Citations

The authors explore the association of remnant cholesterol and CRP with the longitudinal development of cancer in a prospective cohort study and if there is an opportunity to modulate risk with bariatric surgery or GLP‐1 agonist.

Abstract Background Dystonia and Parkinson's disease (PD) exhibit clinical and genetic overlap, but the relevance of dystonia gene variants in PD remains unclear. Objective The aim was to assess the frequency of dystonia‐linked pathogenic variants in PD. Methods We screened sequencing data from 15,684 individuals (8272 PD, 3200 atypical parkinsonism, and 4212 unaffected) from the Global Parkinson's Genetics Program (GP2) and Accelerating Medicines Partnership‐Parkinson's Disease (AMP‐PD) for variants in genes linked to isolated dystonia, dystonia‐parkinsonism, and myoclonus‐dystonia. Results Pathogenic variants were identified only in PD patients. Forty‐five PD individuals (0.54%) carried 26 distinct (likely) pathogenic variants in nine dystonia‐linked genes, most frequently in GCH1 , followed by VPS16 . Conclusion Though rare, pathogenic variants in dystonia‐linked genes are present in clinically and pathologically diagnosed PD. Our results reinforce GCH1 as a PD‐relevant gene with clinical implications, whereas variants identified in other genes are rare and of uncertain relation to the PD phenotype. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

ABSTRACT Importance Despite the current enthusiasm for anti‐obesity medications, there is a critical need for effective lifestyle interventions that can be broadly implemented. Objective To assess the effectiveness of REWIND, an obesity and diabetes treatment program. Design Prospective cohort study. Setting Virtual, community‐based weight management program. Participants Two‐hundred and nineteen participants, BMI ≥ 30 kg/m 2 , with and without type 2 diabetes (T2D). Intervention Three phases: “induction” with meal replacement diet to promote ≥ 15% weight loss; “transition” to everyday foods; and “maintenance” to prevent weight regain. REWIND leveraged teams including community leaders, media personalities, nationally recognized athletes and participants, to disseminate information, and foster belonging, commitment, and resilience. Main Outcomes The co‐primary outcomes were the change in weight at 6‐, 12‐, and 18 months. Secondary outcomes were the proportion of patients achieving ≥ 5%, 10%, 15%, and 20% weight loss at 6, 12, and 18 months, and remission rates in T2D at 6‐, 12‐, and 18‐month, respectively. Results Two hundred and nineteen participants (mean age 49.1 [SD 10.5] years), 37% men, 12% Black, 26% with T2D. Weight decreased from: 114.4 (20.8) kg to 96.1 (18.4) kg at 6 months, to 98.5 (18.1) kg at 12 months, and to 101.1 (20.5) kg at 18 months. Weight loss percentages at 6, 12, and 18 months were ≥ 5%: 95%, 91%, 84%; ≥ 10%: 79%, 72%, 67%; ≥ 15%: 58%, 46%, 47%; and ≥ 20%: 24%, 25%, 22%. Diabetes remission was achieved by 52% and 43% of participants with T2D at 12‐ and 18‐month, respectively. Limitations Single geographic region. Weight was self‐reported. Conclusion A virtual community‐based program for individuals with obesity and T2D facilitated substantial weight loss and T2D remission. This model has the potential for wide‐scale implementation in diverse settings.

