INTRODUCTION:Dementia prevalence is associated with modifiable factors. We quantified the contribution of dementia risk factors in midlife (45-64 years) and late life (≥ 65 years) in the United States. METHODS:Data from six community-based cohorts in the Dementia Risk Prediction Project (DRPP) were used. We estimated risk factor prevalence using nationally representative data. Cohort-specific Cox regression models were used to estimate the association between modifiable risk factors and incident dementia in midlife and late life. Hazard ratios were pooled using meta-analysis then used to calculate population attributable fractions (PAFs) and potential impact fractions. RESULTS:Midlife and late-life risk factors contributed to 22.7% and 16.5% of total dementia cases, respectively. Midlife obesity (PAF: 7.7%; 95% confidence interval [CI]: 4.9%-10.5%), lower education (PAF: 8.1%; 95% CI: 5.2%-11.1%), and late-life physical inactivity (PAF: 10.4%; 95% CI: 6.2%-14.5%) were the greatest contributors. DISCUSSION:Midlife and late-life modifiable risk factors contribute to dementia risk, highlighting a need for interventions across the life course. HIGHLIGHTS:Our sample included 37,931 participants across six pooled, longitudinal US cohorts. We observed midlife and late-life risk factors contributed to 22.7% and 16.5% of dementia cases, respectively. Midlife obesity, late-life physical inactivity, and lower education appear to be the greatest contributors to dementia risk.

BACKGROUND:Early-onset dementia (onset before age 65 years) is an important health concern, but much of our understanding of its risk factors is inferred from studies of late-onset dementia (onset after age 65 years). We investigated associations between several demographic, clinical, and lifestyle factors with early-onset dementia and compared those estimates against their associations with late-onset dementia. METHODS:Data from five community-based longitudinal cohort studies from the UK and USA were pooled and rigorously harmonised: UK Biobank, Atherosclerosis Risk in Communities Study, Framingham Heart Study, Multi-Ethnic Study of Atherosclerosis, and Whitehall II Study. Dementia was ascertained via hospitalisation and death records with or without clinical assessments according to each cohort's protocol. Risk factors included sex, self-reported race or ethnicity (Hispanic, White, Black, Asian, and Other), low education, hypertension, diabetes, obesity, hypercholesterolaemia, depression, alcohol overconsumption, smoking, and physical inactivity. Cox regression models, with age as the timescale and time-varying coefficients, were fitted to estimate hazard ratios (HRs) for early-onset dementia and late-onset dementia and to test whether the HRs differed by age of onset. FINDINGS:In 544 442 participants, there were 807 incident early-onset dementia cases and 14 253 incident late-onset dementia cases over a median follow-up of 13·7 years (IQR 12·9-14·4). Female participants had a lower hazard of early-onset dementia compared with males (HR 0·70 [95% CI 0·61-0·80]). Black versus White race (1·61 [1·23-2·11]), grade school education or less (1·99 [1·67-2·38]), diabetes (2·45 [1·99-3·03]), depression (2·73 [2·34-3·20]), smoking (1·86 [1·56-2·22]), obesity (1·24 [1·04-1·48]), physical inactivity (1·33 [1·11-1·59]), and alcohol overconsumption (1·22 [1·01-1·47]) were independently associated with higher hazards of early-onset dementia. Hypertension stage 1 (HR 1·19 [95% CI 0·97-1·47]), hypertension stage 2 (1·16 [0·94-1·43]), and hypercholesterolaemia (1·11 [0·92-1·34]) had positive effect estimates but were not statistically significant. All risk factors had stronger associations with early-onset dementia than with late-onset dementia except race, physical inactivity, and alcohol overconsumption. INTERPRETATION:Our findings demonstrate the importance of modifiable risk factors in the development of early-onset dementia and guide future research for identifying high-priority targets for primary prevention. FUNDING:US National Institutes of Health, the National Institute for Neurologic Disorders and Stroke, and the National Institute of Aging.

