Automated wave labelling of the auditory brainstem response using machine learning.

OBJECTIVE: To compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates. DESIGN: Secondary data analysis of a previously published ABR dataset. Five types of machine learning algorithm were compared within a nested k-fold cross-validation procedure. STUDY SAMPLE: A set of 482 suprathreshold ABR waveforms were used. These were recorded from 81 participants with audiometric thresholds within normal limits. RESULTS: A convolutional recurrent neural network (CRNN) outperformed the other algorithms evaluated. The algorithm labelled 95.9% of ABR waves within ±0.1 ms of the target. The mean absolute error was 0.025 ms, averaged across the outer validation folds of the nested cross-validation procedure. High confidence levels were generally associated with greater wave-labelling accuracy. CONCLUSIONS: Machine learning algorithms have the potential to assist clinicians with ABR interpretation. The present work identifies a promising machine learning approach, but any algorithm to be used in clinical practice would need to be trained on a large, accurately labelled, heterogeneous dataset and evaluated in clinical settings in follow-on work.

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility.

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England's 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an "FFPEImpact" score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

Audiogram Estimation Performance Using Auditory Evoked Potentials and Gaussian Processes.

OBJECTIVES: Auditory evoked potentials (AEPs) play an important role in evaluating hearing in infants and others who are unable to participate reliably in behavioral testing. Discriminating the AEP from the much larger background activity, however, can be challenging and time-consuming, especially when several AEP measurements are needed, as is the case for audiogram estimation. This task is usually entrusted to clinicians, who visually inspect the AEP waveforms to determine if a response is present or absent. The drawback is that this introduces a subjective element to the test, compromising quality control of the examination. Various objective methods have therefore been developed to aid clinicians with response detection. In recent work, the authors introduced Gaussian processes (GPs) with active learning for hearing threshold estimation using auditory brainstem responses (ABRs). The GP is attractive for this task, as it can exploit the correlation structure underlying AEP waveforms across different stimulus levels and frequencies, which is often overlooked by conventional detection methods. GPs with active learning previously proved effective for ABR hearing threshold estimation in simulations, but have not yet been evaluated for audiogram estimation in subject data. The present work evaluates GPs with active learning for ABR audiogram estimation in a sample of normal-hearing and hearing-impaired adults. This involves introducing an additional dimension to the GP (i.e., stimulus frequency) along with real-time implementations and active learning rules for automated stimulus selection. METHODS: The GP's accuracy was evaluated using the "hearing threshold estimation error," defined as the difference between the GP-estimated hearing threshold and the behavioral hearing threshold to the same stimuli. Test time was evaluated using the number of preprocessed and artifact-free epochs (i.e., the sample size) required for locating hearing threshold at each frequency. Comparisons were drawn with visual inspection by examiners who followed strict guidelines provided by the British Society of Audiology. Twenty-two normal hearing and nine hearing-impaired adults were tested (one ear per subject). For each subject, the audiogram was estimated three times: once using the GP approach, once using visual inspection by examiners, and once using a standard behavioral hearing test. RESULTS: The GP's median estimation error was approximately 0 dB hearing level (dB HL), demonstrating an unbiased test performance relative to the behavioral hearing thresholds. The GP additionally reduced test time by approximately 50% relative to the examiners. The hearing thresholds estimated by the examiners were 5 to 15 dB HL higher than the behavioral thresholds, which was consistent with the literature. Further testing is still needed to determine the extent to which these results generalize to the clinic. CONCLUSIONS: GPs with active learning enable automatic, real-time ABR audiogram estimation with relatively low test time and high accuracy. The GP could be used to automate ABR audiogram estimation or to guide clinicians with this task, who may choose to override the GP's decisions if deemed necessary. Results suggest that GPs hold potential for next-generation ABR hearing threshold and audiogram-seeking devices.

Do iron homeostasis biomarkers mediate the associations of liability to type 2 diabetes and glycemic traits in liver steatosis and cirrhosis: a two-step Mendelian randomization study.

