A biological age based on common clinical markers predicts health trajectory and mortality risk in dogs.
Biological age
Biomarkers of aging
Calorie restriction
Companion animals
Comparative biology
Veterinary care
Journal
GeroScience
ISSN: 2509-2723
Titre abrégé: Geroscience
Pays: Switzerland
ID NLM: 101686284
Informations de publication
Date de publication:
01 Oct 2024
01 Oct 2024
Historique:
received:
14
11
2023
accepted:
13
09
2024
medline:
1
10
2024
pubmed:
1
10
2024
entrez:
30
9
2024
Statut:
aheadofprint
Résumé
Predicting aging trajectories through biomarkers of biological aging can guide interventions that optimize healthy lifespan in humans and companion animals. Differences in physiology, genetics, nutrition, and lifestyle limit the generalization of such biomarkers and may therefore require species-specific algorithms. Here, we compared correlations of standard clinical blood parameters with survival probability in humans with those of the two most common mammalian companion animals, cats and dogs, and highlighted universal and species-specific relationships. Based on this comparative analysis, we generated and validated an algorithm that predicts biological age in canines using a longitudinal dataset with health records, blood count, and clinical chemistry from 829 dogs spanning over 12 years. Positive deviations of biological from chronological age (AgeDev) measured by this composite score significantly correlated with a decreased survival probability (hazard ratio = 1.75 per 1 year of AgeDev, p = 3.7e - 06). Importantly, in nearly half of the dogs whose biological age was accelerated by more than 1 year, none or only a single individual marker scored outside its respective reference range, suggesting practical applications for the detection of unfavorable health trajectories. Analyzing samples from a unique 14-year life-long diet restriction study, we show that restricted caloric intake lowers biological age, an effect that can be quantified at midlife years before a difference in survival is observed. Thus, a biological age clock based on clinical blood tests predicts the health trajectories of dogs for use in research and veterinary practice.
Identifiants
pubmed: 39349737
doi: 10.1007/s11357-024-01352-4
pii: 10.1007/s11357-024-01352-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Niccoli T, Partridge L. Ageing as a risk factor for disease. Curr Biol. 2012;22(17):R741-752.
pubmed: 22975005
doi: 10.1016/j.cub.2012.07.024
Harman D. The aging process: major risk factor for disease and death. Proc Natl Acad Sci U S A. 1991;88(12):5360–3.
pubmed: 2052612
pmcid: 51872
doi: 10.1073/pnas.88.12.5360
Hoffman JM, Creevy KE, Franks A, O’Neill DG, Promislow DEL. The companion dog as a model for human aging and mortality. Aging Cell. 2018;17(3):e12737.
pubmed: 29457329
pmcid: 5946068
doi: 10.1111/acel.12737
Ray M, Carney HC, Boynton B, Quimby J, Robertson S, St Denis K, Tuzio H, Wright B. 2021 AAFP feline senior care guidelines. J Feline Med Surg. 2021;23(7):613–38.
pubmed: 34167339
pmcid: 10812122
doi: 10.1177/1098612X211021538
McKenzie BA, Chen FL, Gruen ME, Olby NJ. Canine geriatric syndrome: a framework for advancing research in veterinary geroscience. Front Vet Sci. 2022;9:853743.
pubmed: 35529834
pmcid: 9069128
doi: 10.3389/fvets.2022.853743
Salvin HE, McGreevy PD, Sachdev PS, Valenzuela MJ. The canine cognitive dysfunction rating scale (CCDR): a data-driven and ecologically relevant assessment tool. Vet J. 2011;188(3):331–6.
pubmed: 20542455
doi: 10.1016/j.tvjl.2010.05.014
Chen FL, Ullal TV, Graves JL, Ratcliff ER, Naka A, McKenzie B, Carttar TA, Super KM, Austriaco J, Weber SY, Vaughn J, LaCroix-Fralish ML. Evaluating instruments for assessing healthspan: a multi-center cross-sectional study on health-related quality of life (HRQL) and frailty in the companion dog. Geroscience. 2023;45(4):2089–108.
pubmed: 36781597
pmcid: 10651603
doi: 10.1007/s11357-023-00744-2
Fick LJ, Fick GH, Li Z, Cao E, Bao B, Heffelfinger D, Parker HG, Ostrander EA, Riabowol K. Telomere length correlates with life span of dog breeds. Cell Rep. 2012;2(6):1530–6.
pubmed: 23260664
doi: 10.1016/j.celrep.2012.11.021
Belsky DW, Moffitt TE, Cohen AA, Corcoran DL, Levine ME, Prinz JA, Schaefer J, Sugden K, Williams B, Poulton R, Caspi A. Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? Am J Epidemiol. 2018;187(6):1220–30.
pubmed: 29149257
Moqri M, Herzog C, Poganik JR, Biomarkers of aging C, Justice J, Belsky DW, Higgins-Chen A, Moskalev A, Fuellen G, Cohen AA, Bautmans I, Widschwendter M, Ding J, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758–75.
pubmed: 37657418
pmcid: 11088934
doi: 10.1016/j.cell.2023.08.003
Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, Hagg S, Marioni RE, Widschwendter M, et al. Validation of biomarkers of aging. Nat Med. 2024; 30(2):360–372.
Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.
pubmed: 23177740
doi: 10.1016/j.molcel.2012.10.016
Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA, Assimes TL, Ferrucci L, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019;11(2):303–27.
pubmed: 30669119
doi: 10.18632/aging.101684
Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–84.
pubmed: 29643443
doi: 10.1038/s41576-018-0004-3
Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573–91.
pubmed: 29676998
doi: 10.18632/aging.101414
Horvath S, Lu AT, Haghani A, Zoller JA, Li CZ, Lim AR, Brooke RT, Raj K, Serres-Armero A, Dreger DL, Hogan AN, Plassais J, Ostrander EA. DNA methylation clocks for dogs and humans. Proc Natl Acad Sci U S A. 2022;119(21):e2120887119.
pubmed: 35580182
pmcid: 9173771
doi: 10.1073/pnas.2120887119
Thompson MJ, vonHoldt B, Horvath S, Pellegrini M. An epigenetic aging clock for dogs and wolves. Aging (Albany NY). 2017;9(3):1055–68.
pubmed: 28373601
doi: 10.18632/aging.101211
Wang T, Ma J, Hogan AN, Fong S, Licon K, Tsui B, Kreisberg JF, Adams PD, Carvunis AR, Bannasch DL, Ostrander EA, Ideker T. Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome. Cell Syst. 2020;11(2):176-185 e176.
pubmed: 32619550
pmcid: 7484147
doi: 10.1016/j.cels.2020.06.006
Nakamura E, Miyao K. A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol A Biol Sci Med Sci. 2007;62(10):1096–105.
pubmed: 17921421
doi: 10.1093/gerona/62.10.1096
Kwon D, Belsky DW. A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. Geroscience. 2021;43(6):2795–808.
pubmed: 34725754
pmcid: 8602613
doi: 10.1007/s11357-021-00480-5
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 A Biol Sci Med Sci. 2017;73(1):4–10.
pubmed: 28531269
pmcid: 5861848
doi: 10.1093/gerona/glx096
Metzger FL, Rebar AH. Clinical pathology interpretation in geriatric veterinary patients. Vet Clin North Am Small Anim Pract. 2012;42(4):615–29.
pubmed: 22720804
doi: 10.1016/j.cvsm.2012.04.004
Lawler DF, Larson BT, Ballam JM, Smith GK, Biery DN, Evans RH, Greeley EH, Segre M, Stowe HD, Kealy RD. Diet restriction and ageing in the dog: major observations over two decades. Br J Nutr. 2008;99(4):793–805.
pubmed: 18062831
doi: 10.1017/S0007114507871686
Kealy RD, Lawler DF, Ballam JM, Mantz SL, Biery DN, Greeley EH, Lust G, Segre M, Smith GK, Stowe HD. Effects of diet restriction on life span and age-related changes in dogs. J Am Vet Med Assoc. 2002;220(9):1315–20.
pubmed: 11991408
doi: 10.2460/javma.2002.220.1315
Middleton RP, Lacroix S, Scott-Boyer MP, Dordevic N, Kennedy AD, Slusky AR, Carayol J, Petzinger-Germain C, Beloshapka A, Kaput J. Metabolic differences between dogs of different body sizes. J Nutr Metab. 2017;2017:4535710.
pubmed: 29225968
pmcid: 5684564
doi: 10.1155/2017/4535710
Urfer SR, Greer K, Wolf NS. Age-related cataract in dogs: a biomarker for life span and its relation to body size. Age (Dordr). 2011;33(3):451–60.
pubmed: 20607428
doi: 10.1007/s11357-010-9158-4
GrambschTMTaPM. Modeling survival data: extending the Cox model. New York: Springer; 2000.
NHANES. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National health and nutrition examination survey data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [1988–1994] https://wwwn.cdc.gov/nchs/nhanes/nhanes3/default.aspx ].
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Jackson CH. Flexsurv: a platform for parametric survival modeling in R. J Stat Softw. 2016; 70.
Contal C, O’Quigley J. An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal. 1999;30(3):253–70.
doi: 10.1016/S0167-9473(98)00096-6
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2022, https://www.R-project.org/ .
