Mapping Novel Frataxin Mitochondrial Networks Through Protein- Protein Interactions.

Friedreich's Ataxia (FRDA) is a neuromuscular degenerative disorder caused by trinucleotide expansions in the first intron of the frataxin (FXN) gene, resulting in insufficient levels of functional FNX protein. Deficits in FXN involve mitochondrial disruptions including iron-sulfur cluster synthesis and impaired energetics. These studies were to identify unique protein-protein interactions with FXN to better understand its function and design therapeutics. Two complementary approaches were employed, BioID and Co-IP, to identify protein interactions with FXN at the direct binding, indirect binding, and non-proximal levels. Forty-one novel protein interactions were identified by BioID and IP techniques. The FXN protein landscape was further analyzed incorporating both interaction type and functional pathways using a maximum path of 6 proteins with a potential direct interaction between FXN and NFS1. Probing the intersection between FXN-protein landscape and biological pathways associated with FRDA, we identified 41 proteins of interest. Peroxiredoxin 3 (Prdx3) was chosen for further analysis because of its role in mitochondrial oxidative injury. Our data has demonstrated the strengths of employing complementary methods to identify a unique interactome for FXN. Our data provides new insights into FXN function and regulation, a potential direct interaction between FXN and NFS1, and pathway interactions between FXN and Prdx3.

Emulation of randomized trials of direct oral anticoagulants with claims data and implications for new Factor XI inhibitors.

Direct oral anticoagulants (DOACs) revolutionized the management of thromboembolic disorders. Clinical care may be further improved as Factor XIs undergo large-scale outcome trials. What role can non-randomized database studies play in expediting understanding of these drugs in clinical practice? The RCT-DUPLICATIVE Initiative emulated the design of eight DOAC randomized clinical trials (RCT) using non-randomized claims database studies. RCT study design parameters and measurements were closely emulated by the database studies and produced highly concordant results. The results of the single database study that did not meet all agreement metrics with the specific RCT it was emulating were aligned with a meta-analysis of six trials studying similar questions, suggesting the trial result was an outlier. Well-designed database studies using fit-for-purpose data came to the same conclusions as DOAC trials, illustrating how database studies could complement RCTs for Factor XI inhibitors-by accelerating insights in underrepresented populations, demonstrating effectiveness and safety in clinical practice, and testing broader indications.

Individualized Treatment Effect Prediction with Machine Learning - Salient Considerations.

BACKGROUND: Machine learning-based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition. METHODS: We describe key methodologic considerations for individualized treatment effect prediction models using data from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial for spironolactone in heart failure with preserved ejection fraction. The causal survival forest algorithm was used for model development. Calibration and discrimination were evaluated using a bootstrapping-based internal validation procedure. Observed benefits were described for predicted benefit quartiles and quartiles of a known effect modifier: ejection fraction. A negative control analysis with noncardiovascular death as the outcome was implemented to detect confounding. RESULTS: Among 3445 participants, 671 events occurred over a median of 3.3 years of follow-up. In internal validation, a higher average observed benefit was noted among patients in the highest quartile of predicted benefit. The median (interquartile range) of the observed restricted mean survival time difference at 3.3 years at the highest quartile of model-predicted benefit was 62 days (32 to 83) and was 47 days (26 to 67) at the lowest quartile of ejection fraction. Body-mass index had higher contribution to prediction of benefit relative to other included measures (33.7% vs. glomerular filtration rate [27.3%], ejection fraction [15.1%], and younger age [12.8%]) No benefit was observed for noncardiovascular death at higher model-predicted benefit quartiles, although benefit for noncardiovascular death was observed at lower quartiles. CONCLUSIONS: Carefully applied and validated predictive models hold promise in identifying heterogeneous treatment effects and are useful for hypothesis generation regarding the role of phenotypic characteristics in modifying the benefit of experimental interventions in clinical trials. (Funded by the National Heart, Lung, and Blood Institute; ClinicalTrials.gov number, NCT00094302.).

Data Checks Before Registering Study Protocols for Health Care Database Analyses.

This Viewpoint discusses the challenges involved with secondary health care data collection vs primary data collection and provides a list of suggested data checks before registration of a study protocol using secondary data.

Selenium Deficiency Exacerbates Hyperoxia-Induced Lung Injury in Newborn C3H/HeN Mice.

