Brown Fat-Activating Lipokine 12,13-diHOME in Human Milk Is Associated With Infant Adiposity

CONTEXT

Little is known about the specific breastmilk components responsible for protective effects on infant obesity. Whether 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), an oxidized linoleic acid metabolite and activator of brown fat metabolism, is present in human milk, or linked to infant adiposity, is unknown.

OBJECTIVE

To examine associations between concentrations of 12,13-diHOME in human milk and infant adiposity.

DESIGN

Prospective cohort study from 2015 to 2019, following participants from birth to 6 months of age.

SETTING

Academic medical centers.

PARTICIPANTS

Volunteer sample of 58 exclusively breastfeeding mother-infant pairs; exclusion criteria included smoking, gestational diabetes, and health conditions with the potential to influence maternal or infant weight gain.

MAIN OUTCOME MEASURES

Infant anthropometric measures including weight, length, body mass index (BMI), and body composition at birth and at 1, 3, and 6 months postpartum.

RESULTS

We report for the first time that 12,13-diHOME is present in human milk. Higher milk 12,13-diHOME level was associated with increased weight-for-length Z-score at birth (β = 0.5742, P = 0.0008), lower infant fat mass at 1 month (P = 0.021), and reduced gain in BMI Z-score from 0 to 6 months (β = -0.3997, P = 0.025). We observed similar associations between infant adiposity and milk abundance of related oxidized linoleic acid metabolites 12,13-Epoxy-9(Z)-octadecenoic acid (12,13-epOME) and 9,10-Dihydroxy-12-octadecenoic acid (9,10-diHOME), and metabolites linked to thermogenesis including succinate and lyso-phosphatidylglycerol 18:0. Milk abundance of 12,13-diHOME was not associated with maternal BMI, but was positively associated with maternal height, milk glucose concentration, and was significantly increased after a bout of moderate exercise.

CONCLUSIONS

We report novel associations between milk abundance of 12,13-diHOME and adiposity during infancy.

Endogenous Fatty Acid Synthesis Drives Brown Adipose Tissue Involution

Thermoneutral conditions typical for standard human living environments result in brown adipose tissue (BAT) involution, characterized by decreased mitochondrial mass and increased lipid deposition. Low BAT activity is associated with poor metabolic health, and BAT reactivation may confer therapeutic potential. However, the molecular drivers of this BAT adaptive process in response to thermoneutrality remain enigmatic. Using metabolic and lipidomic approaches, we show that endogenous fatty acid synthesis, regulated by carbohydrate-response element-binding protein (ChREBP), is the central regulator of BAT involution. By transcriptional control of lipogenesis-related enzymes, ChREBP determines the abundance and composition of both storage and membrane lipids known to regulate organelle turnover and function. Notably, ChREBP deficiency and pharmacological inhibition of lipogenesis during thermoneutral adaptation preserved mitochondrial mass and thermogenic capacity of BAT independently of mitochondrial biogenesis. In conclusion, we establish lipogenesis as a potential therapeutic target to prevent loss of BAT thermogenic capacity as seen in adult humans.

Schlein et al. show that carbohydrate-response element-binding protein (ChREBP) controls de novo lipogenesis (DNL) in brown adipose tissue (BAT) and determines BAT whitening in response to thermoneutral housing. ChREBP deficiency prevents enrichment of DNL-derived lipids and mitophagy during BAT involution, which is associated with higher thermogenic capacity.

