Stage-Dependent Biomarker Changes in Spinocerebellar Ataxia Type 3.

Spinocerebellar ataxia type 3/Machado-Joseph disease is the most common autosomal dominant ataxia. In view of the development of targeted therapies, knowledge of early biomarker changes is needed. We analyzed cross-sectional data of 292 spinocerebellar ataxia type 3/Machado-Joseph disease mutation carriers. Blood concentrations of mutant ATXN3 were high before and after ataxia onset, whereas neurofilament light deviated from normal 13.3 years before onset. Pons and cerebellar white matter volumes decreased and deviated from normal 2.2 years and 0.6 years before ataxia onset. We propose a staging model of spinocerebellar ataxia type 3/Machado-Joseph disease that includes a biomarker stage characterized by objective indicators of neurodegeneration before ataxia onset. ANN NEUROL 2024;95:400-406.

Different inflammatory signatures based on CSF biomarkers relate to preserved or diminished brain structure and cognition.

Neuroinflammation is a hallmark of Alzheimer's disease (AD) and both positive and negative associations of individual inflammation-related markers with brain structure and cognitive function have been described. We aimed to identify inflammatory signatures of CSF immune-related markers that relate to changes of brain structure and cognition across the clinical spectrum ranging from normal aging to AD. A panel of 16 inflammatory markers, Aß42/40 and p-tau181 were measured in CSF at baseline in the DZNE DELCODE cohort (n = 295); a longitudinal observational study focusing on at-risk stages of AD. Volumetric maps of gray and white matter (GM/WM; n = 261) and white matter hyperintensities (WMHs, n = 249) were derived from baseline MRIs. Cognitive decline (n = 204) and the rate of change in GM volume was measured in subjects with at least 3 visits (n = 175). A principal component analysis on the CSF markers revealed four inflammatory components (PCs). Of these, the first component PC1 (highly loading on sTyro3, sAXL, sTREM2, YKL-40, and C1q) was associated with older age and higher p-tau levels, but with less pathological Aß when controlling for p-tau. PC2 (highly loading on CRP, IL-18, complement factor F/H and C4) was related to male gender, higher body mass index and greater vascular risk. PC1 levels, adjusted for AD markers, were related to higher GM and WM volumes, less WMHs, better baseline memory, and to slower atrophy rates in AD-related areas and less cognitive decline. In contrast, PC2 related to less GM and WM volumes and worse memory at baseline. Similar inflammatory signatures and associations were identified in the independent F.ACE cohort. Our data suggest that there are beneficial and detrimental signatures of inflammatory CSF biomarkers. While higher levels of TAM receptors (sTyro/sAXL) or sTREM2 might reflect a protective glia response to degeneration related to phagocytic clearance, other markers might rather reflect proinflammatory states that have detrimental impact on brain integrity.

Single-value brain activity scores reflect both severity and risk across the Alzheimer's continuum.

Single-value scores reflecting the deviation from (FADE score) or similarity with (SAME score) prototypical novelty-related and memory-related functional magnetic resonance imaging (fMRI) activation patterns in young adults have been proposed as imaging biomarkers of healthy neurocognitive aging. Here, we tested the utility of these scores as potential diagnostic and prognostic markers in Alzheimer's disease (AD) and risk states like mild cognitive impairment (MCI) or subjective cognitive decline (SCD). To this end, we analyzed subsequent memory fMRI data from individuals with SCD, MCI, and AD dementia as well as healthy controls (HC) and first-degree relatives of AD dementia patients (AD-rel) who participated in the multi-center DELCODE study (N = 468). Based on the individual participants' whole-brain fMRI novelty and subsequent memory responses, we calculated the FADE and SAME scores and assessed their association with AD risk stage, neuropsychological test scores, CSF amyloid positivity, and ApoE genotype. Memory-based FADE and SAME scores showed a considerably larger deviation from a reference sample of young adults in the MCI and AD dementia groups compared to HC, SCD and AD-rel. In addition, novelty-based scores significantly differed between the MCI and AD dementia groups. Across the entire sample, single-value scores correlated with neuropsychological test performance. The novelty-based SAME score further differed between Aß-positive and Aß-negative individuals in SCD and AD-rel, and between ApoE ε4 carriers and non-carriers in AD-rel. Hence, FADE and SAME scores are associated with both cognitive performance and individual risk factors for AD. Their potential utility as diagnostic and prognostic biomarkers warrants further exploration, particularly in individuals with SCD and healthy relatives of AD dementia patients.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

Benchmark of filter methods for feature selection in high-dimensional gene expression survival data.

