共 50 条
Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes
被引:1
|作者:
Bodnar, Lisa M.
[1
,2
]
Kirkpatrick, Sharon, I
[3
]
Parisi, Sara M.
[1
]
Jin, Qianhui
[1
]
Naimi, Ashley, I
[4
]
机构:
[1] Univ Pittsburgh, Sch Publ Hlth, Dept Epidemiol, Pittsburgh, PA 15260 USA
[2] Univ Pittsburgh, Sch Med, Dept Obstet Gynecol & Reprod Sci, Pittsburgh, PA 15260 USA
[3] Univ Waterloo, Sch Publ Hlth Sci, Waterloo, ON, Canada
[4] Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA USA
关键词:
dietary patterns;
gestational diabetes;
machine learning;
preeclampsia;
pregnancy;
preterm birth;
GESTATIONAL DIABETES-MELLITUS;
HYPERTENSIVE DISORDERS;
VALIDATION;
SCALE;
RISK;
QUESTIONNAIRE;
PREECLAMPSIA;
ASSOCIATION;
D O I:
10.1016/j.tjnut.2023.12.013
中图分类号:
R15 [营养卫生、食品卫生];
TS201 [基础科学];
学科分类号:
100403 ;
摘要:
Background: The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health. Objective: We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes. Methods: We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010-2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used kmeans clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors. Results: The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002). Conclusions: Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.
引用
收藏
页码:680 / 690
页数:11
相关论文