Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446 (2020).
Google Scholar
Chen, H. et al. Association of the Mediterranean Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (MIND) diet with the risk of dementia. JAMA Psychiatry 80, 630–638 (2023).
Google Scholar
Zhang, Y. et al. Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank. Nat. Hum. Behav. 7, 1185–1195 (2023).
Google Scholar
Morris, M. C. et al. MIND diet slows cognitive decline with aging. Alzheimers Dement. 11, 1015–1022 (2015).
Google Scholar
Morris, M. C. et al. MIND diet associated with reduced incidence of Alzheimer’s disease. Alzheimers Dement. 11, 1007–1014 (2015).
Google Scholar
van den Brink, A. C., Brouwer-Brolsma, E. M., Berendsen, A. A. M. & van de Rest, O. The Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets are associated with less cognitive decline and a lower risk of Alzheimer’s disease—a review. Adv. Nutr. 10, 1040–1065 (2019).
Google Scholar
Chen, H. et al. Associations of the Mediterranean‐DASH Intervention for Neurodegenerative Delay diet with brain structural markers and their changes. Alzheimers Dement. 20, 1190–1200 (2024).
Google Scholar
Samuelsson, J. et al. Associations between dietary patterns and dementia‐related neuroimaging markers. Alzheimers Dement. 19, 4629–4640 (2023).
Google Scholar
Agarwal, P. et al. Association of Mediterranean-DASH Intervention for Neurodegenerative Delay and Mediterranean diets with Alzheimer disease pathology. Neurology 100, e2259–e2268 (2023).
Google Scholar
Cornelis, M. C., Agarwal, P., Holland, T. M. & van Dam, R. M. MIND dietary pattern and its association with cognition and incident dementia in the UK Biobank. Nutrients 15, 32 (2022).
Google Scholar
Vu, T. H. T. et al. Adherence to MIND diet, genetic susceptibility, and incident dementia in three US cohorts. Nutrients 14, 2759 (2022).
Google Scholar
de Crom, T. O. E., Mooldijk, S. S., Ikram, M. K., Ikram, M. A. & Voortman, T. MIND diet and the risk of dementia: a population-based study. Alzheimers Res. Ther. 14, 8 (2022).
Google Scholar
Barnes, L. L. et al. Trial of the MIND diet for prevention of cognitive decline in older persons. N. Engl. J. Med. 389, 602–611 (2023).
Google Scholar
Piernas, C. et al. Describing a new food group classification system for UK Biobank: analysis of food groups and sources of macro- and micronutrients in 208,200 participants. Eur. J. Nutr. 60, 2879–2890 (2021).
Google Scholar
Zhang, S., Tomata, Y., Sugiyama, K., Sugawara, Y. & Tsuji, I. Citrus consumption and incident dementia in elderly Japanese: the Ohsaki Cohort 2006 Study. Br. J. Nutr. 117, 1174–1180 (2017).
Google Scholar
Zafra‐Stone, S. et al. Berry anthocyanins as novel antioxidants in human health and disease prevention. Mol. Nutr. Food Res. 51, 675–683 (2007).
Google Scholar
Nakajima, A. et al. Nobiletin, a citrus flavonoid, ameliorates cognitive impairment, oxidative burden, and hyperphosphorylation of tau in senescence-accelerated mouse. Behav. Brain Res. 250, 351–360 (2013).
Google Scholar
Muhammad, T., Ikram, M., Ullah, R., Rehman, S. & Kim, M. Hesperetin, a citrus flavonoid, attenuates LPS-induced neuroinflammation, apoptosis and memory impairments by modulating TLR4/NF-κB signaling. Nutrients 11, 648 (2019).
Google Scholar
Zhang, R. et al. Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses. Nat. Mental Health 2, 535–552 (2024).
Google Scholar
Jeon, K. H. et al. Changes in alcohol consumption and risk of dementia in a nationwide cohort in South Korea. JAMA Netw. Open 6, e2254771 (2023).
