Editorial: Interplay of genetics and environment in pediatric diabetes: insights and innovations
Bibliographic record
Abstract
The field of pediatric diabetes is rapidly evolving while being shaped by the complex interplay of genetic and environmental factors. Type 1 diabetes (T1D), long considered a classic autoimmune disease of childhood, now shows a more heterogeneous course with adult-onset forms and links obesity. Meanwhile, type 2 diabetes (T2D), traditionally confined to adults, is rising sharply in adolescents, driven by the obesity epidemic and disproportionately affecting minority groups. These shifts challenge traditional classifications and highlight the need for a more nuanced understanding of diabetes pathogenesis and management in children and adolescents.The aim of this Research Topic was to explore new scientific findings regarding the interplay between genetic predisposition and environmental factors in the development of pediatric diabetes and metabolic syndromes. While T1D is usually regarded as an autoimmune condition and T2D as a metabolic disorder, their boundaries in children and adolescents are increasingly blurred by overlapping influences such as obesity, insulin resistance, lifestyle, and psychosocial determinants.Our objective was twofold: first, to synthesize emerging evidence that highlights how both genetic factors and modifiable exposures, such as diet, sleep, microbiome, and obesity contribute to the heterogeneity of pediatric diabetes; and second, to identify potential pathways for improved diagnosis, prevention, and management in young populations. By integrating perspectives from molecular genetics, clinical endocrinology, and public health, this Research Topic sought to advance precision medicine approaches and provide actionable insights for clinicians and researchers working to reduce the growing burden of diabetes in children and adolescents.The articles published within this Research Topic collectively highlight the multidimensional nature of pediatric diabetes, where both genetic predisposition and environmental influences act as overlapping drivers of disease development and progression. Rather than examining these forces in isolation, the contributions emphasize how their interplay shapes clinical presentation, metabolic outcomes, and therapeutic challenges. On the clinical and management side, Jia et al. investigated mechanistic and integrative insights, reinforcing the central message of this Research Topic: pediatric diabetes cannot be understood through genetics or environment alone. Instead, the disease represents a nexus of inherited risk factors interacting with modifiable exposures such as diet, physical activity, and psychosocial context. This integrative approach is particularly relevant as obesity and other environmental drivers reshape the epidemiology of pediatric diabetes, blurring the lines between T1D, T2D, and monogenic forms.Taken together, these contributions highlight three major themes. First, modifiable lifestyle factors, including sleep and weight management, remain key targets for prevention and intervention. Second, the identification of genetic variants is essential to resolve the heterogeneous clinical presentations of pediatric diabetes and to enable personalized therapies.Finally, integrating these perspectives points toward a future in which precision medicine can address both the biological and social determinants of health, with the goal of reducing the burden of diabetes in children worldwide.The contributions to this Research Topic illustrate how pediatric diabetes sits at the crossroads of genetics, behavior, and environment. Together, they highlight the limitations of a onedimensional view of disease, instead pointing toward a framework where genetic predisposition interacts with modifiable exposures such as sleep, nutrition, weight status, and psychosocial factors. This multifactorial lens is essential for understanding why diabetes develops in some children but not in others, even among those with shared genetic risk.Importantly, the articles underscore the urgency of prevention and early intervention. The growing prevalence of T2D in youth, coupled with the persistent burden of T1D, reflects broader shifts in lifestyle and environmental pressures. Obesity, sleep disturbances, and sedentary behaviors act as amplifiers of underlying risk, while genetic heterogeneity adds diagnostic and therapeutic complexity. These findings call for more integrative research approaches that move beyond siloed investigations and instead combine molecular genetics, longitudinal epidemiology, and clinical trial data.Looking ahead, three priorities emerge. First, identifying reliable biomarkers that bridge genetic and environmental influences could enable earlier risk stratification and preventive strategies. Second, precision medicine approaches must be adapted to pediatrics, where developmental stage and psychosocial context profoundly shape outcomes. Finally, greater attention should be given to the health disparities that amplify diabetes risk in minority and low-income populations, ensuring that new insights translate into equitable advances in care.By situating genetics and environment within a unified framework, this collection lays the groundwork for the next phase of pediatric diabetes research: one that is preventive, personalized, and equitable.This Research Topic emphasizes that pediatric diabetes is neither solely genetic nor purely environmental, but the result of their dynamic interplay. By integrating perspectives on lifestyle, endocrine consequences, genetic heterogeneity, and clinical management, the contributing articles highlight both the complexity and the opportunities for innovation in this field. We thank all authors and reviewers for their contributions, which together pave the way toward more personalized, preventive, and equitable pediatric diabetes care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".