Longitudinal Changes in Human Milk Macronutrients Across the First Year of Lactation in Indian Mothers

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Although early lactation has been extensively studied, longitudinal data beyond six months postpartum, particularly from low- and middle-income countries, remain limited. In India, prolonged exclusive breastfeeding and continued breastfeeding beyond two years are common, underscoring the need to understand temporal patterns of human milk composition. We aimed to characterize trajectories of human milk protein, carbohydrate, fat, and energy across the first year postpartum. Methods In this community-based longitudinal study in semi-urban India, healthy mother-infant dyads were followed at 1, 3, 6, 9, and 12 months postpartum, with standardized anthropometry performed, feeding practices recorded, and milk samples collected at each visit. Human milk macronutrients were quantified using mid-infrared spectroscopy. Longitudinal trajectories were examined using linear mixed-effects models, adjusting for the socioeconomic status, parity, maternal sum of skinfolds, and infant sex. Inflection points were derived from fitted quadratic models. Results At enrolment (N = 95 dyads, postpartum days 35 ± 10), mothers had a mean age of 25.9 ± 4.7 years (BMI: 22.2 ± 4.1 kg/m²). Substantial inter-individual variability was observed across all macronutrients (401 milk samples). Inclusion of quadratic time terms significantly improved model fit for protein, carbohydrate, fat, and energy (all p < 0.001). Protein concentration declined in early lactation and stabilized around 183 (95% CI: 150, 212) days postpartum. Fat and energy reached nadirs at approximately 196 (160, 234) and 226 (198, 258) days, respectively, followed by modest increases toward late lactation. Carbohydrate showed a gradual decline with later stabilization [inflection point, 341 (272, 454) days]. Higher socioeconomic status and maternal adiposity were independently associated with higher milk fat and energy concentrations. Conclusions Human milk macronutrient composition remains relatively stable during exclusive or predominant breastfeeding, and undergoes measurable shifts around the time complementary feeding is introduced, reflecting dynamic adaptation of lactational biology. Maternal energy reserves appear to influence milk lipid and energy content, with potential implications for infant growth and metabolic health. Context-specific longitudinal data such as these are essential to inform breastfeeding policy and maternal nutrition strategies. Breastfeeding Lactation Human milk Macronutrients Longitudinal study India Figures Figure 1 Figure 2 Background Human milk is the primary and biologically appropriate source of nutrition during early infancy. Breastfeeding confers substantial short- and long-term health benefits for both the mother and the infant and is therefore recognized as a global public health priority [ 1 ]. International agencies, including the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF), recommend exclusive breastfeeding in the first six months of life, followed by the introduction of nutritionally adequate complementary foods with continued breastfeeding thereafter [ 2 , 3 ]. National health policies in India similarly endorse these recommendations [ 4 , 5 ]. Globally, approximately 48% of infants aged 0–6 months are exclusively breastfed [ 6 ]. In India, infant and young child feeding (IYCF) practices remain suboptimal despite improvements over time. Data from the National Family Health Survey of India (NFHS) indicate that 63.7% of infants under six months are exclusively breastfed, while only 11% of breastfed children aged 6–23 months receive a minimum acceptable diet [ 7 ]. Importantly, breastfeeding continuation is common in India. Our earlier cross-sectional study in a rural Indian setting demonstrated that 90% of children continued breastfeeding at 12 months and beyond [ 8 ]. Earlier national estimates suggested a median breastfeeding duration of approximately 12 months, with nearly one-quarter of women breastfeeding for 24 months or longer [ 9 ]. Over the past three decades, NFHS data also show a rising prevalence of prolonged exclusive breastfeeding (PEB), from 14.0% to 21.1% among infants aged 6–8 months, and from 4.3% to 7.7% among children 6 to 23 months [ 10 ]. PEB is more frequently observed among children from socioeconomically disadvantaged households and among mothers with lower educational attainment [ 9 , 10 ]. Continued breastfeeding into the second year of life has been questioned by some agencies, citing limited evidence on nutritional adequacy and potential context-specific risks, particularly in low- and middle-income settings [ 11 ]. These debates underscore the importance of context-specific data on human milk composition during prolonged and extended lactation. Human milk is a complex bioactive fluid containing macronutrients, micronutrients, hormones, immune factors, cells, and other bioactive components that support infant growth, development, and immune function [ 12 ]. Previous research has demonstrated that human milk composition is influenced by maternal nutritional status, body weight or adiposity, dietary intake [ 13 , 14 ], and micronutrient supplementation [ 15 ]. Additionally, the macronutrient composition of human milk varies substantially across lactation and among mothers, reflecting genetic, nutritional, and environmental influences [ 13 , 16 ]. But the extent to which maternal characteristics influence milk macronutrients throughout the course of lactation remains incompletely understood. Emerging evidence also suggests that infants born to mothers with overweight or obesity may be exposed to higher concentrations of metabolic and inflammatory mediators in human milk, including insulin, leptin, cytokines, and C-reactive protein [ 17 ]. These observations reinforce the concept of human milk not merely as a source of nutrition, but as a dynamic biological system shaped by maternal-infant interactions. Despite increasing recognition of the biological complexity of human milk, substantial gaps remain in understanding its composition in relation to lactation duration, optimal feeding periods, and maternal nutritional status [ 18 ], particularly in low- and middle-income countries (LMICs) such as India. Data from India on longitudinal changes in human milk macronutrient composition across infancy are sparse [ 19 ]. Characterizing serial changes in human milk macronutrients and their associations with maternal nutritional status and infant growth has important implications for optimizing infant feeding guidance and breastfeeding policies in settings where prolonged and extended breastfeeding are common. Therefore, the objective of this study was to assess human milk macronutrients (protein, carbohydrate, fat, and energy) serially over the first 12 months of lactation and to explore associated maternal and infant factors. Methods Study design and setting This was a longitudinal, observational, community-based study conducted in a semi-urban population of Akola city, Maharashtra, India, from November 2017 to August 2020. The registry of the local community health centre was used to identify recent antenatal registrations and delivery records. Eligible families were approached at home by a trained research assistant and community health worker (CHW), who provided study information and counselling to mothers and family members, including fathers and in-laws. Mothers who delivered a term infant with no significant congenital anomalies were eligible for inclusion. Mother-infant dyads were excluded if either the mother or the infant had an acute or chronic illness during the postpartum period. Sample size and power consideration Sample size was determined for a longitudinal design with five repeated measurements at 1, 3, 6, 9, and 12 months postpartum. Assuming a two-sided α of 0.05, 80% power, and a within-subject correlation of approximately 0.5, the study was powered to detect a small-to-moderate biologically meaningful change (~ 0.25–0.30 SD) in human milk protein concentration using linear mixed-effects models. A minimum of 75–80 mother-infant dyads were required, and allowing 15–20% attrition over 12 months, at least 95 dyads needed to be enrolled. Participants and follow-up Following written informed consent, mother-infant dyads were enrolled within five weeks postpartum. Baseline information on household characteristics, standard of living, and birth details was recorded at enrolment. Mother-infant dyads were followed longitudinally at 1, 3, 6, 9, and 12 months after enrolment, resulting in a total of five study visits per dyad. At each visit, maternal and infant anthropometry, morbidity data, and infant feeding practices were documented, and a human milk sample was collected. At the 3-month visit, human milk intake was assessed using the deuterium oxide dose-to-mother technique; these data are not reported in the present analysis. Anthropometry of mother-infant dyads Anthropometric measurements of both mothers and infants were obtained at each postpartum visit (1, 3, 6, 9, and 12 months) using standardized procedures. For mothers, measurements included: height, weight, mid-upper arm circumference (MUAC), and four skinfold thicknesses (biceps, triceps, subscapular, and suprailiac). For infants, measurements included: length, weight, MUAC, head circumference, and skinfold thicknesses. Maternal height was measured to the nearest 0.5 cm using a portable stadiometer (Seca 213). Infant weight was measured to the nearest 10 g using a calibrated electronic scale (Salter, Tonbridge, England). Infant length was measured to the nearest 0.1cm using an infantometer (Seca, Hamburg, Germany). Circumferences were measured to the nearest 0.1cm using a non-stretchable Teflon measuring tape (Seca 201). Skinfold thicknesses were measured using a Holtain skinfold calliper. Information on maternal and infant morbidity during the preceding months, healthcare provider consultations, hospitalizations, and medications used was collected at each visit using a pre-designed, structured questionnaire. Human milk sampling and analysis Human milk samples were collected at each visit between 11:00 and 13:00 hours to minimize diurnal variation. Milk was expressed manually from either the left or right breast under standard aseptic precautions. A minimum volume of 10 ml was collected per sample. Samples were transported in insulated containers with cooling packs and stored at -20 0 c until analysis. For analysis, frozen milk samples were thawed at room temperature and subsequently warmed to 40°C in a water bath. Each sample was homogenized using a MIRIS Sonicator at a speed of 1.5 s/ml, as recommended by the manufacturer (MIRIS AB, Sweden) [ 20 ]. Macronutrient concentrations (fat, protein, carbohydrate, and energy) were measured using the Miris Human Milk Analyzer (mid-infrared spectroscopy). Instrument cleaning and calibration were performed using manufacturer-provided solutions. Both the cleaning solution and calibration (Miris Check) solution were warmed to 40 0 C prior to use. Cleaning was conducted with 15 ml of Miris Cleaner Solution, and calibration was performed using 5 ml of Miris Check Solution. Cleaning and calibration procedures were repeated after every tenth sample to ensure analytical accuracy and reliability. The study was approved by the Institutional Ethics Committee of the Government Medical College, Akola (GMCAEC002). Statistical Methods Descriptive statistics are presented as mean (SD) or median (IQR), as appropriate. Violin plots were used to visualize the distribution of HM macronutrient concentrations (fat, protein, carbohydrate, energy) at 1, 3, 6, 9, and 12 months postpartum. The standard-of-living index (SLI) was derived from 27 household assets and infrastructure items, with weighted scores ranging from 0 to 67, consistent with the methodology of the National Family Health Survey of India [ 21 ]. Infant length-for-age (LAZ), weight-for-age (WAZ), and weight-for-length (WLZ) z scores were calculated using the WHO Child Growth Standards [ 22 ]. Associations between milk macronutrient content and maternal, infant, and household characteristics were examined at each time point using Spearman correlation coefficients. Correlation matrices