Background: Thyroid eye disease (TED) is a debilitating autoimmune disorder linked to thyroid dysfunction. There is limited knowledge of TED in Asian populations. This multicenter study characterizes the clinical features and treatment response of TED in a large Chinese cohort. Methods: A retrospective multicenter study included 4157 patients with TED from nine Chinese hospitals from February 2016 to July 2023. Disease severity and activity were evaluated according to the European Group on Graves’ Orbitopathy standards. We examined associations of variables including sex, age, smoking status, I 131 treatment, consultation department, and geographical region with clinical outcomes. Logistic regression and nomogram models were developed to examine associations with sight-threatening TED and, in a subgroup analysis ( n = 126), patients’ responsiveness to intravenous glucocorticoid (IVGC) therapy. Results: We included 4157 patients with mean age and standard deviation (SD) 45.96 ± 16.44 years. Of these, 63.6% ( n = 2644) were females. Over half (55.6%, n = 2310) of participants were in the inactive phase, with a mean clinical activity score of 2.19 ± 1.61 (SD) for all patients. TED severity was categorized as mild (9.3%, n = 385), moderate-to-severe (82.5%, n = 3428), and sight-threatening (8.2%, n = 344). The average degree of exophthalmos was 20.04 ± 5.27 mm, and 48.8% ( n = 2029) of patients had diplopia. Patients treated with I 131 had higher disease activity (47.5%, n = 468, vs. 43.5%, n = 1379, p < 0.05). Coastal region patients exhibited more severe TED (sight-threatening cases: 10.1%, n = 195, vs. 7.2%, n = 147) and higher diplopia scores (1.00 ± 1.10 vs. 0.86 ± 1.09, p < 0.001) than inland counterparts. TED severity was also greater in patients treated in Ophthalmology departments (mild cases: 6.0%, n = 213; moderate-to-severe cases: 85.6%, n = 3055) compared with Endocrinology departments (mild cases: 29.3%, n = 172; moderate-to-severe cases: 63.5%, n = 373). Nomograms had an area under the receiver operating curve of 0.742 (confidence interval [CI] 0.716–0.768) for sight-threatening TED and 0.759 (CI 0.674–0.843) for IVGC therapy responsiveness. Conclusions: We characterized the clinical features and treatment response of TED in a large Chinese cohort. These findings offer valuable insights informing TED risk stratification in Asian patients and forming a foundation for future prospective studies.

Abstract Background Physical activity is associated with lower colorectal cancer (CRC) risk, but its association with molecular subtypes defined by genetic and epigenetic alterations of the disease is unclear. Such information may enhance the understanding of the mechanisms related to the benefits of physical activity. Methods Pooled observational (cases: n = 5386; controls: n = 6798; studies n = 5) and genome-wide association data (cases: n = 8178; controls: n = 10 472; studies n = 5) were used. We used multivariable logistic regression models and Mendelian randomization to assess the association between physical activity and the risk of CRC subtypes defined by individual tumor markers (and marker combinations), namely microsatellite instability status, CpG island methylator phenotype status, and BRAF and KRAS mutations. We used case-only analysis to test for differences between molecular subtypes. We applied Bonferroni correction to account for multiple tests. Results In the pooled observational analysis, higher levels of physical activity were associated with lower CRC risk (Obs-per 1SD, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.90 to 0.97), with an association that was stronger in males (Obs-per 1SD, OR = 0.91, 95% CI = 0.87 to 0.96) than in females (Obs-per 1SD, OR = 0.97, 95% CI = 0.91 to 1.03; Pinteraction = .04). Higher physical activity was associated with a lower risk of CRC across all molecular subtypes, especially in males. There was no difference in the associations by subtypes by pooled observational or Mendelian randomization analyses. The findings did not differ by study design, anatomical site, and early or late age onset of CRC. Conclusions Our findings suggest that physical activity is not differentially associated with the 4 major molecular subtypes involved in colorectal carcinogenesis, indicating that its benefits extend broadly across colorectal cancer pathogenesis.

