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All issues > Volume 69(5); 2026

Mekangkul, Visuthranukul, Sirisabya, Chitsinchayakul, Punnahitananda, and Chomtho: Human milk macronutrient composition and intake in relation to preterm infant growth: a cohort study

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

Eakkarin Mekangkul, MD1,2, Chonnikant Visuthranukul, MD, PhD2, Anongnart Sirisabya, MD3, Thaninee Chitsinchayakul, MD3, Santi Punnahitananda, MD3, Sirinuch Chomtho, MD, PhD2
Corresponding author: Chonnikant Visuthranukul, MD, PhD. Center of Excellence in Pediatric Nutrition, Division of Nutrition, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand Email: chonnikant.v@chula.ac.th
Received October 18, 2025       Revised January 18, 2026       Accepted January 22, 2026
Abstract
Background
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
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
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
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
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.
Key message
Graphical abstract. GA, gestational age; CI, confidence interval; HMA, human milk analyzer. Created using Bio-Render. M E. (2026) (https://BioRender.com/48hudnq.).
Introduction
Introduction
Nutrition is essential for preterm outcomes. Breast milk (BM) remains the optimal source of nutrition for both term and preterm neonates. Nevertheless, compared to term neonates, preterm infants require additional nutrients despite reaching full intakes of BM [1,2]. Macronutrient requirements in preterm infants are higher than those of term infants due to immature physiology and comorbidities [1,3]. Currently, standard fortification based on addition of fixed doses of commercial fortifiers has been routinely employed in many institutes worldwide. Individualized fortification considering different clinical settings has been proposed to optimize the nutritional management in preterm infants [4,5]. Nowadays, this process can be facilitated with a human milk analyzer (HMA) using a midinfrared transmission spectroscopy method which have been proven reliable for clinical use worldwide [6,7].
Intraindividual and interindividual variations in the macronutrient composition of preterm human milk have been documented [8-10]. Studies show many factors affect nutrients in human milk including maternal diets, diurnal effects, maternal health conditions and mode of delivery [11-13]. In terms of gestational age, the macronutrient composition of preterm human milk differs to some extent from that of term human milk [14,15]. Given the variation in macronutrient content of human milk and the delicate nature of individualized fortification methods, exploring the impact of each macronutrient on preterm infant growth may help optimize clinical application of individualized fortification and simplify the preparation process. Therefore, we aimed to identify associations between each macronutrient and the growth of preterm infants and to compare macronutrient intakes calculated using HMA-based values and documentary reference-based values.
Methods
Methods
1. Study design and participants
1. Study design and participants
We conducted a prospective study at the King Chulalongkorn Memorial Hospital, Bangkok, Thailand. The inclusion criteria were preterm infants born with a gestational age equal to or less than 34 weeks and their mothers. All infants were required to receive more than 80% of their total volume intake through enteral nutrition. Recruitment started when infants received human milk intake equal to or more than 50% of their total enteral intake. The mothers were able to consistently provide sufficient human milk for their infants. The exclusion criteria were major abnormalities or congenital gastrointestinal tract diseases that could impair growth or preclude optimal enteral feeding, and a length of stay of less than 7 days. Information pertaining to the study was provided to the parents, and written informed consent was obtained from them. Baseline demographic and clinical data were recorded from the time of recruitment up to 4 weeks, or less than 4 weeks if the infants were discharged earlier. Clinical data of the infants were recorded throughout the study period. Participants were recruited prospectively from April 2023 to February 2025, and additional retrospective data were included to achieve an optimal sample size (Supplementary Fig. 1). We retrospectively collected infants’ clinical data and results of human milk analyses that had been conducted as part of our routine clinical practice from June 2021 to September 2022. The study protocol was approved by the institutional review board (IRB) of the Faculty of Medicine, Chulalongkorn University (IRB No. 0080/66). The study was registered in Thai Clinical Trials Registry (TCTR20230831003).
2. Human milk collection and macronutrient analysis
2. Human milk collection and macronutrient analysis
The participating mothers were informed to collect human milk samples during their routine daily milk expression intended for storage. Researchers provided 2 prelabeled collecting tubes used for a weekly collection and the mothers continued collecting samples for 4 weeks or less if the participating infants were discharged earlier. The mothers were instructed on the expression of human milk starting from the foremilk to the hindmilk or until both breasts were completely emptied. All milk expression methods were acceptable. The minimum required volume per one collecting tube was 6 mL. The samples were then kept in a freezer at the participants’ homes and were delivered to the research team within one week of collection. All samples were stored at -20°C until analysis. Collection date and time were recorded. Samples were analyzed after the infants received the date-matched human milk. No samples of donor human milk were obtained in this study.
