Macronutrients in Human Milk Exposed to Antidepressant and Anti-Inflammatory Medications
Bibliographic record
Abstract
Importance: The association between maternal medications and the macronutrient composition of human milk has not been studied. Objective: To compare macronutrient levels in milk samples from mothers treated with long-term medications with samples from untreated healthy and disease-matched control mothers (DMCs). Design, Setting, and Participants: A cross-sectional study using samples collected between October 2014 and January 2024 from breastfeeding mothers in the US and Canada invited to participate to the Mommy's Milk Human Milk Research Biorepository at the University of California, San Diego. Of 3974 samples from unique individuals in the biorepository, 310 were from mothers treated with 1 of 4 categories of medications, 151 from DMCs with the same underlying disorders, and 73 from healthy untreated mothers, frequency matched on infant age and sex. Of these, 150 were excluded because they had more than 1 medication exposure or were outliers. Data were analyzed from March to June 2024. Exposures: Continuous treatment with selective serotonin reuptake inhibitors (SSRIs), monoclonal antibodies (MABs), systemic steroids, and other anti-inflammatory drugs (ADs) in the 14 days before milk sample collection. Main Outcomes and Measures: Levels of protein, fat, carbohydrate, and total energy were measured with SpectraStar 2400 near infrared analyzer and compared across groups with analysis of covariance adjusted for infant and maternal age, parity, maternal body mass index, infant sex, exclusive breastfeeding, feeding frequency, collection time, maternal cannabis use, and occupation. Results: A total of 384 samples were collected; 194 infants (50.5%) were female; the mean (SD) age of the maternal cohort was 33.5 (4.4) years, and infant age at collection was 6.6 (5.4) months. Mean (SD) protein levels were 15% to 21% lower in samples from exposed mothers (0.92 [0.56] g/100 mL for 63 SSRIs, 0.85 [0.51] g/100 mL for 63 MABs, 0.88 [0.37] g/100 mL for 33 steroids, and 0.85 [0.54] g/100 mL for 20 other ADs) compared with 64 samples from healthy mothers (1.08 [0.50] g/100 mL). Adjusted differences were significant for SSRIs and steroids (F1, 91 = 4.32; P = .04 and F1,59 = 5.00, P = 0.03, respectively). Mean (SD) fat and energy were 10% to 22% lower in samples from mothers with other ADs (3.40 [1.21] g/100 mL for fat and 69.56 [15.35] kcal/100 mL for energy) than from healthy (3.85 [1.66] g/100 mL for fat and 77.16 [22.08] kcal/100 mL for energy) and DMC (4.38 [1.90] g/100 mL for fat and 80.60 [24.70] kcal/100 mL for energy) mothers. Adjusted differences were only significant for fat compared with DMC (F1,88 = 6.22; P = .01). Conclusions and Relevance: In this cross-sectional study, some maternal medications were associated with lower levels of protein and fat in milk, which could impose health risks for breastfed infants. Other factors that could influence macronutrient levels need to be clarified before the clinical implications of these findings can be confirmed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".