Background and Objectives:The apolipoprotein E (APOE) haplotypes are known to be associated with dementia, with the ε4 haplotype associated with higher risk. It has been suggested that the APOE ε2 allele serves as a protective factor for dementia. However, data on the effects of the homozygous APOE ε2/ε2 genotype are limited, likely due to the rarity of the APOE ε2/ε2 genotype. Furthermore, the association between APOE genotypes and dementia may differ across self-reported race. We aim to investigate the association between APOE genotypes and dementia overall and across self-reported race, with a focus on the potential protective effects of the ε2 haplotype and differences across race. Methods:Data from 7 large, community-based, prospective cohort studies from the Dementia Risk Prediction Pooling Project: Age, Gene/Environment Susceptibility-Reykjavik Study, Whitehall II study, Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, Honolulu-Asia Aging Study, Multi-Ethnic Study of Atherosclerosis, and Framingham Heart Study and its associated cohorts were used. Cox proportional hazard models were used to estimate the cause-specific hazard ratios of dementia by APOE genotypes overall and by self-reported race. Results:The study consisted of 45,022 participants (10% Asian, 15% Black, 75% White, 52% male) with a mean age of 56 years (SD: 14.5) at baseline. Compared with participants with an APOE ε3/ε3 genotype, those with an ε2/ε3 genotype had a lower risk of dementia (HR: 0.87, 95% CI 0.80-0.96). There was an indication of protective effects of the ε2/ε2 genotype compared with the ε3/ε3 genotype (HR: 0.98, 95% CI 0.71-1.37). These associations were similar among Black and White participants. The detrimental effect of an ε4/ε4 genotype compared with an ε3/ε3 genotype was also seen overall and among Black and White participants. Those with an APOE ε4/ε4 genotype experienced dementia onset approximately 8 years earlier than those with an APOE ε3/ε3 genotype. Discussion:In this large, pooled cohort, the presence of at least one APOE ε2 allele was associated with lower risk of dementia overall and by self-reported race, suggesting a protective effect. APOE ε4/ε4 was associated with an earlier age of dementia onset with differences across race.

BACKGROUND AND OBJECTIVES:Cerebral small vessel disease (cSVD), characterized by pathologic changes in the structure and function of small brain vessels, is detectable on brain MRI in the absence of clinical symptoms. However, imaging cerebral small vessels themselves in vivo remains costly and challenging. There is growing interest in investigating whether retinal microvascular imaging features could be proxies for changes in the brain microvasculature. Using a multipronged approach, we explored the relation of retinal microvascular characteristics with MRI markers of cSVD (MRI-cSVD). METHODS:First, we explored this relationship in older community persons from the population-based 3C-Dijon cohort. MRI-cSVD was assessed on a 1.5-Tesla MRI at baseline, comprising white matter hyperintensity volume (WMHV), lacunes, and a composite extreme cSVD phenotype (WMHV extreme distributions and presence/absence of lacunes). At 10-year follow-up, participants underwent measurements of retinal microvascular features on fundus using the Singapore "I" Vessel Assessment software. To support 3C-Dijon findings, we conducted a comprehensive literature review up to July 2024 from PubMed/EMBASE and used 2-sample Mendelian randomization (MR) leveraging large-scale genome-wide association studies, to assess causality and directionality. RESULTS:In 670 3C-Dijon participants (median age 70.7, 65.7% women), multivariable analyses (adjusted for age, sex, axial length, and cardiovascular risk factors) showed a significant association of lower arteriolar fractal dimension (FDa) with extreme cSVD (odds ratio [OR] 1.68, 95% CI 1.20-2.34) after multiple testing correction (p < 0.0042), and at p < 0.05, associations of lower FDa and smaller arteriolar caliber with larger WMHV (β = 0.0534 [95% CI 0.0075-0.0569] and 0.0519 [95% CI 0.0045-0.0993]), and of greater venular tortuosity (TORTv) with lacunes (OR 1.45, 95% CI 1.05-2.00). Lower FDa was also associated with poorer executive function. The systematic review of the literature identified 12 studies (N = 7,796) that showed mostly consistent direction of effects for FDa (5/6 studies), TORTv (4/6), and arteriolar caliber (10/11), although statistical significance was observed in 5 individual studies only. Two-sample MR based on large genome-wide association studies (N = 5,292-52,798) showed evidence for a potentially causal association of greater TORTv with extreme cSVD and larger WMHV (p = 0.0017 and 0.049), with no evidence for reverse causation. DISCUSSION:We provide multimodal evidence that geometric characteristics of the retinal microvasculature are associated with increased burden of MRI-cSVD and possibly worse executive function.