BACKGROUND: Previous studies, including Mendelian randomization (MR), have demonstrated type 2 diabetes (T2D) and glycemic traits are associated with increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have explored the underlying pathway, such as the role of iron homeostasis. METHODS: We used a two-step MR approach to investigate the associations of genetic liability to T2D, glycemic traits, iron biomarkers, and liver diseases. We analyzed summary statistics from various genome-wide association studies of T2D (n = 933,970), glycemic traits (n ≤ 209,605), iron biomarkers (n ≤ 246,139), MASLD (n ≤ 972,707), and related biomarkers (alanine aminotransferase (ALT) and proton density fat fraction (PDFF)). Our primary analysis was based on inverse-variance weighting, followed by several sensitivity analyses. We also conducted mediation analyses and explored the role of liver iron in post hoc analysis. RESULTS: Genetic liability to T2D and elevated fasting insulin (FI) likely increased risk of liver steatosis (ORliability to T2D: 1.14 per doubling in the prevalence, 95% CI: 1.10, 1.19; ORFI: 3.31 per log pmol/l, 95% CI: 1.92, 5.72) and related biomarkers. Liability to T2D also likely increased the risk of developing liver cirrhosis. Genetically elevated ferritin, serum iron, and liver iron were associated with higher risk of liver steatosis (ORferritin: 1.25 per SD, 95% CI 1.07, 1.46; ORliver iron: 1.15 per SD, 95% CI: 1.05, 1.26) and liver cirrhosis (ORserum iron: 1.31, 95% CI: 1.06, 1.63; ORliver iron: 1.34, 95% CI: 1.07, 1.68). Ferritin partially mediated the association between FI and liver steatosis (proportion mediated: 7%, 95% CI: 2-12%). CONCLUSIONS: Our study provides credible evidence on the causal role of T2D and elevated insulin in liver steatosis and cirrhosis risk and indicates ferritin may play a mediating role in this association.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Apathy After Stroke: Incidence, Symptom Trajectory, and Impact on Quality of Life and Disability

BACKGROUND AND OBJECTIVES: Apathy is one of the most common symptoms following stroke and is often associated with worse functional outcome and poor quality of life (QoL). The trajectory of apathy symptoms has been previously described, and different trajectories have been identified. We determined group and individual changes in apathy symptomatology from the acute phase until 1 year after stroke. We also examined the association of apathy and depression with disability and QoL 1 year after stroke. METHODS: We measured apathy in a cohort of ischemic stroke survivors at 4 time points from 0 to 12 months after stroke. The Apathy Evaluation Scale (AES) and Dimensional Apathy Scale (DAS) were administered at each time point. Where possible we obtained apathy measured from carers. Depression was assessed with the Geriatric Depression Scale (GDS). Disability and QoL were assessed with the modified Rankin Scale (mRS) and 36-Item Short Form Survey (SF-36). We examined the cross-sectional and individual trajectory of apathy symptoms in each dimension and looked at associations of apathy and depression soon after stroke with mRS and SF-36 at 1 year. RESULTS: Of 200 participants enrolled, 165 completed apathy measures at 12 months. Patient-rated apathy scores increased in both tests at the group level (AES: χ2(3) = 9.86, p = 0.019; DAS: χ2(3) = 8.49, p = 0.037) and individual level (AES: ß = 0.13, p = 0.002; DAS ß = 0.13, p = 0.005; DAS: executive ß = 0.08, p < 0.001). By contrast, carer-rated apathy did not significantly increase (AES: χ2(3) = 0.75, p = 0.862; DAS: χ2(3) = 2.45, p = 0.484). Apathy scores were associated with worse mRS and SF-36, although most associations were no longer significant when controlling for depression. GDS was associated with worse mRS and SF-36 after controlling for covariates and apathy (mRS: ß = 0.08, p = 0.006; SF-36 Mental Component Summary: ß = -1.53, p < 0.001; SF-36 Physical Component Summary: ß = -0.57, p = 0.016). DISCUSSION: Self-reported apathy progressively increases after stroke, especially in the executive dimension. Apathy is associated with worse QoL and greater disability, although some of these associations might be mediated by depression.

