Attention‐deficit/hyperactivity disorder polygenic risk score and incidence of cognitive impairment in older adults: A prospective multicohort study

Douglas Teixeira Leffa; Guilherme Bauer‐Negrini; Livia Amaral; Bruna Bellaver; Pamela C L Ferreira; Guilherme Povala; Firoza Z Lussier; João Pedro Ferrari‐Souza; Ann D Cohen; Dana L Tudorascu; Samuele Cortese; Luis Augusto Rohde; Brooke S G Molina; Derek Archer; Tharick A Pascoal

doi:10.1002/alz.71770

. 2026 Oct 15;22(10):e71770. doi: 10.1002/alz.71770

Attention‐deficit/hyperactivity disorder polygenic risk score and incidence of cognitive impairment in older adults: A prospective multicohort study

Douglas Teixeira Leffa ^1,^✉, Guilherme Bauer‐Negrini ¹, Livia Amaral ¹, Bruna Bellaver ¹, Pamela C L Ferreira ¹, Guilherme Povala ¹, Firoza Z Lussier ¹, João Pedro Ferrari‐Souza ², Ann D Cohen ¹, Dana L Tudorascu ^1,³, Samuele Cortese ^4,^5,^6,^7,⁸, Luis Augusto Rohde ^9,^10,¹¹, Brooke S G Molina ¹², Derek Archer ^13,^14,^15,¹⁶, Tharick A Pascoal ^1,^17,^✉

^¹Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

^²Neuroscience Unit, Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul, Brazil

^³Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

^⁴Developmental EPI (Evidence synthesis, Prediction, Implementation) Lab, Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK

^⁵Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK

^⁶Hampshire and Isle of Wight Healthcare NHS Foundation Trust, Southampton, UK

^⁷New York University Child Study Center, Hassenfeld Children's Hospital at NYU Langone, New York, New York, USA

^⁸DiMePRe‐J‐Department of Precision and Regenerative Medicine‐Ionian Area, University of Bari “Aldo Moro”, Bari, Italy

^⁹ADHD Outpatient Program & Development Psychiatry Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil

^¹⁰National Institute of Developmental Psychiatry & National Center for Innovation and Research in Mental Health, São Paulo, Brazil

^¹¹Medical Council UNIFAJ & UNIMAX, Indaiatuba, Brazil

^¹²Departments of Psychiatry, Psychology, Pediatrics, Clinical and Translational Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

^¹³Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

^¹⁴Department of Neurology, Vanderbilt Health, Nashville, Tennessee, USA

^¹⁵Vanderbilt Genetics Institute, Vanderbilt Health, Nashville, Tennessee, USA

^¹⁶Vanderbilt Brain Institute, Vanderbilt Health, Nashville, Tennessee, USA

^¹⁷Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

Correspondence , Douglas Teixeira Leffa and Tharick A. Pascoal, University of Pittsburgh, 200 Meyran Ave, Pittsburgh, PA 15213, USA. Email: douglas.leffa@gmail.com and pascoalt@upmc.edu

^✉

Corresponding author.

PMCID: PMC13670426 PMID: 42878194

Abstract

INTRODUCTION

Preliminary evidence suggests a link between attention‐deficit/hyperactivity disorder (ADHD) and later‐life cognitive impairment. Whether genetic risk for ADHD, estimated using an ADHD polygenic risk score (ADHD‐PRS), is associated with incident cognitive impairment (mild cognitive impairment or Alzheimer's disease dementia) remains unknown.

METHODS

We analyzed 7761 cognitively unimpaired (CU) older adults with whole‐genome sequencing from eight cohorts participating in the Alzheimer's Disease Sequencing Project (ADSP).

RESULTS

During follow‐up, 1347 participants developed cognitive impairment. Ten‐year cumulative incidence was 33.8% in the highest ADHD‐PRS tertile versus 23.4% in the lowest. In Cox models, higher ADHD‐PRS remained associated with greater risk of cognitive impairment after adjustment for education, apolipoprotein E (APOE) ε4 status, or vascular risk. PRSs for other major psychiatric disorders were not associated with cognitive impairment in the same analytic framework. In a subsample with biomarker data, the association was not modified by baseline amyloid‐β status.

DISCUSSION

These findings support ADHD‐PRS as a novel correlate of cognitive impairment and vulnerability in aging.

Keywords: alzheimer's disease, alzheimer's disease sequencing project, amyloid beta, attention‐deficit/hyperactivity disorder, cognitive aging, cognitive impairment, dementia, mild cognitive impairment, older adults, polygenic risk score

Highlights

Genetic risk for ADHD was estimated using PRSs.
Across eight cohorts comprising 7761 participants, higher ADHD‐PRS was associated with greater risk of incident cognitive impairment.
This association remained after additional adjustment for education, APOE ε4 status, or vascular risk.
PRSs for other major psychiatric disorders were not associated with cognitive impairment in the same analytic framework.
Baseline Aβ status did not modify the association between ADHD‐PRS and cognitive impairment.

1. BACKGROUND

Dementia affects more than 55 million people worldwide, yet the full landscape of factors contributing to late‐life cognitive decline remains incompletely understood. ¹ Attention‐deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 5% of school‐aged children and can persist throughout the lifespan, with prevalence estimates of ∼2.5% in young adults and ∼1% among adults older than 60 years. ² Emerging evidence suggests that ADHD is associated with a higher risk of mild cognitive impairment (MCI) and dementia. ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰ Alzheimer's disease (AD) is the most common cause of dementia, ¹ and a large population‐based study reported elevated risk for AD among relatives of individuals with ADHD, suggesting a potential shared genetic risk. ¹¹

However, studies examining ADHD and later‐life cognitive impairment remain susceptible to residual confounding, including socioeconomic factors, health behaviors, and comorbid psychiatric and medical conditions. ¹² Furthermore, most studies rely solely on electronic health records to ascertain dementia and ADHD diagnosis, ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ¹⁰ which may introduce bias due to differences in healthcare access, clinician recognition, and documentation practices. Crucially, existing cohorts of individuals diagnosed with ADHD in childhood have not yet reached an age where the risk of MCI or dementia can be adequately assessed, ¹³ limiting the feasibility of rigorous longitudinal evaluation. These limitations have prevented firm conclusions from being drawn about whether ADHD represents a genuine risk factor for cognitive decline.

To complement this literature, genetic analyses have been proposed as an alternative approach. ADHD is highly heritable, ¹⁴ with common genetic variants contributing significantly to risk. ¹⁵ The ADHD polygenic risk score (ADHD‐PRS), which aggregates the effects of common variants identified in genome‐wide association studies (GWASs), has been associated with ADHD diagnosis and related traits. ¹⁶ Therefore, ADHD‐PRS may provide a useful tool to investigate the relationship between ADHD genetic risk and late‐life cognitive outcomes. Recent studies have begun to explore whether higher ADHD‐PRS is associated with increased risk for cognitive decline in older age. ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ However, most are focused on cognitive test performance, ¹⁸ , ¹⁹ , ²⁰ and limitations in sample size and follow‐up duration restricted their ability to examine progression to clinical MCI or dementia.

To address these gaps, we aimed to evaluate whether genetic risk for ADHD, estimated with an ADHD‐PRS, is associated with the risk of progression from cognitively unimpaired (CU) status to MCI or AD dementia. We further performed sensitivity analyses to explore the role of potential confounders previously associated with both ADHD and dementia, including vascular risk factors, ¹ , ² assessed possible ADHD specificity by examining PRS for other psychiatric disorders, tested whether baseline amyloid beta (Aβ) status modifies this association, and evaluated the longitudinal relationship between ADHD‐PRS and cognitive performance.

