Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes - a post-hoc analysis of a case–control study

Aug 5, 2026

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Severe hypoglycemia in older adults with type 1 diabetes is linked with major morbidity and mortality, yet its causes are complex and multifactorial. This study uses machine learning to distinguish older adults with and without recent severe hypoglycemia by screening demographic and clinical data, behavioral and lifestyle factors, neurocognitive characteristics, and continuous glucose monitoring (CGM) measures.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

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Abstract

Introduction

Background and rationale

Research design and methods

Results

Key characteristics and ranking

Conclusions

What is already known on this topic

What this study adds

How this study might affect research, practice or policy

Open access Original research Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case– control study Nikki L B Freeman ,1 Rashmi Muthukkumar,2 Ruth S Weinstock,3 M Victor Wickerhauser,4 Anna R Kahkoska 5,6 To cite: Freeman NLB, Muthukkumar R, Weinstock RS, et al. Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a posthoc analysis of a case–control study. BMJ Open Diab Res Care 2024;12:e003748 . doi:10.1136/ bmjdrc-2023-003748 ► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi](https://doi). org/10.1136/bmjdrc-2023- 003748) . Received 4 September 2023 Accepted 30 January 2024 © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. For numbered affiliations see end of article. Correspondence to Dr Nikki L B Freeman; [nlbf@live.unc.edu](nlbf@live.unc.edu) ABSTRACT Introduction Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. Research design and methods Data from a case–control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. Results Data from 191 OAs with type 1 diabetes (47 . 1% female, 92 . 1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM,% time blood glucose below 70 mg/dL, and trail making test B score. Conclusions Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics. INTRODUCTION Older adults with type 1 diabetes area growing population within the USA.1 Although hypoglycemia is a concern at any age for people with type 1 diabetes, older adults are at substantially higher risk of hypoglycemia compared with younger adults. It has previously been reported that the incidence of | WHAT IS ALREADY KNOWN ON THIS TOPIC | | --- | | ⇒ Severe hypoglycemia in older adults with type 1 diabetes is associated with significant morbidity and mortality, and previous work by Weinstock et alidentified the characteristics that distinguish older adults with a recent history of severe hypoglycemia from those without across a wide range of potentially important variables.
WHAT THIS STUDY ADDS | | ⇒ This study aimed to harness machine learning methods to uncover the relative importance of those variables, including demographic, clinical, lifestyle, and neurocognitive characteristics, and continuous glucose monitoring (CGM) measures associated with a history of severe hypoglycemia among older adults with type 1 diabetes.
⇒ The individual-level characteristics associated with a history of severe hypoglycemia were hypoglycemia unawareness, hypoglycemia fear, glycemic vari

FAQs

What problem does the study address for older adults with type 1 diabetes?

The study addresses severe hypoglycemia, which is associated with substantial morbidity and mortality in older adults, while its etiology remains complex and multifactorial.

What data sources and variables are used to differentiate cases from controls?

The analysis uses machine learning on case–control data and evaluates demographic and clinical factors, behavioral and lifestyle characteristics, neurocognitive measures, and continuous glucose monitoring (CGM) metrics.

Which characteristics were most strongly associated with a history of severe hypoglycemia?

Across models, hypoglycemia unawareness was the top characteristic; for the richest-input model, the most important factors included hypoglycemia unawareness, hypoglycemia fear, CGM-derived glycemic variability, % time below 70 mg/dL, and trail making test B score.

Aug 5, 2026

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