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Depression is one of the most poorly understood diseases due to its elusive pathogenesis. There is an urgency to identify molecular and biological mechanisms underlying depression and the gut microbiome is a novel area of interest. Here we investigate the relation of fecal microbiome diversity and composition with depressive symptoms in 1,054 participants from the Rotterdam Study cohort and validate these findings in the Amsterdam HELIUS cohort in 1,539 subjects. We identify association of thirteen microbial taxa, including genera Eggerthella, Subdoligranulum, Coprococcus, Sellimonas, Lachnoclostridium, Hungatella, Ruminococcaceae (UCG002, UCG003 and UCG005), LachnospiraceaeUCG001, Eubacterium ventriosum and Ruminococcusgauvreauiigroup , and family Ruminococcaceae with depressive symptoms. These bacteria are known to be involved in the synthesis of glutamate, butyrate, serotonin and gamma amino butyric acid (GABA), which are key neurotransmitters for depression. Our study suggests that the gut microbiome composition may play a key role in depression. Here, the authors analyze the relation of fecal microbiota diversity and composition with depressive symptoms in 1,054 participants from the Rotterdam Study cohort and in 1,539 subjects of the Amsterdam HELIUS cohort, finding associations with bacteria known to be involved in the synthesis of key neurotransmitters for depression.

Emerging evidence suggested a converging mechanism in neurodegenerative brain diseases (NBD) involving early neuronal network dysfunctions and alterations in the homeostasis of neuronal firing as culprits of neurodegeneration. In this study, we used paired-end short-read and direct long-read whole genome sequencing to investigate an unresolved autosomal dominant dementia family significantly linked to 7q36. We identified and validated a chromosomal inversion of ca. 4 Mb, segregating on the disease haplotype and disrupting the coding sequence of dipeptidyl-peptidase 6 gene (DPP6). DPP6 resequencing identified significantly more rare variants—nonsense, frameshift, and missense—in early-onset Alzheimer’s disease (EOAD, p value = 0.03, OR = 2.21 95% CI 1.05–4.82) and frontotemporal dementia (FTD, p = 0.006, OR = 2.59, 95% CI 1.28–5.49) patient cohorts. DPP6 is a type II transmembrane protein with a highly structured extracellular domain and is mainly expressed in brain, where it binds to the potassium channel Kv4.2 enhancing its expression, regulating its gating properties and controlling the dendritic excitability of hippocampal neurons. Using in vitro modeling, we showed that the missense variants found in patients destabilize DPP6 and reduce its membrane expression (p < 0.001 and p < 0.0001) leading to a loss of protein. Reduced DPP6 and/or Kv4.2 expression was also detected in brain tissue of missense variant carriers. Loss of DPP6 is known to cause neuronal hyperexcitability and behavioral alterations in Dpp6-KO mice. Taken together, the results of our genomic, genetic, expression and modeling analyses, provided direct evidence supporting the involvement of DPP6 loss in dementia. We propose that loss of function variants have a higher penetrance and disease impact, whereas the missense variants have a variable risk contribution to disease that can vary from high to low penetrance. Our findings of DPP6, as novel gene in dementia, strengthen the involvement of neuronal hyperexcitability and alteration in the homeostasis of neuronal firing as a disease mechanism to further investigate.

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions. Here, the authors provide an in-depth study of the metabolome in two large population-based prospective cohorts and identify 32 microbial traits associated with various metabolic biomarkers and specific lipoprotein subfractions, providing insights into the role of microbiota in influencing host lipid levels.

Alzheimer's disease is one of the most heritable diseases in elderly people and the most common type of dementia. In addition to the major genetic determinant of Alzheimer's disease, the APOE gene, 23 genetic variants have been associated with the disease. We assessed the effects of these variants and APOE on cumulative risk and age at onset of Alzheimer's disease and all-cause dementia. We studied incident dementia in cognitively healthy participants (aged >45 years) from the community-based Rotterdam Study, an ongoing prospective cohort study based in Rotterdam, the Netherlands, focusing on neurological, cardiovascular, endocrine, and ophthalmological disorders, and other diseases in elderly people. The Rotterdam Study comprises participants in three baseline cohorts (initiated in 1990, 2000, and 2006), who are re-invited to the research centre every 3–4 years, and continuously monitored by records from general practitioners and medical specialists. Cumulative incidence curves up to age 100 years were calculated for Alzheimer's disease and dementia, taking into account mortality as a competing event. These risk curves were stratified by APOE genotypes, tertiles of a weighted genetic risk score (GRS) of 23 Alzheimer's disease-associated genetic variants, and parental history of dementia. 12 255 of 14 926 participants (58·5% women) from the Rotterdam Study were included in this study. During a median follow-up of 11·0 years (IQR 4·9–15·9; 133 123 person years), 1609 participants developed dementia, of whom 1262 (78%) were classified as having Alzheimer's disease; 3310 people died of causes other than dementia. Both APOE and the GRS significantly modified the risks of Alzheimer's disease and dementia. There was evidence for a significant interaction between APOE genotypes and the GRS for the association with Alzheimer's disease (p=0·03) and dementia (p=0·04); the risk for carriers homozygous for APOE ε4 was modified most by the GRS. In carriers homozygous for APOE ε4, the difference between the high-risk tertile and the low-risk tertile by age 85 years was 27·0% for Alzheimer's disease (p=8·5 × 10−3) and 37·2% for dementia (p=2·2 × 10−4), which translates to a 7–10 year difference in age at onset. Comparing the risk extremes, which were carriers homozygous for APOE ε2 or heterozygous with one copy each of the ε2 and ε3 alleles in the low-risk tertile of the GRS versus carriers homozygous for APOE ε4 in the high-risk tertile of the GRS, the difference in risk by age 85 years was 58·6% (4·1% vs 62·7%; p=6·2 × 10−13) for Alzheimer's disease, and 70·3% (7·2% vs 77·5%; p=3·0 × 10−23) for dementia. These risk differences translate into an 18–29 years difference in age at onset for Alzheimer's disease and an 18–23 year difference in age at onset dementia. This difference becomes more pronounced when parental history of dementia is considered (difference in risk 83·8%; p=1·1 × 10−20). Common variants with small individual effects jointly modify the risk and age at onset of Alzheimer's disease and dementia, particularly in APOE ε4 carriers. These findings highlight the potential of common variants in determining Alzheimer's disease risk. None.

IntroductionType 2 diabetes is a risk factor for dementia and Parkinson’s disease (PD). Drug treatments for diabetes, such as metformin, could be used as novel treatments for these neurological conditions. Using electronic health records from the USA (OPTUM EHR) we aimed to assess the association of metformin with all-cause dementia, dementia subtypes and PD compared with sulfonylureas.Research design and methodsA new user comparator study design was conducted in patients ≥50 years old with diabetes who were new users of metformin or sulfonylureas between 2006 and 2018. Primary outcomes were all-cause dementia and PD. Secondary outcomes were Alzheimer’s disease (AD), vascular dementia (VD) and mild cognitive impairment (MCI). Cox proportional hazards models with inverse probability of treatment weighting (IPTW) were used to estimate the HRs. Subanalyses included stratification by age, race, renal function, and glycemic control.ResultsWe identified 96 140 and 16 451 new users of metformin and sulfonylureas, respectively. Mean age was 66.4±8.2 years (48% male, 83% Caucasian). Over the 5-year follow-up, 3207 patients developed all-cause dementia (2256 (2.3%) metformin, 951 (5.8%) sulfonylurea users) and 760 patients developed PD (625 (0.7%) metformin, 135 (0.8%) sulfonylurea users). After IPTW, HRs for all-cause dementia and PD were 0.80 (95% CI 0.73 to 0.88) and 1.00 (95% CI 0.79 to 1.28). HRs for AD, VD and MCI were 0.81 (0.70–0.94), 0.79 (0.63–1.00) and 0.91 (0.79–1.04). Stronger associations were observed in patients who were younger (<75 years old), Caucasian, and with moderate renal function.ConclusionsMetformin users compared with sulfonylurea users were associated with a lower risk of all-cause dementia, AD and VD but not with PD or MCI. Age and renal function modified risk reduction. Our findings support the hypothesis that metformin provides more neuroprotection for dementia than sulfonylureas but not for PD, but further work is required to assess causality.

Several studies have reported alterations in gut microbiota composition of Alzheimer's disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD). We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE. The machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of and lower abundance of group spp., spp., spp., spp., spp., , and spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of spp., spp., and and higher odds of positive p-tau status. Gut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.

BackgroundMigraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (1H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls.MethodsFasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for 1H-NMR spectroscopy. Data was analysed using elastic net regression analysis.ResultsA total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism.ConclusionThis study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.

Background Over the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account. Results We developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations. Conclusions ProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.

To determine whether classes of diabetes medications are associated with cognitive health and dementia risk, above and beyond their glycemic control properties. Findings were pooled from 5 population-based cohorts: the Framingham Heart Study, the Rotterdam Study, the Atherosclerosis Risk in Communities (ARIC) Study, the Aging Gene-Environment Susceptibility-Reykjavik Study (AGES) and the Sacramento Area Latino Study on Aging (SALSA). Differences between users and non-users of insulin, metformin and sulfonylurea were assessed in each cohort for cognitive and brain MRI measures using linear regression models, and cognitive decline and dementia/AD risk using mixed effect models and Cox regression analyses, respectively. Findings were then pooled using meta-analytic techniques, including 3,590 individuals with diabetes for the prospective analysis. After adjusting for potential confounders including indices of glycemic control, insulin use was associated with increased risk of new-onset dementia (pooled HR (95% CI) = 1.58 (1.18, 2.12);p = 0.002) and with a greater decline in global cognitive function (β = -0.014±0.007;p = 0.045). The associations with incident dementia remained similar after further adjustment for renal function and excluding persons with diabetes whose treatment was life-style change only. Insulin use was not related to cognitive function nor to brain MRI measures. No significant associations were found between metformin or sulfonylurea use and outcomes of brain function and structure. There was no evidence of significant between-study heterogeneity. Despite its advantages in controlling glycemic dysregulation and preventing complications, insulin treatment may be associated with increased adverse cognitive outcomes possibly due to a greater risk of hypoglycemia.

Development of free/libre open source software is usually done by a community of people with an interest in the tool. For scientific software, however, this is less often the case. Most scientific software is written by only a few authors, often a student working on a thesis. Once the paper describing the tool has been published, the tool is no longer developed further and is left to its own device. Here we describe the broad, multidisciplinary community we formed around a set of tools for statistical genomics. The GenABEL project for statistical omics actively promotes open interdisciplinary development of statistical methodology and its implementation in efficient and user-friendly software under an open source licence. The software tools developed withing the project collectively make up the GenABEL suite, which currently consists of eleven tools. The open framework of the project actively encourages involvement of the community in all stages, from formulation of methodological ideas to application of software to specific data sets. A web forum is used to channel user questions and discussions, further promoting the use of the GenABEL suite. Developer discussions take place on a dedicated mailing list, and development is further supported by robust development practices including use of public version control, code review and continuous integration. Use of this open science model attracts contributions from users and developers outside the “core team”, facilitating agile statistical omics methodology development and fast dissemination.