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The convergence of advances in medical science, human biology, data science and technology has enabled the generation of new insights into the phenotype known as ‘diabetes’. Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment) and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e. monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realise its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.

[This corrects the article DOI: 10.3389/fcell.2026.1744112.].

Introduction Personalised prevention aims to delay or avoid disease occurrence, progression, and recurrence of disease through the adoption of targeted interventions that consider the individual biological, including genetic data, environmental and behavioural characteristics, as well as the socio-cultural context. This protocol summarises the main features of a rapid scoping review to show the research landscape on biomarkers or a combination of biomarkers that may help to better identify subgroups of individuals with different risks of developing specific diseases in which specific preventive strategies could have an impact on clinical outcomes. This review is part of the “Personalised Prevention Roadmap for the future HEalThcare” (PROPHET) project, which seeks to highlight the gaps in current personalised preventive approaches, in order to develop a Strategic Research and Innovation Agenda for the European Union. Objective To systematically map and review the evidence of biomarkers that are available or under development in cancer, cardiovascular and neurodegenerative diseases that are or can be used for personalised prevention in the general population, in clinical or public health settings. Methods Three rapid scoping reviews are being conducted in parallel (February–June 2023), based on a common framework with some adjustments to suit each specific condition (cancer, cardiovascular or neurodegenerative diseases). Medline and Embase will be searched to identify publications between 2020 and 2023. To shorten the time frames, 10% of the papers will undergo screening by two reviewers and only English-language papers will be considered. The following information will be extracted by two reviewers from all the publications selected for inclusion: source type, citation details, country, inclusion/exclusion criteria (population, concept, context, type of evidence source), study methods, and key findings relevant to the review question/s. The selection criteria and the extraction sheet will be pre-tested. Relevant biomarkers for risk prediction and stratification will be recorded. Results will be presented graphically using an evidence map. Inclusion criteria Population: general adult populations or adults from specific pre-defined high-risk subgroups; concept: all studies focusing on molecular, cellular, physiological, or imaging biomarkers used for individualised primary or secondary prevention of the diseases of interest; context: clinical or public health settings. Systematic review registration https://doi.org/10.17605/OSF.IO/7JRWD (OSF registration DOI).

IntroductionAlzheimer's disease (AD) is a highly heterogeneous neurodegenerative disorder and the leading cause of dementia characterized by the progressive accumulation of non-modifiable (age, female sex, APOE-ε4 genotype) and modifiable factors [hypertension (HTN), diabetes, obesity (OB), hyperlipidemia (HLP), depression (DEP)]. However, the temporal sequencing and interaction patterns between comorbidity burden and biological subgroups defined by sex and APOE genotype remain not fully understood.MethodsWe applied the Cumulative Event Method (CEM), a novel process mining framework, to longitudinal UK Biobank (UKB) data. Event logs tracked five modifiable risk factors across sex- and APOE-ε4-stratified analyses to identify distinct longitudinal comorbidity patterns associated with AD. Sex-specific findings were validated in an independent CureMD cohort.ResultsAmong 1,916 UK Biobank participants, CEM identified 203 distinct comorbidity sequences across 7,316 clinical events. Females more frequently exhibited a hypertension-preceding-AD sequences than males (7.0% vs. 3.8%; p = 0.005), while males exhibited earlier metabolic-vascular patterns involving hyperlipidemia and hypertension (7.7% vs. 4.5%; p = 0.0085). APOE-ε4 carriers exhibited accelerated multi-comorbidity patterns, whereas non-carriers more frequently transitioned from hypertension to non-AD (p = 1 × 10−4). External validation in CureMD confirmed sex-specific patterns across 191 sequences and 5,176 events.ConclusionLongitudinal comorbidities patterns preceding AD differ by sex and APOE genotype, supporting Alzheimer's as a multisystem failure disease with subgroup-specific comorbidity sequences and clinically relevant windows for precision prevention.