Abstract Background Cryoprecipitated antihemophiliac factor (cryo) is primarily used to replenish fibrinogen in acquired coagulopathy. Little has been reported about its usage patterns within hospitals with respect to patient population, frequency of use, and dosing. Study design and methods Cryo production and usage data were collected over 5 years from July 1, 2016, to June 30, 2021, from nationally based blood collection centers in the United States (U.S. n = 2) and Canada ( n = 2) and from eight large academic hospitals, respectively. Usage data for a similar product, purified fibrinogen concentrate were also collected from four hospitals. Results Two U.S. blood collectors reported increases in cryo production normalized to total whole blood collections from 17.4% in 2016 to 22.3% in 2021 and from 16.4% in 2016 to 20.2% in 2021. In contrast, in Canada cryo manufacturing increased slightly in one region (11.3%–12.9%) and decreased (8.0%–2.0%) in the other. Cryo utilization, defined as numbers of patients treated normalized to the inpatient census, and dose administered per patient did not consistently increase and differed significantly between hospitals participating in the study ( p < .0001). Likewise, the departments that most frequently transfused cryo varied between hospitals. Similarly, variations in practice were observed for fibrinogen concentrate usage. Conclusion While much of Canada and Europe have moved towards using fibrinogen concentrate, two large U.S. collectors have increased cryo distribution. The lack of standardization and variability in the clinical practice regarding the use of cryo and fibrinogen concentrate reported by study sites may be attributable to practitioner preference, availability, and/or cost rather than adherence to published evidence or guidelines.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.

Abstract Background Unresectable hepatocellular carcinoma (uHCC) is a leading cause of cancer death. FDA-approved first-line systemic therapies include atezolizumab/bevacizumab (atezo/bev) and durvalumab/tremelimumab (durva/treme); however, there is a lack of comparative data. Methods We reviewed outcomes of patients with uHCC who initiated atezo/bev or durva/treme between 2017 and 2024, across six institutions. Overall survival (OS) and time to treatment discontinuation (TTD) were analyzed using the Kaplan–Meier and Cox models, adjusting for baseline characteristics. Results Four hundred fifty-two uHCC pts were included. Median age: 68 years; 77% male; 81% white. Most common etiologies were viral hepatitis (38.9%) and metabolic dysfunction-associated steatohepatitis (19.5%). Disease progression was the primary reason for treatment discontinuation, atezo/bev (56%) and durva/treme (42%). Outcomes were not statistically significant (median OS [month, m]: 14.0 vs 14.6 [P = .66]; median TTD [m]: 4.9 vs 3.9 [P = .42] for atezo/bev vs durva/treme). Outcomes were significantly different between Child-Pugh classes (CP: A, B7, B8/9, C) respectively, median OS(m): 19.0, 6.1, 5.1, 2.0 (P &lt; .001); median TTD(m): 6.1, 2.3, 3.0, 1.3 (P &lt; .001). Conclusions In this real-world study of uHCC, no significant difference in clinical outcomes was observed between atezo/bev and durva/treme in the first-line setting. CP scores were a key prognostic variable with both regimens.

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Error analysis is a critical step in evaluating and improving clinical concept extraction models, the most common clinical natural language processing (NLP) task. Unlike corpus annotation, which follows standardized protocols for creating gold-standard datasets, error analysis requires nuanced judgment grounded in both clinical expertise and NLP knowledge. This is especially important given the heterogeneity of clinical text, where variations in documentation style, note structure, and terminology can substantially influence model behavior. Despite its importance, there is currently no standardized, user-level framework to support systematic error analysis in clinical concept extraction task. In this study, we developed and validated MedError, a machine-assisted, human-in-the-loop framework designed to standardize and enhance error analysis for clinical concept extraction tasks. We collected and manually curated a corpus of 1,187 unique errors from a total of 4,237 notes across three different distinct hospitals. The error categories were defined using our previously validated error taxonomy and included 480 false negatives and 707 false positives across 25 error types and 48 clinical concept categories. We evaluated the performance of three proprietary and three open-source large language models (LLMs) in automatically classifying these errors into 26 and 15 predefined categories. We further developed a machine-assisted framework, MedError, which integrates best practices in error analysis, LLM-assisted classification and reasoning, and a user-friendly interface to enable more efficient, reproducible, and context-aware error analysis. The framework supports both single-site and federated multisite error analysis, facilitating the effective translation of clinical NLP systems into real-world settings.