RESEARCH IN CONTEXT

Systematic review: The authors reviewed the literature using traditional sources, including PubMed. A growing number of studies have examined ADHD in relation to later‐life cognitive impairment. Relevant works are appropriately cited.
Interpretation: Our results indicate that ADHD‐PRS, an estimate of genetic risk for ADHD, is associated with vulnerability to later‐life cognitive impairment. The absence of association with other psychiatric PRSs in the same models suggests a degree of specificity to ADHD. Clinically, the results suggest that individuals with higher ADHD genetic risk and those with diagnosed ADHD represent subgroups that may benefit from earlier/more frequent cognitive monitoring and targeted risk‐factor mitigation.
Future directions: Future studies should clarify mechanisms linking ADHD‐PRS to cognitive impairment through comprehensive biomarker phenotyping, follow childhood‐diagnosed ADHD cohorts into later life with repeated assessments, and determine whether individuals with clinically diagnosed ADHD or elevated ADHD‐PRS could benefit from earlier cognitive monitoring.

2. METHODS

2.1. Participants

We analyzed data from the Alzheimer's Disease Sequencing Project (ADSP), a large, multicohort collaborative initiative that integrates extensive longitudinal clinical characterization with large‐scale sequencing to accelerate discovery of genetic and other determinants of risk and resilience for AD and related dementias. ²¹ We included individuals who were CU at baseline and had available age, sex, education, whole‐genome sequencing (WGS), APOE genotype, clinical assessments (including diagnoses and cognitive function), and vascular risk measures. Baseline was defined as the first visit at age ≥50 years with available clinical assessment. Included cohorts were Alzheimer's Disease Neuroimaging Initiative (ADNI), Estudio Familiar de Influencia Genética en Alzheimer (EFIGA), Rush Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), National Alzheimer's Coordinating Center (NACC), Texas Alzheimer's Research and Care Consortium (TARCC), Washington Heights/Inwood Columbia Aging Project (WHICAP), and Wisconsin Registry for Alzheimer's Prevention (WRAP). Further information on each cohort can be found in the Supplementary Material (Tables S1 and S2). Across all cohorts, written informed consent was provided by participants, and research was conducted in accordance with approved Institutional Review Board (IRB) protocols. Secondary analysis of these data was approved by the University of Pittsburgh IRB. Complete data were available for 7761 participants.

2.2. Polygenic risk scores

WGS data were obtained from ADSP Release 4, ²² generated and processed by the Genome Center for Alzheimer's Disease (GCAD). Sequencing was performed at 10 centers using Illumina technology (more details can be found elsewhere ²² ). Genetic data underwent the same robust quality control and imputation pipelines, ²² , ²³ and fully quality‐controlled data were downloaded from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). PRSs were computed using PRSice‐2 (version 2.3.5) ²⁴ based on GWAS summary statistics for ADHD ¹⁵ and (in sensitivity analyses) autism spectrum disorder (ASD), ²⁵ bipolar disorder (BD), ²⁶ major depressive disorder (MDD), ²⁷ and schizophrenia (SCZ). ²⁸ After clumping to retain approximately independent variants (250‐kb window; LD r ² = 0.1), we computed PRSs across 11 GWAS p value thresholds (1, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.005, 0.0005, 5 × 10⁻ ⁶, 5 × 10⁻ ⁸). To reduce multiple testing and summarize liability robustly, we derived the first principal component (PC) across thresholds for each disorder‐specific PRS and used this score as the primary genetic liability measure. ²⁹ PRS values were standardized (z‐scores). To investigate populational structure, principal component analysis was conducted using PLINK 2.0. ³⁰

2.3. Clinical and cognitive outcomes

Clinical diagnoses and cognitive outcomes were obtained from the ADSP Phenotype Harmonization Consortium (ADSP‐PHC) Cognitive Dataset (NIAGADS accession number: NG00067.v18). The ADSP‐PHC was established to harmonize phenotype data across multiple cohorts. ADSP‐PHC diagnostic codes (CU, MCI, AD dementia) were downloaded from NIAGADS. All cohorts included in this study had their own protocols for clinical diagnosis determination, described in cohort publications (see Tables S1 and S2). These protocols typically involved multidisciplinary diagnostic review and applied standard criteria. For neuropsychological outcomes, ADSP‐PHC composite scores for memory, executive function, and language were downloaded from NIAGADS. The ADSP‐PHC developed robust methods to harmonize data from the different neuropsychological batteries across cohorts, with full details found elsewhere. ³¹

2.4. Aβ biomarkers

A subset of participants had Aβ status from positron emission tomography (PET) and/or fluid biomarkers. Aβ PET data were obtained from ADSP‐PHC PET Dataset (NIAGADS accession number: NG00067.v18), processed by NIAGADS using magnetic resonance imaging‐free pipelines with tracer‐specific transformation of global Aβ standardized uptake value ratio to Centiloids. Further information on processing can be found in ADSP‐PHC‐FluidBiomarker_White‐Paper_2024.12.16.pdf, available via NIAGADS. PET‐derived Aβ positivity was defined using a single Centiloid threshold of 25 across tracers, using the harmonized variable (“PHC_AMYLOID_STATUS”) after quality control. Fluid‐derived Aβ status was obtained from the ADSP‐PHC Biomarker Dataset (NIAGADS accession number: NG00067.v18), which harmonized cerebrospinal fluid (CSF) Aβ42 data across cohorts. Further methodological information on the harmonization steps can be found in ADSP‐PHC‐FluidBiomarker_White‐Paper_2024.12.16.pdf, available via NIAGADS. In summary, fluid‐derived Aβ positivity was defined using Gaussian mixture models to determine cutoffs and represented by the harmonized variable “AT_class.” For analyses with individuals with Aβ data, we defined “Aβ‐baseline” as the first clinical assessment at age ≥50 years with an available Aβ measurement (PET and/or fluid). If both PET and fluid were available at the same qualifying visit, PET was prioritized. Time‐to‐event models in the biomarker subsample used this Aβ‐baseline as time zero.

2.5. APOE ε4 status

Apolipoprotein E (APOE) ε2/ε3/ε4 haplotypes were determined using rs429358 and rs7412. APOE ε4 status was modeled as ε4 carrier (≥1 ε4) versus non‐carrier.

2.6. Vascular risk score

Harmonized vascular risk scores were obtained from the ADSP‐PHC Cardiovascular Risk Factor Dataset (NIAGADS accession number: NG00067.v18). They were computed as the first PC summarizing cardiovascular risk indicators (binary history of hypertension, diabetes, heart disease, and stroke, as well as continuous body mass index) and represented by the harmonized variable “PHC_PC1_All.” Further methodological information can be found in ADSP‐PHC‐CardiovascularRisk_README_2024.12.16.docx, available via NIAGADS.