Dementia risk factors in former contact sports participants: prospective cohort study.

Background: The elevated dementia incidence in retired contact sport participants might be explained by a higher prevalence of established risk factors for the disease relative to the general population. Methods: In this cohort study, former elite participants active between 1920 and 1965 in soccer (N=303), boxing (N=281), and wrestling (N=318) were recruited using sports yearbooks and records of sports associations. Men in a population control group were identified using records from a compulsory medical examination (N=1712). All study members were linked to hospital registers (1970-2015) and self-completion questionnaires were circulated (1985, 1995) from which we captured data on nine established risk factors for dementia: hypertension and diabetes status, alcohol intake, loneliness, depressive symptoms, cigarette smoking, body weight, educational attainment, and physical activity. Results: There was little suggestion that former participants in contact sports had a higher prevalence of dementia risk factors relative to the general population. Rather, the balance of evidence was for more favourable risk factor levels in former athletes, as was particularly evident for ever having smoked cigarettes (range in odds ratios [95% confidence interval]: 0.32 [0.21, 0.48] for wrestling to 0.52 [0.36, 0.75] for soccer) and leisure-time physical activity (range in beta coefficients [95% confidence interval]: 1.34 [0.66, 2.02] for soccer to 1.80 [1.07, 2.52] for boxing). Conclusions: The increased dementia rates in retired contact sport participants evident in epidemiological studies is unlikely to be explained by the risk factors examined here. This implicates other characteristics of contact sports, including a history of repeated head impact.

Vestibular evoked myogenic potential (VEMP) test-retest reliability in adults.

BACKGROUND: The technique of measuring ocular vestibular evoked myogenic potentials (oVEMP) in response to Mini-shaker vibration is relatively new, there is a limited normative data to define the presence or absence of a response in the literature. OBJECTIVE: To determine the test-retest reliability of cervical and ocular VEMPs (cVEMP and oVEMP, respectively) to air-conducted sound (ACS) and bone-conducted vibration (BCV) stimulation and to determine normative ranges for the responses. METHODS: Twenty normal-hearing individuals (40 ears) and 20 hearing impaired volunteers with normal balance function (40 ears) were examined in this study. ACS cVEMP and BCV oVEMP (using a Mini-shaker) were recorded from both groups to assess the test-retest reliability and to collect normative VEMP data for P1/N1 latencies and amplitudes from 20 normal hearing individuals. To test reliability, VEMP recordings were repeated within the same session. RESULTS: The test-retest reliability for all the cVEMP parameters showed excellent reliability whereas oVEMP parameters showed between fair and excellent reliability depending on the parameter tested. Normative data for VEMP P1/N1 latencies and amplitudes were established. CONCLUSIONS: Normative data and test-retest reliability for BCV oVEMP using the Mini-shaker at 100 Hz were established in our study for the first time in the literature. Responses appear reliable.

Binge-pattern alcohol consumption and genetic risk as determinants of alcohol-related liver disease.

Alcohol-related liver disease (ARLD) represents a major public health burden. Identification of high-risk individuals would allow efficient targeting of public health interventions. Here, we show significant interactions between pattern of drinking, genetic predisposition (polygenic risk score, PRS) and diabetes mellitus, and risk of incident ARLD, in 312,599 actively drinking adults in UK Biobank. Binge and heavy binge drinking significantly increase the risk of alcohol-related cirrhosis (ARC), with higher genetic predisposition further amplifying the risk. Further, we demonstrate a pronounced interaction between heavy binge drinking and high PRS, resulting in a relative excess risk due to interaction (RERI) of 6.07. Diabetes consistently elevates ARC risk across all drinking and PRS categories, and showed significant interaction with both binge patterns and genetic risk. Overall, we demonstrate synergistic effects of binge drinking, genetics, and diabetes on ARC, with potential to identify high-risk individuals for targeted interventions.