included maternal age, parity, sum of skinfolds, breastfeeding frequency (1 and 3 months only), SLI score, and infant anthropometric z scores, and were displayed using heatmaps. Longitudinal changes in human milk macronutrient concentrations (protein, carbohydrate, fat, and energy) across lactation were examined using linear mixed-effects models (LMMs) with random intercepts for mother-infant dyads to account for within-subject correlation across repeated measurements. Postpartum age (days) was mean-centered and modelled using both linear and quadratic terms to capture potential non-linear trajectories over time. Models were adjusted a priori for covariates (SLI score, parity, maternal sum of four skinfolds, and infant sex). Model-based inflection points (time at nadir or turning point) and their 95% confidence intervals (CIs) were derived post hoc from fitted quadratic models. A two-sided p- value < 0.05 was considered statistically significant. All analyses were performed using R (version 4.5.1). Free versions of Grammarly and ChatGPT were used to refine the English language and improve readability. Results Figure 1 Flowchart illustrating the enrollment and follow-up of mother-infant dyads. Participant characteristics Ninety-five mother-infant dyads were enrolled; follow-up through 12 months is summarized in Fig. 1 . Most families lived in joint households (83%, n = 79), with a median of 4 adults (IQR, 3–6). Mothers had a mean age of 25.9 ± 4.7 years; 84% (n = 80) had completed high school or higher education, and 7.5% (n = 7) were employed. The mean standard-of-living index (SLI) score was 33 ± 6. Vaginal delivery occurred in 84% (n = 80) of births, and 50% of infants were female. Mean birth weight was 2.88 ± 0.47 kg; 11.6% (n = 11) were low-birth-weight (less than 2.5 kg). Maternal-infant anthropometry and feeding practices Table 1 Mother-infant body size at each postpartum time point. 1 month (n = 95) 3 months (n = 86) 6 months (n = 88) 9 months (n = 85) 12 months (n = 87) Table 1 A: Maternal anthropometry Weight, kg 53.53 ± 10.73 54.59 ± 11.63 53.22 ± 11.99 53.34 ± 12.46 53.17 ± 11.92 BMI, kg/m 2 22.23 ± 4.11 22.66 ± 4.46 22.08 ± 4.63 22.18 ± 4.84 22.07 ± 4.58 Sum of skinfolds (triceps, biceps, subscapular, and suprailiac), mm 60.1 ± 18.9 64.3 ± 17.2 62.1 ± 16.9 60.4 ± 17.3 61.0 ± 18.1 Table 1 B: Infant anthropometry Age, months 1.2 ± 0.3 3.1 ± 0.2 6.1 ± 0.2 9.2 ± 0.2 12.2 ± 0.4 Weight, kg 3.85 ± 0.60 5.48 ± 0.85 7.05 ± 0.96 8.17 ± 1.15 8.89 ± 1.08 Length, cm 54.44 ± 2.72 61.05 ± 4.42 68.23 ± 3.66 72.41 ± 3.16 75.87 ± 2.86 Head Circumference, cm 36.89 ± 1.88 39.45 ± 1.74 41.77 ± 3.47 43.78 ± 1.35 45.19 ± 1.52 MUAC, cm 11.78 ± 1.22 13.80 ± 1.62 14.98 ± 1.49 15.10 ± 1.68 15.22 ± 1.38 WLZ score -2.10 ± 1.30 -1.57 ± 1.39 -1.20 ± 1.34 -0.90 ± 1.29 -0.62 ± 1.17 Values are mean ± sd. [BMI: Body mass index; MUAC: Mid-upper arm circumference; WLZ: Weight-for-length z score]. Maternal and infant anthropometry at each visit is shown in Table 1 . At enrolment (35 ± 10 days postpartum), 19% (n = 18) mothers were underweight (BMI < 18.5 kg/m 2 ) and 26% (n = 25) overweight or obese (≥ 25 kg/m 2 ); corresponding proportions at 12 months were 24% (n = 21) and 19.6% (n = 17), respectively. Maternal adiposity remained largely stable during the first 12 months postpartum. Although maternal BMI varied modestly across visits in linear mixed models (Type III ANOVA: F(4342) = 3.10, p = 0.016), only a small decline between 3 and 12 months postpartum was observed (Bonferroni-adjusted p = 0.014), while the sum of four skinfold thicknesses did not differ significantly across visits (F(4344) = 2.16, p = 0.073). All infants were exclusively breastfed at 1 month (by maternal recall), and all but two at 3 months. Breastfeeding frequency declined significantly from 1 to 3 months [14.0 ± 3.2 to 11.2 ± 1.6 feeds/day; mean difference 3.1 (95% CI: 2.4, 3.7), p < 0.001]. Complementary feeding was initiated at a mean age of 6.6 months. At 12 months, 81.6% of mothers continued breastfeeding, with no difference in maternal BMI between those who continued and those who discontinued breastfeeding (21.9 ± 4.7 vs. 22.1 ± 4.5 kg/m 2 , respectively; p = 0.562). At 12 months, 14 infants (16%) were undernourished (WLZ <-2.0). Human milk macronutrient distributions Mean concentrations of protein, carbohydrate, fat, and energy are presented in Table 2 , with distributions across lactation shown in Supplementary Fig. 1 . Protein concentrations showed relatively stable distributions from 1 to 9 months, with increased inter-individual variability at 12 months. Carbohydrate concentrations exhibited modest variability with a slight decline during mid-lactation. Fat and energy concentrations demonstrated greater dispersion during early lactation (1–3 months), with more consolidated distributions thereafter. Table 2 Human milk macronutrient concentrations at each postpartum time point. Postpartum days 35 ± 10 (n = 93) 93 ± 5 (n = 84) 185 ± 5 (n = 81) 276 ± 6 (n = 72) 369 ± 15 (n = 71) Protein, g/100 mL 1.02 ± 0.35 (0.81, 1.20) 0.91 ± 0.31 (0.71, 1.15) 0.85 ± 0.32 (0.63, 1.05) 0.94 ± 0.30 (0.78, 1.16) 1.09 ± 0.54 (0.85, 1.22) Carbohydrate, g/100 mL 5.04 ± 1.99 (3.15, 7.14) 4.55 ± 1.85 (3.14, 6.94) 3.82 ± 1.63 (2.86, 4.51) 3.74 ± 1.26 (3.02, 4.50) 3.59 ± 1.12 (2.95, 4.53) Fat, g/100 mL 3.55 ± 1.25 (2.66, 4.52) 3.18 ± 1.65 (1.83, 4.30) 3.01 ± 1.32 (2.07, 4.01) 3.01 ± 1.18 (2.06, 3.77) 3.73 ± 1.72 (2.53, 4.25) Energy, kcal/100 mL 58.5 ± 15.2 (46.5, 68.0) 52.6 ± 19.3 (37.1, 66.9) 47.7 ± 13.2 (38.6, 55.1) 47.9 ± 12.2 (37.8, 55.1) 54.7 ± 17.6 (41.3, 61.8) Values are mean ± sd and 25th, 75th centiles. Cross-sectional associations of human milk macronutrients with maternal-infant factors In early lactation (at one and three months), several modest but statistically significant associations were observed between human milk macronutrients and maternal-infant factors ( Supplementary Fig. 2) . Breastfeeding frequency showed consistent inverse association with carbohydrate (ρ =-0.44 and − 0.52, p < 0.001), fat (ρ =-0.23, p < 0.05, at 3 months only), and energy (ρ =-0.37 and − 0.40, p < 0.001) concentrations at both visits, whereas a positive association was noted between breastfeeding frequency and protein at visit 1 (ρ = 0.22, p < 0.05). Higher SLI scores and greater maternal skinfold thickness were positively associated with higher milk fat (ρ = 0.30 and 0.25, p < 0.01 and < 0.05) and energy concentrations (ρ = 0.28 and 0.27, p < 0.05), respectively, particularly during early lactation. From 6 months onward, correlations were largely attenuated, although energy content remained positively associated with SLI at 6 months (ρ = 0.23, p < 0.05). At later visits, selected associations emerged between macronutrients and parity and infant anthropometric indices, but these were modest and inconsistent across time points. We compared median concentrations of human milk macronutrients from our study with the respective reference values published recently in the MILQ project [ 23 ]. Across all time points, protein and fat concentrations aligned closely with median reference values, and carbohydrate and energy concentrations aligned with the fifth and 25th percentile, respectively, relative to MILQ reference centiles. Longitudinal changes in human milk macronutrients Linear mixed-effects models (95 dyads; 401 observations) were used to examine longitudinal changes in the human milk macronutrients (Fig. 2 , Table 3 , and Supplementary Table 1 ). Considerable between-mother variability in baseline concentrations and individual trajectories was observed for all macronutrients. (A) Protein, (B) carbohydrate, (C) fat, and (D) energy concentrations plotted against postpartum days. Points represent observed values from serial milk samples. Solid lines depict population-average trajectories estimated from linear mixed-effects models including linear and quadratic terms for postpartum age, with random intercepts and random slopes at the mother–infant dyad level. Shaded bands indicate 95% confidence intervals around the fitted trajectories. Models were adjusted for parity, infant sex, standard-of-living index score, and maternal sum of four skinfolds. Table 3 Longitudinal trajectories and inflection points of human milk macronutrients. Macronutrient Best-fitting model Early-lactation trend Inflection point (postpartum days, 95% CI) Post-inflection trend Protein Quadratic LMM Decline 183 (150–212) Attenuation/plateau Carbohydrate Quadratic LMM Decline 341 (272–454) Minimal change Fat Quadratic LMM Decline 196 (160–234) Increase Energy Quadratic LMM Decline 226 (198–258) Increase Protein : In adjusted LMM including linear and quadratic terms for postpartum age, protein concentration exhibited a significant non-linear trajectory across the first year of lactation (likelihood ratio test comparing linear vs. quadratic time models: χ 2 = 17.1, df = 1, p < 0.001). Protein concentration declined during early lactation, with the rate of decline attenuating over time. The estimated inflection point occurred at 183 (95% CI: 150, 212) days postpartum, beyond which protein concentration stabilized and showed a modest increase toward later lactation. This pattern was independent of parity, infant sex, SLI score, and maternal adiposity. Carbohydrate : Human milk carbohydrate concentration exhibited a significant non-linear trajectory, and inclusion of a quadratic time term significantly improved model fit compared with a linear time model (χ 2 = 16.5, df = 1, p < 0.001). The fitted trajectory indicated a decline in carbohydrate concentration during early and mid-lactation, followed by attenuation of the decline and modest stabilization toward late lactation, with an estimated inflection point at 341 (95% CI: 272, 454) days postpartum. This temporal pattern was independent of parity, infant sex, SLI score, and maternal adiposity. Fat : Like protein and carbohydrate models, inclusion of a quadratic time term significantly improved model fit compared with a linear model (χ 2 = 13.68, df = 1, p = 0.0002). The fitted trajectory showed an early decline in fat concentration, reaching a minimum at approximately 196 (95% CI: 160, 234) days postpartum, followed by an increase towards late lactation. Higher SLI score and greater maternal adiposity were independently associated with higher milk fat concentration, whereas parity and infant sex were not. Energy : The milk energy concentration also exhibited a significant non-linear trajectory, and the inclusion of a quadratic time term significantly improved the model fit compared to a linear time model (χ2 = 26.3, df = 1, p < 0.001). The fitted trajectory showed a decline in energy concentration during early lactation, reaching a nadir at 226 days postpartum (95% CI: 198, 258), followed by stabilization and a modest increase toward later lactation. Higher SLI score and greater maternal adiposity were independently associated with higher milk energy concentration. Discussion In this community-based, longitudinal study, we characterized changes in human milk macronutrient composition across the first year of lactation and examined associations with maternal and infant characteristics. Our findings extend the limited longitudinal evidence from low-and-middle-income countries (LMICs) and demonstrate some peculiarities: (i) marked inter-individual variability in milk macronutrients; (ii) milk composition across lactation is best characterized by non-linear trajectories rather than constant linear trends, (iii) relative stability of macronutrient concentrations during the first six months,; and (iv) stronger and more consistent associations of milk macronutrients with maternal characteristics and feeding pattern than with infant-related factors. The substantial inter-individual variability observed across all macronutrients underscores the importance of mixed-effects approaches and cautions against over-reliance on single “average” values when characterizing human milk composition. By explicitly modelling nonlinearity, our study provides a more nuanced understanding of lactational physiology in a South Asian context. by identifying specific time windows when macronutrient trajectories shift. These findings align with the limited existing [ 24 , 25 ] and emerging [ 26 – 28 ] evidence that human milk composition continues to evolve beyond early lactation, a