A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies

Troisi, Jacopo; Landolfi, Annamaria; Sarno, Laura; Richards, Sean; Symes, Steven A.; Adair, David; Ciccone, Carla; Scala, Giovanni; Martinelli, Pasquale; Guida, Maurizio

doi:10.1007/s11306-018-1370-8

Cited by 26 publications

(20 citation statements)

References 41 publications

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“…Metabolome extraction, purification and derivatization was carried out with the MetaboPrep GC kit (Theoreo srl, Montecorvino Pugliano [SA], Italy) according to the manufacturer’s instructions. Details regarding metabolite extraction and the overall analytical scheme, including QA/QC sample analyses, were reported in Troisi et al [24, 66, 67].…”

Section: Methodsmentioning

confidence: 99%

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

et al. 2019

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BackgroundPolycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet.ResultsFifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment.PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls.ConclusionOur pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan.Trial registrationClinicalTial.gov, NCT03608813. Registered 1st August 2018 - Retrospectively registered, .Electronic supplementary materialThe online version of this article (10.1186/s13048-019-0500-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

et al. 2019

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“…Only a few studies had prediction timing up to the puerperal or neonatal period for LR (2/77, 3%) [74,85], non-LR (3/50, 6%) [114,129,149], or both algorithms (2/15, 13%) [162,168]. This is because some predictors were assessed after delivery, whereas our review question demanded those be assessed up to delivery.…”

Section: Characteristics Of the Studiesmentioning

confidence: 99%

“…[61][62][63]71,98,104,110,113,115,[117][118][119]128,134,141,142,145,147,149,155,157,158,169], 117 (82.4%) high ROB studies [29-37,39-60,64-70,72-97,99-103,105-109,111,112,114,116,120-123, 125-127,129-133,135-140,143,144,146,148, 150-154,156,159-168,170], and 1 (0.7%) unclear ROB study (Table 3) [124]. Among the low ROB studies, the categories of…”

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confidence: 99%

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

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Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

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“…Instrumental analyses were performed with a GC-MS system (GC-2010 Plus gas chromatograph and QP2010SE mass spectrometer; Shimadzu Corp., Kyoto, Japan). The analytical details are reported in Troisi et al [21][22][23][24][25][26].…”

Section: Metabolite Extraction Derivatization and Analysismentioning

confidence: 99%

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients 

Troisi¹,

Cavallo²,

Masarone³

et al. 2020

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Background: One of the most challenging aspects related to Covid-19 is to establish which are the characteristics that may explain clinical variability among patients. To achieve this objective, we compared serum metabolomic profiles of asymptomatic SARS-CoV-2 carriers to patients affected by mild and severe symptoms.Methods: All subjects underwent quantitative assays for anti-SARS-CoV-2 antibody detection. Serum samples for metabolomic analysis were obtained from 109 healthy controls; 15 seroconverted, asymptomatic subjects (AS); 16 Covid-19 patients with mild symptomatology (MI) and 12 patients with severe symptomatology (SE). Metabolites were identified using Mass Spectrometry coupled to Gas Chromatography. Uni- and multivariate approaches were used to select the most relevant metabolites. Results: Anti SARS-CoV-2 IgG showed an increasing trend from controls to asymptomatic and mild severity patients. Tyrosine, phenylalanine, acetoacetate and fumarate showed a decreased concentration in MI compared to both CTRL and AS subjects; on the contrary, mandelic acid showed an increased concertation. The shortest route among these metabolites resulted the Tyrosine metabolism. Aspartic acid, alanine, isoleucine, valine and proline were decreased in MI patients, while methionine and oxo-leucine resulted increased. An increased concertation of fatty acids lauric and myristic acid, phospholipids (phosphatidyl myo-inositol and lyso-phosphatidyl inositol) and histamine were recorded in MI and SE. Conclusion: The reported metabolite levels could be explained by an increase production of L-DOPA, resulting in a following increased production of noradrenaline and adrenaline that could, at least partially, explain the different clinical presentation of the infection.

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A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies

Cited by 26 publications

References 41 publications

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients

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A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies

Cited by 26 publications

References 41 publications

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients&nbsp;

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Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients