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Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

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Evaluating the Effect of Random Multi-Donor Pooling on the Nutritional Variability in Donor Human Milk Using Computer

R Mitchell Smith¹, Scott Richter^1,2, Esther F Iwayemi³

¹Department of Mathematics and Statistics, University of North Carolina Greensboro, Greensboro, North Carolina, USA.

Maternal & Child Nutrition

|January 8, 2026

Summary

This summary is machine-generated.

Pooling donor human milk (DHM) with more donors reduces macronutrient variability. However, even 10 donors per pool could not ensure consistent levels of key micronutrients in DHM.

Keywords:

donor milk milk banking minerals nutrition preterm vitamins

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Area of Science:

Human milk banking and infant nutrition
Nutritional biochemistry and analysis
Bioavailability and nutrient stability

Background:

Donor human milk (DHM) composition, particularly protein and fat, exhibits significant variability.
This variability is influenced by the number of unique donors contributing to a milk pool.
Understanding pooling effects is crucial for optimizing DHM nutritional quality for vulnerable infants.

Purpose of the Study:

To characterize the impact of donor pool size (2-10 donors) on DHM macronutrient, vitamin, and mineral variability.
To determine the minimum number of donors needed to achieve 80% target compliance for true protein, fat, and DSLNT.
To evaluate nutrient variability using a Nutrient Inequality Index (NII).

Main Methods:

Monte Carlo simulations modeled donor lifetime volume and milk bank production constraints.
Nutrient variability was quantified using the Nutrient Inequality Index (NII): ratio of 90th to 10th percentile.
Simulations analyzed pooling effects for macronutrients, vitamins, minerals, and bioactive factors.

Main Results:

Pooling 2-10 donors reduced variability in DHM macronutrients more effectively than in most vitamins and minerals.
Pre-defined targets for true protein, fat, and DSLNT were not consistently achievable across pooling scenarios.
NII stabilized for lactose (<1.1) with 3+ donors, and for fat/protein (<1.3) with 5+ donors.
High NII (>1.5) persisted for micronutrients (e.g., zinc, iron, B vitamins) even with 10 donors.

Conclusions:

Increasing donor numbers in DHM pools mitigates macronutrient variability but has limited impact on micronutrient consistency.
Achieving precise nutritional targets for certain components in DHM requires careful donor selection and potentially larger pool sizes.
Micronutrient variability remains a significant challenge in DHM processing, necessitating further investigation and potential fortification strategies.