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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.

The NCCN Guidelines for Biliary Tract Cancers (BTCs) provide recommendations for the evaluation and comprehensive care of patients with gallbladder cancer, intrahepatic cholangiocarcinoma, and extrahepatic cholangiocarcinoma. The multidisciplinary panel of experts is convened at least once annually to review requests from internal and external entities as well as to evaluate new data on current and emerging therapies. This manuscript focuses on the adjuvant chemotherapy and chemoradiation treatment options for BTCs as well as the systemic treatment recommendations for patients with advanced BTCs.

Importance Obesity is associated with a higher risk of recurrence, mortality, comorbidities, treatment-related adverse effects, and poor quality of life in patients with breast cancer. Scalable interventions are needed to promote weight loss in this population. Objective To evaluate the impact of a remotely delivered weight loss intervention (WLI) on weight change at 1 year in patients with breast cancer and obesity and to explore factors associated with weight change. Design, Setting, and Participants The Breast Cancer Weight Loss trial is a phase 3, randomized clinical trial evaluating the impact of a telephone-based WLI on invasive disease–free survival and other outcomes in women with obesity and early breast cancer at 637 sites across the US and Canada. Participants were enrolled to the study between August 2016 and February 2021. Participants included women with stage II to III, ERBB2 -negative breast cancer and a body mass index (BMI) of 27 or higher. Interventions Participants were randomized to a 2-year, telephone-based WLI plus health education or health education alone control group. Main Outcome and Measures The primary end point for this prespecified secondary analysis was weight change at 1 year. Weight was measured at baseline and 1 year, and changes in weight were compared between groups. Weight change was evaluated with a linear mixed-effects model including treatment group, weight over time, a time-by-group interaction, menopausal status, race and ethnicity, and hormone receptor status. Results A total of 3180 women with breast cancer and BMI of 27 and higher were included in the study; 1591 were randomized to the WLI and 1589 to the control group. At baseline, the mean (SD) age of participants was 53.4 (10.6), and the mean (SD) BMI was 34.4 (5.6). The racial and ethnic breakdown included 406 (12.8%) Black, 231 (7.3%) Hispanic or Latino, 2906 (91.4%) non-Hispanic, and 2555 (80.3%) White participants. WLI participants lost a mean of 4.3 kg (95% CI 3.9-4.6 kg), or 4.7% (95% CI, 4.3%-5.0%) of baseline body weight at 1 year vs control participants, who gained 0.9 kg (95% CI, 0.5-1.3 kg), or 1.0% (95% CI 0.1%-1.4%) of baseline body weight ( P < .001). Participants randomized to WLI experienced significant weight loss (vs control group participants) across demographic and tumor factors. WLI effect differed significantly by menopausal status, with postmenopausal participants having greater weight loss than premenopausal participants, and by race and ethnicity, with Black and Hispanic participants having less weight loss compared to other races and ethnicities. Conclusions and Relevance In this secondary analysis of a randomized clinical trial, a telephone-based WLI induced significant weight loss in patients with breast cancer with overweight and obesity across demographic and treatment factors. Further follow-up of the Breast Cancer Weight Loss trial will evaluate whether the WLI improves disease outcomes. Trial Registration ClinicalTrials.gov Identifier: NCT02750826

Background: Home telemetry has been shown to reduce 30-day readmissions in patients at high risk for readmission. It remains unclear whether this finding is due to the telemetry itself or the nurse contact that follows detection of an abnormal parameter. Purpose: This study was a subgroup analysis from a prior trial comparing patients on home telemetry for 30 days to usual care after discharge to determine if nurse contact affected readmission rates. Methods: Patients randomized to telemetry who had at least 1 nurse contact were compared to those who had none to see if the contact reduced the rate of readmissions. Results: Of 449 home telemetry patients, 292 (65.0%) received 1 or more nurse contacts. Single and multivariable logistic regression models exploring the association of contact with 30-day readmission did not find any statistically significant associations of nurse contact with readmission (all unadjusted P ≥ .13, all adjusted P ≥ .18). Conclusion: Our subset analysis did not show that the nurse contact was the factor that reduced readmission in patients on home telemetry.

