Maternal Dietary Diversity and Birth Weight in Offspring: Evidence from a Chinese Population-Based Study
Abstract
:1. Introduction
2. Methods
2.1. Data and Participants
2.2. Maternal Dietary Intake
2.3. Measures of Maternal Dietary Diversity
2.4. Assessment of Neonate Birth Weight
2.5. Ascertainment of Covariates
2.6. Statistical Analysis
3. Results
3.1. The Characteristics of the Participants
3.2. Maternal Dietary Diversity between LBW and Non-LBW Groups
3.3. Association between Maternal Dietary Diversity and Birth Weight or Low Birth Weight
3.4. Subgroup Analysis and Sensitivity Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhu, L.; Zhang, R.; Zhang, S.L.; Shi, W.J.; Yan, W.L.; Wang, X.L.; Lv, Q.; Liu, L.; Zhou, Q. Chinese neonatal birth weight curve for different gestational age. Zhonghua Er Ke Za Zhi 2015, 53, 97–103. [Google Scholar] [PubMed]
- Wu, W.T.; Zhang, B.Y.; Li, D.N.; Yan, M.J.; Deng, Q.W.; Kang, Y.J.; Du, J.Y.; Gao, X.Y.; Yan, H. Development and validation of nomogram for prediction of low birth weight: A large-scale cross-sectional study in northwest China. J. Matern. Fetal. Neonatal. Med. 2022, 35, 7562–7570. [Google Scholar] [CrossRef] [PubMed]
- WHO/UNICEF. Global Nutrition Targets 2025: Low Birth Weight Policy Brief; WHO: Geneva, Switzerland, 2014. [Google Scholar]
- Zhao, L.Y.; Yu, D.M.; Liu, A.D.; Jia, F.M.; Yu, W.T.; Zhai, F.Y. Analysis of health selective survey result of children and pregnant/lying-in women in China in 2006. Wei Sheng Yan Jiu 2008, 37, 65–67. [Google Scholar] [PubMed]
- Kramer, M.S. The epidemiology of low birthweight. Nestle. Nutr. Inst. Workshop Ser. 2013, 74, 1–10. [Google Scholar]
- Black, R.E.; Victora, C.G.; Walker, S.P.; Bhutta, Z.A.; Christian, P.; de Onis, M.; Ezzati, M.; Grantham-McGregor, S.; Katz, J.; Martorell, R.; et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 2013, 382, 427–451. [Google Scholar] [CrossRef]
- Chen, L.W.; Aubert, A.M.; Shivappa, N.; Bernard, J.Y.; Mensink-Bout, S.M.; Geraghty, A.A.; Mehegan, J.; Suderman, M.; Polanska, K.; Hanke, W.; et al. Associations of maternal dietary inflammatory potential and quality with offspring birth outcomes: An individual participant data pooled analysis of 7 European cohorts in the ALPHABET consortium. PLoS Med. 2021, 18, e1003491. [Google Scholar] [CrossRef]
- Madzorera, I.; Isanaka, S.; Wang, M.; Msamanga, G.I.; Urassa, W.; Hertzmark, E.; Duggan, C.; Fawzi, W.W. Maternal dietary diversity and dietary quality scores in relation to adverse birth outcomes in Tanzanian women. Am. J. Clin. Nutr. 2020, 112, 695–706. [Google Scholar] [CrossRef]
- King, J.C. Physiology of pregnancy and nutrient metabolism. Am. J. Clin. Nutr. 2000, 71 (Suppl. S5), 1218–1225. [Google Scholar] [CrossRef]
- Dewey, K.G. Reducing stunting by improving maternal, infant and young child nutrition in regions such as South Asia: Evidence, challenges and opportunities. Matern. Child. Nutr. 2016, 12 (Suppl. S1), 27–38. [Google Scholar] [CrossRef]
- Zerfu, T.A.; Umeta, M.; Baye, K. Dietary diversity during pregnancy is associated with reduced risk of maternal anemia, preterm delivery, and low birth weight in a prospective cohort study in rural Ethiopia. Am. J. Clin. Nutr. 2016, 103, 1482–1488. [Google Scholar] [CrossRef]
