Symptom clustering in subjects with and without diabetes mellitus: a population-based study of 15,000 Australian adults

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Abstract
GI symptoms form distinct symptom clusters in community samples when factor and cluster analysis is applied. However, this has not been studied in diabetic populations, despite clear evidence that GI complaints are common in patients with diabetes mellitus (DM). This study aimed to describe clustering of GI symptoms among individuals with and without diabetes mellitus, and to describe associations of symptom clustering in diabetes mellitus, with self-reported glucose control and treatment. A large population survey (n = 15,000) was used to identify a cohort with diabetes mellitus. Items assessing therapy and quality of glycemic control were included, as were those assessing 16 common GI symptoms. Latent GI symptom factors were extracted by factor analysis and used in a k-means cluster analysis. The latter serves to group individuals according to commonalities in symptom profiles. The association of cluster group membership to glycemic control and diabetic treatment was described by logistic regression. Factor analysis identified four latent symptom factors, which accounted for 69.3% of the total variance. These were labeled Upper GI/Dysmotility, Diarrhea, Constipation, and Vomiting/Nausea. The k-means analysis produced a five-cluster solution, which included a “health” group and four “diseased” groups, each identified by a predominant symptom: Upper GI/Dysmotility symptoms, Nausea/Vomiting, Diarrhea, and Constipation. After adjustment for age and gender, poor glycemic control predicted membership in all disease clusters, when compared separately with the health group. Oral hypoglycemic drugs predicted membership in the Nausea/Vomiting cluster (OR = 5.13) when used alone, and membership in the Nausea/Vomiting (OR = 10.12) and Upper GI/Dysmotility cluster (OR = 10.12) when used in combination with insulin. Diabetes can be grouped according to common GI symptoms. Glycemic control and treatment for DM predict membership of symptom clusters.