Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study

Dahlberg, 2021, A systematic review of longitudinal risk factors for loneliness in older adults, Aging Mental Health, 1 Pinquart, 2003 Hawkley, 2010, Loneliness matters: a theoretical and empirical review of consequences and mechanisms, Ann. Behav. Med., 40, 218, 10.1007/s12160-010-9210-8 Lim, 2020, Understanding loneliness in the twenty-first century: an update on correlates, risk factors, and potential solutions, Soc. Psychiatry Psychiatr. Epidemiol., 55, 793, 10.1007/s00127-020-01889-7 Holt-Lunstad, 2015, Loneliness and social isolation as risk factors for mortality: a meta-analytic review, Perspect. Psychol. Sci., 10, 227, 10.1177/1745691614568352 Sundström, 2020, Loneliness increases the risk of all-cause dementia and Alzheimer’s disease, J. Gerontol.: Ser. B, 75, 919, 10.1093/geronb/gbz139 Lara, 2019, Does loneliness contribute to mild cognitive impairment and dementia? A systematic review and meta-analysis of longitudinal studies, Ageing Res. Rev., 52, 7, 10.1016/j.arr.2019.03.002 Sutin, 2020, Loneliness and risk of dementia, J. Gerontol.: Ser. B, 75, 1414, 10.1093/geronb/gby112 Shovestul, 2020, Risk factors for loneliness: the high relative importance of age versus other factors, PLOS ONE, 15, e0229087, 10.1371/journal.pone.0229087 Elovainio, 2017, Contribution of risk factors to excess mortality in isolated and lonely individuals: an analysis of data from the UK Biobank cohort study, Lancet Public Health, 2, e260, 10.1016/S2468-2667(17)30075-0 Steptoe, 2013, Social isolation, loneliness, and all-cause mortality in older men and women, Proc. Natl. Acad. Sci. U.S.A., 110, 5797, 10.1073/pnas.1219686110 Fotenos, 2008, Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve, Arch. Neurol., 65, 113, 10.1001/archneurol.2007.27 Demnitz, 2020, Association of trajectories of depressive symptoms with vascular risk, cognitive function and adverse brain outcomes: the Whitehall II MRI sub-study, J. Psychiatr. Res., 131, 85, 10.1016/j.jpsychires.2020.09.005 de Lange, 2020, Multimodal brain-age prediction and cardiovascular risk: the Whitehall II MRI sub-study, NeuroImage, 222, 117292, 10.1016/j.neuroimage.2020.117292 Topiwala, 2017, Moderate alcohol consumption as risk factor for adverse brain outcomes and cognitive decline: longitudinal cohort study, BMJ, 357, j2353, 10.1136/bmj.j2353 Zsoldos, 2018, Allostatic load as a predictor of grey matter volume and white matter integrity in old age: the Whitehall II MRI study, Sci. Rep., 8, 6411, 10.1038/s41598-018-24398-9 Han, 2020, Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group, Mol. Psychiatry, 1 Cacioppo, 2014, Toward a neurology of loneliness, Psychol. Bull., 140, 1464, 10.1037/a0037618 Office for National Statistics, 2015 Luhmann, 2016, Age differences in loneliness from late adolescence to oldest old age, Dev. Psychol., 52, 943, 10.1037/dev0000117 Penninkilampi, 2018, The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis, J. Alzheimer’s Dis., 66, 1619, 10.3233/JAD-180439 Cacioppo, 2006, Loneliness within a nomological net: an evolutionary perspective, J. Res. Pers., 40, 1054, 10.1016/j.jrp.2005.11.007 Victor, 2012, The prevalence of loneliness among adults: a case study of the United Kingdom, J. Psychol., 146, 85, 10.1080/00223980.2011.613875 Nolen-Hoeksema, 2002, Age differences and similarities in the correlates of depressive symptoms, Psychol. Aging, 17, 116, 10.1037/0882-7974.17.1.116 Buecker, 2020, Loneliness and the big five personality traits: a meta-analysis, Eur. J. Pers., 34, 8, 10.1002/per.2229 Kendler, 2006, Personality and major depression: a Swedish longitudinal, population-based twin study, Arch. Gen. Psychiatry, 63, 1113, 10.1001/archpsyc.63.10.1113 Rhebergen, 2009, The three-year naturalistic course of major depressive disorder, dysthymic disorder and double depression, J. Affect. Disord., 115, 450, 10.1016/j.jad.2008.10.018 Abdellaoui, 2019, Associations between loneliness and personality are mostly driven by a genetic association with neuroticism, J. Pers., 87, 386, 10.1111/jopy.12397 Cole, 2017, Predicting age using neuroimaging: innovative brain ageing biomarkers, Trends Neurosci., 40, 681, 10.1016/j.tins.2017.10.001 Franke, 2019, Ten years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained?, Front. Neurol., 