Dataset Information

Lymphangioleiomyomatosis: Searching for potential biomarkers.

ABSTRACT:

Background

Vascular endothelial growth factor-D (VEGF-D) is the most commonly used biomarker for diagnosing lymphangioleiomyomatosis (LAM). However, lung biopsy is often necessary as well; therefore, defining new biomarkers for LAM is crucial. The aim of this study was to describe the diagnostic accuracy of a variety of biomarkers.

Methods

We assessed 13 analytes in serum related to extracellular matrix remodeling, lymphatic involvement and angiogenesis in a cohort of patients with LAM, comparing them with patients with other cystic lung diseases (OCLD) and healthy women. A scoring method based on the cut-point of each VEGF-D and metalloproteinase-2 (MMP-2) was used to evaluate the diagnostic performance of the marker combination.

Results

A total of 97 subjects were recruited: 59 (61%) LAM patients, 18 (19%) OCLD patients, and 20 (20%) healthy female controls. MMP-2 was the only extracellular matrix remodeling biomarker able to differentiate LAM patients from OCLD and healthy patients. Serum MMP-2 was higher in LAM patients [median 578 (465-832) ng/ml] than in patients with OCLD and healthy controls [medians 360 (314-546) and 427 (365-513) ng/ml, respectively (p < 0.0001)]. The area under ROC curve (AUC) of MMP-2 was 0.785 and that of VEGF-D 0.815 (p = 0.6214). The sensitivity/specificity profiles of each biomarker (54/92% for MMP-2, 59/95% for VEGF-D) yielded a composite score (-6.36 + 0.0059 × VEGF-D + 0.0069 × MMP-2) with higher accuracy than each component alone (AUC 0.88 and sensitivity/specificity 79/87%).

Conclusion

Combining MMP-2 and VEGF-D may increase diagnostic accuracy for LAM.

SUBMITTER: Revilla-Lopez E

PROVIDER: S-EPMC9931739 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lymphangioleiomyomatosis: Searching for potential biomarkers.

Revilla-López Eva E Ruiz de Miguel Victoria V López-Meseguer Manuel M Berastegui Cristina C Boada-Pérez Meritxell M Mendoza-Valderrey Alberto A Arjona-Peris Marta M Zapata-Ortega Marta M Monforte Victor V Bravo Carlos C Roman Antonio A Gómez-Ollés Susana S Sáez-Giménez Berta B

Frontiers in medicine 20230202

<h4>Background</h4>Vascular endothelial growth factor-D (VEGF-D) is the most commonly used biomarker for diagnosing lymphangioleiomyomatosis (LAM). However, lung biopsy is often necessary as well; therefore, defining new biomarkers for LAM is crucial. The aim of this study was to describe the diagnostic accuracy of a variety of biomarkers.<h4>Methods</h4>We assessed 13 analytes in serum related to extracellular matrix remodeling, lymphatic involvement and angiogenesis in a cohort of patients wit ...[more]

PMID: 36817769

Similar Datasets

Project description:BackgroundPolycystic ovary syndrome (PCOS) is a heterogeneous endocrinological and metabolic disorder which is the common cause of female infertility. The dysmetabolism displayed in it has not been completely ascertained. Metabonomics may shed light on understanding many small molecule endogenous metabolites and their associated metabolic pathways.ObjectiveTo analyze the different metabolites and related metabolic pathways in follicular fluid and embryo culture fluid of PCOS and non-PCOS groups. Finding markers predictable for clinical outcomes of in vitro fertilization-embryo transfer (IVF-ET) treatment.Population and sample60 women who underwent IVF-ET were selected, including 30 with PCOS and 30 with the fallopian tubal issues only. We collected the first tube follicular fluid (FF) of all patients at the time of oocyte pick up and the waste embryo culture medium (ECM) after D3 high-quality embryo transplant.MethodsAll samples were performed nontargeted Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-QE-MS) analysis. Related metabolic pathways were screened by KEGG annotation. To search potential indicators, the logistic regression was made combined with clinical data.Mean outcome measuresPredictive performance of markers of clinical outcomes (pregnancy rate, delivery rate, live birth rate, miscarriage rate) of assisted reproductive technology (ART).ResultsComparing the PCOS group against the non-PCOS group, we found 11 significantly different metabolites in the FF and 56 in the ECM. There are a total of 11 kinds of biomarkers associated with clinical outcomes. Androsterone sulfate, Glycerophosphocholine, and Elaidic carnitine seem robust to predict the abortion rate of the PCOS group, with an AUC of 0.941, 0.933, 0.933, respectively. The glycerol phospholipid metabolic pathway is enriched in both the follicular fluid and embryo culture fluid.ConclusionsThe differential metabolites were mainly a variety of lipids. Some of them can predict clinical outcomes to a certain extent.

Project description:BackgroundProgress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.MethodsWe leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.ResultsAlthough we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.ConclusionsConnectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Dataset Information

Lymphangioleiomyomatosis: Searching for potential biomarkers.

Background

Methods

Results

Conclusion

Publications

Lymphangioleiomyomatosis: Searching for potential biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets