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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.

Plain english summary Traditional eating disorder (ED) treatment approaches often use a “one-size-fits-all” method, despite the fact EDs are complex and can vary greatly from person to person. This review discusses how personalised treatment can transform care for people with EDs. Personalised care tailors treatment to each person’s unique biology, mental health, and life circumstances, with the understanding that a more flexible and individualised approach could lead to better outcomes. We explore new discoveries in genetic research, machine learning, and advanced tracking methods to predict how someone might respond to specific treatments and identify what works best for them. We also emphasise the importance of addressing changes in the illness experience over time and including patients’ perspectives in their care. While these approaches show great promise, challenges remain, such as ensuring we have evidence to guide effective personalisation, and that treatments are ethical, widely available and easy for clinicians to use. The paper highlights a future where ED treatments are more precise, effective, and adapted to the individual, offering new hope for recovery. Eating disorders (EDs) are complex and heterogeneous conditions, which are often not resolved with conventional, manualised treatments. Arguments for the development of holistic, person-centred treatments accounting for individual variability have been mounting amongst researchers, clinicians and people with lived experience alike. This review explores the transformative potential of personalised medicine in ED care, emphasising the integration of precision diagnostics and tailored interventions based on individual genetic, biological, psychological and environmental profiles. Building on advancements in genomics, neurobiology, and computational technologies, it advocates for a shift from categorical diagnostic frameworks to symptom-based and dimensional approaches. The paper summarises emerging evidence supporting precision psychiatry, including the development of biomarkers, patient-reported outcomes, predictive modelling, and staging models, and discusses their application in ED research and clinical care. It highlights the utility of machine learning and idiographic statistical methods in optimising therapeutic outcomes and identifies key challenges, such as ethical considerations, scalability and implementation.

The tumor microenvironment is highly complex, and immune escape is currently considered an important hallmark of cancer, largely contributing to tumor progression and metastasis. Named for their capability of killing target cells autonomously, natural killer (NK) cells serve as the main effector cells toward cancer in innate immunity and are highly heterogeneous in the microenvironment. Most current treatment options harnessing the tumor microenvironment focus on T cell-immunity, either by promoting activating signals or suppressing inhibitory ones. The limited success achieved by T cell immunotherapy highlights the importance of developing new-generation immunotherapeutics, for example utilizing previously ignored NK cells. Although tumors also evolve to resist NK cell-induced cytotoxicity, cytokine supplement, blockade of suppressive molecules and genetic engineering of NK cells may overcome such resistance with great promise in both solid and hematological malignancies. In this review, we summarized the fundamental characteristics and recent advances of NK cells within tumor immunometabolic microenvironment, and discussed potential application and limitations of emerging NK cell-based therapeutic strategies in the era of presicion medicine.

Anorexia nervosa (AN) is a severe psychiatric disorder for which effective treatment and sustained recovery are contingent upon successful weight restoration, yet the efficacy of existing treatments is suboptimal. This narrative review considers the potential of precision nutrition for tailoring dietary interventions to individual characteristics to enhance acute and longer-term weight outcomes in AN. We review key factors that drive variation in nutritional requirements, including energy expenditure, fecal energy loss, the gut microbiota, genetic factors, and psychiatric comorbidities. Although scientific evidence supporting precision nutrition in AN is limited, preliminary findings suggest that individualized nutrition therapies, particularly those considering duration of illness and the gut microbiota, may augment weight gain. Some patients may benefit from microbiota-directed dietary plans that focus on restoring microbial diversity, keystone taxa, or functions that promote energy absorption, which could enhance weight restoration—although stronger evidence is needed to support this approach. Furthermore, accounting for psychiatric comorbidities such as depression and anxiety as well as genetic factors influencing metabolism may help refine nutrition prescriptions improving upon existing energy estimation equations, which were not developed for patients with AN. Given the reliance on large sample sizes, costly data collection, and the need for computationally intensive artificial intelligence algorithms to assimilate deep phenotypes into personalized interventions, we highlight practical considerations related to the implementation of precision nutrition approaches in clinical practice. More research is needed to identify which factors, including metabolic profiles, genetic markers, demographics, and habitual lifestyle behaviors, are most critical to target for individualizing weight restoration, and whether personalized recommendations can be practicably applied to improve and sustain patient recovery from this debilitating disorder with high relapse and mortality rates. Plain English Summary Anorexia nervosa (AN) is a serious mental health condition for which successful treatment and recovery depend on restoring weight to a healthy level. Current treatments often fall short of being effective. This review examines the potential of precision nutrition, which tailors dietary plans to individual characteristics, to improve weight outcomes and promote long-term recovery in AN while minimizing psychological and physiological discomfort during treatment. We explore various factors that influence nutritional needs in AN and may therefore be used to tailor dietary prescriptions, including loss of dietary energy in stool, gut microorganisms inhabiting the colon, genetic differences, and mental health conditions co-occurring with AN. Although there is limited evidence supporting the use of precision nutrition in AN, early research suggests that nutrition therapies personalized to AN duration may improve weight gain. More recent precision nutrition designs consider not just one or a few factors, but aggregate numerous patient factors including biology, personal preferences, and sociodemographics to find the optimal diet with the aid of artificial intelligence. Before precision nutrition therapies can be implemented in clinical practice, extensive research needs to be conducted in AN, and practical considerations related to the implementation of precision nutrition in clinical care must be addressed.

Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

Eating disorders (EDs) are multisystemic, debilitating, and complex illnesses that affect many young Canadians. These disorders are associated with high rates of medical complications, psychiatric and physical comorbidities, functional impairment, family distress, and financial burden. Despite the severity and increasing prevalence of EDs in youth, advancements in understandings of the pathophysiology and treatment of EDs have remained limited over the past three decades. This trend may be shaped by the chronic underfunding of the field, reliance on small sampled cross-sectional studies, and the notable lack of research focused on youth with EDs from historically underrepresented communities. Current treatment practices demonstrate modest efficacy and often omit the complex, heterogeneous presentations, development, and maintenance of pediatric EDs. Large-scale, multiaxial datasets are necessary to elucidate ED etiology and enable phenotyping. This is a critical step towards implementing future precision psychiatry and personalized treatment advances. In this commentary, we share our experience of conceptualizing a precision ED data and bio-registry, EDBioMAP: Eating Disorder Bio-Registry and Multiaxial Precision Health Platform, and suggest necessary pillars to inform, implement, and drive the successful use of precision psychiatry in pediatric ED care. Effective data utilization requires actionable steps and includes: (1) establishing strategic partnerships; (2) incorporating measurement-based care into clinical practice; (3) collecting novel biological markers; (4) developing minimum datasets; and (5) leveraging predictive modelling techniques. Strategic and standardized data integration is imperative to informing the future use of precision psychiatry for EDs. It can lend well to igniting multi-site collaboration to enhance large datasets necessary for this type of work and offers avenues for future development of personalized treatment interventions and clinical decision-making tools for youth with EDs.

Dynamic Time Warping (DTW) is an emerging analytic technique that offers a flexible approach to modeling symptom dynamics in psychological and psychiatric research. Unlike traditional network models, which often rely on linear associations, DTW aligns symptom trajectories even when changes unfold at slightly different speeds or time intervals. This tutorial offers a brief introduction into DTW and demonstrates how to apply DTW to panel or time series data. We illustrate the workflow using clinical case data from patients with eating disorders, to capture temporal patterns that cannot be detected with conventional network analysis techniques, as these require more intensive time-series data. Key advantages include its applicability to non-stationary data, flexibility in handling irregular time intervals, and reduced reliance on frequent assessments, which patients often cannot maintain due to the burden. We also discuss some of the limitations such as noise, scaling decisions and lack of Granger causality associations. Finally, we outline directions for future research. By expanding the methodological toolkit available for studying therapy processes, DTW holds promise for advancing both research and clinical practice in personalized mental health care.

For over a decade, the mental health field has been interested in precision treatment using psychopharmacological interventions. More recently, this interest has expanded to include psychotherapy, which is the primary treatment modality for eating disorders. Personalised medicine and precision treatment are also seen as priorities for the eating disorder field by those with lived experience and carers, clinicians and researchers. However, precision treatment necessitates the collection of large amounts of clinical data. Three frameworks exist or have been proposed for the purpose of gathering large-scale routine clinical outcomes in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australia national minimum dataset, and the Eating Disorders Clinical Research Network. Despite the emergence of these frameworks, challenges exist with implementation. This paper outlines the rationale for the collection of routine outcome data in eating disorder treatment settings, the three existing frameworks proposed, and considerations for implementation and scaling. These include clinical and practice applications, technical aspects, statistics, and contextual factors. We invite attention to our recommendations and collaborative approaches to facilitate progress towards precision treatment in eating disorders. Plain English summary Precision treatment, also known as precision medicine, involves tailoring treatment to the individual characteristics of each patient. Precision treatment for eating disorders is seen as a priority by individuals with lived experience, their carers, clinicians and researchers. However, precision treatment depends on large amounts of clinical data being collected. Currently, eating disorder services do not collect the same information from or about patients. There is no large clinical database to inform precision medicine decisions. Three main frameworks have been proposed to support largescale and consistent data collection in eating disorder services: The International Consortium for Health Outcomes Measurement (ICHOM) eating disorder set, the Australian national minimum dataset, and the UK Eating Disorders Clinical Research Network. These frameworks hold promise but there are challenges with applying them. This paper summarises why collecting routine outcome data is important, the three main frameworks proposed, and the factors which may help to progress data collection and precision treatment for eating disorders. We consider clinical, practical, technical, statistical and contextual factors. It is important that progress in this area is collaborative and involves individuals with lived experience, carers, clinicians and researchers.