A novel human iPSC model of COL4A1/A2 small vessel disease unveils a key pathogenic role of matrix metalloproteinases.

Cerebral small vessel disease (SVD) affects the small vessels in the brain and is a leading cause of stroke and dementia. Emerging evidence supports a role of the extracellular matrix (ECM), at the interface between blood and brain, in the progression of SVD pathology, but this remains poorly characterized. To address ECM role in SVD, we developed a co-culture model of mural and endothelial cells using human induced pluripotent stem cells from patients with COL4A1/A2 SVD-related mutations. This model revealed that these mutations induce apoptosis, migration defects, ECM remodeling, and transcriptome changes in mural cells. Importantly, these mural cell defects exert a detrimental effect on endothelial cell tight junctions through paracrine actions. COL4A1/A2 models also express high levels of matrix metalloproteinases (MMPs), and inhibiting MMP activity partially rescues the ECM abnormalities and mural cell phenotypic changes. These data provide a basis for targeting MMP as a therapeutic opportunity in SVD.

Development and validation of a nomogram for predicting internal mammary sentinel node metastasis in breast cancer patients.

OBJECTIVE: Internal mammary nodes are important in breast cancer prognosis, but their diagnosis is often missed in clinical practice, leading to inaccurate staging and treatment. We developed a validated nomogram to predict the presence of internal mammary sentinel nodes (IMSN) metastasis. METHODS: A total of 864 sequential IMSN biopsy procedures from a prospective studies database of 1505 cases were used for model development and validation. Multivariable logistic regression was performed on 519 sequential IMSN biopsy procedures from multi-center data between August 2018 and July 2022 to predict the presence of IMSN metastasis. A nomogram was developed based on the logistic regression model and subsequently applied to 345 sequential IMSN biopsy procedures from single-center data between November 2011 and July 2018. The model's discrimination was assessed using the area under the receiver operating characteristic curve. RESULTS: The overall frequency of IMSN metastasis was 17.0% in our study. A predictive model for IMSN metastasis was constructed using tumor size, tumor location, lymphovascular invasion, the number of positive axillary nodes (P < 0.05 for all variables in multivariate analysis), and histological grade (P < 0.05 only in univariate analysis). The nomogram was accurate, with a concordance index of 0.84 in the bootstrapping analysis and an area under the receiver operating characteristic curve of 0.80 in the validation population. CONCLUSION: Our nomogram provides an accurate and validated multivariable predictive model for estimating the individual likelihood of having IMSN metastasis. This may be useful for personalized treatment decisions regarding internal mammary radiotherapy in breast cancer patients.

Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models.

Background: Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods. Methods: We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models. Results: We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature. Conclusions: When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features.

Liquids with High Compressibility.

Compressibility is a fundamental property of all materials. For fluids, that is, gases and liquids, compressibility forms the basis of technologies such as pneumatics and hydraulics and determines basic phenomena such as the propagation of sound and shock waves. In contrast to gases, liquids are almost incompressible. If the compressibility of liquids could be increased and controlled, new applications in hydraulics and shock absorption could result. Here, it is shown that dispersing hydrophobic porous particles into water gives aqueous suspensions with much greater compressibilities than any normal liquids such as water (specifically, up to 20 times greater over certain pressure ranges). The increased compressibility results from water molecules being forced into the hydrophobic pores of the particles under applied pressure. The degree of compression can be controlled by varying the amount of porous particles added. Also, the pressure range of compression can be reduced by adding methanol or increased by adding salt. In all cases, the liquids expand back to their original volume when the applied pressure is released. The approach shown here is simple and economical and could potentially be scaled up to give large amounts of highly compressible liquids.

SERS as a Probe of Surface Chemistry Enabled by Surface-Accessible Plasmonic Nanomaterials.

