上海交通大学学报（医学版） ›› 2025, Vol. 45 ›› Issue (1): 113-121.doi: 10.3969/j.issn.1674-8115.2025.01.014
糜小扬1,2(
), 丁莹1,2, 陈奕静3, 贾洁1,2,4()
收稿日期:2024-07-04
接受日期:2024-11-20
出版日期:2025-01-28
发布日期:2025-01-28
通讯作者:
贾 洁，副教授，博士；电子信箱：jiejia@shsmu.edu.cn。作者简介:糜小扬（2003—），女，本科生；电子信箱：15021302508@163.com。
基金资助:
MI Xiaoyang1,2(), DING Ying1,2, CHEN Yijing3, JIA Jie1,2,4()
Received:2024-07-04
Accepted:2024-11-20
Online:2025-01-28
Published:2025-01-28
Contact:
JIA Jie, E-mail: jiejia@shsmu.edu.cn.Supported by:摘要：
近年来超加工食品（ultra-processed foods，UPFs）在全球范围内消费量迅速增长。UPFs是NOVA分类体系中描述的第四类食品：为工业配方食品，完全或主要由从食物中提取的物质（油、脂肪、糖、淀粉和蛋白质等）、来自食物成分的衍生物（氢化脂肪和改性淀粉等）或多种食品添加剂制成；制造技术包括挤压、成型和预煎炸等。作为高能量密度食品，UPFs通常具有高糖、脂肪和盐，低膳食纤维、蛋白质、维生素和矿物质的特点，营养密度较低。多项研究显示UPFs高摄入能够增加多种慢性疾病的发生风险。孕期营养是影响妊娠结局的重要因素，孕期均衡且充分的营养摄入能够保障母婴的健康。UPFs营养密度有限，孕期对其的高摄入可能不利于母婴健康，但孕期UPFs摄入影响母婴健康的研究有限。该文针对UPFs对妊娠结局影响的相关文献进行了综述，旨在为深入研究UPFs对孕期健康的影响以及进行个性化膳食指导提供研究依据。
中图分类号:
糜小扬, 丁莹, 陈奕静, 贾洁. 孕期超加工食品摄入与妊娠结局关系的研究进展[J]. 上海交通大学学报（医学版）, 2025, 45(1): 113-121.
MI Xiaoyang, DING Ying, CHEN Yijing, JIA Jie. Research progress in the relationship between ultra-processed food intake and pregnancy outcomes[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(1): 113-121.
| Author | Year Country | Study design | Outcome | Number (Case) | Dietary Investigation tool | UPF consumption | OR (95%CI or P value) | Covariate |
|---|---|---|---|---|---|---|---|---|
| MARTIN[ | 2015 USA | Cohort | Preterm birth | 3 143 (364) | FFQ | Western dietary pattern maximum vs minimum | OR=1.53 (1.02, 2.30) | Maternal age, race, pre-pregnancy BMI, educational level, marital status, parity, family income, smoking status in the first 6 months of pregnancy and energy intake |
| ENGLUND-ÖGGE[ | 2014 Norway | Cohort | Preterm birth | 66 000 (3 505) | FFQ | Western dietary pattern | HR=1.12 (1.01, 1.25) P>0.05 | Maternal history of previous preterm delivery, maternal age, height, pre-pregnancy BMI, marital status, parity, smoking, education level, family income, and total energy intake |
| ENGLUND-ÖGGE[ | 2012 Norway | Cohort | Preterm birth | 60 761 (3 281) | MoBa FFQ | Sugar -sweetened beverages maximum vs minimum Artificially sweetened maximum vs minimum | OR=1.11 (1.00, 1.24) OR=1.25 (1.08, 1.45) | Maternal history of previous preterm delivery, maternal age, pre-pregnancy BMI, marital status, parity, smoking, education level and total energy intake |
| RODRIGUES[ | 2020 Brazil | Cross -sectional | LBW | 99 (13) | Form of food consumption markers in the food and nutrition surveillance system | maximum vs minimum | OR=1.46 (1.02, 2.10) | Maternal age, marital status, education level, per capita income, prenatal weight, final fetal weight, increase in gestational age and gestational age, type of delivery, number of pregnancies |
| FERREIRA[ | 2022 Brazil | Cross -sectional | LBW | 260 (8) | FFQ | Dietary pattern 3 (rich in ultra-processed foods) | OR=1.27 (1.11, 1.45) | Maternal age, pre-pregnancy BMI, education level, marital status, family income and parity |
| VIEIRA E SOUZA[ | 2022 Brazil | Cross -sectional | LBW | 626 (43) | FFQ | maximum vs minimum | OR=1.72 (1.09, 2.70) | Maternal age, pre-pregnancy BMI, education level, percapita income, marital status, gestational weight gain, parity, number of prenatal consultations, smoking and physical activity level |
| SCHRUBBE[ | 2024 Brazil | Cohort | LGA | 214 (22) | 24hDietary Rcall | maximum vs minimum | OR=1.03 (1.00, 1.06) | Maternal age, pre-pregnancy BMI, race, education level, family income, marital status, parity, smoking and alcohol consumption |
| ROCHA[ | 2022 Brazil | Cross -sectional | SGA | 300 (17) | FFQ | UPF energy proportion>2.06% vs <0.31% | OR=10.4 (1.33, 8090) | Maternal age, pre-pregnancy BMI, total energy intake, education level, family income, marital status, parity, smoking, alcohol consumption and race |
