Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective

Abstract

Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy.

Erchen Decoction Alleviates High-Fat Diet + AOM/DSS-Induced CRC by Ameliorating Cholesterol-Bile Acid Metabolism Disorder through Regulating FXR and the Gut Microbiota

“…According to some studies, being obese or overweight due to a high-fat diet is strongly linked to the development of colorectal cancer and resistance to treatment. Furthermore, obesity and its metabolic alterations have a signi cant impact on the formation and spread of CRC tumors [23,24]. Although numerous studies suggest that dysbiosis resulting from a high-fat diet independently promotes colorectal cancer, other research indicates that the intake of animal protein and fat and how they interact with the gut bacteria pose more signi cant risks for CRC development than obesity alone [25,26].…”

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

“…In other words, for smaller colorectal adenomas, microbiomics can be used as a complementary test to colonoscopy to reduce the probability of their being missed. In addition, Seo et al (2022) . developed a gradient boosting machine (GBM) model for CRC and IBD differential diagnosis.…”

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

“…The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020 [1][2][3][4][5][6] . Thus, new methods for modeling this increase in obesity overtime is necessary to best combat this public health emergency [7][8][9][10] . Multiple studies have identi ed major risk factors for obesity, including demographic factors (race, sex, age), lifestyle factors (exercise, sleep), and clinical comorbidities (hypertension, diabetes.…”