MRI-based early diagnosis: a diabetic Charcot spine case report.

BACKGROUND: Spinal neuroarthropathy (SNA), also known as Charcot spine, is an uncommon aggressive arthropathy, secondary to loss of proprioceptive and nociceptive feedback from the spine. A diagnosis of SNA is frequently delayed due to the scarcity of symptoms in its early stages, leading to significant neurological deterioration. Therefore, prompt suspicion of the disease is critical to providing better outcomes. This case assembles two rare characteristics of SNA: diabetic aetiology and a precocious time of diagnosis, and aims to highlight the magnetic resonance imaging (MRI) findings that allowed for the diagnosis. CASE PRESENTATION: A 44-year-old woman, with long-term type 1 diabetes, presented with a two-month history of progressive lumbar pain, difficulty in maintaining an upright position, and discrete trunk forward-leaning. Diabetes-related vasculopathy and nephropathy were already known, and laboratory test results did not show any new abnormalities. A lumbar MRI revealed extensive signal intensity changes of the L2 and L3 vertebral bodies associated with marginal areas of enhancement and the involvement of regions adjacent to interapophyseal articulations and spinous processes from L2-L3 to L5-S1, in association with degenerative changes of the thoracolumbar spine. These findings were identified by the radiologist as suggestive of SNA. To rule out neoplastic and infectious disease, a bone biopsy at the L2-L3 level was executed. The pathology report revealed intervertebral disc material and fragments of fibrous tissue, with a complete absence of inflammatory cells. It was decided to perform a six-month MRI follow-up, which showed stability of the findings, confirming the hypothesis of Charcot spine. The patient was under clinical and radiological follow-up and did not require surgical fixation at the moment of diagnosis. After 2.5 years from the initial diagnosis, a new MRI revealed progression of the lesions with oedema and enlarged paravertebral soft tissues; these findings are compatible with the patient's latest complaints of lumbar pain recurrence. CONCLUSION: To the best of our knowledge, this is the first case report of an MRI-based early diagnosis of diabetic SNA, a rare disease with nonspecific symptoms in its initial stages and a wide spectrum of differential diagnoses. The MRI findings, distinctly the involvement of both anterior and posterior spinal elements, were the key to allowing for the proper diagnosis. A precocious diagnosis, although challenging, is fundamental to providing early intervention and to preventing further neurological impairment.

Accuracy of insulin resistance indices for metabolic syndrome: a cross-sectional study in adults.

BACKGROUND: This study aimed to determine the ability of commonly used insulin resistance indices to identify the metabolic syndrome. METHODS: 183 people referred for outpatient care at the Metabolism Unit of Hospital de Clínicas de Porto Alegre were evaluated with anthropometric, blood pressure, lipid profile, and adiponectin measurements. Glucose tolerance status was determined by 2-h 75-g oral glucose tolerance test and glycosylated hemoglobin. Definition of metabolic syndrome was based on the Joint Interim Statement of different medical associations. Twenty-one indices of insulin resistance were estimated from published equations. The accuracy of these indices was determined by area under the ROC curve (AUC) analysis. In addition, we determined an optimal cut point for each index and its performance as a diagnostic test. RESULTS: The study population was comprised of 183 people (73.2% women; 78.7% white; age 52.6 ± 12.0 years, mean ± standard deviation), of whom 140 (76.5%) had metabolic syndrome. The reciprocal of the Gutt index provided the greatest AUC for identification of metabolic syndrome, but there were no statistical differences between Gutt and 11 AUC indices. Gutt presented 86.4% sensitivity and 76.7% specificity to identify metabolic syndrome. CONCLUSIONS: A number of commonly employed indices of insulin resistance are capable of identifying individuals with the metabolic syndrome.