Extremely preterm infants are often treated with supraphysiological oxygen, which contributes to the development of bronchopulmonary dysplasia (BPD). These same infants exhibit compromised antioxidant capacities due in part to selenium (Se) deficiency. Se is essential for basal and inducible antioxidant responses. The present study utilized a perinatal Se deficiency (SeD) mouse model to identify the combined effects of newborn hyperoxia exposure and SeD on alveolarization and antioxidant responses, including the identification of affected developmental pathways. Se-sufficient (SeS) and SeD C3H/HeN breeding pairs were generated, and pups were exposed to room air or 85% O2 from birth to 14 d. Survival, antioxidant protein expression, and RNA seq analyses were performed. Greater than 40% mortality was observed in hyperoxia-exposed SeD pups. Surviving SeD pups had greater lung growth deficits than hyperoxia-exposed SeS pups. Gpx2 and 4 protein and Gpx activity were significantly decreased in SeD pups. Nrf2-regulated proteins, Nqo1 and Gclc were increased in SeD pups exposed to hyperoxia. RNA seq revealed significant decreases in the Wnt/ß-catenin and Notch pathways. Se is a biologically relevant modulator of perinatal lung development and antioxidant responses, especially in the context of hyperoxia exposure. The RNA seq analyses suggest pathways essential for normal lung development are dysregulated by Se deficiency.

Targeted Learning with an Undersmoothed Lasso Propensity Score Model for Large-Scale Covariate Adjustment in Healthcare Database Studies.

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

An open-source implementation of tree-based scan statistics.

PURPOSE: We develop an open-source R package to implement tree-based scan statistics (TBSS) analyses. METHODS: TBSS are data mining methods used by the United States Food and Drug Administration and the Centers for Disease Control. They simultaneously screen thousands of hierarchically aggregated outcomes to identify unsuspected adverse effects of drugs or vaccines, accounting for multiple comparisons. The general structure of TBSS is highly adaptable, with four essential components: (1) a hierarchical outcome structure, (2) a test statistic to be computed for each element of the hierarchy, (3) an algorithm to generate data replicates under a null distribution, and (4) observed outcomes at the lower level of the hierarchy. We encode the general TBSS framework in a convenient R package that offers user-friendly functions for the most used TBSS methods. To illustrate the performance of our software, we evaluated two examples of archetypical TBSS analyses previously analyzed using proprietary, closed-source TreeScan™ software. The first considers the risk of congenital malformations associated with first-trimester exposure to valproate, and the second compares exposure to newly prescribed canagliflozin with a dipeptidyl peptidase 4 inhibitor in adults affected by type 2 diabetes. RESULTS: The results of the original studies are replicated. CONCLUSIONS: The diffusion of an open-source implementation of TBSS can enhance innovation of TBSS methods and foster collaborations. We offer an intuitive R package implementing standard TBSS methods with accompanying tutorials. Our unified object-oriented implementation allows expert users to extend the framework, introduce new features, or enhance existing ones.

Design differences and variation in results between randomised trials and non-randomised emulations: meta-analysis of RCT-DUPLICATE data.

Objective: To explore how design emulation and population differences relate to variation in results between randomised controlled trials (RCT) and non-randomised real world evidence (RWE) studies, based on the RCT-DUPLICATE initiative (Randomised, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology). Design: Meta-analysis of RCT-DUPLICATE data. Data sources: Trials included in RCT-DUPLICATE, a demonstration project that emulated 32 randomised controlled trials using three real world data sources: Optum Clinformatics Data Mart, 2004-19; IBM MarketScan, 2003-17; and subsets of Medicare parts A, B, and D, 2009-17. Eligibility criteria for selecting studies: Trials where the primary analysis resulted in a hazard ratio; 29 RCT-RWE study pairs from RCT-DUPLICATE. Results: Differences and variation in effect sizes between the results from randomised controlled trials and real world evidence studies were investigated. Most of the heterogeneity in effect estimates between the RCT-RWE study pairs in this sample could be explained by three emulation differences in the meta-regression model: treatment started in hospital (which does not appear in health insurance claims data), discontinuation of some baseline treatments at randomisation (which would have been an unusual care decision in clinical practice), and delayed onset of drug effects (which would be under-reported in real world clinical practice because of the relatively short persistence of the treatment). Adding the three emulation differences to the meta-regression reduced heterogeneity from 1.9 to almost 1 (absence of heterogeneity). Conclusions: This analysis suggests that a substantial proportion of the observed variation between results from randomised controlled trials and real world evidence studies can be attributed to differences in design emulation.

Avoidant/restrictive food intake disorder differs from anorexia nervosa in delay discounting.

BACKGROUND: Avoidant/restrictive food intake disorder (ARFID) and anorexia nervosa (AN) are the two primary restrictive eating disorders; however, they are driven by differing motives for inadequate dietary intake. Despite overlap in restrictive eating behaviors and subsequent malnutrition, it remains unknown if ARFID and AN also share commonalities in their cognitive profiles, with cognitive alterations being a key identifier of AN. Discounting the present value of future outcomes with increasing delay to their expected receipt represents a core cognitive process guiding human decision-making. A hallmark cognitive characteristic of individuals with AN (vs. healthy controls [HC]) is reduced discounting of future outcomes, resulting in reduced impulsivity and higher likelihood of favoring delayed gratification. Whether individuals with ARFID display a similar reduction in delay discounting as those with AN (vs. an opposing bias towards increased delay discounting or no bias) is important in informing transdiagnostic versus disorder-specific cognitive characteristics and optimizing future intervention strategies. METHOD: To address this research question, 104 participants (ARFID: n = 57, AN: n = 28, HC: n = 19) completed a computerized Delay Discounting Task. Groups were compared by their delay discounting parameter (ln)k. RESULTS: Individuals with ARFID displayed a larger delay discounting parameter than those with AN, indicating steeper delay discounting (M ± SD = -6.10 ± 2.00 vs. -7.26 ± 1.73, p = 0.026 [age-adjusted], Hedges' g = 0.59), with no difference from HC (p = 0.514, Hedges' g = -0.35). CONCLUSION: Our findings provide a first indication of distinct cognitive profiles among the two primary restrictive eating disorders. The present results, together with future research spanning additional cognitive domains and including larger and more diverse samples of individuals with ARFID (vs. AN), will contribute to identifying maintenance mechanisms that are unique to each disorder as well as contribute to the optimization and tailoring of treatment strategies across the spectrum of restrictive eating disorders.

Avoidant/restrictive food intake disorder (ARFID) and anorexia nervosa (AN) are both restrictive eating disorders. However, the reasons for restricting food intake differ between the two diagnoses. A key question in further understanding similarities and differences between ARFID and AN is to understand whether individuals with these disorders process information and make decisions in similar or distinct ways. When humans decide between two different outcomes (e.g., a smaller immediate or a larger delayed reward), outcomes decrease in their value the farther in the future we expect to receive them (delay discounting). Individuals with AN exhibit a reduced discounting of future outcomes, which makes them more likely to forego immediate gratification for later rewards. However, whether this holds true for individuals with ARFID too (or whether they show the opposite or no bias) is unknown. Our investigation is the first to compare delay discounting between individuals with ARFID, AN, and healthy controls (HC). Our results show that individuals with ARFID show more delay discounting than those with AN, with no difference from HC. Knowing how rewards are being chosen and decisions made (and knowing differences between diagnoses) will be helpful in further optimizing and tailoring treatments for restrictive eating disorders.

The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R.

Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.

High-Dose vs. Standard-Dose Influenza Vaccine and Cardiopulmonary Hospitalization or Mortality: Emulating the INVESTED Trial Using Insurance Claims Data.

The INVESTED trial did not show benefits of high-dose (HD) vaccine vs. standard-dose (SD) for a primary composite outcome of cardiopulmonary hospitalization or all-cause mortality (hazard ratio (HR) = 1.05, 95% confidence interval (CI) = 0.96-1.15) and its components (all-cause mortality HR = 1.01, 95% CI = 0.84-1.21, cardiopulmonary hospitalization HR = 1.05, 95% CI = 0.96-1.16) during three influenza seasons (2016-2019) among participants with recent myocardial infarction or hospitalization for heart failure (HHF). We emulated INVESTED using Medicare claims data to assess whether the real-world evidence (RWE) study reached similar conclusions. We identified 1:1 propensity score (PS)-matched trial-eligible Medicare beneficiaries aged > 65 years and with prior HHF who received an HD or SD vaccine for the 2016-2019 seasons. We also re-analyzed the INVESTED trial data restricting to participants > 65 years with prior HHF to align eligibility criteria more closely with the RWE study. We compared HRs from the trial and RWE study for the main outcomes. Among 53,393 pairs of PS-matched Medicare beneficiaries, the HD vaccine group showed lower risk of the primary composite outcome (HR = 0.96, 95% CI = 0.95-0.98) and all-cause mortality (HR = 0.93, 95% CI = 0.91-0.95), and similar risk of cardiopulmonary hospitalization (HR = 0.98, 95% CI = 0.96-1.00), compared with SD. The RWE and trial results were closely concordant after the trial population was limited to participants > 65 years with prior HHF: trial-based results for the primary composite outcome (HR = 1.02, 95% CI = 0.89-1.17), all-cause mortality (HR = 0.92, 95% CI = 0.72-1.16), and cardiopulmonary hospitalization (HR = 1.02, 95% CI = 0.88-1.18). Although similar to the main trial results, the RWE was closer to the results from trial participants with aligned eligibility criteria. This study affirms the importance of considering different distributions of baseline patient characteristics when comparing trial findings to RWE.

