Citations

Abstract Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort ( n = 5136) and transcriptomic data from a prospective cohort ( n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS‐M). We further assessed whether the taxonomy could guide management in patients receiving disease‐modifying therapies. Except for the “ TP53 ‐complex” subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS‐M and the taxonomy capture disease biology, other non‐genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML‐like, bi TET2 , SF3B1 , and TP53 ‐complex subgroups. Further integration of multi‐omics data may refine MDS classification, improving clinical decision‐making and guiding the development of targeted therapies.

Abstract Background Adipose tissue influences blood pressure regulation through depot-specific endocrine, inflammatory, and exosome-dependent mechanisms. While obesity is a well-established risk factor for hypertension, emerging evidence suggests that distinct fat depots may contribute to vascular dysfunction independent of total adiposity. Methods: This review synthesizes post-2020 literature on 6 anatomically defined depots: visceral, perivascular, epicardial, bone marrow, brown, and beige adipose tissue, with an emphasis on their mechanistic roles, imaging biomarkers, and translational relevance. Results Alterations in adipose depot function can impair nitric oxide signaling, promote oxidative stress, and modulate sympathetic tone, even in the absence of obesity. Exosomal microRNAs from dysfunctional adipose tissue further disrupt vascular homeostasis. Although clinical tools for assessing depot dysfunction are limited, advances in imaging and molecular profiling may enable targeted therapeutic strategies. Conclusions Reframing adipose tissue as a modular regulator of blood pressure, rather than a passive consequence of obesity, may unlock new venues for precision management of hypertension.

Abstract Background The tumor, node, metastasis (TNM) classification at pathological evaluation determines prognosis following cancer treatment. For rectal cancer patients undergoing neoadjuvant therapy, whether ypTNM likely confers different prognosis than pTNM. We compared survival outcomes of patients classified by ypTNM vs pTNM staging. Methods Patients with resected locoregional rectal cancer from the National Cancer Database who underwent upfront surgery (pTNM) and had ≥5 years follow-up (2010-2017) were compared with those receiving neoadjuvant therapy followed by surgery (ypTNM). To address treatment selection bias, analyses were stratified by receipt of guideline-concordant care. Overall survival (OS) was compared using Kaplan-Meier and multivariable Cox regression analyses. Results Of 50 436 patients (median age = 60; 61.4% male), 11 732 (23.3%) underwent upfront surgery and 38 704 (76.7%) received neoadjuvant therapy; 78.8% of the upfront surgery group received guideline-concordant care. In analyses stratified by guideline-concordant care, overall survival was similar for stage 0-I (hazard ratio [HR] = 1.05; 95% CI = 0.96 to 1.15) but significantly worse for ypTNM vs pTNM in stage II (HR = 1.50; 95% CI = 1.27 to 1.77; 5-year OS = 75.6% vs 83.2%) and stage III (HR = 1.60; 95% CI = 1.46 to 1.75; 5-year OS = 67.0% vs 78.2%). Multivariable analysis confirmed ypTNM was associated with worse survival at each stage (stage 0-I: HR = 1.14, P = .004; stage II: HR = 1.49, P < .001; stage III: HR = 1.60, P < .001). Conclusion Stage for stage, ypTNM classification is associated with worse survival than pTNM classification, with increasing difference at higher stages. Future staging systems and management guidelines should account for these differences to optimize prognostication and stratification for clinical trials and surveillance strategies.

ABSTRACT Objective This study assessed the impact of sex and age on weight loss outcomes in patients receiving tirzepatide in real‐world clinical practice. Methods We conducted a retrospective cohort study of adults with overweight or obesity who initiated tirzepatide at Mayo Clinic between June 2022 and May 2024 and completed ≥ 12 months of continuous therapy. Primary outcome was total body weight loss percentage (TBWL%) at 15 months, stratified by sex and age groups (≤ 45, 46–59, ≥ 60 years). Results Among 1039 patients (57% women; mean age 56 ± 11 years), women achieved significantly greater TBWL% than men after 15 months of treatment (15.1% vs. 10.7%, p < 0.001). Patients aged ≤ 45 years had greater weight loss than those ≥ 60 years (15.1% vs. 12.3%, p = 0.024). In multivariable analyses, greater weight loss was independently associated with female sex, absence of type 2 diabetes, no prior obesity medication use, no concomitant weight gain‐promoting medications, and higher tirzepatide dosage. Age was not an independent predictor. Conclusions Sex, but not age, predicted weight loss with tirzepatide among individuals using the medication continuously for ≥ 12 months. These findings underscore the importance of incorporating sex‐specific considerations into personalized obesity treatment strategies.

Introduction To assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals. Methods A total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, paid subscription), Gemini, and OpenEvidence. OpenEvidence was used only for clinician-based questions. Responses were evaluated by three expert reviewers blinded to the LLM used who rated them as accurate and complete, accurate but incomplete, or inaccurate. A fourth reviewer resolved discordant scores. Readability for patient-level questions was assessed using the Flesch Reading Ease Score (FRES) and word count. Lower FRES scores indicate more difficult reading. Accuracy and completeness were compared using odds ratios (95% CI) with ChatGPT 3.5 as the reference model, and differences in readability were analyzed using Friedman’s test. Results LLM performance varied across question types. For patient-level questions, ChatGPT 4.0 achieved the highest accuracy (9 of 11; 82%), followed by ChatGPT 3.5 and Gemini (each 8 of 11; 73%), with no statistically significant differences. Among clinician-level questions, OpenEvidence demonstrated perfect accuracy (15 of 15; 100%), compared with 93% for and 80% for ChatGPT 4.0 and Gemini. Completeness followed similar patterns, with OpenEvidence providing the most complete clinician responses (93%) and ChatGPT 4.0 the most complete patient-level responses (89%). Readability differed significantly among models ( p = 0.012), with Gemini producing the most readable patient-level content (median FRES 43.5 [IQR 36.8–53.4]) compared with ChatGPT 3.5 (30.6 [16.8–48.4]) and ChatGPT 4.0 (28.8 [22.1–37.6]). Word counts did not differ significantly ( p = 0.39). Discussion LLMs demonstrated good overall performance in answering FHA-related questions but often provided incorrect or incomplete information. Fine tuning field-specific data, engineered prompts, and obtaining human-in-the-loop feedback may help improve the accuracy of these models.