LET’S MAKE A DIFFERENCE: EARLY SCREENING FOR PREDIABETES AND TYPE 2 DIABETES IN CANADIAN ABORIGINAL YOUTH
Notice bibliographique
Résumé
Type 2 diabetes (T2D) is one of the fastest growing paediatric chronic diseases worldwide.1 Canadian Aboriginal children are disproportionately affected by T2D, with an incidence rate estimated at 1.54 per 100,000 children per year.2 Recent research found that Aboriginal youth with diabetes experience higher risk for early complications and premature death than non-Aboriginal.3 These authors argued that young people with diabetes have a prolonged exposure to the metabolic consequences of this disease. Although a number of studies have examined the projected incidence rates and potential risk factors for T2D in the adolescent population, the true prevalence rate of prediabetes and T2D is underestimated specifically in the Aboriginal adolescent cohort. To our knowledge, this is the first population-based investigation into the prevalence of prediabetes in Canadian Aboriginal adolescents in the last decade. To investigate the risk factors and prevalence rates of prediabetes and type 2 diabetes among adolescents living in northern Canadian communities of which the majority were of Aboriginal decent. In this novel quantitative study, 160 high school students (aged 13–20) were recruited from three northern, pre-dominantly Canadian Aboriginal communities and were screened for risk for prediabetes and type 2 diabetes. Screening included demographic data, family history, anthropometrical measurements, blood pressure, and A1C. Descriptive and inferential statistics were computed using the Statistical Package for Social Sciences (SPSS v.22.0). Further, chi-square analyses were conducted to investigate if the risk factors of hypertension and obesity occurred at higher frequencies for males and females who presented with an increased HbA1c level. At least half of the adolescents presented with multiple risk factors for type 2 diabetes including Aboriginal ancestry, family history, overweight/obesity, and hypertension. In this sample, 10% had an A1C greater than 5.7%, 22.5% were overweight and 17.5% were obese, and 26.6% had hypertension or prehypertension. Further analysis showed that the 50% of the participants with an elevated HbA1c were also overweight/obese and 31% were prehypertensive/hypertensive. Of the females who were prediabetic, 14% were overweight and 43% were obese. For the males who presented with prediabetes, 22% were overweight and 22% were obese. In addition, 43% of females with an elevated HbA1c and who were classified as prediabetic were hypertensive and 14% were prehypertensive. In comparison, only 11% of the males who were classified as prediabetic were hypertensive and none were prehypertensive (Figure 1). Prediabetes is a growing health concern for young Aboriginal Canadians and there is an urgent need for early screening of both prediabetes and type 2 diabetes. To enhance positive health outcomes, interventions that are specific to the modifiable risk factors including overweight/obesity and hypertension are suggested. This has the potential to prevent the progression to diabetes and reduce related complications.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».