period that has historically received less research attention. Recent advances in dynamic modelling of human milk composition to explore its trajectories emphasize that lactational changes are inherently nonlinear, phase-dependent, and better conceptualized as continuous biological processes rather than discrete stages [ 29 ]. In that framework, early postpartum trajectories (up to four months postpartum) were characterized by rapid change followed by progressive convergence toward a steady state, with between-mother variability exceeding temporal effects [ 29 ]. Our longitudinal quadratic modelling with estimation of inflection points is conceptually aligned with this perspective, as it captures the acceleration-deceleration pattern of macronutrient change across twelve months while preserving postpartum time as a continuous variable. Although we adopted a polynomial, mixed-effects approach rather than a two-phase saturation model, both strategies converge on the same biological inference: macronutrient trajectories are curvilinear, dominated by individual-level heterogeneity, and require nonlinear modelling to adequately represent lactation dynamics. By quadratic modelling, we could capture biologically meaningful shifts in the rate of change that were obscured in linear models, particularly during the transition from early to later lactation. The observed inflection points clustered around mid-lactation (around 200 days) for protein, fat, and energy. This suggests a coordinated regulation of milk composition as infants transition from exclusive or predominant breastfeeding to partial breastfeeding with the introduction of complementary feeding. As reported in the results, the mean age at initiation of complementary feeding in our cohort was 198 days. Most longitudinal studies of human milk macronutrients have focused on early lactation, typically up to 24 weeks postpartum, with sparse data extending beyond six months, particularly from LMIC settings. Available studies on prolonged lactation have demonstrated that protein concentration typically declines during early lactation before stabilizing or modestly increasing later, whereas fat concentrations tend to increase with advancing lactation [ 25 – 27 ]. In contrast, carbohydrate concentrations generally remain relatively stable throughout lactation [ 25 , 26 ]. However, most analyses have relied on categorical time points [ 27 , 28 ], retrospective cohort [ 26 ], or linear assumptions [ 24 ]. Our findings are broadly consistent with these observations and add evidence from an Indian cohort characterized by prolonged breastfeeding practices. The stabilization of protein concentration after approximately six months may reflect alignment with declining relative protein requirements per unit body weight [ 30 ]. Meanwhile, the late lactation increase in fat and energy likely supports the rising absolute energy needs associated with increased physical activity and growth [ 12 , 30 , 31 ]. In contrast, carbohydrate concentration showed a more prolonged decline, with stabilization only toward the end of the first year, consistent with the relatively conserved lactose synthesis pathway and its role in maintaining milk osmolarity [ 12 , 31 , 32 ]. Maternal BMI and the sum of skinfold thicknesses showed little change across the first year postpartum, indicating relatively stable maternal energy reserves during lactation. This stability suggests that temporal variation in human milk macronutrient concentrations is more likely related to physiological changes in lactation rather than to major shifts in maternal adiposity. The positive association of socioeconomic status and maternal adiposity with fat and energy concentrations is consistent with earlier reports linking maternal energy reserves to milk lipid synthesis [ 31 , 33 , 34 ]. Associations with breastfeeding frequency were most evident at 1 and 3 months, where higher feeding frequency correlated with lower fat and energy concentrations, likely reflecting dilution effects related to shorter inter-feed intervals [ 27 , 35 , 36 ]. Infant anthropometric indices showed limited and inconsistent associations with milk macronutrients as reported in a recent review [ 37 ], and we did not observe sex-specific differences in milk composition. Recently published reference values of human milk macronutrient concentrations from the MILQ project [ 23 ] provide harmonized, cross-population estimates across the first 8.5 months of lactation. Our findings are broadly concordant with the MILQ reference pattern. The identification of cohort-specific inflection points highlights the potential for contextual variation in the timing of macronutrient shifts, particularly in populations with prolonged breastfeeding and extended lactation practices, such as those observed in India [ 9 , 10 ]. Strengths and limitations A key strength of our study is the use of dense, repeated measurements across the first year of lactation, analysed using linear mixed-effects models with continuous postpartum age, which allows for the efficient use of irregularly timed observations and accounts for inter-individual heterogeneity. At the same time, as with all observational lactation studies, unmeasured maternal or infant factors, like details of breastfeeding and complementary feeding practices beyond the first six months of life, collection of 10 ml of breast milk, and not complete emptying of the breast, may contribute to residual variability in macronutrient trajectories. Methodological considerations are important when interpreting our findings. The availability of repeated maternal and infant anthropometry strengthened our ability to examine associations of milk macronutrients and body size. Nonetheless, the absence of data on pre-pregnancy BMI, gestational weight gain, maternal dietary intake, and detailed complementary feeding practices limited further exploration of mediating pathways, particularly beyond six months of lactation. In our study, milk samples were collected as standardized mid-day spot samples (10 ml), which likely represent foremilk. Foremilk is known to contain lower fat and energy concentrations than hindmilk, and therefore, absolute values may underestimate full-feed or 24-hour averages [ 25 , 38 , 39 ]. However, the use of a consistent sampling protocol across visits enhances internal validity and supports the interpretation of longitudinal trends. Mid-infrared spectroscopy, used for estimating macronutrients, is widely applied in clinical and milk bank settings, allowing for efficient and simultaneous measurement of multiple components [ 40 ]. Carbohydrate concentrations measured using MIR represent an indirect measure, and these estimates are sensitive to calibration datasets and population-specific milk composition [ 41 – 43 ], highlighting the need for regionally derived reference values. Conclusions Understanding longitudinal changes in human milk macronutrient composition has important implications for infant feeding guidance and maternal nutrition, especially in LMIC settings experiencing a double burden of malnutrition. The stability of human milk composition during early infancy supports current recommendations for exclusive breastfeeding, while the observed changes beyond six months reinforce the importance of timely, nutritionally adequate complementary feeding. In contexts where maternal undernutrition persists, prolonged exclusive breastfeeding without appropriate complementary foods may increase the risk of growth faltering. Conversely, in settings with rising maternal overweight and obesity, higher maternal adiposity may influence milk fat and energy content, with potential implications for early-life metabolic programming. Overall, our findings underscore the dynamic nature of human milk composition across lactation. Longitudinal, context-specific data such as these are essential for refining breastfeeding and complementary feeding recommendations in diverse populations. Abbreviations BMI Body mass index LAZ Length-for-age z score LMIC Low and middle-income countries MIR Mid infra-red spectroscopy MUAC Mid-upper arm circumference NFHS National Family Health Survey SLI Standard-of-living Index UNICEF United Nations Children’s Fund WAZ Weight-for-age z score WHO World Health Organization WLZ Weight-for-length z score Declarations Ethics approval and consent to participate This study was approved by the Institutional Ethics Committee of the Government Medical College, Akola, Maharashtra, India (Approval: GMCAEC002). In this study, all participants provided written informed consent. Ethics declaration The study was conducted in accordance with the Declaration of Helsinki and the national guideline: National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (Indian Council of Medical Research, 2017). Consent for publication Not applicable Availability of data and materials The datasets used during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Division of Reproductive and Child Health of the Indian Council of Medical Research, New Delhi, India (Ref No. 5/7/1138/2014-RCH). Author contributions UD and AK conceptualized the study and acquired funding. UD, SB, and SM collected data and conducted the assays. UD and SS analysed the data, and UD wrote the main manuscript text. KM, VW, and AK reviewed the subsequent manuscript drafts. All authors reviewed and approved the final manuscript Acknowledgement We would like to thank all the families who were involved in the study. We acknowledge the institutional support from Prof Rajesh Karyakarte, Dean of the Government Medical College, Akola, Maharashtra. We also thank the District Health Officer, Akola, for providing the necessary permissions to work with the Community Health Centres in the study area. We are grateful to the project assistant, Ms. Jayashree Ingole, and to the team of community health workers who helped with home visits and data collection. Author information 1. Dr. Urmila Deshmukh, [email protected] ORCID ID: 0000-0002-3372-5628 2. Prof Kavitha Menon, [email protected] ORCID ID: 0000-0001-8624-2868 3. Prof Vinit Warthe, [email protected] 4. Ms Swati Balapure, [email protected] 5. Ms Shraddha Mandale, [email protected] 6. Prof Sharvari Shukl, [email protected] ORCID ID: 0000-0002-7029-5532 7. Prof Anura Kurpad, [email protected] ORCID ID: 0000-0001-7998-2438 References Victora CG, Bahl R, Barros AJ, França GV, Horton S, Krasevec J, et al. Lancet Breastfeeding Series Group. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387(10017):475–90. 10.1016/S0140-6736(15)01024-7 . ASEAN, UNICEF and Alive & Thrive, UNICEF. 2022. Guidelines and Minimum Standards for the Protection, Promotion and Support of Breastfeeding and Complementary Feeding. Jakarta;. Available from: https://www.unicef.or g/eap/media/10676/file/Guidelines%20and %20Min imum%20Standards%20for%20the%20Pro tection,%20Promotion%20and%20Support %20of%20Breastfeeding%20and%20Comp lementary%20Feeding.pdf Accessed 20 Jan 2026. Global strategy for infant and young child feeding. Available from: https://iris.who.int/server/api/core/bitstreams/a70b1144-75b7-4b25-b3a9-bd5ee5098e95/content Accessed 12 Jan 2026. Tiwari S, Bharadva K, Yadav B, Malik S, Gangal P, Banapurmath CR, Zaka-Ur-Rab Z, Deshmukh U, Visheshkumar -, Agrawal RK, Infant. Indian Pediatr. 2016;53(8):703–13. 10.1007/s13312-016-0914-0 . and Young Child Feeding Guidelines, 2016. Guidelines for enhancing Infant and Young child feeding. Ministry of Health and Family Welfare, Government of India., 2013. Available from: https://nhm.gov.in/images/pdf/programmes/child-health/guidelines/Enhancing-optimal-IYCF-practices.pdf Accessed 10 Jan 2026. Global Breastfeeding Scorecard. 2024. Available from: https://knowledge.unicef.org/child-nutrition-and-development/resource/global-breastfeeding-scorecard-2024 , Accessed 20 Jan 2026. International Institute for Population Sciences (IIPS) and ICF. 2021. National Family Health Survey (NFHS-5), 2019- 21: India: Volume I. Mumbai: IIPS. Available from: https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf . Accessed 19 Dec 2025. Deshmukh U, Thomas T, Swaminathan S, Kurpad A. Breastfeeding Practices and Dietary Diversity among Infants and Young Children in Rural and Urban-Slum Populations in India: An Observational Study. Int J Child Health Nutr. 2018;7(4):175–83. doi.org/10.6000/1929-4247.2018.07.04.7 . Mehta AR, Panneer S, Ghosh-Jerath S, Racine EF. Factors Associated With Extended Breastfeeding in India. J Hum Lact. 2017;33(1):140–8. 