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The Predictive Potential of Altered Voxel-Based Morphometry in Severely Obese Patients With Meibomian Gland Dysfunction

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Objective To investigate voxel-based morphometry (VBM) by using magnetic resonance imaging (MRI) in meibomian gland dysfunction patients with severe obesity (PATs) and to explore the application of VBM in the… Click to show full abstract

Objective To investigate voxel-based morphometry (VBM) by using magnetic resonance imaging (MRI) in meibomian gland dysfunction patients with severe obesity (PATs) and to explore the application of VBM in the early diagnosis, prevention of cognitive impairment and targeted treatment of this disease. Methods Sixteen PATs and 12 healthy controls (HCs) were enrolled and underwent MRI. Whole-head images were analyzed using VBM and data were compared between groups using an independent samples t-test. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic value of this approach. Mini-mental state examination (MMSE) scores were used to assess cognitive impairment and were analyzed using an independent samples t-test. Results Compared with HCs, the VBM values in PATs were reduced in the left cerebellum and right thalamus but increased in the right brainstem, right precuneus and right paracentral lobule. The results of ROC curve analysis indicated that VBM may be useful in meibomian gland disease diagnosis. Comparison of MMSE scores between groups showed mild cognitive impairment in PATs. Conclusion PATs showed altered VBM values in some brain areas. These findings may provide information about the pathophysiology of meibomian gland dysfunction and may help to explain the underlying mechanisms of clinical manifestations in PATs, such as cognitive impairment. Abnormal VBM values in these brain areas may serve as predictive factors for development of meibomian gland disease in severely obese people and as indicators for individualized treatment.

Keywords: vbm; meibomian gland; gland; gland dysfunction; voxel based

Journal Title: Frontiers in Neuroscience
Year Published: 2022

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