Human milk analysis was done using the Miris HMA (Miris AB, Sweden). The calibration control procedure was performed at daily start-up before further sample analyses. The unfortified human milk samples were thawed and subsequently warmed in the Miris Heater (Miris AB) until their temperatures reached 40°C for 5–10 minutes. Before each measurement, the samples were homogenized at 1.5 sec/mL for 12 seconds using the Miris ultrasonic processor. Macronutrient and energy contents were analyzed using the Miris HMA, which operates based on a midinfrared transmission spectroscopy method. Each sample was measured twice (3 mL/measurement). Carbohydrate, crude protein, true protein (excluding nonprotein nitrogen), and fat content results were reported in g/100 mL. Total energy content reported in kcal/100 mL was calculated based on the formula: (9.25×fat result)+(4.4×crude protein result)+(4.0×carbohydrate result). The analysis was completed by well-trained staff.
In terms of macronutrient intakes, we calculated carbohydrate, protein, fat and energy intakes from human milk, human milk fortifiers and medical formulas. Associations between macronutrient intakes and growth parameters were analyzed as the primary outcome. Furthermore, as a secondary outcome, we compared intake data derived from the HMA result-based calculations and those obtained from the documentary reference-based calculations [16].
3. Anthropometric measurements
3. Anthropometric measurements
Once the infants received human milk corresponding to the date-matched samples, accounting for 50% or more of the total enteral intake, their body weights were measured to the nearest 1 g by bedside nurses using standard electronic scales (Seca digital baby scale model 727, Germany) on both the first and eighth days. Weight gain velocity, reported in g/kg/day, was calculated by dividing the difference in weight between the first and eighth days by 7 times the weight on the first day. Supine length and head circumference at the maximal occipitofrontal circumference were measured to the nearest 0.1 cm. The rates of length and head circumference gain were reported as changes measured in centimeters per week (cm/wk).
4. Clinical and demographic cofactors
4. Clinical and demographic cofactors
Potential confounding factors were recorded and these included gestational age, small-for-gestational age, duration of parenteral nutrition (PN) support, positive pressure ventilation, early-onset neonatal sepsis, late-onset neonatal sepsis, patent ductus arteriosus (PDA), intraventricular hemorrhage, history of necrotizing enterocolitis (based on the modified Bell staging criteria), bronchopulmonary dysplasia, history of vasopressor, steroid and antibiotic use. In this study, bronchopulmonary dysplasia was defined based on the NICHD 2001 criteria [17]. Early-onset neonatal sepsis was defined as onset within 72 hours after birth. Early-onset neonatal sepsis was categorized into a culture-positive group and a culture-negative group [18]. Late-onset neonatal sepsis was defined as onset after 72 hours of life. Late-onset neonatal sepsis was categorized into a culture-positive group and a culture-negative group [18]. PDA was diagnosed by echocardiography. Hemodynamically significant PDA was defined as a PDA diameter ≥1.5 mm or a left atrial-to-aortic ratio >1.5. All infants diagnosed with hemodynamically significant PDA were treated with either medical or surgical closure [19].
5. Statistical analysis
5. Statistical analysis
The Shapiro-Wilk test was used to assess whether the data were normally distributed. Categorical data were presented with percentages. Continuous data such as gestational age, growth parameters (weight, length, and head circumference) were reported as mean±standard deviation or median and interquartile range depending on the data distribution. Paired t tests were performed to compare macronutrient levels in human milk based on the 2 different approaches. Associations between macronutrient intakes and the growth parameters were determined using generalized estimating equations with a linear model to control correlated data and to evaluate the overtime changes. A P value <0.05 was considered statistically significant. All primary analyses were performed using complete case analysis, utilizing only participants with complete data for the variables included in each specific model, as detailed in the participant flow diagram (Supplementary Fig. 1). No imputation methods were applied to estimate missing values. Participants lost to followup were excluded to ensure data completeness. Statistical analyses were performed using Stata 18.5 (StataCorp LLC, USA).
Results
Results
During the prospective period, 39 infants completed data collection. Additionally, 82 preterm infants with available human milk macronutrient analysis data who met the inclusion criteria were retrospectively included, resulting in a total of 121 infants recruited for the study.
Baseline characteristics of the participants are shown in Table 1. No missing data were observed for any variables of interest among participants. In addition to the information in Table 1, 94 infants were diagnosed with culture-negative early-onset neonatal sepsis (77.7%), and 16 infants were diagnosed with culture-negative late-onset neonatal sepsis (13.2%). The mean volume of human milk intake was 103 mL/kg/day (minimum 45 and maximum 173 mL/kg/day). The mean volume of medical formula intake, including either a preterm formula or a nutritionally complete, energy-dense formula, was 64 mL/kg/day (minimum 38 and maximum 88 mL/kg/day). Notably, all infants received human milk comprising at least 50% of their total enteral intake. Forty percent of infants received supplemental medical formula for 50% of total volume intake. Of 107 infants who received PN during their hospitalization, only one infant received PN during the first week of data collection, which was weaned off after 2 days.
