Gulf War Illness (GWI) is a multi-symptom chronic condition that affects Veterans who served in the 1990–1991 Gulf War (GW). To generate novel information about GWI pathogenesis, we used genome-wide data available from 33 523 Veterans of diverse ancestral backgrounds who served during the 1990–1991 Gulf War era (34% deployed). Polygenic score (PGS) analysis showed GWI pleiotropy for several traits with the strongest evidence for type-2 diabetes (T2D), anxiety, and depression. While T2D PGS was associated with higher GWI odds in GW Veterans, anxiety and depression PGSs were associated with higher odds of GWI in non-deployed GW-era Veterans. Seven independent variants were identified (P < 5 × 10-8). Two of them were supported by independent transcriptomic and phenome-wide analyses. Rs4675853 was associated with AGXT, MAB21L4, and ATG4Btranscriptomic regulation and with sex hormone-binding globulin levels. Rs138168412 was associated with AOPEPtranscriptomic regulation and with respiratory function and physical strength. The TWAS identified five additional loci such as CEMIPin the cerebellum and SNCGin the adrenal gland. The results provide a comprehensive assessment of the polygenic architecture of GWI research definitions, identifying mechanisms potentially relevant to the disease pathogenesis.

Real-world evidence (RWE) derived from real-world data (RWD) can complement randomized controlled trials (RCTs), yet the validity of RWD relative to RCT data remains insufficiently characterized. We obtained post hoc consent and linked individual participant data from the US-based INVESTED trial (2016-2019) with Medicare fee-for-service claims (2012-2020) to validate demographic factors, baseline characteristics (using 183-, 365-, and 730-day lookback periods) and outcomes, and to assess post-trial events. Among 5260 trial participants, 126 were enrolled and eligible for linkage. Among 115 participants with demographic information available from Medicare enrollment files, agreement between RCT- and RWD-based demographic factors was high: only one major age discrepancy, 100% agreement for sex, and an overall agreement of 0.89 for race. Participants with Medicare claims data (n = 65) were older and more likely to be White compared with the overall RCT population. For the 365-day lookback period, baseline comorbidities showed high sensitivity (median 0.80) and specificity (0.89), as did medication use (sensitivity 1.00, specificity 0.88). Lengthening the lookback period to 730 days increased sensitivity but decreased specificity, whereas shortening to 183 days decreased sensitivity but increased specificity. Clinical outcomes showed high specificity (0.88-0.94) but low sensitivity (0.18-0.50). Among those with Medicare coverage beyond the trial end date (n = 45), 22% experienced cardiopulmonary, 18% cardiovascular, and 7% heart failure (HF) hospitalizations, highlighting the value of RWD for extending RCT evidence. Proactive planning of future RCT-RWD linkage initiatives can improve the efficiency of linkage studies, leading to more actionable results.

The integration of molecular biology in medicine represents a remarkable achievement of modern science. Nonetheless, the success of molecular methods reshaped medicine's epistemology over time such that biological explanation came to mean explanation exclusively at the cellular or molecular level. This review argues that medicine committed a philosophical error-molecular investigation came to determine epistemic status, displacing person-level clinical research as an entire domain of rigorous inquiry. Person-level clinical research encompasses clinical observation, psychosocial and behavioral medicine, patient-reported outcomes, and the biology of biography. Each tradition satisfies established criteria used to define foundational science. For example, KRAS mutation testing shares its epistemic structure with biographical constructs such as chronic neuroendocrine dysregulation and allostatic load. Both traditions identify specific activated pathways, predict therapeutic response, stratify patients into biologically distinct populations, and operate upstream of pharmacologic interactions. The biological significance of a pathway depends on the person-level context in which it is activated. Two patients sharing the same mutation but carrying profoundly different neuroimmune histories, inflammatory set-points, and stress exposure are not the same biological system. Precision medicine, as currently practiced, is often precise at the wrong level of analysis. Evidence supporting biography as mechanistically foundational has not been refuted; it was marginalized by self-reinforcing institutional forces. Federal funding concentrated in molecular science created financial dependencies that transformed an epistemological preference into a structural imperative. Tenure structures systematically protected molecular faculty whereas clinical scientists were moved to contingent appointments. Successive generations of trainees oriented rationally toward the molecular model. The displacement of person-level clinical research was not a scientific decision. It was a historical accident compounded by the financial and administrative architecture of modern academic medicine. Restoring this domain to foundational status is not a humanistic plea; it is a scientific corrective-and its costs, if deferred, are borne by patients.