10.1177/0890334416680179 . Chen Z, Sharma S, Chen S, Kim R, Subramanian SV, Li Z. Prevalence, trend, and inequality of prolonged exclusive breastfeeding among children aged 6–23 months old in India from 1992–2021: A cross-sectional study of nationally representative, individual-level data. J Glob Health. 2024;14:04026. 10.7189/jogh.14.04026 . Paediatric Gastroenterology H et al. & Nutrition (ESPGHAN), European Academy of Paediatrics (EAP), European Society for Paediatric Research (ESPR),. World Health Organization (WHO) guideline on the complementary feeding of infants and young children aged 6 – 23 months 2023: a multisociety response. J Pediatr Gastroenterol Nutr. 2024;79(1):1-8. 10.1002/jpn3.12248 Ballard O, Morrow AL. Human milk composition: nutrients and bioactive factors. Pediatr Clin North Am. 2013;60(1):49–74. 10.1016/j.pcl.2012.10.002 . Bravi F, Wiens F, Decarli A, Dal Pont A, Agostoni C, Ferraroni M. Impact of maternal nutrition on breast-milk composition: a systematic review. Am J Clin Nutr. 2016;104(3):646–62. https://doi.org/10.3945/ajcn.115.120881 . Daniel AI, Shama S, Ismail S, et al. Maternal BMI is positively associated with human milk fat: a systematic review and meta-regression analysis. Am J Clin Nutr. 2021;113(4):1009–22. 10.1093/ajcn/nqaa410 . Shinde S, Yelverton CA, Yussuf M, Nurhussien L, Wang D, Fawzi WW. Effects of vitamin and multiple micronutrient supplementation for pregnant and/or lactating women on maternal and infant nutritional status in low- and middle-income countries: a systematic review and meta-analysis. Adv Nutr. 2025;16(12):100487. 10.1016/j.advnut.2025.100487 . AlSulaiti B, Ferguson-Smith AC, Hanin G. From Mammary Glands to Nutrients: Genetic Insights into Milk Composition. Biol Reprod. 2025;ioaf237. 10.1093/biolre/ioaf237 . Sims CR, Lipsmeyer ME, Turner DE, Andres A. Human milk composition differs by maternal BMI in the first 9 months postpartum. Am J Clin Nutr. 2020;112(3):548–57. 10.1093/ajcn/nqaa098 . Christian P, Smith ER, Lee SE, Vargas AJ, Bremer AA, Raiten DJ. The need to study human milk as a biological system. Am J Clin Nutr. 2021;113(5):1063–72. 10.1093/ajcn/nqab075 . Khanna D, Yalawar M, Verma G, Gupta S. Century Wide Changes in Macronutrient Levels in Indian Mothers' Milk: A Systematic Review. Nutrients. 2022;14(7):1395. 10.3390/nu14071395 . Miris Human Milk Analyzer User Manual. Available from: https://www.mirissolutions.com/media/46f0e58a-14c6-48cf-8dea-b651cae01cb3 Accessed 8 Jan 2026. IIPS and Macro International. National Family Health Survey (NFHS-3), India, 2005–06: Maharashtra. International Institute for Population Sciences and Macro International, Mumbai; 2008. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization; 2006. p. 312. Lewis JI, Dror DK, Hampel D, Kac G, Mølgaard C, Moore SE, MILQ Study Consortium, et al. Reference Values for Macronutrients in Human Milk: the Mothers, Infants and Lactation Quality (MILQ) Study. Adv Nutr. 2025;16(Suppl 1):100501. 10.1016/j.advnut.2025.100501 . Dewey KG, Finley DA, Lönnerdal B. Breast milk volume and composition during late lactation (7–20 months). J Pediatr Gastroenterol Nutr. 1984;3(5):713–20. 10.1097/00005176-198411000-00014 . Mitoulas LR, Kent JC, Cox DB, Owens RA, Sherriff JL, Hartmann PE. Variation in fat, lactose, and protein in human milk over 24 h and throughout the first year of lactation. Br J Nutr. 2002;88(1):29–37. 10.1079/BJN2002579 . Muts J, Lukowski JIA, Twisk JWR, Schoonderwoerd A, van Goudoever JB, van Keulen BJ, van den Akker CHP. Macronutrient concentrations in human milk beyond the first half year of lactation: a cohort study. Arch Dis Child Fetal Neonatal Ed. 2025;110(3):248–52. 10.1136/archdischild-2024-327319 . Czosnykowska-Łukacka M, Królak-Olejnik B, Orczyk-Pawiłowicz M. Breast Milk Macronutrient Components in Prolonged Lactation. Nutrients. 2018;10(12):1893. 10.3390/nu10121893 . Perrin MT, Fogleman AD, Newburg DS, Allen JC. A longitudinal study of human milk composition in the second year postpartum: implications for human milk banking. Matern Child Nutr. 2017;13(1):e12239. 10.1111/mcn.12239 . Baranyi J, Pacza T, Martins ML, Thakkar SK, Samuel TM. Modelling the temporal trajectories of human milk components. BMC Pregnancy Childbirth. 2024;24(1):739. 10.1186/s12884-024-06896-z . Lönnerdal B, Erdmann P, Thakkar SK, Sauser J, Destaillats F. Longitudinal evolution of true protein, amino acids and bioactive proteins in breast milk: a developmental perspective. J Nutr Biochem. 2017:1–11. 10.1016/j.jnutbio.2016.06.001 Rocha-Pinto I, Pereira-da-Silva L, Silva DE, Cardoso M. Factors That May Affect Breast Milk Macronutrient and Energy Content: A Critical Review. Nutrients. 2025;17(15):2503. 10.3390/nu17152503 . Sadovnikova A, Garcia SC, Hovey RC. A Comparative Review of the Cell Biology, Biochemistry, and Genetics of Lactose Synthesis. J Mammary Gland Biol Neoplasia. 2021;26(2):181–96. 10.1007/s10911-021-09490-7 . Adhikari S, Kudla U, Nyakayiru J, Brouwer-Brolsma EM. Maternal dietary intake, nutritional status and macronutrient composition of human breast milk: systematic review. Br J Nutr. 2022;127(12):1796–820. 10.1017/S0007114521002786 . Chathyushya KB, Hemalatha R, Ananthan R, Babu JJ, Devraj G, Banjara JP. Macronutrient composition of term and preterm human milk of different socioeconomic groups. Prostaglandins Leukot Essent Fat Acids. 2023;192:102571. 10.1016/j.plefa.2023.102571 . Kent JC. How breastfeeding works. J Midwifery Womens Health. 2007;52(6):564–70. 10.1016/j.jmwh.2007.04.007 . Neville MC, Morton J, Umemura S, Lactogenesis. The transition from pregnancy to lactation. Pediatr Clin North Am. 2001;48(1):35–52. 10.1016/s0031-3955(05)70284-4 . Brockway MM, Daniel AI, Reyes SM, Granger M, McDermid JM, Chan D, et al. Human Milk Macronutrients and Child Growth and Body Composition in the First Two Years: A Systematic Review. Adv Nutr. 2024;15(1):100149. 10.1016/j.advnut.2023.100149 . Takumi H, Kato K, Nakanishi H, Tamura M, Ohto-N T, Nagao S, Hirose J. Comprehensive Analysis of Lipid Composition in Human Foremilk and Hindmilk. J Oleo Sci. 2022;71(7):947–57. 10.5650/jos.ess21449 . van Sadelhoff JHJ, Mastorakou D, Weenen H, Stahl B, Garssen J, Hartog A. Short Communication: Differences in Levels of Free Amino Acids and Total Protein in Human Foremilk and Hindmilk. Nutrients. 2018;10(12):1828. 10.3390/nu10121828 . Zhu M, Yang Z, Ren Y, Duan Y, Gao H, Liu B, Ye W, Wang J, Yin S. Comparison of macronutrient contents in human milk measured using mid-infrared human milk analyser in a field study vs. chemical reference methods. Matern Child Nutr. 2017;13(1):e12248. 10.1111/mcn.12248 . Billard H, Simon L, Desnots E, Sochard A, Boscher C, Riaublanc A, Alexandre-Gouabau MC, Boquien CY. Calibration Adjustment of the Mid-infrared Analyzer for an Accurate Determination of the Macronutrient Composition of Human Milk. J Hum Lact. 2016;32(3):NP19–27. 10.1177/0890334415588513 . Fusch G, Rochow N, Choi A, Fusch S, Poeschl S, Ubah AO, et al. Rapid measurement of macronutrients in breast milk: How reliable are infrared milk analyzers? Clin Nutr. 2015;34(3):465–76. 10.1016/j.clnu.2014.05.005 . Leghi GE, Lai CT, Narayanan A, Netting MJ, Dymock M, Rea A, Wlodek ME, Geddes DT, Muhlhausler BS. Daily variation of macronutrient concentrations in mature human milk over 3 weeks. Sci Rep. 2021;11(1):10224. 10.1038/s41598-021-89460-5 . Additional Declarations No competing interests reported. Supplementary Files HMCpaperSupplementaryFile.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 21 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 08 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9064095","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605323531,"identity":"78113bbf-007b-4158-96c6-c7f780945b51","order_by":0,"name":"Urmila Deshmukh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDCCAwxsQDIhAcxJKGCQAws+IF6LAYMxWDCBaC0MBgyJDWC9eHTwHW9/9uhmW1oeg0Tu0w0PDOzS54cdfgi0xU5OtwG7FskzZ8yNc9tyihkk0s1uJBgk5268nWYA1JJsbHYAuxaDGzls0rltFYkNEmlsQC3MuRtnJ4C0HEjchkvL/efPkLXUpxvOTv+AX8sNBjOglhyYlsMJ8tI5+G2RPJNjJp1zLi2xjecZSMtxww3SOQUHgJ7C6Re+48efSeeUJSf2s6ex3fxRUS0vPzt984cPFXZyuLTAARvcqWCVBgSUowD5BlJUj4JRMApGwUgAACcOZGhjn37TAAAAAElFTkSuQmCC","orcid":"","institution":"Symbiosis International University","correspondingAuthor":true,"prefix":"","firstName":"Urmila","middleName":"","lastName":"Deshmukh","suffix":""},{"id":605323532,"identity":"f8649603-d003-4a11-9744-22f4488fcc2d","order_by":1,"name":"Vinit Warthe","email":"","orcid":"","institution":"Government Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Vinit","middleName":"","lastName":"Warthe","suffix":""},{"id":605323533,"identity":"c69ab9d5-0a91-4d04-b130-e9eab7d7a307","order_by":2,"name":"Kavitha Menon","email":"","orcid":"","institution":"Symbiosis International University","correspondingAuthor":false,"prefix":"","firstName":"Kavitha","middleName":"","lastName":"Menon","suffix":""},{"id":605323534,"identity":"35e6880f-dbf8-49d3-996c-c95a010e1a69","order_by":3,"name":"Swati Balapure","email":"","orcid":"","institution":"Government Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Swati","middleName":"","lastName":"Balapure","suffix":""},{"id":605323535,"identity":"328e814c-2cc6-4d4c-af00-707ffaa124fa","order_by":4,"name":"Shraddha Mandale","email":"","orcid":"","institution":"Government Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shraddha","middleName":"","lastName":"Mandale","suffix":""},{"id":605323536,"identity":"8637589d-8a62-47ac-9626-56e4e89599eb","order_by":5,"name":"Sharvari Shukla","email":"","orcid":"","institution":"Symbiosis International University","correspondingAuthor":false,"prefix":"","firstName":"Sharvari","middleName":"","lastName":"Shukla","suffix":""},{"id":605323537,"identity":"4420a816-9712-4d67-a983-e39f83e0f8b8","order_by":6,"name":"Anura Kurpad","email":"","orcid":"","institution":"St.John's Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anura","middleName":"","lastName":"Kurpad","suffix":""}],"badges":[],"createdAt":"2026-03-08 12:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9064095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9064095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104708126,"identity":"143aa178-a1ee-4bfa-85de-a4f0138a80a7","added_by":"auto","created_at":"2026-03-16 09:45:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130340,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the enrollment and follow-up of mother-infant dyads\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9064095/v1/de83385ab9ad8b223c2db013.png"},{"id":104708124,"identity":"c22c56df-8273-4acb-92bf-c00599c0bd34","added_by":"auto","created_at":"2026-03-16 09:45:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1493430,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trajectories of human milk macronutrient concentrations across lactation.\u003c/p\u003e\n\u003cp\u003e(A) Protein, (B) carbohydrate, (C) fat, and (D) energy concentrations plotted against postpartum days. Points represent observed values from serial milk samples. Solid lines depict population-average trajectories estimated from linear mixed-effects models including linear and quadratic terms for postpartum age, with random intercepts and random slopes at the mother–infant dyad level. Shaded bands indicate 95% confidence intervals around the fitted trajectories. Models were adjusted for parity, infant sex, standard-of-living index score, and maternal sum of four skinfolds.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9064095/v1/6d3ff4c25646b08fcaa94390.png"},{"id":104786116,"identity":"80179c33-2b71-4088-8231-27bfa439ecc6","added_by":"auto","created_at":"2026-03-17 08:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1956059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064095/v1/60391946-056b-4331-b0cd-c1e2df3a69a5.pdf"},{"id":104782499,"identity":"1486d81a-d4ac-4eef-98fa-0c18bf4c3ef0","added_by":"auto","created_at":"2026-03-17 07:57:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":679483,"visible":true,"origin":"","legend":"","description":"","filename":"HMCpaperSupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9064095/v1/1de84b24c0c77a50e7add218.