Stratified analysis in comparative effectiveness studies that emulate randomized trials.

PURPOSE: For observational cohort studies that employ matching by propensity scores (PS), preliminary stratification by consequential predictors of outcome better emulates stratified randomization and potentially reduces variance and bias through relaxed dependence on modeling assumptions. We assessed the impact of pre-stratification in two real-life examples. For both, prior evidence from placebo-controlled randomized clinical trials (RCTs) suggested small or no risk reduction, but observational analysis suggested protection, presumably the result of confounding bias. STUDY DESIGN AND SETTING: The study populations consisted of Medicare beneficiaries (2014-18) with type 2 diabetes initiating either (i) empagliflozin versus dipeptidyl peptidase-4 inhibitors (DPP-4i) or (ii) empagliflozin versus glucagon-like peptide-1 receptor agonists (GLP-1RA). The outcome was myocardial infarction or stroke. We estimated hazard ratios (HR) and rate differences (RD) after controlling for 143 pre-exposure covariates via 1:1 PS matching after (1) PS estimation in the total cohort (total-cohort PS-matching) and (2) PS estimation separately by baseline cardiovascular disease (stratified PS matching). RESULTS: Stratified PS matching resulted in HRs that exceeded those from total-cohort PS-matching by 13% and 9%, respectively, for the comparisons of empagliflozin to DPP-4i and GLP-1RA. Against both comparators, HRs and RDs after stratified PS matching were closer to the null, with slightly higher variances (2%-3%) than those after total-cohort PS matching. CONCLUSION: Stratified PS matching produced effect estimates closer to the expected trial findings than total-cohort PS matching. The price paid in increased variance was minimal.

Separation of Plasmid DNA Topological Forms, Messenger RNA, and Lipid Nanoparticle Aggregates Using an Ultrawide Pore Size Exclusion Chromatography Column.

Health authorities have highlighted the need to determine oligonucleotide aggregates. However, existing technologies have limitations that have prevented the reliable analysis of size variants for large nucleic acids and lipid nanoparticles (LNPs). In this work, nucleic acid and LNP aggregation was examined using prototype, low adsorption ultrawide pore size exclusion chromatography (SEC) columns. A preliminary study was conducted to determine the column's physicochemical properties. A large difference in aggregate content (17.8 vs 59.7 %) was found for a model messenger RNA (mRNA) produced by different manufacturers. We further investigated the nature of the aggregates via a heat treatment. Interestingly, thermal stress irreversibly decreased the amount of aggregates from 59.7 to 4.1% and increased the main peak area 3.3-fold. To the best of our knowledge, for the first time, plasmid DNA topological forms and multimers were separated by analytical SEC. The degradation trends were compared to the data obtained with an anion exchange chromatography method. Finally, unconjugated and fragment antigen-binding (Fab)-guided LNPs were analyzed and their elution times were plotted against their sizes as measured by DLS. Multi-angle light scattering (MALS) was coupled to SEC in order to gain further insights on large species eluting before the LNPs, which were later identified as self-associating LNPs. This study demonstrated the utility of ultrawide pore SEC columns in characterizing the size variants of large nucleic acid therapeutics and LNPs.

Comparative Effectiveness and Safety of Generic Versus Brand-Name Fluticasone-Salmeterol to Treat Chronic Obstructive Pulmonary Disease.

BACKGROUND: In 2019, the U.S. Food and Drug Administration (FDA) approved the first generic maintenance inhaler for asthma and chronic obstructive pulmonary disease (COPD). The inhaler, Wixela Inhub (fluticasone-salmeterol; Viatris), is a substitutable version of the dry powder inhaler Advair Diskus (fluticasone-salmeterol; GlaxoSmithKline). When approving complex generic products like inhalers, the FDA applies a special "weight-of-evidence" approach. In this case, manufacturers were required to perform a randomized controlled trial in patients with asthma but not COPD, although the product received approval for both indications. OBJECTIVE: To compare the effectiveness and safety of generic (Wixela Inhub) and brand-name (Advair Diskus) fluticasone-salmeterol among patients with COPD treated in routine care. DESIGN: A 1:1 propensity score-matched cohort study. SETTING: A large, longitudinal health care database. PATIENTS: Adults older than 40 years with a diagnosis of COPD. MEASUREMENTS: Incidence of first moderate or severe COPD exacerbation (effectiveness outcome) and incidence of first pneumonia hospitalization (safety outcome) in the 365 days after cohort entry. RESULTS: Among 45 369 patients (27 305 Advair Diskus users and 18 064 Wixela Inhub users), 10 012 matched pairs were identified for the primary analysis. Compared with Advair Diskus use, Wixela Inhub use was associated with a nearly identical incidence of first moderate or severe COPD exacerbation (hazard ratio [HR], 0.97 [95% CI, 0.90 to 1.04]) and first pneumonia hospitalization (HR, 0.99 [CI, 0.86 to 1.15]). LIMITATIONS: Follow-up times were short, reflecting real-world clinical practice. The possibility of residual confounding cannot be completely excluded. CONCLUSION: Use of generic and brand-name fluticasone-salmeterol was associated with similar outcomes among patients with COPD treated in routine practice. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.