Abstract There is an urgent need to improve dementia ascertainment robustness in real-world studies assessing drug effects on dementia risk. We developed algorithms to dementia identification algorithms using Medicare claims (inpatient/outpatient/prescription) from 3318 Visit 5 (2011-2013) and 1828 Visit 6 (2016-2017) participants of the Atherosclerosis Risk in Communities (ARIC) Study, validated against ARIC's rigorous syndromic dementia classification. Algorithm performance was compared to existing algorithms (Jain, Bynum, Lee). We further evaluated algorithms effectiveness in a 20% random Medicare sample aged ≥70 years who initiating liraglutide or dipeptidyl peptidase 4 inhibitors (DPP4i) to assess 3-year adjusted risk difference (aRD) for dementia. Our incident dementia algorithm required two dementia diagnostic codes within 1-year, or one dementia code plus a new dementia prescription within 90 days. It achieved a positive predictive value (PPV) of 69.2%, specificity of 99.0%, and sensitivity of 34.6% (population prevalence: 8.8%), comparable to extant algorithms (PPV, 58.7–68.6%; sensitivity 25.5–40.4%). Prevalent dementia algorithm (without requiring incident diagnoses/prescriptions) demonstrated similar performance. In the Medicare sample, dementia risk ranged from 3.0% to 12.5%, aRD comparing liraglutide to DPP4i varied −1.2% to −3.6%, with our algorithm closely matching the Bynum algorithm. Algorithm selection significantly impacts treatment effect estimates, highlighting its importance in in pharmacoepidemiologic research.

Background: Obstructive Sleep Apnea (OSA) is a common occurrence in the perioperative patient population but is often undiagnosed. Point-of-Care Ultrasound (POCUS) has emerged as a promising tool for perioperative assessment; however, its effectiveness in detecting the presence or severity of OSA needs to be evaluated. Objective: We assessed the ability of airway POCUS as a screening and severity evaluation tool for OSA by examining its correlation with STOP-BANG scores and the Apnea–Hypopnea Index (AHI). Design: Cross-sectional observational study. Setting: A single-center study in a tertiary care hospital between June 2020 to May 2021. Patients: Adult patients aged 18–65 with prior Polysomnography (PSG) for OSA workup were screened. Interventions: The participants completed the STOP-BANG questionnaire and subsequently underwent POCUS examinations, either pre- or post-surgery. Ten different POCUS views previously used for evaluating OSA were acquired in a predefined sequence, with subsequent measurements of airway parameters. Outcome measures: Generalized linear modeling was used to explore and assess the relationships between the measured parameters, STOP-BANG, and AHI scores (modeled continuously and categorized into risk levels of STOP-BANG and AHI). Results: A total of 260 patients were screened, of which 142 were enrolled and 127 completed the scanning studies. The median AHI was 16.71, while the STOP-BANG scores were mostly between 5 and 6, indicating a moderate-to-high OSA risk in the study population. Notably, only neck circumference was significantly associated with AHI severity (p = 0.012), whereas none of the other POCUS measures were. Among the POCUS measures, significant associations with STOP-BANG scores were observed for the Tongue Cross-Sectional Area (T-CSA) (p = 0.002), Retro-Palatal Diameter (RPD) (p = 0.034), Distance Between Lingual Arteries (DLA) (p = 0.034), and Geniohyoid Muscle Thickness (GMT) (p = 0.040). Conclusions: Neck circumference is a more reliable predictor of OSA severity (AHI) compared to other POCUS measurements. Many of the POCUS measures had a good correlation with the STOP-BANG scores, highlighting the utility of POCUS as a screening tool for OSA rather than as a severity evaluation tool.

Importance SARS-CoV-2 infection has been linked to neurotoxic effects and cognitive deficits. Objective To determine whether decreases in cognitive function were accelerated after SARS-CoV-2 infection compared with individuals not infected. Design, Setting, and Participants Multicenter, prospective cohort study from 2016 to 2022 among 3525 participants alive on March 1, 2020, and enrolled in The Atherosclerosis Risk in Communities (ARIC) study and the Collaborative Cohort of Cohorts for COVID-19 Research study who completed a prepandemic cognitive assessment and a pandemic-era assessment of SARS-CoV-2 infection. Final analyses performed in November 2024. Exposure SARS-CoV-2 infection determined via self-report of a positive SARS-CoV-2 test or health care professional diagnosis of COVID-19, a positive SARS-CoV-2 antinucleocapsid antibody response, or presence of an administrative code for COVID-19 on medical records. Main outcomes and measures A neuropsychological battery assessed multiple cognitive domains, and a cocalibrated confirmatory factor analysis generated factor scores for global cognitive function. The primary outcome was the rate of excess change in cognitive function. Results The 3525 eligible participants had a mean (SD) age of 80.8 (4.7) years, 2085 (59.1%) were female, 752 (21.4%) were Black, and 2773 (78.6%) were White. SARS-CoV-2 infection was detected among 307 participants (8.7%), 103 of whom (33.6%) were hospitalized. Among uninfected participants, the mean annualized change in cognitive function was −0.09 (95% CI, −0.13 to −0.04). Compared with this rate, change was faster (β = −0.06; 95% CI, −0.09 to −0.02) among participants hospitalized for infection, but not different from participants who were infected but not hospitalized (β = 0.00; 95% CI, −0.02 to 0.03). The association among participants hospitalized for infection was evident in the cognitive domains of memory and executive function, but not language. Conclusions and relevance This cohort study of older participants found accelerated decreases in cognition among individuals hospitalized for SARS-CoV-2 infection, but not nonhospitalized infection, in comparison with individuals not yet infected.

