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Clinical Heterogeneity Of Acquired Idiopathic Isolated Adrenocorticotropic Hormone Deficiency

FRONTIERS IN ENDOCRINOLOGY(2021)

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Abstract
ObjectiveHeterogeneous clinical characteristics are observed in acquired isolated adrenocorticotropic hormone (ACTH) deficiency (IAD); however, its classification remains to be established because of its largely unknown pathophysiology. In IAD, anti-pituitary antibodies have been detected in some patients, although their significance remains unclear. Therefore, this study aimed to classify patients with IAD and to clarify the significance of anti-pituitary antibodies.Design and MethodsWe analyzed 46 consecutive patients with IAD. Serum anti-pituitary antibodies were analyzed via immunofluorescence staining using a mouse pituitary tissue. Principal component and cluster analyses were performed to classify IAD patients based on clinical characteristics and autoantibodies.ResultsImmunofluorescence analysis using the sera revealed that 58% of patients showed anti-corticotroph antibodies and 6% of patients showed anti-follicular stellate cell (FSC) antibodies. Principal component analysis demonstrated that three parameters could explain 70% of the patients. Hierarchical cluster analysis showed three clusters: Groups A and B comprised patients who were positive for anti-corticotroph antibodies, and plasma ACTH levels were extremely low. Groups A and B comprised middle-aged or elderly men and middle-aged women, respectively. Group C comprised patients who were positive for the anti-FSC antibody and elderly men; plasma ACTH levels were relatively high.ConclusionsPatients with IAD were classified into three groups based on clinical characteristics and autoantibodies. The presence of anti-corticotroph antibody suggested severe injury to corticotrophs. This new classification clearly demonstrated the heterogeneity in the pathogenesis of IAD.
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Key words
isolated ACTH deficiency, hypopituitarism, anti-pituitary antibody, anti-corticotroph antibody, anti-follicular stellate cell antibody, classification, principal component analyses, cluster analyses
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