Montoya M, Morrison JA, Arrignon F, Spofford N, Charles H, Hours MA, Biourge V. Life expectancy tables for dogs and cats derived from clinical data. Front Vet Sci. 2023;10:1082102.
pubmed: 36896289
pmcid: 9989186
doi: 10.3389/fvets.2023.1082102
McCay CM, Crowell MF, Maynard LA. The effect of retarded growth upon the length of life span and upon the ultimate body size. 1935. Nutrition. 1989;5(3):155–71 discussion 172.
pubmed: 2520283
Mattison JA, Colman RJ, Beasley TM, Allison DB, Kemnitz JW, Roth GS, Ingram DK, Weindruch R, de Cabo R, Anderson RM. Caloric restriction improves health and survival of rhesus monkeys. Nat Commun. 2017;8:14063.
pubmed: 28094793
pmcid: 5247583
doi: 10.1038/ncomms14063
Flanagan EW, Most J, Mey JT, Redman LM. Calorie restriction and aging in humans. Annu Rev Nutr. 2020;40:105–33.
pubmed: 32559388
pmcid: 9042193
doi: 10.1146/annurev-nutr-122319-034601
Fong S, Pabis K, Latumalea D, Dugersuren N, Unfried M, Tolwinski N, Kennedy B and Gruber J. Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention. Nat Aging. 2024; 4(8):1137–1152.
Greeley EH, Spitznagel E, Lawler DF, Kealy RD, Segre M. Modulation of canine immunosenescence by life-long caloric restriction. Vet Immunol Immunopathol. 2006;111(3–4):287–99.
pubmed: 16567002
doi: 10.1016/j.vetimm.2006.02.002
Brewer RA, Gibbs VK, Smith DL Jr. Targeting glucose metabolism for healthy aging. Nutr Healthy Aging. 2016;4(1):31–46.
pubmed: 28035340
pmcid: 5166514
doi: 10.3233/NHA-160007
Palliyaguru DL, Shiroma EJ, Nam JK, Duregon E, Vieira Ligo Teixeira C, Price NL, Bernier M, Camandola S, Vaughan KL, Colman RJ, Deighan A, Korstanje R, Peters LL, et al. Fasting blood glucose as a predictor of mortality: lost in translation. Cell Metab. 2021;33(11):2189-2200 e2183.
pubmed: 34508697
pmcid: 9115768
doi: 10.1016/j.cmet.2021.08.013
Karaphillis E, Goldstein R, Murphy S, Qayyum R. Serum alanine aminotransferase levels and all-cause mortality. Eur J Gastroenterol Hepatol. 2017;29(3):284–8.
pubmed: 27787263
doi: 10.1097/MEG.0000000000000778
Diaz-Toro F, Nazar G, Araya AX, Petermann-Rocha F. Predictive ability of both the healthy aging index and the frailty index for all-cause mortality. Geroscience. 2024;46(3):3471–9.
pubmed: 38388917
pmcid: 11009179
doi: 10.1007/s11357-024-01097-0
Fielding RA, Atkinson EJ, Aversa Z, White TA, Heeren AA, Achenbach SJ, Mielke MM, Cummings SR, Pahor M, Leeuwenburgh C, LeBrasseur NK. Associations between biomarkers of cellular senescence and physical function in humans: observations from the lifestyle interventions for elders (LIFE) study. Geroscience. 2022;44(6):2757–70.
pubmed: 36367600
pmcid: 9768064
doi: 10.1007/s11357-022-00685-2
ElorteguiPascual P, Rolands MR, Eldridge AL, Kassis A, Mainardi F, Le KA, Karagounis LG, Gut P, Varady KA. A meta-analysis comparing the effectiveness of alternate day fasting, the 5:2 diet, and time-restricted eating for weight loss. Obesity (Silver Spring). 2023;31 Suppl 1(Suppl 1):9–21.
doi: 10.1002/oby.23568
de Cabo R, Mattson MP. Effects of intermittent fasting on health, aging, and disease. N Engl J Med. 2019;381(26):2541–51.
pubmed: 31881139
doi: 10.1056/NEJMra1905136
Brandhorst S, Levine ME, Wei M, Shelehchi M, Morgan TE, Nayak KS, Dorff T, Hong K, Crimmins EM, Cohen P, Longo VD. Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk. Nat Commun. 2024;15(1):1309.
pubmed: 38378685
pmcid: 10879164
doi: 10.1038/s41467-024-45260-9
Creevy KE, Grady J, Little SE, Moore GE, Strickler BG, Thompson S, Webb JA. 2019 AAHA canine life stage guidelines. J Am Anim Hosp Assoc. 2019;55(6):267–90.
pubmed: 31622127
doi: 10.5326/JAAHA-MS-6999