Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios

Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies

Psychopathological characteristics in patients with arginine vasopressin deficiency (central diabetes insipidus) and primary polydipsia compared to healthy controls

Objective: Distinguishing arginine vasopressin deficiency (AVP-D; central diabetes insipidus) from primary polydipsia (PP), commonly referred to as psychogenic polydipsia, is challenging. Psychopathologic findings, commonly used for PP diagnosis in clinical practice, are rarely evaluated in AVP-D patients, and no comparative data between the two conditions currently exist. Design: Data from two studies involving 82 participants [39 AVP-D, 28 PP, and 15 healthy controls (HC)]. Methods: Psychological evaluations were conducted using standardized questionnaires measuring anxiety [State-Trait Anxiety Inventory (STAI)], alexithymia [Toronto Alexithymia Scale (TAS-20)], depressive symptoms (Beck’s Depression Inventory-II (BDI-II), and overall mental health [Short Form-36 Health Survey (SF-36)]. Higher STAI, TAS-20, and BDI-II scores suggest elevated anxiety, alexithymia, and depression, while higher SF-36 scores signify better overall mental health. Results: Compared to HC, patients with AVP-D and PP showed higher levels of anxiety (HC 28 points [24–31] vs AVP-D 36 points [31–45]; vs PP 38 points [33–46], P &lt; .01), alexithymia (HC 30 points [29–37] vs AVP-D 43 points [35–54]; vs PP 46 points [37–55], P &lt; .01), and depression (HC 1 point [0–2] vs AVP-D 7 points [4–14]; vs PP 7 points [3–13], P &lt; .01). Levels of anxiety, alexithymia, and depression showed no difference between both patient groups (P = .58, P = .90, P = .50, respectively). Compared to HC, patients with AVP-D and PP reported similarly reduced self-reported overall mental health scores (HC 84 [68–88] vs AVP-D 60 [52–80], P = .05; vs PP 60 [47–74], P &lt; .01).Conclusion: This study reveals heightened anxiety, alexithymia, depression, and diminished overall mental health in patients with AVP-D and PP. The results emphasize the need for careful interpretation of psychopathological characteristics to differentiate between AVP-D and PP.</p

Psychopathological characteristics in patients with arginine vasopressin deficiency (central diabetes insipidus) and primary polydipsia compared to healthy controls

Objective: Distinguishing arginine vasopressin deficiency (AVP-D; central diabetes insipidus) from primary polydipsia (PP), commonly referred to as psychogenic polydipsia, is challenging. Psychopathologic findings, commonly used for PP diagnosis in clinical practice, are rarely evaluated in AVP-D patients, and no comparative data between the two conditions currently exist. Design: Data from two studies involving 82 participants [39 AVP-D, 28 PP, and 15 healthy controls (HC)]. Methods: Psychological evaluations were conducted using standardized questionnaires measuring anxiety [State-Trait Anxiety Inventory (STAI)], alexithymia [Toronto Alexithymia Scale (TAS-20)], depressive symptoms (Beck’s Depression Inventory-II (BDI-II), and overall mental health [Short Form-36 Health Survey (SF-36)]. Higher STAI, TAS-20, and BDI-II scores suggest elevated anxiety, alexithymia, and depression, while higher SF-36 scores signify better overall mental health. Results: Compared to HC, patients with AVP-D and PP showed higher levels of anxiety (HC 28 points [24–31] vs AVP-D 36 points [31–45]; vs PP 38 points [33–46], P &lt; .01), alexithymia (HC 30 points [29–37] vs AVP-D 43 points [35–54]; vs PP 46 points [37–55], P &lt; .01), and depression (HC 1 point [0–2] vs AVP-D 7 points [4–14]; vs PP 7 points [3–13], P &lt; .01). Levels of anxiety, alexithymia, and depression showed no difference between both patient groups (P = .58, P = .90, P = .50, respectively). Compared to HC, patients with AVP-D and PP reported similarly reduced self-reported overall mental health scores (HC 84 [68–88] vs AVP-D 60 [52–80], P = .05; vs PP 60 [47–74], P &lt; .01).Conclusion: This study reveals heightened anxiety, alexithymia, depression, and diminished overall mental health in patients with AVP-D and PP. The results emphasize the need for careful interpretation of psychopathological characteristics to differentiate between AVP-D and PP.</p

Machine learning-based clinical outcome prediction in surgery for acromegaly

Purpose Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. Methods Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. Results The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59–0.88) for GTR, 0.63 (0.40–0.82) for BR, as well as 0.77 (0.62–0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. Conclusions Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization

Arginine or Hypertonic Saline-Stimulated Copeptin to Diagnose AVP Deficiency

BACKGROUND Distinguishing between arginine vasopressin (AVP) deficiency and primary polydipsia is challenging. Hypertonic saline-stimulated copeptin has been used to diagnose AVP deficiency with high accuracy but requires close sodium monitoring. Arginine-stimulated copeptin has shown similar diagnostic accuracy but with a simpler test protocol. However, data are lacking from a head-to-head comparison between arginine-stimulated copeptin and hypertonic saline-stimulated copeptin in the diagnosis of AVP deficiency. METHODS In this international, noninferiority trial, we assigned adult patients with polydipsia and hypotonic polyuria or a known diagnosis of AVP deficiency to undergo diagnostic evaluation with hypertonic-saline stimulation on one day and with arginine stimulation on another day. Two endocrinologists independently made the final diagnosis of AVP deficiency or primary polydipsia with use of clinical information, treatment response, and the hypertonic-saline test results. The primary outcome was the overall diagnostic accuracy according to prespecified copeptin cutoff values of 3.8 pmol per liter after 60 minutes for arginine and 4.9 pmol per liter once the sodium level was more than 149 mmol per liter for hypertonic saline. RESULTS Of the 158 patients who underwent the two tests, 69 (44%) received the diagnosis of AVP deficiency and 89 (56%) received the diagnosis of primary polydipsia. The diagnostic accuracy was 74.4% (95% confidence interval [CI], 67.0 to 80.6) for arginine-stimulated copeptin and 95.6% (95% CI, 91.1 to 97.8) for hypertonic saline-stimulated copeptin (estimated difference, -21.2 percentage points; 95% CI, -28.7 to -14.3). Adverse events were generally mild with the two tests. A total of 72% of the patients preferred testing with arginine as compared with hypertonic saline. Arginine-stimulated copeptin at a value of 3.0 pmol per liter or less led to a diagnosis of AVP deficiency with a specificity of 90.9% (95% CI, 81.7 to 95.7), whereas levels of more than 5.2 pmol per liter led to a diagnosis of primary polydipsia with a specificity of 91.4% (95% CI, 83.7 to 95.6). CONCLUSIONS Among adult patients with polyuria polydipsia syndrome, AVP deficiency was more accurately diagnosed with hypertonic saline-stimulated copeptin than with arginine-stimulated copeptin. (Funded by the Swiss National Science Foundation; CARGOx ClinicalTrials.gov number, NCT03572166.)

Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension

Introduction: Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing’s syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Methods: Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. Results: After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites – 18 in PPGL, 15 in CS, and 23 in PA – were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Discussions: Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL’s metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.</p

Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension

Introduction: Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing’s syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Methods: Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. Results: After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites – 18 in PPGL, 15 in CS, and 23 in PA – were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Discussions: Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL’s metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.</p