Clinical trial NCT06500000: The Plasma Metabolomics Profiling of Primary Aldosteronism
Official title: The Plasma Metabolomics Profiling of Primary Aldosteronism
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Primary Aldosteronism
Essential Hypertension
Idiopathic Aldosteronism
Aldosterone-producing Adenoma
Lead sponsor: Third Military Medical University
Sponsor class: OTHER
First posted: 2024-07-15
Enrollment: 95
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: 75 hypertensive patients diagnosed with PA and 20 additional age-, sex-, body mass index (BMI)- and waist circumference- matched essential hypertensive patients were recruited from the Department of Hypertension and Endocrinology, Army Medical Center, Army Medical University, Chongqing, China, from August to October 2022. Informed consent was obtained from each participant.

Brief summary: Primary aldosteronism (PA), characterized by overt renin-independent aldosterone production, is the most common form endocrine hypertension. Compared with blood pressure-matched cases of essential hypertension (EH), PA is associated with a higher risk of cardiovascular morbidity and mortality. It is estimated that PA affects at least 10% of hypertensive patients and up to 25% of treatment-resistant hypertension. The major subtypes of PA are comprised of bilateral idiopathic hyperaldosteronism (IHA) and unilateral aldosterone-producing adenoma (APA). The screening, confirmatory testing, and subtype differentiation of PA for therapeutic management is a multi-step and complex process, resulting in low screening rates and poor clinical recognition.

PA is an independent risk factor for metabolic morbidity. Metabolomic profiling is a relatively new strategy for the diagnosis and prognosis of disease through identification and quantification of various metabolites. In the current study, we aimed to investigate the potential biomakers for discriminating PA from EH, as well as subtype classification for PA, by untargeted metabolomics.


Eligibility criteria: Inclusion Criteria:

* According to 2010 Chinese guidelines for the management of Essential hypertension (EH), EH was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, and use of antihypertensive medicine within 2 weeks and excluded from PA through ARR or confirmatory testing.
* Patients were confirmed to be diagnosed with Primary aldosteronism (PA) in accordance with the Endocrine Society Clinical Practice Guideline criteria. Patients with an aldosterone-to-renin ratio (ARR) \> 3.7 (ng/dL) further conformed with one of the following confirmatory tests: saline infusion test or captopril-inhibition test. Adrenal CT scans and Adrenal venous sampling (AVS) were performed for PA subtype classification.
* Patients with idiopathic hyperaldosteronism (IHA) were determined based on the absence of obvious adenoma on adrenal CT and bilateral aldosterone overproduction.
* Patients with aldosterone-producing adenoma (APA) were identified based on macroadenoma \>1 cm on adrenal CT, unilateral hypersecretion of aldosterone, and pathological confirmation.
* Signed informed consent and agreed to participate in this study.

Exclusion Criteria:

* other subtypes of secondary hypertension, including renal hypertension, renovascular hypertension, and adrenal hypertension (i.e., pheochromocytoma and Cushing syndrome).
* adrenal cortical carcinoma
* acute infection at the time of assessment
* severe cardiovascular or cerebrovascular disease, liver or renal dysfunction, tumors, autoimmune disease or mental disorders.
* history of adrenalectomy
* alcohol abuse or pregnancy

Primary outcomes:
measure: The potential biomarkers for primary aldosteronism diagnosis via untargeted metabolomics
timeFrame: 4 months
measure: The potential biomarkers for primary aldosteronism subtype classification via untargeted metabolomics
timeFrame: 4 months
measure: The predictive models for PA diagnosis and subtype classification by machine learning
timeFrame: 4 months
Secondary outcomes:
Other outcomes:
Locations:
city: Chongqing
country: China
facility: China Chongqing The third hospital affiliated to the Third Millitary Medical University
state: Chongqing
Study officials:
References: