scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2020 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pregnant Women ◽  
Clinical Relevance ◽  
Chinese Population ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
New Findings

Abstract Background Metabolomics has provided new insights into the pathology of GDM and has revealed potential biomarkers related to GDM; however, previous findings lack consistency, and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods Using ultra-performance liquid chromatography coupled to tandem mass spectrometry system, metabolites were quantitated with the serum samples of GDM and normal pregnancies in second- and third-trimester stages in a Chinese population. Samples were obtained from 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester at the University of Hong Kong-Shenzhen Hospital. Both function and pathway analyses were applied to find biological roles involved in the two sets of metabolites. We then identified the trimester stage-specific GDM metabolite biomarkers by combining a few machine learning approaches, and the logistic regression models based on selected biomarkers were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.Results The associations of metabolites with GDM were evaluated, with 57 differentially expressed metabolites (DEMs) in the second-trimester group and 72 DEMs in the third-trimester group. The DEMs, functions, and pathways showed remarkable differences between second- and third-trimester groups. Thus, stage specific GDM biomarkers were further identified, and the logistic regression models for these metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups, respectively. The significant associations between the discovered DEMs/biomarkers and GDM-related indices suggest their clinical relevance with GDM and hyperglycemia in pregnant women.Conclusions The present study shows the metabolomics profile in the second- and third-trimester stages in pregnant women with and without GDM. Pathways, DEMs and their associations with GDM related indices have been found. Indeed, further studies are warranted to confirm our findings.

scholarly journals Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ilhame Diboun ◽  
Manjunath Ramanjaneya ◽  
Yasser Majeed ◽  
Lina Ahmed ◽  
Mohammed Bashir ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Metabolic Profiling ◽  
Generalized Linear Model ◽  
Regression Models ◽  
Second Trimester ◽  
Linear Regression Models ◽  
Term Delivery ◽  
Term Labor

Abstract Background Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome. Methods Sixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP® Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor. Results The gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84). Conclusions This study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted.

scholarly journals Asupan Karbohidrat Harian terhadap Risiko Kejadian Diabetes Melitus Gestasi (GDM) melalui Glyco-Hemoglobin Ibu Hamil

2021 ◽  
Vol 4 (2) ◽  
pp. 510-518
Author(s):  
Def Primal ◽  
Tetra Anestasia Putri ◽  
Wira Meiriza
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Daily Intake ◽  
Carbohydrate Intake ◽  
Third Trimester ◽  
Cross Sectional ◽  
The Third ◽  
Diabetes Status

This study aims to identify the relationship between the amount of carbohydrate intake in pregnant women in the third trimester with the incidence of gestational diabetes mellitus (GDM) in the health office area of the City of Bukittinggi. This research method is a cross-sectional study using an experimental approach. The results showed that the daily intake of carbohydrates (grams) of pregnant women in the third trimester had a significant relationship with the incidence of GDM in the working area of the Bukittinggi City Health Office based on 34 pregnant women who had been examined. This can be seen from the higher the daily intake of carbohydrates for pregnant women, the increase in the percentage value of HbA1c. In conclusion, there is a correlation between the consistent daily intake of carbohydrates and the HbA1c weight, which refers to pre-diabetes status and gestational diabetes in the third trimester of pregnant women.   Keywords: Carbohydrate Intake; Gestational Diabetes Mellitus; HbA1c

scholarly journals Dietary patterns of pregnant women, maternal excessive body weight and gestational diabetes

2019 ◽  
Vol 53 ◽  
pp. 52 ◽  
Author(s):  
Daniela Cristina Candelas Zuccolotto ◽  
Lívia Castro Crivellenti ◽  
Laércio Joel Franco ◽  
Daniela Saes Sarotelli
Keyword(s):  
Diabetes Mellitus ◽  
Body Weight ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Dietary Patterns ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Excessive Body Weight ◽  
The Relationship