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal Changes in Human Milk Macronutrients Across the First Year of Lactation in Indian Mothers","fulltext":[{"header":"Background","content":"\u003cp\u003eHuman milk is the primary and biologically appropriate source of nutrition during early infancy. Breastfeeding confers substantial short- and long-term health benefits for both the mother and the infant and is therefore recognized as a global public health priority [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. International agencies, including the World Health Organization (WHO) and the United Nations Children\u0026rsquo;s Fund (UNICEF), recommend exclusive breastfeeding in the first six months of life, followed by the introduction of nutritionally adequate complementary foods with continued breastfeeding thereafter [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. National health policies in India similarly endorse these recommendations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, approximately 48% of infants aged 0\u0026ndash;6 months are exclusively breastfed [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In India, infant and young child feeding (IYCF) practices remain suboptimal despite improvements over time. Data from the National Family Health Survey of India (NFHS) indicate that 63.7% of infants under six months are exclusively breastfed, while only 11% of breastfed children aged 6\u0026ndash;23 months receive a minimum acceptable diet [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Importantly, breastfeeding continuation is common in India. Our earlier cross-sectional study in a rural Indian setting demonstrated that 90% of children continued breastfeeding at 12 months and beyond [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Earlier national estimates suggested a median breastfeeding duration of approximately 12 months, with nearly one-quarter of women breastfeeding for 24 months or longer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Over the past three decades, NFHS data also show a rising prevalence of prolonged exclusive breastfeeding (PEB), from 14.0% to 21.1% among infants aged 6\u0026ndash;8 months, and from 4.3% to 7.7% among children 6 to 23 months [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PEB is more frequently observed among children from socioeconomically disadvantaged households and among mothers with lower educational attainment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContinued breastfeeding into the second year of life has been questioned by some agencies, citing limited evidence on nutritional adequacy and potential context-specific risks, particularly in low- and middle-income settings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These debates underscore the importance of context-specific data on human milk composition during prolonged and extended lactation.\u003c/p\u003e \u003cp\u003eHuman milk is a complex bioactive fluid containing macronutrients, micronutrients, hormones, immune factors, cells, and other bioactive components that support infant growth, development, and immune function [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous research has demonstrated that human milk composition is influenced by maternal nutritional status, body weight or adiposity, dietary intake [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and micronutrient supplementation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the macronutrient composition of human milk varies substantially across lactation and among mothers, reflecting genetic, nutritional, and environmental influences [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. But the extent to which maternal characteristics influence milk macronutrients throughout the course of lactation remains incompletely understood. Emerging evidence also suggests that infants born to mothers with overweight or obesity may be exposed to higher concentrations of metabolic and inflammatory mediators in human milk, including insulin, leptin, cytokines, and C-reactive protein [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These observations reinforce the concept of human milk not merely as a source of nutrition, but as a dynamic biological system shaped by maternal-infant interactions.\u003c/p\u003e \u003cp\u003eDespite increasing recognition of the biological complexity of human milk, substantial gaps remain in understanding its composition in relation to lactation duration, optimal feeding periods, and maternal nutritional status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], particularly in low- and middle-income countries (LMICs) such as India. Data from India on longitudinal changes in human milk macronutrient composition across infancy are sparse [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Characterizing serial changes in human milk macronutrients and their associations with maternal nutritional status and infant growth has important implications for optimizing infant feeding guidance and breastfeeding policies in settings where prolonged and extended breastfeeding are common. Therefore, the objective of this study was to assess human milk macronutrients (protein, carbohydrate, fat, and energy) serially over the first 12 months of lactation and to explore associated maternal and infant factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis was a longitudinal, observational, community-based study conducted in a semi-urban population of Akola city, Maharashtra, India, from November 2017 to August 2020. The registry of the local community health centre was used to identify recent antenatal registrations and delivery records. Eligible families were approached at home by a trained research assistant and community health worker (CHW), who provided study information and counselling to mothers and family members, including fathers and in-laws. Mothers who delivered a term infant with no significant congenital anomalies were eligible for inclusion. Mother-infant dyads were excluded if either the mother or the infant had an acute or chronic illness during the postpartum period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size and power consideration\u003c/h3\u003e\n\u003cp\u003eSample size was determined for a longitudinal design with five repeated measurements at 1, 3, 6, 9, and 12 months postpartum. Assuming a two-sided α of 0.05, 80% power, and a within-subject correlation of approximately 0.5, the study was powered to detect a small-to-moderate biologically meaningful change (~ 0.25–0.30 SD) in human milk protein concentration using linear mixed-effects models. A minimum of 75–80 mother-infant dyads were required, and allowing 15–20% attrition over 12 months, at least 95 dyads needed to be enrolled.\u003c/p\u003e\n\u003ch3\u003eParticipants and follow-up\u003c/h3\u003e\n\u003cp\u003eFollowing written informed consent, mother-infant dyads were enrolled within five weeks postpartum. Baseline information on household characteristics, standard of living, and birth details was recorded at enrolment. Mother-infant dyads were followed longitudinally at 1, 3, 6, 9, and 12 months after enrolment, resulting in a total of five study visits per dyad.\u003c/p\u003e \u003cp\u003eAt each visit, maternal and infant anthropometry, morbidity data, and infant feeding practices were documented, and a human milk sample was collected. At the 3-month visit, human milk intake was assessed using the deuterium oxide dose-to-mother technique; these data are not reported in the present analysis.\u003c/p\u003e\n\u003ch3\u003eAnthropometry of mother-infant dyads\u003c/h3\u003e\n\u003cp\u003eAnthropometric measurements of both mothers and infants were obtained at each postpartum visit (1, 3, 6, 9, and 12 months) using standardized procedures. For mothers, measurements included: height, weight, mid-upper arm circumference (MUAC), and four skinfold thicknesses (biceps, triceps, subscapular, and suprailiac). For infants, measurements included: length, weight, MUAC, head circumference, and skinfold thicknesses.\u003c/p\u003e \u003cp\u003eMaternal height was measured to the nearest 0.5 cm using a portable stadiometer (Seca 213). Infant weight was measured to the nearest 10 g using a calibrated electronic scale (Salter, Tonbridge, England). Infant length was measured to the nearest 0.1cm using an infantometer (Seca, Hamburg, Germany). Circumferences were measured to the nearest 0.1cm using a non-stretchable Teflon measuring tape (Seca 201). Skinfold thicknesses were measured using a Holtain skinfold calliper.\u003c/p\u003e \u003cp\u003eInformation on maternal and infant morbidity during the preceding months, healthcare provider consultations, hospitalizations, and medications used was collected at each visit using a pre-designed, structured questionnaire.\u003c/p\u003e\n\u003ch3\u003eHuman milk sampling and analysis\u003c/h3\u003e\n\u003cp\u003eHuman milk samples were collected at each visit between 11:00 and 13:00 hours to minimize diurnal variation. Milk was expressed manually from either the left or right breast under standard aseptic precautions. A minimum volume of 10 ml was collected per sample. Samples were transported in insulated containers with cooling packs and stored at -20\u003csup\u003e0\u003c/sup\u003e c until analysis.\u003c/p\u003e \u003cp\u003eFor analysis, frozen milk samples were thawed at room temperature and subsequently warmed to 40°C in a water bath. Each sample was homogenized using a MIRIS Sonicator at a speed of 1.5 s/ml, as recommended by the manufacturer (MIRIS AB, Sweden) [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Macronutrient concentrations (fat, protein, carbohydrate, and energy) were measured using the Miris Human Milk Analyzer (mid-infrared spectroscopy). Instrument cleaning and calibration were performed using manufacturer-provided solutions. Both the cleaning solution and calibration (Miris Check) solution were warmed to 40 \u003csup\u003e0\u003c/sup\u003eC prior to use. Cleaning was conducted with 15 ml of Miris Cleaner Solution, and calibration was performed using 5 ml of Miris Check Solution. Cleaning and calibration procedures were repeated after every tenth sample to ensure analytical accuracy and reliability.\u003c/p\u003e \u003cp\u003e The study was approved by the Institutional Ethics Committee of the Government Medical College, Akola (GMCAEC002).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eDescriptive statistics are presented as mean (SD) or median (IQR), as appropriate. Violin plots were used to visualize the distribution of HM macronutrient concentrations (fat, protein, carbohydrate, energy) at 1, 3, 6, 9, and 12 months postpartum.\u003c/p\u003e \u003cp\u003eThe standard-of-living index (SLI) was derived from 27 household assets and infrastructure items, with weighted scores ranging from 0 to 67, consistent with the methodology of the National Family Health Survey of India [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Infant length-for-age (LAZ), weight-for-age (WAZ), and weight-for-length (WLZ) \u003cem\u003ez\u003c/em\u003e scores were calculated using the WHO Child Growth Standards [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAssociations between milk macronutrient content and maternal, infant, and household characteristics were examined at each time point using Spearman correlation coefficients. Correlation matrices included maternal age, parity, sum of skinfolds, breastfeeding frequency (1 and 3 months only), SLI score, and infant anthropometric z scores, and were displayed using heatmaps.\u003c/p\u003e \u003cp\u003eLongitudinal changes in human milk macronutrient concentrations (protein, carbohydrate, fat, and energy) across lactation were examined using linear mixed-effects models (LMMs) with random intercepts for mother-infant dyads to account for within-subject correlation across repeated measurements. Postpartum age (days) was mean-centered and modelled using both linear and quadratic terms to capture potential non-linear trajectories over time. Models were adjusted a priori for covariates (SLI score, parity, maternal sum of four skinfolds, and infant sex). Model-based inflection points (time at nadir or turning point) and their 95% confidence intervals (CIs) were derived post hoc from fitted quadratic models. A two-sided \u003cem\u003ep-\u003c/em\u003evalue \u0026lt; 0.05 was considered statistically significant. All analyses were performed using R (version 4.5.1). Free versions of Grammarly and ChatGPT were used to refine the English language and improve readability.