Diseases, G. B. D. & Injuries, C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 396, 1204–1222 (2020).
Google Scholar
Chi, H. C. et al. Adult renal dysfunction and risk of dementia or cognitive decline: brain-kidney axis hypothesis based on a systematic review and meta-analysis. J. Prev. Alzheimers Dis. 10, 443–452 (2023).
Google Scholar
Licher, S. et al. Lifetime risk and multimorbidity of non-communicable diseases and disease-free life expectancy in the general population: a population-based cohort study. PLoS Med. 16, e1002741 (2019).
Google Scholar
Jadoul, M., Aoun, M. & Masimango Imani, M. The major global burden of chronic kidney disease. Lancet Glob. Health 12, e342–e343 (2024).
Google Scholar
Kidney Disease: Improving Global Outcomes, CKD Work Group. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 105, S117–S314 (2024).
Google Scholar
GBD 2021 Nervous System Disorders Collaborators. Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol. 23, 344–381 (2024).
Google Scholar
Jack, C. R. Jr. et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s association workgroup. Alzheimers Dement. 20, 5143–5169 (2024).
Google Scholar
Petersen, R. C. et al. A new framework for dementia nomenclature. JAMA Neurol. 80, 1364–1370 (2023).
Google Scholar
Dubois, B. et al. Clinical diagnosis of Alzheimer’s disease: recommendations of the international working group. Lancet Neurol. 20, 484–496 (2021).
Google Scholar
Farrell, D. R. & Vassalotti, J. A. Screening, identifying, and treating chronic kidney disease: why, who, when, how, and what? BMC Nephrol. 25, 34 (2024).
Google Scholar
Andrassy, K. M. Comments on ‘KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease’. Kidney Int. 84, 622–623 (2013).
Google Scholar
Vaidya, S. R., Aeddula, N. R. & Doerr, C. Chronic kidney disease (nursing). In StatPearls (2024).
Bello, A. K. et al. An update on the global disparities in kidney disease burden and care across world countries and regions. Lancet Glob. Health 12, e382–e395 (2024).
Google Scholar
GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. Lancet Public. Health 7, e105–e125 (2022).
Google Scholar
Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the lancet standing commission. Lancet 404, 572–628 (2024).
Google Scholar
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).
Google Scholar
Andersson, C., Johnson, A. D., Benjamin, E. J., Levy, D. & Vasan, R. S. 70-year legacy of the Framingham Heart Study. Nat. Rev. Cardiol. 16, 687–698 (2019).
Google Scholar
Mittelmark, M. B. et al. Prevalence of cardiovascular diseases among older adults. Cardiovascular health study. Am. J. Epidemiol. 137, 311–317 (1993).
Google Scholar
Ikram, M. A. et al. The Rotterdam study. Design update and major findings between 2020 and 2024. Eur. J. Epidemiol. 39, 183–206 (2024).
Google Scholar
The Atherosclerosis Risk In Communities (ARIC) study: design and objectives. The ARIC investigators. Am J Epidemiol 129, 687–702 (1989).
Bild, D. E. et al. Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156, 871–881 (2002).
Google Scholar
Batty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J. & Bell, S. Comparison of risk factor associations in UK biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 368, m131 (2020).
Google Scholar
Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Google Scholar
Peters, A. et al. Framework and baseline examination of the German national cohort (NAKO). Eur. J. Epidemiol. 37, 1107–1124 (2022).
Google Scholar
Stefanou, E., Tountas, C., Ioannidis, E. & Kole, C. Biomarkers in cardiorenal syndrome, a potential use in precision medicine. J. Nephrol. 37, 2127–2138 (2024).
Google Scholar
Provenzano, M. et al. Estimated glomerular filtration rate in observational and interventional studies in chronic kidney disease. J. Nephrol. 37, 573–586 (2024).
Google Scholar
Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).
Google Scholar
Watanabe, K., Watanabe, T. & Nakayama, M. Cerebro-renal interactions: impact of uremic toxins on cognitive function. Neurotoxicology 44, 184–193 (2014).