Tobacco smoking accounts for nearly 90% of lung cancer deaths worldwide, yet the mechanisms underlying persistent cancer risk in former smokers are not fully understood. Epidemiological evidence shows that more than 40% of lung cancers develop over 15 years after cessation, demonstrating that while some smoking-induced molecular alterations resolve rapidly, others remain as long-lasting scars that promote carcinogenesis. This review synthesizes longitudinal and cross-sectional genomic, epigenomic, and transcriptomic studies of airway and lung tissues to distinguish persistent from nonpersistent smoking-induced molecular alterations. Persistent alterations include somatic mutations in TP53 and KRAS, DNA methylation at tumor suppressor loci, dysregulated noncoding RNAs, chromosomal instability, and epigenetic age acceleration. Nonpersistent changes, such as acute inflammatory responses and detoxification pathways, generally normalize within months to several years following cessation. Multi-omics profiling reveals coordinated patterns of dysregulation consistent with field cancerization in former smokers. In addition, the integration of multi-omics data with artificial intelligence may enable composite molecular signatures for stratifying high-risk former smokers, link molecular persistence to clinical outcomes, and inform chemoprevention strategies. Collectively, these observations clarify which molecular alterations sustain long-term cancer risk despite smoking cessation and highlight opportunities for precision prevention and earlier detection in high-risk populations.

Prevention of cancer is an appealing strategy to reduce the burden of illness associated with cancer, but despite the rapidly advancing understanding of the early phases of carcinogenesis, translation of biologic insights into actionable public health strategies has been challenging. Phase III clinical trials have historically required large numbers of participants and lengthy durations to show effects in the minority of participants who develop cancer during the finite span of each trial. Early-phase trials help to refine intervention strategies and provide preliminary human safety and efficacy data to justify phase III trials. Recent advances in trial methodology and developments in immunopreventive strategies have energized the field of cancer prevention and provide potential paths for prevention of multiple cancer types. In this review we discuss the history and current state of cancer prevention trials, with a focus on overcoming inherent biologic and methodologic barriers to preventive agent development.

Interventions to teach protective behaviors may be differentially effective within an adolescent population. Identifying the characteristics of youth who are less likely to respond to an intervention can guide program modifications to improve its effectiveness. Using comprehensive longitudinal data on adolescent risk behaviors, perceptions, sensation-seeking, peer and family influence, and neighborhood risk factors from 2564 grade 10–12 students in The Bahamas, this study employs machine learning approaches (support vector machines, logistic regression, decision tree, and random forest) to identify important predictors of non-responsiveness for precision prevention. We used 80% of the data to train the models and the rest for model testing. Among different machine learning algorithms, the random forest model using longitudinal data and the Boruta feature selection approach predicted intervention non-responsiveness best, achieving sensitivity of 85.4%, specificity of 78.4% and AUROC of 0.93 on the training data, and sensitivity of 84.3%, specificity of 67.1%, and AUROC of 0.85 on the test data. Key predictors include self-efficacy, perceived response cost, parent monitoring, vulnerability, response efficacy, HIV/AIDS knowledge, communication about condom use, and severity of HIV/STI. Machine learning can yield powerful predictive models to identify adolescents who are unlikely to respond to an intervention. Such models can guide the development of alternative strategies that may be more effective with intervention non-responders.

Background/objectives: The global incidence of skin cancer is rising, creating a need to strengthen prevention strategies. In this review, we examine the contributions of public health, dermatology, behavioural science, and emerging technologies such as artificial intelligence and bioinformatics, which have collectively shaped prevention in recent decades. Methods: Using a narrative scoping review approach guided by the PRISMA-ScR framework, we synthesised research across these disciplines to highlight their roles in enhancing skin cancer prevention. Results: Initial efforts focused on increasing public knowledge through sun protection campaigns and symptom recognition. Dermatologists enhanced early detection through refined techniques and clinical guidelines. Initiatives such as Euromelanoma enabled broader collaboration and population-level screening. As more disciplines joined, advances in risk stratification, digital imaging, artificial intelligence, molecular and genetic diagnostics and bioinformatics became possible. Beyond skin cancer prevention, these tools may have additional applications for systemic health issues. However, a number of challenges remain, particularly regarding data privacy concerns, cost-effectiveness, equitable access, and the validation of artificial intelligence tools in diverse populations. Conclusions: The prevention of skin cancer brings together knowledge spanning the fields of public health and dermatology to behavioural research and digital innovation. Working together, these disciplines have improved early detection and awareness. However, fragmented collaboration across regions throughout the world continue to limit their impact. Improved equity alongside stronger, more coordinated partnerships will be essential for the next phase of progress.

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a “staging” model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.