The US Food and Drug Administration (FDA) and National Institutes of Health (NIH) share a mutual interest in facilitating efficient, well-designed clinical studies of drugs, devices, and biological products. Recent advances in science and technology, as well as innovative approaches to research design and methodology, provide opportunities to enhance efficiency in medical product development and improve participant engagement in clinical trials. Recent initiatives across the FDA and NIH focus on evidence modernization approaches. Fostering appropriate use of novel designs and sources of evidence, such as real-world data (RWD) to support marketing authorizations and satisfy postapproval study requirements, may be enhanced by using consensus terminology for innovative study designs. To facilitate effective communication within the scientific community, FDA and NIH formed an interagency collaborative initiative to define clinical research terms related to innovative study designs, with a focus on studies using RWD, for FDA-regulated medical products or broader research and foster a shared understanding of terms across the clinical research ecosystem. The FDA-NIH Modernizing Research and Evidence (MoRE) Glossary Working Group (MGWG) was initiated in April 2023 to evaluate terms inadequately defined within the clinical research community that would benefit from development of a consensus definition. The MGWG conducted a landscape evaluation of common innovative design terminology that may lack clarity or concordance. Subsequently, the MGWG reviewed whether and how existing regulations, guidance, and policies use or define such terms. Following the landscape evaluation, the MGWG engaged in rigorous review to seek consensus definitions. In addition, federal agencies sought public input via a request for information before publishing the included terms and definitions. The MGWG developed the MoRE Consensus Definitions, comprising 40 clinical research terms and definitions related to innovative clinical study designs that support scientific, patient, clinical, and regulatory decision-making. The MoRE Consensus Definitions are intended to facilitate effective communication about clinical research and enable transparency around innovative clinical study designs. This publication makes available the glossary developed through this collaboration and serves as an accessible resource for the clinical research enterprise. Furthermore, as clinical research is continuously evolving, additional efforts may focus on emerging new vocabulary and evolving use of current terms to benefit medical product development.

OBJECTIVE:Using national claims databases, we sought to emulate the design of the ongoing SEPRA trial and predict its findings, comparing the effects of once weekly semaglutide to SoC medications on glycemic control (A1C <7%) in type-2 diabetes mellitus (T2D). RESEARCH DESIGN AND METHODS:Using Optum Clinformatics (July 2017 - May 2022), we identified a 1:1 propensity score-matched (PSM) cohort of adults with T2D on metformin monotherapy, who had recorded A1C and initiated either injectable semaglutide or SoC medications (dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter-2 inhibitors, SUs, or glucagon-like peptide-1 agonists) and met eligibility criteria adapted from the SEPRA trial. The primary outcome was the proportion of patients achieving A1C <7%. The study protocol was preregistered (NCT05577728, ClinicalTrials.gov) before any etiologic analyses. Risk ratios and corresponding 95% CIs were estimated. RESULTS:We identified 1,144 PSM pairs of injectable semaglutide and SoC initiators with balance in pre-exposure covariates. Semaglutide initiators were 30% (risk ratio (95% CI), 1.30 (1.16 to 1.45)) more likely to achieve glycemic control (A1C <7%) than those initiating SoC. Additionally, semaglutide initiators had a 1.3% reduction in A1C, compared with a 1.1% reduction in the SoC group. These results were consistent with interim results of the SEPRA trial, which were released after the protocol for our database study was preregistered. CONCLUSION:This claims database study, designed to predict the results of the SEPRA trial, found results consistent with interim trial results. Our findings support the notion that well-designed non-randomized studies using fit-for-purpose data can effectively complement pragmatic randomized controlled trials.

Advances in the availability and analysis of real-world data (RWD) have enabled the generation of robust real-world evidence (RWE) to support regulatory decision making by the US Food and Drug Administration. Realizing the full potential of RWE in a regulatory environment requires cross-discipline expertise and collaboration to increase confidence in RWE-based approaches. The FDA's Advancing RWE Program was established to address this need by providing a new option for regulatory interactions on RWE-based approaches.

