Progressive myelopathy associated with spinal epidural lipomatosis in three non-obese patients with type 1 diabetes mellitus

BACKGROUND

Spinal epidural lipomatosis (SEL) is a rare condition defined as pathological overgrowth of the normally present epidural fat within the spinal canal. SEL is associated with Cushing disease, obesity and chronic corticosteroid therapy. Diabetes mellitus type 1 (DM1) has not known to be a risk factor for SEL. The neurological symptoms of SEL are attributed mainly to mechanical compression on the spinal cord and the cauda equina.

METHODS

A retrospective chart review of patients evaluated at NYU Multiple Sclerosis Care Center identified three diabetic patients with progressive myelopathy associated with SEL. We report the clinical course, diagnostic workup and outcomes in these three patients with SEL-associated myelopathy.

RESULTS

Three patients (2 females and 1 male) had long-standing DM1 and developed progressive myelopathy in their early 40's. All were found to have thoracic SEL (extensive extradural T1, T2 hyperintense signal; biopsy confirmed in one case) with associated extensive abnormal cord signal in lower cervical/upper thoracic spinal cord. A comprehensive evaluation for metabolic, infectious, autoimmune and vascular causes of myelopathy that included serologies, cerebrospinal fluid analyses, and spinal angiography did not reveal an alternative cause for myelopathy. One of the patients underwent a surgical decompression of SEL with subsequent clinical and radiologic improvement.

CONCLUSIONS

Our case series suggest that patients with DM1 and myelopathy of unknown cause should be evaluated for SEL. Timely diagnosis and appropriate intervention may forestall progression of neurological disability and even result in neurologic improvement. SEL should be considered on the short list of diagnoses that cause potentially reversible progressive myelopathy.

Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis

Automated segmentation of brain MRI scans into tissue classes is commonly used for the assessment of multiple sclerosis (MS). However, manual correction of the resulting brain tissue label maps by an expert reader remains necessary in many cases. Since automated segmentation data awaiting manual correction are "missing", we proposed to use multiple imputation (MI) to fill-in the missing manually-corrected MRI data for measures of normalized whole brain volume (brain parenchymal fraction-BPF) and T2 hyperintense lesion volume (T2LV). Automated and manually corrected MRI measures from 1300 patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) were identified. Simulation studies were conducted to assess the performance of MI with missing data both missing completely at random and missing at random. An imputation model including the concurrent automated data as well as clinical and demographic variables explained a high proportion of the variance in the manually corrected BPF (R(2)=0.97) and T2LV (R(2)=0.89), demonstrating the potential to accurately impute the missing data. Further, our results demonstrate that MI allows for the accurate estimation of group differences with little to no bias and with similar precision compared to an analysis with no missing data. We believe that our findings provide important insights for efficient correction of automated MRI measures to obviate the need to perform manual correction on all cases.