Recent high-profile failures of Alzheimer disease treatments at the clinical trial stage have led to renewed efforts to identify and test novel interventions for Alzheimer disease and related dementias (ADRD). In this Perspective, we highlight the importance of including well-designed observational studies as part of these efforts. Observational research is an important cornerstone for gathering evidence on risk factors and causes of ADRD; this evidence can then be combined with data from preclinical studies and randomized controlled trials to inform the development of effective interventions. Observational study designs can be particularly beneficial for hypothesis generation, posing questions that are unethical or impractical for a trial setting, studying life-course associations, research in populations typically not included in trials, and public health surveillance. Here, we discuss each of these situations in the specific context of ADRD research. We also highlight novel approaches to enhance causal inference and provide a timely discussion on how observational epidemiological studies help provide a bridge between preclinical studies and successful interventions for ADRD.
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Alzheimer’s Disease International, Guerchet, M., Prince, M. & Prina, M. Numbers of People with Dementia Worldwide: An Update to the Estimates in the World Alzheimer Report 2015 (Alzheimer’s Disease International, 2020).
Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 16, 391–460 (2020).
Jack, C. R. Jr et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207–216 (2013).
Barnes, D. E. & Yaffe, K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 10, 819–828 (2011).
Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446 (2020).
Long, J. M. & Holtzman, D. M. Alzheimer disease: an update on pathobiology and treatment strategies. Cell 179, 312–339 (2019).
Vandenbroucke, J. P. Observational research, randomised trials, and two views of medical science. PLoS Med. 5, e67 (2008).
Frieden, T. R. Evidence for health decision making-Beyond randomized, controlled trials. N. Engl. J. Med. 377, 465–475 (2017).
Stoiljkovic, M., Horvath, T. L. & Hajós, M. Therapy for Alzheimer’s disease: missing targets and functional markers? Ageing Res. Rev. 68, 101318 (2021).
Freedman, B. Equipoise and the ethics of clinical research. N. Engl. J. Med. 317, 141–145 (1987).
Fewell, Z., Davey Smith, G. & Sterne, J. A. C. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am. J. Epidemiol. 166, 646–655 (2007).
Hernán, M. A. & Robins, J. M. Estimating causal effects from epidemiological data. J. Epidemiol. Community Health 60, 578–586 (2006).
Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).
Balshem, H. et al. GRADE guidelines: 3. Rating the quality of evidence. J. Clin. Epidemiol. 64, 401–406 (2011).
Lash, T. L. et al. Modern Epidemiology (Wolters Kluwer, 2021).
Koepsell, T. D. Epidemiologic Methods: Studying the Occurrence of Illness (Oxford University Press, 2014).
Dougherty, D. & Conway, P. H. The “3T’s” road map to transform US health care: the “How” of high-quality care. JAMA 299, 2319–2321 (2008).
Trochim, W., Kane, C., Graham, M. J. & Pincus, H. A. Evaluating translational research: a process marker model. Clin. Transl. Sci. 4, 153–162 (2011).
Sperling, R. A., Jack, C. R. & Aisen, P. S. Testing the right target and right drug at the right stage. Sci. Transl. Med. 3, 111cm33 (2011).
Schneider, J. A., Arvanitakis, Z., Bang, W. & Bennett, D. A. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 69, 2197–2204 (2007).
Nelson, P. T. et al. ‘New Old Pathologies’: AD, PART, and cerebral age-related TDP-43 with sclerosis (CARTS). J. Neuropathol. Exp. Neurol. 75, 482–498 (2016).
McAleese, K. E. et al. Concomitant neurodegenerative pathologies contribute to the transition from mild cognitive impairment to dementia. Alzheimers Dement. J. Alzheimers Assoc. 17, 1121–1133 (2021).
Vemuri, P. et al. Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain 138, 761–771 (2015).
White, L. R. et al. Neuropathologic comorbidity and cognitive impairment in the Nun and Honolulu-Asia Aging Studies. Neurology 86, 1000–1008 (2016).
Brenowitz, W. D. et al. Mixed neuropathologies and estimated rates of clinical progression in a large autopsy sample. Alzheimers Dement. 13, 654–662 (2017).
Beach, T. G., Monsell, S. E., Phillips, L. E. & Kukull, W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. J. Neuropathol. Exp. Neurol. 71, 266–273 (2012).
Ackley, S. F. et al. Effect of reductions in amyloid levels on cognitive change in randomized trials: instrumental variable meta-analysis. BMJ 372, n156 (2021).