Comparison of EHR Data-Completeness in Patients with Different Types of Medical Insurance Coverage in the United States.

Prior studies have demonstrated that misclassification of study variables due to electronic health record (EHR)-discontinuity can be mitigated by restricting EHR-based analyses to subjects with high predicted EHR-continuity based on a simple algorithm. In this study, we compared EHR continuity in populations covered by Medicare, Medicaid, or commercial insurance. Using claims-linked EHRs from a multicenter network in Massachusetts, including Medicare (MA EHR-Medicare cohort) and Medicaid (MA EHR-Medicaid cohort) claims data; and TriNetX (TriNetX cohort) claims-linked EHR data from 11 US-based healthcare organizations, we assessed (1) EHR-continuity quantified by proportion of encounters captured by EHR (capture proportion (CP)); (2) area under receiver operating curve (AUROC) of previously validated model to identify patients with high EHR-continuity (CP ≥ 0.6); (3) misclassification of 40 patient characteristics, quantified by average standardized absolute mean difference (ASAMD). Study participants were ≥ 65 years (Medicare) or ≥ 18 years (Medicaid, TriNetX) with ≥ 365 days of continuous insurance enrollment overlapping with an EHR encounter. We found that the mean CP was 0.30, 0.18, and 0.19 and AUROC of the prediction model to identify patients with high EHR-continuity was 0.92, 0.89, and 0.77 in the MA EHR-Medicare, MA EHR-Medicaid, and TriNetX cohorts, respectively. Restricting to patients with predicted EHR-continuity percentile of top 20%, 50%, and 50% in MA EHR-Medicare, MA EHR-Medicaid, and TriNetX cohorts resulted in acceptable levels of misclassification (ASAMD < 0.1). Using a prediction model to identify cohorts with high EHR-continuity can improve validity, but cutoffs to achieve this goal vary by population.

Comparing self-harming intentions underlying eating disordered behaviors and nonsuicidal self-injury: Replication and extension in adolescents.

OBJECTIVE: Eating disorder (ED) behaviors are often characterized as indirect forms of self-harm. However, recent research has found less clear demarcations between direct self-harming behaviors (e.g., nonsuicidal self-injury [NSSI], suicidal behaviors) than previously assumed. The aim of this study was to replicate findings of this prior research on adult populations in adolescents with a history of restrictive eating. METHOD: A total of 117 adolescents between ages 12-14 were included in the study. Participants reported the presence and frequency of binge eating, compensatory, restrictive eating, and NSSI. Participants also reported thoughts of and intentions to hurt and kill themselves when engaging in each behavior on average. The t-tests and linear effects models were conducted to compare self-harming thoughts and intentions across behaviors. RESULTS: Participants reported at least some intent to hurt themselves physically in the moment and in the long-term when engaging in all ED behaviors and NSSI, and reported engaging in these behaviors while thinking about suicide. Direct self-harming knowledge and intentions were most frequently reported with NSSI and longer-term knowledge and intentions via NSSI and restrictive eating. Additionally, participants reported some suicidal thoughts and intentions across behaviors. DISCUSSION: This study replicates prior research, suggesting that adolescents engage in ED behaviors and NSSI with non-zero self-harming and suicidal thoughts and intentions. ED behaviors and NSSI may better be explained on a continuum. Implications include the recommendation of safety planning during ED treatment. PUBLIC SIGNIFICANCE STATEMENT: This study highlights the overlap between eating disorder (ED) behaviors, nonsuicidal self-injury (NSSI), and suicide. Though clear distinctions typically exist for motives of self-harming behavior between ED behaviors (i.e., indirect, in the long run) and NSSI (i.e., direct, in the moment), this research suggests that intentions for self-harming and suicide may exist on a continuum. Clinical ED treatment should consider safety planning as part of routine interventions.