Age and life expectancy clocks based on machine learning analysis of mouse frailty

Michael B. Schultz, Alice E. Kane, Sarah J. Mitchell and 7 more

Nature Communications · 2020 · CC BY

doi:10.1101/2019.12.20.884452

Abstract

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict the life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs are scored longitudinally until death and machine learning is employed to develop two clocks. A random forest regression is trained on FI components for chronological age to generate the FRIGHT ( Fr ailty I nferred G eriatric H ealth T imeline) clock, a strong predictor of chronological age. A second model is trained on remaining lifespan to generate the AFRAID ( A nalysis of Frai lty and D eath) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of longevity genes and aging interventions.

References 71

1.
Butler RN, et al. Aging: the reality: biomarkers of aging: from primitive organisms to humans. J. Gerontol. A Biol. Sci. Med. Sci. 2004;59:B560-B567.
Not in our library10.1093/gerona/59.6.b560
2.
Rantanen T, et al. Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. J. Gerontol. A Biol. Sci. Med. Sci. 2000;55:168-173.
Not in our library10.1093/gerona/55.3.m168
3.
Bittner V, et al. Prediction of mortality and morbidity with a 6-minute walk test in patients with left ventricular dysfunction. JAMA. 1993;270:1702-1707.
Not in our library
4.
Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019).
Not in our library10.1038/s41591-019-0381-y
5.
Martínez de Toda I, Vida C, Sanz San Miguel L, De la Fuente M. When will my mouse die? Life span prediction based on immune function, redox and behavioural parameters in female mice at the adult age. Mech. Ageing Dev. 2019;182:111125.
Not in our library10.1016/j.mad.2019.111125
6.
Mather KA, Jorm AF, Parslow RA, Christensen H. Is telomere length a biomarker of aging? A review. J. Gerontol. A Biol. Sci. Med. Sci. 2011;66 A:202-213.
Not in our library10.1093/gerona/glq180
7.
Krištić J, et al. Glycans are a novel biomarker of chronological and biological ages. J. Gerontol. A Biol. Sci. Med. Sci. 2014;69:779-789.
Not in our library10.1093/gerona/glt190
8.
Biomarkers of Cellular Senescence and Skin Aging · 2018
No notice
9.
DNA methylation age of human tissues and cell types · 2013
Corrected
10.
The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age · 2017
No notice
11.
Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 2018;19:371-384.
Not in our library10.1038/s41576-018-0004-3
12.
A standard procedure for creating a frailty index · 2008
No notice
13.
Accumulation of Deficits as a Proxy Measure of Aging · 2001
No notice
14.
Whitehead JC, et al. A clinical frailty index in aging mice: comparisons with frailty index data in humans. J. Gerontol. Biol. Sci. Med. Sci. 2014;69:621-632.
Not in our library10.1093/gerona/glt136
15.
Kane AE, Keller KM, Heinze-Milne S, Grandy SA, Howlett SE. A murine frailty index based on clinical and laboratory measurements: links between frailty and pro-inflammatory cytokines differ in a sex-specific manner. J. Gerontol. A Biol. Sci. Med. Sci. 2019;74:275-282.
Not in our library10.1093/gerona/gly117
16.
Feridooni HAA, et al. The impact of age and frailty on ventricular structure and function in C57BL/6J mice. J. Physiol. 2017;595:3721-3742.
Not in our library10.1113/jp274134
17.
A Frailty Index Based On Deficit Accumulation Quantifies Mortality Risk in Humans and in Mice · 2017
No notice
18.
Kane A, et al. Impact of longevity interventions on a validated mouse clinical frailty index. J. Gerontol. A Biol. Sci. Med. Sci. 2016;71:333-339.
Not in our library10.1093/gerona/glu315
19.
Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech. Ageing Dev. 2006;127:240-248.
Not in our library10.1016/j.mad.2005.10.004
20.
Breiman L. Random forests. Mach. Learn. 2001;45:5-32.
Not in our library
21.
Keller K, Kane A, Heinze-Milne S, Grandy SA, Howlett SE. Chronic treatment with the ACE inhibitor enalapril attenuates the development of frailty and differentially modifies pro- and anti-inflammatory cytokines in aging male and female C57BL/6 mice. J. Gerontol. A. 2019;74:1149-1157.