- Ashworth, A. Effects of intrauterine growth retardation on mortality and morbidity in infants and young children. Eur. J. Clin. Nutr. 1998, 52 (Suppl. 1), S34–S41. [Google Scholar]
- Christian, P.; Lee, S.E.; Angel, M.D.; Adair, L.S.; Arifeen, S.E.; Ashorn, P.; Barros, F.C.; Fall, C.H.; Fawzi, W.W.; Hao, W.; et al. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int. J. Epidemiol. 2013, 42, 1340–1355. [Google Scholar] [CrossRef]
- Perälä, M.M.; Männistö, S.; Kaartinen, N.E.; Kajantie, E.; Osmond, C.; Barker, D.J.; Valsta, L.M.; Eriksson, J.G. Body size at birth is associated with food and nutrient intake in adulthood. PLoS ONE 2012, 7, e46139. [Google Scholar] [CrossRef]
- Ochieng, J.; Afari-Sefa, V.; Lukumay, P.J.; Dubois, T. Determinants of dietary diversity and the potential role of men in improving household nutrition in Tanzania. PLoS ONE 2017, 12, e0189022. [Google Scholar] [CrossRef]
- WHO. Preparation and Use of Food-Based Dietary Guidelines; WHO Technical Report, Series 880. Report of a Joint FAO/WHO consultation; WHO: Geneva, Switzerland, 1996. [Google Scholar]
- Agustina, R.; Nadiya, K.; Andini, E.A.; Setianingsih, A.A.; Sadariskar, A.A.; Prafiantini, E.; Wirawan, F.; Karyadi, E.; Raut, M.K. Associations of meal patterning, dietary quality and diversity with anemia and overweight-obesity among Indonesian school-going adolescent girls in West Java. PLoS ONE 2020, 15, e0231519. [Google Scholar] [CrossRef]
- FAO and FHI 360. Minimum Dietary Diversity for Women: A Guide for Measurement; FAO: Rome, Italy, 2016. [Google Scholar]
- Yves, M.P.; Allemand, P.; Wiesmann, D.; Arimond, M.; Ballard, T.; Deichler, M.; Dop, M.; Kennedy, G.; Lee, W.T.K.; Moursi, M. Moving Forward on Choosing a Standard Operational Indicator of Women’s Dietary Diversity; FAO: Rome, Italy, 2015. [Google Scholar]
- FANTA. Measuring the Quality of Women’s Diets: Consensus on a Global Indicator for Women’s Dietary Diversity; FHI 360/FANTA: Washington, DC, USA, 2015. [Google Scholar]
- Madzorera, I.; Ghosh, S.; Wang, M.; Fawzi, W.; Isanaka, S.; Hertzmark, E.; Namirembe, G.; Bashaasha, B.; Agaba, E.; Turyashemererwa, F.; et al. Prenatal dietary diversity may influence underweight in infants in a Ugandan birth-cohort. Matern. Child. Nutr. 2021, 17, e13127. [Google Scholar] [CrossRef]
- Cano-Ibáñez, N.; Martínez-Galiano, J.M.; Amezcua-Prieto, C.; Olmedo-Requena, R.; Bueno-Cavanillas, A.; Delgado-Rodríguez, M. Maternal dietary diversity and risk of small for gestational age newborn: Findings from a case–control study. Clin. Nutr. 2020, 39, 1943–1950. [Google Scholar] [CrossRef]
- Zerfu, T.A.; Pinto, E.; Baye, K. Consumption of dairy, fruits and dark green leafy vegetables is associated with lower risk of adverse pregnancy outcomes (APO): A prospective cohort study in rural Ethiopia. Nutr. Diabetes 2018, 8, 52. [Google Scholar] [CrossRef]
- Gunderson, E.P.; Abrams, B.; Selvin, S. The relative importance of gestational gain and maternal characteristics associated with the risk of becoming overweight after pregnancy. Int. J. Obes. Relat. Metab. Disord. 2000, 24, 1660–1668. [Google Scholar] [CrossRef]
- Yang, J.M.; Dang, S.N.; Cheng, Y.; Qiu, H.Z.; Mi, B.B.; Jiang, Y.F.; Qu, P.F.; Zeng, L.X.; Wang, Q.L.; Li, Q.; et al. Dietary intakes and dietary patterns among pregnant women in Northwest China. Public Health Nutr. 2017, 20, 282–293. [Google Scholar] [CrossRef]
