Management of type 2 diabetes in young adults aged 18–30 years: ADS/ADEA/APEG consensus statement

Wong, Jencia; Ross, Glynis P.; Zoungas, Sophia; Craig, Maria E.; Davis, Elizabeth A.; Donaghue, Kim C.; Maple-Brown, Louise; McGill, Margaret; Shaw, Jonathan E.; Speight, Jane; Wischer, Natalie; Stranks, Stephen N

doi:10.5694/mja2.51482

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…Our findings upheld current guidelines that type 2 diabetes onset at a younger age should be given special attention regarding psychological burden (37,38). The integration of mental health support within a coordinated multidisciplinary diabetes care team should be encouraged (38).…”

Section: Discussionsupporting

confidence: 69%

Early-Onset Type 2 Diabetes and Mood, Anxiety, and Stress-Related Disorders: A Genetically Informative Register-Based Cohort Study

et al. 2022

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OBJECTIVE To assess the association and familial coaggregation between early-onset type 2 diabetes (diagnosed before age 45 years) and mood, anxiety, and stress-related disorders and estimate the contribution of genetic and environmental factors to their co-occurrence. RESEARCH DESIGN AND METHODS This population-based cohort study included individuals born in Sweden during 1968–1998, from whom pairs of full siblings, half-siblings, and cousins were identified. Information on diagnoses of early-onset type 2 diabetes and mood (including unipolar depression and bipolar disorder), anxiety, and stress-related disorders was obtained from the National Patient Register. Logistic and Cox regression models were used to assess the phenotypic association and familial coaggregation between type 2 diabetes and psychiatric disorders. Quantitative genetic modeling was conducted in full and maternal half-sibling pairs to estimate the relative contributions of genetic and environmental factors to the association. RESULTS Among a total of 3,061,192 individuals, 7,896 (0.3%) were diagnosed with early-onset type 2 diabetes. These individuals had higher risks of any diagnosis (odds ratio [OR] 3.62 [95% CI 3.44, 3.80]) and specific diagnosis of unipolar depression (3.97 [3.75, 4.22]), bipolar disorder (4.17 [3.68, 4.73]), anxiety (3.76 [3.54, 3.99]), and stress-related disorders (3.35 [3.11, 3.61]). Relatives of individuals with early-onset type 2 diabetes also had higher overall risks of the examined psychiatric disorders (ORs 1.03–1.57). These associations are largely explained by genetic factors (51–78%), with the rest explained by nonshared environmental factors. CONCLUSIONS Our findings highlight the burden of mood, anxiety, and stress-related disorders in early-onset type 2 diabetes and demonstrate that shared familial liability may contribute to their co-occurrence, suggesting that in future research investigators should aim to identify shared risk factors and ultimately refine preventive and intervention strategies.

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Section: Discussionsupporting

confidence: 69%

Early-Onset Type 2 Diabetes and Mood, Anxiety, and Stress-Related Disorders: A Genetically Informative Register-Based Cohort Study

et al. 2022

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“…The American Diabetes Association (ADA) also changed the universal screening age from 45 to 35 years in their 2022 guideline [ 10 ]. The Australian Diabetes Society recently published a consensus statement on T2DM screening in young adults (aged 18 to 30 years) [ 16 ]. In this statement, screening for diabetes in young adults who are overweight or obese and with additional risk factors, including in utero exposure to T2DM or gestational diabetes mellitus, was recommended [ 16 ].…”

Section: Diabetes Screening Policies In Different Countriesmentioning

confidence: 99%

Screening for Prediabetes and Diabetes in Korean Nonpregnant Adults: A Position Statement of the Korean Diabetes Association, 2022

et al. 2022

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Diabetes screening serves to identify individuals at high-risk for diabetes who have not yet developed symptoms and to diagnose diabetes at an early stage. Globally, the prevalence of diabetes is rapidly increasing. Furthermore, obesity and/or abdominal obesity, which are major risk factors for type 2 diabetes mellitus (T2DM), are progressively increasing, particularly among young adults. Many patients with T2DM are asymptomatic and can accompany various complications at the time of diagnosis, as well as chronic complications develop as the duration of diabetes increases. Thus, proper screening and early diagnosis are essential for diabetes care. Based on reports on the changing epidemiology of diabetes and obesity in Korea, as well as growing evidence from new national cohort studies on diabetes screening, the Korean Diabetes Association has updated its clinical practice recommendations regarding T2DM screening. Diabetes screening is now recommended in adults aged ≥35 years regardless of the presence of risk factors, and in all adults (aged ≥19) with any of the risk factors. Abdominal obesity based on waist circumference (men ≥90 cm, women ≥85 cm) was added to the list of risk factors.

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“…It is important to prevent both hypo and hyper-glycemic conditions for effective diabetes management. The blood glucose concentration is affected by multiple factors, and it is ideal to use the historic values as predictive inputs (3,12).…”

Section: Introductionmentioning

confidence: 99%

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes

Wang

Cates

et al. 2023

Front. Endocrinol.

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ObjectiveFor the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose.Patients and methods273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status et al were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis.ResultsThe statistical variables for which p< 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice.ConclusionWhen it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.

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Management of type 2 diabetes in young adults aged 18–30 years: ADS/ADEA/APEG consensus statement

Cited by 10 publications

References 48 publications

Early-Onset Type 2 Diabetes and Mood, Anxiety, and Stress-Related Disorders: A Genetically Informative Register-Based Cohort Study

Early-Onset Type 2 Diabetes and Mood, Anxiety, and Stress-Related Disorders: A Genetically Informative Register-Based Cohort Study

Screening for Prediabetes and Diabetes in Korean Nonpregnant Adults: A Position Statement of the Korean Diabetes Association, 2022

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes

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