Human milk macronutrient composition and intake in relation to preterm infant growth: a cohort study.

Background: Individualized targeted fortification based on human milk (HM) analysis reportedly achieves optimal outcomes in preterm infants. Therefore, understanding the effects of macronutrients in HM on preterm infant growth is essential. Purpose: This study aimed to determine the association between HM macronutrients and the growth of preterm infants. We also compared macronutrient intake data obtained from an HM analyzer (HMA)–based calculation with those derived from a reference-based calculation. Methods: This prospective-retrospective cohort study included infants born at 34 weeks' gestation or less. HM samples were collected weekly for up to 4 weeks or until discharge, whichever occurred first. Clinical outcomes were recorded. The macronutrient composition was analyzed using midinfrared HMA. Associations were determined using a linear regression model. Results: Of 121 preterm infants, 65 (51.2%) were male. A total of 200 HM samples were analyzed. Fat composition showed a significant positive association with weight gain velocity, with an adjusted unstandardized coefficient (aB) of 3.07 (95% confidence interval [CI], 0.22–5.93). Total protein and fat intakes were positively associated with weight gain (aB, 3.41; 95% CI, 0.83–5.98; and aB, 7.07; 95% CI, 1.73–12.42, respectively). When using the HMA-based calculation, protein intake was lower and carbohydrate intake was higher throughout the 4-week period compared with those obtained based on the reference-based calculation. Conclusion: Higher protein and fat intakes could potentially enhance weight gain in preterm infants. These findings provide further evidence to support the concept of individualized HM fortification. Our findings underscore the importance of using HMA-based methods to calculate macronutrient intakes among preterm infants. [ABSTRACT FROM AUTHOR]

Copyright of Clinical & Experimental Pediatrics is the property of Korean Pediatric Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)