Understanding the potential for, and direction and magnitude of uncontrolled confounding is critical for generating informative real-world evidence. Many sensitivity analyses are available to assess robustness of study results to residual confounding, but it is unclear how researchers are using these methods. We conducted a systematic review of published active-comparator cohort studies of drugs or biologics to summarize use of sensitivity analyses aimed at assessing uncontrolled confounding from an unmeasured variable. We reviewed articles in 5 medical and 7 epidemiologic journals published between January 1, 2017, and June 30, 2022. We identified 158 active-comparator cohort studies: 76 from medical and 82 from epidemiologic journals. Residual, unmeasured, or uncontrolled confounding was noted as a potential concern in 93% of studies, but only 84 (53%) implemented at least 1 sensitivity analysis to assess uncontrolled confounding from an unmeasured variable. The most common analyses were E-values among medical journal articles (21%) and restriction on measured variables among epidemiologic journal articles (22%). Researchers must rigorously consider the role of residual confounding in their analyses and the best sensitivity analyses for assessing this potential bias.This article is part of a Special Collection on Pharmacoepidemiology.

Importance:The US Food and Drug Administration (FDA) and National Institutes of Health (NIH) share a mutual interest in facilitating efficient, well-designed clinical studies of drugs, devices, and biological products. Recent advances in science and technology, as well as innovative approaches to research design and methodology, provide opportunities to enhance efficiency in medical product development and improve participant engagement in clinical trials. Recent initiatives across the FDA and NIH focus on evidence modernization approaches. Fostering appropriate use of novel designs and sources of evidence, such as real-world data (RWD) to support marketing authorizations and satisfy postapproval study requirements, may be enhanced by using consensus terminology for innovative study designs. Objective:To facilitate effective communication within the scientific community, FDA and NIH formed an interagency collaborative initiative to define clinical research terms related to innovative study designs, with a focus on studies using RWD, for FDA-regulated medical products or broader research and foster a shared understanding of terms across the clinical research ecosystem. Evidence Review:The FDA-NIH Modernizing Research and Evidence (MoRE) Glossary Working Group (MGWG) was initiated in April 2023 to evaluate terms inadequately defined within the clinical research community that would benefit from development of a consensus definition. The MGWG conducted a landscape evaluation of common innovative design terminology that may lack clarity or concordance. Subsequently, the MGWG reviewed whether and how existing regulations, guidance, and policies use or define such terms. Following the landscape evaluation, the MGWG engaged in rigorous review to seek consensus definitions. In addition, federal agencies sought public input via a request for information before publishing the included terms and definitions. Findings:The MGWG developed the MoRE Consensus Definitions, comprising 40 clinical research terms and definitions related to innovative clinical study designs that support scientific, patient, clinical, and regulatory decision-making. Conclusions and Relevance:The MoRE Consensus Definitions are intended to facilitate effective communication about clinical research and enable transparency around innovative clinical study designs. This publication makes available the glossary developed through this collaboration and serves as an accessible resource for the clinical research enterprise. Furthermore, as clinical research is continuously evolving, additional efforts may focus on emerging new vocabulary and evolving use of current terms to benefit medical product development.

Real-world evidence involving healthcare database studies is well established for making causal inferences in post-market drug safety studies and methods, data, and research infrastructure for evaluating effectiveness have advanced in recent years. The rapidly expanding field of etiologic research using insurance claims and electronic health records databases is being evaluated for supporting effectiveness claims. One such use case to support regulatory decision-making on effectiveness is for expanding indications beyond existing effectiveness claims. Confidence in the validity of findings from cohort studies conducted using databases (hereafter "database study") to support indication expansions could be increased through a structured benchmarking process of an initial database study against RCT evidence followed by calibration of a subsequent database study based on differences in results observed in the initial RCT-database pair. This paper proposes a benchmark, expand, and calibration (BenchExCal) approach to trial emulation and describes the design and process for evaluating the performance of the approach through both simulation studies; five planned empirical examples are also described. The project will provide insights regarding how a first-stage benchmarking emulation of a completed trial for an existing indication can be used to calibrate, increase confidence, and improve interpretation of the results for a second-stage emulation of a hypothetical trial that could potentially provide evidence for an expanded indication. Although the examples have been selected to provide a variety of learnings, five use cases do not address all clinical and data scenarios that may be encountered when seeking a supplemental indication for a marketed drug.