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.

Associations of Urine and Plasma Metabolites With Kidney Failure and Death in a Chronic Kidney Disease Cohort.

RATIONALE & OBJECTIVE: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. STUDY DESIGN: Prospective metabolome-wide association study. SETTING & PARTICIPANTS: Persons with CKD enrolled in the GCKD (German CKD) study with metabolite measurements, with external validation within the ARIC (Atherosclerosis Risk in Communities) Study. EXPOSURES: 1,513 urine and 1,416 plasma metabolites (Metabolon Inc) measured at study entry using untargeted mass spectrometry. OUTCOMES: Main end points were kidney failure (KF) and a composite kidney end point (CKE) of KF, estimated glomerular filtration rate<15mL/min/1.73m2, or a 40% decrease in estimated glomerular filtration rate. Death from any cause was a secondary end point. After a median of 6.5 years of follow-up, 500 persons had experienced KF, 1,083 had experienced the CKE, and 680 had died. ANALYTICAL APPROACH: Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design with external validation. RESULTS: 5,088 GCKD study participants were included in analyses of urine metabolites, and 5,144 were included in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with the CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (HR, 1.95; 95% CI, 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, the CKE, and death. External validation of the identified associations of metabolites with KF or the CKE revealed directional consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LIMITATIONS: Use of observational data and semiquantitative metabolite measurements at a single time point. CONCLUSIONS: The observed associations between metabolites and KF, the CKE, or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts. PLAIN-LANGUAGE SUMMARY: Incomplete understanding of the variability of chronic kidney disease (CKD) progression motivated the search for new biomarkers that would help identify people at increased risk. We explored metabolites in plasma and urine for their association with unfavorable kidney outcomes or death in persons with CKD. Metabolomic analyses revealed 182 metabolites significantly associated with CKD progression or death. Many of these associations confirmed previously reported findings or were validated by analysis in an external study population. Our comprehensive screen of the metabolome serves as a valuable foundation for future investigations into biomarkers associated with CKD progression.

Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the "Flip-Flop" phenomenon).

BACKGROUND: Atopic dermatitis (AD) and psoriasis vulgaris (PV) are almost mutually exclusive diseases with different immune polarizations, mechanisms and therapeutic targets. Switches to the other disease ("Flip-Flop" [FF] phenomenon) can occur with or without systemic treatment and are often referred to as paradoxical reactions under biological therapy. METHODS: The objective was to develop a diagnostic algorithm by combining clinical criteria of AD and PV to identify FF patients. The algorithm was prospectively validated in patients enrolled in the CK-CARE registry in Bonn, Germany. Afterward, algorithm refinements were implemented based on machine learning. RESULTS: Three hundred adult Caucasian patients were included in the validation study (n = 238 with AD, n = 49 with PV, n = 13 with FF; mean age 41.2 years; n = 161 [53.7%] female). The total FF scores of the PV and AD groups differed significantly from the FF group in the validation data (p < .001). The predictive mean generalized Youden-Index of the initial model was 78.9% [95% confidence interval 72.0%-85.6%] and the accuracy was 89.7%. Disease group-specific sensitivity was 100% (FF), 95.0% (AD), and 61.2% (PV). The specificity was 89.2% (FF), 100% (AD), and 100% (PV), respectively. CONCLUSION: The FF algorithm represents the first validated tool to identify FF patients.

Interactive exploration of adverse events and multimorbidity in CKD.

