Headache and type 2 diabetes association: a US national ambulatory case-control study

Objective We investigate the joint observation between type 2 diabetes and headache using a case-control study of a US ambulatory dataset. Background Recent whole-population cohort studies propose that type 2 diabetes may have a protective effect against headache prevalence. With headaches ranked as a leading cause of disability, headache-associated comorbidities could help identify shared molecular mechanisms. Methods We performed a case-control study using the US National Ambulatory Medical Care Survey, 2009, on the joint observation between headache and specific comorbidities, namely type 2 diabetes, hypertension and anxiety, for all patients between 18 and 65 years of age. The odds ratio of having a headache and a comorbidity were calculated using conditional logistic regression, controlling for gender and age over a study population of 3,327,947 electronic health records in the absence of prescription medication data. Results We observed estimated odds ratio of 0.89 (95% CI: 0.83-0.95) of having a headache and a record of type 2 diabetes over the population, and 0.83 (95% CI: 2.02-2.57) and 0.89 (95% CI: 3.00-3.49) for male and female, respectively. Conclusions We find that patients with type 2 diabetes are less likely to present a recorded headache indication. Patients with hypertension are almost twice as likely of having a headache indication and patients with an anxiety disorder are almost three times as likely. Given the possibility of confounding indications and prescribed medications, additional studies are recommended

Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions

Validation of UK Biobank data for mental health outcomes : a pilot study using secondary care electronic health records

The study was funded by the MRC Pathfinder Grant (MC_PC_17215); the National Institute for Health Research’s (NIHR) Oxford Health Biomedical Research Centre (BRC-1215-20005) and the Virtual Brain Cloud from European Commission (grant no. H2020SC1-DTH-2018-1). This work was supported by the UK Clinical Record Interactive Search (UK-CRIS) system funded by the National Institute for Health Research (NIHR) and the Medical Research Council, with the University of Oxford, using data and systems of the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005).UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.Peer reviewe

High blood pressure and risk of dementia : a two-sample Mendelian randomization study in the UK biobank

This work was supported by Janssen Research and Development , LLC (of Johnson & Johnson).Background: Findings from randomized controlled trials have yielded conflicting results on the association between blood pressure (BP) and dementia traits. We tested the hypothesis that a causal relationship exists between systolic BP (SBP) and/or diastolic BP (DBP) and risk of Alzheimer's disease (AD). Methods: We performed a generalized summary Mendelian randomization (GSMR) analysis using summary statistics of a genome-wide association study meta-analysis of 299,024 individuals of SBP or DBP as exposure variables against three different outcomes: 1) AD diagnosis (International Genomics of Alzheimer's Project), 2) maternal family history of AD (UK Biobank), and 3) paternal family history of AD (UK Biobank). Finally, a combined meta-analysis of 368,440 individuals that included these three summary statistics was used as final outcome. Results: GSMR applied to the International Genomics of Alzheimer's Project dataset revealed a significant effect of high SBP lowering the risk of AD (βGSMR = −0.19, p =.04). GSMR applied to the maternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.12, p =.02], DBP [βGSMR = −0.10, p =.05]) and to the paternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.16, p =.02], DBP [βGSMR = −0.24, p = 7.4 × 10−4]) showed the same effect. A subsequent combined meta-analysis confirmed the overall significant effect for the other SBP analyses (βGSMR = −0.14, p =.03). The DBP analysis in the combined meta-analysis also confirmed a DBP effect on AD (βGSMR = −0.14, p =.03). Conclusions: A causal effect exists between high BP and a reduced late-life risk of AD. The results were obtained through careful consideration of confounding factors and the application of complementary MR methods on independent cohorts.Peer reviewe

Comparative effect of metformin versus sulfonylureas with dementia and Parkinson's disease risk in US patients over 50 with type 2 diabetes mellitus

This work was supported by Janssen Pharmaceuticals. LJL is supported by the National Institute on Aging Intramural Research Program, USA. Additional funds were provided by Rosetrees Trust (M937) and John Black Charitable Fund (ID A2926). AJN-H has received funding from Janssen Pharmaceuticals, GlaxoSmithKline and Ono Pharma. QSL is an employee of Janssen Research & Development, Johnson & Johnson, and may hold equity in Johnson & Johnson.Introduction Type 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 methods A 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. Results We 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. Conclusions Metformin 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.Peer reviewe

Oscillatory activity in the medial prefrontal cortex and nucleus accumbens correlates with impulsivity and reward outcome.