BackgroundIdentifying genetic variants conferring resilience to Alzheimer's disease and related dementia (ADRD) may hold promise for developing therapeutics.ObjectiveTo determine genetic associations with being dementia-free at age 85 (DF85).MethodsWe examined genetic associations, using whole genome sequencing data, with DF85 in three Trans-Omics for Precision Medicine cohorts and the Alzheimer's Disease Sequencing Project Phenotype Harmonization Consortium. We tested common variants individually and aggregation of rare (MAF ≤ 1%) coding and non-coding variants in DF85 participants (n = 3657) against individuals who were not DF85 (n = 20,010). We verified associations using a stricter control set who developed dementia before age 85 (n = 5552).ResultsWe observed an association at APOE (rs429358, MAF = 0.21, odds ratio [OR] = 0.49, 95% confidence interval [CI] = 0.46-0.53, p = 1.0 × 10-92) as well as for two common variants (rs16892237-A near MAL2, MAF = 0.08, OR = 1.34, 95% CI = 1.21-1.48, p = 1.1 × 10-8 and rs8004018-G near GCH1, MAF = 0.16, OR = 1.24, 95% CI = 1.15-1.34, p = 1.7 × 10-9) and an aggregate of rare loss of function and disruptive missense variants in FBXW10 on chr 17 (p = 1.4 × 10-7) associated with DF85.ConclusionsThrough a genome-wide assessment of a resilience-focused outcome, we identified common and rare genetic variants contributing to DF85 status. Genes associated with DF85 may delay onset of ADRD and provide translational impact.

​Background: To optimise management of patients with covert brain infarction (CBI), personalised estimates of stroke and dementia risks are needed. We determined age- and sex-specific stroke and dementia risks in individuals with CBI and identified clinical and imaging prognostic indicators. Methods: We included 2287 participants with MRI-defined CBI from eight community-based cohorts across Europe and the USA, who were age/sex-matched within each cohort to 6861 participants without CBI (1:3). We used Cox models to determine relative risks of stroke and dementia, and subdistribution hazard models to estimate 10-year absolute risks and prognostic indicators among participants with CBI. Findings: Of 9148 participants (mean age 74 years, 53% women), 716 (7·8%) were diagnosed with a stroke and 1311 (14·3%) with dementia during 10-year follow-up. Individuals with CBI had a 2·01-fold (95%CI: 1·66−2·42) increased risk of stroke and a 1·34-fold (1·11−1·62) increased risk of dementia compared to those without CBI. Stroke and dementia risks relative to no CBI were higher with multiple CBI than with single CBI (hazard ratio stroke: 2·51 [1·94–3·26] vs 1·79 [1·49–2·15]; dementia: 1·74 [1·39–2·17] vs 1·22 [1·04–1·44]), but did not differ between lacunes, cortical infarcts or cerebellar infarcts. In individuals with CBI, smoking, hypertension, diabetes, atrial fibrillation, education, APOE-ε4 genotype, and multiple CBI predisposed to stroke and/or dementia. With CBI, ten-year absolute stroke risk was 10·9% (95%CI: 7·4%–15·1%) and 10-year absolute dementia risk was 18·9% (95%CI: 10·5%–29·4%). Absolute risks increased with age from 5·4% (50-59 years) to 16·2% (aged >80) for stroke and from 2·5% to 31·2% for dementia. Interpretation: Community-dwelling individuals with CBI are at increased risk of stroke and dementia, particularly in the presence of multiple infarcts. Demographic, clinical, and imaging characteristics may guide risk factor management, interventions, and trial inclusion.

INTRODUCTION:An integrative polygenic risk score (iPRS) capturing the neurodegenerative and vascular contribution to dementia could identify high-risk individuals and improve risk prediction. METHODS:We developed an iPRS for dementia (iPRS-DEM) in Europeans (aged 65+), comprising genetic risk for Alzheimer's disease (AD) and 23 vascular or neurodegenerative traits (excluding apolipoprotein E [APOE]). iPRS-DEM was evaluated across cohorts comprising older community-dwelling people (N = 3702), a multi-ancestry biobank (N = 130,797 Europeans; 105,404 non-Europeans), and dementia-free memory clinic participants (N = 2032). RESULTS:iPRS-DEM was associated with dementia risk independently of APOE in the elderly (subdistribution hazard ratio [sHR]per1SD = 1.15, 95% confidence interval [CI]: 1.03 to 1.28), which generalized to Europeans (EUR-sHRper1SD = 1.28, 95% CI: 1.09 to 1.51]), East-Asians (EAS-sHRper1SD = 5.29, 95% CI: 1.43 to 34.36), and memory-clinic participants (sHRper1SD = 1.25, 95% CI: 1.11 to 1.42). Prediction was comparable to clinical risk factors in older community-dwelling people, with improved performance among memory-clinic patients. Risk stratification was enhanced by defining four genetic risk groups with iPRS-DEM and APOE ε4, reaching five-fold increased risk in APOE ε4+/iPRS-DEM+ memory-clinic participants. DISCUSSION:Alongside APOE ε4, iPRS-DEM may refine risk stratification for the enrichment of dementia clinical trials and prevention programs. HIGHLIGHTS:iPRS-DEM reflects neurodegenerative and vascular contribution to dementia. We show iPRS-DEM captures additional dementia genetic risk beyond APOE and AD-PRS. iPRS-DEM, in combination with APOE ε4, shows promise for dementia risk stratification. Our results generalize across both population-based and memory-clinic settings. We show transportability of iPRS-DEM to East Asian ancestry.