Abstract 2860: Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development

Biomarker analysis is critically dependent on the quality of biofluid or tissue samples obtained from human research studies. Although proteomic, lipidomic, and metabolomic analyses can be dramatically impacted by the time of sample collection, fasting status, and participant demographics, the hemolysis status of plasma, serum or buffy coat samples is a poorly understood confounder of sample quality. Hemoglobin levels can range between 0 - 10 g/L in samples (referred to as 1-4 in plasma/serum and 0-4 level in buffy coat) as a marker of hemolysis severity and sample contamination by reticulocyte-derived analytes. In oncology clinical trials, patients can be more susceptible to hemolysis due to chemotherapy treatment, which can impact sample assessment and study results. Herein, we analyzed 941 plasma and 950 serum samples using comprehensive proteomics, structural lipidomics, signaling lipidomics, and metabolomics in a pancreatic cancer biomarker discovery program referred to as Project Survival as well as 951 buffy coat samples using only proteomic analysis. Project survival is a 7-year longitudinal pancreatic cancer biomarker discovery trial analyzing 400+ pancreatic cancer and at-risk patients using multi-omic and multiple biofluid assessment. To date this study yielded samples in plasma with 92.3% - #1, 6.5% - #2, 1.2% - #3, and 0% - #4 hemolysis, serum with 94.8% - #1, 3.8% - #2, 1.4% - #3, and 0% - #4 hemolysis and buffy coat 42.7% - #0, 25.6% - #1, 20.8% -#2, 10.4% - #3, and 0.5% - #4 hemolysis. Multi-omic and regression analysis of sample data for hemolysis status revealed a distinct pattern of OMIC variables correlated with the degree of hemolysis. Proteomics analysis was the greatest impacted in terms of the protein identification and quantitation. Additionally, pathway analysis revealed expected pathways associated with hemolysis and coagulation, but also unknown pathways and corresponding proteins that were differentially correlated with hemolysis state. Additionally, metabolomics and lipidomics analysis also revealed distinct differentials associated with hemolysis state. Herein, our analysis is the first to analyze thousands of samples using multi-omics revealing critically informative molecular differentials across OMIC technologies demonstrating that caution should be given to avoid these identified biomarkers for translational development.

Citation Format: Michael A. Kiebish, Punit Shah, Valerie Bussberg, Vladimir Tolstikov, Rick Searfoss, Kennedy Ofori-Mensa, Eric M. Grund, Abena Darkwah, Emily Y. Chen, Bennett Greenwood, Ellaine Adu Ntoso, Leonardo Rodrigues, Mia Liu, Elder Granger, Chas Bountra, Rangaprasad Sarangarajan, A J. Moser, Niven R. Narain. Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2860.

Abstract 4945: Project Survival: Engineering a phenomic and artificial intelligence driven precision medicine biomarker pipeline for pancreatic adenocarcinomas

Pancreatic cancer is a complex and dynamic disorder necessitating a comprehensive clinical design integrated with robust OMICS technologies and AI analytics to identify potential molecular and clinical signatures of diagnosis, progression, and treatment outcomes. Project Survival is a multisite prospective longitudinal study currently in the 4th year of a 6 year initiative of sampling and analysis of subjects in 6 categories: healthy volunteers with a first degree relative with pancreatic cancer (N=39), pancreatitis (N=34), pancreatic cystic neoplasm (N=52), suspicious pancreatic masses with pathology other than pancreatic cancer (N=22), early stage (N=66), locally advanced (N=123), and metastatic pancreatic cancer (N=99). All diseased patients are longitudinally sampled multiple times per year for sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissue. The BERG Interrogative Biology® platform is employed for multi-omic mass spectrometry analysis (metabolomics, lipidomics and proteomics) and applies artificial intelligence (bAIcis®, BERG Artificial Intelligence Clinical Information System) technologies. bAIcis® is harnessed to align the multi-omic profiles with longitudinal clinical information to infer probabilistic cause-and-effect relationships among molecular and clinical variables in a network-based model. Multiple longitudinal time points continue to be collected during the course of the six-year timeline enabling dynamic modeling. The value of this longitudinal study is in the epidemiological assessment of patient type progression to more advanced stages and identification of biomarkers and clinical features that align with the shifts observed in the patient populations. Collectively, we are incorporating patient progression with longitudinal sampling to investigate predictive signatures of disease advancement. Biomarker panels with AUC > 0.7 will be pursued in a further prospective clinical study with a larger subject number. The integration of multi-omic analysis with artificial intelligence has identified several biomarker panels that meet numerous unmet needs for the identification and clinical stratification of pancreatic adenocarcinoma.

Citation Format: Eric Michael Grund, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, John J. Crowley, Amy Stoll-D'Astice, Tori Singer, Corinne Decicco, Wendy Hori, Abena Darkwah, Lixia Zhang, Valerie Bussberg, Leonardo O. Rodrigues, Emily Y. Chen, Tomislav Dragovich, Manuel Hidalgo, Niven R. Narain, A James Moser. Project Survival: Engineering a phenomic and artificial intelligence driven precision medicine biomarker pipeline for pancreatic adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4945.