BACKGROUND AND HYPOTHESIS: Persons with chronic kidney disease (CKD) are at increased risk of adverse events, early mortality, and multimorbidity. A detailed overview of adverse event types and rates from a large CKD cohort under regular nephrological care is missing. We generated an interactive tool to enable exploration of adverse events and their combinations in the prospective, observational German CKD (GCKD) study. METHODS: The GCKD study enrolled 5217 participants under regular nephrological care with an estimated glomerular filtration rate of 30-60 or >60 mL/min/1.73m2 and an overt proteinuria. Cardio-, cerebro- and peripheral vascular, kidney, infection, and cancer events, as well as deaths were adjudicated following a standard operation procedure. We summarized these time-to-event data points for exploration in interactive graphs within an R shiny app. Multivariable adjusted Cox models for time to first event were fitted. Cumulative incidence functions, Kaplan-Meier curves and intersection plots were used to display main adverse events and their combinations by sex and CKD etiology. RESULTS: Over a median of 6.5 years, 10 271 events occurred in total and 680 participants (13.0%) died while 2947 participants (56.5%) experienced any event. The new publicly available interactive platform enables readers to scrutinize adverse events and their combinations as well as mortality trends as a gateway to better understand multimorbidity in CKD: incident rates per 1000 patient-years varied by event type, CKD etiology, and baseline characteristics. Incidence rates for the most frequent events and their recurrence were 113.6 (cardiovascular), 75.0 (kidney), and 66.0 (infection). Participants with diabetic kidney disease and men were more prone to experiencing events. CONCLUSION: This comprehensive explorative tool to visualize adverse events (https://gckd.diz.uk-erlangen.de/), their combination, mortality, and multimorbidity among persons with CKD may manifest as a valuable resource for patient care, identification of high-risk groups, health services, and public health policy planning.

Identification and comparison of sex-specific serious adverse drug reactions in spontaneous reports and systematically collected reports (ADRED).

AIMS: Adverse drug reactions (ADRs) are known to show sex-specific differences in occurrence and phenotype. The aim of this study was to analyse sex-specific differences in ADR-drug combinations that required hospitalization based on two different datasets. METHODS: We performed a complementary analysis of (i) spontaneously reported (n = 12 564, female = 51.7%) and (ii) systematically collected ADR reports from a prospective multicentre observational study (ADRED, n = 2355, female = 48.2%) from Germany in the ADR database EudraVigilance (EV). Both datasets were analysed separately concerning the suspected drugs, ADRs and ADR-drug combinations more frequently reported for females or males by calculating reporting odds ratios (ROR) with 95% confidence intervals. ADR-drug combinations more frequently reported for either females or males in EV reports were related to prescription data. Finally, the results from both datasets were discussed with regard to their (dis-)concordance. RESULTS: In both datasets, some antineoplastic agents and nervous system drugs were found to be reported more often for females than males (RORs ranging from 1.5 [1.1-2.1] for quetiapine in spontaneous reports to 41.3 [13.1-130.0] for trastuzumab in spontaneous reports). ADRs of the respiratory system, and haemorrhages were described predominantly for males in both datasets. In spontaneous reports the ADR-drug combination self-injurious behaviour-quetiapine was more often reported for females without and with consideration of drug prescriptions (ROR: 3.8 [1.3-11.0]). Quetiapine and psychiatric disorders (superordinate level) was exclusively reported for females in ADRED reports. CONCLUSIONS: Our results can contribute to raise awareness and further knowledge regarding sex-specific ADRs. The findings require further in-depth investigation.

Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach.

Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor-response relationships and CIF estimates of renal events.

Plasma amyloid beta X-42/X-40 ratio and cognitive decline in suspected early and preclinical Alzheimer's disease.

