Combinatorial Analysis of Circulating Biomarkers and Maternal Characteristics for Preeclampsia Prediction in the First and Third Trimesters in Asia

We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two...

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Published inDiagnostics (Basel) Vol. 12; no. 7; p. 1533
Main Authors Lin, Willie, Teng, Sen-Wen, Lin, Tzu-Yi, Lovel, Ronald, Sung, Hsin-Yu, Chang, Wen-Ying, Wu, Tang Bo-Chung, Chen, Hsuan-Yu, Wang, Le-Ming, Shaw, Steven W.
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 23.06.2022
MDPI
Subjects
Biomarkers
Blood pressure
Consent
Edema
Growth factors
MicroRNAs
miR-181a
miR-210
miR-223
Placenta
Preeclampsia
Pregnancy
risk algorithm
sFlt-1/PlGF
Thrombocytopenia
Womens health
Minneapolis Minnesota
United States > US
Germany
Taiwan
miR-223
sFlt-1/PlGF
risk algorithm
miR-181a
miR-210
Online AccessGet full text
ISSN2075-4418
2075-4418
DOI10.3390/diagnostics12071533

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Abstract We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73–0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74–0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.
AbstractList We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73–0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74–0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.
We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73−0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74−0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.
We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73−0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74−0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73−0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74−0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.
Author Wang, Le-Ming
Teng, Sen-Wen
Lovel, Ronald
Shaw, Steven W.
Lin, Willie
Sung, Hsin-Yu
Chen, Hsuan-Yu
Lin, Tzu-Yi
Chang, Wen-Ying
Wu, Tang Bo-Chung
AuthorAffiliation 3 School of Medicine, Fu-Jen Catholic University, New Taipei 242, Taiwan
4 College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; forever841230@gmail.com
8 Department of Obstetrics and Gynecology, Taipei Chang Gung Memorial Hospital, No. 199, Dun-Hua North Road, Taipei 105, Taiwan
6 Department of Obstetrics and Gynecology, Wan Fang Hospital, Taipei 116, Taiwan; lemingmd90@gmail.com
7 Graduate Institute of Clinical Medicine, Taipei Medical University, Taipei 110, Taiwan
2 Department of Obstetrics and Gynecology, Cardinal Tien Hospital, New Taipei 231, Taiwan; senwen1955@yahoo.com.tw
5 Meribank Biotech Co., Ltd., Taipei 114, Taiwan; lovel.ronald@gmail.com (R.L.); dora.sung@meribank.com.tw (H.-Y.S.); nancy.chang@meribank.com.tw (W.-Y.C.); sherry.chen@meribank.com.tw (H.-Y.C.)
1 Meridigen Biotech Co., Ltd., Taipei 114, Taiwan; willie.lin@meridigen.com (W.L.); linus.tang@meridigen.com (T.B.-C.W.)
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CitedBy_id crossref_primary_10_15829_1728_8800_2024_4193
crossref_primary_10_1016_j_preghy_2023_10_011
crossref_primary_10_31083_j_ceog5009195
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risk algorithm
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StartPage 1533
SubjectTerms Biomarkers
Blood pressure
Consent
Edema
Growth factors
MicroRNAs
miR-181a
miR-210
miR-223
Placenta
Preeclampsia
Pregnancy
risk algorithm
sFlt-1/PlGF
Thrombocytopenia
Womens health
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Title Combinatorial Analysis of Circulating Biomarkers and Maternal Characteristics for Preeclampsia Prediction in the First and Third Trimesters in Asia
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