1. Macronutrient content in human milk
1. Macronutrient content in human milk
Macronutrient contents in human milk are presented in Table 2. Among the 200 HM samples, 121, 45, 21 and 13 were collected during the first, second, third and fourth collection weeks, respectively. There were no significant differences in carbohydrate, fat and energy content across the 4 weeks of collection. Protein content showed a significant decreasing trend from the first to the fourth week of collection (P=0.001).
2. Associations between macronutrient intakes and growth parameters
2. Associations between macronutrient intakes and growth parameters
The associations between macronutrient intake and growth are demonstrated in Table 3. In terms of weight gain, protein and fat intakes had statistically significant positive associations with the rates of weight gain (g/kg/day). Fat intake showed a higher association than protein intake with adjusted unstandardized coefficients of 7.07 (95% confidence interval [CI], 1.73–12.42; P=0.009) and 3.41 (95% CI, 0.83–5.98; P=0.010), respectively. Carbohydrate and energy intakes were not significantly associated with weight gain. There was not a significant association between macronutrient and energy intakes and gains in length and head circumference.
Regarding the protein-to-energy intake ratio, the means (95% CI) at the first, second, third, and fourth week were 2.15 (2.10–2.20), 2.18 (2.10–2.25), 2.22 (2.10–2.35), and 2.26 (2.06–2.47) g/100 kcal, P>0.05. The protein-to-energy intake ratio exhibited a significant association with the rate of body weight gain, as shown in Supplementary Table 1. Concerning length and head circumference gains, the associations were not significant, with unstandardized coefficients of -0.06 (95% CI, -0.38 to 0.28; P=0.743) and -0.04 (95% CI, -0.26 to 0.19; P=0.756), respectively.
Considering macronutrient and energy intakes calculated solely from human milk, excluding fortifiers, the present study did not demonstrate significant associations with growth parameters, as shown in Table 4.
As indicated in Table 5, among all prefortified human milk macronutrient levels, fat content was the only macronutrient significantly associated with weight gain. No significant associations were found between macronutrient content and gains in length or head circumference.
3. Comparison of macronutrient intakes between the 2 calculation approaches
3. Comparison of macronutrient intakes between the 2 calculation approaches
Carbohydrate intakes estimated from the HMA-based method were consistently higher than those estimated from the reference-based method across the 4 weeks of collection, as demonstrated in Fig. 1. In contrast, protein intakes estimated from the HMA-based method showed a consistently lower trend across the 4 weeks of collection. The mean differences in fat intake were significantly lower when using the HMA-based method at the third and fourth weeks. The energy intake estimated using the HMA-based method was significantly higher in the first week and significantly lower in the fourth week.
The comparison of macronutrient intakes derived exclusively from the human milk component using 2 calculation methods is illustrated in Supplementary Fig. 2. The mean differences in all macronutrient and energy intakes exhibited similar trends. Additional details are provided in Supplementary Tables 23.
Discussion
Discussion
The present study demonstrates the distinct impact of each macronutrient intake on the rates of weight gain in preterm infants. Among all macronutrients, higher fat content in human milk was associated with greater weight gain velocity. With respect to macronutrient intakes, fat and protein intakes showed the strongest association with weight gain velocity.
From the present study, an increase in total fat intake may significantly enhance weight gain in preterm infants, aligning with Belfort et al. [20], who reported that higher fat intake from prefortified HM was associated with higher weight z score, fat mass and fat-free mass. Preterm infants demonstrate rapid postnatal gains in weight and fat mass compared with term infants, potentially explained by the “thrifty catch-up fat” concept [21-23]. Increased energy and fat intakes were positively associated with greater body fat mass in preterm infants [24,25]. Increased body fat mass is linked to long-term metabolic syndrome in preterm infants [26].
Protein intake was significantly associated with the rates of weight gain. This finding is consistent with previous studies [27,28]. Lin et al. [27] found that increased daily protein intake and protein concentration in human milk were related to increased weight gain velocity in preterm infants. Amissah et al. [28] conducted an intervention review and found greater weight gain in the protein supplementation group. Adequate protein intake is crucial for building tissues and supporting catch-up growth in preterm infants [29,30]. Lingwood et al. [31] reported that lean body mass significantly increased with higher protein intake, but not fat mass in preterm infants. Protein intake positively relates to insulin-like growth factor I and increased lean mass prior to term age [32].
We did not find a significant association between carbohydrate intake and growth parameters. This result aligns with the results of previous studies [20,27]. Conversely, Collins et al. [33] reported that energy intake from carbohydrates significantly impacted growth, proposing a non–nitrogen-sparing effect as the mechanism. Additionally, Amissah et al. [34] conducted a systematic review and found a positive relationship between carbohydrate supplementation in the form of prebiotics and weight gain. However, the reviewed study was of low quality, and the researchers suggested that more studies focusing on different types of carbohydrate supplementation were needed. The relatively low average carbohydrate intake observed in our study, attributable to overall lower milk intake compared to the former study, may have limited our ability to obtain comparable results.
