Proteomic pattern of breast milk discriminates obese mothers with infants of delayed weight gain from normal-weight mothers with infants of normal weight gain

母乳蛋白质组学模式可将婴儿体重增长延迟的肥胖母亲与婴儿体重增长正常的正常母亲区分开来

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作者:Christo Atanassov, Etienne Viallemonteil, Charlotte Lucas, Marylise Perivier, Stéphane Claverol, Roland Raimond, Régis Hankard

Abstract

We previously reported that exclusively breastfed infants born to mothers with pregestational obesity gain less weight during the first month after birth than those born to mothers of normal pregestational weight. This issue is potentially important since lower weight gain in breastfed infants of obese mothers might increase the risk of developing later obesity. Breast milk quality and quantity, together with breastfeeding practice, possibly influence infants' feeding behavior, appetite control, and regulation of growth later in life. The issue of whether breast milk protein patterns from obese mothers differ in composition from those of non-obese mothers remains largely unexplored. Here, we established a breast milk proteomic pattern that discriminates obese mothers and infants with delayed weight gain at 1 month after birth from normal-weight mothers with infants of the same age and with normal weight gain. Obese mothers were matched to normal-weight mothers (n = 26; body mass index 33.5 ± 3.2 vs 21.5 ± 1.5 kg·m-2). The mean weight gain of infants in the obese group at 1 month after birth was 430.8 g lower than that of the infants in the control group. Analysis of the breast milk delipidized fraction by surface-enhanced laser desorption/ionization on CM10 and Q10 arrays was followed by MS-assisted purification and LC-MS/MS microsequencing of a selected biomarker. We identified 15 candidate protein biomarkers, seven of which were overexpressed in the obese group and eight in the normal-weight group. One of the most significant candidate biomarkers, overexpressed in the obese group, was identified as a fragment of the sixth extracellular domain of the polymeric immunoglobulin receptor. Further structural identification of these candidate biomarkers and their validation in clinical assays may facilitate the development of a predictive immunoassay.

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