Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Rotterdam, Netherlands Organization for the 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. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS-I, RS-II, RS-III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the

The exact etiology of dementia is still unclear, but both genetic and lifestyle factors are thought to be key drivers of this complex disease. The recognition of familial patterns of dementia has led to the discovery of genetic factors that play a role in the pathogenesis of dementia, including the apolipoprotein E (APOE) genotype and a large and still growing number of genetic variants. 1,2 Beyond the genetic architecture, several modifiable risk factors have been implicated in the development of dementia. 3 Prevention trials to halt or delay cognitive decline are increasingly recruiting older individuals who are genetically predisposed to dementia. However, it remains unclear whether targeted health and lifestyle interventions can attenuate or even offset this increased genetic risk. Here, we leverage long-term data on both genetic and modifiable factors from 6352 individuals aged 55 years and older within the population-based Rotterdam Study. In this study, we demonstrate that among individuals at low-and intermediate genetic risk, favorable Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Background Human prion diseases are rare and usually rapidly fatal neurodegenerative disorders, the most common being sporadic Creutzfeldt-Jakob disease (sCJD). Variants in the PRNP gene that encodes prion protein are strong risk factors for sCJD but, although the condition has similar heritability to other neurodegenerative disorders, no other genetic risk loci have been confirmed. We aimed to discover new genetic risk factors for sCJD, and their causal mechanisms.Methods We did a genome-wide association study of sCJD in European ancestry populations (patients diagnosed with probable or definite sCJD identified at national CJD referral centres) with a two-stage study design using genotyping arrays and exome sequencing. Conditional, transcriptional, and histological analyses of implicated genes and proteins in brain tissues, and tests of the effects of risk variants on clinical phenotypes, were done using deep longitudinal clinical cohort data. Control data from healthy individuals were obtained from publicly available datasets matched for country. FindingsSamples from 5208 cases were obtained between 1990 and 2014. We found 41 genome-wide significant single nucleotide polymorphisms (SNPs) and independently replicated findings at three loci associated with sCJD risk; within PRNP (rs1799990; additive model odds ratio [OR] 1•23 [95% CI 1•17-1•30], p=2•68 × 10 -¹⁵; heterozygous model p=1•01 × 10 -¹³⁵), STX6 (rs3747957; OR 1•16 [1•10-1•22], p=9•74 × 10 -⁹), and GAL3ST1 (rs2267161; OR 1•18 [1•12-1•25], p=8•60 × 10 -¹⁰). Follow-up analyses showed that associations at PRNP and GAL3ST1 are likely to be caused by common variants that alter the protein sequence, whereas risk variants in STX6 are associated with increased expression of the major transcripts in disease-relevant brain regions.Interpretation We present, to our knowledge, the first evidence of statistically robust genetic associations in sporadic human prion disease that implicate intracellular trafficking and sphingolipid metabolism as molecular causal mechanisms. Risk SNPs in STX6 are shared with progressive supranuclear palsy, a neurodegenerative disease associated with misfolding of protein tau, indicating that sCJD might share the same causal mechanisms as prion-like disorders.

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Introduction Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). Methods We studied associations of 143 plasma‐based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta‐analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. Results Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. Discussion Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials.

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer, and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold pmeta = 6.5x10−4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-ce %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-ce) became significantly associated with LTL (p = 3.6x10−4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (p = 1.9x10−4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.