INTRODUCTION:White matter hyperintensities (WMHs), a major cerebral small vessel disease (cSVD) marker, may arise from different pathologies depending on their location. We explored clinical and genetic correlates of agnostically derived spatial WMH patterns in two longitudinal population-based cohorts (Three-City Study [3C]-Dijon, LIFE-Adult). METHODS:We derived seven WMH spatial patterns using Bullseye segmentation in 2878 individuals aged 65+ and explored their associations with vascular and genetic risk factors, cognitive performance, dementia and stroke incidence. RESULTS:WMHs in the frontoparietal and anterior periventricular region were associated with blood pressure traits, WMH genetic risk score (GRS), baseline and decline in general cognitive performance, incident all-cause dementia, and ischemic stroke. Juxtacortical-deep occipital WMHs were not associated with vascular risk factors and WMH GRS, but with incident all-cause dementia and intracerebral hemorrhage. DISCUSSION:Accounting for WMH spatial distribution is key to deciphering mechanisms underlying cSVD subtypes, an essential step towards personalized therapeutic approaches. HIGHLIGHTS:We studied spatial patterns of WMHs in 2878 participants. Blood pressure was associated with frontoparietal and anterior PV WMHs. Anterior PV WMHs predicted dementia and stroke risk. Juxtacortical-deep occipital WMH burden was not associated with blood pressure or WMH genetic risk. Juxtacortical-deep occipital WMH burden predicted dementia and intracerebral hemorrhage.

Introduction Optimal management of covert brain infarction (CBI) is hampered by a lack of personalised estimates of dementia risk, for which individual studies are usually underpowered. We aimed to determine age- and sex-specific dementia risks, and identify clinical and imaging prognostic indicators in individuals with CBI. Methods We included participants from 7 population-based cohorts across Europe and the USA, and aggregated data through coordinated meta-analysis. Within each cohort, participants with CBI on MRI were matched 1:3 on age and sex to individuals without CBI. We used Cox models to estimate the relative risk of dementia, and subdistribution hazard models to compute 10-year absolute risks accounted for competing mortality. In participants with CBI, we also applied subdistribution hazard models to identify prognostic indicators of dementia. Results Of a total 8176 participants (mean age 73 years, 54% women), 2044 had a CBI. During a mean follow-up of 7.0 years, 369 (18%) participants with CBI developed dementia, compared to 859 (14%) without CBI. Individuals with CBI had a 1.4-fold (95%CI: 1.1−1.7) increased risk of all-cause dementia, and a 3.0-fold (1.3−6.7) increased risk of vascular dementia. In individuals with CBI, ten-year absolute risk of dementia ranged from 2.1% (0.4−7.2%) in those aged 50-59 years, to 34.9% (23.9-46.2%) in those aged ≥80.Dementia risk was elevated with CBI particularly from age 70 onwards (Figure 1). Dementia risk was higher among individuals with multiple CBI than in those with a single CBI (22.9% [10.4−38.4%] versus 17.9% [9.0−29.2%]; hazard ratios of 1.8 [1.5−2.2] versus 1.2 [1.0−1.5]), but was similar for cortical infarcts, cerebellar infarcts, and lacunes. Age, lower education, hypertension, diabetes, history of smoking, APOE-e4 genotype, and multiple CBI were risk indicators of dementia in people with CBI. Excess risk of dementia with CBI was only slightly attenuated by accounting for occurrence of ischemic stroke during follow-up (HR: 1.3 [1.1−1.6]). Conclusions Individuals with CBI are at increased risk of dementia, particularly after age 70 and with multiple infarcts. Demographic, clinical, and imaging characteristics may further guide personalised interventions and future trial design.