Google Scholar
Rao, R. T. Methodological difficulties of studying alcohol consumption and dementia. BMJ 362, k3894 (2018).
Google Scholar
Zhang, Y. et al. Intakes of fish and polyunsaturated fatty acids and mild-to-severe cognitive impairment risks: a dose-response meta-analysis of 21 cohort studies. Am. J. Clin. Nutr. 103, 330–340 (2016).
Google Scholar
Wan, Y. et al. Association between changes in carbohydrate intake and long term weight changes: prospective cohort study. BMJ 382, e073939 (2023).
Google Scholar
Ayoob, K. T. Carbohydrate confusion and dietary patterns: unintended public health consequences of ‘food swapping’. Front. Nutr. 10, 1266308 (2023).
Google Scholar
Drewnowski, A., Maillot, M. & Vieux, F. Multiple metrics of carbohydrate quality place starchy vegetables alongside non-starchy vegetables, legumes, and whole fruit. Front. Nutr. 9, 867378 (2022).
Google Scholar
Ylilauri, M. P. et al. Association of dietary cholesterol and egg intakes with the risk of incident dementia or Alzheimer disease: the Kuopio Ischaemic Heart Disease Risk Factor Study. Am. J. Clin. Nutr. 105, 476–484 (2017).
Google Scholar
Institute of Medicine (US) Standing Committee on the Scientific Evaluation of Dietary Reference Intakes and its Panel on Folate, Other B Vitamins, and Choline. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline (National Academies Press, 1998).
Vishwanathan, R., Kuchan, M. J., Sen, S. & Johnson, E. J. Lutein and preterm infants with decreased concentrations of brain carotenoids. J. Pediatr. Gastroenterol. Nutr. 59, 659–665 (2014).
Google Scholar
Chen, Y. et al. Associations of sugar-sweetened, artificially sweetened, and naturally sweet juices with Alzheimer’s disease: a prospective cohort study. GeroScience 46, 1229–1240 (2023).
Google Scholar
Liu, H. et al. Meta-analysis of sugar-sweetened beverage intake and the risk of cognitive disorders. J. Affect. Disord. 313, 177–185 (2022).
Google Scholar
Chen, H. et al. Sugary beverages and genetic risk in relation to brain structure and incident dementia: a prospective cohort study. Am. J. Clin. Nutr. 117, 672–680 (2023).
Google Scholar
Scheltens, P. et al. Alzheimer’s disease. Lancet 397, 1577–1590 (2021).
Google Scholar
Schliebs, R. & Arendt, T. The cholinergic system in aging and neuronal degeneration. Behav. Brain Res. 221, 555–563 (2011).
Google Scholar
O’Brien, J. T. Clinical significance of white matter changes. Am. J. Geriatr. Psychiatry 22, 133–137 (2014).
Google Scholar
Roseborough, A., Ramirez, J., Black, S. E. & Edwards, J. D. Associations between amyloid β and white matter hyperintensities: a systematic review. Alzheimers Dement. 13, 1154–1167 (2017).
Google Scholar
Peng, M. et al. Dietary inflammatory index, genetic susceptibility and risk of incident dementia: a prospective cohort study from UK Biobank. J. Neurol. 271, 1286–1296 (2024).
Google Scholar
Chen, X., Maguire, B., Brodaty, H. & O’Leary, F. Dietary patterns and cognitive health in older adults: a systematic review. J. Alzheimers Dis. 67, 583–619 (2019).
Google Scholar
Dyall, S. C. Long-chain omega-3 fatty acids and the brain: a review of the independent and shared effects of EPA, DPA and DHA. Front. Aging Neurosci. 7, 52 (2015).
Google Scholar
Chen, H. et al. Circulating metabolomic profile of the MIND diet and its relation to cognition in middle-aged and older adults. iMetaOmics 2, e61 (2025).
Google Scholar
McGrattan, A. M. et al. Diet and inflammation in cognitive ageing and Alzheimer’s disease. Curr. Nutr. Rep. 8, 53–65 (2019).
