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

Type 2 diabetes in children and adolescents: Exploring the disease heterogeneity and research gaps to optimum management

Over the past 20 years, the incidence and prevalence of type 2 diabetes mellitus (T2DM) in children and adolescents have increased, particularly in racial and ethnic minorities. Despite the rise in T2DM in children and adolescents, the pathophysiology and progression of disease in this population are not clearly understood. Youth-onset T2DM has a more adverse clinical course than is seen in those who develop T2DM in adulthood or those with T1DM. Furthermore, the available therapeutic options are more limited for children and adolescents with T2DM compared to adult patients, mostly due to the challenges of implementing clinical trials. A better understanding of the mechanisms underlying the de-velopment and aggressive disease phenotype of T2DM in youth is important to finding effective prevention and management strategies. This review highlights the key evidence about T2DM in children and adolescents and its current burden and challenges both in clinical care and research activities.

Cardiometabolic health markers among Aboriginal adolescents from the Next Generation Youth Wellbeing Cohort Study

OBJECTIVE

The objective of this study was to investigate cardiometabolic health markers among Aboriginal adolescents aged 10-24 years and relationships with age, gender, and body composition.

METHODS

Baseline data (2018-2020) from the Next Generation Youth Wellbeing Cohort Study (Western Australia, New South Wales, and Central Australia) on clinically assessed body mass index, waist/height ratio, blood pressure, glycated haemoglobin (HbA1c), total and high-density lipoprotein cholesterol, total/high-density lipoprotein cholesterol ratio, and triglycerides were analysed.

RESULTS

Among 1100 participants, the proportion with individual health markers within the ideal range ranged from 59% for total cholesterol to 91% for HbA1c. Four percent had high blood pressure, which was more common with increasing age and among males; 1% had HbA1c indicative of diabetes. Healthier body composition (body mass index and waist/height ratio) was associated with having individual health markers in the ideal range and with an ideal cardiometabolic profile.

CONCLUSIONS

Most Aboriginal adolescents in this study had cardiometabolic markers within the ideal range, though markers of high risk were present from early adolescence. Ideal health markers were more prevalent among those with healthy body composition.

IMPLICATIONS FOR PUBLIC HEALTH

Specific screening and management guidelines for Aboriginal adolescents and population health initiatives that support maintenance of healthy body composition could help improve cardiometabolic health in this population.

Cardiometabolic risk profile of young and old adults with diabetes: Pooled, cross-sectional analysis of 42 national health surveys

AIMS

To compare levels of cardiometabolic risk factors in young and old adults with diabetes.

METHODS

Pooled analysis of 42 STEPS Surveys (n = 133,717). Diabetes was defined as self-reported diagnosis or fasting plasma glucose ≥ 126 mg/dl. There were two age groups: < 40 and ≥ 40 years. We summarized the mean levels of four cardiometabolic risk factors by country, sex and age group.

RESULTS

In 11 (men) and seven countries (women), the mean BMI seemed higher in young versus old adults; largest difference was found in men in Qatar (∼6 kg/m2). For waist circumference, such pattern was observed in two (men) and in three (women) countries; largest difference in men in Tuvalu (∼7 cm). Regarding systolic blood pressure, in one country (Myanmar) the mean was higher in young men with ∼8 mmHg difference. Women in the oldest group always had higher mean systolic blood pressure. For total cholesterol, in 13 (men) and five (women) countries the mean was higher in young adults (difference was always <1 mmol/l).

CONCLUSIONS

Levels of cardiometabolic risk factors in young versus old adults with diabetes were heterogenous across 42 countries and depended on the risk factor. This calls to monitor cardiometabolic risk factors in young adults with diabetes.

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

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 the future research investigators should aim to identify shared risk factors and ultimately refine preventive and intervention strategies.

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

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.

Synbiotics and Gut Microbiota: New Perspectives in the Treatment of Type 2 Diabetes Mellitus

The number of people with type 2 diabetes mellitus (T2DM) has increased sharply over the past decades. Apart from genetic predisposition, which may cause some of the diagnosed cases, an unhealthy diet and lifestyle are incentive triggers of this global epidemic. Consumption of probiotics and prebiotics to gain health benefits has become increasingly accepted by the public in recent years, and their critical roles in alleviating T2DM symptoms are confirmed by accumulating studies. Microbiome research reveals gut colonization by probiotics and their impacts on the host, while oral intake of prebiotics may stimulate existing metabolisms in the colon. The use of synbiotics (a combination of prebiotics and probiotics) can thus show a synergistic effect on T2DM through modulating the gastrointestinal microenvironment. This review summarizes the research progress in the treatment of T2DM from the perspective of synbiotics and gut microbiota and provides a class of synbiotics which are composed of lactulose, arabinose, and Lactobacillus plantarum, and can effectively adjust the blood glucose, blood lipid, and body weight of T2DM patients to ideal levels.

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

OBJECTIVE

For 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 METHODS

273 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.

RESULTS

The 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.

CONCLUSION

When 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.