Journal Article Cohort Profile: Dementia Risk Prediction Project (DRPP) Get access Amy E Krefman, Amy E Krefman Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Corresponding author. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA. E-mail: amy.krefman@northwestern.edu https://orcid.org/0000-0002-6692-0104 Search for other works by this author on: Oxford Academic PubMed Google Scholar John Stephen, John Stephen Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA https://orcid.org/0000-0001-7309-9193 Search for other works by this author on: Oxford Academic PubMed Google Scholar Padraig Carolan, Padraig Carolan Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Sanaz Sedaghat, Sanaz Sedaghat Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Maxwell Mansolf, Maxwell Mansolf Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Aïcha Soumare, Aïcha Soumare UMR1219 Bordeaux Population Health Center (Team VINTAGE), INSERM-University of Bordeaux, Bordeaux, France Search for other works by this author on: Oxford Academic PubMed Google Scholar Alden L Gross, Alden L Gross Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Allison E Aiello, Allison E Aiello Robert N Butler Columbia Aging Center and Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Archana Singh-Manoux, Archana Singh-Manoux Université Paris Cité, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Paris, FranceDepartment of Epidemiology and Public Health, University College London, London, UK https://orcid.org/0000-0002-1244-5037 Search for other works by this author on: Oxford Academic PubMed Google Scholar M Arfan Ikram, M Arfan Ikram Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more Catherine Helmer, Catherine Helmer Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France Search for other works by this author on: Oxford Academic PubMed Google Scholar Christophe Tzourio, Christophe Tzourio Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France https://orcid.org/0000-0002-6517-2984 Search for other works by this author on: Oxford Academic PubMed Google Scholar Claudia Satizabal, Claudia Satizabal Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USAThe Framingham Heart Study, Framingham, MA, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Deborah A Levine, Deborah A Levine Department of Internal Medicine and Cognitive Health Services Research Program, University of Michigan, Ann Arbor, MI, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Donald Lloyd-Jones, Donald Lloyd-Jones Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Emily M Briceño, Emily M Briceño Department of Physical Medicine & Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Farzaneh A Sorond, Farzaneh A Sorond Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Frank J Wolters, Frank J Wolters Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The NetherlandsDepartments of Radiology & Nuclear Medicine, and Alzheimer Centre Erasmus MC, Erasmus MC University Medical Centre, Rotterdam, The Netherlands Search for other works by this author on: Oxford Academic PubMed Google Scholar Jayandra Himali, Jayandra Himali Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USAThe Framingham Heart Study, Framingham, MA, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Lenore J Launer, Lenore J Launer Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Lihui Zhao, Lihui Zhao Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Mary Haan, Mary Haan Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, CA, USA https://orcid.org/0000-0001-9312-4501 Search for other works by this author on: Oxford Academic PubMed Google Scholar Oscar L Lopez, Oscar L Lopez Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Stéphanie Debette, Stéphanie Debette UMR1219 Bordeaux Population Health Center (Team VINTAGE), INSERM-University of Bordeaux, Bordeaux, France https://orcid.org/0000-0001-8675-7968 Search for other works by this author on: Oxford Academic PubMed Google Scholar Sudha Seshadri, Sudha Seshadri Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases and Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USAThe Framingham Heart Study, Framingham, MA, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Suzanne E Judd, Suzanne E Judd Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Timothy M Hughes, Timothy M Hughes Departments of Internal Medicine and Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Vilmundur Gudnason, Vilmundur Gudnason Icelandic Heart Association, Kopavogur, IcelandFaculty of Medicine, University of Iceland, Reykjavik, Iceland Search for other works by this author on: Oxford Academic PubMed Google Scholar Denise Scholtens, Denise Scholtens Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Norrina B Allen Norrina B Allen Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 53, Issue 1, February 2024, dyae012, https://doi.org/10.1093/ije/dyae012 Published: 09 February 2024 Article history Received: 16 March 2023 Editorial decision: 02 January 2024 Accepted: 18 January 2024 Published: 09 February 2024

