Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai
The objective of this study was to analyse representative dietary patterns during pregnancy in Shanghai and explore the effects of dietary patterns during pregnancy on preterm birth. Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, strat...
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description | The objective of this study was to analyse representative dietary patterns during pregnancy in Shanghai and explore the effects of dietary patterns during pregnancy on preterm birth. Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, stratified random sampling was used to select survey participants from 16 districts in Shanghai, which were divided into five sampling areas; 40–70 pregnant women were selected from each area. A total of 4361 pregnant women and their offspring were involved in the study. The male-to-female ratio of the babies was 1.04:1, and the incidence of single preterm birth was 4.2%. Three dietary patterns were extracted by factor analysis: a ‘Vegetarian Pattern’, an ‘Animal Food Pattern’ (AFP), and a ‘Dairy and Egg Pattern’. These patterns explained 40.513% of the variance in dietary intake. Binary logistic regression, which was used to analyse the association between birth outcomes and scores measuring maternal dietary patterns, found only the AFP was a significant risk factor for preterm birth. Higher AFP scores were positively associated with preterm birth (Q2 vs. Q1 OR = 1.487, 95% CI: 1.002–2.207; Q3 vs. Q1 OR = 1.885, 95% CI: 1.291–2.754). After adjusting for other potential contributors, a higher AFP score was still a significant risk factor for preterm birth (Q2 vs. Q1 OR = 1.470, 95% CI: 0.990–2.183; Q3 vs. Q1 OR = 1.899, 95% CI: 1.299–2.776). The incidence of preterm birth was 4.2%. A higher score of AFP was significantly associated with a higher risk of preterm birth. The animal food intake of pregnant women should be reasonably consumed during pregnancy. |
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Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, stratified random sampling was used to select survey participants from 16 districts in Shanghai, which were divided into five sampling areas; 40–70 pregnant women were selected from each area. A total of 4361 pregnant women and their offspring were involved in the study. The male-to-female ratio of the babies was 1.04:1, and the incidence of single preterm birth was 4.2%. Three dietary patterns were extracted by factor analysis: a ‘Vegetarian Pattern’, an ‘Animal Food Pattern’ (AFP), and a ‘Dairy and Egg Pattern’. These patterns explained 40.513% of the variance in dietary intake. Binary logistic regression, which was used to analyse the association between birth outcomes and scores measuring maternal dietary patterns, found only the AFP was a significant risk factor for preterm birth. Higher AFP scores were positively associated with preterm birth (Q2 vs. Q1 OR = 1.487, 95% CI: 1.002–2.207; Q3 vs. Q1 OR = 1.885, 95% CI: 1.291–2.754). After adjusting for other potential contributors, a higher AFP score was still a significant risk factor for preterm birth (Q2 vs. Q1 OR = 1.470, 95% CI: 0.990–2.183; Q3 vs. Q1 OR = 1.899, 95% CI: 1.299–2.776). The incidence of preterm birth was 4.2%. A higher score of AFP was significantly associated with a higher risk of preterm birth. The animal food intake of pregnant women should be reasonably consumed during pregnancy.</description><identifier>ISSN: 2072-6643</identifier><identifier>EISSN: 2072-6643</identifier><identifier>DOI: 10.3390/nu13072367</identifier><identifier>PMID: 34371874</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Birth weight ; Body mass index ; Childbirth & labor ; Cohort analysis ; Diet ; Dietary intake ; Factor analysis ; Food ; Food and nutrition ; Food habits ; Food intake ; Gestational age ; Health risks ; Infants (Premature) ; Iodine ; Multiple births ; Nutrition research ; Offspring ; Overweight ; Pattern analysis ; Pregnancy ; Pregnant women ; Premature birth ; Principal components analysis ; Questionnaires ; Random sampling ; Risk analysis ; Risk factors ; Sampling ; Statistical sampling ; Surveys ; Vegetarianism ; Womens health</subject><ispartof>Nutrients, 2021-07, Vol.13 (7), p.2367</ispartof><rights>COPYRIGHT 2021 MDPI