Not in our library10.1093/gerona/gly219
22.
Harrison DE, et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature. 2009;460:392-395.
Not in our library10.1038/nature08221
23.
Miller RA, et al. Methionine-deficient diet extends mouse lifespan, slows immune and lens aging, alters glucose, T4, IGF-I and insulin levels, and increases hepatocyte MIF levels and stress resistance. Aging Cell. 2005;4:119-125.
Not in our library10.1111/j.1474-9726.2005.00152.x
24.
Orentreich N, Matias J, DeFelice A, Zimmerman J. Low methionine ingestion by rats extends life span. J. Nutr. 1993;123:269-274.
Not in our library10.1093/jn/123.2.269
25.
Sun L, Sadighi Akha AA, Miller RA, Harper JM. Life-span extension in mice by preweaning food restriction and by methionine restriction in middle age. J. Gerontol. A Biol. Sci. Med. Sci. 2009;64:711-722.
Not in our library10.1093/gerona/glp051
26.
Horvath S, Levine AJ. HIV-1 infection accelerates age according to the epigenetic clock. J. Infect. Dis. 2015;212:1563-1573.
Not in our library10.1093/infdis/jiv277
27.
Accelerated epigenetic aging in Down syndrome · 2015
No notice
28.
An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease · 2016
No notice
29.
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors · 2017
No notice
30.
Accelerated epigenetic aging in Werner syndrome · 2017
No notice
31.
An epigenetic biomarker of aging for lifespan and healthspan · 2018
No notice
32.
A whole lifespan mouse multi-tissue DNA methylation clock · 2018
No notice
33.
Petkovich DA, et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 2017;25:954-960.e6.
Not in our library10.1016/j.cmet.2017.03.016
34.
Diverse interventions that extend mouse lifespan suppress shared age-associated epigenetic changes at critical gene regulatory regions · 2017
No notice
35.
Multi-tissue DNA methylation age predictor in mouse · 2017
No notice
36.
Baumann CW, Kwak D, Thompson LDV. Assessing onset, prevalence and survival in mice using a frailty phenotype. Aging (Albany NY). 2018;10:4042-4053.
Not in our library10.18632/aging.101692
37.
Trammell RA, Toth LA. Markers for predicting death as an outcome for mice used in infectious disease research. Comp. Med. 2011;61:492-498.
Not in our library
38.
Ray MA, Johnston NA, Verhulst S, Trammell RA, Toth LA. Identification of markers for imminent death in mice used in longevity and aging research. J. Am. Assoc. Lab. Anim. Sci. 2010;49:282-288.
Not in our library
39.
Ingram DK, Archer JR, Harrison DE, Reynolds MA. Physiological and behavioral correlates of lifespan in aged C57BL/6J mice. Exp. Gerontol. 1982;17:295-303.
Not in our library10.1016/0531-5565(82)90019-5
40.
Miller RA. Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population. J. Gerontol. A Biol. Sci. Med. Sci. 2001;56:180-186.
Not in our library10.1093/gerona/56.4.b180
41.
Miller RA, Harper JM, Galecki A, Burke DT. Big mice die young: early life body weight predicts longevity in genetically heterogeneous mice. Aging Cell. 2002;1:22-29.
Not in our library10.1046/j.1474-9728.2002.00006.x
42.
Harper JM, Wolf N, Galecki AT, Pinkosky SL, Miller RA. Hormone levels and cataract scores as sex-specific, mid-life predictors of longevity in genetically heterogeneous mice. Mech. Ageing Dev. 2003;124:801-810.
Not in our library10.1016/s0047-6374(03)00133-7
43.
Fahlström A, Zeberg H, Ulfhake B. Changes in behaviors of male C57BL/6J mice across adult life span and effects of dietary restriction. Age (Omaha). 2012;34:1435-1452.
Not in our library10.1007/s11357-011-9320-7
44.
Swindell W, Harper J, Miller R. How long will my mouse live? Machine learning approaches for the prediction of mouse lifespan. J. Gerontol. A Biol. Sci. Med. Sci. 2008;63:895-906.
Not in our library10.1093/gerona/63.9.895
45.
Song X, Mitnitski A, Rockwood K. Prevalence and 10-Year outcomes of frailty in older adults in relation to deficit accumulation. J. Am. Geriatr. Soc. 2010;58:681-687.
Not in our library10.1111/j.1532-5415.2010.02764.x
46.