Alexander, G. C. et al. Revisiting FDA approval of aducanumab. N. Engl. J. Med. 385, 769–771 (2021).
Anderson, T. S., Ayanian, J. Z., Souza, J. & Landon, B. E. Representativeness of participants eligible to be enrolled in Clinical Trials of Aducanumab for Alzheimer disease compared with Medicare beneficiaries with Alzheimer Disease and Mild Cognitive Impairment. JAMA 326, 1627–1629 (2021).
Berchtold, N. C. & Cotman, C. W. Evolution in the conceptualization of dementia and Alzheimer’s disease: Greco-Roman period to the 1960s. Neurobiol. Aging 19, 173–189 (1998).
Braak, H. & Braak, E. Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol. Aging 18, 351–357 (1997).
Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019).
Chartier-Harlin, M. C. et al. Early-onset Alzheimer’s disease caused by mutations at codon 717 of the beta-amyloid precursor protein gene. Nature 353, 844–846 (1991).
Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921–923 (1993).
Hamilton, R. L. Lewy bodies in Alzheimer’s disease: a neuropathological review of 145 cases using alpha-synuclein immunohistochemistry. Brain Pathol. 10, 378–384 (2000).
Mirra, S. S. et al. The consortium to establish a registry for Alzheimer’s disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 41, 479–486 (1991).
Newell, K. L., Hyman, B. T., Growdon, J. H. & Hedley-Whyte, E. T. Application of the National Institute on Aging (NIA)-Reagan Institute criteria for the neuropathological diagnosis of Alzheimer disease. J. Neuropathol. 58, 1147–1155 (1999).
McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology 65, 1863–1872 (2005).
Hardy, J. A. & Higgins, G. A. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185 (1992).
Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).
Pascoal, T. A. et al. In vivo quantification of neurofibrillary tangles with [18F]MK-6240. Alzheimers Res. Ther. 10, 74 (2018).
Clark, C. M. et al. Use of florbetapir-PET for imaging β-amyloid pathology. JAMA 305, 275–283 (2011).
Shaw, L. M. et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann. Neurol. 65, 403–413 (2009).
McDade, E. et al. Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease. Neurology 91, e1295–e1306 (2018).
Vermunt, L. et al. Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimers Dement. J. Alzheimers Assoc. 15, 888–898 (2019).
Jack, C. R. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. J. Alzheimers Assoc. 14, 535–562 (2018).
Amaducci, L. A. et al. Risk factors for clinically diagnosed Alzheimer’s disease: a case-control study of an Italian population. Neurology 36, 922–931 (1986).
Molgaard, C. A. et al. Epidemiology of head trauma and neurocognitive impairment in a multi-ethnic population. Neuroepidemiology 9, 233–242 (1990).
Fratiglioni, L., Ahlbom, A., Viitanen, M. & Winblad, B. Risk factors for late-onset Alzheimer’s disease: a population-based, case-control study. Ann. Neurol. 33, 258–266 (1993).
Stern, Y. et al. Influence of education and occupation on the incidence of Alzheimer’s disease. JAMA 271, 1004–1010 (1994).
Yu, J.-T. et al. Evidence-based prevention of Alzheimer’s disease: systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials. J. Neurol. Neurosurg. Psychiatry 91, 1201–1209 (2020).
Baumgart, M. et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement. J. Alzheimers Assoc. 11, 718–726 (2015).
Markun, S. et al. Effects of vitamin B12 supplementation on cognitive function, depressive symptoms, and fatigue: a systematic review, meta-analysis, and meta-regression. Nutrients 13, 923 (2021).
de Souto Barreto, P., Demougeot, L., Vellas, B. & Rolland, Y. Exercise training for preventing dementia, mild cognitive impairment, and clinically meaningful cognitive decline: a systematic review and meta-analysis. J. Gerontol. A. Biol. Sci. Med. Sci. 73, 1504–1511 (2018).
Woods, B., Aguirre, E., Spector, A. E. & Orrell, M. Cognitive stimulation to improve cognitive functioning in people with dementia. Cochrane Database Syst. Rev. 2, CD005562 (2012).
SPRINT MIND Investigators for the SPRINT Research Group et al. Effect of intensive vs standard blood pressure control on probable dementia: a randomized Clinical Trial. JAMA 321, 553–561 (2019).
Moll van Charante, E. P. et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet 388, 797–805 (2016).
Rosenberg, A. et al. Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: the FINGER trial. Alzheimers Dement. J. Alzheimers Assoc. 14, 263–270 (2018).
