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Background: Blood plasma proteins have been associated with Alzheimer’s disease (AD), but understanding which proteins are on the causal pathway remains challenging. Objective: Investigate the genetic overlap between candidate proteins and AD using polygenic risk scores (PRS) and interrogate their causal relationship using bi-directional Mendelian randomization (MR). Methods: Following a literature review, 31 proteins were selected for PRS analysis. PRS were constructed for prioritized proteins with and without the apolipoprotein E region (APOE+/–PRS) and tested for association with AD status across three cohorts (n = 6,244). An AD PRS was also tested for association with protein levels in one cohort (n = 410). Proteins showing association with AD were taken forward for MR. Results: For APOE ɛ3, apolipoprotein B-100, and C-reactive protein (CRP), protein APOE+ PRS were associated with AD below Bonferroni significance (pBonf, p

Attention-deficit/hyperactivity disorder (ADHD) persists in older age and is postulated as a risk factor for cognitive impairment and Alzheimer's Disease (AD). However, these findings rely primarily on electronic health records and can present biased estimates of disease prevalence. An obstacle to investigating age-related cognitive decline in ADHD is the absence of large-scale studies following patients with ADHD into older age. Alternatively, this study aimed to determine whether genetic liability for ADHD, as measured by a well-validated ADHD polygenic risk score (ADHD-PRS), is associated with cognitive decline and the development of AD pathophysiology in cognitively unimpaired (CU) older adults. We calculated a weighted ADHD-PRS in 212 CU individuals without a clinical diagnosis of ADHD (55-90 years). These individuals had baseline amyloid-β (Aβ) positron emission tomography, longitudinal cerebrospinal fluid (CSF) phosphorylated tau at threonine 181 (p-tau

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) persists in older age and is postulated to be a risk factor for cognitive impairment and Alzheimer’s Disease (AD). However, this notion relies exclusively on epidemiological associations, and no previous study has linked ADHD with a decline in cognitive performance in older adults or with AD progression. Therefore, this study aimed to determine whether genetic liability for ADHD, as measured by a well-validated ADHD polygenic risk score (ADHD-PRS), is associated with longitudinal cognitive decline and the development of AD pathophysiology in cognitively unimpaired (CU) older adults.MethodsWe calculated a weighted ADHD-PRS in 212 CU individuals without a clinical diagnosis of ADHD (55-90 years) using whole-genome information. These individuals had baseline amyloid-β (Aβ) positron emission tomography, as well as longitudinal cerebrospinal fluid (CSF) phosphorylated tau at threonine 181, structural magnetic resonance imaging, and cognitive assessments for up to 6 years. Linear mixed-effects models were used to test the association of ADHD-PRS with cognition and AD biomarkers.OutcomesHigher ADHD-PRS was associated with greater cognitive decline over 6 years. The combined effect between high ADHD-PRS and brain Aβ deposition on cognitive deterioration was more significant than each individually. Additionally, higher ADHD-PRS was associated with increased CSF p-tau181 levels and frontoparietal atrophy in CU Aβ-positive individuals.InterpretationOur results suggest that genetic liability for ADHD is associated with cognitive deterioration and the development of AD pathophysiology in the CU elderly. These findings indicate that ADHD-PRS might inform the risk of developing cognitive decline in this population.FundingNational Institute of Health and Brain & Behavioral Research Foundation.

Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography–mass spectrometry (age 60–64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed ‘hub’ metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = −0.10, 95% confidence interval = −0.15 to −0.052, P = 5.99 × 10−5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P

BackgroundBlood plasma proteins are modifiable and have been associated with Alzheimer’s disease (AD), but understanding which proteins are on the causal pathway remains challenging.ObjectiveInvestigate the genetic overlap between candidate proteins and AD using polygenic risk scores (PRS) and interrogate their causal relationship using bi-directional Mendelian Randomization (MR).MethodsFollowing a literature review, 31 proteins were selected for PRS analysis. PRS were constructed for prioritised proteins with and without the apolipoprotein E region (APOE+/- PRS) and tested for association with AD status across three cohorts (n=6244). An AD PRS was also tested for association with protein levels in one cohort (n=410). Proteins showing association with AD were taken forward for MR.ResultsFor APOE e3, apolipoprotein B-100, and C-reactive protein (CRP), protein APOE+ PRS were associated with AD below Bonferroni significance (pBonf, p-value ConclusionApolipoproteins and CRP PRS are associated with AD and provide a genetic signal linked to a specific, modifiable risk factor. Whilst evidence of causality was limited, this study was conducted in a moderate sample size and provides a framework for larger samples with greater statistical power.