Google Scholar
Zheng, Y., Ji, B., Chen, S., Zhou, R. & Ni, R. The impact of uremic toxins on Alzheimer’s disease. Curr. Alzheimer Res. 19, 104–118 (2022).
Google Scholar
Greenberg, J. H. et al. Urine biomarkers of kidney tubule health, injury, and inflammation are associated with progression of CKD in children. J. Am. Soc. Nephrol. 32, 2664–2677 (2021).
Google Scholar
Zhao, K., Seeliger, E., Niendorf, T. & Liu, Z. Noninvasive assessment of diabetic kidney disease with MRI: hype or hope? J. Magn. Reson. Imaging 59, 1494–1513 (2024).
Google Scholar
Singla, R. K., Kadatz, M., Rohling, R. & Nguan, C. Kidney ultrasound for nephrologists: a review. Kidney Med. 4, 100464 (2022).
Google Scholar
Scheltens, P. et al. Alzheimer’s disease. Lancet 397, 1577–1590 (2021).
Google Scholar
Darweesh, S. K. L. et al. Quantitative gait, cognitive decline, and incident dementia: the Rotterdam study. Alzheimers Dement. 15, 1264–1273 (2019).
Google Scholar
Wolters, F. J. et al. Orthostatic hypotension and the long-term risk of dementia: a population-based study. PLoS Med. 13, e1002143 (2016).
Google Scholar
Mooldijk, S. S. & Ikram, M. A. Cerebral small vessel disease in population-based research: what are we looking at — and what not? Aging Dis. 15, 1438–1446 (2024).
Google Scholar
Jack, C. R. Jr Advances in Alzheimer’s disease research over the past two decades. Lancet Neurol. 21, 866–869 (2022).
Google Scholar
Hainsworth, A. H., Markus, H. S. & Schneider, J. A. Cerebral small vessel disease, hypertension, and vascular contributions to cognitive impairment and dementia. Hypertension 81, 75–86 (2024).
Google Scholar
Tang, X. et al. Association of kidney function and brain health: a systematic review and meta-analysis of cohort studies. Ageing Res. Rev. 82, 101762 (2022).
Google Scholar
Mund, M. & Nestler, S. Beyond the cross-lagged panel model: next-generation statistical tools for analyzing interdependencies across the life course. Adv. Life Course Res. 41, 100249 (2019).
Google Scholar
Xie, Z., Tong, S., Chu, X., Feng, T. & Geng, M. Chronic kidney disease and cognitive impairment: the kidney-brain axis. Kidney Dis. 8, 275–285 (2022).
Google Scholar
Kelly, D. M. & Rothwell, P. M. Disentangling the relationship between chronic kidney disease and cognitive disorders. Front. Neurol. 13, 830064 (2022).
Google Scholar
Bossola, M. & Picconi, B. Uremic toxins and the brain in chronic kidney disease. J. Nephrol. 37, 1391–1395 (2024).
Google Scholar
Lu, R., Kiernan, M. C., Murray, A., Rosner, M. H. & Ronco, C. Kidney-brain crosstalk in the acute and chronic setting. Nat. Rev. Nephrol. 11, 707–719 (2015).
Google Scholar
Seliger, S. L., Wendell, C. R., Waldstein, S. R., Ferrucci, L. & Zonderman, A. B. Renal function and long-term decline in cognitive function: the Baltimore Longitudinal Study of Aging. Am. J. Nephrol. 41, 305–312 (2015).
Google Scholar
Lee, S. et al. The association between kidney function and cognitive decline in community-dwelling, elderly Japanese people. J. Am. Med. Dir. Assoc. 16, 349.e1–5 (2015).
Google Scholar
Berger, I. et al. Cognition in chronic kidney disease: a systematic review and meta-analysis. BMC Med. 14, 206 (2016).
Google Scholar
Bugnicourt, J. M., Godefroy, O., Chillon, J. M., Choukroun, G. & Massy, Z. A. Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. J. Am. Soc. Nephrol. 24, 353–363 (2013).
