Biomarkers of Low Milk Supply †
by
, , , , , and
Ching Tat Lai
1,2,3,*
,
Xuehua Jin
1,2,3,
Xiaojie Zhou
1,2,3,
Lisa F. Stinson
1,2,3,
Mary E. Wlodek
1,4,
Sharon L. Perrella
1,2,3,
Zoya Gridneva
1,2,3 and
Donna T. Geddes
1,2,3 1
School of Molecular Sciences, The University of Western Australia, Crawley, WA 6009, Australia
2
ABREAST Network, Perth, WA 6000, Australia
3
UWA Centre for Human Lactation Research and Translation, Crawley, WA 6009, Australia
4
Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
†
Presented at the Australian Breastfeeding + Lactation Research and Science Translation Conference (ABREAST Conference 2023), Perth, Australia, 10 November 2023.
Proceedings 2023, 93(1), 5; https://doi.org/10.3390/proceedings2023093005
Published: 19 December 2023
(This article belongs to the Proceedings of Australian Breastfeeding + Lactation Research and Science Translation Conference 2023)
It is estimated that 5–10% of breastfeeding mothers are unable to produce enough milk for their infants. Causes of low milk supply are multifactorial, including factors such as gene mutations, endocrine disorders and infrequent milk removal. All these factors affect the normal function of the mammary gland and potentially the concentrations of human milk components. Therefore, an array of milk components may act as biomarkers for the detection of low milk supply. Various biochemical assays were used to measure an array of milk components (fat, total protein, casein, whey, lactose, citrate, calcium, copper, iron, potassium, magnesium, sodium, phosphorous and zinc) in 48 women with low milk supply (<600 mL/24 h) and 65 with normal milk supply (>600 mL/24 h). Univariable linear regression and correlation analysis were used to examine associations between milk component concentrations and milk supply. The dataset was then randomly split into a training set (70%) and a test set (30%). Multivariable logistic regression with the training set was used to develop prediction models with various combinations of milk component concentrations, and the test set was used to validate the predictivity of these models.
Univariable linear regression showed that concentrations of fat (p = 0.039), total protein (p = 0.002), whey (p = 0.001), potassium (p = 0.02), sodium (p = 0.001) and zinc (p = 0.001) and the Na:K ratio (p = 0.008) were significantly different between samples from low- and normal-milk-supply mothers. In addition, total protein, casein, whey, lactose, citrate, calcium, potassium, sodium, phosphorous, zinc and the Na:K ratio were strongly correlated with each other (r > 0.6 or r < −0.6). The predictive model with fat content as the sole predictor had an accuracy of 61%, precision of 80% and recall of 52%, while the model with fat, total protein and lactose as predictors had an accuracy of 53%, precision of 100% and recall of 33% in predicting low milk supply. Further, the model with only sodium and potassium had an accuracy of 64%, precision of 80% and recall of 50%. The best predictive model included all measured milk components, and had an accuracy of 79%, precision of 84% and recall of 76% in predicting low milk supply.
These preliminary findings suggest that differences in milk composition between women with low and normal milk supply have the potential to be used in testing for the detection of low milk production.
Author Contributions
Conceptualization, C.T.L. and D.T.G.; methodology, D.T.G., C.T.L., X.J., X.Z., L.F.S., S.L.P. and Z.G.; data collection, C.T.L., X.J., X.Z., S.L.P. and Z.G.; formal analysis, C.T.L., X.J. and X.Z.; investigation, D.T.G. and C.T.L.; resources, D.T.G.; data curation, D.T.G., C.T.L. and X.J.; writing—original draft preparation, C.T.L.; writing—review and editing, D.T.G., X.J., X.Z., L.F.S., M.E.W., S.L.P. and Z.G.; visualization, C.T.L.; supervision, D.T.G.; funding acquisition, D.T.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by an unrestricted research grant from Medela AG (Switzerland). C.T.L., X.Z., L.F.S., S.L.P., Z.G. and D.T.G. receive a salary from an unrestricted research grant paid by Medela AG and administered by The University of Western Australia. X.J. is supported by a UWA—CSC Joint PhD Scholarship and a UWA—CSC Higher Degree by Research Top-Up Scholarship.
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Human Research Ethics Committee at The University of Western Australia (2019/RA/4/20/4023) and conducted in accordance with the relevant guidelines and regulations.
Informed Consent Statement
Informed consent was obtained from all subjects involved in this study.
Data Availability Statement
Data sharing is not applicable.
Acknowledgments
We thank all our participants and their families for their time and help with this research.
Conflicts of Interest
D.T.G. declares participation in the Scientific Advisory Board of Medela AG. C.T.L., X.Z., L.F.S., S.L.P., Z.G. and D.T.G. are/were supported by an unrestricted research grant from Medela AG, administered by The University of Western Australia. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. All other authors declare no conflicts of interest.
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Share and Cite
MDPI and ACS Style
Lai, C.T.; Jin, X.; Zhou, X.; Stinson, L.F.; Wlodek, M.E.; Perrella, S.L.; Gridneva, Z.; Geddes, D.T. Biomarkers of Low Milk Supply. Proceedings 2023, 93, 5. https://doi.org/10.3390/proceedings2023093005
AMA Style
Lai CT, Jin X, Zhou X, Stinson LF, Wlodek ME, Perrella SL, Gridneva Z, Geddes DT. Biomarkers of Low Milk Supply. Proceedings. 2023; 93(1):5. https://doi.org/10.3390/proceedings2023093005
Chicago/Turabian StyleLai, Ching Tat, Xuehua Jin, Xiaojie Zhou, Lisa F. Stinson, Mary E. Wlodek, Sharon L. Perrella, Zoya Gridneva, and Donna T. Geddes. 2023. "Biomarkers of Low Milk Supply" Proceedings 93, no. 1: 5. https://doi.org/10.3390/proceedings2023093005
