First trimester metabolomics 1H-NMR study of the urinary profile predicts gestational diabetes mellitus development in obese women

Piras, Cristina; Neri, Isabella; Pintus, Roberta; Noto, Antonio; Petrella, Elisabetta; Monari, Francesca; Dessì, Angelica; Fanos, Vassilios; Atzori, Luigi; Facchinetti, Fabio

doi:10.1080/14767058.2021.1970133

Cited by 11 publications

(10 citation statements)

References 36 publications

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“…Other articles use NMR spectroscopy metabolomics-based methods to predict GDM at different points in pregnancy [ 23 , 52 , 53 , 54 ]. Their predictive power is variable but tends to be better at the time of GDM diagnosis than earlier, e.g., with AUROCs of 0.62 and 0.59, respectively, in the study of McBride et al [ 52 ], and NERs of 0.695–0.885 and 0.635–0.825, respectively, in the study of Pinto et al [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…Their predictive power is variable but tends to be better at the time of GDM diagnosis than earlier, e.g., with AUROCs of 0.62 and 0.59, respectively, in the study of McBride et al [ 52 ], and NERs of 0.695–0.885 and 0.635–0.825, respectively, in the study of Pinto et al [ 53 ]. Studies that focus particularly on early detection of GDM achieve moderate predictive power, with AUROCs of 0.610–0.719 [ 54 ] and 0.796 [ 23 ]. Papers presenting this kind of strategy do not usually mention details about duration of analysis in the methodology section.…”

Section: Discussionmentioning

confidence: 99%

“…This review revealed that many of the predictive models for this pregnancy disease derive from data acquired by instrumental techniques, such as liquid or gas chromatography coupled to mass spectrometry (LC-MS or GC-MS, respectively), nuclear magnetic resonance (NMR) spectroscopy, and polymerase chain reaction (PCR), among others [ 17 ]. More recently, new articles have been published in the context of GDM diagnosis based on the same techniques, i.e., LC-MS [ 18 , 19 , 20 , 21 ], GC-MS [ 22 ], NMR spectroscopy [ 23 ], and PCR [ 24 , 25 , 26 , 27 ]. Methods based on these techniques are very time consuming, as they require tedious sample preparation procedures or prolonged instrumental runs.…”

Section: Introductionmentioning

confidence: 99%

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Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Mennickent,

Romero-Albornoz,

Gutiérrez-Vega

et al. 2024

Biomedicines

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Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Mennickent,

Romero-Albornoz,

Gutiérrez-Vega

et al. 2024

Biomedicines

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“…Modifications of hippurate concentrations in urine samples have been previously associated with several conditions, including metabolic disorders and systemic infectious diseases [ 30 , 31 , 32 , 33 ]. As an example, patients with tuberculosis infection are characterized by a lower concentration of urine hippurate, probably due to the synthesis of aromatic amino acids such as tryptophan, tyrosine, and phenylalanine [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

First-Void Urine Microbiome in Women with Chlamydia trachomatis Infection

Gaspari

Ceccarani

Orioni

et al. 2022

IJMS

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Background: Chlamydia trachomatis (CT) is the agent of the most common bacterial sexually transmitted infection worldwide. Until now, little information is available about the microbial composition of urine samples during CT urethritis. Therefore, in this study, we characterized the microbiome and metabolome profiles of first-void urines in a cohort of women with CT urethral infection attending an STI clinic. Methods: Based on CT positivity by nucleic acid amplification techniques on urine samples, the enrolled women were divided into two groups, i.e., “CT-negative” (n = 21) and “CT-positive” (n = 11). Urine samples were employed for (i) the microbiome profile analysis by means of 16s rRNA gene sequencing and (ii) the metabolome analysis by 1H-NMR. Results: Irrespective of CT infection, the microbiome of first-void urines was mainly dominated by Lactobacillus, L. iners and L. crispatus being the most represented species. CT-positive samples were characterized by reduced microbial biodiversity compared to the controls. Moreover, a significant reduction of the Mycoplasmataceae family—in particular, of the Ureaplasma parvum species—was observed during CT infection. The Chlamydia genus was positively correlated with urine hippurate and lactulose. Conclusions: These data can help elucidate the pathogenesis of chlamydial urogenital infections, as well as to set up innovative diagnostic and therapeutic approaches.

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“…Other articles use NMR metabolomics-based methods to predict GDM at different points in pregnancy [40][41][42][43][44]. Their predictive power is variable, but tends to be better at the time of GDM diagnosis than earlier, e.g.…”

Section: Nir Data-based Prediction Has Advantages Over Other Instrume...mentioning

confidence: 99%

Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy

Mennickent,

Romero-Albornoz,

Gutiérrez-Vega

et al. 2024

Preprint

View full text Add to dashboard Cite

Gestational diabetes mellitus (GDM) is a hyperglycemia state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, poorly reproducible, and tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to get a result. Near-infrared (NIR) spectroscopy, a simple, fast and low-cost analytical technique has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and time of analysis. Serum samples from the first (before GDM diagnosis) and the second (at the time of GDM diagnosis) trimester of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best first and second trimester models got areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR spectroscopy-based methods for the prediction of GDM. The developed methods allow to predict GDM from 10 µL of serum in only 32 minutes. They are simple and fast, and have a great potential to be applied in clinical practice, especially as alternative screening tools to the OGTT at the time of GDM diagnosis.

show abstract

First trimester metabolomics 1H-NMR study of the urinary profile predicts gestational diabetes mellitus development in obese women

Cited by 11 publications

References 36 publications

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

First-Void Urine Microbiome in Women with Chlamydia trachomatis Infection

Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy

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