2.7. Statistical analyses

Kaplan–Meier methods were used to estimate the 10‐year cumulative incidence of cognitive impairment under right‐censoring and to visualize unadjusted differences across PRS tertiles (high vs low). We modeled progression from CU status to incident cognitive impairment (MCI or AD dementia) using Cox proportional hazards models, with ADHD‐PRS as a continuous predictor. Time was measured from baseline to first incident diagnosis, censored at last follow‐up or 10 years. This analytic window was selected to limit the influence of less stable estimates from sparse later follow‐up. Primary models adjusted for baseline age, sex, ancestry (first three PCs), and cohort. We then fitted three additional sensitivity models, each adding one of the following variables: education, vascular risk, or APOE ε4 status. To evaluate specificity, we repeated these models using PRS for other psychiatric disorders (ASD, BD, MDD, and SCZ) as predictors, applying the same modeling framework. In the biomarker subsample, we fitted analogous Cox models anchored at Aβ‐baseline and tested effect modification by Aβ status via an ADHD‐PRS × Aβ interaction term, additionally reporting Aβ‐stratified estimates. For continuous neuropsychological outcomes, we fitted linear mixed‐effects models to harmonized domain composites with fixed effects for ADHD‐PRS, time from baseline, their interaction (PRS × time), and covariates (baseline age, sex, ancestry PCs, and cohort), with participant‐specific random intercepts and random slopes for time. The PRS main effect was interpreted as baseline differences and the PRS × time interaction as differential rate of change. To evaluate whether the primary findings were influenced by diagnostic reversion after incident MCI/AD dementia, we identified participants who developed MCI or AD dementia and subsequently returned to CU status during the 10‐year analytic follow‐up window. We classified these participants as sustained CU reverters or transient CU reverters. Sustained CU reverters were defined as participants who returned to CU after incident MCI/AD dementia and remained CU at all subsequent visits. Transient CU reverters were defined as participants who returned to CU status after incident MCI/AD dementia but later reconverted to MCI or AD dementia. We then performed a sensitivity analysis in which sustained CU reverters were treated as nonevents and censored at their last available visit, whereas transient CU reverters were retained as events. All analyses were conducted in R (version 4.4.1) with two‐sided α = 0.05.

3. RESULTS

We analyzed data from 7761 CU participants from eight cohorts (Table 1). Mean (SD) baseline age was 70.97 (9.72) years and 5252 (67.7%) were women. Across follow‐up, we evaluated a total of 38,087 diagnostic assessments. Participants contributed a mean (SD) of 4.91 (2.88) diagnostic visits, and the median (interquartile range) observation time to incident cognitive impairment or censoring was 5.08 (1.99 to 9.47) years.

TABLE 1.

Baseline demographic and clinical characteristics of the population.

Characteristic	Total sample (N = 7761)	ADNI (N = 481)	EFIGA (N = 790)	MAP (N = 190)	NACC (N = 4129)	ROS (N = 219)	TARCC (N = 625)	WHICAP (N = 357)	WRAP (N = 970)
Age at baseline, mean (SD), years	70.97 (9.72)	74.27 (5.77)	69.76 (8.18)	81.79 (5.18)	74.39 (7.56)	76.18 (6.33)	63.65 (7.79)	74.75 (5.24)	55.79 (4.76)
Sex, no. (%)
Female	5252 (67.7)	250 (52.0)	516 (65.3)	133 (70.0)	2820 (68.3)	142 (64.8)	452 (72.3)	266 (74.5)	673 (69.4)
Male	2509 (32.3)	231 (48.0)	274 (34.7)	57 (30.0)	1309 (31.7)	77 (35.2)	173 (27.7)	91 (25.5)	297 (30.6)
Race, no. (%)
American Indian or Alaska Native	49 (0.6)	1 (0.2)	0 (0.0)	0 (0.0)	40 (1.0)	0 (0.0)	3 (0.5)	5 (1.4)	0 (0.0)
Asian	55 (0.7)	8 (1.7)	1 (0.1)	0 (0.0)	12 (0.3)	0 (0.0)	32 (5.1)	0 (0.0)	2 (0.2)
Black or African American	1282 (16.5)	30 (6.2)	45 (5.7)	0 (0.0)	1068 (25.9)	0 (0.0)	0 (0.0)	95 (26.6)	44 (4.5)
Native Hawaiian or Other Pacific Islander	1 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	1 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
White	5467 (70.4)	436 (90.6)	116 (14.7)	190 (100.0)	2940 (71.2)	219 (100.0)	590 (94.4)	53 (14.8)	923 (95.2)
Other/unknown/more than one race	907 (11.7)	6 (1.2)	628 (79.5)	0 (0.0)	68 (1.6)	0 (0.0)	0 (0.0)	204 (57.2)	1 (0.1)
Ethnicity, no. (%)
Hispanic or Latino	2175 (28.0)	15 (3.1)	775 (98.1)	0 (0.0)	463 (11.2)	1 (0.5)	625 (100.0)	288 (80.7)	8 (0.8)
Not Hispanic or Latino	5560 (71.6)	462 (96.0)	1 (0.1)	190 (100.0)	3663 (88.7)	218 (99.5)	0 (0.0)	65 (18.2)	961 (99.1)
Not available	26 (0.3)	4 (0.8)	14 (1.8)	0 (0.0)	3 (0.1)	0 (0.0)	0 (0.0)	4 (1.1)	1 (0.1)
Education, mean (SD), years	14.18 (4.76)	16.45 (2.65)	8.25 (5.77)	14.79 (2.88)	15.48 (3.20)	18.53 (3.46)	10.28 (4.71)	7.41 (4.43)	16.27 (2.78)
APOE ε4, no. carriers (%)	2382 (30.7)	139 (28.9)	213 (27.0)	33 (17.4)	1346 (32.6)	52 (23.7)	123 (19.7)	90 (25.2)	386 (39.8)
Vascular risk factors
Vascular risk score, mean (SD)	0.08 (1.19)	−0.93 (0.73)	−0.18 (1.03)	0.39 (1.18)	0.18 (1.07)	0.63 (1.25)	0.37 (1.33)	0.98 (1.31)	−0.36 (1.29)
Hypertension, no. (%)	6358 (81.9)	243 (50.5)	663 (83.9)	184 (96.8)	3836 (92.9)	211 (96.3)	489 (78.2)	348 (97.5)	384 (39.6)
Diabetes, no. (%)	1699 (21.9)	18 (3.7)	205 (25.9)	40 (21.1)	856 (20.7)	52 (23.7)	240 (38.4)	154 (43.1)	134 (13.8)
Heart disease, no. (%)	2179 (28.1)	2 (0.4)	82 (10.4)	69 (36.3)	1335 (32.3)	105 (47.9)	110 (17.6)	172 (48.2)	304 (31.3)
Stroke, no. (%)	577 (7.4)	8 (1.7)	30 (3.8)	31 (16.3)	254 (6.2)	47 (21.5)	65 (10.4)	82 (23.0)	60 (6.2)
BMI (SD)	27.65 (5.65)	27.16 (4.79)	26.42 (4.97)	26.45 (5.45)	27.19 (5.40)	26.39 (5.17)	31.04 (6.29)	27.54 (4.89)	29.23 (6.41)
Composite score at baseline, mean (SD)
Memory composite	0.66 (0.53)	0.83 (0.46)	0.34 (0.56)	0.35 (0.38)	0.67 (0.49)	0.46 (0.37)	0.52 (0.50)	0.28 (0.51)	1.08 (0.44)
Executive function composite	0.44 (0.81)	0.75 (0.48)	−0.58 (0.88)	0.47 (0.48)	0.57 (0.64)	0.54 (0.55)	−0.14 (0.62)	−0.53 (0.68)	1.10 (0.60)
Language composite	0.45 (0.71)	0.78 (0.53)	−0.40 (0.64)	0.34 (0.45)	0.69 (0.57)	0.39 (0.53)	−0.13 (0.55)	−0.37 (0.54)	0.67 (0.55)

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Note: Values are mean (SD) for continuous variables and no. (%) for categorical variables. Percentages are calculated within each cohort. Age and education are in years; BMI is in kg/m². APOE ε4 carriers are participants with ≥1 ε4 allele. Baseline memory, executive function, and language composites represent harmonized cognitive composite scores at baseline (higher values indicate better performance). Vascular risk score reflects a standardised composite vascular risk index (described in Methods). BMI missingness: TARCC missing n = 3; EFIGA missing n = 3.

Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative; EFIGA, Estudio Familiar de Influencia Genética en Alzheimer; MAP, Rush Memory and Aging Project; NACC, National Alzheimer's Coordinating Center; ROS, Religious Orders Study; TARCC, Texas Alzheimer's Research and Care Consortium; WHICAP, Washington Heights/Inwood Columbia Aging Project; WRAP, Wisconsin Registry for Alzheimer's Prevention.

ADHD‐PRS data distribution is displayed in Figure S1. Associations between ADHD‐PRS and baseline characteristics are shown in Table S3. Individuals with higher ADHD‐PRSs were more likely to be female, have lower educational attainment, and have higher vascular risk scores. There was no significant association between ADHD‐PRS and baseline age or APOE ε4 status.

3.1. ADHD‐PRS and incident cognitive impairment

Over 10 years of follow‐up, 1347 participants developed incident cognitive impairment, including 1163 transitions from CU to MCI and 184 transitions from CU to AD dementia. The Kaplan–Meier estimated 10‐year cumulative incidence of cognitive impairment was 33.8% (95% CI: 30.7 to 36.8) in the upper ADHD‐PRS tertile and 23.4% (95% CI: 21.4 to 25.4) in the lower tertile (log‐rank p < 0.0001). This corresponds to an absolute difference of 10.4 percentage points, or approximately 10 additional cases per 100 individuals over the course of 10 years. Kaplan–Meier curves by ADHD‐PRS strata are shown in Figure 1A. In adjusted Cox models, higher ADHD‐PRS was associated with greater risk of incident cognitive impairment per 1‐SD increase in ADHD‐PRS (hazard ratio [HR] = 1.11, 95% CI: 1.02 to 1.20, P = 0.014). Cohort‐specific estimates were not statistically significant and were broadly consistent in direction (Figure 1B), with HR > 1 in all cohorts except one. Results were similar after additional adjustment for education, APOE ε4 status, or vascular risk score (Figure 1C). In sensitivity analyses, PRSs for ASD, BD, MDD, and SCZ were not significantly associated with incident cognitive impairment in adjusted models (Figure 2A–H). Although Kaplan–Meier curves suggested separation for MDD‐PRS (Figure 2E), adjusted Cox models did not show a significant association (Figure 2F).

ADHD‐PRS and risk of incident cognitive impairment. Kaplan–Meier curves and Cox proportional hazards models evaluating associations between ADHD‐PRS and incident cognitive impairment (MCI or AD dementia) for up to 10 years of follow‐up. (A) Kaplan–Meier curves comparing lower versus upper ADHD‐PRS tertile. Shaded bands indicate 95% CIs, and the log‐rank p values compare tertiles. (B) Overall and cohort‐specific aHRs for ADHD‐PRS. The overall estimate is adjusted for ancestry, baseline age, sex, and cohort, whereas cohort‐specific estimates are adjusted for ancestry, baseline age, and sex. (C) Adjusted associations between ADHD‐PRS and incident cognitive impairment as aHRs per 1‐SD increase in PRS from sequential Cox models with covariates as listed in the figure. Cox models additionally adjusted for ancestry (using the first three PCs) and cohort. ADHD‐PRS, attention‐deficit/hyperactivity disorder polygenic risk score; aHR, adjusted hazard ratio; CI, confidence interval; AD, Alzheimer's disease; MCI, mild cognitive impairment; PC, principal component; ADNI, Alzheimer's Disease Neuroimaging Initiative; EFIGA, Estudio Familiar de Influencia Genética en Alzheimer; MAP, Rush Memory and Aging Project; ROS, Rush Religious Orders Study; NACC, National Alzheimer's Coordinating Center; TARCC, Texas Alzheimer's Research and Care Consortium; WHICAP, Washington Heights/Inwood Columbia Aging Project; WRAP, Wisconsin Registry for Alzheimer's Prevention.

PRS for other psychiatric disorders and risk of incident cognitive impairment. Kaplan–Meier curves and Cox proportional hazards models evaluating associations between psychiatric PRS and incident cognitive impairment (MCI or AD dementia) during up to 10 years of follow‐up. (A, C, E, and G) Kaplan–Meier curves comparing lower versus upper PRS tertile for ASD‐PRS, BD‐PRS, MDD‐PRS, and SCZ‐PRS, respectively. Shaded bands indicate 95% CIs, and the log‐rank p values compare tertiles. (B, D, F, and H) show adjusted associations between each PRS and incident cognitive impairment as aHRs per 1‐SD increase in PRS from sequential Cox models with covariates as listed in the figure. Cox models additionally adjusted for ancestry (using the first three PCs) and cohort. PRS, polygenic risk score; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; SCZ, schizophrenia; aHR, adjusted hazard ratio; CI, confidence interval; AD, Alzheimer's disease; MCI, mild cognitive impairment; PC, principal component; ADNI, Alzheimer's Disease Neuroimaging Initiative; EFIGA, Estudio Familiar de Influencia Genética en Alzheimer; MAP, Rush Memory and Aging Project; ROS, Rush Religious Orders Study; NACC, National Alzheimer's Coordinating Center; TARCC, Texas Alzheimer's Research and Care Consortium; WHICAP, Washington Heights/Inwood Columbia Aging Project; WRAP, Wisconsin Registry for Alzheimer's Prevention.

Among the 1347 participants with incident MCI or AD dementia, 458 (34.0%) later returned to CU status within the analytic follow‐up window. Of these, 299 (65.3%) were sustained CU reverters who remained CU at all subsequent follow‐up visits, and 159 (34.7%) were transient CU reverters who later reconverted to MCI or AD dementia. After treating sustained CU reverters as non‐events and retaining transient CU reverters as events, 1048 events remained. In this analysis, higher ADHD‐PRS remained associated with incident cognitive impairment (HR = 1.11, 95% CI: 1.01 to 1.22; P = 0.027).

3.2. Aβ subsample analyses

A total of 1146 participants had available Aβ status (ADNI n = 411, NACC n = 436, WRAP n = 299). Most Aβ measures were PET‐based (n = 766). Overall, 815 (71.1%) participants were Aβ‐negative and 331 (28.9%) were Aβ‐positive. Demographic and clinical variables at Aβ‐baseline can be found in Table S4. ADHD‐PRS was not associated with Aβ positivity after adjustment for age at baseline, sex, study, and ancestry (odds ratio [OR] = 1.04, 95% CI: 0.88 to 1.23, p = 0.65). In Cox models anchored at Aβ‐baseline, ADHD‐PRS remained associated with incident cognitive impairment (HR = 1.32, 95% CI: 1.09 to 1.60, p = 0.005; Figure 3A,B). There was no evidence of effect modification by Aβ status (ADHD‐PRS × Aβ interaction HR = 0.95, 95% CI: 0.69 to 1.30, p = 0.75). Aβ‐stratified estimates are shown in Figure 3C–F.