| VICTOR[ | 2023 Brazil | Cross -sectional | SGA LBW | 2 632 314 (186 206/188 450) | / | maximum vs minimum | OR=1.04 (1.02, 1.06) OR=1.13 (1.11, 1.16) | Maternal age, marital status, education level, gestational age, number of prenatal appointments, newborn gender and race |
| LEONE[ | 2021 Spain | Cohort | GDM | 3 730 (186) | FFQ | <3 servings/d vs >4.5servings/d | OR=1.10 P=0.82 | Maternal age, pre-pregnancy BMI, education level, smoking, physical activity, family history of diabetes, parity, time spent watching TV, hypertension, nutritional therapy and total energy intake |
| LAMYIAN[ | 2017 Iran | Cohort | GDM | 1 026 (71) | FFQ | maximum vs minimum | OR=2.12 (1.12, 5.43) | Maternal age, pre-pregnancy BMI, physical activity, family history of diabetes, history of GDM, smoking, drug use and total energy intake |
| YISAHAK[ | 2022 USA | Cohort | GDM PE | 1 948 (85/63) | FFQ | maximum vs minimum | OR=0.68 (0.44, 1.05) OR=0.99 P=0.85 OR=1.33 P=0.63 | Maternal age, pre-pregnancy BMI, race, family income, education level, marital status, parity, physical activity, sleep duration and total energy intake |
| SEDAGHAT[ | 2017 Iran | Case -control | GDM | 388 (122) | FFQ | Western dietary pattern | OR=1.68 (1.04, 2.27) | Maternal age, pre-pregnancy BMI, gestational age, education level, socioeconomic status, smoking, family history of diabetes and supplement use |
| ASADI[ | 2019 Iran | Case -control | GDM | 278 (130) | FFQ | Western dietary pattern | OR=1.50 (0.74, 3.03) P=0.2 | Maternal age, physical activity level, family history of diabetes, pre-pregnancy BMI, education level, occupational status, history of fetal macrosomia and total energy intake |
| DONAZAR-EZCURRA[ | 2018 Spain | Cohort | GDM | 3 396 (172) | FFQ | Soft drink intake > 400 g/w | OR=2.03 (1.25, 3.31) | Maternal age, pre-pregnancy BMI, family history of diabetes, smoking, total energy intake, physical activity, parity, fast food intake, adherence to Mediterranean dietary patterns, alcohol intake, multiple pregnancies and cardiovascular disease/hypertension |
| BRANTSAETER[ | 2009 Norway | Cohort | PE | 23 423 (1 267) | FFQ | Western dietary pattern | OR=1.21 (1.03, 1.42) | Maternal age, pre-pregnancy BMI, education level, smoking, pre-pregnancy hypertension status, dietary supplement use and total energy intake |
| Cohort | Gestationalhypertension PE | 55 139 (5 491) | FFQ | Western dietary pattern | Gestational hypertension OR=1.18 (1.05, 1.33) PE OR=1.40 (1.11, 1.76) | Maternal age, pre-pregnancy BMI, parity, smoking, physical activity level, paired sociodemographic status, total energy intake and previous history of hypertension |
表1 UPFs摄入对妊娠结局的影响
Tab 1 Effect of intake of UPFs on gestational outcomes
| Author | Year Country | Study design | Outcome | Number (Case) | Dietary Investigation tool | UPF consumption | OR (95%CI or P value) | Covariate |
|---|---|---|---|---|---|---|---|---|
| MARTIN[ | 2015 USA | Cohort | Preterm birth | 3 143 (364) | FFQ | Western dietary pattern maximum vs minimum | OR=1.53 (1.02, 2.30) | Maternal age, race, pre-pregnancy BMI, educational level, marital status, parity, family income, smoking status in the first 6 months of pregnancy and energy intake |
| ENGLUND-ÖGGE[ | 2014 Norway | Cohort | Preterm birth | 66 000 (3 505) | FFQ | Western dietary pattern | HR=1.12 (1.01, 1.25) P>0.05 | Maternal history of previous preterm delivery, maternal age, height, pre-pregnancy BMI, marital status, parity, smoking, education level, family income, and total energy intake |
| ENGLUND-ÖGGE[ | 2012 Norway | Cohort | Preterm birth | 60 761 (3 281) | MoBa FFQ | Sugar -sweetened beverages maximum vs minimum Artificially sweetened maximum vs minimum | OR=1.11 (1.00, 1.24) OR=1.25 (1.08, 1.45) | Maternal history of previous preterm delivery, maternal age, pre-pregnancy BMI, marital status, parity, smoking, education level and total energy intake |
| RODRIGUES[ | 2020 Brazil | Cross -sectional | LBW | 99 (13) | Form of food consumption markers in the food and nutrition surveillance system | maximum vs minimum | OR=1.46 (1.02, 2.10) | Maternal age, marital status, education level, per capita income, prenatal weight, final fetal weight, increase in gestational age and gestational age, type of delivery, number of pregnancies |
