A metabolomics approach identified toxins associated with uremic symptoms in advanced chronic kidney disease.

Uremic symptoms are common in patients with advanced chronic kidney disease, but the toxins that cause these symptoms are unknown. To evaluate this, we performed a cross-sectional study of the 12 month post-randomization follow-up visit of Modification of Diet in Renal Disease (MDRD) participants reporting uremic symptoms who also had available stored serum. We quantified 1,163 metabolites by liquid chromatography-tandem mass spectrometry. For each uremic symptom, we calculated a score as the severity multiplied by the number of days the symptom was experienced. We analyzed the associations of the individual symptom scores with metabolites using linear models with empirical Bayesian inference, adjusted for multiple comparisons. Among 695 participants, the mean measured glomerular filtration rate (mGFR) was 28 mL/min/1.73 m2. Uremic symptoms were more common in the subgroup of 214 patients with an mGFR under 20 mL/min/1.73 m2 (mGFR under 20 subgroup) than in the full group. For all metabolites with significant associations, the direction of the association was concordant in the full group and the subgroup. For gastrointestinal symptoms (bad taste, loss of appetite, nausea, and vomiting), eleven metabolites were associated with symptoms. For neurologic symptoms (decreased alertness, falling asleep during the day, forgetfulness, lack of pep and energy, and tiring easily/weakness), seven metabolites were associated with symptoms. Associations were consistent across sensitivity analyses. Thus, our proof-of-principle study demonstrates the potential for metabolomics to understand metabolic pathways associated with uremic symptoms. Larger, prospective studies with external validation are needed.

Comparison of beginning R students' perceptions of peer-made plots created in two plotting systems: a randomized experiment.

We performed an empirical study of the perceived quality of scientific graphics produced by beginning R users in two plotting systems: the base graphics package ("base R") and the ggplot2 add-on package. In our experiment, students taking a data science course on the Coursera platform were randomized to complete identical plotting exercises using either base R or ggplot2. This exercise involved creating two plots: one bivariate scatterplot and one plot of a multivariate relationship that necessitated using color or panels. Students evaluated their peers on visual characteristics key to clear scientific communication, including plot clarity and sufficient labeling. We observed that graphics created with the two systems rated similarly on many characteristics. However, ggplot2 graphics were generally perceived by students to be slightly more clear overall with respect to presentation of a scientific relationship. This increase was more pronounced for the multivariate relationship. Through expert analysis of submissions, we also find that certain concrete plot features (e.g., trend lines, axis labels, legends, panels, and color) tend to be used more commonly in one system than the other. These observations may help educators emphasize the use of certain plot features targeted to correct common student mistakes.

A screen of 1,049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential.

Recent genome-wide association studies (GWAS) identified numerous schizophrenia (SZ) and Alzheimer's disease (AD) associated loci, most outside protein-coding regions and hypothesized to affect gene transcription. We used a massively parallel reporter assay to screen, 1,049 SZ and 30 AD variants in 64 and nine loci, respectively for allele differences in driving reporter gene expression. A library of synthetic oligonucleotides assaying each allele five times was transfected into K562 chronic myelogenous leukemia lymphoblasts and SK-SY5Y human neuroblastoma cells. One hundred forty eight variants showed allelic differences in K562 and 53 in SK-SY5Y cells, on average 2.6 variants per locus. Nine showed significant differences in both lines, a modest overlap reflecting different regulatory landscapes of these lines that also differ significantly in chromatin marks. Eight of nine were in the same direction. We observe no preference for risk alleles to increase or decrease expression. We find a positive correlation between the number of SNPs in linkage disequilibrium and the proportion of functional SNPs supporting combinatorial effects that may lead to haplotype selection. Our results prioritize future functional follow up of disease associated SNPs to determine the driver GWAS variant(s), at each locus and enhance our understanding of gene regulation dynamics.

Linear models enable powerful differential activity analysis in massively parallel reporter assays.

BACKGROUND: Massively parallel reporter assays (MPRAs) have emerged as a popular means for understanding noncoding variation in a variety of conditions. While a large number of experiments have been described in the literature, analysis typically uses ad-hoc methods. There has been little attention to comparing performance of methods across datasets. RESULTS: We present the mpralm method which we show is calibrated and powerful, by analyzing its performance on multiple MPRA datasets. We show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets. We investigate theoretical and real-data properties of barcode summarization methods and show an unappreciated impact of summarization method for some datasets. Finally, we use our model to conduct a power analysis for this assay and show substantial improvements in power by performing up to 6 replicates per condition, whereas sequencing depth has smaller impact; we recommend to always use at least 4 replicates. An R package is available from the Bioconductor project. CONCLUSIONS: Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments.

Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics.

As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis.

Controlling time-varying confounding in difference-in-differences studies using the time-varying treatments framework.

This article clarifies how the biostatistical literature on time-varying treatments (TVT) can provide tools for dealing with time-varying confounding in difference-in-differences (DiD) studies. I use a simulation study to compare the bias and standard error of inverse probability weighting estimators from the TVT framework, a DiD framework, and hybrid approaches that combine ideas from both frameworks. I simulated longitudinal data with treatment effect heterogeneity over multiple time points using linear and logistic models. Simulation settings looked at both time-invariant confounders and time-varying confounders affected by prior treatment. Estimators that combined ideas from both frameworks had lower bias than standard TVT and DiD estimators when assumptions were unmet. The TVT framework provides estimation tools that can complement DiD tools in a wide range of applied settings. It also provides alternate estimands for consideration in policy settings.

A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis.

Introduction: Uremic toxins contributing to increased risk of death remain largely unknown. We used untargeted metabolomics to identify plasma metabolites associated with mortality in patients receiving maintenance hemodialysis. Methods: We measured metabolites in serum samples from 522 Longitudinal US/Canada Incident Dialysis (LUCID) study participants. We assessed the association between metabolites and 1-year mortality, adjusting for age, sex, race, cardiovascular disease, diabetes, body mass index, serum albumin, Kt/Vurea, dialysis duration, and country. We modeled these associations using limma, a metabolite-wise linear model with empirical Bayesian inference, and 2 machine learning (ML) models: Least absolute shrinkage and selection operator (LASSO) and random forest (RF). We accounted for multiple testing using a false discovery rate (pFDR) adjustment. We defined significant mortality-metabolite associations as pFDR < 0.1 in the limma model and metabolites of at least medium importance in both ML models. Results: The mean age of the participants was 64 years, the mean dialysis duration was 35 days, and there were 44 deaths (8.4%) during a 1-year follow-up period. Two metabolites were significantly associated with 1-year mortality. Quinolinate levels (a kynurenine pathway metabolite) were 1.72-fold higher in patients who died within year 1 compared with those who did not (pFDR, 0.009), wheras mesaconate levels (an emerging immunometabolite) were 1.57-fold higher (pFDR, 0.002). An additional 42 metabolites had high importance as per LASSO, 46 per RF, and 9 per both ML models but were not significant per limma. Conclusion: Quinolinate and mesaconate were significantly associated with a 1-year risk of death in incident patients receiving maintenance hemodialysis. External validation of our findings is needed.

Incidence and Risk Factors for Pruritus in Patients with Nondialysis CKD.

BACKGROUND: Pruritus is a common symptom experienced by patients with nondialysis CKD, but risk factors for incident pruritus in this patient population have not been evaluated. METHODS: We identified 1951 participants with CKD in the Chronic Renal Insufficiency Cohort Study without pruritus at the baseline assessment. Pruritus was assessed by the Kidney Disease Quality of Life-36 (KDQOL-36) instrument, and moderate-to-severe pruritus was defined as a response of 3 or higher on a Likert scale of 1-5. We used time-updated multivariable joint models to evaluate the association of patient clinical characteristics, eGFR, and laboratory parameters with incident pruritus. RESULTS: Over a median follow-up of 6 years, 660 (34%) participants developed incident moderate-to-severe pruritus, with a higher incidence rate observed among participants with more advanced CKD. In multivariable models, the hazard ratio (95% confidence interval [CI]) for pruritus associated with a 10 ml/min per 1.73 m 2 lower eGFR was 1.16 (95% CI, 1.10 to 1.23). Older age (≥65 years), higher body mass index, diabetes, current smoking, opioid use, depressive symptoms, and serum parathyroid hormone were also associated with a higher risk of incident pruritus, whereas low serum calcium (<9 mg/dl) was associated with a lower risk (all P <0.05). Serum phosphate was not associated with incident pruritus in the primary analysis. CONCLUSIONS: A substantial proportion of patients with nondialysis CKD develop moderate-to-severe pruritus. Although lower eGFR is associated with the risk of pruritus, other comorbidities, particularly depressive symptoms, were potential risk factors. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09480822.mp3.

