Tag: pubmed

Annu Rev Biomed Data Sci. 2026 Mar 25. doi: 10.1146/annurev-biodatasci-092724-035434. Online ahead of print.

ABSTRACT

Genetic influences on how human traits change over time remain underexplored and may play an important role in disease processes. In this review, we explore emerging statistical approaches for incorporating longitudinal data on trait trajectories into genetic epidemiology studies, including longitudinal genome-wide association studies, polygenic scores, and Mendelian randomization. We discuss the caution required when analyzing longitudinal data focused on disease progression, where analyses are conducted within a group of patients rather than the general population. Finally, we outline the large longitudinal data resources that are available and discuss future directions in trajectory-based genetic epidemiological studies. Embracing time as a critical dimension of human traits offers deeper insight into disease pathways and intervention opportunities.

PMID:41880638 | DOI:10.1146/annurev-biodatasci-092724-035434

JMIR AI. 2026 Mar 25;5:e83425. doi: 10.2196/83425.

ABSTRACT

BACKGROUND: Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in health care modeling, especially in environments marked by uncertainty, nonlinearity, and missing information. Although its use in prediction, classification, and risk stratification is well established, its application to explicit causal inference remains limited, varied, and methodologically premature.

OBJECTIVE: This systematic review aimed to examine how fuzzy logic frameworks have been used to address causal questions in health care, focusing on their methodological characteristics, comparative performance, and degree of integration with formal causal inference approaches.

METHODS: A systematic search across 6 databases (PubMed, Web of Science, ScienceDirect, SpringerLink, Scopus, and IEEE Xplore) identified peer-reviewed studies published between 2014 and 2025 that applied fuzzy modeling in health care settings with explicit or implicit causal objectives. The review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and used a modified PICO (population, intervention, comparator, and outcome) framework for study selection. Data were extracted on the health care domain, fuzzy method, comparator use, and causal framing. Risk of bias was evaluated using the Joanna Briggs Institute (JBI) checklist and the PROBAST+AI tool, according to study design.

RESULTS: A total of 37 studies met the inclusion criteria. The most frequently applied approaches were fuzzy inference systems, fuzzy cognitive maps, and neuro-fuzzy models, with applications spanning infectious diseases, cancer, cardiovascular health, mental health, and occupational health. Fourteen studies included comparator models; among these, 5 reported superior performance of fuzzy approaches, 3 showed comparable results, and 6 lacked sufficient detail for a robust comparison. Only 2 studies explicitly implemented formal causal inference frameworks, while most relied on predictive or associative modeling with implicit causal assumptions. Overall, the risk of bias was moderate to high.

CONCLUSIONS: Fuzzy logic offers interpretability and flexibility well suited to complex health care problems, yet its application to explicit causal inference remains fragmented. Greater methodological transparency, systematic benchmarking, and integration with formal causal designs-such as counterfactual and target trial frameworks-are required to establish fuzzy logic as a robust paradigm for causal inference in health care.

PMID:41880635 | DOI:10.2196/83425

N Engl J Med. 2026 Mar 26;394(12):1155-1166. doi: 10.1056/NEJMoa2507874.

ABSTRACT

BACKGROUND: Standard adjuvant chemotherapy for stage III colon cancer consists of a fluoropyrimidine-plus-oxaliplatin regimen. Whether the addition of atezolizumab (an anti-programmed death ligand 1 agent) to a modified FOLFOX6 regimen (fluorouracil, oxaliplatin, and leucovorin; called mFOLFOX6) would improve outcomes in patients with stage III colon cancer with mismatch repair-deficient (dMMR) status is unclear.

METHODS: In a phase 3 trial, we randomly assigned, in a 1:1 ratio, patients with resected stage III dMMR tumors to receive either adjuvant atezolizumab plus mFOLFOX6 for 6 months, with atezolizumab continued as monotherapy (for a total of 12 months of therapy), or mFOLFOX6 alone for 6 months. The primary end point was disease-free survival. Secondary end points were overall survival and the adverse-event profile.

RESULTS: A total of 355 patients were assigned to receive atezolizumab plus mFOLFOX6 and 357 to receive mFOLFOX6 alone. The median age of the patients was 64 years, 55.1% were women, and 53.9% had tumors that were T4, N2, or both (indicating high risk). At a median follow-up of 40.9 months, the 3-year disease-free survival was 86.3% (95% confidence interval [CI], 81.8 to 89.8) in the atezolizumab-mFOLFOX6 group, as compared with 76.2% (95% CI, 70.9 to 80.6) in the mFOLFOX6 group (hazard ratio for disease recurrence or death, 0.50; 95% CI, 0.35 to 0.73; P<0.001). Adverse events of grade 3 or 4 occurred in 84.1% of the patients who received atezolizumab plus mFOLFOX6 and in 71.9% of those who received mFOLFOX6 alone.

