Metabolomics prospect of obesity and metabolic syndrome; a systematic review

Exploring causal effects of gut microbiota and metabolites on body fat percentage using two-sample Mendelian randomization

AIM

The relationship between the gut microbiota, metabolites and body fat percentage (BFP) remains unexplored. We systematically assessed the causal relationships between gut microbiota, metabolites and BFP using Mendelian randomization analysis.

MATERIALS AND METHODS

Single nucleotide polymorphisms associated with gut microbiota, blood metabolites and BFP were screened via a genome-wide association study enrolling individuals of European descent. Summary data from genome-wide association studies were extracted from the MiBioGen consortium and the UK Biobank. The inverse variance-weighted model was the primary method used to estimate these causal relationships. Sensitivity analyses were performed using pleiotropy, Mendelian randomization-Egger regression, heterogeneity tests and leave-one-out tests.

RESULTS

In the aspect of phyla, classes, orders, families and genera, we observed that o_Bifidobacteriales [β = -0.05; 95% confidence interval (CI): -0.07 to -0.03; false discovery rate (FDR) = 2.76 × 10-3], f_Bifidobacteriaceae (β = -0.05; 95% CI: -0.07 to -0.07; FDR = 2.76 × 10-3), p_Actinobacteria (β = -0.06; 95% CI: -0.09 to -0.03; FDR = 6.36 × 10-3), c_Actinobacteria (β = -0.05; 95% CI: -0.08 to -0.02; FDR = 1.06 × 10-2), g_Bifidobacterium (β = -0.05; 95% CI: -0.07 to -0.02; FDR = 1.85 × 10-2), g_Ruminiclostridium9 (β = -0.03; 95% CI: -0.06 to -0.01; FDR = 4.81 × 10-2) were negatively associated with BFP. G_Olsenella (β = 0.02; 95% CI: 0.01-0.03; FDR = 2.16 × 10-2) was positively associated with BFP. Among the gut microbiotas, f_Bifidobacteriales, o_Bifidobacteriales, c_Actinobacteria and p_Actinobacteria were shown to be significantly associated with BFP in the validated dataset. In the aspect of metabolites, we only observed that valine (β = 0.77; 95% CI: 0.5-1.04; FDR = 8.65 × 10-6) was associated with BFP.

CONCLUSIONS

Multiple gut microbiota and metabolites were strongly associated with an increased BFP. Further studies are required to elucidate the mechanisms underlying this putative causality. In addition, BFP, a key indicator of obesity, suggests that obesity-related interventions can be developed from gut microbiota and metabolite perspectives.

Serum apolipoproteins and mortality risk: evidence from observational and Mendelian randomization analyses

BACKGROUND

Apolipoproteins (APOs) have emerged as significant players in lipid metabolism that affects the risk of chronic disease. However, the impact of circulating APO concentrations on premature death remains undetermined.

OBJECTIVES

This study aimed to investigate the associations of serum APOs with all-cause, cardiovascular disease (CVD)-related, and cancer-related mortality.

METHODS

We included 340,737 participants who had serum APO measurements from the UK Biobank. Restricted cubic splines and multivariable Cox regression models were used to assess the associations between APOs and all-cause and cause-specific mortality by computing hazard ratios (HRs) and 95% confidence intervals (CIs). Based on 1-sample Mendelian randomization (MR) design, including 398,457 participants of White ancestry who had genotyping data from the UK Biobank, we performed instrumental variable analysis with 2-stage least squares regression to assess the association between genetically predicted APOs and mortality.

RESULTS

After adjusting for potential confounders including high-density and low-density lipoprotein particles, we observed nonlinear inverse relationships of APOA1 with all-cause, CVD-related, and cancer-related mortality (P-nonlinear < 0.001). By contrast, positive relationships were observed for APOB and all-cause (P-nonlinear < 0.001), CVD-related (P-linear < 0.001), and cancer-related (P-linear = 0.03) mortality. MR analysis showed consistent results, except that the association between APOB and cancer mortality was null. Furthermore, both observational and MR analyses found an inverse association between APOA1 and lung cancer-related mortality (HR comparing extreme deciles: 0.46; 95% CI: 0.26, 0.80; and HR: 0.78; 95% CI: 0.63, 0.97, respectively).

