# Examining Foot Shape Variations in Individuals With and Without Diabetes

## TL;DR

This study compares foot shape differences in people with and without diabetes to help design better shoes and prevent foot ulcers.

## Contribution

The study provides new 3D foot shape data and analysis methods for people with diabetes and neuropathy.

## Key findings

- Feet of people with diabetes and neuropathy show more pronounced shape variations compared to those without.
- PCA analysis revealed a 43% larger mean pairwise distance in foot shape for individuals with diabetes and neuropathy.
- PLSR analysis suggests potential for predicting diabetes and neuropathy based on foot shape features.

## Abstract

For people with diabetes, a good‐fitting shoe is essential for reducing the risk of ulcers and eventual amputation. However, there is a lack of 3D data explaining differences in foot shape between people with and without diabetes and how individual factors might influence these differences; these data are vital for adequate shoe design and prevention of diabetic foot ulcers. This study quantifies the differences in external foot shapes of people with and without diabetes and peripheral neuropathy and examines which factors might affect these variations. One‐hundred thirty‐six foot scans of older adults with and without diabetes and peripheral neuropathy were used to create and assess foot shape models against demographic and health factors. Principal component analysis (PCA) showed that the feet of people with diabetes and with neuropathy are not necessarily clustered into a particular foot shape but have more pronounced features in specific foot variations (e.g., ankle width, arch height, hallux abduction, and edema vs. atrophic feet) compared to people without diabetes and neuropathy. The mean pairwise distance (intrascore spread) in PC1 and PC2 for individuals with diabetes and neuropathy was 43% larger than for those without diabetes and neuropathy and 24% larger than for those with neuropathy but no diabetes. Partial least squares regression (PLSR) showed potential predicting the presence of diabetes and neuropathy; however, additional data are required to support the trend. Analyses, such as PCA and PLSR, could be useful for determining how to quantify these changes to design more appropriate footwear for these populations.