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At least 73 records · Page 4Linked to original sources
PubMed2026

Prediction of incident heart failure in established atherosclerotic cardiovascular disease: the SMART2-HF model.

BACKGROUND AND AIMS: Patients with established atherosclerotic cardiovascular disease (ASCVD) are at high risk of developing heart failure (HF). However, incident HF is not part of the risk assessment of current guideline-recommended models. The aim of this study was to develop and externally validate the SMART2-HF model for prediction of incident HF in patients with ASCVD. METHODS: SMART2-HF was developed in 7698 individuals with established ASCVD (coronary, cerebrovascular, or peripheral artery disease, or abdominal aortic aneurysm) but without prior HF from the UCC-SMART cohort. Cox proportional hazards models including sex-predictor interactions and with age as the time scale were derived to estimate the 10-year and lifetime risk of incident HF (hospitalization for HF or HF-related death), accounting for competing non-HF mortality. Predictors, limited to routinely available clinical characteristics, were aligned with the SMART2 risk model for recurrent cardiovascular (CV) risk in the same population. External validation was performed in 240 741 patients with ASCVD from six data sources: the Clinical Practice Research Datalink, the HUNT3 study, the SWEDEHEART Registry, the ASCVD-Particles cohort, the Estonian Biobank and the international REACH Registry. RESULTS: During a median follow-up of 11.2 years (interquartile range 6.1-16.4 years), 1031 incident HF events (13%) occurred in the UCC-SMART cohort. In the external validation data sources, a total of 24 885 incident HF events (10%) occurred. The pooled C-statistic was .696 (95% confidence interval .674-.717), with consistent performance in subgroups by sex and type of ASCVD. Predicted risks matched observed incidence in external validation. CONCLUSIONS: The SMART2-HF model enables the prediction of incident HF in patients with ASCVD. Aligned with the guideline-recommended SMART2 model for recurrent CV risk, SMART2-HF can be used as a complementary tool in this population.

Humans↗
PubMed1995

Is it clinically possible to distinguish nonhemorrhagic infarct from hemorrhagic stroke?

BACKGROUND AND PURPOSE: Diagnosis of the nonhemorrhagic ischemic type of stroke by analysis of patients' clinical features is considered unreliable because no clinical feature is specific. The diagnosis is so difficult to establish that we cannot hope to use the same method to make a reliable diagnosis in all stroke cases. In this study, we propose a simple scoring system with a positive predictive value of close to 100% to distinguish nonhemorrhagic infarct from hemorrhagic stroke. This scoring is available for all physicians in bedside diagnosis even if this score can be applied to a subgroup of patients. METHODS: Twenty-six clinical variables that might potentially distinguish cerebral hemorrhage from infarction were recorded in patients consecutively admitted to our stroke unit for stroke lasting more than 24 hours with at least unilateral motor weakness affecting face and/or arm and/or leg (internal validity study). Patients previously receiving anticoagulant therapy were excluded. We used CT scan as the gold standard. We used multivariate logistic regression to establish a clinical score from which we derived the classification rule. This rule was validated with data from the next 200 consecutive patients hospitalized in the stroke unit (external validity study). RESULTS: Three hundred sixty-eight patients were enrolled in the internal study. The obtained score was (2 x alcohol consumption) + (1.5 x plantar response) + (3 x headache) + (3 x history of hypertension)--(5 x history of transient neurological deficit)--(2 x peripheral arterial disease)--(1.5 x history of hyperlipidemia)--(2.5 x atrial fibrillation on admission). All patients with a score less than 1 (n = 123) had a nonhemorrhagic infarct (ie, 40% of the 305 patients with a nonhemorrhagic infarct). No threshold was found to diagnose cerebral hemorrhage with a sufficiently high positive predictive value. Among the 200 patients enrolled in the external validity study, 72 patients with a score below 1 had a nonhemorrhagic infarct (ie, 43% of patients with a nonhemorrhagic infarct). CONCLUSIONS: Diagnosis of nonhemorrhagic infarct can be made in 36% (95% confidence interval [CI], 29 to 43) of patients with a high level of accuracy (100% in the external validity study, which gives a 95% CI of 93 to 100). Thus, 43% (95% CI, 36 to 50) of patients with a nonhemorrhagic infarct could receive a bedside diagnosis. The score is simple and can be calculated from information available to all physicians.

Adult↗
PubMed1993

An examination of cluster-based classification schemes for DUI offenders.

This study examines the utility of cluster-based classification schemes for DUI offenders. Variables from previous empirical typologies and multiple domains were used in a series of cluster analyses in order to examine replicability across independent samples, across clustering algorithms and across sets of psychometric indicators. The arbitrary nature of cluster solutions and the external validity of cluster-based schemes, with respect to an outcome criterion constructed synthetically from earlier research, was examined. Only three of eight cluster techniques (Ward's, K means, and complete linkage) yielded meaningful results. For these techniques, replicability was poor across samples, algorithms and sets of psychometric indicators. Results indicated that identified clusters were arbitrary. Although external validity analysis yielded positive results, the possibility that external validity was a function of underlying dimensions was discussed, as were other implications of the findings.

