The interplay between the microbiota, the epigenome, and nutrition among obese adults during behavioral weight reduction

By Pooja Toshniwal PahariaReviewed by Lily Ramsey, LLMAug 21 2023

In a recent study published in Nutrients, researchers evaluated associations between gut microbiome (MB), deoxyribonucleic acid (DNA) methylation (DNAme), and nutrition before and during a one-year behavioral weight reduction intervention.

Study: The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss. Image Credit: MeekoMedia/Shutterstock.com

Background

The microbiome and DNA are crucial in regulating weight and metabolic homeostasis. Modifiable factors like physical exercise and dietary patterns impact obesity prevention and treatment.

The mechanisms underlying the impact of lifestyle on body weight are complicated, with probable causative pathways involving genetic changes such as DNA methylation.

Lifestyle interventions may regulate the genetic activity for phenotype improvements during obesity treatments. The gut microbiome mediates inflammation, appetite regulation, and energy balance.

Although obesity is associated with the gut microbiota, diet, and epigenome, their interplay during obesity therapy remains scarce. Understanding the interactions between these factors is essential for effective treatment and prevention of obesity and its sequelae.

About the study

In the present study, researchers assessed interactions between the intestinal microbiome, diet, and host epigenome concerning obesity.

The study included 47 adults, aged 18 to 55 years, who were overweight or obese (BMI ranging between 27.0 and 45.0 kg/m²) and who were recruited for a randomized clinical trial between April 2018 and February 2019 to compare weight reduction induced by intermittent fasting (IMF) or daily calorie restriction (DCR) during a 1.0-year behavioral-type intervention for weight loss. The participants were evaluated at study initiation and after three months (3.0 m).

The EPIC array assessed Fecal microbiomes by 16S ribosomal ribonucleic acid (rRNA) sequencing and DNA methylation in whole blood.

Polymerase chain reaction (PCR) was performed, and food groups, nutrient consumption, and Healthy Eating Index (HEI) scores were determined using one-week dietary records. Linear modeling was performed to evaluate the impact of microbial taxa abundance on DNA methylation and diet, with data adjustments for a 5.0% false discovery rate and potential confounders.

All individuals were motivated to decrease caloric intake by 34.0% weekly and engage in moderate-to-vigorous physical exercise for 300.0 minutes weekly.

Anthropometric measurements such as waist circumference and body weight, clinical evaluations such as systolic and diastolic blood pressure, and cardiometabolic assays were performed using standardized protocols.

Sensitivity analyses were performed by using 82,889 CpG sites within genes which were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways (n=130) involved in metabolism (such as that of carbohydrate and lipid), organ systems (such as endocrinal, immune, and digestive), and diseases (such as cardiovascular and metabolic diseases), considered linked to obesity.

Dietary records were analyzed by trained registered dietitian nutritionists (RDNs) using the Nutrition Data System for Research (NDSR) nutrient analysis software. Serum samples were assessed using liquid chromatography/mass spectroscopy (LC/MS).

Results

The mean age of the participants was 41 years, and their mean BMI was 34 kg/m². Most (77%) of the participants were female. A 6.2% weight reduction was observed in three months, with the mean BMI and waist circumference reducing by 2.1 kg/m² and 9.0 cm, respectively.

Likewise, there were improvements in serological cardiometabolic indicators, including significant decreases in triglycerides, cholesterol, insulin, and glucose levels within three months.

At baseline, Ruminiclostridium, was positively related to DNA methylation of the genes 5-Nucleotidase Ecto (NT5E), Collagen Type XX Alpha 1 Chain (COL20A1), and Collagen Type XVIII Alpha 1 Chain (COL18A1).

At three months, 14 distinct microbiomes: DNA methylation relationships were observed, including a moderate and inverse association between Akkermansia counts and DNA methylation of Glucuronidase Beta (GUSB), Crystallin Lambda 1 (CRYL1), complement 9 (C9), and GDP-Mannose 4,6-Dehydratase (GMDS).

At 3.0 months, two statistically significant relationships between the gut microbiome and DNA methylation were observed.

These included an inverse and moderate relationship between Lachnospiraceae NK4A136 abundance and DNA methylation in an ornithine transcarbamylase (OTC) intron and a robustly positive association between Megasphaera abundance and DNA methylation in enhancer-binding protein delta (CEBPD)/cytosine-cytosine-adenosine-adenosine-thymidine promoter region.

Lachnospiraceae UCG-001 abundance was inversely and moderately linked to DNA methylation in genes such as Hes Family BHLH Transcription Factor 1 (HES1), Nuclear Receptor Subfamily 5 Group A Member 2 (NR5A2), Lecithin Retinol Acyltransferase (LRAT), and Piccolo Presynaptic Cytomatrix Protein (PCLO).

In contrast, a positive association was observed between Ruminococcus gnavus and DNA methylation in the carbonic anhydrase 3 (CA3) gene at three months.

Individuals self-documented decreasing caloric intake by 500 kcal daily in three months, and dietary quality improved. Fat-obtained calories were reduced by 4.0%, and protein intake increased similarly.

Integrated analysis showed no relationships between dietary components; however, at three months, Ruminococcaceae NK4A214 was related to food-group-level low-fat margarine intake.

In the change analyses, Ruminococcus gnavus abundance was related to the intakes of trans-octadecenoic acid and total trans-fats, which significantly reduced in three months.

Conclusion

Overall, the study findings showed that DNA methylation of phenotypically significant genes is linked to microorganisms involved in mucin breakdown, short-chain fatty acid synthesis, and body weight. The relationships provide a basic grasp of the potential pathways via which alterations in the gut microbiota may impact metabolism during weight loss.