CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS: We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION: Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

Adaptive digital tissue deconvolution.

MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. RESULTS: We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION: A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362).

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.

Potential and Uncertainties of RejectClass in Acute Kidney Graft Dysfunction: An Independent Validation Study.

BACKGROUND: Kidney graft rejections are classified based on the Banff classification. The RejectClass algorithm, initially derived from a cohort comprising mostly protocol biopsies, identifies data-driven phenotypes of acute rejection and chronic pathology using Banff lesion scores. It also provides composite scores for inflammation activity and chronicity. This study independently evaluates the performance of RejectClass in a cohort consisting entirely of indication biopsies. METHODS: We retrospectively applied RejectClass to 441 patients from the German TRABIO (TRAnsplant BIOpsies) cohort who had received indication biopsies. The primary endpoint was death-censored graft failure during 2 y of follow-up. RESULTS: The application of RejectClass to our cohort demonstrated moderately comparable phenotypic features with the derivation cohort, and most clusters indicated an elevated risk of graft loss. However, the reproduction of all phenotypes and the associated risks of graft failure, as depicted in the original studies, was not fully accomplished. In contrast, adjusted Cox proportional hazards analyses substantiated that both the inflammation score and the chronicity score are independently associated with graft loss, exhibiting hazard ratios of 1.7 (95% confidence interval, 1.2-2.3; P = 0.002) and 2.2 (95% confidence interval, 1.8-2.6; P < 0.001), respectively, per 0.25-point increment (scale: 0.0-1.0). CONCLUSIONS: The composite inflammation and chronicity scores may already have direct utility in quantitatively assessing the disease stage. Further refinement and validation of RejectClass clusters are necessary to achieve more reliable and accurate phenotyping of rejection.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data.

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

The German Transplantation Registry Reveals Deficiencies in the Listing Process for Kidney Transplantation.

Introduction: The time from dialysis onset to enrollment on the kidney waiting list (listing time) is a crucial step on the path to receiving a kidney allograft; however, this process has received very little research attention in the Eurotransplant (ET) area. Methods: We retrospectively analyzed data from the German transplantation registry, including patients who were on the waiting list for a first kidney transplant in Germany between 2006 and 2016. Listing time was evaluated using a mixed linear model. The outcomes on the kidney waiting list were assessed using competing risk analyses. Results: We assessed a total of 43,955 patients. Listing occurred at a higher pace in patients receiving living donor transplantations (median 0.4 years from dialysis onset) than in deceased donor transplantations (Eurotransplant Kidney Allocation System [ETKAS] 1.1 years, European Senior Program [ESP] 1.4 years, Acceptable Mismatch program 1.3 years), with 28.5% of living donor transplantations performed preemptively. There was only modest variation in listing time between the transplant centers. Patients with a history of viral infection, high immunization; hemodialysis patients; and patients with a higher body mass index (BMI) had a delayed listing process. Two of 3 patients listed in the ETKAS, excluding those with potential bonus points (pediatric, other organ transplantations), were eventually transplanted. Older patients, male patients, patients with blood type O, and patients with diabetic nephropathy as the underlying renal disease had the highest risk not to proceed to transplantation. Conclusion: Although long waiting times remain the biggest hurdle for transplantation in Germany, there is ample room for improvement of the listing process.

Expectation of clinical decision support systems: a survey study among nephrologist end-users.