Improvements in the relevance and reliability of routinely collected clinical data and statistical methods to analyze the available data have enhanced the adoption of real-world data (RWD) to generate real-world evidence (RWE) for regulatory decision making of medical products. As part of the reauthorization of the Prescription Drug User Fee Act (PDUFA VII), the US Food and Drug Administration (FDA) committed to issuing annual reports describing such uses for drugs and biological products. The first report covered fiscal year (FY) 2023 and described two approvals based, at least in part, on RWE: tocilizumab (trade name Actemra) and lacosamide (trade name Vimpat). This article describes New Drug Applications and Biologics Licensing Applications approved by the Center for Drug Evaluation and Research (CDER) in FYs 2020-2022 with RWE that (1) contributed to substantial evidence of effectiveness or (2) provided safety data necessary for approval. RWE contributed to substantial evidence of effectiveness for the approval of applications for fosdenopterin (trade name Nulibry) and tacrolimus (trade name Prograf) in FY 2021 and abatacept (trade name Orencia), vosoritide (trade name Voxzogo), and alpelisib (trade name Vijoice) in FY 2022. No studies provided only safety data necessary for approval. The five approvals included six total studies that provided RWE pivotal for the applications approval. Four studies leveraged registry data, and two leveraged medical record data. In parallel with annual RWE public reporting under PDUFA VII, this report can inform interested parties regarding how RWD are used to generate RWE that can support regulatory decision making for medical products.

Clinical interviewing is the basic method to understand how a person feels and what are the presenting complaints, obtain medical history, evaluate personal attitudes and behavior related to health and disease, give the patient information about diagnosis, prognosis, and treatment, and establish a bond between patient and physician that is crucial for shared decision making and self-management. However, the value of this basic skill is threatened by time pressures and emphasis on technology. Current health care trends privilege expensive tests and procedures and tag the time devoted to interaction with the patient as lacking cost-effectiveness. Instead, the time spent to inquire about problems and life setting may actually help to avoid further testing, procedures, and referrals. Moreover, the dialogue between patient and physician is an essential instrument to increase patient's motivation to engage in healthy behavior. The aim of this paper was to provide an overview of clinical interviewing and its optimal use in relation to style, flow and hypothesis testing, clinical domains, modifications according to settings and goals, and teaching. This review points to the primacy of interviewing in the clinical process. The quality of interviewing determines the quality of data that are collected and, eventually, of assessment and treatment. Thus, interviewing deserves more attention in educational training and more space in clinical encounters than it is currently receiving.

Affecting an estimated 88 million Americans, prediabetes increases the risk for developing type 2 diabetes mellitus (T2DM), and independently, cardiovascular disease, retinopathy, nephropathy, and neuropathy. Nevertheless, little is known about the use of metformin for diabetes prevention among patients in the Veterans Health Administration, the largest integrated healthcare system in the U.S. This is a retrospective observational cohort study of the proportion of Veterans with incident prediabetes who were prescribed metformin at the Veterans Health Administration from October 2010 to September 2019. Among 1,059,605 Veterans with incident prediabetes, 12,009 (1.1%) were prescribed metformin during an average 3.4 years of observation after diagnosis. Metformin prescribing was marginally higher (1.6%) among those with body mass index (BMI) >= 35 kg/m2, age <60 years, HbA1c >= 6.0%, or those with a history of gestational diabetes, all subgroups at a higher risk for progression to T2DM. In a multivariable model, metformin was more likely to be prescribed for those with BMI >= 35 kg/m2 incidence rate ratio [IRR] 2.6 [95% confidence intervals (CI): 2.1-3.3], female sex IRR, 2.4 [95% CI: 1.8-3.3], HbA1c >= 6% IRR, 1.93 [95% CI: 1.5-2.4], age <60 years IRR, 1.7 [95% CI: 1.3-2.3], hypertriglyceridemia IRR, 1.5 [95% CI: 1.2-1.9], hypertension IRR, 1.5 [95% CI: 1.1-2.1], Major Depressive Disorder IRR, 1.5 [95% CI: 1.1-2.0], or schizophrenia IRR, 2.1 [95% CI: 1.2-3.8]. Over 20% of Veterans with prediabetes attended a comprehensive structured lifestyle modification clinic or program. Among Veterans with prediabetes, metformin was prescribed to 1.1% overall, a proportion that marginally increased to 1.6% in the subset of individuals at highest risk for progression to T2DM.

The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration (FDA) to evaluate the potential use of real-world evidence (RWE) to support new indications for use for previously approved drugs, and to satisfy post-approval study requirements. Extracting reliable evidence from real-world data (RWD) is often complicated by a lack of treatment randomization, potential intercurrent events, and informative loss to follow up. Targeted Learning (TL) is a sub-field of statistics that provides a rigorous framework to help address these challenges. The TL Roadmap offers a step-by-step guide to generating valid evidence and assessing its reliability. Following these steps produces an extensive amount of information for assessing whether the study provides reliable scientific evidence in support regulatory decision making. This paper presents two case studies that illustrate the utility of following the roadmap. We use targeted minimum loss-based estimation combined with super learning to estimate causal effects. We also compared these findings with those obtained from an unadjusted analysis, propensity score matching, and inverse probability weighting. Non-parametric sensitivity analyses illuminate how departures from (untestable) causal assumptions would affect point estimates and confidence interval bounds that would impact the substantive conclusion drawn from the study. TL's thorough approach to learning from data provides transparency, allowing trust in RWE to be earned whenever it is warranted.