Studies have demonstrated a positive relationship between energy intake and weight gain in preterm infants [35,36]. This association was not significant in our analysis. Considering the source of energy is essential, as excess energy with inadequate protein intake may result in increased fat mass and reduced lean mass [1]. In the present study, the protein-to-energy intake ratio was significantly associated with weight gain velocity. This finding underscores the importance of macronutrient distribution in preterm feeding, particularly the protein component. Benefits in growth and neurocognitive outcomes have been reported with the achievement of higher optimal protein-to-energy ratios [29,37].
When considering intakes derived exclusively from human milk, Belfort et al. [20] reported significantly positive associations between fat and energy intakes from prefortified human milk and weight z scores. Conversely, our study found no significant associations. The longer collection period and relatively higher total volume of human milk intake in the previous study may contribute to the variation in results. Crucially, over a short period, we observed significant positive associations of fat and protein intakes following human milk fortification, supporting the distinct impacts of each macronutrient in preterm feeding.
Regarding macronutrient composition in human milk, the current study found that fat content was significantly and positively associated with the rate of weight gain. However, findings on this association have varied. Lin et al. [27] found no significant association between fat content in human milk and the rate of preterm infant growth. Prentice et al. [38] reported an inverse relationship between fat levels in human milk and changes in body weight between 3 and 12 months of age. Several possible factors may influence fat content and contribute to the variations in findings, such as the method of human milk expression. Throughout the study period, human milk samples were collected while the infants were hospitalized; therefore, there were no specified pre-feed or post-feed periods. Macronutrient content is consistent regardless of collection time [39]. Beyond composition, actual nutrient intake may better reflect true associations with growth outcomes.
In the present study, we found significant differences in macronutrient intakes when calculated using data derived from the HMA compared to those based on standard reference values [16]. Protein intake was significantly lower, while carbohydrate intake was higher over the 4-week data collection period. These significant deficits in actual protein intake underscore the need for accurate evaluations of nutritional intake in preterm infants. The actual carbohydrate intake was higher than expected based on the reference data. Given the controversial impact of carbohydrate intake on growth in preterm infants, this difference requires further investigation. Fat intake was significantly lower during the late third and fourth weeks of data collection, which may have contributed to the reduced energy intake observed in the fourth week. Calculation of macronutrient intake using reference data may be more convenient and time-efficient; however, reference data are broad and only approximate. In light of our findings, we recommend the use of HMA-based methods for evaluating macronutrient intake in preterm infants whenever possible. To the best of our knowledge, no studies have previously reported the differences between the 2 calculation methods observed in our study. Given the variability in human milk macronutrients, the present study may provide fundamental data to help optimize the principle of individualized human milk fortification.
Our data further support the concept of individualized feeding for preterm infants by indicating the distinct impact of each macronutrient on preterm weight gain. The strengths of this study are as follows. First, we utilized a reliable bedside HMA that provided real-time and accurate macronutrient measurements. Our results may support healthcare providers in more routinely adopting the use of a HMA in clinical practice. Second, our findings may be applicable to preterm infants in settings where exclusive human milk feeding and mixed feeding patterns are routine clinical practices. Third, our study underlines the importance of the method used to calculate macronutrient intake for preterm infants, and we suggest using the HMA-based method as the first priority to optimize daily nutritional intake. However, this study had some limitations. First, we were unable to demonstrate significant associations between macronutrient intake or macronutrient content in human milk and length or head circumference gain. This finding is consistent with the previous study by Lin et al. [27]. We suggest that a longer duration of data collection might solve this limitation. Second, the methods of human milk sample collection may present challenges. Lastly, the retrospective nature of the additional method may introduce potential biases, despite our rigorous efforts to control confounding factors.
In conclusion, higher protein and fat intakes among macronutrients could contribute to greater weight gain in preterm infants. We support the use of HMA-based methods for calculating macronutrient intake in this population, as significant intake deficits in protein and fat were observed when using standard reference values. Overall, our findings provide additional evidence to support the principle of individualized human milk fortification.