Introduction: Early-onset dementia (EOD) is defined as dementia diagnosed before age 65. While much is known about late-onset dementia (LOD), research on EOD is scarce. The Dementia Risk Pooling Project (DRPP) harmonized data from nine community-based prospective studies, including under-represented racial/ethnic groups. We hypothesized that EOD incidence rates are higher for men than women, and non-Hispanic (NH) Black participants than other race/ethnicity groups. Methods: We used data from eight DRPP cohorts: Age, Gene/Environment Susceptibility-Reykjavik Study, Whitehall II study, Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, Honolulu-Asia Aging Study, Multi-Ethnic Study of Atherosclerosis, Sacramento Area Latino Study on Aging, and six Framingham Heart Study cohorts. Individual data were pooled and rigorously harmonized. Race/ethnicity was categorized as self-reported Hispanic, NH White, NH Black, and NH other/unknown. Crude incidence rates and ratios were calculated per 10,000 person-years with 95% confidence intervals. Results: The analytic sample consisted of 56,607 participants, who experienced 7,504 cases of LOD and 124 cases of EOD over a median follow-up of 19.13 (IQR 10.46 - 31.11) years. EOD and LOD incidence rates per 10,000 person-years were 1.95 (95% CI 1.62 - 2.32) and 123.35 (95% CI 120.49 - 126.26), respectively. Compared to women, men had 1.17 (95% CI 0.82 - 1.66) times the rate of EOD but 0.82 (95% CI 0.79 - 0.86) times the rate of LOD. Similarly, compared to NH Whites, Hispanics and participants of other or unknown race/ethnicity had higher rates of EOD but lower rates of LOD. NH Blacks had a higher rate of both EOD and LOD compared to NH Whites, but the association with EOD was more pronounced. Conclusion: In this multi-cohort study with individual participant data, EOD and LOD incidence rates were different across sex and race/ethnicity groups. Some of these differences may be due to longer life expectancy in women and some race/ethnicity groups.

Introduction: Apolipoprotein E ( APOE ) is a glycoprotein that mediates and regulates lipid transport and uptake. The APOE e4 allele is associated with increased risk of mortality and Alzheimer’s Disease and related dementias. It has been suggested that the APOE e2 allele is associated with increased survival and serves as a protective factor for cognitive decline and dementia. However, due to the rarity of the e2 allele, this association is not fully understood. Furthermore, the association between APOE genotypes and dementia may differ by race/ethnicity. Methods: Data from six large, community-based, prospective cohort studies from the Dementia Risk Pooling Project (DRPP) were used. Cumulative incidence estimates for dementia and mortality by race and APOE genotype (combining one or two e2 or e4 alleles: 24, 22/23, 33, 34/44 ) were estimated. Cox regression models were used to estimate the APOE genotype ( 22, 23, 33, 34, 44 ) cause-specific hazard ratios (HRs) for dementia and mortality adjusting for age, sex, and cohort. Results: The sample consisted of 43,404 participants (average age 54, 52% female, 16% Black participants). Overall, the 5-year cumulative incidence of dementia was higher in White participants across different genotypes, while mortality rate was higher in Black participants (Figure 1). The 5-year cumulative incidence of dementia was higher among both Black and White participants with one or two e4 alleles. Using the APOE e2/e4 genotype as a reference, the dementia HRs for the e2/e2 and e2/e3 genotypes were 0.73 (95% CI 0.51-1.06) and 0.64 (95% CI 0.53-0.76), respectively. e3/e4 and e4/e4 genotypes were associated with higher HRs of 1.32 (95% CI 1.12-1.56) and 2.68 (95% CI 2.20-3.27), respectively. There was no association between APOE genotypes and mortality. Conclusion: In this large, pooled cohort, the presence of an e2 allele was associated with lower risk of dementia, while the presence of an e4 allele was associated with higher risk in both Black and White adults. APOE genotype was not associated with mortality.