Google Scholar
Kelly, D. M., et al. Impaired kidney function, cerebral small vessel disease and cognitive disorders: the Framingham Heart Study. Nephrol. Dial. Transplant. 39, 1911–1922 (2024).
Google Scholar
Sedaghat, S. et al. The AGES-Reykjavik study suggests that change in kidney measures is associated with subclinical brain pathology in older community-dwelling persons. Kidney Int. 94, 608–615 (2018).
Google Scholar
Sedaghat, S. et al. Kidney function and microstructural integrity of brain white matter. Neurology 85, 154–161 (2015).
Google Scholar
Scheppach, J. B. et al. Association of kidney function measures with signs of neurodegeneration and small vessel disease on brain magnetic resonance imaging: the Atherosclerosis Risk In Communities (ARIC) study. Am. J. Kidney Dis. 81, 261–269.e1 (2023).
Google Scholar
Akoudad, S. et al. Kidney function and cerebral small vessel disease in the general population. Int. J. Stroke 10, 603–608 (2015).
Google Scholar
Sedaghat, S. et al. Kidney function and cerebral blood flow: the Rotterdam study. J. Am. Soc. Nephrol. 27, 715–721 (2016).
Google Scholar
Seliger, S. L. et al. Cystatin C and subclinical brain infarction. J. Am. Soc. Nephrol. 16, 3721–3727 (2005).
Google Scholar
Farrah, T. E., Dhillon, B., Keane, P. A., Webb, D. J. & Dhaun, N. The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons. Kidney Int. 98, 323–342 (2020).
Google Scholar
Toyoda, K. Cerebral small vessel disease and chronic kidney disease. J. Stroke 17, 31–37 (2015).
Google Scholar
Yan, Q. et al. Kidney–brain axis in the pathogenesis of cognitive impairment. Neurobiol. Dis. 200, 106626 (2024).
Google Scholar
Viggiano, D. et al. Mechanisms of cognitive dysfunction in CKD. Nat. Rev. Nephrol. 16, 452–469 (2020).
Google Scholar
Viggiano, D. et al. Mild cognitive impairment and kidney disease: clinical aspects. Nephrol. Dial. Transpl. 35, 10–17 (2020).
Google Scholar
Kang, D. H. et al. Role of the microvascular endothelium in progressive renal disease. J. Am. Soc. Nephrol. 13, 806–816 (2002).
Google Scholar
O’Rourke, M. F. & Safar, M. E. Relationship between aortic stiffening and microvascular disease in brain and kidney: cause and logic of therapy. Hypertension 46, 200–204 (2005).
Google Scholar
Kurella Tamura, M. et al. Kidney disease, intensive hypertension treatment, and risk for dementia and mild cognitive impairment: the systolic blood pressure intervention trial. J. Am. Soc. Nephrol. 31, 2122–2132 (2020).
Google Scholar
Babroudi, S. et al. Blood pressure, incident cognitive impairment, and severity of CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) study. Am. J. Kidney Dis. 82, 443–453.e1 (2023).
Google Scholar
Kim, E. D. et al. Associations between kidney disease measures and regional pulse wave velocity in a large community-based cohort: the Atherosclerosis Risk In Communities (ARIC) study. Am. J. Kidney Dis. 72, 682–690 (2018).
Google Scholar
Zierler, R. E. et al. Carotid and lower extremity arterial disease in patients with renal artery atherosclerosis. Arch. Intern. Med. 158, 761–767 (1998).
Google Scholar
Miralles, M. et al. Screening for carotid and renal artery stenoses in patients with aortoiliac disease. Ann. Vasc. Surg. 12, 17–22 (1998).
Google Scholar
Wong, T. Y. et al. Retinal microvascular abnormalities and renal dysfunction: the Atherosclerosis Risk In Communities study. J. Am. Soc. Nephrol. 15, 2469–2476 (2004).
Google Scholar
Fernando, M. S. et al. White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury. Stroke 37, 1391–1398 (2006).