| FERREIRA[ | 2022 Brazil | Cross -sectional | LBW | 260 (8) | FFQ | Dietary pattern 3 (rich in ultra-processed foods) | OR=1.27 (1.11, 1.45) | Maternal age, pre-pregnancy BMI, education level, marital status, family income and parity |
| VIEIRA E SOUZA[ | 2022 Brazil | Cross -sectional | LBW | 626 (43) | FFQ | maximum vs minimum | OR=1.72 (1.09, 2.70) | Maternal age, pre-pregnancy BMI, education level, percapita income, marital status, gestational weight gain, parity, number of prenatal consultations, smoking and physical activity level |
| SCHRUBBE[ | 2024 Brazil | Cohort | LGA | 214 (22) | 24hDietary Rcall | maximum vs minimum | OR=1.03 (1.00, 1.06) | Maternal age, pre-pregnancy BMI, race, education level, family income, marital status, parity, smoking and alcohol consumption |
| ROCHA[ | 2022 Brazil | Cross -sectional | SGA | 300 (17) | FFQ | UPF energy proportion>2.06% vs <0.31% | OR=10.4 (1.33, 8090) | Maternal age, pre-pregnancy BMI, total energy intake, education level, family income, marital status, parity, smoking, alcohol consumption and race |
| VICTOR[ | 2023 Brazil | Cross -sectional | SGA LBW | 2 632 314 (186 206/188 450) | / | maximum vs minimum | OR=1.04 (1.02, 1.06) OR=1.13 (1.11, 1.16) | Maternal age, marital status, education level, gestational age, number of prenatal appointments, newborn gender and race |
| LEONE[ | 2021 Spain | Cohort | GDM | 3 730 (186) | FFQ | <3 servings/d vs >4.5servings/d | OR=1.10 P=0.82 | Maternal age, pre-pregnancy BMI, education level, smoking, physical activity, family history of diabetes, parity, time spent watching TV, hypertension, nutritional therapy and total energy intake |
| LAMYIAN[ | 2017 Iran | Cohort | GDM | 1 026 (71) | FFQ | maximum vs minimum | OR=2.12 (1.12, 5.43) | Maternal age, pre-pregnancy BMI, physical activity, family history of diabetes, history of GDM, smoking, drug use and total energy intake |
| YISAHAK[ | 2022 USA | Cohort | GDM PE | 1 948 (85/63) | FFQ | maximum vs minimum | OR=0.68 (0.44, 1.05) OR=0.99 P=0.85 OR=1.33 P=0.63 | Maternal age, pre-pregnancy BMI, race, family income, education level, marital status, parity, physical activity, sleep duration and total energy intake |
| SEDAGHAT[ | 2017 Iran | Case -control | GDM | 388 (122) | FFQ | Western dietary pattern | OR=1.68 (1.04, 2.27) | Maternal age, pre-pregnancy BMI, gestational age, education level, socioeconomic status, smoking, family history of diabetes and supplement use |
| ASADI[ | 2019 Iran | Case -control | GDM | 278 (130) | FFQ | Western dietary pattern | OR=1.50 (0.74, 3.03) P=0.2 | Maternal age, physical activity level, family history of diabetes, pre-pregnancy BMI, education level, occupational status, history of fetal macrosomia and total energy intake |
| DONAZAR-EZCURRA[ | 2018 Spain | Cohort | GDM | 3 396 (172) | FFQ | Soft drink intake > 400 g/w | OR=2.03 (1.25, 3.31) | Maternal age, pre-pregnancy BMI, family history of diabetes, smoking, total energy intake, physical activity, parity, fast food intake, adherence to Mediterranean dietary patterns, alcohol intake, multiple pregnancies and cardiovascular disease/hypertension |
| BRANTSAETER[ | 2009 Norway | Cohort | PE | 23 423 (1 267) | FFQ | Western dietary pattern | OR=1.21 (1.03, 1.42) | Maternal age, pre-pregnancy BMI, education level, smoking, pre-pregnancy hypertension status, dietary supplement use and total energy intake |
| Cohort | Gestationalhypertension PE | 55 139 (5 491) | FFQ | Western dietary pattern | Gestational hypertension OR=1.18 (1.05, 1.33) PE OR=1.40 (1.11, 1.76) | Maternal age, pre-pregnancy BMI, parity, smoking, physical activity level, paired sociodemographic status, total energy intake and previous history of hypertension |
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