Supplementary materials

Supplementary materials

Supplementary Tables 1-3 and Supplementary Figs. 1-2 are available at https://doi.org/10.3345/cep.2025.02509.
Supplementary Table 1.
Association between the protein-to-energy intake ratio and the rate of weight gain
cep-2025-02509-Supplementary-Table-1.pdf
Supplementary Table 2.
Comparison of total macronutrient intakes based on two calculation methods: HMA-based versus documentary reference-based.
cep-2025-02509-Supplementary-Table-2.pdf
Supplementary Table 3.
Comparison of macronutrient intakes derived exclusively from the human milk portion using two calculation methods
cep-2025-02509-Supplementary-Table-3.pdf
Supplementary Fig.1.
Flow diagram of participant recruitment
cep-2025-02509-Supplementary-Fig-1.pdf
Supplementary Fig.2.
Comparison of macronutrient intakes derived exclusively from the human milk portion using two calculation methods * Statistically significant difference (p-value <0.05). Data were reported in mean (95% CI) and p-values were assessed using pair-t-tests. HMA: human milk analyzer, (A) Carbohydrate intake (B) Protein intake (C) Fat intake (D) Energy intake
cep-2025-02509-Supplementary-Fig-2.pdf
Footnotes

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Funding

This study was funded by the Ratchadapiseksompotch Fund, Faculty of Medicine, Chulalongkorn University (Grant number: RA66/032).

Acknowledgments

The authors appreciate the efforts of the research team and all those involved in this study. The authors sincerely thank all participants and their parents for their contributions to the study.

Author contribution

Conceptualization: EM, CV, SC; Methodology: EM, CV, AS, TC, SP, SC; Investigation: EM, CV; Data curation: EM, CV; Project administration: EM, CV; Formal Analysis: EM; Visualization: EM; Writing - original draft: EM; Writing - review and editing: EM, CV, AS, TC, SP, SC

