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Enregistrement W4317568190 · doi:10.1111/jdi.13961

Update information on type 1 diabetes in children/adolescents and adults

2023· article· en· W4317568190 sur OpenAlexaboutno aff
Junnosuke Miura, Yasuko Uchigata

Notice bibliographique

RevueJournal of Diabetes Investigation · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueDiabetes and associated disorders
Établissements canadiensnon disponible
Organismes subventionnairesSanofi K.K.Novo Nordisk PharmaEli Lilly JapanNovartis PharmaTerumoAstellas PharmaMylanSanofiKowa CompanyAbbott JapanNovo NordiskMitsubishi Tanabe Pharma CorporationEli Lilly and CompanyAstraZenecaOno Pharmaceutical
Mots-clésMedicineType 2 diabetesType 1 diabetesDiabetes mellitusPediatricsGerontologyFamily medicineEndocrinology

Résumé

récupéré en direct d'OpenAlex

In 2000, the world's age-adjusted prevalence of type 1 diabetes with onset under 15 years was reported using data from 1990 to 19941. The lowest incidence rate (/105 person-years) was 0.1 in China and Venezuela, and the highest was 36.8 in Sardinia and 36.5 in Finland; revealing a difference of more than 350-fold between the lowest and highest incidence rates. Other high-incidence countries included Sweden, Norway, Canada, the United Kingdom, and New Zealand. East Asia belonged to the low incidence group, and Japan had an incidence rate of 1.1–2.2. In Asia and Africa, with an overall low incidence rate, the incidence rate is high in girls, while in Europe and the United States, with an overall high incidence rate, it is the same or higher in boys2. Different reasons have been proposed for this discrepancy including genetics, racial differences, epidemiological sampling problems, autoimmunity, and pregnancy; however, the mechanism of this discrepancy is not completely understood. The 2021 International Diabetes Federation (IDF) Atlas 10th edition3 published an estimate of 108,300 (149,500) annual new-onset type 1 diabetes cases under 15 years of age (under 20 years of age). The estimated prevalence was 651,700 (1,211,900), and compared with previous IDF Atlas estimates, there has been an increase in the incidence in many IDF regions. The reasons for this increase in incidence have been not clear, but in addition to the presence of susceptible genomic background in type 1 diabetes, environmental factors including changes milk intake, exposure to heterologous proteins from early birth, and rapid postnatal growth have been proposed to be associated with an increased incidence of type 1 diabetes. Furthermore, lifestyle changes, including a decrease in the morbidity of infectious diseases, have been proposed to affect the incidence of type 1 diabetes. Furthermore, lifestyle changes, including a decrease in the morbidity of infectious diseases, have been proposed to affect the incidence of type 1 diabetes. The male-to-female ratio of incidence in the countries and regions, described in the previous paragraph, has not changed significantly from the previous state. From the end of 2019, it was reported that Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-Cov-2) infection spread worldwide and caused various symptoms. It has been reported that the incidence of childhood-onset type 1 diabetes increased after SARS-Cov-2 infection4, 5. Additionally, there are some reports of type 1 diabetes developing after vaccination against Coronavirus disease (COVID)-196-8. However, currently it is impossible to clarify the causal relationship between the COVID-19 ribonucleic acid (RNA)-based vaccine and the onset of type 1 diabetes. As the relationship between the Coxsackie virus and the onset of type 1 diabetes has been reported, it is necessary to closely monitor future trends focused on susceptible HLA haplotypes for type 1 diabetes. Type 1 diabetes is classified into two types, autoimmune and idiopathic, based on etiology9. Positive islet-associated autoantibody detection is essential for the diagnosis of autoimmune acute-onset type 1 diabetes10. Currently, anti-glutamic acid decarboxylase (GAD) antibody, anti-insulinoma-associated antigen-2 (IA-2) antibody, insulin autoantibody (IAA), islet cell antigen (ICA), and anti-zinc transporter-8 (ZincT-8) antibody can be measured. In Japan, the former three are covered by insurance, and the positive rate of pancreatic islet-related autoantibodies in new acute-onset type 1 diabetes is 82% for anti-GAD antibody, 58% for anti-IA-2 antibody, 55% for IAA, and 50% for ZincT8 antibody, and combining these four antibodies gives a positive rate of 94%11. In Japan, anti-GAD antibody was measured by radio-immunoassay (RIA), but in December 2015, the method used was changed to enzyme-linked immunosorbent assay (ELISA). Accordingly, different diagnostic results have been reported especially in slowly progressive insulin dependent diabetes mellitus (SPIDDM) cases with low antibody titers. Among the cases originally diagnosed as SPIDDM by the GAD-RIA method, the GAD-ELISA-positive cases had significantly lower C-peptide levels than the GAD-ELISA-negative cases12. Even in 30 SPIDDM cases with an anti-GAD-RIA antibody titer of ≤10 U/mL, the C-peptide values were significantly lower in the GAD-ELISA-positive cases than in the negative cases, and HLA-DR9(+) cases were significantly more common among positive cases (P < 0.05)13. On the other hand, the method for detection of anti-IA-2 antibody was also