ADHD‐PRS and incident cognitive impairment by Aβ status. Kaplan–Meier curves and Cox proportional hazards models evaluating associations between ADHD‐PRS and incident cognitive impairment (MCI or AD dementia) among participants with available Aβ biomarkers. Baseline was defined as the visit at which Aβ status was ascertained. (A, C, and E) Kaplan–Meier curves comparing lower and upper ADHD‐PRS tertile in total biomarker subsample, Aβ‐negative individuals, and Aβ‐positive individuals, respectively. Shaded bands indicate 95% CIs, and log‐rank p value compares tertiles. (B, D, and F) Overall and cohort‐specific Cox model estimates, reported as aHRs per 1‐SD increase in ADHD‐PRS with 95% CIs. Pooled Cox models were adjusted for age, sex, ancestry (using the first three PCs), and cohort. Cohort‐specific Cox models were adjusted for age, sex, and ancestry. ADHD‐PRS, attention‐deficit/hyperactivity disorder polygenic risk score; Aβ, amyloid beta; aHR, adjusted hazard ratio; CI, confidence interval; MCI, mild cognitive impairment; AD, Alzheimer's disease; PC, principal component; ADNI, Alzheimer's Disease Neuroimaging Initiative; NACC, National Alzheimer's Coordinating Center; WRAP, Wisconsin Registry for Alzheimer's Prevention.

3.3. ADHD‐PRS and cognition

In linear mixed‐effects models of harmonized cognitive composites, higher ADHD‐PRS was associated with lower baseline performance in memory (β = −0.031, 95% CI: −0.046 to −0.016, p < 0.0001), executive function (β = −0.056, 95% CI: −0.075 to −0.037, p < 0.0001), and language (β = −0.029, 95% CI: −0.046 to −0.012, p = 0.0007) (Table S5). For longitudinal change, ADHD‐PRS was not associated with the rate of change in memory (PRS × time β = 0.0017, 95% CI: −0.00039 to 0.00385, p = 0.11) or language (PRS × time β = 0.00027, 95% CI: −0.00168 to 0.00221, p = 0.79) (Figure S2). However, there was a significant interaction between ADHD‐PRS and time for executive function (PRS × time β = 0.0040, 95% CI: 0.0021 to 0.0058, p < 0.0001), indicating that individuals with higher ADHD‐PRS experienced a less steep decline in executive function over time.

4. DISCUSSION

This prospective multicohort analysis of 7761 CU older adults provides evidence that higher ADHD‐PRS, a probabilistic estimate of genetic risk for ADHD, is associated with greater risk of clinically ascertained incident cognitive impairment over 10 years. Baseline Aβ status did not modify this risk in the subsample with available biomarker data. The specificity of this association to ADHD, in contrast to the null findings for four other psychiatric disorders tested within the same analytic framework, suggests that higher ADHD‐PRS confers a distinct and previously underappreciated contribution to late‐life cognitive vulnerability.

Our primary finding that higher ADHD‐PRS associates with progression from CU to MCI/AD aligns with epidemiological studies linking ADHD diagnosis to later‐life cognitive impairment and dementia. ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰ Unlike prior observational studies, which relied largely on administrative records, the ADHD‐PRS provides a measure derived from inherited common genetic variation that is not dependent on healthcare utilization or clinical recognition.This is particularly relevant in older cohorts, where ADHD is likely underdiagnosed. ³² Furthermore, MCI and AD dementia were ascertained through standardized clinical evaluations, providing a more rigorous approach than electronic health record definitions. Participants in the highest ADHD‐PRS tertile had approximately 10 additional cases of cognitive impairment per 100 individuals over 10 years compared with the lowest tertile. This provides a rationale for further work to determine whether individuals with higher ADHD‐PRS or clinically recognized ADHD warrant heightened vigilance for cognitive decline.

Notably, we found no evidence that PRSs for other psychiatric disorders (ASD, BD, MDD, and SCZ) were associated with incident cognitive impairment. This is relevant since psychiatric disorders show substantial genetic overlap, ³³ raising the possibility that a shared transdiagnostic liability could underlie later cognitive decline. Our results do not support that explanation in this sample. Additionally, ADHD is not the only psychiatric condition proposed to increase risk for cognitive decline. The evidence is more extensive for MDD, ³⁴ and SCZ ³⁵ and BD ³⁶ have also been associated with dementia risk, with more limited evidence for ASD. ³⁷ Importantly, our study evaluated genetic risk captured by PRS rather than clinical diagnoses. For some disorders, particularly MDD, PRS may capture a smaller fraction of risk, ²⁷ potentially contributing to the null findings. Thus, although only ADHD‐PRS was associated with cognitive impairment in this analytic framework, these results do not preclude clinical associations between other psychiatric disorders and late‐life cognitive decline.

In the biomarker subsample, ADHD‐PRS was not associated with Aβ positivity, and Aβ did not modify the association between ADHD‐PRS and clinical progression. These findings support that higher ADHD‐PRS may be associated with cognitive vulnerability through mechanisms that are at least partially independent of baseline Aβ pathology. Although prior studies reported mixed findings regarding ADHD‐PRS × Aβ interactions, ¹⁸ , ¹⁹ our results do not support a markedly different effect of ADHD‐PRS in individuals above conventional Aβ positivity thresholds. Still, because Aβ accumulation is a continuous process, dichotomizing Aβ for clinical classification may obscure more subtle susceptibility effects. ³⁸ It remains possible that individuals with higher ADHD‐PRSs experience adverse cognitive consequences at lower levels of Aβ burden, but this would need to be explored in future work.

Higher ADHD‐PRS was associated with lower cognitive performance across memory, executive function, and language domains, without evidence of faster decline over time. Together with prior research showing an association between ADHD‐PRS and lower cognitive performance in structured testing, ¹⁵ , ¹⁶ these findings are consistent with lifelong traits that predate the observation window. Importantly, cognitive performance declined over time across all ADHD‐PRS strata, and individuals with higher ADHD‐PRS consistently performed worse throughout follow‐up. The less steep executive function decline observed among individuals with higher ADHD‐PRS likely reflects a floor effect, whereby lower baseline performance limits measurable further decline rather than indicating a protective trajectory. These findings suggest that persistently lower performance in individuals with higher ADHD‐PRS may place them closer to the threshold at which clinical impairment becomes detectable.

Currently, the mechanisms underlying the association of ADHD and ADHD‐PRS with cognitive impairment in older age remain unclear. Lower educational attainment and higher vascular risk have been proposed as shared factors, but our findings, along with those of prior studies, ⁶ , ⁷ , ⁹ , ¹⁸ suggest that they do not fully explain the association. ADHD may instead confer risk through multiple interacting processes across the lifespan, including long‐standing differences in attention and executive‐control networks ³⁹ that could reduce cognitive reserve, as well as the cumulative burden of health‐related exposures more common in ADHD, such as sleep disturbance, ⁴⁰ substance use, ⁴¹ traumatic brain injury, ⁴² obesity, ⁴³ and poorer physical health. ⁴⁴ Disentangling these pathways is challenging, particularly because many relevant exposures fluctuate over decades and may not be well captured in older‐age cohorts. Long‐term prospective studies following childhood‐diagnosed ADHD into later life, with repeated neurocognitive, comorbidity, and biomarker assessments, will be important to clarify whether risk is driven primarily by cumulative exposures, reduced reserve, non‐AD neuropathologies, or their combination.

Findings from this study should be interpreted with consideration of the following limitations. ADHD‐PRS is an indirect measure of common‐variant genetic risk that explains only a modest proportion of variance in clinical ADHD and ADHD‐related traits, estimated at approximately 0.7% to 5.5%, ¹⁶ , ⁴⁵ and therefore does not capture the full clinical phenotype in older adults. The ADHD‐PRS captures only one component of ADHD risk and does not directly account for rare genetic variation, environmental exposures, or gene–environment interplay. In addition, the ADHD‐PRS was derived from a GWAS that was predominantly composed of individuals of European ancestry, ¹⁵ and the generalizability across diverse ancestral groups may be limited. Volunteer‐based cohorts may preferentially include individuals with less severe psychiatric illness, ⁴⁶ and some cohorts included here excluded or may have underrepresented individuals with significant psychiatric or neurodevelopmental conditions. This may affect the distribution of psychiatric PRS in the analytic sample relative to the general older adult population, particularly reducing representation of individuals at the highest psychiatric common‐variant genetic risk. Biomarker data were limited relative to the full sample. Although we adjusted for vascular risk factors using a harmonized score, we did not systematically adjust for all medical comorbidities that could influence cognitive outcomes, and residual confounding remains possible. Due to data limitations, we were unable to evaluate longitudinal changes in Aβ in relation to ADHD‐PRS and cognitive decline. Finally, since ADHD symptoms can overlap with those of MCI, the possibility that individuals with high ADHD‐PRS may have been misdiagnosed with MCI should be considered.