Google Scholar
Theuerle, J. D. et al. Retinal microvascular function predicts chronic kidney disease in patients with cardiovascular risk factors. Atherosclerosis 341, 63–70 (2022).
Google Scholar
Qureshi, A. I. & Caplan, L. R. Intracranial atherosclerosis. Lancet 383, 984–998 (2014).
Google Scholar
Sacco, R. L., Kargman, D. E., Gu, Q. & Zamanillo, M. C. Race-ethnicity and determinants of intracranial atherosclerotic cerebral infarction. The Northern Manhattan Stroke Study. Stroke 26, 14–20 (1995).
Google Scholar
Wong, L. K. Global burden of intracranial atherosclerosis. Int. J. Stroke 1, 158–159 (2006).
Google Scholar
Gorelick, P. B., Wong, K. S., Bae, H. J. & Pandey, D. K. Large artery intracranial occlusive disease: a large worldwide burden but a relatively neglected frontier. Stroke 39, 2396–2399 (2008).
Google Scholar
Gorelick, P. B. et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 42, 2672–2713 (2011).
Google Scholar
Pajewski, N. M. et al. The legacy effect of intensive versus standard BP control on the incidence of needing dialysis or kidney transplantation. J. Am. Soc. Nephrol. 35, 1737–1745 (2024).
Google Scholar
Syrjanen, J. A. et al. Associations of amyloid and neurodegeneration plasma biomarkers with comorbidities. Alzheimers Dement. 18, 1128–1140 (2022).
Google Scholar
Sedaghat, S. et al. The association of kidney function with plasma amyloid-β levels and brain amyloid deposition. J. Alzheimers Dis. 92, 229–239 (2023).
Google Scholar
Sarto, J. et al. Impact of demographics and comorbid conditions on plasma biomarkers concentrations and their diagnostic accuracy in a memory clinic cohort. J. Neurol. 271, 1973–1984 (2024).
Google Scholar
Dittrich, A. et al. Association of chronic kidney disease with plasma NfL and other biomarkers of neurodegeneration: the H70 Birth Cohort Study in Gothenburg. Neurology 101, e277–e288 (2023).
Google Scholar
Wu, J. et al. The impact of kidney function on plasma neurofilament light and phospho-tau 181 in a community-based cohort: the Shanghai Aging Study. Alzheimers Res. Ther. 16, 32 (2024).
Google Scholar
Adesso, S. et al. Indoxyl sulfate affects glial function increasing oxidative stress and neuroinflammation in chronic kidney disease: interaction between astrocytes and microglia. Front. Pharmacol. 8, 370 (2017).
Google Scholar
Lipton, S. A. et al. Neurotoxicity associated with dual actions of homocysteine at the N-methyl-D-aspartate receptor. Proc. Natl Acad. Sci. USA 94, 5923–5928 (1997).
Google Scholar
GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).
Google Scholar
Stefan, N. & Schulze, M. B. Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment. Lancet Diabetes Endocrinol. 11, 426–440 (2023).
Google Scholar
Nilsson, P. M., Tuomilehto, J. & Rydén, L. The metabolic syndrome — what is it and how should it be managed? Eur. J. Prev. Cardiol. 26, 33–46 (2019).
Google Scholar
Alberti, K. G. & Zimmet, P. Z. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15, 539–553 (1998).
Google Scholar
Kjaergaard, A. D. et al. Kidney function and risk of dementia: observational study, meta-analysis, and two-sample mendelian randomization study. Eur. J. Epidemiol. 37, 1273–1284 (2022).
Google Scholar
Zhao, H., Yuan, H. & Wang, E. Causal effects of kidney function and chronic kidney disease on Alzheimer’s disease by analyzing large-scale genome-wide association study datasets. J. Alzheimers Dis. 102, 321–328 (2024).
Google Scholar
Marini, S. et al. Genetic overlap and causal inferences between kidney function and cerebrovascular disease. Neurology 94, e2581–e2591 (2020).