INTRODUCTION: Blood-based biomarkers are a cost-effective and minimally invasive method for diagnosing the early and preclinical stages of amyloid positivity (AP). Our study aims to investigate our novel immunoprecipitation-immunoassay (IP-IA) as a test for predicting cognitive decline. METHODS: We measured levels of amyloid beta (Aß)X-40 and AßX-42 in immunoprecipitated eluates from the DELCODE cohort. Receiver-operating characteristic (ROC) curves, regression analyses, and Cox proportional hazard regression models were constructed to predict AP by Aß42/40 classification in cerebrospinal fluid (CSF) and conversion to mild cognitive impairment (MCI) or dementia. RESULTS: We detected a significant correlation between AßX-42/X-40 in plasma and CSF (r = 0.473). Mixed-modeling analysis revealed a substantial prediction of AßX-42/X-40 with an area under the curve (AUC) of 0.81 for AP (sensitivity: 0.79, specificity: 0.74, positive predictive value [PPV]: 0.71, negative predictive value [NPV]: 0.81). In addition, lower AßX-42/X-40 ratios were associated with negative PACC5 slopes, suggesting cognitive decline. DISCUSSION: Our results suggest that assessing the plasma AßX-42/X-40 ratio via our semiautomated IP-IA is a promising biomarker when examining patients with early or preclinical AD. HIGHLIGHTS: New plasma Aß42/Aß40 measurement using immunoprecipitation-immunoassay Plasma Aß42/Aß40 associated with longitudinal cognitive decline Promising biomarker to detect subjective cognitive decline at-risk for brain amyloid positivity.

Pseudo-value regression trees.

This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression framework, modeling individual-specific survival probabilities by computing pseudo-values and relating them to a set of covariates. The standard approach to pseudo-value regression is to fit a main-effects model using generalized estimating equations (GEE). PRT extend this approach by building a multivariate regression tree with pseudo-value outcome and by successively fitting a set of regularized additive models to the data in the nodes of the tree. Due to the combination of tree learning and additive modeling, PRT are able to perform variable selection and to identify relevant interactions between the covariates, thereby addressing several limitations of the standard GEE approach. In addition, PRT include time-dependent effects in the node-wise models. Interpretability of the PRT fits is ensured by controlling the tree depth. Based on the results of two simulation studies, we investigate the properties of the PRT method and compare it to several alternative modeling techniques. Furthermore, we illustrate PRT by analyzing survival in 3,652 patients enrolled for a randomized study on primary invasive breast cancer.

IL-13, periostin and dipeptidyl-peptidase-4 reveal endotype-phenotype associations in atopic dermatitis.

BACKGROUND: The heterogeneous (endo)phenotypes of atopic dermatitis (AD) require precision medicine. Currently, systemic therapy is recommended to patients with an Eczema Area and Severity Index (EASI)≥16. Previous studies have demonstrated an improved treatment response to the anti-interleukin (IL)-13 antibody tralokinumab in AD subgroups with elevated levels of the IL-13-related biomarkers dipeptidyl-peptidase (DPP)-4 and periostin. METHODS: Herein, 373 AD patients aged≥12 years were stratified by IL-13high , periostinhigh and DPP-4high endotypes using cross-sectional data from the ProRaD cohort Bonn. "High" was defined as >80th quantile of 47 non-atopic controls. We analyzed endotype-phenotype associations using machine-learning gradient boosting compared to logistic regression. RESULTS: AD severity and eosinophils correlated with IL-13 and periostin levels. Correlations of IL-13 with EASI were stronger in patients with increased (rs=0.482) than with normal (rs=0.342) periostin levels. We identified eosinophilia>6% and an EASI range of 5.5-17 dependent on the biomarker combination to be associated with increasing probabilities of biomarkerhigh endotypes. Also patients with mild-to-low-moderate severity (EASI<16) featured increased biomarkers (IL-13high : 41%, periostinhigh : 48.4%, DPP-4high : 22.3%). Herthoge sign (adjusted Odds Ratio (aOR)=1.89, 95% Confidence Interval (CI) [1.14-3.14]) and maternal allergic rhinitis (aOR=2.79-4.47) increased the probability of an IL-13high -endotype, "dirty neck" (aOR=2.83 [1.32-6.07]), orbital darkening (aOR=2.43 [1.08-5.50]), keratosis pilaris (aOR=2.21 [1.1-4.42]) and perleche (aOR=3.44 [1.72-6.86]) of a DPP-4high -endotype. CONCLUSIONS: A substantial proportion of patients with EASI<16 featured high biomarker levels suggesting systemic impact of skin inflammation already below the current cut-off for systemic therapy. Our findings facilitate the identification of patients with distinct endotypes potentially linked to response to IL-13-targeted therapy.