Adult↗
PubMed1981

[Quasi experimental evaluation of public health interventions (author's transl)].

The classic experiment, the randomised controlled trial, is the best known and most revered of evaluation research methods. Randomization in community-based intervention trials, however, is not always possible because of ethical problems arising from with holding the experimental treatment from the control groups or the difficulties in conducting experiments in field settings which do not approach controlled laboratory conditions. In such circumstances, quasi-experimental or observational designs must be used. Two major principles are involved in using quasi-experimental methods: (1) the logic for establishing causality between treatment and effect is the same as that for randomised experiments, but the problems of assessing causality or internal validity are greater, and (2) assessment of the external validity or generalizability of quasi-experimental findings crucial to the interpretation of results. Selected quasi-experimental designs using time series and comparison groups are described with examples from public health intervention trials where threats to internal validity have been assessed by using different analytic techniques or gathering additional evidence. Quasi-experimental evaluations are most useful when opportunities exist for testing rival hypotheses concerning the internal and external validity, or the findings can be used to complement true experiments.

Epidemiologic Methods↗
PubMed2026

Risk prediction in patients with heart failure with preserved ejection fraction: the LIFE-Preserved model.

BACKGROUND AND AIMS: Heart failure (HF) with preserved ejection fraction (HFpEF) constitutes a heterogeneous disease with varying prognosis. Given the rising incidence of HFpEF, accurate risk prediction for these patients is needed to identify high-risk individuals, who may benefit the most from preventive treatments. The LIFE-Preserved model was developed and validated for the prediction of individual short-term and lifetime risk for HF hospitalization or cardiovascular (CV) death in patients with HFpEF. METHODS: LIFE-Preserved was derived in 20 332 patients aged 40-90 years with a left ventricular ejection fraction ≥ 50% from the Swedish HF Registry. Cause- and sex-specific Cox models were derived to predict the risk of HF hospitalization or CV death using 14 routinely available predictors. Use of age as the timescale allowed for predictions beyond the maximum follow-up duration in the derivation data, adjusted for competing risks. External validation was performed in two trials (EMPEROR-Preserved and TOPCAT-Americas) and three registries (NHS England Secure Data Environment, Veterans Affairs, and HF-Particles). Model performance was assessed by discrimination and calibration. RESULTS: During a median follow-up of 1.8 years (interquartile range .6-4.2, maximum 19 years), 9341 first HF hospitalizations or CV deaths (46%) were observed in Swedish HF Registry. External validation included data from 28 062 patients with HFpEF [9930 (35%) first HF hospitalizations or CV deaths]. Pooled C-statistics were .714 (95% confidence interval .652-.775) in trials and .658 (95% confidence interval .599-.717 in registries, with adequate calibration in all external validation sources. Performance was similar in men and women. An interactive calculator of the LIFE-Preserved model has been made available here. CONCLUSIONS: The LIFE-Preserved model enables prediction of short-term and lifetime risk of HF hospitalization or CV death in patients with HFpEF. The model could serve as a tool to identify high-risk HFpEF patients, guiding clinical management and shared decision-making.

Humans↗
PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗
PubMed1991

Who enrolls in prevention trials? Discordance in perception of risk by professionals and participants.

Internal and external validity problems permeate all intervention studies but are accentuated in primary preventive intervention research, particularly when studies target or recruit individuals based on their risk for psychopathology. Since many people who are at risk do not yet experience distress, they may not perceive the need for intervention. Recruitment tactics based on explaining extent of risk are unlikely to be persuasive and may have negative consequences. If respondents are not motivated to participate, a small or biased subset of the target population will participate in the intervention. Bias is of special concern when those enrolled represent only part of the continuum of risk. Selective enrollment may compromise both internal validity (the interpretation of the research results) and external validity (the generalizability of the findings) of intervention trials in primary prevention. This article discusses the effects of partial enrollment and the resultant bias. It suggests several strategies for increasing the enrollment of the target population and examines some of their ethical ramifications. It also stresses the importance of collecting systematic data documenting how the participants in the intervention differ from the target group as a whole.

Bias↗
PubMed1975

Changes in obsessive/compulsive patients as measured by the Leyton Inventory before and after treatment with clomipramine.

The Leyton Obsessional Inventory has been found to be a useful measure in assessing patients before and after treatment with clomipramine. Mean scores for symptoms and interference altered significantly during the course of treatment. The Leyton Obsessional Inventory, however, lacks external validation owing to the absence of some valid alternative quantification. In the absence of such external validation it seems justifiable to use the mean Leyton score diagnostically but not as a sole indication of severity or response to treatment.