Google Scholar
Pohl, D. J. et al. Relationship between residential segregation, later-life cognition, and incident dementia across race/ethnicity. Int. J. Env. Res. Public. Health 18, 11233 (2021).
Google Scholar
Lee, H., Caldwell, J. T., Maene, C., Cagney, K. A. & Saunders, M. R. Racial/ethnic inequities in access to high-quality dialysis treatment in Chicago: does neighborhood racial/ethnic composition matter? J. Racial Ethn. Health Disparities 7, 854–864 (2020).
Google Scholar
Park, S. et al. Association between visit-to-visit blood pressure variability and risks of dementia in CKD patients: a nationwide observational cohort study. Clin. Kidney J. 15, 1506–1513 (2022).
Google Scholar
Drew, D. A. et al. Blood pressure and cognitive decline in prevalent hemodialysis patients. Am. J. Nephrol. 49, 460–469 (2019).
Google Scholar
Olczyk, P., Kusztal, M., Gołębiowski, T., Letachowicz, K. & Krajewska, M. Cognitive impairment in end stage renal disease patients undergoing hemodialysis: markers and risk factors. Int. J. Env. Res. Public. Health 19, 2389 (2022).
Google Scholar
Yang, S. et al. Association between cognitive function and risk of chronic kidney disease: a longitudinal cohort and mendelian randomization study. Mayo Clin. Proc. 99, 1399–1410 (2024).
Google Scholar
Hayat, S. A. et al. Understanding the relationship between cognition and death: a within cohort examination of cognitive measures and mortality. Eur. J. Epidemiol. 33, 1049–1062 (2018).
Google Scholar
Tanaka, S. & Okusa, M. D. Crosstalk between the nervous system and the kidney. Kidney Int. 97, 466–476 (2020).
Google Scholar
Tanaka, S. et al. Vagus nerve stimulation activates two distinct neuroimmune circuits converging in the spleen to protect mice from kidney injury. Proc. Natl Acad. Sci. USA 118, e2021758118 (2021).
Google Scholar
Mooldijk, S. S., Labrecque, J. A., Ikram, M. A. & Ikram, M. K. Ratios in regression analyses with causal questions. Am J Epidemiol 194, 311–313 (2025).
Google Scholar
Brookhart, M. A. et al. Variable selection for propensity score models. Am. J. Epidemiol. 163, 1149–1156 (2006).
Google Scholar
Knottnerus, J. A. & Tugwell, P. Confounding obscures our view, effect modification is part of reality. J. Clin. Epidemiol. 114, v–vi (2019).
Google Scholar
Knol, M. J. & VanderWeele, T. J. Recommendations for presenting analyses of effect modification and interaction. Int. J. Epidemiol. 41, 514–520 (2012).
Google Scholar
VanderWeele, T. J. A unification of mediation and interaction: a 4-way decomposition. Epidemiology 25, 749–761 (2014).
Google Scholar
Ikram, M. A. & VanderWeele, T. J. A proposed clinical and biological interpretation of mediated interaction. Eur. J. Epidemiol. 30, 1115–1118 (2015).
Google Scholar
Bos, D. et al. Thyroid function and atrial fibrillation: is there a mediating role for epicardial adipose tissue? Clin. Epidemiol. 10, 225–234 (2018).
Google Scholar
Ma, Y. et al. APOE ε4 and late-life cognition: mediation by structural brain imaging markers. Eur. J. Epidemiol. 37, 591–601 (2022).
Google Scholar
Berry, D. & Willoughby, M. T. On the practical interpretability of cross-lagged panel models: rethinking a developmental workhorse. Child. Dev. 88, 1186–1206 (2017).
Google Scholar
Hamaker, E. L., Kuiper, R. M. & Grasman, R. P. A critique of the cross-lagged panel model. Psychol. Methods 20, 102–116 (2015).
Google Scholar
Murray, A. M. & Vemuri, P. Kidney disease and brain health: current knowledge and next steps. Am. J. Kidney Dis. 81, 253–255 (2023).
