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Qin J, Zhou Y, Li H, Meng Y, Tanumihardjo SA, Liu J. A Correlation Study of Plasma and Breast Milk Retinol Concentrations in Breastfeeding Women in China. Nutrients 2023; 15:5085. [PMID: 38140344 PMCID: PMC10745653 DOI: 10.3390/nu15245085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Retinol in breast milk is related to plasma concentration among breastfeeding women, but the linear or curvilinear relationships between the two remains unclear. We conducted a cross-sectional study in 403 Chinese breastfeeding women at 42 ± 7 days postpartum. Plasma and breast milk samples were assayed using high performance liquid chromatography to determine the concentration of retinol. Partial Spearman correlation and multivariable fractional polynomial regression were used to examine the relationships between the two retinol concentrations and between plasma retinol concentration and milk-to-plasma (M/P) retinol. The median (interquartile range, IQR) of the retinol concentration in the plasma was 1.39 (1.21, 1.63) μmol/L and 1.15 (0.83, 1.49) μmol/L in the breast milk, respectively. The partial correlation coefficient between them was 0.17 (p < 0.01). A linear relationship was observed with an adjusted regression coefficient of 0.34 (95% CI: 0.19, 0.49). The relationship between the plasma retinol and M/P ratio was nonlinear and segmented at 1.00 μmol/L of plasma retinol. The regression coefficients, below and above the segmented point, were -1.69 (95% CI: -2.75, -0.62) and -0.29 (95% CI: -0.42, -0.16), respectively. Plasma and breast milk retinol were positively correlated, whereas women with a low concentration of plasma retinol showed a stronger capacity of transferring retinol to breast milk.
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Affiliation(s)
- Jing Qin
- National Health Commission Key Laboratory of Reproductive Health/Institute of Reproductive and Child Health, Peking University Health Science Center, Beijing 100191, China; (J.Q.); (H.L.); (Y.M.)
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Yubo Zhou
- National Health Commission Key Laboratory of Reproductive Health/Institute of Reproductive and Child Health, Peking University Health Science Center, Beijing 100191, China; (J.Q.); (H.L.); (Y.M.)
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Hongtian Li
- National Health Commission Key Laboratory of Reproductive Health/Institute of Reproductive and Child Health, Peking University Health Science Center, Beijing 100191, China; (J.Q.); (H.L.); (Y.M.)
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Ying Meng
- National Health Commission Key Laboratory of Reproductive Health/Institute of Reproductive and Child Health, Peking University Health Science Center, Beijing 100191, China; (J.Q.); (H.L.); (Y.M.)
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Sherry A. Tanumihardjo
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Jianmeng Liu
- National Health Commission Key Laboratory of Reproductive Health/Institute of Reproductive and Child Health, Peking University Health Science Center, Beijing 100191, China; (J.Q.); (H.L.); (Y.M.)
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100191, China
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