Actions expressed prematurely without regard for their consequences are considered impulsive. Such behaviour is governed by a network of brain regions including the prefrontal cortex (PFC) and nucleus accumbens (NAcb) and is prevalent in disorders including attention deficit hyperactivity disorder (ADHD) and drug addiction. However, little is known of the relationship between neural activity in these regions and specific forms of impulsive behaviour. In the present study we investigated local field potential (LFP) oscillations in distinct sub-regions of the PFC and NAcb on a 5-choice serial reaction time task (5-CSRTT), which measures sustained, spatially-divided visual attention and action restraint. The main findings show that power in gamma frequency (50-60 Hz) LFP oscillations transiently increases in the PFC and NAcb during both the anticipation of a cue signalling the spatial location of a nose-poke response and again following correct responses. Gamma oscillations were coupled to low-frequency delta oscillations in both regions; this coupling strengthened specifically when an error response was made. Theta (7-9 Hz) LFP power in the PFC and NAcb increased during the waiting period and was also related to response outcome. Additionally, both gamma and theta power were significantly affected by upcoming premature responses as rats waited for the visual cue to respond. In a subgroup of rats showing persistently high levels of impulsivity we found that impulsivity was associated with increased error signals following a nose-poke response, as well as reduced signals of previous trial outcome during the waiting period. Collectively, these in-vivo neurophysiological findings further implicate the PFC and NAcb in anticipatory impulsive responses and provide evidence that abnormalities in the encoding of rewarding outcomes may underlie trait-like impulsive behaviour.RCUK, Wellcome, OtherThis is the final version of the article. It first appeared at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111300

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis:A multi-national multi-database case-control study

Background: Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk. Methods: A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from ele

Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals

This project was funded by DPUK through MRC (grant no. MR/L023784/2) and the UK Medical Research Council Award to the University of Oxford (grant no. MC_PC_17215). L.S is funded by the Virtual Brain Cloud from European comission (grant no. H2020-SC1-DTH-2018-1). C.R.B is funded by National Institutes of Health (NIH) research grant R01AG054628. S.R.C is funded by National Institutes of Health (NIH) research grant (R01AG054628), Medical Research Council (MR/R024065/1), Age UK and Economic and Social Research Council. R.E.M. was supported by Alzheimer's Research UK major project grant ARUKPG2017B-10. C.H was supported by an MRC Human Genetics Unit programme grant “Quantitative traits in health and disease” (U.MC_UU_00007/10). H.C.W received funding from Wellcome Trust. J.W is funded by TauRx pharmaceuticals Ltd and received Educational grant from Biogen paid to Alzheimer Scotland/Brain Health Scotland. G.W received GRAMPIAN UNIVERSITY HOSPITALS NHS TRUST, Scottish Government—Chief Scientist Office, ROLAND SUTTON ACADEMIC TRUST, Medical Research Scotland, Sutton Academic Trust and ROLAND SUTTON ACADEMIC TRUST. J.M.W received Wellcome Trust Strategic Award, MRC UK Dementia Research Institute and MRC project grants, Fondation Leducq, Stroke Association, British Heart Foundation, Alzheimer Society, and the European Union H2020 PHC-03-15 SVDs@Target grant (666881). D.S received MRC (MR/S010351/1), MRC (MR/W002388/1) and MRC (MR/W002566/1). A.M is supported by the Wellcome Trust (104036/Z/14/Z, 216767/Z/19/Z, 220857/Z/20/Z) and UKRI MRC (MC_PC_17209, MR/S035818/1). This work is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 847776. In addition, A.M has received grant support from The Sackler Trust, outside of the work presented. N.B received grant to institution from GSK as part of GSK/Oxford FxG initiative. A.N.H received John Black Charitable Fund-Rosetrees, H2020 funding from European Comission-Project Virtual Brain Cloud, AI for the Discovery of new therapies in Parkinson's (A2926), Rising Start Initiative—stage 2, Brain-Gut Microbiome (Call: PAR-18-296; Award ID: 1U19AG063744-01), Gut-liver-brain biochemical axis in Alzheimer's disease (5RF1AG057452-01), Virtual Brain Cloud (Call: H2020-SC1-DTH- 2018-1; Grant agreement ID: 826421). Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006) and is currently supported by the Wellcome Trust (216767/Z/19/Z). Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” [STRADL] Reference 104036/Z/14/Z). We are grateful to all the families who took part; the general practitioners and the Scottish School of Primary Care for their help in recruiting them; and the whole Generation Scotland team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health-care assistants, and nurses.Peer reviewedPublisher PD