CONCLUSIONS: The addition of atezolizumab to mFOLFOX6 significantly improved disease-free survival among patients with stage III dMMR colon cancer. (Funded by the National Cancer Institute of the National Institutes of Health and Genentech; ATOMIC ClinicalTrials.gov number, NCT02912559.).

PMID:41880612 | DOI:10.1056/NEJMoa2507874

J Med Internet Res. 2026 Mar 25;28:e80895. doi: 10.2196/80895.

ABSTRACT

BACKGROUND: The targeted application and design of digital health interventions (DHIs) require an understanding of usage determinants. Usage includes uptake (initial use) and frequency (extent of use), but it is unclear whether both components are driven by the same determinants.

OBJECTIVE: This study aimed to examine the determinants of uptake and frequency of use and assess whether they differ.

METHODS: The investigated DHI was a web portal provided in an intervention for improving disease-related self-management. This study is a secondary analysis of intervention group data from a parallel-group randomized controlled trial. Eligibility criteria were being an adult and being diagnosed with type 2 diabetes and/or coronary heart disease. Sociodemographic, psychological, and health-related variables were examined as determinants. Determinants were analyzed using simple and multiple regression models. Uptake was analyzed using logistic regression, and frequency was analyzed using negative binomial regression with robust SEs. Frequency was analyzed for those who used the DHI at least once. Except for sociodemographic variables, all other variables were standardized to a range from 0 to 1. For simple regression, inflation of the α error due to multiple testing was controlled via the approach of Benjamini and Hochberg, and for multiple regression, it was controlled via the significance of the complete multiple regression model.

RESULTS: Of 462 intervention group members, 199 (43.1%) used the web portal at least once. After controlling for inflation of the α error, simple regression for uptake yielded significant effects for higher education (B=0.56, 95% CI 0.18-0.95; P=.004), openness (B=1.08, 95% CI 0.33-1.83; P=.005), intention regarding physical activity (B=2.28, 95% CI 1.30-3.26; P<.001), and intention regarding healthy nutrition (B=2.30, 95% CI 1.30-3.31; P<.001). The multiple regression model for uptake was highly significant (P<.001), with significant positive associations for intentions regarding physical activity (B=1.86, 95% CI 0.74-2.97; P=.001) and healthy nutrition (B=2.22, 95% CI 1.00-3.44; P<.001), as well as a significant negative association for patient activation (B=-3.20, 95% CI -4.95 to -1.46; P<.001). After controlling for inflation of the α error, simple regression for frequency yielded no statistically significant effect, and the multiple regression model for frequency was not significant (P=.07).

CONCLUSIONS: This study is innovative in jointly examining determinants of the uptake and frequency of use of the same DHI within a single context and sample. By demonstrating that factors driving uptake do not necessarily increase the frequency of use, it advances existing research. The study contributes to a more differentiated understanding of DHI use and shows that distinct strategies are required to promote adoption versus sustained engagement. Applying this approach to other DHIs and settings may support more targeted and equitable digital health implementation in real-world contexts, thereby optimizing digital health deployment strategies overall.

PMID:41880606 | DOI:10.2196/80895

JMIR Res Protoc. 2026 Mar 25;15:e78589. doi: 10.2196/78589.

ABSTRACT

BACKGROUND: Adjuvant endocrine therapy is a cornerstone in managing estrogen receptor-positive early breast cancer but may adversely affect metabolic health, including weight gain, insulin resistance, and dyslipidemia. These changes increase the risk of cardiovascular disease and may influence breast cancer outcomes. However, the timing and magnitude of early metabolic changes following endocrine therapy initiation remain poorly characterized. Conventional definitions such as metabolic syndrome rely on dichotomous thresholds and may lack sensitivity to detect early treatment-related metabolic changes, highlighting the need for refined assessment approaches.

OBJECTIVE: This prospective follow-up study aims to investigate early metabolic effects of initiating adjuvant endocrine therapy in women with estrogen receptor-positive early breast cancer and to compare conventional and expanded approaches to metabolic health classification.