Atopic dermatitis: Correlation of distinct risk factors with age of onset in adulthood compared to childhood.

BACKGROUND: Atopic dermatitis (AD) has long been regarded as a primarily pediatric disease. However, there is growing evidence for a high rate of adult-onset AD. We aimed to characterize factors associated with adult-onset versus childhood-onset AD and controls. METHODS: We analyzed cross-sectional data of the CK-CARE-ProRaD cohorts Bonn, Augsburg, Davos, Zürich of 736 adult patients stratified by age of AD onset (childhood-onset <18 years: 76.4% (subsets: 0 to 2; ≥2 to 6; ≥7 to 11; ≥12 to 18); adult-onset ≥18 years: 23.6% (subsets: ≥18 to 40; ≥41 to 60; ≥61) and 167 controls (91 atopic, 76 non-atopic)). RESULTS: We identified active smoking to be associated with adult-onset AD versus controls (adjusted Odds Ratio (aOR) = 5.54 [95% Confidence Interval: 1.06-29.01] vs. controlsnon-atopic , aOR = 4.03 [1.20-13.45] vs. controlsatopic ). Conjunctivitis showed a negative association versus controlsatopic (aOR = 0.36 [0.14-0.91]). Food allergy (aOR = 2.93 [1.44-5.96]), maternal food allergy (aOR = 9.43 [1.10-80.95]), palmar hyperlinearity (aOR = 2.11 [1.05-4.25]), and academic background (aOR = 2.14 [1.00-4.54]) increased the odds of childhood-onset AD versus controlsatopic . Shared AD-associated factors were maternal AD (4-34x), increased IgE (2-20x), atopic stigmata (2-3x) with varying effect sizes depending on AD onset and control group. Patients with adult-compared to childhood-onset had doubled odds of allergic rhinitis (aOR = 2.15 [1.12-4.13]), but reduced odds to feature multiple (3-4) atopic comorbidities (aOR = 0.34 [0.14-0.84]). Adult-onset AD, particularly onset ≥61 years, grouped mainly in clusters with low contributions of personal and familial atopy and high frequencies of physical inactivity, childhood-onset AD, particularly infant-onset, mainly in "high-atopic"-clusters. CONCLUSIONS: The identified associated factors suggest partly varying endo- and exogeneous mechanisms underlying adult-onset versus childhood-onset AD. Our findings might contribute to better assessment of the individual risk to develop AD throughout life and encourage prevention by non-smoking and physical activity as modifiable lifestyle factors.

Evolution of Clinical Outcome Measures and Biomarkers in Sporadic Adult-Onset Degenerative Ataxia.

BACKGROUND: Sporadic adult-onset ataxias without known genetic or acquired cause are subdivided into multiple system atrophy of cerebellar type (MSA-C) and sporadic adult-onset ataxia of unknown etiology (SAOA). OBJECTIVES: To study the differential evolution of both conditions including plasma neurofilament light chain (NfL) levels and magnetic resonance imaging (MRI) markers. METHODS: SPORTAX is a prospective registry of sporadic ataxia patients with an onset >40 years. Scale for the Assessment and Rating of Ataxia was the primary outcome measure. In subgroups, blood samples were taken and MRIs performed. Plasma NfL was measured via a single molecule assay. Regional brain volumes were automatically measured. To assess signal changes, we defined the pons and middle cerebellar peduncle abnormality score (PMAS). Using mixed-effects models, we analyzed changes on a time scale starting with ataxia onset. RESULTS: Of 404 patients without genetic diagnosis, 130 met criteria of probable MSA-C at baseline and 26 during follow-up suggesting clinical conversion to MSA-C. The remaining 248 were classified as SAOA. At baseline, NfL, cerebellar white matter (CWM) and pons volume, and PMAS separated MSA-C from SAOA. NfL decreased in MSA-C and did not change in SAOA. CWM and pons volume decreased faster, whereas PMAS increased faster in MSA-C. In MSA-C, pons volume had highest sensitivity to change, and PMAS was a predictor of faster progression. Fulfillment of possible MSA criteria, NfL and PMAS were risk factors, CWM and pons volume protective factors for conversion to MSA-C. CONCLUSIONS: This study provides detailed information on differential evolution and prognostic relevance of biomarkers in MSA-C and SAOA. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Association of osteopontin with kidney function and kidney failure in chronic kidney disease patients: the GCKD study.