Fig. 1.
Total macronutrient intakes based on 2 calculation methods: human milk analyzer (HMA)-based versus reference-based. *Statistically significant difference (P<0.05). Data are reported as mean (95% confidence interval) and were assessed using paired t tests. (A) Carbohydrate intake. (B) Protein intake. (C) Fat intake. (D) Energy intake.
cep-2025-02509f1.tif
cep-2025-02509f2.tif
Table 1.
Participant demographic data and clinical characteristics (n=121)
Characteristics Value
Male sex 62 (51.2)
Gestational age (wk)a) 30.4±2.7
 <28 18 (14.9)
 28–32 53 (43.8)
 33–34 50 (41.3)
Postnatal complications
 Culture-positive early-onset neonatal sepsis 0 (0)
 Culture-positive late-onset neonatal sepsis 3 (2.5)
 Ventilator-associated pneumonia 7 (5.8)
 Patent ductus arteriosus 49 (40.5)
 Necrotizing enterocolitisb) 9 (7.4)
 Intraventricular hemorrhage 15 (12.4)
 Bronchopulmonary dysplasia 33 (27.3)
Maternal age at delivery (yr) 33.3±4.5
Mode of delivery
 Vaginal delivery 19 (15.7)
 Cesarean section 102 (84.3)
Anthropometric data at birth
 Weight (g) 1,366 (1,044–1,715)
 Weight (z score) 0.64 (-0.23 to 1.27)
 Small-for-gestational age 23 (19)
 Length (cm) 38.6±4.2
 Length (z score) 0.49 (-0.50 to 1.13)
 Head circumference (cm) 27.5±2.9
 Head circumference (z score) -0.08 (-0.98 to 0.62)
Patterns of feeding
 FBM and MF 34 (28.1)
 BM and MF 15 (12.4)
 Only FBM 51 (42.1)
 Only BM 21 (17.4)
Receiving PN nutrition during admission 107 (88.4)
Duration of PN nutrition support (day) 10 (6–17)

Values are presented as number (%), mean±standard deviation, or median (interquartile range).

BM, breast milk; FBM, fortified BM; MF, medical formulas including preterm or nutritionally complete energy-dense formula; PN, parenteral nutrition.

a) Subgroups are based on the World Health Organization classification. [40]

b) The maximal stage in this study was stage 2a based on the modified Bell staging criteria.

Table 2.
Macronutrient content of human milk across 4 weekly collections
Macronutrients and energy Week 1 (n=121) Week 2 (n=45) Week 3 (n=21) Week 4 (n=13) P value
Carbohydrate (g/100 mL) 0.322
 Mean (95% CI) 8.02 (7.93–8.12) 7.98 (7.82–8.14) 8.01 (7.77–8.24) 7.95 (7.67–8.22)
P value Reference 0.316 0.113 0.179
Protein (g/100 mL) 0.001
 Mean (95% CI) 1.29 (1.24–1.34) 1.18 (1.1–1.26) 1.19 (1.06–1.31) 1.19 (1.04–1.34)
P value Reference 0.003 0.008 0.001
Fat (g/100 mL) 0.666
 Mean (95% CI) 3.57 (3.42–3.72) 3.54 (3.29–3.79) 3.5 (3.12–3.87) 3.22 (2.79–3.65)
P value Reference 0.912 0.680 0.243
Energy (kcal/100 mL) 0.687
 Mean (95% CI) 71.95 (70.39–73.51) 70.89 (68.71–73.07) 70.6 (67.17–74.04) 67.84 (63.91–71.77)
P value Reference 0.968 0.684 0.239

Values are presented as mean (95% CI) and were assessed using population-averaged generalized estimating equations with a linear model.

CI, confidence interval.

Boldface indicates a statistically significant difference with P<0.05.

Table 3.
Associations between total macronutrient intakea) and growth parameters
Variable Weight gain (g/kg/day) Length gain (cm/wk) Head circumference gain (cm/wk)
Total intakes B 95% CI P value B 95% CI P value B 95% CI P value
 Carbohydrate (g/kg/day) 1.03 0.67–1.39 <0.001 0.05 0.01–0.10 0.019 0.01 -0.02 to 0.04 0.505
 Protein (g/kg/day) 4.38 3.11–5.65 <0.001 0.15 -0.01 to 0.31 0.069 0.03 -0.08 to 0.14 0.567
 Fat (g/kg/day) 2.08 1.55–2.60 <0.001 0.08 0.01–0.15 0.018 0.05 0–0.1 0.051
 Energy (kcal/kg/day) 0.15 0.11–0.19 <0.001 0.01 0–0.01 0.012 0.002 -0.001 to 0.01 0.197
Total intakes aBb) 95% CI P value aBc) 95% CI P value aBb) 95% CI P value
 Carbohydrate (g/kg/day) 2.19 -0.27 to 4.66 0.081 0.12 -0.20 to 0.43 0.472 (-) (-) (-)
 Protein (g/kg/day) 3.41 0.83–5.98 0.010 0 -0.33 to 0.32 0.985 (-) (-) (-)
 Fat (g/kg/day) 7.07 1.73–12.42 0.009 0.2 -0.43 to 0.83 0.526 0.04 -0.10 to 0.17 0.618
 Energy (kcal/kg/day) -0.45 -0.99 to 0.10 0.102 -0.02 -0.08 to 0.05 0.641 0 -0.01 to 0.01 0.642

B, unadjusted unstandardized coefficient; CI, confidence interval; aB, adjusted unstandardized coefficient.