\u003c/p\u003e \u003c/div\u003e\n\n\n"},{"header":"Results","content":"\u003cp\u003e \u003cstrong\u003eFigure 1\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eFlowchart illustrating the enrollment and follow-up of mother-infant dyads.\u003c/p\u003e\u003ch3\u003eParticipant characteristics\u003c/h3\u003e\u003cp\u003eNinety-five mother-infant dyads were enrolled; follow-up through 12 months is summarized in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. Most families lived in joint households (83%, n = 79), with a median of 4 adults (IQR, 3–6). Mothers had a mean age of 25.9 ± 4.7 years; 84% (n = 80) had completed high school or higher education, and 7.5% (n = 7) were employed. The mean standard-of-living index (SLI) score was 33 ± 6. Vaginal delivery occurred in 84% (n = 80) of births, and 50% of infants were female. Mean birth weight was 2.88 ± 0.47 kg; 11.6% (n = 11) were low-birth-weight (less than 2.5 kg).\u003c/p\u003e\u003ch3\u003eMaternal-infant anthropometry and feeding practices\u003c/h3\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMother-infant body size at each postpartum time point.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e1 month (n = 95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e3 months (n = 86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e6 months (n = 88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e9 months (n = 85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e12 months (n = 87)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA: Maternal anthropometry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.53 ± 10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e54.59 ± 11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.22 ± 11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.34 ± 12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e53.17 ± 11.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.23 ± 4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.66 ± 4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.08 ± 4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.18 ± 4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.07 ± 4.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSum of skinfolds (triceps, biceps, subscapular, and suprailiac), mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60.1 ± 18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e64.3 ± 17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e62.1 ± 16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60.4 ± 17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e61.0 ± 18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB: \u003cb\u003eInfant anthropometry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.2 ± 0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.1 ± 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.1 ± 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.2 ± 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.2 ± 0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.85 ± 0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.48 ± 0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.05 ± 0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.17 ± 1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.89 ± 1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLength, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e54.44 ± 2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e61.05 ± 4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e68.23 ± 3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e72.41 ± 3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e75.87 ± 2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHead Circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e36.89 ± 1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e39.45 ± 1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41.77 ± 3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.78 ± 1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e45.19 ± 1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMUAC, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11.78 ± 1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13.80 ± 1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14.98 ± 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.10 ± 1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.22 ± 1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWLZ score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-2.10 ± 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-1.57 ± 1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-1.20 ± 1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.90 ± 1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.62 ± 1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eValues are mean ± sd.\u003c/p\u003e\u003cp\u003e[BMI: Body mass index; MUAC: Mid-upper arm circumference; WLZ: Weight-for-length z score].\u003c/p\u003e\u003cp\u003eMaternal and infant anthropometry at each visit is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. At enrolment (35 ± 10 days postpartum), 19% (n = 18) mothers were underweight (BMI \u0026lt; 18.5 kg/m\u003csup\u003e2\u003c/sup\u003e) and 26% (n = 25) overweight or obese (≥ 25 kg/m\u003csup\u003e2\u003c/sup\u003e); corresponding proportions at 12 months were 24% (n = 21) and 19.6% (n = 17), respectively. Maternal adiposity remained largely stable during the first 12 months postpartum. Although maternal BMI varied modestly across visits in linear mixed models (Type III ANOVA: F(4342) = 3.10, p = 0.016), only a small decline between 3 and 12 months postpartum was observed (Bonferroni-adjusted p = 0.014), while the sum of four skinfold thicknesses did not differ significantly across visits (F(4344) = 2.16, p = 0.073).\u003c/p\u003e\u003cp\u003eAll infants were exclusively breastfed at 1 month (by maternal recall), and all but two at 3 months. Breastfeeding frequency declined significantly from 1 to 3 months [14.0 ± 3.2 to 11.2 ± 1.6 feeds/day; mean difference 3.1 (95% CI: 2.4, 3.7), p \u0026lt; 0.001]. Complementary feeding was initiated at a mean age of 6.6 months. At 12 months, 81.6% of mothers continued breastfeeding, with no difference in maternal BMI between those who continued and those who discontinued breastfeeding (21.9 ± 4.7 vs. 22.1 ± 4.5 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively; \u003cem\u003ep\u003c/em\u003e = 0.562). At 12 months, 14 infants (16%) were undernourished (WLZ \u0026lt;-2.0).\u003c/p\u003e\u003ch2\u003eHuman milk macronutrient distributions\u003c/h2\u003e\u003cp\u003eMean concentrations of protein, carbohydrate, fat, and energy are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, with distributions across lactation shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Protein concentrations showed relatively stable distributions from 1 to 9 months, with increased inter-individual variability at 12 months. Carbohydrate concentrations exhibited modest variability with a slight decline during mid-lactation. Fat and energy concentrations demonstrated greater dispersion during early lactation (1–3 months), with more consolidated distributions thereafter.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHuman milk macronutrient concentrations at each postpartum time point.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePostpartum days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e35 ± 10\u003c/p\u003e \u003cp\u003e(n = 93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e93 ± 5\u003c/p\u003e \u003cp\u003e(n = 84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e185 ± 5\u003c/p\u003e \u003cp\u003e(n = 81)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e276 ± 6\u003c/p\u003e \u003cp\u003e(n = 72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e369 ± 15\u003c/p\u003e \u003cp\u003e(n = 71)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProtein, g/100 mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.02 ± 0.35\u003c/p\u003e \u003cp\u003e(0.81, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.91 ± 0.31\u003c/p\u003e \u003cp\u003e(0.71, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.85 ± 0.32\u003c/p\u003e \u003cp\u003e(0.63, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.94 ± 0.30\u003c/p\u003e \u003cp\u003e(0.78, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.09 ± 0.54\u003c/p\u003e \u003cp\u003e(0.85, 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCarbohydrate, g/100 mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.04 ± 1.99 (3.15, 7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.55 ± 1.85\u003c/p\u003e \u003cp\u003e(3.14, 6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.82 ± 1.63\u003c/p\u003e \u003cp\u003e(2.86, 4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.74 ± 1.26\u003c/p\u003e \u003cp\u003e(3.02, 4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.59 ± 1.12\u003c/p\u003e \u003cp\u003e(2.95, 4.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFat, g/100 mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.55 ± 1.25\u003c/p\u003e \u003cp\u003e(2.66, 4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.18 ± 1.65\u003c/p\u003e \u003cp\u003e(1.83, 4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.01 ± 1.32\u003c/p\u003e \u003cp\u003e(2.07, 4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.01 ± 1.18\u003c/p\u003e \u003cp\u003e(2.06, 3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.73 ± 1.72\u003c/p\u003e \u003cp\u003e(2.53, 4.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEnergy, kcal/100 mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e58.5 ± 15.2\u003c/p\u003e \u003cp\u003e(46.5, 68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52.6 ± 19.3\u003c/p\u003e \u003cp\u003e(37.1, 66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.7 ± 13.2\u003c/p\u003e \u003cp\u003e(38.6, 55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.9 ± 12.2\u003c/p\u003e \u003cp\u003e(37.8, 55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e54.7 ± 17.6\u003c/p\u003e \u003cp\u003e(41.3, 61.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eValues are mean ± sd and 25th, 75th centiles.\u003c/p\u003e\u003ch2\u003eCross-sectional associations of human milk macronutrients with maternal-infant factors\u003c/h2\u003e\u003cp\u003eIn early lactation (at one and three months), several modest but statistically significant associations were observed between human milk macronutrients and maternal-infant factors (\u003cb\u003eSupplementary Fig.\u0026nbsp;2)\u003c/b\u003e. Breastfeeding frequency showed consistent inverse association with carbohydrate (ρ =-0.44 and − 0.52, p \u0026lt; 0.001), fat (ρ =-0.23, p \u0026lt; 0.05, at 3 months only), and energy (ρ =-0.37 and − 0.40, p \u0026lt; 0.001) concentrations at both visits, whereas a positive association was noted between breastfeeding frequency and protein at visit 1 (ρ = 0.22, p \u0026lt; 0.05). Higher SLI scores and greater maternal skinfold thickness were positively associated with higher milk fat (ρ = 0.30 and 0.25, p \u0026lt; 0.01 and \u0026lt; 0.05) and energy concentrations (ρ = 0.28 and 0.27, p \u0026lt; 0.05), respectively, particularly during early lactation.\u003c/p\u003e\u003cp\u003eFrom 6 months onward, correlations were largely attenuated, although energy content remained positively associated with SLI at 6 months (ρ = 0.23, p \u0026lt; 0.05). At later visits, selected associations emerged between macronutrients and parity and infant anthropometric indices, but these were modest and inconsistent across time points.\u003c/p\u003e\u003cp\u003eWe compared median concentrations of human milk macronutrients from our study with the respective reference values published recently in the MILQ project [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Across all time points, protein and fat concentrations aligned closely with median reference values, and carbohydrate and energy concentrations aligned with the fifth and 25th percentile, respectively, relative to MILQ reference centiles.