Kane AE, Gregson E, Theou O, Rockwood K, Howlett SE. The association between frailty, the metabolic syndrome, and mortality over the lifespan. GeroScience. 2017;39:221-229.
Not in our library10.1007/s11357-017-9967-9
47.
Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: comparing the frailty index and phenotype. Arch. Gerontol. Geriatr. 2015;60:464-470.
Not in our library10.1016/j.archger.2015.01.016
48.
Hoogendijk EO, et al. Development and validation of a frailty index in the Longitudinal Aging Study Amsterdam. Aging Clin. Exp. Res. 2017;29:927-933.
Not in our library10.1007/s40520-016-0689-0
49.
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;68:667-674.
Not in our library10.1093/gerona/gls233
50.
Levine ME, Crimmins EM. A comparison of methods for assessing mortality risk. Am. J. Hum. Biol. 2014;26:768-776.
Not in our library10.1002/ajhb.22595
51.
Deep biomarkers of human aging: Application of deep neural networks to biomarker development · 2016
No notice
52.
Mamoshina P, et al. Population specific biomarkers of human aging: a big data study using South Korean, Canadian, and Eastern European patient populations. J. Gerontol. A Biol. Sci. Med. Sci. 2018;73:1482-1490.
Not in our library10.1093/gerona/gly005
53.
Cole JH, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017;163:115-124.
Not in our library10.1016/j.neuroimage.2017.07.059
54.
Zhavoronkov A, Li R, Ma C, Mamoshina P. Deep biomarkers of aging and longevity: from research to applications. Aging (Albany NY). 2019;11:10771-10780.
Not in our library10.18632/aging.102475
55.
Gialluisi A, Di Castelnuovo A, Donati MB, de Gaetano G, Iacoviello L. Machine learning approaches for the estimation of biological aging: the road ahead for population studies. Front. Med. 2019;6:1-7.
Not in our library10.3389/fmed.2019.00146
56.
Repeatedly measured predictors: a comparison of methods for prediction modeling · 2018
Not checked
57.
Furgal AKC, Sen A, Taylor JMG. Review and comparison of computational approaches for joint longitudinal and time-to-event models. Int. Stat. Rev. 2019;87:393-418.
Not in our library10.1111/insr.12322
58.
Li K, Luo S. Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease. Stat. Methods Med. Res. 2019;28:327-342.
Not in our library10.1177/0962280217722177
59.
Standard laboratory tests to identify older adults at increased risk of death · 2014
No notice
60.
Gordon EH, Hubbard RE. The pathophysiology of frailty: why sex is so important. J. Am. Med. Dir. Assoc. 2018;19:4-5.
Not in our library10.1016/j.jamda.2017.10.009
61.
Austad SN, Fischer KE. Perspective sex differences in lifespan. Cell Metab. 2016;23:1022-1033.
Not in our library10.1016/j.cmet.2016.05.019
62.
Austad SN, Bartke A. Sex differences in longevity and in responses to anti-aging interventions: a mini-review. Gerontology. 2016;62:40-46.
Not in our library10.1159/000381472
63.
DNA methylation GrimAge strongly predicts lifespan and healthspan · 2019
No notice
64.
DNA methylation age of human tissues and cell types · 2013
Corrected
65.
DNA methylation signatures in peripheral blood strongly predict all-cause mortality · 2017
No notice
66.
Ackert‐Bicknell CL, et al. Aging research using mouse models. Curr. Protoc.Mouse Biol. 2015;5:95-133.
Not in our library10.1002/9780470942390.mo140195
67.
Mitchell S J, et al. Daily fasting improves health and survival in male mice independent of diet composition and calories. Cell metab. 2019;29:221-228.
Not in our library10.1016/j.cmet.2018.08.011
68.
Festing, M. in Inbred Strains in Biomedical Research Ch, 7, 137–266 (Palgrave, London, 1979).
Not in our library
69.
Kunstyr I, Leuenberger HGW. Gerontological data of C57BL/6J Mice. I. Sex Differences in survival curves. J. Gerontol. 1975;30:157-162.
Not in our library10.1093/geronj/30.2.157
70.
Pedregosa F, et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011;12:2825-2830.
Not in our library
71.
Belsky DW, et al. Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? Am. J. Epidemiol. 2018;187:1220-1230.
Not in our library10.1093/aje/kwx346

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