- Yang, J.M.; Cheng, Y.; Pei, L.L.; Jiang, Y.F.; Lei, F.L.; Zeng, L.X.; Wang, Q.L.; Li, Q.; Kang, Y.J.; Shen, Y.; et al. Maternal iron intake during pregnancy and birth outcomes: A crosssectional study in Northwest China. Br. J. Nutr. 2017, 117, 862–871. [Google Scholar] [CrossRef] [PubMed]
- Meltzer, H.M.; Brantsaeter, A.L.; Ydersbond, T.A.; Alexander, J.; Haugen, M. Methodological challenges when monitoring the diet of pregnant women in a large study: Experiences from the Norwegian Mother and Child Cohort Study (MoBa). Matern. Child. Nutr. 2008, 4, 14–27. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Y.; Yan, H.; Dibley, M.J.; Shen, Y.; Li, Q.; Zeng, L.X. Validity and reproducibility of a semi-quantitative food frequency questionnaire for use among pregnant women in rural China. Asia Pac. J. Clin. Nutr. 2008, 17, 166–177. [Google Scholar] [PubMed]
- Cheng, Y.; Dibley, M.J.; Zhang, X.L.; Zeng, L.X.; Yan, H. Assessment of dietary intake among pregnant women in a rural area of western China. BMC Public Health 2009, 9, 222. [Google Scholar] [CrossRef] [PubMed]
- Chinese Nutrition Society. Dietary Guidelines for Chinese Residents 2007; The Tibet People’s Publishing House: Lhasa, China, 2008. [Google Scholar]
- Gicevic, S.; Gaskins, A.J.; Fung, T.T.; Rosner, B.; Tobias, D.K.; Isanaka, S.; Willett, W.C. Evaluating pre-pregnancy dietary diversity vs. dietary quality scores as predictors of gestational diabetes and hypertensive disorders of pregnancy. PLoS ONE 2018, 13, e0195103. [Google Scholar] [CrossRef]
- Tian, X.; Wu, M.; Zang, J.; Zhu, Y.; Wang, H. Dietary diversity and adiposity in Chinese men and women: An analysis of four waves of cross-sectional survey data. Eur. J. Clin. Nutr. 2017, 71, 506–511. [Google Scholar] [CrossRef]
- UNICEF. WHO: Low Birthweight: Country, Regional and Global Estimates; UNICEF: New York, NY, USA, 2004. [Google Scholar]
- Inglis, V.; Ball, K.; Crawford, D. Why do women of low socioeconomic status have poorer dietary behaviours than women of higher socioeconomic status? A qualitative exploration. Appetite 2005, 45, 334–343. [Google Scholar] [CrossRef]
- Bloch, J.R.; Dawley, K.; Suplee, P.D. Application of the Kessner and Kotelchuck Prenatal Care Adequacy Indices in a Preterm Birth Population. Public Health Nurs. 2010, 26, 449–459. [Google Scholar] [CrossRef]
- Ramakrishnan, U.; Young, M.F.; Martorell, R. Maternal Nutrition and Birth Outcomes. In Nutrition and Health in a Developing World, 3rd ed.; de Pee, S., Taren, D., Bloem, M., Eds.; Humana Press: Cham, Switzerland, 2017; pp. 487–502. [Google Scholar]
- Filmer, D.; Pritchett, L.H. Estimating wealth effects without expenditure data—Or tears: An application to educational enrollments in states of India. Demography 2001, 38, 115–132. [Google Scholar]
- Wahl, S.; Boulesteix, A.L.; Zierer, A.; Thorand, B.; van de Wiel, M.A. Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. BMC Med. Res. Methodol. 2016, 16, 144. [Google Scholar] [CrossRef]
- Roux, A.V.D. Neighborhoods and health: Where are we and were do we go from here? Rev. Epidemiol. Sante. Publique 2007, 55, 13–21. [Google Scholar] [CrossRef]
- Yang, J.X.; Wang, M.; Tobias, D.K.; Rich-Edwards, J.W.; Darling, A.M.; Abioye, A.I.; Noor, R.A.; Madzorera, I.; Fawzi, W.W. Dietary diversity and diet quality with gestational weight gain and adverse birth outcomes, results from a prospective pregnancy cohort study in urban Tanzania. Matern. Child. Nutr. 2022, 18, e13300. [Google Scholar] [CrossRef]