In conclusion, ADHD‐PRS was associated with incident late‐life cognitive impairment in pooled multicohort analyses of older adults, through mechanisms distinct from AD Aβ pathways. These findings support ADHD genetic risk as a potential contributor to cognitive aging and suggest that the lifespan trajectory of ADHD may intersect with dementia risk through pathways that remain to be clarified. Individuals with ADHD, including older adults in whom the condition has gone unrecognized, may represent an underidentified at‐risk population for whom earlier cognitive monitoring and proactive management of modifiable risk factors could be beneficial. Future studies integrating ADHD phenotyping with biomarker panels and mechanistic analyses will be important for translating these findings into prevention strategies.

CONFLICT OF INTEREST STATEMENT

Tharick A. Pascoal serves on a Data Safety Monitoring Board for NIH award 5 R01 AG084834 based at the University of Cincinnati. He also has served on a scientific advisory board at Johnson & Johnson. Luis Augusto Rohde has received grant or research support from, served as a consultant to, and served on the speakers’ bureau of Abdi Ibrahim, Abbott, Aché, Adium, Apsen, Bial, Cellera, EMS, Hypera Pharma, Knight Therapeutics, Libbs, Medice, Novartis/Sandoz, Pfizer/Upjohn/Viatris, Shire/Takeda, and Torrent in the last three years. The ADHD and Juvenile Bipolar Disorder Outpatient Programs chaired by Luis Augusto Rohde have received unrestricted educational and research support from the following pharmaceutical companies in the last three years: Novartis/Sandoz and Shire/Takeda. Luis Augusto Rohde has received authorship royalties from Oxford Press and ArtMed. Samuele Cortese has declared reimbursement for travel and accommodation expenses from the Association for Child and Adolescent Central Health (ACAMH) in relation to lectures delivered for ACAMH, the Canadian AADHD Alliance Resource, the British Association of Psychopharmacology, Healthcare Convention and CCM Group team for educational activity on ADHD, and has received honoraria from Medice. Douglas Teixeira Leffa and Brooke S. G. Molina received travel awards to attend the American Professional Society of ADHD and Related Disorders (APSARD) 2024, 2025, and 2026 Annual Meetings, and to attend the 10th World Congress on ADHD. All other authors report no conflicts of interest. Author disclosures are available in the Supporting Information.

CONSENT

Across all cohorts, written informed consent was provided by participants, and research was conducted in accordance with approved Institutional Review Board protocols. Secondary analysis of these data was approved by the University of Pittsburgh IRB.

Supporting information

Supporting Information

ALZ-22-e71770-s002.pdf^{(2MB, pdf)}

Supporting Information

ALZ-22-e71770-s001.docx^{(312.9KB, docx)}

ACKNOWLEDGMENTS

DA is supported by the National Institute on Aging (NIA) (K01‐073584). TAP is supported by the following National Institutes of Health (NIH) awards: 5 R01 AG073267; 5 R01 AG075336. GBN, BB, and GP are supported by the Alzheimer's Association Research Fellowship Program (AARFD‐23‐1150249; AARFD‐22‐974627; 24AARFD‐1243899). BSGM is supported by the Alzheimer's Association (AARG‐NTF‐23‐1151371). SC, National Institute for Health and Care Research (NIHR) Research Professor (NIHR303122), is funded by the NIHR for this research project. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS, or the UK Department of Health and Social Care. SC is also supported by NIHR grants NIHR203684, NIHR203035, NIHR130077, NIHR128472, RP‐PG‐0618‐20003 and by grant 101095568‐HORIZONHLTH‐ 2022‐DISEASE‐07‐03 from the European Research Executive Agency. (www.fnih.org. https://neurogenomics.wustl.edu/)

The Alzheimer's Disease Sequencing Project (ADSP) is comprised of two Alzheimer's Disease (AD) genetics consortia and three National Human Genome Research Institute (NHGRI) funded Large Scale Sequencing and Analysis Centers (LSAC). The two AD genetics consortia are the Alzheimer's Disease Genetics Consortium (ADGC) funded by NIA (U01 AG032984), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by NIA (R01 AG033193), the National Heart, Lung, and Blood Institute (NHLBI), other National Institute of Health (NIH) institutes and other foreign governmental and non‐governmental organizations. The Discovery Phase analysis of sequence data is supported through UF1AG047133 (to Drs. Schellenberg, Farrer, Pericak‐Vance, Mayeux, and Haines); U01AG049505 to Dr. Seshadri; U01AG049506 to Dr. Boerwinkle; U01AG049507 to Dr. Wijsman; and U01AG049508 to Dr. Goate and the Discovery Extension Phase analysis is supported through U01AG052411 to Dr. Goate, U01AG052410 to Dr. Pericak‐Vance and U01 AG052409 to Drs. Seshadri and Fornage.

Sequencing for the Follow Up Study (FUS) is supported through U01AG057659 (to Drs. PericakVance, Mayeux, and Vardarajan) and U01AG062943 (to Drs. Pericak‐Vance and Mayeux). Data generation and harmonization in the Follow‐up Phase is supported by U54AG052427 (to Drs. Schellenberg and Wang). The FUS Phase analysis of sequence data is supported through U01AG058589 (to Drs. Destefano, Boerwinkle, De Jager, Fornage, Seshadri, and Wijsman), U01AG058654 (to Drs. Haines, Bush, Farrer, Martin, and Pericak‐Vance), U01AG058635 (to Dr. Goate), RF1AG058066 (to Drs. Haines, Pericak‐Vance, and Scott), RF1AG057519 (to Drs. Farrer and Jun), R01AG048927 (to Dr. Farrer), and RF1AG054074 (to Drs. Pericak‐Vance and Beecham).