10, 789, 10.3389/fneur.2019.00789 Cole, 2019, Brain age and other bodily ‘ages’: implications for neuropsychiatry, Mol. Psychiatry, 24, 266, 10.1038/s41380-018-0098-1 Franke, 2020, In vivo biomarkers of structural and functional brain development and aging in humans, Neurosci. Biobehav. Rev., 117, 142, 10.1016/j.neubiorev.2017.11.002 Smith, 2020, Brain aging comprises multiple modes of structural and functional change with distinct genetic and biophysical associations, eLife, 9, e52677, 10.7554/eLife.52677 Cole, 2019, Quantification of the biological age of the brain using neuroimaging, Biomarkers of Human Aging, 293, 10.1007/978-3-030-24970-0_19 Cole, 2020, Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors, Neurobiol. Aging, 10.1016/j.neurobiolaging.2020.03.014 Beck, 2021, Cardiometabolic risk factors associated with brain age and accelerate brain ageing, medRxiv Dunås, 2021, Multimodal image analysis of apparent brain age identifies physical fitness as predictor of brain maintenance, Cereb. Cortex, 10.1093/cercor/bhab019 Anatürk, 2021, Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging, Human Brain Mapp., 42, 1626, 10.1002/hbm.25316 Kaufmann, 2019, Common brain disorders are associated with heritable patterns of apparent aging of the brain, Nat. Neurosci., 22, 1617, 10.1038/s41593-019-0471-7 Rokicki, 2021, Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders, Human Brain Mapp., 42, 1714, 10.1002/hbm.25323 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 Düzel, 2019, Structural brain correlates of loneliness among older adults, Sci. Rep., 9, 1, 10.1038/s41598-019-49888-2 Elovainio, 2020, Association of social isolation, loneliness, and genetic risk with incidence of dementia: UK Biobank cohort study, medRxiv Donovan, 2016, Association of higher cortical amyloid burden with loneliness in cognitively normal older adults, JAMA Psychiatry, 73, 1230, 10.1001/jamapsychiatry.2016.2657 Hakulinen, 2016, Structural and functional aspects of social support as predictors of mental and physical health trajectories: Whitehall II cohort study, J. Epidemiol. Community Health, 70, 710, 10.1136/jech-2015-206165 Anatürk, 2020, Mid-life and late life activities and their relationship with mri measures of brain structure and functional connectivity in the UK Biobank cohort, bioRxiv Eysenck, 1984 Deary, 2011, Some origins and evolution of the epq-r (short form) neuroticism and extraversion items, Pers. Individ. Diff., 50, 1213, 10.1016/j.paid.2011.02.011 Spitzer, 1999, Validation and utility of a self-report version of prime-md: the phq primary care study, JAMA, 282, 1737, 10.1001/jama.282.18.1737 Smith, 2013, Prevalence and characteristics of probable major depression and bipolar disorder within UK Biobank: cross-sectional study of 172,751 participants, PLOS ONE, 8, e75362, 10.1371/journal.pone.0075362 Unger, 2020, International society of hypertension global hypertension practice guidelines, Hypertension, 75, 1334, 10.1161/HYPERTENSIONAHA.120.15026 Elliott, 2008, The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine, Int. J. Epidemiol., 37, 234, 10.1093/ije/dym276 Townsend, 1987, Deprivation, J. Social Policy, 16, 125, 10.1017/S0047279400020341 Townsend, 1988 Alfaro-Almagro, 2018, Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank, Neuroimage, 166, 400, 10.1016/j.neuroimage.2017.10.034 Miller, 2016, Multimodal population brain imaging in the UK Biobank prospective epidemiological study, Nat. Neurosci., 19, 1523, 10.1038/nn.4393 Fischl, 2002, Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain, Neuron, 33, 341, 10.1016/S0896-6273(02)00569-X de Lange, 2019, Population-based neuroimaging reveals traces of childbirth in the maternal brain, Proc. Natl. Acad. Sci. U.S.A., 10.1073/pnas.1910666116 de Lange, 2020, Women’s brain aging: effects of sex-hormone exposure, pregnancies, and genetic risk for Alzheimer’s disease, Human Brain Mapp., 10.1002/hbm.25180 Glasser, 2016, A multi-modal parcellation of human cerebral cortex, Nature, 536, 171, 10.1038/nature18933 Rosen, 2018, Quantitative assessment of structural image quality, Neuroimage, 169, 407, 10.1016/j.neuroimage.2017.12.059 Chen, 2016, Xgboost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785, 10.1145/2939672.2939785 Le, 2018, A nonlinear