CONCLUSIONS

Our findings indicate that circulating APOA1 has potential beneficial effects on all-cause, CVD-related, and lung cancer-related death risk, whereas APOB may confer detrimental effects on all-cause and CVD-related death risk.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

The "crosstalk" between microbiota and metabolomic profile in high-fat-diet-induced obese mice supplemented with Bletilla striata polysaccharides and composite polysaccharides

The potential prebiotic feature of Bletilla striata polysaccharides (BSP) has been widely accepted, while the beneficial effect of BSP on high-fat-diet-induced obesity is unclear. Moreover, the "crosstalk" between microbiota and metabolomic profile in high-fat-diet-induced obese mice supplemented with BSP still need to be further explored. The present study attempted to illustrate the effect of BSP and/or composite polysaccharides on high-fat-diet-induced obese mice by combining multi-matrix (feces, urine, liver) metabolomics and gut microbiome. The results showed that BSP and/or composite polysaccharides were able to reduce the abnormal weight gain induced by high-fat diet. A total of 175 molecules were characterized by proton nuclear magnetic resonance (1H NMR) in feces, urine and liver, suggesting that multi-matrix metabolomics could provide a comprehensive view of metabolic regulatory mechanism of BSP in high-fat-diet-induced obese mice. Several pathways were altered in response to BSP supplementation, mainly pertaining to amino acid, purine, pyrimidine, ascorbate and aldarate metabolisms. In addition, BSP ameliorated high-fat-diet-induced imbalanced gut microbiome, by lowering the ratio of Firmicutes/Bacteroidetes. Significant correlations were illustrated between particular microbiota's features and specific metabolites. Overall, the anti-obesity effect of BSP could be attributed to the amelioration of the disorders of gut microbiota and to the regulation of the "gut-liver axis" metabolism.

The link between obesity and insulin resistance among children: Effects of key metabolites

BACKGROUND

Childhood obesity became a severe public health challenge, and insulin resistance (IR) was one of the common complications. Both obesity and IR were considered as the basis of metabolic disorders. However, it is unclear which common key metabolites are associated with childhood obesity and IR.

METHODS

The children were divided into normal weight and overweight/obese groups. Fasting blood glucose and fasting insulin were measured, and homeostasis model assessment of insulin resistance was calculated. Liquid chromatography-tandem mass spectrometry was applied for metabonomic analysis. Multiple linear regression analysis and correlation analysis explored the relationships between obesity, IR, and metabolites. Random forests were used to rank the importance of differential metabolites, and relative operating characteristic curves were used for prediction.

RESULTS

A total of 88 normal-weight children and 171 obese/overweight children participated in the study. There was a significant difference between the two groups in 30 metabolites. Childhood obesity was significantly associated with 10 amino acid metabolites and 20 fatty acid metabolites. There were 12 metabolites significantly correlated with IR. The ranking of metabolites in random forest showed that glutamine, tyrosine, and alanine were important in amino acids, and pyruvic-ox-2, ethylmalonic-2, and phenyllactic-2 were important in fatty acids. The area under the curve of body mass index standard deviation  score (BMI-SDS) combined with key amino acid metabolites and fatty acid metabolites for predicting IR was 80.0% and 76.6%, respectively.

CONCLUSIONS

There are common key metabolites related to IR and obese children, and these key metabolites combined with BMI-SDS could effectively predict the risk of IR.

Epigenome-wide association study of systemic effects of obesity susceptibility in human twins

The current study was designed to use an epigenome-wide association approach (EWAS) to identify potential systemic DNA methylation alterations that are associated with obesity using 22 discordant twin pairs. Buccal cells (from a cheek swab) were used as a non-obesity relevant purified marker cell for the epigenetic analysis. Analysis of differential DNA methylation regions (DMRs) was used to identify epigenetic associations with metabolic and dietary measures related to obesity with discordant twins. An edgeR analysis provided a DMR signature with p < 1e-04, but statistical significance was reduced due to low sample size and known multiple origins of obesity. A weighted gene coexpression network analysis (WGCNA) was performed and identified modules (p < 0.005) of epigenetic sites that correlated with different metabolic and dietary measures. The DMR and WGCNA epigenetic sites were near genes (e.g., CIDEC, SPP1, ZFPG9, and POMC) with previously identified obesity associated pathways (e.g., metabolism, cholesterol, and fat digestion). Observations demonstrate the feasibility of identifying systemic epigenetic biomarkers for obesity, which can be further investigated for clinical relevance in future research with larger sample sizes. The availability of a systemic epigenetic biomarker for obesity susceptibility may facilitate preventative medicine and clinical management of the disease early in life.