Significance The absence of disease-modifying therapeutics for Alzheimer’s disease (AD) continues, and an understanding of early, easily accessible biomarkers to inform treatment strategies remains elusive. This study uses knowledge of blood metabolites previously associated with midlife cognition—a preclinical predictor of AD—to systematically investigate causal associations with later AD status. Given that the pathological changes underlying AD are thought to develop years before clinical manifestations of the disease, developing these findings further could hold special utility in informing early treatment intervention., There are currently no disease-modifying treatments for Alzheimer’s disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition—a preclinical predictor of AD—translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR–BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs—particularly XL.HDL.FC—as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.

Plasma phosphorylated tau at threonine-181 (P-tau181) demonstrates promise as an accessible blood-based biomarker specific to Alzheimer's Disease (AD), with levels recently demonstrating high predictive accuracy for AD-relevant pathology. The genetic underpinnings of P-tau181 levels, however, remain elusive. This study presents the first genome-wide association study of plasma P-tau181 in a total sample of 1153 participants from 2 independent cohorts. No loci, other than those within the APOE genomic region (lead variant = rs429358, beta = 0.32, p =8.44 × 10-25) demonstrated association with P-tau181 at genome-wide significance (p < 5 × 10-08), though rs60872856 on chromosome 2 came close (beta = -0.28, p = 3.23 × 10-07, nearest gene=CYTIP). As the APOE e4 allele is already a well-established genetic variant associated with AD, this study found no evidence of novel genetic associations relevant to plasma P-tau181, though presents rs60872856 on chromosome 2 as a candidate locus to be further evaluated in future larger size GWAS.

BACKGROUNDPhysiological differences between males and females could contribute to the development of AD. Here, we examined metabolic pathways that may lead to precision medicine initiatives.METHODSWe explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, CSF biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites’ discriminatory performance in AD.RESULTSIn females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (AUC = 0.83, SE = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046).CONCLUSIONSMetabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, associated to females, paving the way to personalised treatment.

Background Recent studies suggest that plasma phosphorylated tau181 (p-tau181) is a highly specific biomarker for Alzheimer’s disease (AD)-related tau pathology. It has great potential for the diagnostic and prognostic evaluation of AD, since it identifies AD with the same accuracy as tau PET and CSF p-tau181 and predicts the development of AD dementia in cognitively unimpaired (CU) individuals and in those with mild cognitive impairment (MCI). Plasma p-tau181 may also be used as a biomarker in studies exploring disease pathogenesis, such as genetic or environmental risk factors for AD-type tau pathology. The aim of the present study was to investigate the relation between polygenic risk scores (PRSs) for AD and plasma p-tau181. Methods Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to examine the relation between AD PRSs, constructed based on findings in recent genome-wide association studies, and plasma p-tau181, using linear regression models. Analyses were performed in the total sample (n = 818), after stratification on diagnostic status (CU (n = 236), MCI (n = 434), AD dementia (n = 148)), and after stratification on Aβ pathology status (Aβ positives (n = 322), Aβ negatives (n = 409)). Results Associations between plasma p-tau181 and APOE PRSs (p = 3e−18–7e−15) and non-APOE PRSs (p = 3e−4–0.03) were seen in the total sample. The APOE PRSs were associated with plasma p-tau181 in all diagnostic groups (CU, MCI, and AD dementia), while the non-APOE PRSs were associated only in the MCI group. The APOE PRSs showed similar results in amyloid-β (Aβ)-positive and negative individuals (p = 5e−5–1e−3), while the non-APOE PRSs were associated with plasma p-tau181 in Aβ positives only (p = 0.02). Conclusions Polygenic risk for AD including APOE was found to associate with plasma p-tau181 independent of diagnostic and Aβ pathology status, while polygenic risk for AD beyond APOE was associated with plasma p-tau181 only in MCI and Aβ-positive individuals. These results extend the knowledge about the relation between genetic risk for AD and p-tau181, and further support the usefulness of plasma p-tau181 as a biomarker of AD.

