Fell, Vanessa

Abstract Context Patients with mild autonomous cortisol secretion (MACS) have higher rates of vertebral fractures compared to patients with non-functioning adrenal nodules, yet bone density is not always reduced. Objective To characterize the effect of MACS on bone density, metabolism, and microstructure as measured by high-resolution peripheral quantitative computed tomography (HR-pQCT). Design Cross-sectional study, 2019-2022 Setting Tertiary referral center Participants Seventy-five patients with MACS and 75 age-, sex-, and gonadal status–matched referent subjects. Measurements Bone turnover markers, dual-energy x-ray absorptiometry (DXA) scan, and HR-pQCT. Outcomes Areal and volumetric bone mineral density (aBMD, vBMD), trabecular bone score (TBS), microstructural parameters including trabecular bone volume to total volume (BV/TV) ratio. Results While no differences in aBMD were observed at any skeletal site, patients with MACS had decreased TBS (1.389 vs 1.475, P < .001) and lower osteocalcin concentration (17.1 vs 19.5 ng/mL, P = .030) than referent subjects. On HR-pQCT at the tibia, patients with MACS had reduced trabecular BV/TV (0.220 vs 0.233, P = .015) compared to referent subjects. On multivariable analysis, MACS was associated with lower TBS (odds ratio [OR] per SD decrease 2.87 [1.65-5.00]), lower trabecular BV/TV at the radius (OR per SD decrease 2.58 [1.33-5.00]), and lower trabecular BV/TV at the tibia (OR per SD decrease 4.06 [2.10-7.86]). Conclusion Patients with MACS have microstructural deterioration that may not be evident on routine DXA aBMD testing.

Abstract Context Data on cognition in patients with primary aldosteronism (PA) are scarce. Mild autonomous cortisol secretion (MACS), defined based on post-dexamethasone cortisol >1.8 mcg/dL, is diagnosed in up to 30% of patients with PA. Objectives (1) Assess cognition in patients with PA vs referent subjects with hypertension and (2) determine differences in cognition between patients with PA and those with MACS. Methods A prospective, cross-sectional study, 2019-2023. Cognition was assessed by National Institute of Health Toolbox Cognition Battery and reported as T-scores corrected for age, sex, race, and education. Only subjects with hypertension were included. Results Cognitive assessment was performed in 52 patients with PA (including 11 patients with both PA and MACS, median age of 51.0 years, 30 (57.7%) women), 47 patients with MACS (median age of 57.0 years, 31 (66.0%) women), and 52 referent subjects (median age of 66.1 years, 30 (57.7%) women). When compared to referents, and after adjusting for age, smoking, systolic blood pressure, and alcohol use, patients with PA had lower total cognition (β = −5.7, P = .023), notably fluid cognition (β = −6.8, P = .017), including attention and executive function (β = −5.6, P = .012) and cognitive flexibility and executive function (β = −8.2, P = .006). When compared to patients with MACS, patients with PA had a higher processing speed (β = 9.0, P = .003). No differences in cognition were found between patients with PA with and without MACS. Conclusion Patients with PA demonstrated lower cognition compared with referents and processing speed compared with MACS. Future studies should characterize the impact of MACS on the cognition of patients with PA.

BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limited availability, and often difficult interpretation pose challenges. This study explores the utility of circulating microRNAs (miRNAs) and machine learning in distinguishing between unilateral and bilateral forms of primary aldosteronism. METHODS: MiRNA profiling was conducted on plasma samples from 18 patients with primary aldosteronism taken during adrenal venous sampling on an Illumina MiSeq platform. Bioinformatics and machine learning identified 9 miRNAs for validation by reverse transcription real-time quantitative polymerase chain reaction. Validation was performed on a cohort consisting of 108 patients with known subdifferentiation. A 30-patient subset of the validation cohort involved both adrenal venous sampling and peripheral, the rest only peripheral samples. A neural network model was used for feature selection and comparison between adrenal venous sampling and peripheral samples, while a deep-learning model was used for classification. RESULTS: Our model identified 10 miRNA combinations achieving >85% accuracy in distinguishing unilateral primary aldosteronism and bilateral adrenal hyperplasia on a 30-sample subset, while also confirming the suitability of peripheral samples for analysis. The best model, involving 6 miRNAs, achieved an area under curve of 87.1%. Deep learning resulted in 100% accuracy on the subset and 90.9% sensitivity and 81.8% specificity on all 108 samples, with an area under curve of 86.7%. CONCLUSIONS: Machine learning analysis of circulating miRNAs offers a minimally invasive alternative for primary aldosteronism lateralization. Early identification of bilateral adrenal hyperplasia could expedite treatment initiation without the need for further localization, benefiting both patients and health care providers.