Clinical Trials as Topic↗
PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗
PubMed2026

Machine learning-enabled multi-omics discovery of prognostic biomarkers and signaling targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to subtype using single omics layers. We conducted an exploratory investigation integrating reverse-phase protein array (RPPA) and DNA methylation data from the cancer genome atlas (TCGA)- pancreatic adenocarcinoma (PAAD) to assess the feasibility of multi-omics subtyping, alongside a supervised machine learning analysis of a small gene expression omnibus (GEO) transcriptomic cohort (n = 26) to identify candidate diagnostic genes. RPPA-based K-means clustering suggested a weak, possible two-subtype structure (silhouette ≈ 0.16) that remained unassociated with overall survival (log-rank p = 0.113) and lacked independent prognostic value. An independently performed similarity network fusion (SNF) analysis integrating RPPA and methylation data showed low concordance with RPPA-derived subtypes (Adjusted Rand Index (ARI) = 0.014), indicating limited convergence between molecular modalities. Supervised machine learning analysis of the GEO cohort using a fully nested leave-one-out cross-validation pipeline achieved a mean (area under the curve) AUC of 0.896 across four classifiers and identified four-fold-stable candidate genes (ESCO2, COL17A1, BCL2L14, and SOWAHB). However, this gene panel demonstrated limited external validity across two independent PDAC cohorts (log-rank p = 0.438 for both GSE62452 and GSE28735), indicating limited generalizability despite robust internal performance. Collectively, these findings provide limited evidence for a robust, prognostically significant multi-omics subtype or a validated diagnostic gene signature; instead, this study serves as a hypothesis-generating resource and highlights the importance of rigorous cross-validation and independent external validation in small-sample transcriptomic biomarker discovery.

Humans↗
PubMed1992

Analysis of randomized and nonrandomized patients in clinical trials using the comprehensive cohort follow-up study design.

In clinical research, randomized trials are widely accepted as the definitive method of evaluating the efficacy of therapies. The random assignment of patients to their treatment ensures the internal validity of the comparison of new treatments with controls. An assessment of the external validity of trial results can best be achieved by comparing the study population to the population of patients who met the eligibility criteria but did not consent to randomization. A part of the data of the Coronary Artery Surgery Study (CASS), in which coronary artery bypass surgery is compared to conventional medical therapy in patients with coronary artery disease, is used to illustrate a strategy of multivariate analysis of randomized and nonrandomized patients which allows an investigation of both internal and external validity. The method used Cox's proportional hazards regression model with inclusion of covariates for randomization status and corresponding interactions in addition to the usual covariates for treatment and the important prognostic factors.

Cohort Studies↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed1987

How generalizable are the effects of smoking prevention programs? Refusal skills training and parent messages in a teacher-administered program.

This study investigated both substantive and methodological issues associated with school-based smoking prevention programs. Substantive issues included the efficacy of a refusal skills training curriculum and of parent messages mailed to students' homes. Methodological issues included the effects of assigning classrooms versus entire schools to experimental conditions and determination of the effects of attrition on internal and external validity. Results revealed differential impact for different subgroups of adolescents. The refusal skills program produced lower rates of smoking than the control condition for students who were smokers at the pretreatment assessment but may have produced detrimental effects among males who were nonsmokers at pretest. The provision of parent messages did not affect outcome. Method of assignment (schools versus classrooms) failed to produce significant effects, and attrition did not affect internal validity. However, the above differential findings, as well as the impact of attrition on external validity, raise questions concerning the generalizability of smoking prevention programs.

Adolescent↗
PubMedDate not supplied

[Interobserver and intraobserver variation: a problem of validity in epidemiologic studies of arterial pressure].

In carrying out blood pressure epidemiologic studies there may be different factors that can affect internal and external validity and thus eliminate the inferential process. As part of the Hypertension and Risk Factors Associated Study conducted in March 1987 in Cuajimalpa de Morelos, Mexico City, 23 nursing students were standardized on the blood pressure auscultatory method using a sound picture and measuring intraobserver and interobserver agreement through intraclass correlation coefficient. Even though initial standardization sessions showed difficulties in the use of instruments and in the reading of blood pressure levels, final K (kappa) values measuring interobserver agreement increased from 0.25 to 0.86. Omega values measuring intraobserver agreement fluctuated between 0.86 and 0.98. This epidemiologic technique is proposed in order to improve internal and external validity of blood pressure studies.

Blood Pressure↗
PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗
PubMed1991

Subject attrition in prevention research.

Subject attrition threatens the internal validity of substance abuse prevention studies because differences in the rate of attrition and the substance use behavior of remaining subjects in the different conditions could account for any differences found in substance use rates. Attrition threatens the external validity of prevention studies because, to the extent that study dropouts are different from remaining subjects, the results of the study may not be generalizable to study dropouts. Analysis of these threats to the validity of prevention studies should be routinely conducted. However, studies of alcohol and drug abuse prevention have generally failed to report or analyze subject attrition. Smoking prevention studies have more frequently reported attrition, and they have recently begun to analyze the degree to which attrition may affect the internal and external validity of the study. Evidence thus far suggests that differences in attrition across conditions do occur occasionally. The evidence is substantial that study dropouts are systematically more likely to smoke, to use other substances, and to score highly on other risk-taking measures.

Alcoholism↗
PubMed2026

Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.

Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.

Humans↗