METHODS: This single-center, prospective observational study was conducted at Aarhus University Hospital, Denmark. Women aged≥18 years with early-stage estrogen receptor-positive breast cancer initiating adjuvant endocrine therapy and without pre-existing diabetes were eligible. Metabolic health was assessed at baseline and after 3 months using biometric measurements (weight, waist and hip circumference, waist-to-hip ratio, and blood pressure) and non-fasting blood samples (plasma glucose; hemoglobin A1c, (HbA1c); lipid profile; and estradiol). The 3-month follow-up was selected to capture early metabolic changes while aligning with routine clinical care to minimize additional visits and reduce selection bias. Metabolic health will be evaluated using two conventional measures and two extended, exploratory measures. Conventional measures are metabolic syndrome (MetS), defined as meeting ≥3 of 5 established criteria (blood pressure ≥130/85 mmHg, triglycerides >2 mmol/l, high-density lipoprotein cholesterol <1.295 mmol/l, waist circumference >88 cm, and plasma glucose >7.8 mmol/l), and the Metabolic Syndrome z score (MetS-Z), a continuous standardized composite of the MetS components. Additional extended measures are exploratory: the extended MetS, which expands the standard MetS definition by incorporating low-density lipoprotein cholesterol (>3 mmol/l), body mass index (≥30 kg/m²), waist-to-hip ratio (>0.85), and HbA1c (≥42 mmol/mol), and the EMETA score, a standardized composite of the extended MetS components calculated using the same approach as the MetS-z score.

RESULTS: The study was funded in July 2024. Recruitment occurred between November 2024 and April 2025, and follow-up was completed in September 2025. Statistical analyses are planned for February 2026, with results expected to be published in summer 2026.

CONCLUSIONS: This study is expected to provide insights into early metabolic changes following initiation of adjuvant endocrine therapy and evaluate different approaches to classifying metabolic health. The aim to inform future research by helping to identify patients at increased risk of cardiometabolic complications and adverse breast cancer outcomes, warranting confirmation and validation of expanded metabolic measures in longer-term, larger cohorts.

PMID:41880605 | DOI:10.2196/78589

JMIR Form Res. 2026 Mar 25;10:e83556. doi: 10.2196/83556.

ABSTRACT

BACKGROUND: As physical education classes are lost to budget cuts and recess is canceled to meet standardized testing goals, the modern school day has become dominated by sedentary digital activities. To reverse this trend, current interventions have focused on reducing screen time. However, instead of fighting this digital invasion, this study examined the use of technology, specifically mixed reality, to turn screen time from sedentary into active time, promoting physical activity in a classroom setting.

OBJECTIVE: The primary aim of this study was to iteratively develop and test a mixed reality prototype that promotes physical activity (eg, jumping, squatting, and punching) during a digital classroom activity. The primary outcomes were the percentage of active time during the activity, a breakdown of the intensity of that active time, and an evaluation of the prototype’s usability.

METHODS: Between November 2023 and April 2025, a multidisciplinary research team developed a prototype and evaluated it during 2 rounds of pilot-testing. Participants were aged 10 to 15 years and attended local middle schools. Physical activity was assessed using a medical-grade, hip-worn accelerometer (ActiGraph wGT3X-BT). Acceptability was assessed using a validated questionnaire (the System Usability Scale) that has a maximum score of 100. To collect feedback for prototype improvements, semistructured interviews were conducted after each round of pilot-testing.

RESULTS: In the first round of pilot-testing, students (n=22) were active for 46.0% (6.9, SD 2.7 minutes) of the headset session, which lasted 15 (SD 0) minutes. After improving the prototype using feedback from the first round, students in the second round (n=10) were active for 5.8 (SD 3.1) minutes (62.4%) of the web-based assignment, which lasted 9.3 (SD 2.41) minutes, while still reporting “good” acceptability scores (mean 73.8, SD 17.2). There were no significant differences in acceptability ratings between the 2 pilot-testing rounds (P=.16), nor were there differences between boys and girls in round 1 (P=.79) or round 2 (P=.61).

CONCLUSIONS: The results of this iterative study indicate that mixed reality can be used to elicit physical activity in a classroom setting, at least for short assignments. However, further research is needed to determine longer-term use and effectiveness.

PMID:41880604 | DOI:10.2196/83556

J Med Econ. 2026 Dec;29(1):957-971. doi: 10.1080/13696998.2026.2646075. Epub 2026 Mar 25.

ABSTRACT

BACKGROUND AND OBJECTIVE: By shortening the time after U.S. drug approval when substantial revenue reductions typically occur, the Inflation Reduction Act’s (IRA) Drug Price Negotiation Program (DPNP) alters investment incentives. Particular concerns include disincentives for post-approval clinical testing of new indications (begun after U.S. drug approval), and investment in small molecules relative to biologics. Post-approval development faces shortened times to earn returns; small molecule drugs are eligible to be selected for the DPNP four years earlier than biologics. The purpose of this study is to investigate these potential disincentives by quantifying trends in clinical trial starts, before and after IRA passage, for biologics and small molecules, further segmenting trials into those begun prior to U.S. drug approval and those begun later, seeking additional indications (pre-approval and post-approval trials, respectively).