OBJECTIVE: To investigate the relationship between the dietary patterns of pregnant women with maternal excessive body weight and gestational diabetes mellitus . METHODS: A cross-sectional study conducted with a convenience sample of 785 adult pregnant women attended by the Unified Health System of Ribeirão Preto, state of São Paulo, between 2011 and 2012. Two 24-hour dietary recalls, corrected by the multiple source method, were employed. For the classification of the body mass index and the diagnosis of gestational diabetes mellitus, the criteria by Atalah and the World Health Organization were used, respectively. Dietary patterns were obtained by principal component analysis using the Varimax rotation method. The relationship between adherence to patterns, overweight and obesity was analyzed by multinomial logistic regression models and the relationship with gestational diabetes mellitus by adjusted unconditional logistic regression models. RESULTS: We identified four dietary patterns: “traditional Brazilian”; “snacks”; “coffee” and “healthy”. Women with a higher adherence to the “Healthy” (OR = 0.52; 95%CI 0.33–0.83) and “Brazilian Traditional” patterns (OR = 0.61; 95%CI 0.38–0.96) presented a lower chance of obesity, when compared to women with lower adherence, regardless of confounding factors. After adjustment for maternal excessive body weight, there was no association between dietary patterns and gestational diabetes mellitus. CONCLUSIONS: Among the pregnant women, greater adherence to “traditional Brazilian” and “healthy” patterns was inversely associated with obesity, but no relationship was identified with gestational diabetes mellitus after adjusting for excessive body weight. Prospective studies are recommended to investigate the relationship between dietary patterns, overweight and gestational diabetes mellitus, reducing the chance of reverse causality

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

Trends in Heart Rate and Heart Rate Variability during Pregnancy and Three Months Postpartum: Continuous Monitoring in a Free-Living Context (Preprint)

2021 ◽  
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilen ◽  
Pasi Liljeberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Time Domain ◽  
Second Trimester ◽  
Third Trimester ◽  
Non Invasive ◽  
The Third ◽  
Invasive Method ◽  
Frequency Power

BACKGROUND Heart rate variability (HRV) is a non-invasive method reflecting autonomic nervous system (ANS) regulations. Altered HRV is associated with adverse mental or physical health complications. ANS also has a central role in physiological adaption during pregnancy causing normal changes in HRV. OBJECTIVE Assessing trends in heart rate (HR) and HRV parameters as a non-invasive method for remote maternal health monitoring during pregnancy and three months postpartum. METHODS Fifty-eight pregnant women were monitored using an Internet-of-Things (IoT)-based remote monitoring system during pregnancy and 3-months postpartum. Pregnant women were asked to continuously wear Gear sport smartwatch to monitor their HR and HRV. In addition, a cross-platform mobile application was utilized for collecting pregnancy-related information. The trends of HR and HRV parameters were extracted using reliable data. We also analyzed the trends of normalized HRV parameters based on HR to remove the effect of HR changes on HRV trends. Finally, we exploited hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. RESULTS HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P<.01). Time-domain HRV parameters, average normal interbeat intervals (AVNN), standard deviation of normal interbeat intervals (SDNN), root mean square of the successive difference of normal interbeat intervals (RMSSD), normalized SDNN (nSDNN), and normalized RMSSD (nRMSSD) decreased significantly during the second trimester (P<.001) then increased significantly during the third trimester (P<.01). Some of the frequency domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF (nHF) decreased significantly during the second trimester (P<.01), and HF increased significantly during the third trimester (P<.01). In the postpartum period, nRMSSD decreased (P<.05), and the LF to HF ratio (LF/HF) increased significantly (P<.01). CONCLUSIONS Our study showed that HR increased and HRV parameters decreased as the pregnancy proceeded, and the values returned to normal after the delivery. Moreover, our results show that HR started to decrease while time-domain HRV parameters and HF started to increase during the third trimester. Our results also demonstrate the possibility of continuous HRV monitoring in everyday life settings.

scholarly journals Visfatin concentration is decreased in women with gestational diabetes mellitus in the third trimester

2008 ◽  
Vol 31 (7) ◽  
pp. 610-613 ◽  
Author(s):  
M. Akturk ◽  
A. E. Altinova ◽  
I. Mert ◽  
U. Buyukkagnici ◽  
A. Sargin ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Third Trimester ◽  
The Third

