Diabetes Meets Machine Learning, Part 1

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Of all the disorders that have responded well to artificial intelligence and machine learning, diabetes mellitus probably tops the list. The evidence supporting a role for Machine Learning (ML)-enhanced algorithms in managing the disease is persuasive and applies to several components of patient care, including screening, diagnosis, treatment, and prognosis.

Let's start with screening: As most clinicians know, there's a difference between screening for disease and diagnosing it. The former casts a much wider net, which means it will include more false positives. Once this larger cohort has been identified, more precise diagnostic testing can be performed to pinpoint patients who have the disorder.  As we explain in our next book, The Digital Reconstruction of Healthcare, screening tools for diabetes have been in existence for many years, including tools to help clinicians and patients identify the presence of prediabetes. Still, new technology has taken the screening process to a new level.

The American Diabetes Association (ADA) defines prediabetes as fasting plasma glucose between 100 and 126 mg/dl, a 2-hour oral glucose tolerance test reading between 140 and 200 mg/dl, or hemoglobin A1c of 5.7% to 6.5%.¹ The Association has developed a risk assessment tool to help clinicians and patients; it asks for a patient's age, gender, family history of diabetes, the presence of hypertension, whether the patient is physically active, their ethnicity, and height and weight. Although this kind of scoring system will detect many persons with prediabetes, it has its limitations and ignores many changes in a patient's medical history over time. An artificial intelligence (AI)-enabled assessment tool that takes advantage of gradient boosting, on the other hand, is capable of factoring in many more risk factors gleaned from a patient’s electronic health record. This type of assessment system can accurately predict which patients will progress to full-blown diabetes, with an area under the curve (AUC) of 0.865.² The gradient boosting approach has proven more accurate than logistic regression and evaluation of standard clinical cutoffs of blood glucose ≥ 110 mg/dl and HbA1c ≥ 6.0%. An analysis by Avivit Cahn from the Hebrew University of Jerusalem and her colleagues found the said ML algorithm incorporated 69 variables to achieve its superior AUC.

There is also reason to believe that AI-enhanced algorithms can help identify individuals before they become prediabetic. The Centers for Disease Control and Prevention (CDC) has a screening test to help predict prediabetes that asks one’s age, family history of diabetes, the existence of hypertension, race or ethnicity, whether one is physically active, gender, height and weight.

But logistic regression, artificial neural networks, random forest analysis, and gradient boosting have uncovered numerous other risk factors to predict prediabetes. Among the predictors that may help refine the risk stratification process: food security, citizenship, monocyte count, uric acid level, ALT, RBC count, and serum calcium and potassium.³ Whereas the CDC screening tool returned an area under the receiver operating characteristic (AUROC) of 64.4% in a large cohort from the National Health and Nutrition Survey, several AI-based algorithms generated AUROCs at or above 70%.

Several research projects suggest machine learning can improve the treatment of diabetes. In our book on mobile medicine, we discussed DreaMed,*  an Israeli vendor that developed DreaMed Diabetes to provide clinical decision assistance to clinicians managing Type 1 diabetes in patients who are using an insulin pump and continuous glucose monitoring (CGM). Its Advisor Pro algorithms collect several data types, including the patient’s daily blood glucose readings, meal carbohydrate data, insulin dosing records, and physical activity level. The software then uses event-driven machine learning and adaptive technology to create a more individualized regimen, adjusting its insulin dosing recommendations and offering behavior-modification recommendations accordingly. DreaMed Diabetes relies on a clinical AI program called MD-Logic. It uses fuzzy logic and adaptive learning algorithms to approximate how diabetes specialists reason as they adjust an individual's insulin regimen. The company claims that the software provides “faster analysis and deeper insights” than would normally be available with routine endocrinologist care. There is research support for this automated approach to Type 1 diabetes management.⁴

This coordinated interplay between an insulin pump and CGM sometimes referred to as closed-loop control or an artificial pancreas, has also been a focus of Mayo Clinic researchers. Yogish Kudva, MBBS, along with colleagues Sandeep Gupta and Ayan Bannerjee from Arizona State University, has been analyzing continuous glucose monitoring data from people with type 1 diabetes on the Medtronic MiniMed 670G closed-loop system* to develop software that would help each patient and clinic provider improve closed-loop control. They have also developed a system for measuring heart rate, respiratory rate, and self-report symptoms of viral infections in individuals with diabetes. The aim is to investigate the role of glycemic control on the progression of signs and symptoms of infectious disease in both at-home and hospital settings.

Similarly, Dr. Kudva participated in a recent investigation on the role of a closed-loop system to improve glycemic control in adolescents and young adults, part of the International Diabetes Closed-Loop Trial.⁵

Unfortunately, until recently, CGM has been out of reach for most Type 2 patients. But with the introduction of new, state-of-the-art technology, it is now available to them as well. We’ll explore these innovative solutions in part 2 of our series on machine learning-enhanced diabetes.

*Products and services mentioned are not endorsements.  Mayo Clinic has no relationship with any of these vendors.

 

References

1. American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes 2019. Diabetes Care. 2019;42(Suppl. 1):S13–S28. doi .org/10.2337/dc19-S002

2.  Cahn A, Shoshan A, Sagiv T el al. Prediction of progression from pre-diabetes to diabetes: development and validation of a machine learning model. Diabetes Metab Res Rev. 2019; e3252. doi.org/10.1002/dmrr.3252

3. De Silva K, Jonsson D, Demmer RT. A combined strategy of feature selection and machine learning to identify predictors of prediabetes. JAMIA. 2020;27: 396–406.

4. Nimri R, Muller I, Atlas E, et al. MD-logic overnight control for 6 weeks of home use in patients with type 1 diabetes: randomized crossover trial. Diabetes Care. 2014;37:3025_32.

5. Isganaitis E, Raghinaru D, Amber-Osborn L et al. Closed-Loop Insulin Therapy Improves Glycemic Control in Adolescents and Young Adults: Outcomes from the International Diabetes Closed-Loop Trial. Diabetes Technol. Ther. 2021l Jan 21. doi: 10.1089/dia.2020.0572.  Online ahead of print.