Yaffe, K. et al. Systematic multi-domain Alzheimer’s risk reduction trial (SMARRT): study protocol. J. Alzheimers Dis. 70, S207–S220 (2019).
Li, J. et al. Mid- to Late- life body mass index and dementia risk: 38 years of follow-up of the Framingham study. Am. J. Epidemiol. 190, 2503–2510 (2021).
Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Lopatko Lindman, K. et al. Herpesvirus infections, antiviral treatment, and the risk of dementia-a registry-based cohort study in Sweden. Alzheimers Dement. 7, e12119 (2021).
Levine, D. A. et al. Association between blood pressure and later-life cognition among black and white individuals. JAMA Neurol. 77, 810–819 (2020).
Leng, Y., Musiek, E. S., Hu, K., Cappuccio, F. P. & Yaffe, K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol. 18, 307–318 (2019).
Deal, J. A. et al. Hearing impairment and incident dementia and cognitive decline in older adults: the health ABC study. J. Gerontol. A. Biol. Sci. Med. Sci. 72, 703–709 (2017).
Power, M. C., Adar, S. D., Yanosky, J. D. & Weuve, J. Exposure to air pollution as a potential contributor to cognitive function, cognitive decline, brain imaging, and dementia: a systematic review of epidemiologic research. Neurotoxicology 56, 235–253 (2016).
Saji, N. et al. Analysis of the relationship between the gut microbiome and dementia: a cross-sectional study conducted in Japan. Sci. Rep. 9, 1008 (2019).
Tranah, G. J. et al. Mitochondrial DNA sequence variation associated with dementia and cognitive function in the elderly. J. Alzheimers Dis. 32, 357–372 (2012).
Nagarajan, N. et al. Vision impairment and cognitive decline among older adults: a systematic review. BMJ Open. 12, e047929 (2022).
Lee, C. S. et al. Association Between cataract extraction and development of dementia. JAMA Intern. Med. 182, 134–141 (2022).
Carlson, M. C. et al. Hormone replacement therapy and reduced cognitive decline in older women: the Cache County Study. Neurology 57, 2210–2216 (2001).
Espeland, M. A. et al. Long-term effects on cognitive function of postmenopausal hormone therapy prescribed to women aged 50 to 55 years. JAMA Intern. Med. 173, 1429–1436 (2013).
Black, N. Why we need observational studies to evaluate the effectiveness of health care. BMJ 312, 1215–1218 (1996).
Bahorik, A. L. et al. Early to midlife smoking trajectories and cognitive function in middle-aged US adults: the CARDIA study. J. Gen. Intern. Med. 37, 1023–1030 (2022).
Emmerzaal, T. L., Kiliaan, A. J. & Gustafson, D. R. 2003-2013: a decade of body mass index, Alzheimer’s disease, and dementia. J. Alzheimers Dis. 43, 739–755 (2015).
Coogan, P. et al. Experiences of racism and subjective cognitive function in African American women. Alzheimers Dement. 12, e12067 (2020).
Grasset, L. et al. Relation between 20-year income volatility and brain health in midlife: the CARDIA study. Neurology 93, e1890–e1899 (2019).
Dacks, P. A. et al. Dementia prevention: optimizing the use of observational data for personal, clinical, and public health decision-making. J. Prev. Alzheimers Dis. 1, 117–123 (2014).
Lange, M. et al. Cognitive complaints in cancer survivors and expectations for support: results from a web-based survey. Cancer Med. 8, 2654–2663 (2019).
Ospina-Romero, M. et al. Association between Alzheimer disease and cancer with evaluation of study biases: a systematic review and meta-analysis. JAMA Netw. Open 3, e2025515 (2020).
U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Social Determinants of Health. Healthy People 2030 https://health.gov/healthypeople/objectives-and-data/social-determinants-health (2022).
Marmot, M., Friel, S., Bell, R., Houweling, T. A. & Taylor, S. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet 372, 1661–1669 (2008).
Cadar, D. et al. Individual and area-based socioeconomic factors associated with dementia incidence in England. JAMA Psychiatry 75, 723–732 (2018).
Mayeda, E. R., Glymour, M. M., Quesenberry, C. P. & Whitmer, R. A. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimers Dement. 12, 216–224 (2016).
Petersen, J. D. et al. Association of socioeconomic status with dementia diagnosis among older adults in Denmark. JAMA Netw. Open 4, e2110432 (2021).