\u003c/p\u003e\u003ch2\u003eLongitudinal changes in human milk macronutrients\u003c/h2\u003e\u003cp\u003eLinear mixed-effects models (95 dyads; 401 observations) were used to examine longitudinal changes in the human milk macronutrients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eand Supplementary Table\u0026nbsp;1\u003c/b\u003e). Considerable between-mother variability in baseline concentrations and individual trajectories was observed for all macronutrients.\u003c/p\u003e\u003cp\u003e(A) Protein, (B) carbohydrate, (C) fat, and (D) energy concentrations plotted against postpartum days. Points represent observed values from serial milk samples. Solid lines depict population-average trajectories estimated from linear mixed-effects models including linear and quadratic terms for postpartum age, with random intercepts and random slopes at the mother–infant dyad level. Shaded bands indicate 95% confidence intervals around the fitted trajectories. Models were adjusted for parity, infant sex, standard-of-living index score, and maternal sum of four skinfolds.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal trajectories and inflection points of human milk macronutrients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eMacronutrient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eBest-fitting model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEarly-lactation trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eInflection point (postpartum days, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePost-inflection trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQuadratic LMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDecline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e183 (150–212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAttenuation/plateau\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQuadratic LMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDecline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e341 (272–454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinimal change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQuadratic LMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDecline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e196 (160–234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQuadratic LMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDecline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e226 (198–258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eProtein\u003c/em\u003e: In adjusted LMM including linear and quadratic terms for postpartum age, protein concentration exhibited a significant non-linear trajectory across the first year of lactation (likelihood ratio test comparing linear vs. quadratic time models: χ\u003csup\u003e2\u003c/sup\u003e = 17.1, df = 1, p \u0026lt; 0.001). Protein concentration declined during early lactation, with the rate of decline attenuating over time. The estimated inflection point occurred at 183 (95% CI: 150, 212) days postpartum, beyond which protein concentration stabilized and showed a modest increase toward later lactation. This pattern was independent of parity, infant sex, SLI score, and maternal adiposity.\u003c/p\u003e\u003cp\u003e \u003cem\u003eCarbohydrate\u003c/em\u003e: Human milk carbohydrate concentration exhibited a significant non-linear trajectory, and inclusion of a quadratic time term significantly improved model fit compared with a linear time model (χ\u003csup\u003e2\u003c/sup\u003e = 16.5, df = 1, p \u0026lt; 0.001). The fitted trajectory indicated a decline in carbohydrate concentration during early and mid-lactation, followed by attenuation of the decline and modest stabilization toward late lactation, with an estimated inflection point at 341 (95% CI: 272, 454) days postpartum. This temporal pattern was independent of parity, infant sex, SLI score, and maternal adiposity.\u003c/p\u003e\u003cp\u003e \u003cem\u003eFat\u003c/em\u003e: Like protein and carbohydrate models, inclusion of a quadratic time term significantly improved model fit compared with a linear model (χ\u003csup\u003e2\u003c/sup\u003e = 13.68, df = 1, p = 0.0002). The fitted trajectory showed an early decline in fat concentration, reaching a minimum at approximately 196 (95% CI: 160, 234) days postpartum, followed by an increase towards late lactation. Higher SLI score and greater maternal adiposity were independently associated with higher milk fat concentration, whereas parity and infant sex were not.\u003c/p\u003e\u003cp\u003e \u003cem\u003eEnergy\u003c/em\u003e: The milk energy concentration also exhibited a significant non-linear trajectory, and the inclusion of a quadratic time term significantly improved the model fit compared to a linear time model (χ2 = 26.3, df = 1, p \u0026lt; 0.001). The fitted trajectory showed a decline in energy concentration during early lactation, reaching a nadir at 226 days postpartum (95% CI: 198, 258), followed by stabilization and a modest increase toward later lactation. Higher SLI score and greater maternal adiposity were independently associated with higher milk energy concentration.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this community-based, longitudinal study, we characterized changes in human milk macronutrient composition across the first year of lactation and examined associations with maternal and infant characteristics. Our findings extend the limited longitudinal evidence from low-and-middle-income countries (LMICs) and demonstrate some peculiarities: (i) marked inter-individual variability in milk macronutrients; (ii) milk composition across lactation is best characterized by non-linear trajectories rather than constant linear trends, (iii) relative stability of macronutrient concentrations during the first six months,; and (iv) stronger and more consistent associations of milk macronutrients with maternal characteristics and feeding pattern than with infant-related factors.\u003c/p\u003e \u003cp\u003eThe substantial inter-individual variability observed across all macronutrients underscores the importance of mixed-effects approaches and cautions against over-reliance on single \u0026ldquo;average\u0026rdquo; values when characterizing human milk composition. By explicitly modelling nonlinearity, our study provides a more nuanced understanding of lactational physiology in a South Asian context. by identifying specific time windows when macronutrient trajectories shift. These findings align with the limited existing [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and emerging [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] evidence that human milk composition continues to evolve beyond early lactation, a period that has historically received less research attention. Recent advances in dynamic modelling of human milk composition to explore its trajectories emphasize that lactational changes are inherently nonlinear, phase-dependent, and better conceptualized as continuous biological processes rather than discrete stages [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In that framework, early postpartum trajectories (up to four months postpartum) were characterized by rapid change followed by progressive convergence toward a steady state, with between-mother variability exceeding temporal effects [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our longitudinal quadratic modelling with estimation of inflection points is conceptually aligned with this perspective, as it captures the acceleration-deceleration pattern of macronutrient change across twelve months while preserving postpartum time as a continuous variable. Although we adopted a polynomial, mixed-effects approach rather than a two-phase saturation model, both strategies converge on the same biological inference: macronutrient trajectories are curvilinear, dominated by individual-level heterogeneity, and require nonlinear modelling to adequately represent lactation dynamics.\u003c/p\u003e \u003cp\u003eBy quadratic modelling, we could capture biologically meaningful shifts in the rate of change that were obscured in linear models, particularly during the transition from early to later lactation. The observed inflection points clustered around mid-lactation (around 200 days) for protein, fat, and energy. This suggests a coordinated regulation of milk composition as infants transition from exclusive or predominant breastfeeding to partial breastfeeding with the introduction of complementary feeding. As reported in the results, the mean age at initiation of complementary feeding in our cohort was 198 days.\u003c/p\u003e \u003cp\u003eMost longitudinal studies of human milk macronutrients have focused on early lactation, typically up to 24 weeks postpartum, with sparse data extending beyond six months, particularly from LMIC settings. Available studies on prolonged lactation have demonstrated that protein concentration typically declines during early lactation before stabilizing or modestly increasing later, whereas fat concentrations tend to increase with advancing lactation [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast, carbohydrate concentrations generally remain relatively stable throughout lactation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, most analyses have relied on categorical time points [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], retrospective cohort [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], or linear assumptions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our findings are broadly consistent with these observations and add evidence from an Indian cohort characterized by prolonged breastfeeding practices.\u003c/p\u003e \u003cp\u003eThe stabilization of protein concentration after approximately six months may reflect alignment with declining relative protein requirements per unit body weight [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Meanwhile, the late lactation increase in fat and energy likely supports the rising absolute energy needs associated with increased physical activity and growth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, carbohydrate concentration showed a more prolonged decline, with stabilization only toward the end of the first year, consistent with the relatively conserved lactose synthesis pathway and its role in maintaining milk osmolarity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Maternal BMI and the sum of skinfold thicknesses showed little change across the first year postpartum, indicating relatively stable maternal energy reserves during lactation. This stability suggests that temporal variation in human milk macronutrient concentrations is more likely related to physiological changes in lactation rather than to major shifts in maternal adiposity.\u003c/p\u003e \u003cp\u003eThe positive association of socioeconomic status and maternal adiposity with fat and energy concentrations is consistent with earlier reports linking maternal energy reserves to milk lipid synthesis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Associations with breastfeeding frequency were most evident at 1 and 3 months, where higher feeding frequency correlated with lower fat and energy concentrations, likely reflecting dilution effects related to shorter inter-feed intervals [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Infant anthropometric indices showed limited and inconsistent associations with milk macronutrients as reported in a recent review [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and we did not observe sex-specific differences in milk composition.\u003c/p\u003e \u003cp\u003eRecently published reference values of human milk macronutrient concentrations from the MILQ project [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] provide harmonized, cross-population estimates across the first 8.5 months of lactation. Our findings are broadly concordant with the MILQ reference pattern. The identification of cohort-specific inflection points highlights the potential for contextual variation in the timing of macronutrient shifts, particularly in populations with prolonged breastfeeding and extended lactation practices, such as those observed in India [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA key strength of our study is the use of dense, repeated measurements across the first year of lactation, analysed using linear mixed-effects models with continuous postpartum age, which allows for the efficient use of irregularly timed observations and accounts for inter-individual heterogeneity. At the same time, as with all observational lactation studies, unmeasured maternal or infant factors, like details of breastfeeding and complementary feeding practices beyond the first six months of life, collection of 10 ml of breast milk, and not complete emptying of the breast, may contribute to residual variability in macronutrient trajectories.