Subgroups of older adults with osteoarthritis based upon differing comorbid symptom presentations and potential underlying pain mechanisms

Susan L Murphy, Angela K Lyden, Kristine Phillips, Daniel J Clauw, David A Williams, Susan L Murphy, Angela K Lyden, Kristine Phillips, Daniel J Clauw, David A Williams

Abstract

Introduction: Although people with knee and hip osteoarthritis (OA) seek treatment because of pain, many of these individuals have commonly co-occurring symptoms (for example, fatigue, sleep problems, mood disorders). The purpose of this study was to characterize adults with OA by identifying subgroups with the above comorbid symptoms along with illness burden (a composite measure of somatic symptoms) to begin to examine whether subsets may have differing underlying pain mechanisms.

Methods: Community-living older adults with symptomatic knee and hip OA (n = 129) participated (68% with knee OA, 38% with hip OA). Hierarchical agglomerative cluster analysis was used. To determine the relative contribution of each variable in a cluster, multivariate analysis of variance was used.

Results: We found three clusters. Cluster 1 (n = 45) had high levels of pain, fatigue, sleep problems, and mood disturbances. Cluster 2 (n = 38) had intermediate degrees of depression and fatigue, but low pain and good sleep. Cluster 3 (n = 42) had the lowest levels of pain, fatigue, and depression, but worse sleep quality than Cluster 2.

Conclusions: In adults with symptomatic OA, three distinct subgroups were identified. Although replication is needed, many individuals with OA had symptoms other than joint pain and some (such as those in Cluster 1) may have relatively stronger central nervous system (CNS) contributions to their symptoms. For such individuals, therapies may need to include centrally-acting components in addition to traditional peripheral approaches.

Figures

Figure 1
Figure 1
Illustration of required canonical discriminant functions to differentiate Clusters 1, 2 and 3. Two discriminant functions were identified that significantly differentiated the clusters accounting for 71% and 29% of the variance among them (Wilk's Lambda for Function 1: χ2 = 168.28, P < .0001; and for Function 2: χ2 = 59.10, P < .0001).

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