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Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
(2022)
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.
Introduction
Malnutrition in cancer patients often remains undetected and underestimated in clinical practice despite studies revealing prevalences from 20 to 70%. Therefore, this study aimed to identify patient groups exposed to an increased nutritional risk in a university oncological outpatient center.
Methods
Between May 2017 and January 2018 we screened oncological patients there using the malnutrition universal screening tool (MUST). Qualitative data were collected by a questionnaire to learn about patients’ individual information needs and changes in patients’ diets and stressful personal nutrition restrictions.
Results
We included 311 patients with various cancers. 20.3% (n = 63) were found to be at high risk of malnutrition, 16.4% (n = 51) at moderate risk despite a mean body mass index (BMI) of 26.5 ± 4.7 kg/m2. The average age was 62.7 (± 11.8) with equal gender distribution (52% women, n = 162). In 94.8% (n = 295) unintended weight loss led to MUST scoring. Patients with gastrointestinal tumors (25%, n = 78) and patients >65 years (22%, n = 68) were at higher risk. Furthermore, there was a significant association between surgery or chemotherapy within six months before survey and a MUST score ≥2 (OR = 3.6). Taste changes, dysphagia, and appetite loss were also particular risk factors (OR = 2.3–3.2). Young, female and normal-weight patients showed most interest in nutrition in cancer. However, only 38% (n = 118) had a nutritional counseling.
Conclusion
This study confirms that using the MUST score is a valid screening procedure to identify outpatients at risk of developing malnutrition. Here one in five was at high risk, but only 1% would have been detected by BMI alone. Therefore, an ongoing screening procedure with meaningful parameters should be urgently implemented into the clinical routine of cancer outpatients as recommended in international guidelines.
Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug–drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim\(^®\) Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (C\(_{max}\)) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes.
Background
Anderson–Fabry disease (FD) is an X-linked lysosomal storage disorder with varying organ involvement and symptoms, depending on the underlying mutation in the alpha-galactosidase A gene (HGNC: GLA). With genetic testing becoming more readily available, it is crucial to precisely evaluate pathogenicity of each genetic variant, in order to determine whether there is or might be not a need for FD-specific therapy in affected patients and relatives at the time point of presentation or in the future.
Methods
This case series investigates the clinical impact of the specific GLA gene variant c.376A>G (p.Ser126Gly) in five (one heterozygous and one homozygous female, three males) individuals from different families, who visited our center between 2009 and 2021. Comprehensive neurological, nephrological and cardiac examinations were performed in all cases. One patient received a follow-up examination after 12 years.
Results
Index events leading to suspicion of FD were mainly unspecific neurological symptoms. However, FD-specific biomarkers, imaging examinations (i.e., brain MRI, heart MRI), and tissue-specific diagnostics, including kidney and skin biopsies, did not reveal evidence for FD-specific symptoms or organ involvement but showed normal results in all cases. This includes findings from 12-year follow-up in one patient with renal biopsy.
Conclusion
These findings suggest that p.Ser126Gly represents a benign GLA gene variant which per se does not cause FD. Precise clinical evaluation in individuals diagnosed with genetic variations of unknown significance should be performed to distinguish common symptoms broadly prevalent in the general population from those secondary to FD.
Impact of cardiovascular risk factors on myocardial work-insights from the STAAB cohort study
(2022)
Myocardial work is a new echocardiography-based diagnostic tool, which allows to quantify left ventricular performance based on pressure-strain loops, and has been validated against invasively derived pressure-volume measurements. Myocardial work is described by its components (global constructive work [GCW], global wasted work [GWW]) and indices (global work index [GWI], global work efficiency [GWE]). Applying this innovative concept, we characterized the prevalence and severity of subclinical left ventricular compromise in the general population and estimated its association with cardiovascular (CV) risk factors. Within the Characteristics and Course of Heart Failure STAges A/B and Determinants of Progression (STAAB) cohort study we comprehensively phenotyped a representative sample of the population of Würzburg, Germany, aged 30-79 years. Indices of myocardial work were determined in 1929 individuals (49.3% female, mean age 54 ± 12 years). In multivariable analysis, hypertension was associated with a mild increase in GCW, but a profound increase in GWW, resulting in higher GWI and lower GWE. All other CV risk factors were associated with lower GCW and GWI, but not with GWW. The association of hypertension and obesity with GWI was stronger in women. We conclude that traditional CV risk factors impact selectively and gender-specifically on left ventricular myocardial performance, independent of systolic blood pressure. Quantifying active systolic and diastolic compromise by derivation of myocardial work advances our understanding of pathophysiological processes in health and cardiac disease.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.