The ADGC cohorts include: Adult Changes in Thought (ACT) (U01 AG006781, U19 AG066567), the Alzheimer's Disease Research Centers (ADRC) (P30 AG062429, P30 AG066468, P30 AG062421, P30 AG066509, P30 AG066514, P30 AG066530, P30 AG066507, P30 AG066444, P30 AG066518, P30 AG066512, P30 AG066462, P30 AG072979, P30 AG072972, P30 AG072976, P30 AG072975, P30 AG072978, P30 AG072977, P30 AG066519, P30 AG062677, P30 AG079280, P30 AG062422, P30 AG066511, P30 AG072946, P30 AG062715, P30 AG072973, P30 AG066506, P30 AG066508, P30 AG066515, P30 AG072947, P30 AG072931, P30 AG066546, P20 AG068024, P20 AG068053, P20 AG068077, P20 AG068082, P30 AG072958, P30 AG072959), the Chicago Health and Aging Project (CHAP) (R01 AG11101, RC4 AG039085, K23 AG030944), Indiana Memory and Aging Study (IMAS) (R01 AG019771), Indianapolis Ibadan (R01 AG009956, P30 AG010133), the Memory and Aging Project (MAP) (R01 AG17917), Mayo Clinic (MAYO) (R01 AG032990, U01 AG046139, R01 NS080820, RF1 AG051504, P50 AG016574), Mayo Parkinson's Disease controls (NS039764, NS071674, 5RC2HG005605), University of Miami (R01 AG027944, R01 AG028786, R01 AG019085, IIRG09133827, A2011048), the Multi‐Institutional Research in Alzheimer's Genetic Epidemiology Study (MIRAGE) (R01 AG09029, R01 AG025259), the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD) (U24 AG021886), the National Institute on Aging Late Onset Alzheimer's Disease Family Study (NIA‐ LOAD) (U24 AG056270), the Religious Orders Study (ROS) (P30 AG10161, R01 AG15819), the Texas Alzheimer's Research and Care Consortium (TARCC) (funded by the Darrell K Royal Texas Alzheimer's Initiative), Vanderbilt University/Case Western Reserve University (VAN/CWRU) (R01 AG019757, R01 AG021547, R01 AG027944, R01 AG028786, P01 NS026630, and Alzheimer's Association), the Washington Heights‐Inwood Columbia Aging Project (WHICAP) (RF1 AG054023), the University of Washington Families (VA Research Merit Grant, NIA: P50AG005136, R01AG041797, NINDS: R01NS069719), the Columbia University Hispanic Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA) (RF1 AG015473), the University of Toronto (UT) (funded by Wellcome Trust, Medical Research Council, Canadian Institutes of Health Research), and Genetic Differences (GD) (R01 AG007584). The CHARGE cohorts are supported in part by National Heart, Lung, and Blood Institute (NHLBI) infrastructure grant HL105756 (Psaty), RC2HL102419 (Boerwinkle) and the neurology working group is supported by the National Institute on Aging (NIA) R01 grant AG033193.

The CHARGE cohorts participating in the ADSP include the following: Austrian Stroke Prevention Study (ASPS), ASPS‐Family study, and the Prospective Dementia Registry‐Austria (ASPS/PRODEM‐Aus), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). ASPS is funded by the Austrian Science Fond (FWF) grant number P20545‐P05 and P13180 and the Medical University of Graz. The ASPS‐Fam is funded by the Austrian Science Fund (FWF) project I904), the EU Joint Programme – Neurodegenerative Disease Research (JPND) in frame of the BRIDGET project (Austria, Ministry of Science) and the Medical University of Graz and the Steiermärkische Krankenanstalten Gesellschaft. PRODEM‐Austria is supported by the Austrian Research Promotion agency (FFG) (Project No. 827462) and by the Austrian National Bank (Anniversary Fund, project 15435. ARIC research is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data in ARIC is collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD), and with previous brain MRI examinations funded by R01‐HL70825 from the NHLBI. CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grants U01HL080295 and U01HL130114 from the NHLBI with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629, R01AG15928, and R01AG20098 from the NIA. FHS research is supported by NHLBI contracts N01‐HC‐25195 and HHSN268201500001I. This study was also supported by additional grants from the NIA (R01s AG054076, AG049607 and AG033040 and NINDS (R01 NS017950). The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG‐CT‐2006‐01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007‐2013)/grant agreement HEALTH‐F4‐ 2007‐201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2‐CT‐2002‐ 01254). High‐throughput analysis of the ERF data was supported by a joint grant from the Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO‐RFBR 047.017.043). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the municipality of Rotterdam. Genetic data sets are also supported by the Netherlands Organization of Scientific Research NWO Investments (175.010.2005.011, 911‐03‐012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014‐93‐015; RIDE2), and the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project 050‐060‐810. All studies are grateful to their participants, faculty and staff. The content of these manuscripts is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Health and Human Services.

The FUS cohorts include: the Alzheimer's Disease Research Centers (ADRC) (P30 AG062429, P30 AG066468, P30 AG062421, P30 AG066509, P30 AG066514, P30 AG066530, P30 AG066507, P30 AG066444, P30 AG066518, P30 AG066512, P30 AG066462, P30 AG072979, P30 AG072972, P30 AG072976, P30 AG072975, P30 AG072978, P30 AG072977, P30 AG066519, P30 AG062677, P30 AG079280, P30 AG062422, P30 AG066511, P30 AG072946, P30 AG062715, P30 AG072973, P30 AG066506, P30 AG066508, P30 AG066515, P30 AG072947, P30 AG072931, P30 AG066546, P20 AG068024, P20 AG068053, P20 AG068077, P20 AG068082, P30 AG072958, P30 AG072959), Alzheimer's Disease Neuroimaging Initiative (ADNI) (U19AG024904), Amish Protective Variant Study (RF1AG058066), Cache County Study (R01AG11380, R01AG031272, R01AG21136, RF1AG054052), Case Western Reserve University Brain Bank (CWRUBB) (P50AG008012), Case Western Reserve University Rapid Decline (CWRURD) (RF1AG058267, NU38CK000480), CubanAmerican Alzheimer's Disease Initiative (CuAADI) (3U01AG052410), Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA) (5R37AG015473, RF1AG015473, R56AG051876), Genetic and Environmental Risk Factors for Alzheimer Disease Among African Americans Study (GenerAAtions) (2R01AG09029, R01AG025259, 2R01AG048927), Gwangju Alzheimer and Related Dementias Study (GARD) (U01AG062602), Hillblom Aging Network (2014‐A‐004‐NET, R01AG032289, R01AG048234), Hussman Institute for Human Genomics Brain Bank (HIHGBB) (R01AG027944, Alzheimer's Association “Identification of Rare Variants in Alzheimer Disease”), Ibadan Study of Aging (IBADAN) (5R01AG009956), Longevity Genes Project (LGP) and LonGenity (R01AG042188, R01AG044829, R01AG046949, R01AG057909, R01AG061155, P30AG038072), Mexican Health and Aging Study (MHAS) (R01AG018016), Multi‐Institutional Research in Alzheimer's Genetic Epidemiology (MIRAGE) (2R01AG09029, R01AG025259, 2R01AG048927), Northern Manhattan Study (NOMAS) (R01NS29993), Peru Alzheimer's Disease Initiative (PeADI) (RF1AG054074), Puerto Rican 1066 (PR1066) (Wellcome Trust (GR066133/GR080002), European Research Council (340755)), Puerto Rican Alzheimer Disease Initiative (PRADI) (RF1AG054074), Reasons for Geographic and Racial Differences in Stroke (REGARDS) (U01NS041588), Research in African American Alzheimer Disease Initiative (REAAADI) (U01AG052410), the Religious Orders Study (ROS) (P30 AG10161, P30 AG72975, R01 AG15819, R01 AG42210), the RUSH Memory and Aging Project (MAP) (R01 AG017917, R01 AG42210Stanford Extreme Phenotypes in AD (R01AG060747), University of Miami Brain Endowment Bank (MBB), University of Miami/Case Western/North Carolina A&T African American (UM/CASE/NCAT) (U01AG052410, R01AG028786), Wisconsin Registry for Alzheimer's Prevention (WRAP) (R01AG027161 and R01AG054047), Mexico‐Southern California Autosomal Dominant Alzheimer's Disease Consortium (R01AG069013), Center for Cognitive Neuroscience and Aging (R01AG047649), and the A4 Study (R01AG063689, U19AG010483 and U24AG057437).

The four LSACs are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54 HG003273), the Broad Institute Genome Center (U54HG003067), The American Genome Center at the Uniformed Services University of the Health Sciences (U01AG057659), and the Washington University Genome Institute (U54HG003079). Genotyping and sequencing for the ADSP FUS is also conducted at John P. Hussman Institute for Human Genomics (HIHG) Center for Genome Technology (CGT).

Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions, and at the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD, U24AG021886) at Indiana University funded by NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Study Investigators, the NIA funded Alzheimer's Disease Centers (ADCs), and the National Alzheimer's Coordinating Center (NACC, U24AG072122) and the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA. Harmonized phenotypes were provided by the ADSP Phenotype Harmonization Consortium (ADSP‐PHC), funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716) and Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks (AI4AD, U01 AG068057). This research was supported in part by the Intramural Research Program of the National Institutes of health, National Library of Medicine. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by NIA, and other NIH institutes, and by private U.S. organizations, or foreign governmental or nongovernmental organizations.

The ADSP Phenotype Harmonization Consortium (ADSP‐PHC) is funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716). The harmonized cohorts within the ADSP‐PHC include: the Anti‐Amyloid Treatment in Asymptomatic Alzheimer's study (A4 Study), a secondary prevention trial in preclinical Alzheimer's disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public‐private‐philanthropic partnership, including funding from the National Institutes of Health‐National Institute on Aging, Eli Lilly and Company, Alzheimer's Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in‐kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer's Association and GHR Foundation. The A4 and A4‐LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women's Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer's Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de‐identified data in order to advance the quest to find a successful treatment for Alzheimer's disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: ; the Adult Changes in Thought study (ACT), U01 AG006781, U19 AG066567; Alzheimer's Disease Neuroimaging Initiative (ADNI): Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann‐La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California; Brain tissue and additional biological samples were collected from individuals who were assessed and followed in the Case Western ADC which was funded by NIA P50AG008012; Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA): 5R37AG015473, RF1AG015473, R56AG051876; the Health & Aging Brain Study – Health Disparities (HABS‐HD), supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG054073, R01AG058533, R01AG070862, P41EB015922, and U19AG078109; brain tissues were obtained from the Brain Bank at the John P. Hussman Institute for Human Genomics at the University of Miami Miller School of Medicine, with funding provided by Louis D. Scientific Prize from the Institut de France; the Indiana Alzheimer's Disease Research Center (IADRC) supported by National Institutes of Health (NIH) grant P30AG072976; the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE), which was supported by a grant from Ministry of Science, ICT and Future Planning (Grant No: NRF‐2014M3C7A1046042); Memory & Aging Project at Knight Alzheimer's Disease Research Center (MAP at Knight ADRC): The Memory and Aging Project at the Knight‐ADRC (Knight‐ADRC). This work was supported by the National Institutes of Health (NIH) grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: ) and the Departments of Neurology and Psychiatry at Washington University School of Medicine; the University of Miami Brain Endowment Bank (Miami Brain Bank); National Alzheimer's Coordinating Center (NACC): The NACC database is funded by NIA/NIH Grant U24 AG072122. SCAN is a multi‐institutional project that was funded as a U24 grant (AG067418) by the National Institute on Aging in May 2020. Data collected by SCAN and shared by NACC are contributed by the NIA‐funded ADRCs as follows: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD); National Institute on Aging Alzheimer's Disease Family Based Study (NIA‐AD FBS): U24 AG056270; Religious Orders Study (ROS): P30AG10161,R01AG15819, R01AG42210; Memory and Aging Project (MAP ‐ Rush): R01AG017917, R01AG42210; Minority Aging Research Study (MARS): R01AG22018, R01AG42210; the Texas Alzheimer's Research and Care Consortium (TARCC), funded by the Darrell K Royal Texas Alzheimer's Initiative, directed by the Texas Council on Alzheimer's Disease and Related Disorders; Washington Heights/Inwood Columbia Aging Project (WHICAP): RF1 AG054023;and Wisconsin Registry for Alzheimer's Prevention (WRAP): R01AG027161 and R01AG054047. Additional acknowledgments include the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA.

Contributor Information

Douglas Teixeira Leffa, Email: douglas.leffa@gmail.com.

Tharick A. Pascoal, Email: pascoalt@upmc.edu.

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[alz71770-bib-0004] 4. Fluegge K, Fluegge K. Antecedent ADHD, dementia, and metabolic dysregulation: a U.S. based cohort analysis. Neurochem Int. 2018;112:255‐258. doi: 10.1016/j.neuint.2017.08.005 [DOI] [PubMed] [Google Scholar]

[alz71770-bib-0005] 5. Du Rietz E, Brikell I, Butwicka A, et al. Mapping phenotypic and aetiological associations between ADHD and physical conditions in adulthood in Sweden: a genetically informed register study. Lancet Psychiatry. 2021;8(9):774‐783. doi: 10.1016/s2215-0366(21)00171-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71770-bib-0006] 6. Dobrosavljevic M, Zhang L, Garcia‐Argibay M, et al. Attention‐deficit/hyperactivity disorder as a risk factor for dementia and mild cognitive impairment: a population‐based register study. Eur Psychiatry. 2021;65(1):1‐19. doi: 10.1192/j.eurpsy.2021.2261 [DOI] [PMC free article] [PubMed] [Google Scholar]

[alz71770-bib-0007] 7. Levine SZ, Rotstein A, Kodesh A, et al. Adult attention‐deficit/hyperactivity disorder and the risk of dementia. JAMA Netw Open. 2023;6(10):e2338088‐e2338088. doi: 10.1001/jamanetworkopen.2023.38088 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[alz71770-bib-0009] 9. Golimstok Á, Basalo MJG, Majul M, et al. Adult attention deficit‐hyperactivity disorder is associated with lewy body disease and cognitive impairment: a prospective cohort study with 15‐year follow‐up. Am J Geriatr Psychiatry. 2024;32(9):1063‐1077. doi: 10.1016/j.jagp.2024.04.005 [DOI] [PubMed] [Google Scholar]

[alz71770-bib-0010] 10. Rast JE, Rosso AL, James BD, et al. Association of neurodevelopmental conditions with Alzheimer's disease and related dementias and parkinson's disease. J Gerontol A Biol Sci Med Sci. 2026;81(4):glaf281. doi: 10.1093/gerona/glaf281 [DOI] [PMC free article] [PubMed] [Google Scholar]

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[alz71770-bib-0012] 12. Becker S, Sharma MJ, Callahan BL. ADHD and neurodegenerative disease risk: a critical examination of the evidence. Front Aging Neurosci. 2021;13:826213. doi: 10.3389/fnagi.2021.826213 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Attention‐deficit/hyperactivity disorder polygenic risk score and incidence of cognitive impairment in older adults: A prospective multicohort study

Douglas Teixeira Leffa

Guilherme Bauer‐Negrini

Livia Amaral

Bruna Bellaver

Pamela C L Ferreira

Guilherme Povala

Firoza Z Lussier

João Pedro Ferrari‐Souza

Ann D Cohen

Dana L Tudorascu

Samuele Cortese

Luis Augusto Rohde

Brooke S G Molina

Derek Archer

Tharick A Pascoal

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

RESEARCH IN CONTEXT

2. METHODS

2.1. Participants

2.2. Polygenic risk scores

2.3. Clinical and cognitive outcomes

2.4. Aβ biomarkers

2.5. APOE ε4 status

2.6. Vascular risk score

2.7. Statistical analyses

3. RESULTS

TABLE 1.

3.1. ADHD‐PRS and incident cognitive impairment

FIGURE 1.

FIGURE 2.

3.2. Aβ subsample analyses

FIGURE 3.

3.3. ADHD‐PRS and cognition

4. DISCUSSION

CONFLICT OF INTEREST STATEMENT

CONSENT

Supporting information

ACKNOWLEDGMENTS

Contributor Information

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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