- Saaka, M. Maternal dietary diversity and infant outcome of pregnant women in northern Ghana. Int. J. Child. Health Nutr. 2012, 1, 148–156. [Google Scholar] [CrossRef]
- Martínez-Galiano, J.M.; Amezcua-Prieto, C.; Salcedo-Bellido, I.; González-Mata, G.; Bueno-Cavanillas, A.; Delgado-Rodríguez, M. Maternal dietary consumption of legumes, vegetables and fruit during pregnancy, does it protect against small for gestational age? BMC Pregnancy Childbirth 2018, 18, 486. [Google Scholar] [CrossRef]
- Yang, J.M.; Chang, Q.Q.; Tian, X.Y.; Zhang, B.Y.; Zeng, L.X.; Yan, H.; Dang, S.N.; Li, Y.H. Dietary protein intake during pregnancy and birth weight among Chinese pregnant women with low intake of protein. Nutr. Metab. 2022, 19, 43. [Google Scholar] [CrossRef]
- Takimoto, H.; Yoshiike, N.; Katagiri, A.; Ishida, H.; Abe, S. Nutritional status of pregnant and lactating women in Japan: A comparison with non-pregnant/non-lactating controls in the National Nutrition Survey. J. Obstet. Gynaecol. Res. 2003, 29, 96–103. [Google Scholar] [CrossRef]
- Anonymous. Infant and young child nutrition and progress in implementing the International Code of Marketing of Breast-milk Substitutes: Report by the Secretariat. Am. J. Roentgenol. Radium Ther. Nucl. Med. 2004, 115, 751–759. [Google Scholar]
- He, Y.N.; Yang, X.G.; Xia, J.; Zhao, L.Y.; Yang, Y.X. Consumption of meat and dairy products in China: A review. Proc. Nutr. Soc. 2016, 75, 385–391. [Google Scholar] [CrossRef]
- Lachat, C.; Raneri, J.E.; Smith, K.W.; Kolsteren, P.; Van Damme, P.; Verzelen, K.; Penafiel, D.; Vanhove, W.; Kennedy, G.; Hunter, D.; et al. Dietary species richness as a measure of food biodiversity and nutritional quality of diets. Proc. Natl. Acad. Sci. USA 2018, 115, 127–132. [Google Scholar] [CrossRef]
MDD-W | p | |||
---|---|---|---|---|
T1 (Lowest) n = 2594 | T2 n = 2447 | T3 (Highest) n = 1764 | ||
Maternal age (year), | 26.0 ± 4.8 | 26.1 ± 4.6 | 26.3 ± 4.4 | 0.131 |
Gestational weeks (week), | 39.6 ± 1.3 | 39.5 ± 1.3 | 39.5 ± 1.3 | 0.071 |
Neonate birth weight (g), | 3248.1 ± 461.6 | 3271.6 ± 443.5 | 3291.4 ± 438.0 | 0.007 |
Neonate gender, n (%) | ||||
male | 1356 (52.3) | 1313 (53.7) | 958 (54.3) | 0.379 |
female | 1238 (47.7) | 1134 (46.3) | 806 (45.7) | |
Family economic status, n (%) * | ||||
low | 904 (34.8) | 809 (33.1) | 550 (31.2) | <0.001 |
middle | 938 (36.2) | 852 (34.8) | 578 (32.8) | |
high | 752 (29.0) | 786 (32.1) | 636 (36.1) | |
Maternal registered permanent residence, n (%) | ||||
urban | 282 (10.9) | 376 (15.4) | 337 (19.1) | <0.001 |
rural | 2313 (89.1) | 2071 (84.6) | 1427 (80.9) | |
Antenatal examination level, n (%) ** | ||||
low | 924 (35.6) | 724 (29.6) | 396 (22.4) | <0.001 |
middle | 912 (35.2) | 796 (32.5) | 518 (29.4) | |
high | 758 (29.2) | 927 (37.9) | 850 (48.2) | |
Folic acid supplements, n (%) | ||||
yes | 1815 (70.0) | 1837 (75.1) | 1378 (78.1) | <0.001 |
no | 779 (30.0) | 610 (24.9) | 386 (21.9) | |
Illness during pregnancy, n (%) | ||||
yes | 1532 (59.1) | 1433 (58.6) | 1040 (59.0) | 0.933 |
no | 1062 (40.9) | 1014 (41.4) | 724 (41.0) | |
Maternal education level, n (%) | ||||
low | 306 (11.8) | 207 (8.5) | 107 (6.1) | <0.001 |
