Research progress in the relationship between ultra-processed food intake and pregnancy outcomes

doi:10.3969/j.issn.1674-8115.2025.01.014

Abstract

Abstract:

In recent years, the global consumption of ultra-processed foods (UPFs) has increased. UPFs are classified as the fourth group of food in the NOVA classification system: industrially formulated foods made entirely or mostly from substances extracted from foods (oils, fats, sugar, starch, proteins, etc), derivatives of food constituents (hydrogenated fats, modified starches, etc), or multiple food additives. Common manufacturing techniques include extrusion, moulding, and pre-frying. As high-energy-density foods, UPFs are typically characterized by high levels of sugar, fat, and salt, and low levels of dietary fiber, protein, vitamins, and minerals, resulting in low nutrient density. Studies have shown that a high intake of UPFs increases the risk of various chronic diseases. Nutrition during pregnancy is a crucial factor influencing pregnancy outcomes, and balanced and adequate nutrient intake is essential for the health of both the mother and child. Given that UPFs have limited nutritional density, high intake during pregnancy may be detrimental to maternal and infant health. However, the impact of consuming UPFs during pregnancy on maternal and infant health is not extensively studied. This article reviews the literature on the effects of UPFs on pregnancy outcomes, aiming to provide a foundation for further research and personalized dietary guidance.

Key words: ultra-processed food, NOVA classification, gestational nutrition, pregnancy outcomes

CLC Number:

R153.1

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.

Figures/Tables 1

TrendMD

References 57

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Author	Year Country	Study design	Outcome	Number (Case)	Dietary Investigation tool	UPF consumption	OR (95%CI or P value)	Covariate
MARTIN^[23]	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^[24]	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^[25]	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^[26]	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^[27]	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^[28]	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^[29]	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^[30]	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^[31]	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^[32]	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^[33]	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^[34]	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^[14]	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^[35]	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^[36]	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^[15]	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
IKEM^[37]	2019 Danmark	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

Author	Year Country	Study design	Outcome	Number (Case)	Dietary Investigation tool	UPF consumption	OR (95%CI or P value)	Covariate
MARTIN^[23]	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^[24]	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^[25]	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^[26]	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^[27]	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^[28]	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^[29]	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^[30]	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^[31]	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^[32]	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^[33]	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^[34]	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^[14]	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^[35]	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^[36]	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^[15]	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
IKEM^[37]	2019 Danmark	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