How hypoxia affects microbiota metabolism in mice

Objective

To investigate the relationship between gut microbiota and the fecal metabolites of hypoxic environments in mice.

Methods

High-fat diet-induced obese mice (n = 20) and normal diet-fed mice (n = 20) were randomly divided into four groups: high altitude obese group (HOB), high altitude normal weight group (HN), low altitude obese group LOB (LOB), and low altitude normal weight group (LN). Fecal samples from each group were 16S rRNA gene sequenced, and five samples from each of the four groups above were selected for non-targeted fecal metabolomics analysis using liquid chromatography-mass spectrometry. The relationship between gut microbiota and fecal metabolites was analyzed using SIMCA 14.1, MetaboAnalyst 5.0 and R 4.1.11.

Results

(A) Body weight was significantly lower in the hypoxic obesity group than in the normoxic obesity group. (B) Differences in α-diversity and β-diversity were found in the fecal gut microbiota of mice of different body weights and altitude, and the diversity of gut microbiota was higher in the normal group than in the obese group; the results of the comparison between the two groups showed that Faecalibaculum, Romboutsia, Lactobacillus, and A2 were associated with obesity; Romboutsia was associated with hypoxia. (C) The metabolic profiles of fecal metabolites differed between groups: gut microbiota were associated with nucleotide and amino acid metabolism in the same body groups, while gut microbiota were associated with lipid and amino acid metabolism in the same oxygen concentration groups.

Conclusion

(a) Gut microbiota diversity was reduced in obese groups. Romboutsia was the dominant microbiota in the hypoxia group. (b) Gut microbiota were associated with nucleotide and amino acid metabolism in the same body weight groups, while they were associated with lipid and amino acid metabolism in the same altitude groups.

Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes

BACKGROUND

A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk.

METHODS

In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models.

RESULTS

The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile.

CONCLUSIONS

In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD.

TRIAL REGISTRATION

The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

BACKGROUND

Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways.

METHODS

We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed.

RESULTS

We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism.

CONCLUSION

Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case-Control Studies

Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case-control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9-75%) and 28% (95% CI: 2-62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.

A high Diabetes Risk Reduction Score (DRRS) is associated with a better cardio-metabolic profile among obese individuals

BACKGROUND

Dietary indices and scores are valuable predictive markers against chronic diseases. Several previous studies have revealed the beneficial effects of diabetes risk reduction score (DRRS) against diabetes and cancer incidence. However, its association with metabolic abnormalities among obese individuals have not been revealed before. In the current study, we aimed to investigate the association between DRRS and metabolic risk factors among obese individuals.

METHODS

In the current cross-sectional study, 342 obese individuals [Body mass index (BMI) ≥ 30 kg/m²] aged 20-50 years were included. Dietary intake was assessed by a validated semi-quantitative food frequency questionnaire (FFQ) of 168 food items and DRRS was calculated. Metabolic syndrome (MetS) was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III). Enzymatic methods were used to assess serum lipids, glucose, and insulin concentrations. Blood pressure was measured by a sphygmomanometer and body composition with bioelectrical impedance analysis (BIA).

RESULTS

Those with a higher adherence to DRRS had a significantly higher intake of energy, fiber, and lower protein compared with those in the lower quartiles. Moreover, lower intakes of trans fats, meat, sugar sweetened beverages (SSB), and glycemic index (GI) with higher intakes of fruits, cereal fiber, polyunsaturated fatty acids/ saturated fatty acids (PUFA/ SFA) ratio, coffee, and nuts were observed in the highest versus lowest DRRS categories. Lower systolic blood pressure, diastolic blood pressure, triglyceride and, higher high-density lipoprotein values were observed in higher DRRS categories. Logistic regression analysis showed that hypertension was significantly associated with adherence to DRRS among obese individuals, the odds ratio (OR) was 0.686 (95% confidence interval [CI], 0.26-0.84) after adjustment for potential confounders. But the risk of other components of MetS was not significantly associated with higher quartiles of adherence to DRRS. Also, a non-significantly lower prevalence of MetS was observed in the higher quartile of DRRS.

CONCLUSIONS

According to the results of the current study, higher DRRS was associated with lower blood pressure, modified serum lipids, and lower Mets prevalence. Further studies in different populations are warranted for better generalization of the obtained findings.

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.