Explanation of observational data engenders a causal belief about smoking and cancer.

Most researchers do not deliberately claim causal results in an observational study. But do we lead our readers to draw a causal conclusion unintentionally by explaining why significant correlations and relationships may exist? Here we perform a randomized controlled experiment in a massive open online course run in 2013 that teaches data analysis concepts to test the hypothesis that explaining an analysis will lead readers to interpret an inferential analysis as causal. We test this hypothesis with a single example of an observational study on the relationship between smoking and cancer. We show that adding an explanation to the description of an inferential analysis leads to a 15.2% increase in readers interpreting the analysis as causal (95% confidence interval for difference in two proportions: 12.8%-17.5%). We then replicate this finding in a second large scale massive open online course. Nearly every scientific study, regardless of the study design, includes an explanation for observed effects. Our results suggest that these explanations may be misleading to the audience of these data analyses and that qualification of explanations could be a useful avenue of exploration in future research to counteract the problem. Our results invite many opportunities for further research to broaden the scope of these findings beyond the single smoking-cancer example examined here.

Factors Associated With Gaps in Medicaid Enrollment Among People With HIV and the Effect of Gaps on Viral Suppression.

INTRODUCTION: Gaps in Medicaid enrollment may affect HIV outcomes. We evaluated factors associated with Medicaid enrollment gaps and their effect on viral suppression (VS) within the HIV Research Network. METHODS: We used a combined data set with Medicaid enrollment files from 2006 to 2010 and HIV Research Network demographic and clinical data. A gap was defined as ≥1 month without Medicaid and gap length was determined. We used multivariable logistic regression to determine factors associated with a gap and multivariable logistic regression with generalized estimated equations to evaluate factors associated with VS after gap. RESULTS: Of 5836 participants, the majority were male, of black race, and aged 25-50 years. More than half had a gap in Medicaid. Factors associated with a gap included male sex [adjusted odds ratio (aOR) 1.79, (1.53, 2.08)] and younger age (aORs ranging from 1.50 to 4.13 comparing younger age groups to age >50, P < 0.05 for all). About a quarter of gaps had VS information before and after gap. Of those, 53.7% had VS both before and after gap and 25.8% were unsuppressed both before and after gap. The strongest association with VS after gap was VS before gap [aOR 15.76 (10.48, 23.69)]. Transition into Ryan White HIV/AIDS Program coverage during Medicaid gaps was common (28% of all transitions). CONCLUSIONS: Gaps in Medicaid enrollment were common and many individuals with pre-gap VS maintained VS after gap, possibly due to accessing other sources of antiretroviral therapy coverage. Implementing initiatives to maintain Medicaid enrollment and to expedite Medicaid reenrollment and having alternate resources available in gaps are important to ensure continuous antiretroviral therapy to optimize HIV outcomes.

Equivocal significance of post-ictal generalized EEG suppression as a marker of SUDEP risk.

PURPOSE: Our objective was to determine the significance of PGES as a possible EEG marker of increased risk for SUDEP and explore factors that influence PGES. METHODS: We identified 17 patients who died due to definite or probable SUDEP and 52 living control patients with drug resistant focal epilepsy who underwent EEG monitoring and least one seizure recorded on EEG. We reviewed 305 seizures on EEG and when available, on video, for presence or absence of PGES, the duration of PGES immediately after seizure end, seizure type, state seizure occurred (sleep vs. wake), tonic duration and time from seizure onset to initial nursing intervention. We noted that majority (93% in SUDEP group and 83% living controls) with PGES had additional brief bursts of suppression. We measured the time from the end of seizure to end of last brief suppression to determine the time to final PGES. RESULTS: SUDEP patients had statistically significant shorter PGES duration compared to living controls (unadjusted: -32.8s, 95%CI[-54.5, -11.2], adjusted: -39.5s, 95% CI[-59.4, -19.6]). SUDEP status was associated with longer time to final PGES compare to living controls, but this was not statistically significant. Earlier nursing intervention was associated with shorter seizure duration. PGES occurred only after GCS. Time to nursing intervention, tonic duration or state did not have a statistically significant effect on PGES. CONCLUSIONS: PGES is an equivocal marker of increased SUDEP risk. Earlier nursing intervention is associated with shorter seizure duration and may play a role in reducing risk of SUDEP.