Association between functional genetic variants in retinoid X receptor<i>-α/γ</i>and the risk of gestational diabetes mellitus in a southern Chinese population

Yu, Xiangyuan; Song, Li-Ping; Zheng, Huaping; Wei, Shudan; Wen, Xiaolan; Huang, Bo; Liu, Dabin

doi:10.1042/bsr20211338

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Such methods are valid to predict GDM at any point of life. Their predictive power is moderate, e.g., with an NER of 0.531–0.552 [ 55 ] and an AUROC of 0.7694 [ 27 ]. Moreover, they are associated with long analysis times.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, they are associated with long analysis times. For example, the strategy of Yu et al [ 55 ] consists of the extraction of genomic DNA, and its analysis by PCR-restriction fragment length polymorphism (PCR-RFLP). The PCR-RFLP protocol alone takes more than 2 h. Likewise, the method of Zulueta et al [ 27 ] consists of genomic DNA extraction, and its analysis by iPLEX-PCR using the MassARRAY system from Agena Bioscience.…”

Section: Discussionmentioning

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%

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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“…Other significant variants in other RA metabolism genes that could impact the severity of alcohol teratogenicity in a PAE individual were also found (Figure 1 and Table 1). One risk variant in RXRG, rs2134095, which affects the splicing of RXRG [41] was found to be enriched in the FASD cohort. RXRG is necessary to mediate RA-regulated transcription; therefore, rs2134095 may potentially hinder RA signaling efficiency in impacted individuals.…”

Section: The Fasd-diagnosed Cohort Has Increased Frequencies Of Ra An...mentioning

confidence: 99%

Risk and Resilience Variants in the Retinoic Acid Metabolic and Developmental Pathways Associated with Risk of FASD Outcomes

McKay,

Petrelli,

Pind

et al. 2024

Biomolecules

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Fetal Alcohol Spectrum Disorder (FASD) is a common neurodevelopmental disorder that affects an estimated 2–5% of North Americans. FASD is induced by prenatal alcohol exposure (PAE) during pregnancy and while there is a clear genetic contribution, few genetic factors are currently identified or understood. In this study, using a candidate gene approach, we performed a genetic variant analysis of retinoic acid (RA) metabolic and developmental signaling pathway genes on whole exome sequencing data of 23 FASD-diagnosed individuals. We found risk and resilience alleles in ADH and ALDH genes known to normally be involved in alcohol detoxification at the expense of RA production, causing RA deficiency, following PAE. Risk and resilience variants were also identified in RA-regulated developmental pathway genes, especially in SHH and WNT pathways. Notably, we also identified significant variants in the causative genes of rare neurodevelopmental disorders sharing comorbidities with FASD, including STRA6 (Matthew–Wood), SOX9 (Campomelic Dysplasia), FDG1 (Aarskog), and 22q11.2 deletion syndrome (TBX1). Although this is a small exploratory study, the findings support PAE-induced RA deficiency as a major etiology underlying FASD and suggest risk and resilience variants may be suitable biomarkers to determine the risk of FASD outcomes following PAE.

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“…Their predictive power is moderate, e.g. with an accuracy of 0.531-0.552 [46] and an AUROC of 0.7694 [47]. Moreover, they are associated with long analysis times.…”

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

confidence: 99%

“…Moreover, they are associated with long analysis times. For example, the strategy of Yu et al [46] consists of the extraction of genomic DNA, and its analysis by PCR-restriction fragment length polymorphism (PCR-RFLP). The PCR-RFLP protocol alone takes more than 2 hours.…”

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

Association between functional genetic variants in retinoid X receptor-α/γand the risk of gestational diabetes mellitus in a southern Chinese population

Cited by 7 publications

References 41 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

Risk and Resilience Variants in the Retinoic Acid Metabolic and Developmental Pathways Associated with Risk of FASD Outcomes

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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