METHODS: Combining data from Citeline Trialtrove and Pharmaprojects databases, a linear regression model estimated separately for trials beginning pre- and post-U.S. drug approval yielded best-fit estimates of trends and percentage changes in average monthly Phase I-III industry-sponsored interventional clinical trial starts for the periods before (January 2010-July 2022) and after (August 2022-December 2024) passage of the IRA, in biologic and small molecule drugs.

RESULTS: During the first twenty-nine months after IRA passage, the estimated number of small molecule drug clinical trials started monthly dropped by 25.2% (95% CI: -39.0% to -7.1%) and 29.5% (95% CI: -42.4% to -13.2%) for pre-approval trials for new drugs and post-approval trials, respectively. In comparison, for biologics, the model estimates non-significant decreases for pre-approval trials (-2.1%, 95% CI: -9.4% to +5.8%) and post-approval trials (-13.0%, 95% CI: -30.0% to +6.4%).

CONCLUSIONS: While descriptive, this study offers early evidence of reductions in industry-sponsored new small molecule pre-approval and post-approval drug trials after IRA passage. These trends should be monitored as responses work their way through drug pipeline investment decisions.

PMID:41880583 | DOI:10.1080/13696998.2026.2646075

Proc Natl Acad Sci U S A. 2026 Mar 31;123(13):e2525390123. doi: 10.1073/pnas.2525390123. Epub 2026 Mar 25.

ABSTRACT

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori-Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

PMID:41880563 | DOI:10.1073/pnas.2525390123

Sci Transl Med. 2026 Mar 25;18(842):eady6269. doi: 10.1126/scitranslmed.ady6269. Epub 2026 Mar 25.

ABSTRACT

Relentless mechanical work of the heart is powered by continuous oxygen consumption. How the heart uses oxygen is a defining feature of its health. Invasive studies have established that impaired oxygen consumption by the myocardium predicts contractile dysfunction and adverse outcomes. Despite its importance, noninvasive quantification of myocardial oxygen use remains limited. Magnetic resonance imaging (MRI) signal is known to be sensitive to blood oxygenation and has the potential to quantify myocardial oxygen consumption noninvasively, without exogenous contrast agents and free of ionizing radiation. However, its clinical translation has been impeded by the need for complex biophysical calibration, vulnerability to imaging artifacts and consistent vital motions, and the requirement of lengthy acquisition times. Here, we introduce a rapid, self-calibrated cardiac MRI framework that overcomes these barriers through high-resolution, motion-resolved coronary sinus oximetry, which can quantify myocardial oxygen extraction of the whole heart within 3 minutes. We optimized the imaging parameters via numerical simulations and validated them against invasive coronary sinus catheterization in a porcine model. We combined the method with clinical MRI sequences and demonstrated the feasibility of quantifying myocardial oxygen consumption and myocardial oxygen efficiency in patients with and without heart failure secondary to myocardial infarction in a single institution. This needle-free approach establishes a practical framework for noninvasive characterization of myocardial oxygen metabolism. It holds the potential to facilitate early disease detection, inform personalized therapeutic strategies, and guide the development of cardiometabolic therapies aimed at addressing the ongoing heart failure epidemic.

PMID:41880521 | DOI:10.1126/scitranslmed.ady6269

Sci Adv. 2026 Mar 27;12(13):eady7595. doi: 10.1126/sciadv.ady7595. Epub 2026 Mar 25.

ABSTRACT

Arctic precipitation has increased in recent decades (hereafter, Arctic wetting), but the drivers remain uncertain. Using observations, reanalyses, and single-model initial-condition large ensembles (SMILEs), we show that enhanced evaporation due to sea ice loss has been the primary driver of Arctic wetting during 1979-2024, especially in the Atlantic sector. However, the externally forced component in most SMILEs explains only ~69% of sea ice loss and 75% of wetting in the observations and reanalyses. Further analysis reveals that the observed transition of one of the Northern Hemisphere’s interdecadal internal variability-Atlantic multidecadal variability (AMV)-from a negative to a positive phase substantially enhanced Arctic sea ice loss, thereby accelerating wetting by about 31%. Under SSP3-7.0, if the AMV switches phase in the near future from the current +1 to a -1 standard deviation anomaly, then the rates of Arctic sea ice loss and wetting would slow by nearly 29 and 33%, respectively, relative to the externally forced response alone. These results underscore the pivotal role of AMV in modulating Arctic sea ice loss and wetting and highlight the need to account for AMV phase changes in near-term Arctic climate projections.

PMID:41880515 | DOI:10.1126/sciadv.ady7595