Updated risk factors should be used to predict development of diabetes

American Association of Clinical Endocrinology Consensus Statement: Comprehensive Type 2 Diabetes Management Algorithm - 2023 Update

OBJECTIVE

This consensus statement provides (1) visual guidance in concise graphic algorithms to assist with clinical decision-making of health care professionals in the management of persons with type 2 diabetes mellitus to improve patient care and (2) a summary of details to support the visual guidance found in each algorithm.

METHODS

The American Association of Clinical Endocrinology (AACE) selected a task force of medical experts who updated the 2020 AACE Comprehensive Type 2 Diabetes Management Algorithm based on the 2022 AACE Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan and consensus of task force authors.

RESULTS

This algorithm for management of persons with type 2 diabetes includes 11 distinct sections: (1) Principles for the Management of Type 2 Diabetes; (2) Complications-Centric Model for the Care of Persons with Overweight/Obesity; (3) Prediabetes Algorithm; (4) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Dyslipidemia; (5) Atherosclerotic Cardiovascular Disease Risk Reduction Algorithm: Hypertension; (6) Complications-Centric Algorithm for Glycemic Control; (7) Glucose-Centric Algorithm for Glycemic Control; (8) Algorithm for Adding/Intensifying Insulin; (9) Profiles of Antihyperglycemic Medications; (10) Profiles of Weight-Loss Medications (new); and (11) Vaccine Recommendations for Persons with Diabetes Mellitus (new), which summarizes recommendations from the Advisory Committee on Immunization Practices of the U.S. Centers for Disease Control and Prevention.

CONCLUSIONS

Aligning with the 2022 AACE diabetes guideline update, this 2023 diabetes algorithm update emphasizes lifestyle modification and treatment of overweight/obesity as key pillars in the management of prediabetes and diabetes mellitus and highlights the importance of appropriate management of atherosclerotic risk factors of dyslipidemia and hypertension. One notable new theme is an emphasis on a complication-centric approach, beyond glucose levels, to frame decisions regarding first-line pharmacologic choices for the treatment of persons with diabetes. The algorithm also includes access/cost of medications as factors related to health equity to consider in clinical decision-making.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

Machine learning for diabetes clinical decision support: a review

Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning

BACKGROUND

Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care.

PATIENTS AND METHODS

Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods-namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected.

RESULTS

After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model.

CONCLUSION

The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.

Combined effects of continuous exercise and intermittent active interruptions to prolonged sitting on postprandial glucose, insulin, and triglycerides in adults with obesity: a randomized crossover trial

BACKGROUND

Postprandial glucose, insulin, and triglyceride metabolism is impaired by prolonged sitting, but enhanced by exercise. The aim of this study was to assess the effects of a continuous exercise bout with and without intermittent active interruptions to prolonged sitting on postprandial glucose, insulin, and triglycerides.

METHODS

Sedentary adults who were overweight to obese (n = 67; mean age 67 yr SD ± 7; BMI 31.2 kg∙m- 2 SD ± 4.1), completed three conditions: SIT: uninterrupted sitting (8-h, control); EX+SIT: sitting (1-h), moderate-intensity walking (30-min), uninterrupted sitting (6.5-h); EX+BR: sitting (1-h), moderate-intensity walking (30- min), sitting interrupted every 30-min with 3-min of light-intensity walking (6.5 h). Participants consumed standardized breakfast and lunch meals and blood was sampled at 13 time-points.

RESULTS

When compared to SIT, EX+SIT increased total area under the curve (tAUC) for glucose by 2% [0.1-4.1%] and EX+BR by 3% [0.6-4.7%] (all p < 0.05). Compared to SIT, EX+SIT reduced insulin and insulin:glucose ratio tAUC by 18% [11-22%] and 21% [8-33%], respectively; and EX+BR reduced values by 25% [19-31%] and 28% [15-38%], respectively (all p < 0.001 vs SIT, all p < 0.05 EX+SIT-vs-EX+BR). Compared to SIT, EX+BR reduced triglyceride tAUC by 6% [1-10%] (p = 0.01 vs SIT), and compared to EX+SIT, EX+BR reduced this value by 5% [0.1-8.8%] (p = 0.047 vs EX+SIT). The magnitude of reduction in insulin tAUC from SIT-to-EX+BR was greater in those with increased basal insulin resistance. No reduction in triglyceride tAUC from SIT-to-EX+BR was apparent in those with high fasting triglycerides.

CONCLUSIONS

Additional reductions in postprandial insulin-glucose dynamics and triglycerides may be achieved by combining exercise with breaks in sitting. Relative to uninterrupted sitting, this strategy may reduce postprandial insulin more in those with high basal insulin resistance, but those with high fasting triglycerides may be resistant to such intervention-induced reductions in triglycerides.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry ( ACTRN12614000737639 ).