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<p>Initial evidence of intermittence in clinical symptoms of Cushing’s syndrome (CS) dates back to the 1930s and 1940s. However, thanks to the development of the colorimetric methods for measurement of different urinary steroids, in 1956 the first case of CS with cyclicity in urinary corticosteroids was published. In those very years, Gregoy Pincus unravelled the circadian rhythm of corticoadrenal steroids, showing it had a dynamic pattern very different from that occurring in cyclic CS. At the beginning of the 1970s, the concept of “periodic hormonogenesis” was introduced, and up to nowadays numerous cases of cyclic CS have been described. Difficulties still remain to reach a correct diagnosis in a short time and to discover cortisol fluctuations, especially in cases with long periodicity whereas peripheral effects of pulsatile glucocorticoids remain elusive, primarily on bones. For these reasons, it is realistic to believe that in the near future machine learning technologies will allow for building up data-driven classifications of patients with cyclic CS based on integration of their clinical dataset with “omics” matrices from the cellular sources of the secreting lesions, leading to a personalised medical approach.</p>

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La Sindrome di Cushing ciclica

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Roberto Toni,
  • Fulvio Barbaro,
  • Giusy Di Conza,
  • Francesca Pia Quartulli,
  • Salvatore Mosca,
  • Silvio Caravelli,
  • Giammarco Gardini,
  • Massimiliano Mosca

摘要

Initial evidence of intermittence in clinical symptoms of Cushing’s syndrome (CS) dates back to the 1930s and 1940s. However, thanks to the development of the colorimetric methods for measurement of different urinary steroids, in 1956 the first case of CS with cyclicity in urinary corticosteroids was published. In those very years, Gregoy Pincus unravelled the circadian rhythm of corticoadrenal steroids, showing it had a dynamic pattern very different from that occurring in cyclic CS. At the beginning of the 1970s, the concept of “periodic hormonogenesis” was introduced, and up to nowadays numerous cases of cyclic CS have been described. Difficulties still remain to reach a correct diagnosis in a short time and to discover cortisol fluctuations, especially in cases with long periodicity whereas peripheral effects of pulsatile glucocorticoids remain elusive, primarily on bones. For these reasons, it is realistic to believe that in the near future machine learning technologies will allow for building up data-driven classifications of patients with cyclic CS based on integration of their clinical dataset with “omics” matrices from the cellular sources of the secreting lesions, leading to a personalised medical approach.