Evidence map›Paper›PMID 39198813›Full record

ArticleNutrition journal2024

Maternal dietary patterns during pregnancy and birth weight: a prospective cohort study.

Tongtong Li, Yusa He, Nan Wang, Chengwu Feng, Puchen Zhou, Ye Qi, Zhengyuan Wang, Xiaojun Lin, Dou Mao, Zhuo Sun and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nutrition journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Tongtong Li *CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Yusa He *Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Nan WangCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Chengwu FengCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Puchen ZhouCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Ye QiSchool of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Zhengyuan WangDivision of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, China.
Xiaojun LinDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Dou MaoCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Zhuo SunDivision of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, China.
Aili ShengCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Yang SuCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Liping ShenDivision of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, China.
Fengchang LiCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
Xueying CuiDepartment of Nutrition, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China.
Changzheng YuanSchool of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Liang WangDepartment of Public Health, Marshall University, West Virginia, USA.
Jiajie Zang *Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, China. zangjiajie@scdc.sh.cn.
Geng Zong *CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China. zonggeng@sinh.ac.cn.

Funding

National Natural Science Foundation of China 82373576Special Foundation for National Science and Technology Basic Research Program of China 2019FY101002the National Science Fund for Excellent Young Scholars 81922060
6 · The paper itself

Abstract

backgroundExisting data on maternal dietary patterns and birth weight remains limited and inconsistent, especially in non-Western populations. We aimed to examine the relationship between maternal dietary patterns and birth weight among a cohort of Chinese.

methodsIn this study, 4,184 mother-child pairs were included from the Iodine Status in Pregnancy and Offspring Health Cohort. Maternal diet during pregnancy was evaluated using a self-administered food frequency questionnaire with 69 food items. Principal component analysis was used to identify dietary patterns. Information on birth weight and gestational age was obtained through medical records. Adverse outcomes of birth weight were defined according to standard clinical cutoffs, including low birth weight, macrosomia, small for gestational age, and large for gestational age.

resultsThree maternal dietary patterns were identified: plant-based, animal-based, and processed food and beverage dietary patterns, which explained 23.7% variance in the diet. In the multivariate-adjusted model, women with higher adherence to the plant-based dietary patten had a significantly higher risk of macrosomia (middle tertile vs. low tertile: odds ratio (OR) 1.45, 95% CI 1.00-2.10; high tertile vs. low tertile: OR 1.55, 95% CI 1.03-2.34; P-trend = 0.039). For individual food groups, potato intake showed positive association with macrosomia (high tertile vs. low tertile: OR 1.72, 95% CI 1.20-2.47; P-trend = 0.002). Excluding potatoes from the plant-based dietary pattern attenuated its association with macrosomia risk. No significant associations was observed for the animal-based or processed food and beverage dietary pattern with birth weight outcomes.

conclusionsAdherence to a plant-based diet high in carbohydrate intake was associated with higher macrosomia risk among Chinese women. Future studies are required to replicate these findings and explore the potential mechanisms involved.

Indexed as

Birth WeightDietFetal MacrosomiaMaternal Nutritional Physiological PhenomenaAdultChinaCohort StudiesDiet, VegetarianFemaleGestational AgeHumansInfant, Low Birth WeightInfant, NewbornPregnancyProspective StudiesSurveys and QuestionnairesBirth weightChineseMacrosomiaPlant-based dietary pattern

Identifiers

PMID39198813
PMCPMC11351029

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.