BACKGROUND: Osteopontin (OPN), synthesized in the thick ascending limb of Henle's loop and in the distal tubule, is involved in the pathogenesis of kidney fibrosis, a hallmark of kidney failure (KF). In a cohort of chronic kidney disease (CKD) patients, we evaluated OPN's association with kidney markers and KF. METHODS: OPN was measured from baseline serum samples of German Chronic Kidney Disease study participants. Cross-sectional regression models for estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR) as well as Cox regression models for all-cause mortality and KF were evaluated to estimate the OPN effect. Additionally, the predictive ability of OPN and time-dependent population-attributable fraction were evaluated. RESULTS: Over a median follow-up of 6.5 years, 471 KF events and 629 deaths occurred among 4950 CKD patients. One-unit higher log(OPN) was associated with 5.5 mL/min/1.73 m2 lower eGFR [95% confidence interval (95% CI) -6.4 to -4.6] and 1% change in OPN with 0.7% higher UACR (estimated effect 0.7, 95% CI 0.6-0.8). Moreover, higher OPN levels were associated with a higher risk of KF [hazard ratio (HR) 1.4, 95% CI 1.2-1.7] and all-cause mortality (HR 1.5, 95% CI 1.3-1.8). After 6 years, 31% of the KF events could be attributed to higher OPN levels (95% CI 3%-56%). CONCLUSIONS: In this study, higher OPN levels were associated with kidney function markers worsening and a higher risk for adverse outcomes. A larger proportion of KF could be attributed to higher OPN levels, warranting further research on OPN with regards to its role in CKD progression and possible treatment options.

Calibrating machine learning approaches for probability estimation: A comprehensive comparison.

Statistical prediction models have gained popularity in applied research. One challenge is the transfer of the prediction model to a different population which may be structurally different from the model for which it has been developed. An adaptation to the new population can be achieved by calibrating the model to the characteristics of the target population, for which numerous calibration techniques exist. In view of this diversity, we performed a systematic evaluation of various popular calibration approaches used by the statistical and the machine learning communities for estimating two-class probabilities. In this work, we first provide a review of the literature and, second, present the results of a comprehensive simulation study. The calibration approaches are compared with respect to their empirical properties and relationships, their ability to generalize precise probability estimates to external populations and their availability in terms of easy-to-use software implementations. Third, we provide code from real data analysis allowing its application by researchers. Logistic calibration and beta calibration, which estimate an intercept plus one and two slope parameters, respectively, consistently showed the best results in the simulation studies. Calibration on logit transformed probability estimates generally outperformed calibration methods on nontransformed estimates. In case of structural differences between training and validation data, re-estimation of the entire prediction model should be outweighted against sample size of the validation data. We recommend regression-based calibration approaches using transformed probability estimates, where at least one slope is estimated in addition to an intercept for updating probability estimates in validation studies.

Analysis of associations between dietary patterns, genetic disposition, and cognitive function in data from UK Biobank.