To address gaps in understanding the pathophysiology of Gulf War Illness (GWI), the VA Million Veteran Program (MVP) developed and implemented a survey to MVP enrollees who served in the U.S. military during the 1990–1991 Persian Gulf War (GW). Eligible Veterans were invited via mail to complete a survey assessing health conditions as well as GW-specific deployment characteristics and exposures. We evaluated the representativeness of this GW-era cohort relative to the broader population by comparing demographic, military, and health characteristics between respondents and non-respondents, as well as with all GW-era Veterans who have used Veterans Health Administration (VHA) services and the full population of U.S. GW-deployed Veterans. A total of 109,976 MVP GW-era Veterans were invited to participate and 45,270 (41%) returned a completed survey. Respondents were 84% male, 72% White, 8% Hispanic, with a mean age of 61.6 years (SD = 8.5). Respondents were more likely to be older, White, married, better educated, slightly healthier, and have higher socioeconomic status than non-respondents, but reported similar medical conditions and comparable health status. Although generally similar to all GW-era Veterans using VHA services and the full population of U.S. GW Veterans, respondents included higher proportions of women and military officers, and were slightly older. In conclusion, sample characteristics of the MVP GW-era cohort can be considered generally representative of the broader GW-era Veteran population. The sample represents the largest research cohort of GW-era Veterans established to date and provides a uniquely valuable resource for conducting in-depth studies to evaluate health conditions affecting 1990–1991 GW-era Veterans.

Background This report describes the U.S. Food and Drug Administration (FDA) experience in establishing a dedicated mailbox, and in publishing related guidance, to address concerns among interested parties regarding the conduct of clinical trials during the COVID-19 public health emergency (PHE).Methods Six hundred and thirty-four mailbox inquiries were received from March 2020 through February 2022. Qualitative methods were used to provide a structured description of, and identify common themes among, these inquiries.Results Most inquiries came from U.S.-based interested parties, including sponsors, industry trade associations, academic institutions, hospitals, clinics, research sites, trial participants, and individual persons. Approximately one-fifth of questions were related directly to COVID-19 (e.g., proposals for treatment); other inquiries were related to conduct of routine trial-related activities, and concerns were often focused on maintaining compliance with good clinical practice. In March 2020, FDA published a guidance titled Conduct of Clinical Trials of Medical Products During the COVID-19 Public Health Emergency; the document was subsequently revised eight times based in part on issues raised in mailbox inquiries.Conclusions The dedicated mailbox enabled expedited communication among invested parties during the COVID-19 PHE; FDA also provided updates of the aforementioned guidance. These efforts supported the continuance of ongoing trials and the initiation of new trials during the PHE in accordance with good clinical practice guidelines, thereby helping to ensure the safety of trial participants while maintaining the quality of trial data. By soliciting and responding to trial-related inquiries and addressing corresponding needs and concerns, FDA improved transparency and communication.

Real-world data (RWD) and real-world evidence (RWE) are increasingly used to support regulatory decision making, but regulatory agencies and stakeholders may apply different definitions for RWD and use different criteria to determine when analysis of such data are considered RWE in decisions on drug approvals. To explore this issue, we reviewed two prominent publications that operationalized the definitions of RWD and RWE when describing the use of RWE in drug approvals by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Both publications considered noninterventional (observational) studies, RWD as a comparator arm for a single-arm trial, product-related literature reviews, and RWD to support clinical trial implementation (e.g., to identify potential participants) as generating RWE. In contrast, inconsistencies were identified regarding types of data sources and study designs that were considered as not generating RWE. For example, a lack of agreement existed regarding whether RWE is generated when RWD describe therapeutic contexts or are used in phase I/II interventional trials, open-label extension studies, or pharmacovigilance activities. These discrepancies highlight opportunities to develop a consistent understanding of the role of RWE in regulatory decision making for drug approvals among regulatory agencies and stakeholders.

This survey study describes changes in the use of prescription medications in individuals aged 65 years or older from 1999 through March 2020.

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.