Marmot, M. G., Shipley, M. J. & Rose, G. Inequalities in death–specific explanations of a general pattern? Lancet 1, 1003–1006 (1984).
Dow, W. H., Schoeni, R. F., Adler, N. E. & Stewart, J. Evaluating the evidence base: Policies and interventions to address socioeconomic status gradients in health. Ann. N. Y. Acad. Sci. 1186, 240–251 (2010).
National Academies of Sciences, Engineering, and Medicine. Preventing Cognitive Decline and Dementia: a Way Forward (The National Academies Press, 2017).
Ritchie, K., Ritchie, C. W., Yaffe, K., Skoog, I. & Scarmeas, N. Is late-onset Alzheimer’s disease really a disease of midlife? Alzheimers Dement. Transl. Res. Clin. Interv. 1, 122–130 (2015).
Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 280–292 (2011).
Sperling, R., Mormino, E. & Johnson, K. The evolution of preclinical Alzheimer’s disease: implications for prevention trials. Neuron 84, 608–622 (2014).
Lee, A. T. C. et al. Risk of incident dementia varies with different onset and courses of depression. J. Affect. Disord. 282, 915–920 (2021).
Dotson, V. M., Beydoun, M. A. & Zonderman, A. B. Recurrent depressive symptoms and the incidence of dementia and mild cognitive impairment. Neurology 75, 27–34 (2010).
Nicolau, B., Thomson, W. M., Steele, J. G. & Allison, P. J. Life-course epidemiology: concepts and theoretical models and its relevance to chronic oral conditions. Community Dent. Oral. Epidemiol. 35, 241–249 (2007).
Watson, J. L., Ryan, L., Silverberg, N., Cahan, V. & Bernard, M. A. Obstacles and opportunities In Alzheimer’s clinical trial recruitment. Health Aff. 33, 574–579 (2014).
Barnes, L. L. et al. Mixed pathology is more likely in black than white decedents with Alzheimer dementia. Neurology 85, 528–534 (2015).
Manly, J. J. & Glymour, M. M. What the aducanumab approval reveals about Alzheimer disease research. JAMA Neurol. 78, 1305–1306 (2021).
Kawas, C. H. et al. Multiple pathologies are common and related to dementia in the oldest-old: the 90+ study. Neurology 85, 535–542 (2015).
Gill, S. S. et al. Representation of patients with dementia in clinical trials of donepezil. Can. J. Clin. Pharmacol. J. Can. Pharmacol. Clin. 11, e274–e285 (2004).
Dou, K.-X. et al. Comparative safety and effectiveness of cholinesterase inhibitors and memantine for Alzheimer’s disease: a network meta-analysis of 41 randomized controlled trials. Alzheimers Res. Ther. 10, 126 (2018).
Xu, H. et al. Long-term effects of cholinesterase inhibitors on cognitive decline and mortality. Neurology 96, e2220–e2230 (2021).
Richardson, K. et al. Anticholinergic drugs and risk of dementia: case-control study. BMJ 361, k1315 (2018).
Gray, S. L. et al. Benzodiazepine use and risk of incident dementia or cognitive decline: prospective population based study. BMJ 352, i90 (2016).
Brookmeyer, R., Abdalla, N., Kawas, C. H. & Corrada, M. M. Forecasting the prevalence of pre-clinical and clinical Alzheimer’s disease in the United States. Alzheimers Dement. J. Alzheimers Assoc. 14, 121–129 (2018).
GBD 2016 Dementia Collaborators. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 88–106 (2019).
Wolters, F. J. et al. Twenty-seven-year time trends in dementia incidence in Europe and the United States: the Alzheimer Cohorts Consortium. Neurology 95, e519–e531 (2020).
Mukadam, N., Sommerlad, A., Huntley, J. & Livingston, G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. Lancet Glob. Health 7, e596–e603 (2019).
Schünemann, H. J. et al. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J. Clin. Epidemiol. 111, 105–114 (2019).
Yaffe, K., Sawaya, G., Lieberburg, I. & Grady, D. Estrogen therapy in postmenopausal women: effects on cognitive function and dementia. JAMA 279, 688–695 (1998).
Shumaker, S. A. et al. Estrogen plus progestin and the incidence of dementia and mild cognitive impairment in postmenopausal women: the Women’s Health Initiative Memory Study: a randomized controlled trial. JAMA 289, 2651–2662 (2003).
Robins, J. M., Hernán, M. A. & Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550–560 (2000).
Lunceford, J. K. & Davidian, M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat. Med. 23, 2937–2960 (2004).