BACKGROUND: Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug information, with medical knowledge. Clinical decision support systems (CDSS) can tackle these issues and improve patient management. Knowledge about the awareness and implementation of CDSS in Germany within the field of nephrology is scarce. PURPOSE: Nephrologists' attitude towards any CDSS and potential CDSS features of interest, like adverse event prediction algorithms, is important for a successful implementation. This survey investigates nephrologists' experiences with and expectations towards a useful CDSS for daily medical routine in the outpatient setting. METHODS: The 38-item questionnaire survey was conducted either by telephone or as a do-it-yourself online interview amongst nephrologists across all of Germany. Answers were collected and analysed using the Electronic Data Capture System REDCap, as well as Stata SE 15.1, and Excel. The survey consisted of four modules: experiences with CDSS (M1), expectations towards a helpful CDSS (M2), evaluation of adverse event prediction algorithms (M3), and ethical aspects of CDSS (M4). Descriptive statistical analyses of all questions were conducted. RESULTS: The study population comprised 54 physicians, with a response rate of about 80-100% per question. Most participants were aged between 51-60 years (45.1%), 64% were male, and most participants had been working in nephrology out-patient clinics for a median of 10.5 years. Overall, CDSS use was poor (81.2%), often due to lack of knowledge about existing CDSS. Most participants (79%) believed CDSS to be helpful in the management of CKD patients with a high willingness to try out a CDSS. Of all adverse event prediction algorithms, prediction of CKD progression (97.8%) and in-silico simulations of disease progression when changing, e. g., lifestyle or medication (97.7%) were rated most important. The spectrum of answers on ethical aspects of CDSS was diverse. CONCLUSION: This survey provides insights into experience with and expectations of out-patient nephrologists on CDSS. Despite the current lack of knowledge on CDSS, the willingness to integrate CDSS into daily patient care, and the need for adverse event prediction algorithms was high.

Anomaly detection in mixed high-dimensional molecular data.

MOTIVATION: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. RESULTS: We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. AVAILABILITY AND IMPLEMENTATION: All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.

Responsibility and decision-making authority in using clinical decision support systems: an empirical-ethical exploration of German prospective professionals' preferences and concerns.

Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals' attitudes to potential changes of responsibility and decision-making authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees' reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians' responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS.

Bayesian network modeling of risk and prodromal markers of Parkinson's disease.

Parkinson's disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data.

DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.

The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.' In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets.

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data.

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

Educational Attainment Is Associated With Kidney and Cardiovascular Outcomes in the German CKD (GCKD) Cohort.

Introduction: Prospective data on impact of educational attainment on prognosis in patients with chronic kidney disease (CKD) are scarce. We investigated the association between educational attainment and all-cause mortality, major adverse cardiovascular (CV) events (MACEs), kidney failure requiring dialysis, and CKD etiology. Methods: Participants (N = 5095, aged 18-74 years) of the ongoing multicenter German Chronic Kidney Disease (GCKD) cohort, enrolled on the basis of an estimated glomerular filtration rate (eGFR) of 30 to 60 ml/min (stages G3, A1-A3) or overt proteinuria (stages G1-G2, A3), were divided into 3 categories according to their educational attainment and were followed for 6.5 years. Results: Participants with low educational attainment (vs. high) had a higher risk for mortality (hazard ratio [HR] 1.48, 95% CI: 1.16-1.90), MACE (HR 1.37, 95% CI: 1.02-1.83), and kidney failure (HR 1.54, 95% CI: 1.15-2.05). Mediators between low educational attainment and mortality were smoking, CV disease (CVD) at baseline, low income, higher body mass index, and higher serum levels of CRP, high-density lipoprotein cholesterol, uric acid, NGAL, BAP, NT-proBNP, OPN, H-FABP, and urea. Low educational attainment was positively associated with diabetic nephropathy (odds ratio [OR] 1.65, 95% CI: 1.36-2.0) and CKD subsequent to acute kidney injury (OR 1.56, 95% CI: 1.03-2.35), but negatively associated with IgA nephropathy (OR 0.68, 95% CI: 0.52-0.90). Conclusion: Low educational attainment is associated with adverse outcomes and CKD etiology. Lifestyle habits and biomarkers mediate associations between low educational attainment and mortality. Recognition of the role of educational attainment and the associated health-relevant risk factors is important to optimize the care of patients with CKD and improve prognosis.

A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests.