Introduction: Atherosclerotic arterial disease is the leading cause of death throughout the world. One of the most advanced forms of atherosclerosis, peripheral arterial disease (PAD), affects over 8.5 million people in the United States and over 230 million globally. This disease begins from an inflammatory lesion, known as the fatty streak, formed by mononuclear phagocytes (MNPs), a broad cell type classification that includes monocytes, dendritic cells, and macrophages. Development of more efficacious treatments for PAD requires that we better understand the cellular and molecular processes by which MNPs form these inflammatory lesions. Methods: To compare gene expression of MNPs, we performed scRNAseq on PBMCs from an initial cohort of 4 patients with PAD, mild claudicants to minor tissue loss (Rutherford 1-5), and 5 healthy individuals, defined as those without any chronic diseases and a hsCRP <2.0 mg/L. We conducted single cell RNA library generation using Parse Bio and sequenced to a depth of 70k reads per cell and downstream bioinformatic and differentially gene expression analysis via R and Seurat. Results: Gene expression differences in healthy versus PAD cells were visualized on a UMAP plot. Aligning our cells to the human PBMC Cell Atlas, we found that the cell type with the greatest transcriptional change (healthy vs. PAD) was CD14 + MNPs. Next, we screened for differentially expressed genes within this CD14 + MNP cluster between healthy and PAD subjects and identified 854 upregulated and 730 downregulated genes (log 2 fold change ≥ 0.25 and p < 0.05, Welch’s t-test). Two of the most significantly upregulated genes in PAD CD14 + MNPs are FAM20A and TMTC1. FAM20A encodes a psuedokinase important in calcium homeostasis as well as bone mineralization and that is upregulated in advanced atherosclerotic plaques. TMTC1 encodes for an endoplasmic reticulum protein also involved in calcium homeostasis, vascular homeostasis, and endothelial cadherin egress. Finally, one gene we found downregulated in PAD is CD83, a molecule implicated in resolution of inflammation, that when downregulated promotes an inflammatory state. Conclusion: Based on these findings we hypothesize that in PAD there exists a distinct transcriptional program along the CD14 + MNP cell type lineage that is pro-inflammatory and that may play a role in disease progression and response to intervention.

Abstract Objective Despite exponential growth in artificial intelligence (AI) research for laboratory medicine and pathology, a significant gap exists between model development and clinical AI implementation. This article introduces a structured framework, the Clinical AI Readiness Evaluator (CARE), to bridge this gap and support the responsible adoption of AI in clinical laboratory settings. Methods Building upon the Machine Learning Technology Readiness Levels framework, we developed CARE specifically for the clinical laboratory environment by incorporating health care–specific requirements, regulatory considerations, and workflow integration needs. This framework was iteratively refined through practical application across diverse AI use cases within laboratory medicine and pathology. Results The CARE framework provides a systematic approach to AI development and implementation through 8 component workstreams: clinical use case, data, data pipeline, code, clinical user experience, clinical technology infrastructure, clinical orchestration, and regulatory compliance. Unlike generic AI frameworks, CARE distinguishes itself by emphasizing both health care and laboratory workflow integration, regulatory requirements, ethical considerations, and comprehensive validation for clinical contexts. The framework accommodates both internally developed models and commercial AI solutions, providing clear guidance through technology readiness levels and structured review processes. Conclusions The CARE framework addresses the unique challenges of implementing AI in laboratory medicine and pathology by providing a comprehensive roadmap from initial concepts through clinical deployment and maintenance. This article, the first in a series of 4, establishes the foundational AI lifecycle framework, while subsequent articles will explore data documentation, ethical AI considerations, and governance structures. By adopting this structured approach, laboratories can responsibly harness AI’s potential to enhance diagnostic accuracy and operational efficiencies and, ultimately, improve patient care.