Gestation specific reference intervals for thyroid function tests in pregnancy

2016 ◽  
Vol 54 (8) ◽  
Author(s):  
Süleyman Akarsu ◽  
Filiz Akbiyik ◽  
Eda Karaismailoglu ◽  
Zeliha Gunnur Dikmen
Keyword(s):  
Pregnant Women ◽  
Thyroid Function ◽  
First Trimester ◽  
Reference Intervals ◽  
Specific Reference ◽  
Thyroid Function Tests ◽  
Second Trimester ◽  
Third Trimester ◽  
The Third ◽  
Function Tests

AbstractThyroid function tests are frequently assessed during pregnancy to evaluate thyroid dysfunction or to monitor pre-existing thyroid disease. However, using non-pregnant reference intervals can lead to misclassification. International guidelines recommended that institutions should calculate their own pregnancy-specific reference intervals for free thyroxine (FT4), free triiodothyronine (FT3) and thyroid-stimulating hormone (TSH). The objective of this study is to establish gestation-specific reference intervals (GRIs) for thyroid function tests in pregnant Turkish women and to compare these with the age-matched non-pregnant women.Serum samples were collected from 220 non-pregnant women (age: 18–48), and 2460 pregnant women (age: 18–45) with 945 (39%) in the first trimester, 1120 (45%) in the second trimester, and 395 (16%) in the third trimester. TSH, FT4 and FT3 were measured using the Abbott Architect i2000SR analyzer.GRIs of TSH, FT4 and FT3 for first trimester pregnancies were 0.49–2.33 mIU/L, 10.30–18.11 pmol/L and 3.80–5.81 pmol/L, respectively. GRIs for second trimester pregnancies were 0.51–3.44 mIU/L, 10.30–18.15 pmol/L and 3.69–5.90 pmol/L. GRIs for third trimester pregnancies were 0.58–4.31 mIU/L, 10.30–17.89 pmol/L and 3.67–5.81 pmol/L. GRIs for TSH, FT4 and FT3 were different from non-pregnant normal reference intervals.TSH levels showed an increasing trend from the first trimester to the third trimester, whereas both FT4 and FT3 levels were uniform throughout gestation. GRIs may help in the diagnosis and appropriate management of thyroid dysfunction during pregnancy which will prevent both maternal and fetal complications.

scholarly journals Plasma Markers of Oxidative Stress in Patients with Gestational Diabetes Mellitus in the Second and Third Trimester

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hongwei Li ◽  
Qian Yin ◽  
Ning Li ◽  
Zhenbo Ouyang ◽  
Mei Zhong
Keyword(s):  
Oxidative Stress ◽  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Paraoxonase 1 ◽  
Third Trimester ◽  
Significant Difference ◽  
Markers Of Oxidative Stress ◽  
Plasma Markers

Objective.To determine plasma markers of oxidative stress during the second and third trimester of pregnancy in patients with gestational diabetes mellitus (GDM).Study Design.We conducted a prospective nested case-control study involving 400 pregnant women, 22 of whom developed GDM. As control group, 30 normal pregnant women were chosen randomly. Plasma samples were analyzed for 8-iso-prostaglandin F2α(8-iso-PGF2α), advanced oxidative protein products (AOPPs), protein carbonyl (PCO), glutathione peroxidase-3 (GPX-3), and paraoxonase-1 (PON1) at 16–20 weeks, 24–28 weeks, and 32–36 weeks of gestation.Results.Compared to control subjects, the plasma levels of PCO, AOPPs, and 8-iso-PGF2αwere elevated at 16–20 weeks’ and 32–36 weeks’ gestation in GDM. There was no significant difference in PCO and 8-iso-PGF2αat 24–28 weeks in GDM. GPX-3 was statistically significantly increased at 16–20 weeks and 32–36 weeks in GDM. PON1 reduced in patients with GDM. No significant differences were found at 24–28 and 32–36 weeks between the GDM and control groups. In GDM, PCO, AOPPs, and 8-iso-PGF2αlevels were higher and GPX-3 and PON1 levels were lower in the second than the third trimester.Conclusion.Oxidation status increased in GDM, especially protein oxidation, which may contribute to the pathogenesis of GDM.

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