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ObjectiveInsufficient sleep is common in postpartum mothers. The main objectives of this study are to explore the sleep duration among Chinese lactating mothers and preliminarily investigate the relationship between sleep duration and feeding pattern. The secondary objectives are to investigate the relationships between sleep duration and milk macronutrients and between maternal-related indicators, including melatonin (MT), growth hormone (GH), ghrelin (GHRL), glucagon-like peptide-1 (GLP-1), prolactin (PRL), and cholecystokinin (CCK).MethodsThe present study comprises a longitudinal and a cross-sectional cohort from December 2019 to December 2021. Postpartum lactating women living in Shanghai were recruited through online and offline recruitment. The subjects were included in the longitudinal cohort or cross-sectional study based on their lactation period at the time of recruitment. The longitudinal cohort included a total of 115 mothers. Human milk and feeding pattern were measured and collected at 2–4 months and 5–7 months postpartum. At four predetermined follow-up time points, data on sleep duration was collected (at the time of recruitment, 2–4 months postpartum, 5–7 months postpartum, and 12–17 months postpartum). The cross-sectional study included 35 lactating mothers (2–12 months postpartum) who reported their sleep duration and provided blood samples. Mid-infrared spectroscopy (MIRS) method was used to analyze the macronutrients of breast milk, while MT, GH, GHRL, GLP-1, PRL, and CCK in maternal blood were determined by ELISA.ResultsThe maternal sleep duration before pregnancy was 8.14 ± 1.18 h/d (n = 115), 7.27 ± 1.31 h/d (n = 113) for 2–4 months postpartum, 7.02 ± 1.05 h/d (n = 105) for 5–7 months postpartum, and 7.45 ± 1.05 h/d (n = 115) for 12–17 months postpartum. The incidence of insufficient sleep (<7 h/d) before pregnancy (12.17%) was significantly less than at any follow-up time after delivery (vs. 2–4 months postpartum, χ2 = 10.101, p = 0.001; vs. 5–7 months postpartum, χ2 = 15.281, p < 0.0001; vs. 12–17 months postpartum, χ2 = 6.426, p = 0.011). The percentage of insufficient maternal sleep was highest at 5–7 months postpartum (34.29%). No significant difference was found between the incidence of insufficient sleep at 5–7 months postpartum, 2–4 months postpartum (29.20%, χ2 = 0.650, p = 0.420), and 12–17 months postpartum (25.22%, χ2 = 2.168, p = 0.141). At 2–4 months postpartum, the frequency of formula feeding per day is related to reduced maternal sleep duration (Standardization coefficient β = −0.265, p = 0.005, Adjusted R2 = 0.061). At 2–4 months and 5–7 months postpartum, the relationship between macronutrients in breast milk and the mother's sleep duration was insignificant (all p > 0.05). Other than the positive correlation found between maternal GHRL and sleep duration (r = 0.3661, p = 0.0305), no significant relationship was observed between sleep duration and other indexes (all p > 0.05).ConclusionsPostpartum mothers generally sleep less, but there is no correlation between insufficient sleep and the macronutrient content of breast milk. Formula feeding may be related to the mother's sleep loss, while breastfeeding (especially direct breastfeeding) may be related to increased maternal sleep duration. The findings suggest that sleep duration is related to maternal serum GHRL. More high-quality studies are needed to clarify the mechanism of these findings and provide a solid theoretical basis and support references for breastfeeding.

Objective Fat, carbohydrates (mainly lactose) and protein in breast milk all provide indispensable benefits for the growth of newborns. The only source of nutrition in early infancy is breast milk, so the energy of breast milk is also crucial to the growth of infants. Some macronutrients composition in human breast milk varies greatly, which could affect its nutritional fulfillment to preterm infant needs. Therefore, rapid analysis of macronutrients (including lactose, fat and protein) and milk energy in breast milk is of clinical importance. This study compared the macronutrients results of a mid-infrared (MIR) analyzer and an ultrasound-based breast milk analyzer and unified the results by machine learning. Methods This cross-sectional study included breastfeeding mothers aged 22–40 enrolled between November 2019 and February 2021. Breast milk samples (n = 546) were collected from 244 mothers (from Day 1 to Day 1086 postpartum). A MIR milk analyzer (BETTERREN Co., HMIR-05, SH, CHINA) and an ultrasonic milk analyzer (Honɡyanɡ Co,. HMA 3000, Hebei, CHINA) were used to determine the human milk macronutrient composition. A total of 465 samples completed the tests in both analyzers. The results of the ultrasonic method were mathematically converted using machine learning, while the Bland-Altman method was used to determine the limits of agreement (LOA) between the adjusted results of the ultrasonic method and MIR results. Results The MIR and ultrasonic milk analyzer results were significantly different. The protein, fat, and energy determined using the MIR method were higher than those determined by the ultrasonic method, while lactose determined by the MIR method were lower (all p < 0.05). The consistency between the measured MIR and the adjusted ultrasound values was evaluated using the Bland-Altman analysis and the scatter diagram was generated to calculate the 95% LOA. After adjustments, 93.96% protein points (436 out of 465), 94.41% fat points (439 out of 465), 95.91% lactose points (446 out of 465) and 94.62% energy points (440 out of 465) were within the LOA range. The 95% LOA of protein, fat, lactose and energy were - 0.6 to 0.6 g/dl, -0.92 to 0.92 g/dl, -0.88 to 0.88 g/dl and - 40.2 to 40.4 kj/dl, respectively and clinically acceptable. The adjusted ultrasonic results were consistent with the MIR results, and LOA results were high (close to 95%). Conclusions While the results of the breast milk rapid analyzers using the two methods varied significantly, they could still be considered comparable after data adjustments using linear regression algorithm in machine learning. Machine learning methods can play a role in data fitting using different analyzers.