Background Interventions targeting core characteristics of eating disorders (EDs) can effectively alleviate symptoms. However, it remains unclear whether these characteristics exhibit cultural specificity within the Chinese population. This study combines exploratory graph analysis (EGA) and network analysis to identify key psychological characteristics in Chinese patients with EDs. Methods The psychological characteristics of 1,001 patients with EDs were assessed using the Eating Disorder Inventory-1 (EDI-1). Nineteen representative items were selected and categorized into different dimensions through EGA. Network analysis was then performed to identify key psychological characteristics by determining central and bridge nodes. Results In addition to the “ED-specific” and “Non-specific” categories, an unexpected category, “Perfectionism,” was identified. Across these three categories, four key psychological characteristics were highlighted: “terrified of gaining weight,” “guilty after overeating,” “worry that feelings will get out of control,” and “must do things perfectly.” Conclusion Beyond drive for thinness, perfectionism and emotional regulation difficulties may represent key psychological characteristics among Chinese individuals with EDs. These findings could help inform the development of culturally tailored treatment strategies for EDs in China. Plain English summary This study looked at the main psychological traits of eating disorders (EDs) in people from China to understand how treatments could better fit their needs. The researchers found that people with EDs often struggle with a strong fear of gaining weight, feeling guilty after eating too much, trouble managing emotions, and putting too much pressure on themselves to be perfect. These traits were grouped into three categories: those directly related to eating disorders, general mental health issues, and perfectionism. Perfectionism and difficulties controlling emotions were found to be just as important as concerns about weight. This means that treating eating disorders isn’t just about food and weight—it’s also about addressing perfectionism and emotional struggles. These findings could help create more supportive and effective treatments for people in China, helping them feel more in control and less overwhelmed in their daily lives.

Background Weight restoration is one of the main goals of treatment for AN. Weight trajectories derive from various elements including baseline personal characteristics and factors linked to the course of treatment. The aim of our research was to identify different BMI trajectories during inpatient treatment, to examine whether patient characteristics were predictive of the nature of these trajectories, and to examine how they affect hospitalization outcomes. Methods The study population consisted of 310 female AN inpatients. To analyse the data, we examined trajectories of change in BMI, using a clustering algorithm: k-means for longitudinal data. Results We chose a four-trajectory model. The most common was the A trajectory, which we labelled “severe and compliant” (38.71%, N  = 120). The second most frequent (28.71%, N  = 89) was B trajectory, labelled “the least severe with weight fluctuations before discharge”, it is situated above the others over the whole period. Trajectory C, which we labelled “severe, with high dissatisfaction scores and long lengths of stay” included 25.16% ( N  = 78) of patients. The D trajectory, which we labelled “resistant and non-compliant”, was the smallest with only 23 subjects (7.42%), situated below the others. Significant differences were found across trajectories concerning: lifetime, admission and target BMI, satisfaction with target BMI, menarcheal status and the duration of amenorrhea, previous inpatient treatments and parental psychiatric disorders. Factors that differed in the course of treatment were: length of stay, dropout, discharge BMI, changes in target weight, tube feeding and transfers to intensive care unit. Conclusion This study is one of the few to examine BMI trajectories during treatment for AN. It shows that different trajectories lead to different outcomes. A better understanding of the underlying clinical profiles associated with trajectories could enable more personalized care and an improved outcome. Plain English summary One of the main goals of treatment for anorexia nervosa (AN) is weight restauration. The aim of our research was to identify different body mass index (BMI; which is a weight to height ratio) trajectories during hospitalization. We wanted also to examine whether patient characteristics influence the nature of these trajectories, and how they affect treatment results. The study population consisted of 310 female patients suffering from AN. We have described four different trajectories. Significant differences were found across trajectories concerning patients’ characteristics, their medical history, the course of the hospitalization and its result. The literature on this subject is sparse. Our study shows that different trajectories lead to different outcomes. Patients with lower lifetime and admission BMIs have worse outcome in term of discharge BMI. A better understanding of the underlying clinical profiles associated with BMI trajectories could enable more personalized care and an improve treatment results.