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Enregistrement W4406274446 · doi:10.1111/dme.15514

The missing piece: The clinical translation of precision diabetes medicine requires precision mental health care: A call to action from the international <scp>PsychoSocial</scp> Aspects of Diabetes (<scp>PSAD</scp>) Study Group

2025· article· en· W4406274446 sur OpenAlexaff
Frans Pouwer, Katharine Barnard‐Kelly, Bryan Cleal, Debbie Cooke, Mary de Groot, Sonya S. Deschênes, Dominic Ehrmann, Anthony Vincent Fernandez, Lisbeth Frostholm, David Hopkins, Norbert Hermanns, Richard I. G. Holt, Marjolein M. Iversen, Thomas Kubiak, Christina Maar Andersen, Briana Mezuk, Giesje Nefs, Susanne S. Pedersen, Miranda T. Schram, Frank J. Snoek, Uffe Søholm, Timothy Skinner, Søren Skovlund, Marietta Stadler, Ragnhild B. Strandberg, Sarah Bro Trasmundi, Michael Vallis, Kirsty Winkley, Per Winterdijk, Maartje de Wit, Natalie Zaremba, Jane Speight

Notice bibliographique

RevueDiabetic Medicine · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Research
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésMedicinePsychosocialCall to actionPrecision medicineAction (physics)Diabetes mellitusMental healthGerontologyFamily medicinePsychiatryEndocrinologyAdvertisingPathology

Résumé

récupéré en direct d'OpenAlex

Diabetes is an increasingly common, long-term condition, requiring 24/7 self-care and constituting one of the greatest health challenges of our time. As with all ‘wicked problems’, a one-size-fits-all approach to care is doomed to fail. In 2020, we welcomed the first international consensus report on precision diabetes medicine, which included a section on patient-centred mental health and quality of life outcomes.1 This included the recommendation that, ‘in the setting of precision diabetes medicine, providers should assess symptoms of diabetes distress, depression, anxiety, disordered eating and cognitive capacities using appropriate standardized and validated tools at the initial visit, at periodic intervals and when there is a change in disease, treatment or life circumstance (..), information that, when combined with other data, are likely to improve the precision of clinical decision making’.1 In 2023, the Precision Medicine in Diabetes Initiative (PMDI) published the second international consensus report, on gaps and opportunities for the clinical translation of precision diabetes medicine.2 This report focused on results ‘from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2)’, to inform the translation of precision medicine research into practice.2 Regrettably, the second consensus omits any such recommendation or discussion of mental health issues. Furthermore, among the ‘key sources of heterogeneity in diabetes’, only ‘behaviour’ was included, while among the ‘pillars of precision medicine’, only ‘lifestyle interventions’ were included.2 The first consensus called for ‘a rigorous review elucidating effective precision medicine strategies, areas of promise and notable gaps across …[diabetes]… to inform an evidence-based road map to optimize the integration of precision medicine into the global response to the diabetes crisis’.1 Of the 15 new systematic reviews conducted to inform the second consensus report, none includes the psychosocial aspects of diabetes.1 Yet, there is a robust evidence base demonstrating the crucial role of psychosocial factors for people living with, or at risk of, diabetes; and this evidence has only strengthened since the first consensus. For example, a recent umbrella review of 25 systematic reviews of longitudinal studies concluded that common mental disorders, such as depression, anxiety disorders, sleep disorders and schizophrenia, are associated with increased risks for developing type 2 diabetes.3 Various psychotropic medications can increase weight, and people living with mental disorders often face additional challenges, such as high stress, lowered self-esteem, lack of energy, as well as socioeconomic disadvantage, all of which may compromise health and healthy behaviours, and need to be considered when managing risk for type 2 diabetes.3 Furthermore, in 2020, a special issue of Diabetic Medicine, commemorating the 25th anniversary of the PsychoSocial Aspects of Diabetes (PSAD) study group, included 14 commissioned reviews of behavioural, psychological and social aspects of diabetes.4 These included diabetes and depression,5 diabetes distress,6 fear of hypoglycaemia,7 disordered eating,8 and disordered sleep,9 other reviews focused on psychological factors related to the use of medications and diabetes technologies, motivation for self-care, importance of social support, the quality of the patient-clinician communication and the impact of diabetes and its management on quality of life.4 These reviews summarized the state-of-the-science regarding the inseparable role of psychology in diabetes, including several effective (and cost-effective) interventions based on psychological and behavioural science, none of which are mentioned in the second international consensus report.1 The systematic removal of essential psychosocial factors from a report focused on the ‘gaps and opportunities for the clinical translation’ of precision diabetes medicine, without any clarification, appears to be a step backwards, creating rather than recognising a gap. Given that approximately one in two people will experience mental health problems at some point in their life, and the crucial role that psychology plays in all self-management behaviours and clinician-patient communications, how can any of the four pillars—prevention, diagnosis, treatment or prognosis—be considered precise without recognizing these issues? The PMDI did not consider these omissions among the potential liabilities of a precision medicine approach. The PMDI statement is also out-of-step with other international consensus reports, which recognize the essential role of psychology in diabetes care, such as that published by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) focused on the management of type 1 diabetes in adults.10 Section 10 describes psychosocial care, providing an overview of psychological comorbidities that can have a negative impact on diabetes outcomes, and explaining how monitoring of these problems should be integrated in diabetes care.10 Consistent with the studies described above, the ADA/EASD consensus statement not only discusses depression, anxiety, anorexia nervosa, bulimia nervosa, binge eating and intentional insulin omission for weight loss, but also different forms of diabetes-specific emotional distress, such as feeling powerless and overwhelmed by the daily self-care demands, fear of hypoglycaemia, worries about complications, a lack of social support or feeling ‘policed’ by family, friends or co-workers.10 Moreover, the ADA/EASD consensus statement explains how validated questionnaires can be used to ‘flag’ these psychological problems that may require psychological support. It is also emphasized that ‘members of the team have a responsibility for providing psychosocial care as an integral component of diabetes care. Preferably, the diabetes care team should include a mental health professional (psychiatrist, psychologist and/or social worker) to advise the team and consult with people with diabetes in need of psychosocial support’.10 Effective psychological therapies are available, including (online) cognitive behavioural therapy (CBT), mindfulness and interpersonal therapies.10 Thus, it is our consensus that psychosocial factors not only affect risks for and the course of diabetes, but that mental health is as important a goal of precision diabetes medicine as physical health. Nearly every mental disorder has a higher prevalence among people with diabetes. Thus, we contend that precision diabetes medicine must also entail precision mental health care. We therefore encourage the PMDI to incorporate phenotypic psychosocial factors into the next revision of the international consensus report, and everyone to recognise that precision diabetes medicine must include precision mental health care. The authors have received no funding for writing this article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,009
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,576
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,054
Tête enseignante GPT0,404
Écart entre enseignants0,350 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2025
Routes d'admission1
Résumé présentoui

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