The immune system plays a vital role in maintaining tissue integrity and organismal homeostasis. The sudden stress caused by myocardial infarction (MI) poses a significant challenge for the immune system: it must quickly substitute dead myocardial with fibrotic tissue while controlling overt inflammatory responses. In this review, we will discuss the central role of myocardial regulatory T-cells (Tregs) in orchestrating tissue repair processes and controlling local inflammation in the context of MI. We herein compile recent advances enabled by the use of transgenic mouse models with defined cardiac antigen specificity, explore whole-heart imaging techniques, outline clinical studies and summarize deep-phenotyping conducted by independent labs using single-cell transcriptomics and T-cell repertoire analysis. Furthermore, we point to multiple mechanisms and cell types targeted by Tregs in the infarcted heart, ranging from pro-fibrotic responses in mesenchymal cells to local immune modulation in myeloid and lymphoid lineages. We also discuss how both cardiac-specific and polyclonal Tregs participate in MI repair. In addition, we consider intriguing novel evidence on how the myocardial milieu takes control of potentially auto-aggressive local immune reactions by shaping myosin-specific T-cell development towards a regulatory phenotype. Finally, we examine the potential use of Treg manipulating drugs in the clinic after MI.
Background: RET (rearranged during transfection) variants are the most prevalent oncogenic events in medullary thyroid cancer (MTC). In advanced disease, multi-tyrosine kinase inhibitors (MKIs) cabozantinib and vandetanib are the approved standard treatment irrespective of RET status. The actual outcome of patients with RET-positive MTC treated with MKIs is ill described. Methods: We here retrospectively determined the RET oncogene variant status with a targeted DNA Custom Panel in a prospectively collected cohort of 48 patients with advanced MTC treated with vandetanib and/or cabozantinib at four German referral centers. Progression-free survival (PFS) and overall survival (OS) probabilities were estimated using the Kaplan-Meier method. Results: In total, 44/48 (92%) patients had germline or somatic RET variants. The M918T variant was found in 29/44 (66%) cases. In total, 2/32 (6%) patients with a somatic RET variant had further somatic variants, while in 1/32 (3%) patient with a germline RET variant, additional variants were found. Only 1/48 (2%) patient had a pathogenic HRAS variant, and no variants were found in 3 cases. In first-line treatment, the median OS was 53 (95% CI (95% confidence interval), 32–NR (not reached); n = 36), and the median PFS was 21 months (12–39; n = 33) in RET-positive MTC patients. In second-line treatment, the median OS was 18 (13–79; n = 22), and the median PFS was 3.5 months (2–14; n = 22) in RET-positive cases. Conclusions: RET variants were highly prevalent in patients with advanced MTC. The treatment results in RET-positive cases were similar to those reported in unselected cohorts.
Purpose
T\(_{1P}\) dispersion quantification can potentially be used as a cardiac magnetic resonance index for sensitive detection of myocardial fibrosis without the need of contrast agents. However, dispersion quantification is still a major challenge, because T\(_{1P}\) mapping for different spin lock amplitudes is a very time consuming process. This study aims to develop a fast and accurate T\(_{1P}\) mapping sequence, which paves the way to cardiac T1ρ dispersion quantification within the limited measurement time of an in vivo study in small animals.
Methods
A radial spin lock sequence was developed using a Bloch simulation-optimized sampling pattern and a view-sharing method for image reconstruction. For validation, phantom measurements with a conventional sampling pattern and a gold standard sequence were compared to examine T\(_{1P}\) quantification accuracy. The in vivo validation of T\(_{1P}\) mapping was performed in N = 10 mice and in a reproduction study in a single animal, in which ten maps were acquired in direct succession. Finally, the feasibility of myocardial dispersion quantification was tested in one animal.
Results
The Bloch simulation-based sampling shows considerably higher image quality as well as improved T\(_{1P}\) quantification accuracy (+ 56%) and precision (+ 49%) compared to conventional sampling. Compared to the gold standard sequence, a mean deviation of - 0.46 ± 1.84% was observed. The in vivo measurements proved high reproducibility of myocardial T\(_{1P}\) mapping. The mean T\(_{1P}\) in the left ventricle was 39.5 ± 1.2 ms for different animals and the maximum deviation was 2.1% in the successive measurements. The myocardial T\(_{1P}\) dispersion slope, which was measured for the first time in one animal, could be determined to be 4.76 ± 0.23 ms/kHz.
Conclusion
This new and fast T\(_{1P}\) quantification technique enables high-resolution myocardial T\(_{1P}\) mapping and even dispersion quantification within the limited time of an in vivo study and could, therefore, be a reliable tool for improved tissue characterization.