Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies

Cohen, 2003, Human population: the next half century, Science., 302, 1172, 10.1126/science.1088665

Mamoshina, 2018, Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations, J Gerontol Ser A., 1, 10.1093/gerona/gly005

Cole, 2017, Predicting age using neuroimaging: innovative brain ageing biomarkers, Trends Neurosci., 40, 681, 10.1016/j.tins.2017.10.001

Cosco, 2017, Healthy ageing, resilience and wellbeing, Epidemiol Psychiatr Sci., 26, 579, 10.1017/S2045796017000324

Cole, 2018, Brain age predicts mortality, Mol Psychiatr., 23, 1385, 10.1038/mp.2017.62

Cole, 2018, Brain age and other bodily ‘ages': implications for neuropsychiatry, Mol Psychiatr., 24, 266, 10.1038/s41380-018-0098-1

Di Giuseppe, 2012, Total dietary antioxidant capacity and lung function in an Italian population: a favorable role in premenopausal/never smoker women, Eur J Clin Nutr., 66, 61, 10.1038/ejcn.2011.148

Yamaguchi, 2012, Novel regression equations predicting lung age from varied spirometric parameters, Respir Physiol Neurobiol., 183, 108, 10.1016/j.resp.2012.06.025

Russoniello, 2013, Heart rate variability and biological age: implications for health and gaming, Cyberpsychol Behav Soc Netw., 16, 302, 10.1089/cyber.2013.1505

Klemera, 2006, A new approach to the concept and computation of biological age, Mech Ageing Dev., 127, 240, 10.1016/j.mad.2005.10.004

Blackburn, 2015, Human telomere biology: a contributory and interactive factor in aging, disease risks, and protection, Science., 350, 1193, 10.1126/science.aab3389

Peters, 2015, The transcriptional landscape of age in human peripheral blood, Nat Commun., 6, 8570, 10.1038/ncomms9570

Holly, 2013, Towards a gene expression biomarker set for human biological age, Aging Cell., 12, 324, 10.1111/acel.12044

Hannum, 2013, Genome-wide methylation profiles reveal quantitative views of human aging rates, Mol Cell., 49, 359, 10.1016/j.molcel.2012.10.016

Horvath, 2013, DNA methylation age of human tissues and cell types, Genome Biol., 14, R115, 10.1186/gb-2013-14-10-r115

Fabris, 2017, A review of supervised machine learning applied to ageing research, Biogerontology., 18, 171, 10.1007/s10522-017-9683-y

Iacoviello, 2007, The Moli-Sani Project, a randomized, prospective cohort study in the Molise region in Italy; design, rationale and objectives, Ital J Public Health., 4, 110, 10.2427/5886

Sebastiani, 2017, Biomarker signatures of aging, Aging Cell., 16, 329, 10.1111/acel.12557

Murabito, 2018, Measures of biologic age in a community sample predict mortality and age-related disease: the framingham offspring study, JGerontol Ser A Biol Sci Med Sci., 73, 757, 10.1093/gerona/glx144

Putin, 2016, Deep biomarkers of human aging : application of deep neural networks to biomarker development, Aging (Albany NY), 8, 1021, 10.18632/aging.100968

Ching, 2018, Opportunities and obstacles for deep learning in biology and medicine, J R Soc Interface., 15, 20170387, 10.1098/rsif.2017.0387

Mamoshina, 2016, Applications of deep learning in biomedicine, Mol Pharm., 13, 1445, 10.1021/acs.molpharmaceut.5b00982

Cole, 2017, Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker, Neuroimage., 163, 115, 10.1016/j.neuroimage.2017.07.059

Mamoshina, 2019, Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers, Sci Rep., 15, 142, 10.1038/s41598-018-35704-w

Liem, 2017, Predicting brain-age from multimodal imaging data captures cognitive impairment, Neuroimage., 148, 179, 10.1016/j.neuroimage.2016.11.005

Franke, 2010, Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters, Neuroimage., 50, 883, 10.1016/j.neuroimage.2010.01.005

