Nutritional Status, Dietary Intake, and Nutrition-Related Interventions Among Older Adults With Type 1 Diabetes: A Systematic Review and Call for More Evidence Toward Clinical Guidelines

There is an emerging population of older adults (≥65 years) living with type 1 diabetes. Optimizing health through nutrition during this life stage is challenged by multiple and ongoing changes in diabetes management, comorbidities, and lifestyle factors. There is a need to understand nutritional status, dietary intake, and nutrition-related interventions that may maximize well-being throughout the life span in type 1 diabetes, in addition to nutrition recommendations from clinical guidelines and consensus reports. Three reviewers used Cochrane guidelines to screen original research (January 1993-2023) and guidelines (2012-2023) in two databases (MEDLINE and CENTRAL) to characterize nutrition evidence in this population. We found limited original research explicitly focused on nutrition and diet in adults ≥65 years of age with type 1 diabetes (six experimental studies, five observational studies) and meta-analyses/reviews (one scoping review), since in the majority of analyses individuals ≥65 years of age were combined with those age ≥18 years, with diverse diabetes durations, and also individuals with type 1 and type 2 diabetes were combined. Further, existing clinical guidelines (n = 10) lacked specificity and evidence to guide clinical practice and self-management behaviors in this population. From a scientific perspective, little is known about nutrition and diet among older adults with type 1 diabetes, including baseline nutrition status, dietary intake and eating behaviors, and the impact of nutrition interventions on key clinical and patient-oriented outcomes. This likely reflects the population's recent emergence and unique considerations. Addressing these gaps is foundational to developing evidence-based nutrition practices and guidelines for older adults living with type 1 diabetes.

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

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.

Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population

INTRODUCTION

There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM).

METHODS

Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information.

RESULTS

Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources.

CONCLUSIONS

Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

BACKGROUND

The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge.

METHODS

This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node.

RESULTS

Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.