ConspectusWhen the size of materials is reduced, their volume decreases much faster than their surface area, which in the most extreme case leads to 2D nanomaterials which are "all surface". Since atoms at the surface have free energies, electronic states, and mobility which are very different from bulk atoms, nanomaterials that have large surface-to-volume ratios can display remarkable new properties compared to their bulk counterparts. More generally, the surface is where nanomaterials interact with their environment, which in turn places surface chemistry at the heart of catalysis, nanotechnology, and sensing applications. Understanding and utilizing nanosurfaces are not possible without appropriate spectroscopic and microscopic characterization techniques. An emerging technique in this area is surface-enhanced Raman spectroscopy (SERS), which utilizes the interaction between plasmonic nanoparticles and light to enhance the Raman signals of molecules near the nanoparticles' surfaces. SERS has the great advantage that it can provide detailed in situ information on surface orientation and binding between molecules and the nanosurface. A long-standing dilemma that has limited the applications of SERS in surface chemistry studies is the choice between surface-accessibility and plasmonic activity. More specifically, the synthesis of metal nanomaterials with strong plasmonic and SERS-enhancing properties typically involves the use of strongly adsorbing modifier molecules, but these modifiers also passivate the surface of the product material, which prevents the general application of SERS in the analysis of weaker molecule-metal interactions.In this Account, we discuss our efforts in the development of modifier-free synthetic approaches to synthesize surface-accessible, plasmonic nanomaterials for SERS. We start by discussing the definition of "modifiers" and "surface-accessibility", especially in the context of surface chemistry studies in SERS. As a general rule of thumb, the chemical ligands on surface-accessible nanomaterials should be easily displaceable by a wide range of target molecules relevant to potential applications. We then introduce modifier-free approaches for the bottom-up synthesis of colloidal nanoparticles, which are the basic building blocks for nanotechnology. Following this, we introduce modifier-free interfacial self-assembly approaches developed by our group that allow the creation of multidimensional plasmonic nanoparticle arrays from different types of nanoparticle-building blocks. These multidimensional arrays can be further combined with different types of functional materials to form surface-accessible multifunctional hybrid plasmonic materials. Finally, we demonstrate applications for surface-accessible nanomaterials as plasmonic substrates for SERS studies of surface chemistry. Importantly, our studies revealed that the removal of modifiers led to not only significantly enhanced properties but also the observation of new surface chemistry phenomena that had been previously overlooked or misunderstood in the literature. Realizing the current limitations of modifier-based approaches provides new perspectives in manipulating molecule-metal interactions in nanotechnology and can have significant implications in the design and synthesis of the next generation of nanomaterials.

Combining surface-accessible Ag and Au colloidal nanomaterials with SERS for in situ analysis of molecule-metal interactions in complex solution environments.

The interactions between molecules and noble metal nanosurfaces play a central role in many areas of nanotechnology. The surface chemistry of noble metal surfaces under ideal, clean conditions has been extensively studied; however, clean conditions are seldom met in real-world applications. We developed a sensitive and robust characterization technique for probing the surface chemistry of nanomaterials in the complex environments that are directly relevant to their applications. Surface-enhanced Raman spectroscopy (SERS) can be used to probe the interaction of plasmonic nanoparticles with light to enhance the Raman signals of molecules near the surface of nanoparticles. Here, we explain how to couple SERS with surface-accessible plasmonic-enhancing substrates, which are capped with weakly adsorbing capping ligands such as citrate and chloride ions, to allow molecule-metal interactions to be probed in situ and in real time, thus providing information on the surface orientation and the formation and breaking of chemical bonds. The procedure covers the synthesis and characterization of surface-accessible colloids, the preliminary SERS screening with agglomerated colloids, the synthesis and characterization of interfacial nanoparticle assemblies, termed metal liquid-like films, and the in situ biphasic SERS analysis with metal liquid-like films. The applications of the approach are illustrated using two examples: the probing of π-metal interactions and that of target/ligand-particle interactions on hollow bimetallic nanostars. This protocol, from the initial synthesis of the surface-accessible plasmonic nanoparticles to the final in situ biphasic SERS analysis, requires ~14 h and is ideally suited to users with basic knowledge in performing Raman spectroscopy and wet synthesis of metal nanoparticles.