Cole, 2015, Prediction of brain age suggests accelerated atrophy after traumatic brain injury, Ann Neurol., 77, 571, 10.1002/ana.24367

Cole, 2017, Brain-predicted age in down syndrome is associated with beta amyloid deposition and cognitive decline, Neurobiol Aging., 56, 41, 10.1016/j.neurobiolaging.2017.04.006

Valizadeh, 2017, Age prediction on the basis of brain anatomical measures, Hum Brain Mapp., 38, 997, 10.1002/hbm.23434

Franke, 2012, Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and alzheimer's disease 1 data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database adni, GeroPsych., 25, 235, 10.1024/1662-9647/a000074

Cole, 2018, Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury, Brain., 141, 822, 10.1093/brain/awx354

Cole, 2017, Increased brain-predicted aging in treated HIV disease, Neurology., 88, 1349, 10.1212/WNL.0000000000003790

Cole, 2018, Neuroimaging studies illustrate the commonalities between ageing and brain diseases, BioEssays., 40, 1700221, 10.1002/bies.201700221

Jansen, 2015, What twin studies tell us about the heritability of brain development, morphology, and function: a review, Neuropsychol Rev., 25, 27, 10.1007/s11065-015-9278-9

Mosconi, 2018, Lifestyle and vascular risk effects on MRI-based biomarkers of Alzheimer's disease: a cross-sectional study of middle-aged adults from the broader New York City area, BMJ Open., 8, e019362, 10.1136/bmjopen-2017-019362

Mosconi, 2015, Let food be thy medicine: diet, nutrition, and biomarkers' risk of alzheimer's disease, Curr Nutr Rep., 4, 126, 10.1007/s13668-014-0111-5

Staubo, 2017, Mediterranean diet, micronutrients and macronutrients, and MRI measures of cortical thickness, Alzheimer's Dement., 13, 168, 10.1016/j.jalz.2016.06.2359

Luciano, 2017, Mediterranean-Type diet and brain structural change from 73 to 76 years in a Scottish cohort, Neurology., 88, 449, 10.1212/WNL.0000000000003559

Gu, 2015, Mediterranean diet and brain structure in a multiethnic elderly cohort, Neurology., 85, 1744, 10.1212/WNL.0000000000002121

Steffener, 2016, Differences between chronological and brain age are related to education and self-reported physical activity, Neurobiol Aging., 40, 138, 10.1016/j.neurobiolaging.2016.01.014

Luders, 2016, Estimating brain age using high-resolution pattern recognition: younger brains in long-term meditation practitioners, Neuroimage., 134, 508, 10.1016/j.neuroimage.2016.04.007

Rogenmoser, 2018, Keeping brains young with making music, Brain Struct Funct., 223, 297, 10.1007/s00429-017-1491-2

Franke, 2017, Premature brain aging in baboons resulting from moderate fetal undernutrition, Front Aging Neurosci., 9, 92, 10.3389/fnagi.2017.00092

Hatton, 2018, Negative fateful life events in midlife and advanced predicted brain aging, Neurobiol Aging., 67, 1, 10.1016/j.neurobiolaging.2018.03.004

Pardoe, 2017, Structural brain changes in medically refractory focal epilepsy resemble premature brain aging, Epilepsy Res., 133, 28, 10.1016/j.eplepsyres.2017.03.007

Gaser, 2013, BrainAGE in mild cognitive impaired patients: predicting the conversion to alzheimer's disease, PLoS ONE., 8, e67346, 10.1371/journal.pone.0067346

Löwe, 2016, The effect of the APOE genotype on individual BrainAGE in normal aging, Mild cognitive impairment, and Alzheimer's Disease, PLoS ONE., 11, e0157514, 10.1371/journal.pone.0157514

Franceschi, 2018, The continuum of aging and age-related diseases: common mechanisms but different rates, Front Med., 5, 61, 10.3389/fmed.2018.00061

Joshi, 2017, Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity, Nat Commun., 8, 910, 10.1038/s41467-017-00934-5