RATIONALE & OBJECTIVE: Stratification of chronic kidney disease (CKD) patients at risk for progressing to kidney failure requiring kidney replacement therapy (KFRT) is important for clinical decision-making and trial enrollment. STUDY DESIGN: Four independent prospective observational cohort studies. SETTING & PARTICIPANTS: The development cohort comprised 4,915 CKD patients, and 3 independent validation cohorts comprised a total of 3,063. Patients were observed for approximately 5 years. EXPOSURE: 22 demographic, anthropometric, and laboratory variables commonly assessed in CKD patients. OUTCOME: Progression to KFRT. ANALYTICAL APPROACH: A least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model was fit to select laboratory variables that best identified patients at high risk for KFRT. Model discrimination and calibration were assessed and compared against the 4-variable Tangri (T4) risk equation both in a resampling approach within the development cohort and in the validation cohorts using cause-specific concordance (C) statistics, net reclassification improvement, and calibration graphs. RESULTS: The newly derived 6-variable risk score (Z6) included serum creatinine, albumin, cystatin C, and urea, as well as hemoglobin and the urinary albumin-creatinine ratio. In the the resampling approach, Z6 achieved a median C statistic of 0.909 (95% CI, 0.868-0.937) at 2 years after the baseline visit, whereas the T4 achieved a median C statistic of 0.855 (95% CI, 0.799-0.915). In the 3 independent validation cohorts, the Z6C statistics were 0.894, 0.921, and 0.891, whereas the T4C statistics were 0.882, 0.913, and 0.862. LIMITATIONS: The Z6 was both derived and tested only in White European cohorts. CONCLUSIONS: A new risk equation based on 6 routinely available laboratory tests facilitates identification of patients with CKD who are at high risk of progressing to KFRT.

Association Between Obstructive Sleep Apnea and Brain White Matter Hyperintensities in a Population-Based Cohort in Germany.

Importance: Underlying pathomechanisms of brain white matter hyperintensities (WMHs), commonly observed in older individuals and significantly associated with Alzheimer disease and brain aging, have not yet been fully elucidated. One potential contributing factor to WMH burden is chronic obstructive sleep apnea (OSA), a disorder highly prevalent in the general population with readily available treatment options. Objective: To investigate potential associations between OSA and WMH burden. Design, Setting, and Participants: Analyses were conducted in 529 study participants of the Study of Health in Pomerania-Trend baseline (SHIP-Trend-0) study with complete WMH, OSA, and important clinical data available. SHIP-Trend-0 is a general population-based, cross-sectional, observational study to facilitate the investigation of a large spectrum of common risk factors, subclinical disorders, and clinical diseases and their relationships among each other with patient recruitment from Western Pomerania, Germany, starting on September 1, 2008, with data collected until December 31, 2012. Data analysis was performed from February 1, 2019, to January 31, 2021. Exposures: The apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were assessed during a single-night, laboratory-based polysomnography measurement. Main Outcomes and Measures: The primary outcome was WMH data automatically segmented from 1.5-T magnetic resonance images. Results: Of 529 study participants (mean [SD] age, 52.15 [13.58] years; 282 female [53%]), a total of 209 (40%) or 102 (19%) individuals were diagnosed with OSA according to AHI or ODI criteria (mean [SD] AHI, 7.98 [12.55] events per hour; mean [SD] ODI, 3.75 [8.43] events per hour). Both AHI (ß = 0.024; 95% CI, 0.011-0.037; P <.001) and ODI (ß = 0.033; 95% CI, 0.014-0.051; P <. 001) were significantly associated with brain WMH volumes. These associations remained even in the presence of additional vascular, metabolic, and lifestyle WMH risk factors. Region-specific WMH analyses found the strongest associations between periventricular frontal WMH volumes and both AHI (ß = 0.0275; 95% CI, 0.013-0.042, P < .001) and ODI (ß = 0.0381; 95% CI, 0.016-0.060, P < .001) as well as periventricular dorsal WMH volumes and AHI (ß = 0.0165; 95% CI, 0.004-0.029, P = .008). Conclusions and Relevance: This study found significant associations between OSA and brain WMHs, indicating a novel, potentially treatable WMH pathomechanism.

An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D.

NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses.

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression.

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.