Background Recent meta‐analyses of genome‐wide association studies (GWAS) have identified ∼30 susceptibility LOAD loci in addition to APOE, however the majority are common variants (minor allele frequency (MAF)>0.02). We used the dense, high‐resolution Haplotype Reference Consortium (HRC) r1.1 reference panel (64,976 haplotypes/39,235,157 SNPs), which allows imputation of rare variants (MAF>0.0008), to impute 44 GWAS datasets of the IGAP consortia to identify novel rare variant, gene, and pathway associations. Method We imputed 25,192 cases and 40,410 controls to the HRC r1.1 panel using Minimac3 on the Michigan Imputation Server. Converting imputed genotype probabilities to minor allele dosage, we ran logistic regression using SNPTEST on individual variants with MAF > 0.01 (and using generalized linear mixed models in R with family‐based datasets), and performed a fixed‐effects, inverse‐variance‐weighted meta‐analysis using METAL. Variants with MAF ≤ 0.01 were meta‐analyzed using score‐based tests via SeqMeta/R. Both analyses adjusted for age‐at‐onset(cases)/age‐at‐exam(controls), sex, and principal components for population substructure. Gene‐based associations were done with SKAT‐O and burden testing, while pathway associations were examined using VEGAS2. Result Discovery analyses of ∼39.2M genotyped or imputed SNVs confirmed single variant associations in 26 of 30 known IGAP LOAD loci at suggestive levels of significance (P < 10−5), with 12 known loci attaining genome‐wide statistical significance (P < 5 × 10−8) (Figure 1). Newly observed associations included common variants (MAF>0.01) in or near homologs of known AD loci, EPHA5 (rs17086136, OR[95% CI] = 1.23 [1.13,1.33], P = 6.36 × 10−7) and ADAM28 (rs10096379, OR[95% CI] = 0.86 [0.81,0.92], P = 3.02 × 10−6); in/near neuronal development genes including DAB1 (neuronal migration; 1:57700874:T:G, OR[95% CI] = 0.71 [0.62, 0.81], P = 6.94 × 10−7) and DCC (axon guidance; rs2054289, OR[95% CI] = 0.71 [0.62, 0.83], P = 6.69 × 10−6); and in/near genes involved in cardiometabolic traits including THADA (type 2 diabetes; rs77101426, OR[95% CI] = 0.89 [0.85, 0.95], P = 2.37 × 10−6). Several known AD loci demonstrated novel rare variant associations with genome‐wide significance, including CR1, PICALM, and the MS4A region (Figure 2), and novel rare variant associations were observed in or near genes involved in memory and cognitive function, including HS3ST4 and DBX1. Independent replication in external datasets including the UK Biobank is on‐going. Conclusion Several novel LOAD candidate loci, including those with prior associations with neurodevelopment and cardiometabolic traits, were identified using high‐quality imputation of rare and low‐frequency variants in IGAP.

Background There is an important interplay between the gut microbiome and brain, commonly known as the gut‐brain‐axis. Several studies addressed the role of the microbiome in neurologic diseases. Here, we studied the role of microbiota in cognitive function, neurovascular changes, and neurodegenerative changes in the general population. Method In the population‐based Rotterdam Study, we studied the association between microbial taxa and cognitive function in 1,184 participants and the association between microbial taxa and neurovascular and neurodegenerative traits, measured on brain magnetic resonance imaging, in 1,241 participants. Gut microbiota were profiled using 16 s rRNA gene sequencing. We used linear regression analysis to study these associations and adjusted for major confounders, i.e., age, sex, technical covariates, medication use, alcohol, smoking, body mass index, and hypertension. Result We found that the genus Clostridium sensu stricto 1 showed a higher abundance in participants with better cognitive function (beta(β)(standard error(SE)) 0.023(0.01), p = 3.22 × 10−2), larger global brain volume (β(SE) 0.0019(0.0006), p = 1.83 × 10−3), less white matter hyperintensities (β(SE) ‐0.024(0.007), p = 5.38 × 10−4), and smaller lateral ventricular volume (β(SE) ‐0.013(0.005), p = 1.81 × 10−2). The family Clostridiaceae 1 also showed a higher abundance in these traits, with the same direction of effect and very similar effect estimates. The class Clostridia, order Clostridiales, family Christensenellaceae, and genus Christensenellaceae R7 group showed a higher abundance in better cognition. Of note is that the agreement of our findings with those of studies of Alzheimer’s disease is high. Conclusion Clostridium shows a higher abundance in less neurovascular changes, in less neurodegenerative changes, and in better cognition. We find that different microbiota are associated to neurovascular and neurodegenerative pathology.