AG</rights><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c450t-f7cf48725f15b886ddfe729325f8451d6ac5ebdfcf2974827e67b3e02809ab113</citedby><cites>FETCH-LOGICAL-c450t-f7cf48725f15b886ddfe729325f8451d6ac5ebdfcf2974827e67b3e02809ab113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308829/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308829/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53770,53772</link.rule.ids></links><search><creatorcontrib>Wang, Zhengyuan</creatorcontrib><creatorcontrib>Zhao, Shenglu</creatorcontrib><creatorcontrib>Cui, Xueying</creatorcontrib><creatorcontrib>Song, Qi</creatorcontrib><creatorcontrib>Shi, Zehuan</creatorcontrib><creatorcontrib>Su, Jin</creatorcontrib><creatorcontrib>Zang, Jiajie</creatorcontrib><title>Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai</title><title>Nutrients</title><description>The objective of this study was to analyse representative dietary patterns during pregnancy in Shanghai and explore the effects of dietary patterns during pregnancy on preterm birth. Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, stratified random sampling was used to select survey participants from 16 districts in Shanghai, which were divided into five sampling areas; 40–70 pregnant women were selected from each area. A total of 4361 pregnant women and their offspring were involved in the study. The male-to-female ratio of the babies was 1.04:1, and the incidence of single preterm birth was 4.2%. Three dietary patterns were extracted by factor analysis: a ‘Vegetarian Pattern’, an ‘Animal Food Pattern’ (AFP), and a ‘Dairy and Egg Pattern’. These patterns explained 40.513% of the variance in dietary intake. Binary logistic regression, which was used to analyse the association between birth outcomes and scores measuring maternal dietary patterns, found only the AFP was a significant risk factor for preterm birth. Higher AFP scores were positively associated with preterm birth (Q2 vs. Q1 OR = 1.487, 95% CI: 1.002–2.207; Q3 vs. Q1 OR = 1.885, 95% CI: 1.291–2.754). After adjusting for other potential contributors, a higher AFP score was still a significant risk factor for preterm birth (Q2 vs. Q1 OR = 1.470, 95% CI: 0.990–2.183; Q3 vs. Q1 OR = 1.899, 95% CI: 1.299–2.776). The incidence of preterm birth was 4.2%. A higher score of AFP was significantly associated with a higher risk of preterm birth. The animal food intake of pregnant women should be reasonably consumed during pregnancy.</description><subject>Analysis</subject><subject>Birth weight</subject><subject>Body mass index</subject><subject>Childbirth & labor</subject><subject>Cohort analysis</subject><subject>Diet</subject><subject>Dietary intake</subject><subject>Factor analysis</subject><subject>Food</subject><subject>Food and nutrition</subject><subject>Food habits</subject><subject>Food intake</subject><subject>Gestational age</subject><subject>Health risks</subject><subject>Infants (Premature)</subject><subject>Iodine</subject><subject>Multiple births</subject><subject>Nutrition research</subject><subject>Offspring</subject><subject>Overweight</subject><subject>Pattern analysis</subject><subject>Pregnancy</subject><subject>Pregnant women</subject><subject>Premature birth</subject><subject>Principal components analysis</subject><subject>Questionnaires</subject><subject>Random sampling</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Sampling</subject><subject>Statistical sampling</subject><subject>Surveys</subject><subject>Vegetarianism</subject><subject>Womens health</subject><issn>2072-6643</issn><issn>2072-6643</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkl1rFDEUhoNYbNn2xl8Q8EaErZl8jxfCura1ULBQvQ6ZTDKTMpPUJCPsvzfLFm2lyUVOznnOe3jhAPC2QeeEtOhjWBqCBCZcvAInuEZrzil5_SQ-Bmc536P9EUhw8gYcE0pEIwU9AerCOWtKhtHBr94WnXbwVpdiU8iwX5IPA7xNdgg6mB2MYf-pxRl-8amMn-DmEMBtHGMq8K4s_Q76AO9GHYZR-1Nw5PSU7dnjuwI_Ly9-bL-tb75fXW83N2tDGSprJ4yjUmDmGtZJyfveWYFbUhOSsqbn2jDb9c443AoqsbBcdMQiLFGru6YhK_D5oPuwdLPtjQ0l6Uk9JD9XSypqr55Xgh_VEH8rSZCUddIKvH8USPHXYnNRs8_GTpMONi5ZYcYR4hgLVtF3_6H3cUmh2qsUo0K0kj2hBj1Z5YOLda7Zi6qN4IxTyhGu1PkLVL29nb2JwTpf888aPhwaTIo5J-v-emyQ2m-E-rcR5A-L7aUC</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Wang, Zhengyuan</creator><creator>Zhao, Shenglu</creator><creator>Cui, Xueying</creator><creator>Song, Qi</creator><creator>Shi, Zehuan</creator><creator>Su, Jin</creator><creator>Zang, Jiajie</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20210701</creationdate><title>Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai</title><author>Wang, Zhengyuan ; Zhao, Shenglu ; Cui, Xueying ; Song, Qi ; Shi, Zehuan ; Su, Jin ; Zang, Jiajie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c450t-f7cf48725f15b886ddfe729325f8451d6ac5ebdfcf2974827e67b3e02809ab113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analysis</topic><topic>Birth weight</topic><topic>Body mass index</topic><topic>Childbirth & labor</topic><topic>Cohort analysis</topic><topic>Diet</topic><topic>Dietary intake</topic><topic>Factor analysis</topic><topic>Food</topic><topic>Food and nutrition</topic><topic>Food habits</topic><topic>Food intake</topic><topic>Gestational age</topic><topic>Health risks</topic><topic>Infants (Premature)</topic><topic>Iodine</topic><topic>Multiple births</topic><topic>Nutrition research</topic><topic>Offspring</topic><topic>Overweight</topic><topic>Pattern analysis</topic><topic>Pregnancy</topic><topic>Pregnant women</topic><topic>Premature birth</topic><topic>Principal components analysis</topic><topic>Questionnaires</topic><topic>Random sampling</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Sampling</topic><topic>Statistical sampling</topic><topic>Surveys</topic><topic>Vegetarianism</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Zhengyuan</creatorcontrib><creatorcontrib>Zhao, Shenglu</creatorcontrib><creatorcontrib>Cui, Xueying</creatorcontrib><creatorcontrib>Song, Qi</creatorcontrib><creatorcontrib>Shi, Zehuan</creatorcontrib><creatorcontrib>Su, Jin</creatorcontrib><creatorcontrib>Zang, Jiajie</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nutrients</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Zhengyuan</au><au>Zhao, Shenglu</au><au>Cui, Xueying</au><au>Song, Qi</au><au>Shi, Zehuan</au><au>Su, Jin</au><au>Zang, Jiajie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai</atitle><jtitle>Nutrients</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>13</volume><issue>7</issue><spage>2367</spage><pages>2367-</pages><issn>2072-6643</issn><eissn>2072-6643</eissn><abstract>The objective of this study was to analyse representative dietary patterns during pregnancy in Shanghai and explore the effects of dietary patterns during pregnancy on preterm birth. Data were derived from the ‘Iodine Status in Pregnancy and Offspring Health Cohort’ (ISPOHC) study. Multistage, stratified random sampling was used to select survey participants from 16 districts in Shanghai, which were divided into five sampling areas; 40–70 pregnant women were selected from each area. A total of 4361 pregnant women and their offspring were involved in the study. The male-to-female ratio of the babies was 1.04:1, and the incidence of single preterm birth was 4.2%. Three dietary patterns were extracted by factor analysis: a ‘Vegetarian Pattern’, an ‘Animal Food Pattern’ (AFP), and a ‘Dairy and Egg Pattern’. These patterns explained 40.513% of the variance in dietary intake. Binary logistic regression, which was used to analyse the association between birth outcomes and scores measuring maternal dietary patterns, found only the AFP was a significant risk factor for preterm birth. Higher AFP scores were positively associated with preterm birth (Q2 vs. Q1 OR = 1.487, 95% CI: 1.002–2.207; Q3 vs. Q1 OR = 1.885, 95% CI: 1.291–2.754). After adjusting for other potential contributors, a higher AFP score was still a significant risk factor for preterm birth (Q2 vs. Q1 OR = 1.470, 95% CI: 0.990–2.183; Q3 vs. Q1 OR = 1.899, 95% CI: 1.299–2.776). The incidence of preterm birth was 4.2%. A higher score of AFP was significantly associated with a higher risk of preterm birth. The animal food intake of pregnant women should be reasonably consumed during pregnancy.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>34371874</pmid><doi>10.3390/nu13072367</doi><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Birth weight Body mass index Childbirth & labor Cohort analysis Diet Dietary intake Factor analysis Food Food and nutrition Food habits Food intake Gestational age Health risks Infants (Premature) Iodine Multiple births Nutrition research Offspring Overweight Pattern analysis Pregnancy Pregnant women Premature birth Principal components analysis Questionnaires Random sampling Risk analysis Risk factors Sampling Statistical sampling Surveys Vegetarianism Womens health |
title | Effects of Dietary Patterns during Pregnancy on Preterm Birth: A Birth Cohort Study in Shanghai |
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