Renal Disease Risk Factors Among Risk Groups Comprised of African American Women With Type 2 Diabetes: A Secondary Analysis

Machine Learning and Smart Devices for Diabetes Management: Systematic Review

(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.

"I put diabetes on the shelf": African-American Women's Perceptions of Risk for Diabetes Complications

The purpose of this study was to describe the perceptions of African American (AA) women with Type 2 diabetes mellitus (T2DM) about developing diabetes mellitus (DM) complications and explore how their perceived risk influenced DM self-management. Ten (N = 10) AA women participated in the qualitative description study through semi-structured interviews. Thematic analysis informed by the Health Belief Model and Risk Perception Conceptual Model revealed the perceived probability of DM complications by AA women with T2DM and how they made judgments regarding the seriousness, extent, or severity of complications. Those with high levels of DM knowledge perceived themselves at high risk of developing DM complications and those with low DM knowledge perceived themselves at low risk of DM complications. Risk perceptions and health literacy also influenced DM self-management behaviors.

Distinct trajectories of HbA1c in newly diagnosed Type 2 diabetes from the DPV registry using a longitudinal group-based modelling approach

AIM

To identify groups of heterogeneous HbA_1c trajectories over time in newly diagnosed Type 2 diabetes.

METHODS

The study comprised 6355 adults with newly diagnosed Type 2 diabetes (55% men, median age 62 years, baseline BMI 31 kg/m² ) from the Diabetes Patienten Verlaufsdokumentation (DPV) prospective multicentre diabetes registry (Germany, Austria). Individuals were assessed during the first 5 years after diabetes diagnosis if they had ≥ 3 aggregated HbA_1c measurements during follow-up. Latent class growth modelling was used to determine distinct subgroups that followed similar longitudinal HbA_1c patterns (SAS: Proc Traj). Multinomial logistic regression models were used to investigate which variables were associated with the respective HbA_1c trajectory groups.

RESULTS

Four distinct longitudinal HbA_1c trajectory (glycaemic control) groups were found. The largest group (56% of participants) maintained stable good glycaemic control (HbA_1c 42-45 mmol/mol). Twenty-six percent maintained stable moderate glycaemic control (HbA_1c 57-62 mmol/mol). A third group (12%) initially showed severe hyperglycaemia (HbA_1c 97 mmol/mol) but reached good glycaemic control within 1 year. The smallest group (6%) showed stable poor glycaemic control (HbA_1c 79-88 mmol/mol). Younger age at diabetes diagnosis, male sex, and higher BMI were associated with the stable moderate or poor glycaemic control groups. Insulin therapy was strongly associated with the highly improved glycaemic control group.

CONCLUSIONS

Four subgroups with distinct HbA_1c trajectories were determined in newly diagnosed Type 2 diabetes using a group-based modelling approach. Approximately one-third of people with newly diagnosed Type 2 diabetes need either better medication adherence or earlier intensification of glucose-lowering therapy.

Longitudinal trends in HbA1c patterns and association with outcomes: A systematic review

BACKGROUND

This study aimed to review studies that identified patterns of longitudinal HbA1c trends in patients with diabetes and to summarize factors and outcomes associated with distinct trajectory patterns.

METHODS

PubMed and Web of Science were systematically searched for studies examining HbA1c trends among patients with diabetes from database inception through September 2017. Articles were included if they met the following inclusion criteria: (a) longitudinal study of subjects with diabetes only, (b) use of serial measurements of HbA1c , and (c) analysis of the trend of HbA1c using group-based trajectory approaches.

RESULTS

Twenty studies were included, 11 on type 1 diabetes and 9 on type 2 diabetes. These studies identified 2 to 6 HbA1c trajectory patterns. The most commonly identified patterns included stable HbA1c around 7.0% and at levels between 8.0% and 9.9%, which usually captured the HbA1c pattern among the majority of subjects in the study population. Unstable patterns identified included increasing HbA1c trend, decreasing HbA1c trend, and non-linear patterns. These patterns were associated with differential risk of disease outcomes, over and beyond single-point HbA1c measures. Age, gender, ethnicity, diabetes duration, disease management frequency, cardiovascular risk factors, insulin treatment, family environment, and psychosocial factors were the most frequently reported factors associated with membership of specific HbA1c pattern groups.

CONCLUSION

Common patterns of longitudinal HbA1c trends were identified despite heterogeneity among the studies. A better understanding of what underlies these different patterns may provide opportunities to tailor therapies and care for these patients to reduce adverse outcomes.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Background

Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective

The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Methods

A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

Results

We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

Conclusions

We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.