Diet, Gut Microbiome, and Their End Metabolites Associate With Acute Pancreatitis Risk

Author:

Yazici Cemal1,Thaker Sarang2,Castellanos Karla K.1,Al Rashdan Haya1,Huang Yongchao3,Sarraf Paya4,Boulay Brian1,Grippo Paul1,Gaskins H. Rex5,Danielson Kirstie K.6,Papachristou Georgios I.7,Tussing-Humphreys Lisa8,Dai Yang3,Mutlu Ece R.1,Layden Brian T.69

Affiliation:

1. Division of Gastroenterology and Hepatology, Department of Medicine, University of Illinois Chicago, Chicago, Illinois, USA;

2. Division of Gastroenterology and Hepatology, Department of Medicine, Northwestern University, Chicago, Illinois, USA;

3. Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, USA;

4. Department of Medicine, University of Illinois Chicago, Chicago, Illinois, USA;

5. Department of Animal Sciences, Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana-Champaign, Illinois, USA;

6. Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Illinois Chicago, Chicago, Illinois, USA;

7. Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, The Ohio State University, Columbus, Ohio, USA;

8. Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, Illinois, USA;

9. Jesse Brown VA Medical Center, Chicago, Illinois, USA.

Abstract

INTRODUCTION:Diet and decreased gut microbiome diversity has been associated with acute pancreatitis (AP) risk. However, differences in dietary intake, gut microbiome, and their impact on microbial end metabolites have not been studied in AP. We aimed to determine differences in (i) dietary intake (ii) gut microbiome diversity and sulfidogenic bacterial abundance, and (iii) serum short-chain fatty acid (SCFA) and hydrogen sulfide (H2S) concentrations in AP and control subjects.METHODS:This case-control study recruited 54 AP and 46 control subjects during hospitalization. Clinical and diet data and stool and blood samples were collected. 16S rDNA sequencing was used to determine gut microbiome alpha diversity and composition. Serum SCFA and H2S levels were measured. Machine learning (ML) model was used to identify microbial targets associated with AP.RESULTS:AP patients had a decreased intake of vitamin D3, whole grains, fish, and beneficial eicosapentaenoic, docosapentaenoic, and docosahexaenoic acids. AP patients also had lower gut microbiome diversity (P= 0.021) and a higher abundance of sulfidogenic bacteria includingVeillonellasp. andHaemophilussp., which were associated with AP risk. Serum acetate and H2S concentrations were significantly higher in the AP group (P< 0.001 andP= 0.043, respectively). ML model had 96% predictive ability to distinguish AP patients from controls.DISCUSSION:AP patients have decreased beneficial nutrient intake and gut microbiome diversity. An increased abundance of H2S-producing genera in the AP and SCFA-producing genera in the control group and predictive ability of ML model to distinguish AP patients indicates that diet, gut microbiota, and their end metabolites play a key role in AP.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Gastroenterology

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