\u003c/p\u003e \u003cp\u003eMethodological considerations are important when interpreting our findings. The availability of repeated maternal and infant anthropometry strengthened our ability to examine associations of milk macronutrients and body size. Nonetheless, the absence of data on pre-pregnancy BMI, gestational weight gain, maternal dietary intake, and detailed complementary feeding practices limited further exploration of mediating pathways, particularly beyond six months of lactation.\u003c/p\u003e \u003cp\u003eIn our study, milk samples were collected as standardized mid-day spot samples (10 ml), which likely represent foremilk. Foremilk is known to contain lower fat and energy concentrations than hindmilk, and therefore, absolute values may underestimate full-feed or 24-hour averages [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, the use of a consistent sampling protocol across visits enhances internal validity and supports the interpretation of longitudinal trends.\u003c/p\u003e \u003cp\u003eMid-infrared spectroscopy, used for estimating macronutrients, is widely applied in clinical and milk bank settings, allowing for efficient and simultaneous measurement of multiple components [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Carbohydrate concentrations measured using MIR represent an indirect measure, and these estimates are sensitive to calibration datasets and population-specific milk composition [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], highlighting the need for regionally derived reference values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUnderstanding longitudinal changes in human milk macronutrient composition has important implications for infant feeding guidance and maternal nutrition, especially in LMIC settings experiencing a double burden of malnutrition. The stability of human milk composition during early infancy supports current recommendations for exclusive breastfeeding, while the observed changes beyond six months reinforce the importance of timely, nutritionally adequate complementary feeding. In contexts where maternal undernutrition persists, prolonged exclusive breastfeeding without appropriate complementary foods may increase the risk of growth faltering. Conversely, in settings with rising maternal overweight and obesity, higher maternal adiposity may influence milk fat and energy content, with potential implications for early-life metabolic programming.\u003c/p\u003e \u003cp\u003eOverall, our findings underscore the dynamic nature of human milk composition across lactation. Longitudinal, context-specific data such as these are essential for refining breastfeeding and complementary feeding recommendations in diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body mass index \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLAZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Length-for-age z score\u003c/p\u003e\n\u003cp\u003eLMIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low and middle-income countries\u003c/p\u003e\n\u003cp\u003eMIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mid infra-red spectroscopy\u003c/p\u003e\n\u003cp\u003eMUAC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mid-upper arm circumference\u003c/p\u003e\n\u003cp\u003eNFHS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Family Health Survey\u003c/p\u003e\n\u003cp\u003eSLI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standard-of-living Index\u003c/p\u003e\n\u003cp\u003eUNICEF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United Nations Children\u0026rsquo;s Fund\u003c/p\u003e\n\u003cp\u003eWAZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Weight-for-age z score\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;World Health Organization\u003c/p\u003e\n\u003cp\u003eWLZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Weight-for-length z score\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of the Government Medical College, Akola, Maharashtra, India (Approval: GMCAEC002). In this study, all participants provided written informed consent. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and the national guideline: National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (Indian Council of Medical Research, 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Division of Reproductive and Child Health of the Indian Council of Medical Research, New Delhi, India (Ref No. 5/7/1138/2014-RCH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUD and AK conceptualized the study and acquired funding. UD, SB, and SM collected data and conducted the assays. UD and SS analysed the data, and UD wrote the main manuscript text. KM, VW, and AK reviewed the subsequent manuscript drafts. All authors reviewed and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the families who were involved in the study. We acknowledge the institutional support from Prof Rajesh Karyakarte, Dean of the Government Medical College, Akola, Maharashtra. We also thank the District Health Officer, Akola, for providing the necessary permissions to work with the Community Health Centres in the study area. We are grateful to the project assistant, Ms. Jayashree Ingole, and to the team of community health workers who helped with home visits and data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Dr. Urmila Deshmukh, [email protected]\u003c/p\u003e\n\u003cp\u003eORCID ID: 0000-0002-3372-5628\u003c/p\u003e\n\u003cp\u003e2. Prof Kavitha Menon, [email protected]\u003c/p\u003e\n\u003cp\u003eORCID ID: 0000-0001-8624-2868 \u003c/p\u003e\n\u003cp\u003e3. Prof Vinit Warthe, [email protected] \u003c/p\u003e\n\u003cp\u003e4. Ms Swati Balapure, [email protected] \u003c/p\u003e\n\u003cp\u003e5. Ms Shraddha Mandale, [email protected] \u003c/p\u003e\n\u003cp\u003e6. Prof Sharvari Shukl, [email protected] \u003c/p\u003e\n\u003cp\u003eORCID ID: 0000-0002-7029-5532\u003c/p\u003e\n\u003cp\u003e7. Prof Anura Kurpad, [email protected]\u003c/p\u003e\n\u003cp\u003eORCID ID: 0000-0001-7998-2438\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVictora CG, Bahl R, Barros AJ, Fran\u0026ccedil;a GV, Horton S, Krasevec J, et al. Lancet Breastfeeding Series Group. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. 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Daily variation of macronutrient concentrations in mature human milk over 3 weeks. Sci Rep. 2021;11(1):10224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-89460-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-89460-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-breastfeeding-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ibfj","sideBox":"Learn more about [International Breastfeeding Journal](http://internationalbreastfeedingjournal.biomedcentral.com/)","snPcode":"13006","submissionUrl":"https://submission.nature.com/new-submission/13006/3","title":"International Breastfeeding Journal","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breastfeeding, Lactation, Human milk, Macronutrients, Longitudinal study, India","lastPublishedDoi":"10.21203/rs.3.rs-9064095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9064095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHuman milk is a dynamic biological system. Although early lactation has been extensively studied, longitudinal data beyond six months postpartum, particularly from low- and middle-income countries, remain limited. In India, prolonged exclusive breastfeeding and continued breastfeeding beyond two years are common, underscoring the need to understand temporal patterns of human milk composition. We aimed to characterize trajectories of human milk protein, carbohydrate, fat, and energy across the first year postpartum.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this community-based longitudinal study in semi-urban India, healthy mother-infant dyads were followed at 1, 3, 6, 9, and 12 months postpartum, with standardized anthropometry performed, feeding practices recorded, and milk samples collected at each visit. Human milk macronutrients were quantified using mid-infrared spectroscopy. Longitudinal trajectories were examined using linear mixed-effects models, adjusting for the socioeconomic status, parity, maternal sum of skinfolds, and infant sex. Inflection points were derived from fitted quadratic models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt enrolment (N\u0026thinsp;=\u0026thinsp;95 dyads, postpartum days 35\u0026thinsp;\u0026plusmn;\u0026thinsp;10), mothers had a mean age of 25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 years (BMI: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 kg/m\u0026sup2;). Substantial inter-individual variability was observed across all macronutrients (401 milk samples). Inclusion of quadratic time terms significantly improved model fit for protein, carbohydrate, fat, and energy (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Protein concentration declined in early lactation and stabilized around 183 (95% CI: 150, 212) days postpartum. Fat and energy reached nadirs at approximately 196 (160, 234) and 226 (198, 258) days, respectively, followed by modest increases toward late lactation. Carbohydrate showed a gradual decline with later stabilization [inflection point, 341 (272, 454) days]. Higher socioeconomic status and maternal adiposity were independently associated with higher milk fat and energy concentrations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHuman milk macronutrient composition remains relatively stable during exclusive or predominant breastfeeding, and undergoes measurable shifts around the time complementary feeding is introduced, reflecting dynamic adaptation of lactational biology. Maternal energy reserves appear to influence milk lipid and energy content, with potential implications for infant growth and metabolic health. Context-specific longitudinal data such as these are essential to inform breastfeeding policy and maternal nutrition strategies.\u003c/p\u003e","manuscriptTitle":"Longitudinal Changes in Human Milk Macronutrients Across the First Year of Lactation in Indian Mothers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 09:45:21","doi":"10.21203/rs.3.rs-9064095/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-30T21:30:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T03:35:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T17:41:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215472967299403999251138459265291900569","date":"2026-04-07T20:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276510108946239704204462498035775144710","date":"2026-04-06T16:42:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T00:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20508442544485645762091036293827368290","date":"2026-03-23T13:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-21T22:45:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T16:15:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T16:14:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Breastfeeding Journal","date":"2026-03-08T12:09:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-breastfeeding-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ibfj","sideBox":"Learn more about [International Breastfeeding Journal](http://internationalbreastfeedingjournal.biomedcentral.com/)","snPcode":"13006","submissionUrl":"https://submission.nature.com/new-submission/13006/3","title":"International Breastfeeding Journal","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0dcd03ca-9b39-4e6e-8a8c-b4e6d6170141","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-30T21:30:35+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T21:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 09:45:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9064095","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9064095","identity":"rs-9064095","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}