Maternal fibrinogen/fibrin degradation products to high density lipoprotein cholesterol ratio for predicting delivery of small and large for gestational age infants: a pilot study.

Birthweight FDP FHR HDL-C LGA Prediction SGA

Journal

Lipids in health and disease
ISSN: 1476-511X
Titre abrégé: Lipids Health Dis
Pays: England
ID NLM: 101147696

Informations de publication

Date de publication:
12 Dec 2023
Historique:
received: 19 05 2023
accepted: 06 12 2023
medline: 13 12 2023
pubmed: 13 12 2023
entrez: 13 12 2023
Statut: epublish

Résumé

The purpose of this pilot study was to investigate associations between fibrinogen/fibrin degradation products (FDP) to high density lipoprotein-cholesterol (HDL-C) ratio (FHR) of mothers and the risk of delivering large/small for gestational age (LGA/SGA) infants and to evaluate the predictive power of FHR on LGA/SGA. This study retrospectively reviewed 11,657 consecutive women whose lipid profiles and FDP levels were investigated at the time of admission for delivery at a specialized hospital. The FHR was calculated, and perinatal outcomes, including clinical parameters, were analyzed. The prevalence of SGA was 9% (n = 1034), and that of LGA was 15% (n = 1806) in this cohort study. FHR was significantly lower in women who delivered SGA infants (4.0 ± 3.2 vs. 4.7 ± 3.3 mg/mmol, P < 0.01) and higher in women who delivered LGA infants (5.7 ± 3.8 vs. 4.7 ± 3.3 mg/mmol, P < 0.01) compared with those who delivered infants of normal size for their gestational age. Women in the top quartile for FHR (> 5.9 mg/mmol) had a 2.9-fold higher risk of delivering LGA infants [adjusted odds ratio (OR) = 2.9, P < 0.01] and a 47% lower risk of delivering SGA infants (adjusted OR = 0.47, P < 0.01) than those in the bottom quartile (< 2.7 mg/mmol). In addition, adding FHR to the conventional models significantly improved the area under the curve for the prediction of delivering LGA (0.725 vs. 0.739, P < 0.01) and SGA (0.717 vs. 0.727, P < 0.01) infants. These findings suggest that the FHR calculated in late pregnancy is an innovative predictor of delivering LGA and SGA infants. Combining FHR with perinatal parameters could thus enhance the predictive ability for predicting the delivery of LGA/SGA infants.

Sections du résumé

BACKGROUND BACKGROUND
The purpose of this pilot study was to investigate associations between fibrinogen/fibrin degradation products (FDP) to high density lipoprotein-cholesterol (HDL-C) ratio (FHR) of mothers and the risk of delivering large/small for gestational age (LGA/SGA) infants and to evaluate the predictive power of FHR on LGA/SGA.
METHODS METHODS
This study retrospectively reviewed 11,657 consecutive women whose lipid profiles and FDP levels were investigated at the time of admission for delivery at a specialized hospital. The FHR was calculated, and perinatal outcomes, including clinical parameters, were analyzed.
RESULTS RESULTS
The prevalence of SGA was 9% (n = 1034), and that of LGA was 15% (n = 1806) in this cohort study. FHR was significantly lower in women who delivered SGA infants (4.0 ± 3.2 vs. 4.7 ± 3.3 mg/mmol, P < 0.01) and higher in women who delivered LGA infants (5.7 ± 3.8 vs. 4.7 ± 3.3 mg/mmol, P < 0.01) compared with those who delivered infants of normal size for their gestational age. Women in the top quartile for FHR (> 5.9 mg/mmol) had a 2.9-fold higher risk of delivering LGA infants [adjusted odds ratio (OR) = 2.9, P < 0.01] and a 47% lower risk of delivering SGA infants (adjusted OR = 0.47, P < 0.01) than those in the bottom quartile (< 2.7 mg/mmol). In addition, adding FHR to the conventional models significantly improved the area under the curve for the prediction of delivering LGA (0.725 vs. 0.739, P < 0.01) and SGA (0.717 vs. 0.727, P < 0.01) infants.
CONCLUSION CONCLUSIONS
These findings suggest that the FHR calculated in late pregnancy is an innovative predictor of delivering LGA and SGA infants. Combining FHR with perinatal parameters could thus enhance the predictive ability for predicting the delivery of LGA/SGA infants.

Identifiants

DOI: 10.1186/s12944-023-01986-x PMID: 38087267
pubmed: 38087267
doi: 10.1186/s12944-023-01986-x
pii: 10.1186/s12944-023-01986-x
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

221

Subventions

Organisme : Natural Science Research Project of Anhui Educational Committee
ID : KJ2021A1200
Organisme : Changzhou Medical Center of Nanjing Medical University
ID : CMCC202219
Organisme : Jiangsu Maternal and Child Health Research Projects
ID : F201842
Organisme : Changzhou science and technology infrastructure construction project
ID : Key Laboratory, CM20223012

Informations de copyright

© 2023. The Author(s).

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Auteurs

Bin Zhang (B)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China.

Sijie Xi (S)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China.

Renchen Liu (R)

General Education College, Anhui Institute of Information Technology, Wuhu, China.

Xiaoya Han (X)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China.

Wei Long (W)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China.

Xiaosong Yuan (X)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China. yuanxiaosong@126.com.

Bin Yu (B)

Department of Medical Genetics, Changzhou Medical Center, Changzhou Maternal and Child Health Care Hospital, Nanjing Medical University, 16th Ding Xiang Road, Changzhou, 213023, Jiangsu, China. binyu@njmu.edu.cn.

Classifications MeSH