Continuous speech with pauses inserted between words increases cortical tracking of speech envelope.

The decoding multivariate Temporal Response Function (decoder) or speech envelope reconstruction approach is a well-known tool for assessing the cortical tracking of speech envelope. It is used to analyse the correlation between the speech stimulus and the neural response. It is known that auditory late responses are enhanced with longer gaps between stimuli, but it is not clear if this applies to the decoder, and whether the addition of gaps/pauses in continuous speech could be used to increase the envelope reconstruction accuracy. We investigated this in normal hearing participants who listened to continuous speech with no added pauses (natural speech), and then with short (250 ms) or long (500 ms) silent pauses inserted between each word. The total duration for continuous speech stimulus with no, short, and long pauses were approximately, 10 minutes, 16 minutes, and 21 minutes, respectively. EEG and speech envelope were simultaneously acquired and then filtered into delta (1-4 Hz) and theta (4-8 Hz) frequency bands. In addition to analysing responses to the whole speech envelope, speech envelope was also segmented to focus response analysis on onset and non-onset regions of speech separately. Our results show that continuous speech with additional pauses inserted between words significantly increases the speech envelope reconstruction correlations compared to using natural speech, in both the delta and theta frequency bands. It also appears that these increase in speech envelope reconstruction are dominated by the onset regions in the speech envelope. Introducing pauses in speech stimuli has potential clinical benefit for increasing auditory evoked response detectability, though with the disadvantage of speech sounding less natural. The strong effect of pauses and onsets on the decoder should be considered when comparing results from different speech corpora. Whether the increased cortical response, when longer pauses are introduced, reflect improved intelligibility requires further investigation.

Association of gout with brain reserve and vulnerability to neurodegenerative disease.

Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.

A comprehensive evaluation of the longitudinal association between alcohol consumption and a measure of inflammation: Multiverse and vibration of effects analyses.

BACKGROUND: Moderate alcohol consumption appears to be associated with reduced inflammation. Determining whether this association is robust to common variations in research parameters has wide-reaching implications for our understanding of disease aetiology and public health policy. We aimed to conduct comprehensive multiverse and vibration of effects analyses evaluating the associations between alcohol consumption and a measure of inflammation. METHODS: A secondary analysis of the 1970 British Birth Cohort Study was performed, using data from 1970 through 2016. Measurements of alcohol consumption were taken in early/mid-adulthood (ages 34 and 42), and level of inflammation marker high-sensitivity C-reactive protein (hsCRP) at age 46. Multiverse analyses were applied to comparisons of low-to-moderate consumption and consumption above various international drinking guidelines with an 'abstinent' reference. Research parameters of interest related to: definitions of drinking and reference groups; alcohol consumption measurement year; outcome variable transformation; and breadth of covariate adjustment. After identifying various analytic options within these parameters and running the analysis over each unique option combination, specification curve plots, volcano plots, effect ranges, and variance decomposition metrics were used to assess consistency of results. RESULTS: A total of 3101 individuals were included in the final analyses, with primary analyses limited to those where occasional consumers served as reference. All combinations of research specifications resulted in lower levels of inflammation amongst low-to-moderate consumers compared to occasional consumers (1st percentile effect: -0.21; 99th percentile effect: -0.04). Estimates comparing above-guidelines drinking with occasional consumers were less definitive (1st percentile effect: -0.26; 99th percentile effect: 0.43). CONCLUSIONS: The association between low-to-moderate drinking and lower hsCRP levels is largely robust to common variations in researcher-defined parameters, warranting further research to establish whether this relationship is causal. The association between above-guidelines drinking and hsCRP levels is less definitive.