Additional file 1.

Background Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanistic insights relevant to early dementia. Here, we aimed to identify the metabolic underpinnings of cognitive outcomes in late midlife by exploring and integrating associations of single metabolites, metabolic pathways and networks. We further aimed to untangle the influence of life course factors on these relationships; a previously unexplored avenue using a systems biology approach. Methods and Findings Levels of 1019 metabolites were detected by liquid chromatography-mass spectrometry (Metabolon Inc) and quantified at age 60-64 among participants of the British 1946 Birth Cohort (N=1740). Cognitive outcomes were assessed at the same age and 5-9 years later, and included short-term memory (age 60-64, 69 and change), delayed memory (age 60-64), processing speed (age 60-64, 69 and change) and Addenbrooke’s Cognitive Examination III (age 69). Using a combination of linear regression analysis, quantitative pathway analysis and weighted gene correlation network analysis, we evaluated relationships between metabolite measures (single-metabolites, pathways and network modules) and cognitive outcomes. Single-metabolite and network analyses were sequentially adjusted for life course factors across four models, including: sex and blood clinic information (model 1); model 1 + BMI and lipid medication (model 2); model 2 + childhood cognition, education and socioeconomic position (model 3); model 3 + smoking, exercise, alcohol intake, blood pressure and diet (model 4). After correcting for multiple tests, we identified 155 metabolites, 10 pathways and 5 modules to show relationships with cognitive outcomes. Thirty-five metabolites were influential in their module and identified in single-metabolite analyses. Notably, we report independent relationships between a module comprised of acylcarnitines and processing speed, revealing palmitoylcarnitine (C16) as a key driver of associations (model 4: s = −0.10, 95%CI = −0.15 to −0.052). Two modules demonstrated associations with several cognitive outcomes that were partly explained by life course factors: one enriched in modified nucleosides and amino acids (s range (model 1) = −0.12 to −0.09, attenuation (model 4)= 39.2 to 55.5%), and another in vitamin A and C metabolites (s range (model 1) = 0.11 to 0.23, attenuation (model 4) = 68.6 to 92.6%). Our other findings, including a module enriched in sphingolipid pathways (s range (model 1) = 0.085 to 0.10, attenuation (model 4) = 87.0 to 116%), were entirely explained by life course factors particularly childhood cognition and education. The limitations of this study include those commonly seen with population-based cohorts, such as possible residual confounding and generalisability to other populations, as well as a lack of longitudinal metabolite data. Conclusions Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms underlying cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.

There are currently no disease modifying treatments for Alzheimer’s Disease (AD). Epidemiological studies have highlighted blood metabolites as potential biomarkers, but possible confounding and reverse causation prevent causal conclusions. Here, we investigated whether nineteen metabolites previously associated with midlife cognitive function, are on the causal pathway to AD.Summary statistics from the largest Genome-Wide Association Studies (GWAS) for AD and for metabolites were used to perform bi-directional univariable Mendelian Randomisation (MR). Bayesian model averaging MR (MR-BMA) was additionally performed to address high correlation between metabolites and to identify metabolite combinations which may be on the AD causal pathway.Univariable MR indicated three Extra-Large High-Density Lipoproteins (XL.HDL) to be on the causal pathway to AD: Free Cholesterol (XL.HDL.FC: OR=0.86, 95% CI=0.78-0.94), Total Lipids (XL.HDL.L: OR=0.88, 95% CI=0.80-0.97), and Phospholipids (XL.HDL.PL: OR=0.87, 95% CI=0.81-0.97); significant at an adjusted threshold of pOR=0.88, 95% CI=0.79-0.99; GP:OR=1.2, 95% CI=1.05-1.38); significant at the 5% level.This study offers insight into the causal relationship between metabolites previously demonstrating association with mid-life cognition, and AD. It highlights GP in addition to several XL.HDLs as causal candidates which warrant further investigation. As the pathological changes underpinning AD are thought to develop decades prior to symptom onset, progressing these findings could hold special value in informing future risk reduction strategies.

