Clinical, metabolomic, and proteomic profiles associated with reproductive outcomes in unexplained recurrent pregnancy loss - Scorecard - MDSpire

Clinical, metabolomic, and proteomic profiles associated with reproductive outcomes in unexplained recurrent pregnancy loss

  • By

  • Zicheng Song

  • Yishi Jiang

  • Zhixing Zhu

  • Yan Che

  • Shuping Li

  • Aimin Zhao

  • July 10, 2026

  • 0 min

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Clinical Scorecard: Profiles of Clinical, Metabolomic, and Proteomic Factors Linked to Reproductive Outcomes in Cases of Unexplained Recurrent Pregnancy Loss

At a Glance

CategoryDetail
ConditionUnexplained Recurrent Pregnancy Loss (URPL)
Key MechanismsHormonal and metabolic factors influencing conception and early pregnancy maintenance.
Target PopulationWomen aged 18 to 45 years with a history of unexplained recurrent pregnancy loss.
Care SettingReproductive Immunology Clinic

Key Highlights

  • 66.4% of women with URPL conceived during the follow-up period.
  • Higher testosterone levels were associated with lower probability of conception.
  • Among women who conceived, higher prolactin levels correlated with increased odds of early pregnancy loss.
  • Lower bile acid-related metabolites were observed in cases of early pregnancy loss.
  • 7α,12α-dihydroxy-3-oxocholest-4-en-27-oic acid was identified as a candidate metabolomic factor associated with reproductive outcomes.

Guideline-Based Recommendations

Diagnosis

  • Evaluate established causes of recurrent pregnancy loss, including genetic and anatomical factors.

Management

  • Consider hormonal and metabolic factors in the management of URPL.

Monitoring & Follow-up

  • Monitor hormonal levels, including testosterone and prolactin, in women with URPL.

Risks

  • Higher testosterone and prolactin levels may indicate risks for conception and early pregnancy loss.

Patient & Prescribing Data

Women with unexplained recurrent pregnancy loss seeking conception.

Empiric therapeutic interventions may be driven by limited prognostic discrimination.

Clinical Best Practices

  • Utilize multi-omics profiling to assess biological complexity in URPL.
  • Conduct prospective studies to evaluate baseline clinical characteristics before conception.

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