simulation framework supports adjusting for age when analyzing brainage, Front. Aging Neurosci., 10, 10.3389/fnagi.2018.00317 de Lange, 2020, Commentary: correction procedures in brain-age prediction, NeuroImage: Clin., 26 Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc.: Ser. B Methodol., 57, 289 Cohen, 1988 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Kanai, 2012, Brain structure links loneliness to social perception, Curr. Biol., 22, 1975, 10.1016/j.cub.2012.08.045 Sin, 2018, The relationships between brain structural changes and perceived loneliness in older adults suffering from late-life depression, Int. J. Geriatr. Psychiatry, 33, 606, 10.1002/gps.4831 Wong, 2016, Loneliness in late-life depression: structural and functional connectivity during affective processing, Psychol. Med., 46, 2485, 10.1017/S0033291716001033 Spreng, 2020, The default network of the human brain is associated with perceived social isolation, Nat. Commun., 11, 1, 10.1038/s41467-020-20039-w Nakagawa, 2015, White matter structures associated with loneliness in young adults, Sci. Rep., 5, 17001, 10.1038/srep17001 Fjell, 2014, What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus, Prog. Neurobiol., 117, 20, 10.1016/j.pneurobio.2014.02.004 Doan, 2017, Dissociable diffusion mri patterns of white matter microstructure and connectivity in Alzheimer’s disease spectrum, Sci. Rep., 7, 45131, 10.1038/srep45131 Wilson, 2007, Loneliness and risk of Alzheimer disease, Arch. Gen. Psychiatry, 64, 234, 10.1001/archpsyc.64.2.234 Abdellaoui, 2019, Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness, Human Mol. Genet., 28, 3853, 10.1093/hmg/ddz219 Day, 2018, Elucidating the genetic basis of social interaction and isolation, Nat. Commun., 9, 1, 10.1038/s41467-018-04930-1 Åkerlind, 1992, Loneliness and alcohol abuse: a review of evidences of an interplay, Soc. Sci. Med., 34, 405, 10.1016/0277-9536(92)90300-F The Loneliness Action Group, 2016 Heinrich, 2006, The clinical significance of loneliness: a literature review, Clin. Psychol. Rev., 26, 695, 10.1016/j.cpr.2006.04.002 Theeke, 2009, Predictors of loneliness in us adults over age sixty-five, Arch. Psychiatr. Nurs., 23, 387, 10.1016/j.apnu.2008.11.002 Munafò, 2018, Collider scope: when selection bias can substantially influence observed associations, Int. J. Epidemiol., 47, 226, 10.1093/ije/dyx206 Allen, 2020, The associations between loneliness, social exclusion and pain in the general population: an= 502,528 cross-sectional UK biobank study, J. Psychiatr. Res., 130, 68, 10.1016/j.jpsychires.2020.06.028 Borys, 1985, Gender differences in loneliness, Pers. Soc. Psychol. Bull., 11, 63, 10.1177/0146167285111006 Russell, 1996, Ucla loneliness scale (version 3): reliability, validity, and factor structure, J. Pers. Assess., 66, 20, 10.1207/s15327752jpa6601_2 Shiovitz-Ezra, 2012, Use of direct versus indirect approaches to measure loneliness in later life, Res. Aging, 34, 572, 10.1177/0164027511423258 Smith, 2020, Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations, Elife, 9, 10.7554/eLife.52677 Eavani, 2018, Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods, Neurobiol. Aging, 71, 41, 10.1016/j.neurobiolaging.2018.06.013 de Lange, 2020, The maternal brain: region-specific patterns of brain aging are traceable decades after childbirth, Human Brain Mapp., 10.1002/hbm.25152 Wang, 2020, Finding the needle in a high-dimensional haystack: canonical correlation analysis for neuroscientists, NeuroImage, 216, 116745, 10.1016/j.neuroimage.2020.116745 Cole, 2010, Illustrating bias due to conditioning on a collider, Int. J. Epidemiol., 39, 417, 10.1093/ije/dyp334 Liu, 2012, Implications of m bias in epidemiologic studies: a simulation study, Am. J. Epidemiol., 176, 938, 10.1093/aje/kws165 Quadt, 2020, Brain-body interactions underlying the association of loneliness with mental and physical health, Neurosci. Biobehav. Rev., 10.1016/j.neubiorev.2020.06.015 Akhter-Khan, 2020, Why loneliness interventions are unsuccessful: a call for precision health, Adv. Geriatr. Med. Res., 4 Rødevand, 2021, Polygenic overlap and shared genetic loci between loneliness, severe mental disorders, and cardiovascular disease risk factors suggest shared molecular mechanisms, Transl. psychiatry, 11, 1, 10.1038/s41398-020-01142-4