middle | 1536 (59.2) | 1323 (54.1) | 825 (46.8) | |
high | 752 (29.0) | 917 (37.5) | 832 (47.2) | |
Maternal career, n (%) | ||||
farmers | 2005 (77.3) | 1737 (71.0) | 1162 (65.9) | <0.001 |
workers and merchants | 309 (11.9) | 341 (13.9) | 283 (16.0) | |
intellectuals | 280 (10.8) | 369 (15.1) | 319 (18.1) |
β | 95% CI | p | |
---|---|---|---|
Model 1 | |||
Overall MDD-W | 8.55 | 3.07, 14.02 | 0.002 |
Animal-based food | 20.30 | 9.15, 31.44 | <0.001 |
Non-animal-based food | 6.71 | −0.56, 13.97 | 0.070 |
Ratio | 50.69 | 10.52, 90.85 | 0.013 |
Model 2 | |||
Overall MDD-W | 9.15 | 3.92, 14.38 | 0.001 |
Animal-based food | 21.12 | 10.45, 31.80 | <0.001 |
Non-animal-based food | 7.45 | 0.51, 14.40 | 0.035 |
Ratio | 49.54 | 10.95, 88.12 | 0.012 |
Model 3 | |||
Overall MDD-W | 5.37 | 0.07, 10.66 | 0.047 |
Animal-based food | 11.40 | 0.47, 22.32 | 0.041 |
Non-animal-based food | 4.77 | −2.16, 11.71 | 0.177 |
Ratio | 22.48 | −16.26, 61.21 | 0.255 |
MDD-W | p | MDD-W Tertiles | p for Trend | ||||
---|---|---|---|---|---|---|---|
OR | 95% CI | T1 | T2 | T3 | |||
Model 1 | |||||||
Overall MDD-W | 0.93 | 0.86, 0.99 | 0.029 | 1.00 | 0.65 (0.48, 0.88) | 0.64 (0.46, 0.91) | 0.007 |
Animal-based food | 0.77 | 0.66, 0.89 | <0.001 | 1.00 | 0.61 (0.45, 0.84) | 0.62 (0.41, 0.95) | 0.003 |
Non-animal-based food | 0.97 | 0.89, 1.06 | 0.538 | 1.00 | 0.96 (0.69, 1.35) | 0.93 (0.68, 1.27) | 0.638 |
Ratio | 0.33 | 0.17, 0.63 | 0.001 | 1.00 | 0.86 (0.63, 1.16) | 0.56 (0.39, 0.80) | 0.001 |
Model 2 | |||||||
Overall MDD-W | 0.91 | 0.85, 0.98 | 0.014 | 1.00 | 0.61 (0.44, 0.85) | 0.61 (0.43, 0.89) | 0.005 |
Animal-based food | 0.73 | 0.62, 0.86 | <0.001 | 1.00 | 0.57 (0.41, 0.79) | 0.60 (0.38–0.95) | 0.002 |
Non-animal-based food | 0.96 | 0.87, 1.06 | 0.406 | 1.00 | 0.88 (0.62, 1.25) | 0.90 (0.64, 1.25) | 0.493 |
Ratio | 0.28 | 0.14, 0.59 | 0.001 | 1.00 | 0.82 (0.60, 1.13) | 0.53 (0.36, 0.77) | 0.001 |
Model 3 | |||||||
Overall MDD-W | 0.91 | 0.85, 0.98 | 0.015 | 1.00 | 0.60 (0.43, 0.83) | 0.62 (0.43, 0.89) | 0.005 |
Animal-based food | 0.73 | 0.62, 0.86 | <0.001 | 1.00 | 0.57 (0.41, 0.79) | 0.61 (0.38, 0.98) | 0.003 |
Non-animal-based food | 0.96 | 0.87, 1.06 | 0.381 | 1.00 | 0.87 (0.61, 1.23) | 0.89 (0.64, 1.24) | 0.470 |
Ratio | 0.29 | 0.14, 0.61 | 0.001 | 1.00 | 0.83 (0.60, 1.14) | 0.54 (0.37, 0.79) | 0.001 |
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Teng, Y.; Jing, H.; Chacha, S.; Wang, Z.; Huang, Y.; Yang, J.; Yan, H.; Dang, S. Maternal Dietary Diversity and Birth Weight in Offspring: Evidence from a Chinese Population-Based Study. Int. J. Environ. Res. Public Health 2023, 20, 3228. https://doi.org/10.3390/ijerph20043228
Teng Y, Jing H, Chacha S, Wang Z, Huang Y, Yang J, Yan H, Dang S. Maternal Dietary Diversity and Birth Weight in Offspring: Evidence from a Chinese Population-Based Study. International Journal of Environmental Research and Public Health. 2023; 20(4):3228. https://doi.org/10.3390/ijerph20043228
Chicago/Turabian StyleTeng, Yuxin, Hui Jing, Samuel Chacha, Ziping Wang, Yan Huang, Jiaomei Yang, Hong Yan, and Shaonong Dang. 2023. "Maternal Dietary Diversity and Birth Weight in Offspring: Evidence from a Chinese Population-Based Study" International Journal of Environmental Research and Public Health 20, no. 4: 3228. https://doi.org/10.3390/ijerph20043228