:2074-6. doi:10.1111/j.1532-5415.2004.52561.x
12. Terry DF, Wilcox M, McCormick MA, Lawler E, Perls TT. Cardiovascular advantages among the offspring of centenarians. J Gerontol A Biol Sci Med Sci (2003) 58:M425-31. doi:10.1093/gerona/58.5.M425
13. Westendorp RG, van Heemst D, Rozing MP, Frölich M, Mooijaart SP, Blauw GJ, et al. Nonagenarian siblings and their offspring display lower risk of mortality and morbidity than sporadic nonagenarians: the Leiden Longevity Study. J Am Geriatr Soc (2009) 57:1634-7. doi:10.1111/j.1532-5415.2009.02381.x
14. Rozing MP, Westendorp RG, de Craen AJ, Frölich M, de Goeij MC, Heijmans BT, et al. Favorable glucose tolerance and lower prevalence of metabolic syndrome in offspring without diabetes mellitus of nonagenarian siblings: the Leiden longevity study. J Am Geriatr Soc (2010) 58:564-9. doi:10.1111/j.1532-5415.2010.02725.x
15. Rozing MP, Westendorp RG, de Craen AJ, Frölich M, Heijmans BT, Beekman M, et al. Low serum free triiodothyronine levels mark familial longevity: the Leiden Longevity Study. J Gerontol A Biol Sci Med Sci (2010) 65(4):365-8. doi:10.1093/gerona/glp200
16. Atzmon G, Pollin TI, Crandall J, Tanner K, Schechter CB, Scherer PE, et al. Adiponectin levels and genotype: a potential regulator of life span in humans. J Gerontol A Biol Sci Med Sci (2008) 63:447-53. doi:10.1093/gerona/63.5.447
17. Terry DF, Evans JC, Pencina MJ, Murabito JM, Vasan RS, Wolf PA, et al. Characteristics of Framingham offspring participants with long-lived parents. Arch Intern Med (2007) 167:438-44. doi:10.1001/archinte.167.5.438
18. Barzilai N, Atzmon G, Schechter C, Schaefer EJ, Cupples AL, Lipton R, et al. Unique lipoprotein phenotype and genotype associated with exceptional longevity. JAMA (2003) 290:2030-40. doi:10.1001/jama.290.15.2030
19. Heijmans BT, Beekman M, Houwing-Duistermaat JJ, Cobain MR, Powell J, Blauw GJ, et al. Lipoprotein particle profiles mark familial and sporadic human longevity. PLoS Med (2006) 3:e495. doi:10.1371/journal.pmed.0030495
20. Vaarhorst AA, Beekman M, Suchiman EH, van Heemst D, Houwing-Duistermaat JJ, Westendorp RG, et al. Lipid metabolism in long-lived families: the Leiden Longevity Study. Age (Dordr) (2011) 33:219-27. doi:10.1007/s11357-010-9172-6
21. Wijsman CA, van Heemst D, Rozing MP, Slagboom PE, Beekman M, de Craen AJ, et al. Homocysteine and familial longevity: the Leiden Longevity Study. PLoS One (2011) 6:e17543. doi:10.1371/journal.pone.0017543
22. van Vlijmen BJ, van den Maagdenberg AM, Gijbels MJ, van der Boom H, HogenEsch H, Frants RR, et al. Diet-induced hyperlipoproteinemia and atherosclerosis in apolipoprotein E3-Leiden transgenic mice. J Clin Invest (1994) 93:1403-10. doi:10.1172/JCI117117
23. Ruhaak LR, Uh HW, Beekman M, Koeleman CA, Hokke CH, Westendorp RG, et al. Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity. PLoS One (2010) 5:e12566. doi:10.1371/journal.pone.0012566
24. Hu C, van Dommelen J, van der Heijden R, Spijksma G, Reijmers TH, Wang M, et al. RPLC-ion-trap-FTMS method for lipid profiling of plasma: method validation and application to p53 mutant mouse model. J Proteome Res (2008) 7(11):4982-91. doi:10.1021/pr800373m
25. Gonzalez-Covarrubias V, Beekman M, Uh HW, Dane A, Troost J, Paliukhovich I, et al. Lipidomics of familial longevity. Aging Cell (2013) 12:426-34. doi:10.1111/acel.12064
26. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res (2007) 16:219-42. doi:10.1177/0962280206074463
27. Rubin D. Underlying Bayesian Theories. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons (1987).
28. Wu Y, Zhang D, Pang Z, Oksuzyan A, Jiang W, Wang S, et al. Gender-specific patterns in age-related decline in general health among Danish and Chinese: a cross-national comparative study. Geriatr Gerontol Int (2012) 12:431-9. doi:10.1111/j.1447-0594.2011.00784.x
29. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw (2010) 33:1-22. doi:10.18637/jss.v033.i01
30. Breiman L. Random forest. Mach Learn (2001) 45:5-32. doi:10.1023/A:1017934522171
31. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J (2003) 24:987-1003. doi:10.1016/S0195-668X(03)00114-3
32. Do CB, Hinds DA, Francke U, Eriksson N. Comparison of family history and SNPs for predicting risk of complex disease. PLoS Genet (2012) 8:e1002973. doi:10.1371/journal.pgen.1002973
33. Christensen LH, Høyer BB, Pedersen HS, Zinchuk A, Jönsson BA, Lindh C, et al. Prenatal smoking exposure, measured as maternal serum cotinine, and children's motor developmental milestones and motor function: a follow-up study. Neurotoxicology (2016) 53:236-45. doi:10.1016/j.neuro.2016.02.007
34. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell (2013) 153:1194-217. doi:10.1016/j.cell.2013.05.039
35. Li Q, Wang S, Milot E, Bergeron P, Ferrucci L, Fried LP, et al. Homeostatic dysregulation proceeds in parallel in multiple physiological systems. Aging Cell (2015) 14:1103-12. doi:10.1111/acel.12402
36. Cohen AA, Milot E, Li Q, Bergeron P, Poirier R, Dusseault-Bélanger F, et al. Detection of a novel, integrative aging process suggests complex physiological integration. PLoS One (2015) 10:e0116489. doi:10.1371/journal.pone.0116489
37. Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A, Kolosov A, et al. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY) (2016) 8:1021-33. doi:10.18632/aging.100968
38. Fischer K, Kettunen J, Würtz P, Haller T, Havulinna AS, Kangas AJ, et al. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med (2014) 11:e1001606. doi:10.1371/journal.pmed.1001606
39. van de Rest O, Schutte BA, Deelen J, Stassen SA, van den Akker EB, van Heemst D, et al. Metabolic effects of a 13-weeks lifestyle intervention in older adults: the Growing Old Together Study. Aging (Albany NY) (2016) 8:111-26. doi:10.18632/aging.100877