Uni- and multivariate analyses were performed using population-averaged generalized estimating equations and a linear model.

Variables with values of P<0.2 in the univariate analysis were included in the multivariate model.

(-) Data not included in subsequent multivariate analyses.

a) Total macronutrient intake was calculated of human and nonhuman milk components, including human milk fortifier, preterm formula, and nutritionally complete energy-dense formula.

b) Adjusted for ventilator-associated pneumonia. c)Adjusted for ventilator-associated pneumonia and late-onset neonatal sepsis.

Table 4.
Associations between growth parameters and human milk macronutrient intake (excluding fortifiers)
Intakes Weight gain (g/kg/day)
Length gain (cm/wk)
Head circumference gain (cm/wk)
B 95% CI P value B 95% CI P value B 95% CI P value
Carbohydrate (g/kg/day) -0.18 -0.44 to 0.08 0.269 -0.01 -0.04 to 0.02 0.458 -0.01 -0.03 to 0.01 0.169a,b)
Protein (g/kg/day) -0.38 -1.62 to 0.86 0.553 -0.09 -0.23 to 0.05 0.226 -0.08 -0.18 to 0.01 0.085a,b)
Fat (g/kg/day) 0.24 -0.27 to 0.75 0.35 -0.01 -0.07 to 0.05 0.671 -0.01 -0.05 to 0.03 0.774
Energy (kcal/kg/day) 0 -0.03 to 0.03 0.832 -0.001 -0.004 to 0.002 0.530 -0.001 -0.003 to 0.001 0.326

B, unadjusted unstandardized coefficient; CI, confidence interval.

Uni- and multivariate analyses were performed using population-averaged generalized estimating equations with a linear model. Variables with values of P<0.2 in the univariate analysis were included in the multivariate model.

a) Adjusted unstandardized coefficients (aB) for carbohydrate and protein intake were 0 (95% CI, -0.04 to 0.05), P=0.844; and -0.09 (95% CI, -0.31 to 0.13), P=0.415, respectively.

b) Adjusted for gestational age, ventilator-associated pneumonia, positive ventilation support, and intraventricular hemorrhage.

Table 5.
Associations between macronutrient levels and growth parameters in human milk
Macronutrient levels Weight gain (g/kg/day)
Length gain (cm/wk)
Head circumference gain (cm/wk)
B 95% CI P value B 95% CI P value B 95% CI P value
Carbohydrate (g/100 mL) -0.38 -1.90 to 1.13 0.623 0.10 -0.07 to 0.27 0.258 0.01 -0.11 to 0.12 0.924
Protein (g/100 mL) 2.08 -0.66 to 4.83 0.137a,b) -0.17 -0.48 to 0.15 0.310 -0.13 -0.34 to 0.08 0.228
Fat (g/100 mL) 1.40 0.50, 2.31 0.002a,b) -0.04 -0.07 to 0.14 0.498 0.03 -0.04 to 0.11 0.364
Energy (kcal/100 mL) 0.13 0.039, 0.22 0.005a,b) 0.005 -0.01 to 0.02 0.392 0.002 -0.01 to 0.01 0.597

B, unadjusted unstandardized coefficient; CI, confidence interval.

Uni- and multivariate analyses were performed using population-averaged generalized estimating equations with a linear model.

Variables with values of P<0.2 in the univariate analysis were included in the multivariate model.

a) Adjusted unstandardized coefficient for protein, fat, and energy intake were 2.65 (95% CI, -0.40 to 5.69), P=0.089; 3.07 (95% CI, 0.22–5.93), P=0.035; and -0.19 (95% CI, -0.48 to 0.11), P=0.218, respectively.

b) Adjusted for ventilator-associated pneumonia.

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