Google Scholar
Pereira, J. B. et al. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease. Brain 144, 3505–3516 (2021).
Google Scholar
Benedet, A. L. et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. JAMA Neurol. 78, 1471–1483 (2021).
Google Scholar
Pontecorvo, M. J. et al. Association of donanemab treatment with exploratory plasma biomarkers in early symptomatic Alzheimer disease: a secondary analysis of the TRAILBLAZER-ALZ Randomized Clinical Trial. JAMA Neurol. 79, 1250–1259 (2022).
Google Scholar
Huber, H. et al. Biomarkers of Alzheimer’s disease and neurodegeneration in dried blood spots—A new collection method for remote settings. Alzheimers Dement. 20, 2340–2352 (2024).
Google Scholar
Guo, Y. et al. Plasma proteomic profiles predict future dementia in healthy adults. Nat. Aging 4, 247–260 (2024).
Google Scholar
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).
Google Scholar
Liu, B. et al. Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr. 14, 1998–2005 (2011).
Google Scholar
Bradbury, K. E., Young, H. J., Guo, W. & Key, T. J. Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire. J. Nutr. Sci. 7, e6 (2018).
Google Scholar
Perez-Cornago, A. et al. Description of the updated nutrition calculation of the Oxford WebQ questionnaire and comparison with the previous version among 207,144 participants in UK Biobank. Eur. J. Nutr. 60, 4019–4030 (2021).
Google Scholar
Greenwood, D. C. et al. Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers. Am. J. Epidemiol. 188, 1858–1867 (2019).
Google Scholar
Liu, W. et al. Association of biological age with health outcomes and its modifiable factors. Aging Cell 22, e13995 (2023).
Google Scholar
Cheesman, R. et al. Familial influences on neuroticism and education in the UK Biobank. Behav. Genet. 50, 84–93 (2020).
Google Scholar
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
Google Scholar
Bond, J., Townsend, P., Phillimore, P. & Beattie, A. Health deprivation: inequality and the North, Croom Helm, Beckenham, 1987. J. Soc. Policy 18, 291–293 (1989).
Google Scholar
Craig, C. L. et al. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35, 1381–1395 (2003).
Google Scholar
Ke, G. et al. LightGBM: a highly efficient gradient boosting decision tree. In Proc. 31st Conference on Neural Information Processing Systems (eds Guyon, I. et al.) 3147–3155 (NIPS, 2017).
Sonnega, A. et al. Cohort Profile: the Health and Retirement Study (HRS). Int. J. Epidemiol. 43, 576–585 (2014).
Google Scholar
Crimmins, E. M., Kim, J. K., Langa, K. M. & Weir, D. R. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study. J. Gerontol. B 66, i162–i171 (2011).
Google Scholar
NHANES Survey Methods and Analytic Guidelines (CDC, NCHS, year); https://wwwn.cdc.gov/Nchs/Nhanes/AnalyticGuidelines.aspx
Ahluwalia, N., Dwyer, J., Terry, A., Moshfegh, A. & Johnson, C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv. Nutr. 7, 121–134 (2016).
Google Scholar
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).
Google Scholar
Lourida, I. et al. Association of lifestyle and genetic risk with incidence of dementia. JAMA 322, 430–437 (2019).
Google Scholar
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).
Google Scholar
Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).
Google Scholar
Wik, L. et al. Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. Mol. Cell. Proteomics 20, 100168 (2021).
Google Scholar
Hu, F. B. et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am. J. Clin. Nutr. 69, 243–249 (1999).
Google Scholar
Raghunathan, T., Lepkowski, J., Hoewyk, J. V. & Solenberger, P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv. Methodol. 27, 85–95 (2001).
Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).
Google Scholar
Alfaro-Almagro, F. et al. Confound modelling in UK Biobank brain imaging. Neuroimage 224, 117002 (2021).
Google Scholar
Chen, S. J. Diet-ML-Dementia. Zenodo https://doi.org/10.5281/zenodo.15671234 (2025).