Background:Clonal hematopoiesis of indeterminate potential (CHIP) was initially linked to a twofold increase in atherothrombotic events. However, recent investigations have revealed a more nuanced picture, suggesting that CHIP may confer only a modest rise in myocardial infarction (MI) risk. This observed lower risk might be influenced by yet unidentified factors that modulate the pathological effects of CHIP. Mosaic loss of the Y chromosome (mLOY), a common marker of clonal hematopoiesis in men, has emerged as a potential candidate for modulating cardiovascular risk associated with CHIP. In this study, we aimed to ascertain the risk linked to each somatic mutation or mLOY and explore whether mLOY could exert an influence on the cardiovascular risk associated with CHIP.Methods:We conducted an examination for the presence of CHIP and mLOY using targeted high-throughput sequencing and digital PCR in a cohort of 446 individuals. Among them, 149 patients from the CHAth study had experienced a first MI at the time of inclusion (MI(+) subjects), while 297 individuals from the Three-City cohort had no history of cardiovascular events (CVE) at the time of inclusion (MI(-) subjects). All subjects underwent thorough cardiovascular phenotyping, including a direct assessment of atherosclerotic burden. Our investigation aimed to determine whether mLOY could modulate inflammation, atherosclerosis burden, and atherothrombotic risk associated with CHIP.Results:CHIP and mLOY were detected with a substantial prevalence (45.1% and 37.7%, respectively), and their occurrence was similar between MI(+) and MI(-) subjects. Notably, nearly 40% of CHIP(+) male subjects also exhibited mLOY. Interestingly, neither CHIP nor mLOY independently resulted in significant increases in plasma hs-CRP levels, atherosclerotic burden, or MI incidence. Moreover, mLOY did not amplify or diminish inflammation, atherosclerosis, or MI incidence among CHIP(+) male subjects. Conversely, in MI(-) male subjects, CHIP heightened the risk of MI over a 5 y period, particularly in those lacking mLOY.Conclusions:Our study highlights the high prevalence of CHIP and mLOY in elderly individuals. Importantly, our results demonstrate that neither CHIP nor mLOY in isolation substantially contributes to inflammation, atherosclerosis, or MI incidence. Furthermore, we find that mLOY does not exert a significant influence on the modulation of inflammation, atherosclerosis burden, or atherothrombotic risk associated with CHIP. However, CHIP may accelerate the occurrence of MI, especially when unaccompanied by mLOY. These findings underscore the complexity of the interplay between CHIP, mLOY, and cardiovascular risk, suggesting that large-scale studies with thousands more patients may be necessary to elucidate subtle correlations.Funding:This study was supported by the Fondation Cœur & Recherche (the Société Française de Cardiologie), the Fédération Française de Cardiologie, ERA-CVD (« CHEMICAL » consortium, JTC 2019) and the Fondation Université de Bordeaux. The laboratory of Hematology of the University Hospital of Bordeaux benefitted of a convention with the Nouvelle Aquitaine Region (2018-1R30113-8473520) for the acquisition of the Nextseq 550Dx sequencer used in this study.Clinical trial number:NCT04581057.

Importance Vascular disease is a treatable contributor to dementia risk, but the role of specific markers remains unclear, making prevention strategies uncertain. Objective To investigate the causal association between white matter hyperintensity (WMH) burden, clinical stroke, blood pressure (BP), and dementia risk, while accounting for potential epidemiologic biases. Design, Setting, and Participants This study first examined the association of genetically determined WMH burden, stroke, and BP levels with Alzheimer disease (AD) in a 2-sample mendelian randomization (2SMR) framework. Second, using population-based studies (1979-2018) with prospective dementia surveillance, the genetic association of WMH, stroke, and BP with incident all-cause dementia was examined. Data analysis was performed from July 26, 2020, through July 24, 2022. Exposures Genetically determined WMH burden and BP levels, as well as genetic liability to stroke derived from genome-wide association studies (GWASs) in European ancestry populations. Main Outcomes and Measures The association of genetic instruments for WMH, stroke, and BP with dementia was studied using GWASs of AD (defined clinically and additionally meta-analyzed including both clinically diagnosed AD and AD defined based on parental history [AD-meta]) for 2SMR and incident all-cause dementia for longitudinal analyses. Results In 2SMR (summary statistics–based) analyses using AD GWASs with up to 75 024 AD cases (mean [SD] age at AD onset, 75.5 [4.4] years; 56.9% women), larger WMH burden showed evidence for a causal association with increased risk of AD (odds ratio [OR], 1.43; 95% CI, 1.10-1.86; P = .007, per unit increase in WMH risk alleles) and AD-meta (OR, 1.19; 95% CI, 1.06-1.34; P = .008), after accounting for pulse pressure for the former. Blood pressure traits showed evidence for a protective association with AD, with evidence for confounding by shared genetic instruments. In the longitudinal (individual-level data) analyses involving 10 699 incident all-cause dementia cases (mean [SD] age at dementia diagnosis, 74.4 [9.1] years; 55.4% women), no significant association was observed between larger WMH burden and incident all-cause dementia (hazard ratio [HR], 1.02; 95% CI, 1.00-1.04; P = .07). Although all exposures were associated with mortality, with the strongest association observed for systolic BP (HR, 1.04; 95% CI, 1.03-1.06; P = 1.9 × 10 −14 ), there was no evidence for selective survival bias during follow-up using illness-death models. In secondary analyses using polygenic scores, the association of genetic liability to stroke, but not genetically determined WMH, with dementia outcomes was attenuated after adjusting for interim stroke. Conclusions These findings suggest that WMH is a primary vascular factor associated with dementia risk, emphasizing its significance in preventive strategies for dementia. Future studies are warranted to examine whether this finding can be generalized to non-European populations.