PURPOSE: Research suggests that diet influences cognitive function and the risk for neurodegenerative disease. The present study aimed to determine whether a recently developed diet score, based on recommendations for dietary priorities for cardio metabolic health, was associated with fluid intelligence, and whether these associations were modified by individual genetic disposition. METHODS: This research has been conducted using the UK Biobank Resource. Analyses were performed using self-report data on diet and the results for the verbal-numerical reasoning test of fluid intelligence of 104,895 individuals (46% male: mean age at recruitment 57.1 years (range 40-70)). For each participant, a diet score and a polygenic score (PGS) were constructed, which evaluated predefined cut-offs for the intake of fruit, vegetables, fish, processed meat, unprocessed meat, whole grain, and refined grain, and ranged from 0 (unfavorable) to 7 (favorable). To investigate whether the diet score was associated with fluid intelligence, and whether the association was modified by PGS, linear regression analyses were performed. RESULTS: The average diet score was 3.9 (SD 1.4). After adjustment for selected confounders, a positive association was found between baseline fluid intelligence and PGS (P < 0.001). No association was found between baseline fluid intelligence and diet score (P = 0.601), even after stratification for PGS, or in participants with longitudinal data available (n = 9,482). CONCLUSION: In this middle-aged cohort, no evidence was found for an association between the investigated diet score and either baseline or longitudinal fluid intelligence. However, as in previous reports, fluid intelligence was strongly associated with a PGS for general cognitive function.

Association between urinary iodine excretion, genetic disposition and fluid intelligence in children, adolescents and young adults: the DONALD study.

PURPOSE: Iodine deficiency increases the risk of cognitive impairment and delayed physical development in children. It is also associated with cognitive impairment in adults. Cognitive abilities are among the most inheritable behavioural traits. However, little is known about the consequences of insufficient postnatal iodine intake and whether the individual genetic disposition modifies the association between iodine intake and fluid intelligence in children and young adults. METHODS: The cultural fair intelligence test was used to assess fluid intelligence in the participants of the DONALD study (n = 238; mean age, 16.5 [SD = 7.7] years). Urinary iodine excretion, a surrogate iodine intake marker, was measured in 24-h urine. Individual genetic disposition (n = 162) was assessed using a polygenic score, associated with general cognitive function. Linear regression analyses were conducted to determine whether Urinary iodine excretion was associated with fluid intelligence and whether this association was modified by individual genetic disposition. RESULTS: Urinary iodine excretion above the age-specific estimated average requirement was associated with a five-point higher fluid intelligence score than that below the estimated average requirement (P = 0.02). The polygenic score was positively associated with the fluid intelligence score (ß = 2.3; P = 0.03). Participants with a higher polygenic score had a higher fluid intelligence score. CONCLUSION: Urinary iodine excretion above the estimated average requirement in childhood and adolescence is beneficial for fluid intelligence. In adults, fluid intelligence was positively associated with a polygenic score for general cognitive function. No evidence showed that the individual genetic disposition modifies the association between Urinary iodine excretion and fluid intelligence.

Strong seasonal patterns of DOC release by a temperate seaweed community: Implications for the coastal ocean carbon cycle.

Release of dissolved organic carbon (DOC) by seaweed underpins the microbial food web and is crucial for the coastal ocean carbon cycle. However, we know relatively little of seasonal DOC release patterns in temperate regions of the southern hemisphere. Strong seasonal changes in inorganic nitrogen availability, irradiance, and temperature regulate the growth of seaweeds on temperate reefs and influence DOC release. We seasonally surveyed and sampled seaweed at Coal Point, Tasmania, over 1 year. Dominant species with or without carbon dioxide (CO2 ) concentrating mechanisms (CCMs) were collected for laboratory experiments to determine seasonal rates of DOC release. During spring and summer, substantial DOC release (10.06-33.54 µmol C · g DW-1 · h-1 ) was observed for all species, between 3 and 27 times greater than during autumn and winter. Our results suggest that inorganic carbon (Ci ) uptake strategy does not regulate DOC release. Seasonal patterns of DOC release were likely a result of photosynthetic overflow during periods of high gross photosynthesis indicated by variations in tissue C:N ratios. For each season, we calculated a reef-scale net DOC release for seaweed at Coal Point of 7.84-12.9 g C · m-2 · d-1 in spring and summer, which was ~16 times greater than in autumn and winter (0.2-1.0 g C · m-2 · d-1 ). Phyllospora comosa, which dominated the biomass, contributed the most DOC to the coastal ocean, up to ~14 times more than Ecklonia radiata and the understory assemblage combined. Reef-scale DOC release was driven by seasonal changes in seaweed physiology rather than seaweed biomass.