changed from the RIA to ELISA in October 2018, and similar differences between the two methods were examined. Among 138 SPIDDM patients, it has been reported that the fasting C-peptide in anti-IA-2-ELISA antibody-positive cases was significantly lower than in the anti-IA-2-ELISA antibody-negative cases14. Similar to the study on anti-GAD antibodies, there were significantly more DRB1*09:01 carriers in the IA-2-ELISA-positive cases than in the IA-2-ELISA-negative cases. Based on these findings, both the anti-GAD antibody and the anti-IA-2 antibody are considered to serve as indicators of post-onset endogenous insulin deficiency in SPIDDM better than RIA. It has also been reported that the islet-related autoantibody positive rate differs according to the age of onset of type 1 diabetes. The most commonly used anti-GAD antibody had a high positive rate in relatively older-onset type 1 diabetes patients, and anti-IA-2 antibody and anti-ZincT-8 antibody had a higher positive rate in young-onset patients15. Moreover, it was reported that autoantibody disappeared rapidly in patients with onset under the age of 10 years15. There have been great advances in insulin therapy in recent 100 years (Table 1). The first is progress in the technology of blood glucose measuring devices. Since the 1990s, treatment using continuous glucose monitoring (CGM) has become widely used worldwide. In Japan, both intermittently scanned CGM (isCGM) and real time CGM (rtCGM) are covered by health insurance, and the number of patients using CGM has increased. Moreover, according to a questionnaire-based survey of about 1,600 Japanese type 1 diabetic patients with an average age of 48, more than 10% had received some medical support for severe hypoglycemia during the past year16. The greatest contribution of isCGM and rtCGM is shortening the hypoglycemic duration, which has great significance for daily glycemic control17, 18. During lockdown to combat the SARS-Cov-2 pandemic, CGM has also been reported to maintain glycemic control in children under the age of 18 with type 1 diabetes19. The second is that the spread of CGM has led to the establishment of new glycemic control indicators using CGM data20. In addition to the HbA1c value, an index called time in range (TIR: 70–180 mg/dL) has emerged as a treatment target. In fact, it has been reported that frequent scanning with isCGM increases the TIR and decreases HbA1c levels in Japanese children with type 1 diabetes21. Furthermore, sensor augmented pump therapy that links CGM and insulin pump has been developed. It has been reported that the frequency of hypoglycemia associated with insulin treatment was reduced by the predictive low glucose suspend (PLGS) function of CGM22, and hypoglycemia and TIR were further improved by the hybrid closed-loop, and it can be worn even in half marathons23. Recently, the performance of a fully automatic closed loop has also been reported24. There have also been advances in insulin formulations. Second-generation fast-acting insulin products Faster Aspart (Fiasp®, Novo Nordisk, Copenhagen, Denmark) and URLi (Lyumjev®, Eli Lilly Company, Indianapolice, IN, USA) became available in 2020, making it easier to correct postprandial hyperglycemia. In addition, therapeutic agents other than insulin preparations have facilitated glycemic control in type 1 diabetes. Furthermore, the concomitant use of an oral hypoglycemic agent, SGLT2 inhibitor, has been reported to improve glycemic fluctuations and can be expected to improve HbA1c levels25, 26. Continuous glucose monitoring is also effective in evaluating these fluctuations at this time. However, on the other hand, sodium glucose cotransporter (SGLT) 2 inhibitor combination therapy has been associated with a risk of increased hypoglycemia, ketoacidosis (DKA) without hyperglycemia, and urinary tract infections25; therefore, this therapy should be managed by diabetologists. Based on recent advances in insulin preparations, insulin treatment devices, and home blood glucose monitoring devices, insulin therapy for type 1 diabetes, which began with insulin therapy for life support, has entered an era in which mobile devices automatically manage glycemic control. In the near future, it is expected that the precision of devices will be further improved, and improvements will be made in terms of both glycemic control and quality of life. None declared. JM received lecture fees from Taisho Pharmaceutical Co., Ltd, Novo Nordisk Pharma Ltd, Novartis Pharma K.K., Eli Lilly Japan K.K., Sanofi K.K., Life scan K.K., Abbott Japan LLC, Terumo Corporation, Boehringer Ingelheim Co., Ltd, Kyowa Kirin Co., Ltd, Takeda Pharmaceutical Co. Ltd, Mitsubishi Tanabe Pharma Corporation, MSD K.K., Mylan EPD G.K., Kowa Company, Ltd, AstraZeneca K.K., and Astellas Pharma Inc., medical consultation fees from Kanro Inc., manuscript fees from Novo Nordisk Pharma Ltd, YU received honoraria from Novo Nordisk Pharma Ltd, Terumo Corporation, Sanofi KK, Ono Pharmaceutical Co., Ltd. Approval of the research protocol: N/A. Informed consent: N/A. Registry and the registration no. of the study/trial: N/A. Animal studies: N/A.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,360

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,004
Tête enseignante GPT0,200
Écart entre enseignants0,196 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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