While clonal hematopoiesis of indeterminate potential (CHIP) has been initially associated with a 2-fold increased incidence of atherothrombotic events, recent studies showed that in fact, CHIP may be associated with a very modest increase in the risk of Myocardial Infarction (MI). This lower risk than initially suggested could be related to modulation of the pathological effects of CHIP by factors not yet identified. Mosaic loss of Y chromosome (mLOY), a frequent marker of clonal hematopoiesis in men, was recently associated with cardiovascular diseases and as such, represents a candidate modulator of cardiovascular risk associated with CHIP. In this study, we sought to determine the risk associated with each somatic mutation or mLOY and whether mLOY could modulate the cardiovascular risk associated with CHIP. We looked for the presence of CHIP and mLOY using sensitive high-throughput sequencing and digital PCR in 446 subjects enrolled in 2 prospective studies. The 149 patients in the CHAth study had a first myocardial infarction (MI) at inclusion (MI(+) subjects). They were compared with 297 subjects from the 3-city cohort who had no history of a cardiovascular event (CVE) at inclusion (MI(-) subjects). All these subjects underwent fine cardiovascular phenotyping, including a direct assessment of atherosclerotic burden. We searched whether mLOY could modulate the inflammation, atherosclerosis burden and atherothrombotic risk associated with CHIP. CHIP and mLOY were detected with a high prevalence (45.1% and 37.7% respectively), and with a similar frequency between MI(+) and MI(-) subjects. Nearly 40% of CHIP(+) male subjects also carried a mLOY. Separately, neither CHIP nor mLOY increased the plasmatic level of hsCRP, the atherosclerotic burden nor the incidence of MI. mLOY did not increase or decrease the inflammation, atherosclerosis or MI incidence in CHIP(+) male subjects. In MI(-) male subjects, CHIP increased the risk of MI at 5 years, especially in those not carrying a mLOY. CHIP and mLOY are highly prevalent in elderly subjects, but none of them separately increase inflammation, atherosclerosis or MI incidence in an important manner. mLOY do not modulate the inflammation, atherosclerosis burden or atherothrombotic risk associated with CHIP, but CHIP may accelerate the occurrence of MI when not associated with mLOY.

Machine learning models have been used to create accurate prediction models for dementia. However, many suffer from overfitting and external validation often results in decreased performance. Pooling data from various sources for model training can improve the generalizability of prediction models. We show here a prediction model for dementia developed on pooled data from the Dementia Risk Prediction Pooling (DRPP) Consortium. Data from 11 longitudinal disease cohorts within the DRPP and relevant risk factors (25 in total) were collected and harmonized at baseline and follow-up exams. An ensemble tree-based algorithm, LightGBM, was used to create two prediction models for dementia at or before 10 years. The first model contains all variables in the dataset, and the second clinical model excludes the Mini-Mental State Examination (MMSE) score and APOE genotype. 5-fold cross-validation was repeated 1000 times to tune the model hyperparameters (number of leaves, tree depth, learning rate) to yield the greatest area under the curve (AUC). Feature importance of the model was analyzed via individual feature information gain. Analysis was performed in R 4.1.2. Among the 55,614 participants from 11 cohorts included in this analysis (Table 1), the first model with all the variables had a cross-validation AUC of 0.762 (CI: 0.757-0.767). Counterintuitively, the second model of the without the MMSE and APOE variables yielded an AUC of 0.804 (CI: 0.799-0.809), which may indicate overfitting in the first model. Feature importance analysis show that age is the most important variable in both models. APOE, MMSE, fasting glucose, and any physical activity are the next most important predictors in the full model (Figure 1). In the second model, fasting glucose, any physical activity, gender, and A1c levels were the next most important predictors (Figure 2). By pooling various data sources, we can train machine learning models for dementia risk prediction on more diverse data. Further work is needed to compare the performance of these models with models trained on single data sources via external validation. A pooled dataset also offers an opportunity to understand how model performance will change given shifts in the underlying population.