Additional file 1: Table S1. Genetic variants used for associations with lipid and protein modules. Table S2. Top 10 drivers in five selected lipid modules. Table S3. Selected lipid modules and annotation. Table S4. Top 10 drivers in five selected protein modules. Table S5. Summary of gene set enrichment analyses and gene ontology enrichment analysis of protein modules. Table S6. Correlations between lipid modules/protein modules and AD genetic variants. Figure S1. Preservation summary plots for AD, MCI and control sub datasets in lipidomics dataset. Figure S2. Preservation summary plots for AD, MCI and control sub datasets in ANM proteomics dataset. Figure S3. Dendrogram and cross-tabulation based comparison of modules in ANM and ART protein cohort networks. Figure S4. A. Cluster dendrogram of weighted lipid correlation network analysis and the number of lipid features in each module; B. Cluster dendrogram of weighted protein correlation network analysis and the number of proteins in each module. Figure S5. Scatter plots of module membership versus lipids/proteins-diagnosis correlation. Figure S6. Scatter plots of eigenlipids correlation with ROD. Figure S7. Scatter plots of eigenlipids correlation with brain atrophy measures. Figure S8. Scatter plots of lipid module membership versus lipids-phenotypes correlation. Figure S9. DAGs summarize biological processes in five protein modules. Figure S10. Scatter plots of eigenproteins correlation with brain atrophy measures. Figure S11. Scatter plots of protein module membership versus proteins-phenotypes correlation. Figure S12. Scatter plots of protein module membership versus proteins-phenotypes correlation. Figure S13. Correlation networks for lipid greenyellow module and protein lightcyan module. Figure S14. Correlation networks for lipid darkturquoise module and protein lightgreen module.

INTRODUCTIONThere is an urgent need to understand the molecular mechanisms underlying Alzheimer’s Disease (AD) to enable early diagnosis and develop effective treatments. Here we aim to investigate Alzheimer’s dementia using an unsupervised lipid, protein and gene multi-omic integrative approach.METHODSA lipidomics dataset (185 AD, 40 MCI and 185 controls) and a proteomics dataset (201 AD patients, 104 MCI individuals and 97 controls) were utilised for weighted gene co-expression network analyses (WGCNA). An additional proteomics dataset (94 AD, 55 MCI and 100 controls) was included for external proteomics validation. Modules created within each modality were correlated with clinical AD diagnosis, brain atrophy measures and disease progression, as well as with each other. Gene Ontology (GO) enrichment analysis was employed to examine the biological processes and molecular and cellular functions for protein modules associated with AD phenotypes. Lipid species were annotated in the lipid modules associated with AD phenotypes. Associations between established AD risk loci and lipid/protein modules that showed high correlation with AD phenotypes were also explored.RESULTSFive of the 20 identified lipid modules and five of the 17 identified protein modules were correlated with AD phenotypes. Lipid modules comprising of phospholipids, triglycerides, sphingolipids and cholesterol esters, correlated with AD risk loci involved in immune response and lipid metabolism. Five protein modules involved in positive regulation of cytokine production, neutrophil mediated immunity, humoral immune responses were correlated with AD risk loci involved in immune and complement systems.DISCUSSIONWe have shown the first multi-omic study linking genes, proteins and lipids to study pathway dysregulation in AD. Results identified modules of tightly regulated lipids and proteins that were strongly associated with AD phenotypes and could be pathology drivers in lipid homeostasis and innate immunity.Research in ContextLipid and protein modules were preserved amongst Alzheimer’s disease (AD) patients, participants with mild cognitive impairment (MCI) and controls. Protein modules were also externally validated.Five lipid and five protein modules out of a total of thirty-seven correlated with clinical AD diagnosis, brain atrophy measurements and the rate of cognitive decline in AD.Lipid and protein modules associated with AD phenotypes showed associations with established AD risk loci involved in lipid and immune pathways.