Little, R. J. A. & Rubin, D. B. Statistical Analysis with Missing Data (Wiley, 1987).
Williams, B. D., Pendleton, N. & Chandola, T. Cognitively stimulating activities and risk of probable dementia or cognitive impairment in the English Longitudinal Study of Ageing. SSM Popul. Health 12, 100656 (2020).
Shadish, W. R., Cook, T. D. & Campbell, D. T. Experimental and Quasi-Experimental Designs for Generalized Causal Inference (Cengage Learning, 2001).
Seblova, D. et al. Does prolonged education causally affect dementia risk when adult socioeconomic status is not altered? A Swedish natural experiment in 1.3 million individuals. Am. J. Epidemiol. 190, 817–826 (2021).
Nguyen, T. T. et al. Instrumental variable approaches to identifying the causal effect of educational attainment on dementia risk. Ann. Epidemiol. 26, 71–76.e3 (2016).
Huang, W. & Zhou, Y. Effects of education on cognition at older ages: evidence from China’s Great Famine. Soc. Sci. Med. 98, 54–62 (2013).
Angrist, J. D. & Krueger, A. B. Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15, 69–85 (2001).
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Smith, G. D. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).
Henry, A. et al. The relationship between sleep duration, cognition and dementia: a Mendelian randomization study. Int. J. Epidemiol. 48, 849–860 (2019).
Leng, Y., Ackley, S. F., Glymour, M. M., Yaffe, K. & Brenowitz, W. D. Genetic risk of Alzheimer’s disease and sleep duration in non-demented elders. Ann. Neurol. 89, 177–181 (2021).
Mukherjee, S. et al. Genetically predicted body mass index and Alzheimer’s disease related phenotypes in three large samples: Mendelian randomization analyses. Alzheimers Dement. J. Alzheimers Assoc. 11, 1439–1451 (2015).
Walker, V. M., Kehoe, P. G., Martin, R. M. & Davies, N. M. Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. Int. J. Epidemiol. 49, 1132–1140 (2019).
Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 6 (2022).
Burgess, S., Butterworth, A. S. & Thompson, J. R. Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors. J. Clin. Epidemiol. 69, 208–216 (2016).
Bor, J., Moscoe, E., Mutevedzi, P., Newell, M.-L. & Bärnighausen, T. Regression discontinuity designs in epidemiology. Epidemiology 25, 729–737 (2014).
Bärnighausen, T. et al. Quasi-experimental study designs series-paper 7: assessing the assumptions. J. Clin. Epidemiol. 89, 53–66 (2017).
Thompson, J. R. et al. Mendelian randomization incorporating uncertainty about pleiotropy. Stat. Med. 39, 4627–4645 (2017).
Weuve, J. et al. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimers Dement. J. Alzheimers Assoc. 11, 1098–1109 (2015).
Bi, Q., Goodman, K. E., Kaminsky, J. & Lessler, J. What is machine learning? A primer for the epidemiologist. Am. J. Epidemiol. 188, 2222–2239 (2019).
Stamate, D. et al. A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: results from the European medical information framework for Alzheimer disease biomarker discovery cohort. Alzheimers Dement. Transl. Res. Clin. Interv. 5, 933–938 (2019).
Habes, M. et al. The brain chart of aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimers Dement. J. Alzheimers Assoc. 17, 89–102 (2021).
Casanova, R. et al. Investigating predictors of preserved cognitive function in older women using machine learning: women’s health initiative memory study. J. Alzheimers Dis. 84, 1267–1278 (2021).
The research of W.D.B. and K.Y. is supported by the National Institutes of Health (NIH)/National Institute on Aging (NIA) grants NIA K01AG062722 (W.D.B) and R35AG071916 (K.Y.). The authors declare no other competing interests.
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- Amyloid cascade hypothesis
The hypothesis that amyloid-β is the main pathological agent that causes Alzheimer disease.
- Big data
Large-scale data comprising many observations and/or many traits.
- Causal inference
Inferring the independent effect of one factor on an outcome, typically from data of observations.
- Pleiotropic effects
When one gene influences two or more phenotypic traits.
- Pragmatic trials
Clinical trials developed after drug approval to test the effectiveness of a drug in a real-world setting.
- Real-world data
Observational data that represent real-world settings, for example, health-care records in a large health system.
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Brenowitz, W.D., Yaffe, K. Observational studies in Alzheimer disease: bridging preclinical studies and clinical trials. Nat Rev Neurol 18, 747–757 (2022). https://doi.org/10.1038/s41582-022-00733-7