Google Scholar
Mc Causland, F. R. et al. Finerenone and kidney outcomes in patients with heart failure: the FINEARTS-HF trial. J. Am. Coll. Cardiol. 85, 159–168 (2025).
Google Scholar
Zheng, S. L. et al. Association between use of sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 agonists, and dipeptidyl peptidase 4 inhibitors with all-cause mortality in patients with type 2 diabetes: a systematic review and meta-analysis. JAMA 319, 1580–1591 (2018).
Google Scholar
Sohn, M., Dietrich, J. W., Nauck, M. A. & Lim, S. Characteristics predicting the efficacy of SGLT-2 inhibitors versus GLP-1 receptor agonists on major adverse cardiovascular events in type 2 diabetes mellitus: a meta-analysis study. Cardiovasc. Diabetol. 22, 153 (2023).
Google Scholar
Sheets, K. M., Davey, C. S., St Peter, W. L., Reule, S. A. & Murray, A. M. Cognitive impairment, perceived medication adherence, and high-risk medication use in patients with reduced kidney function: a cross-sectional analysis. Health Sci. Rep. 5, e697 (2022).
Google Scholar
Copur, S., Berkkan, M., Sarafidis, P. & Kanbay, M. Intensive blood pressure control on dementia in patients with chronic kidney disease: potential reduction in disease burden. Eur. J. Intern. Med. 101, 8–13 (2022).
Google Scholar
van Buchem, M. A. et al. The heart–brain connection: a multidisciplinary approach targeting a missing link in the pathophysiology of vascular cognitive impairment. J. Alzheimers Dis. 42, S443–S451 (2014).
Google Scholar
Nijskens, C. M. et al. Is it time for Heart–Brain clinics? A clinical survey and proposition to improve current care for cognitive problems in heart failure. Clin. Cardiol. 47, e24200 (2024).
Google Scholar
Li, Y. et al. Neighborhood racial and ethnic segregation and the risk of dementia in older adults living with kidney failure. J. Am. Soc. Nephrol. 35, 936–948 (2024).
Google Scholar
Taubman, S. L., Robins, J. M., Mittleman, M. A. & Hernán, M. A. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. Int. J. Epidemiol. 38, 1599–1611 (2009).
Google Scholar
Young, J. G., Cain, L. E., Robins, J. M., O’Reilly, E. J. & Hernán, M. A. Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula. Stat. Biosci. 3, 119–143 (2011).
Google Scholar
Naimi, A. I., Cole, S. R. & Kennedy, E. H. An introduction to g methods. Int. J. Epidemiol. 46, 756–762 (2017).
Google Scholar
VanderWeele, T. J. & Tchetgen Tchetgen, E. J. Mediation analysis with time varying exposures and mediators. J. R. Stat. Soc. Ser. B Stat. Methodol. 79, 917–938 (2017).
Google Scholar
Robins, J. M., Hernán, M. A. & Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550–560 (2000).
Google Scholar
Wehbe, R. M. et al. Deep learning for cardiovascular imaging: a review. JAMA Cardiol. 8, 1089–1098 (2023).
Google Scholar
Oikonomou, E. K. & Khera, R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc. Diabetol. 22, 259 (2023).
Google Scholar
Haug, C. J. & Drazen, J. M. Artificial intelligence and machine learning in clinical medicine, 2023. N. Engl. J. Med. 388, 1201–1208 (2023).
Google Scholar
Giddings, R. et al. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. Lancet Digit. Health 6, e131–e144 (2024).
Google Scholar
VanderWeele, T. J. Principles of confounder selection. Eur. J. Epidemiol. 34, 211–219 (2019).
Google Scholar
Lin, S. H. & Ikram, M. A. On the relationship of machine learning with causal inference. Eur. J. Epidemiol. 35, 183–185 (2020).
Google Scholar
Feuerriegel, S. et al. Causal machine learning for predicting treatment outcomes. Nat. Med. 30, 958–968 (2024).
Google Scholar
Mitra, N., Roy, J. & Small, D. The future of causal inference. Am. J. Epidemiol. 191, 1671–1676 (2022).
Google Scholar
link