Untangling the relationship between diet and visceral fat mass through blood metabolomics and gut microbiome profiling.

BACKGROUND/OBJECTIVES: Higher visceral fat mass (VFM) is associated with an increased risk for developing cardio-metabolic diseases. The mechanisms by which an unhealthy diet pattern may influence visceral fat (VF) development has yet to be examined through cutting-edge multi-omic methods. Therefore, our objective was to examine the dietary influences on VFM and identify gut microbiome and metabolite profiles that link food intakes to VFM. SUBJECTS/METHODS: In 2218 twins with VFM, food intake and metabolomics data available we identified food intakes most strongly associated with VFM in 50% of the sample, then constructed and tested the 'VFM diet score' in the remainder of the sample. Using linear regression (adjusted for covariates, including body mass index and total fat mass), we investigated associations between the VFM diet score, the blood metabolomics profile and the fecal microbiome (n=889), and confirmed these associations with VFM. We replicated top findings in monozygotic (MZ) twins discordant (⩾1 s.d. apart) for VFM, matched for age, sex and the baseline genetic sequence. RESULTS: Four metabolites were associated with the VFM diet score and VFM: hippurate, alpha-hydroxyisovalerate, bilirubin (Z,Z) and butyrylcarnitine. We replicated associations between VFM and the diet score (beta (s.e.): 0.281 (0.091); P=0.002), butyrylcarnitine (0.199 (0.087); P=0.023) and hippurate (-0.297 (0.095); P=0.002) in VFM-discordant MZ twins. We identified a single species, Eubacterium dolichum to be associated with the VFM diet score (0.042 (0.011), P=8.47 × 10-5), VFM (0.057 (0.019), P=2.73 × 10-3) and hippurate (-0.075 (0.032), P=0.021). Moreover, higher blood hippurate was associated with elevated adipose tissue expression neuroglobin, with roles in cellular oxygen homeostasis (0.016 (0.004), P=9.82x10-6). CONCLUSIONS: We linked a dietary VFM score and VFM to E. dolichum and four metabolites in the blood. In particular, the relationship between hippurate, a metabolite derived from microbial metabolism of dietary polyphenols, and reduced VFM, the microbiome and increased adipose tissue expression of neuroglobin provides potential mechanistic insight into the influence of diet on VFM.

Variation in human genes encoding adhesion and proinflammatory molecules are associated with severe malaria in the Vietnamese.

The genetic basis for susceptibility to malaria has been studied widely in African populations but less is known of the contribution of specific genetic variants in Asian populations. We genotyped 67 single-nucleotide polymorphisms (SNPs) in 1030 severe malaria cases and 2840 controls from Vietnam. After data quality control, genotyping data of 956 cases and 2350 controls were analysed for 65 SNPs (3 gender confirmation, 62 positioned in/near 42 malarial candidate genes). A total of 14 SNPs were monomorphic and 2 (rs8078340 and rs33950507) were not in Hardy-Weinberg equilibrium in controls (P<0.01). In all, 7/46 SNPs in 6 genes (ICAM1, IL1A, IL17RC, IL13, LTA and TNF) were associated with severe malaria, with 3/7 SNPs in the TNF/LTA region. Genotype-phenotype correlations between SNPs and clinical parameters revealed that genotypes of rs708567 (IL17RC) correlate with parasitemia (P=0.028, r(2)=0.0086), with GG homozygotes having the lowest parasite burden. Additionally, rs708567 GG homozygotes had a decreased risk of severe malaria (P=0.007, OR=0.78 (95% CI; 0.65-0.93)) and death (P=0.028, OR=0.58 (95% CI; 0.37-0.93)) than those with AA and AG genotypes. In summary, variants in six genes encoding adhesion and proinflammatory molecules are associated with severe malaria in the Vietnamese. Further replicative studies in independent populations will be necessary to confirm these findings.

Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects.

BACKGROUND: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG-sites in enhancers. METHOD: A smoking-methylation meta-analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population-based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans-ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989-1991 and 1100 African-American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). RESULTS: Altogether 952 CpG-sites in 500 genes were differentially methylated between smokers and never smokers after Bonferroni correction. There were 526 novel smoking-associated CpG-sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta-analysis of the American Indian and African-American samples. Novel CpG sites mapped both to genes containing previously identified smoking-methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7. Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking-associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. CONCLUSION: This study identified novel smoking-associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking-related disease risk.

Perturbation analysis: a simple method for filtering SNPs with erroneous genotyping in genome-wide association studies.

We introduce a simple and yet scientifically objective criterion for identifying SNPs with genotyping errors due to poor clustering. This yields a metric for assessing the stability of the assigned genotypes by evaluating the extent of discordance between the calls made with the unperturbed and perturbed intensities. The efficacy of the metric is evaluated by: (1) estimating the extent of over-dispersion of the Hardy-Weinberg equilibrium chi-square test statistics; (2) an interim case-control study, where we investigated the efficacy of the introduced metric and standard quality control filters in reducing the number of SNPs with evidence of phenotypic association which are attributed to genotyping errors; (3) investigating the call and concordance rates of SNPs identified by perturbation analysis which have been genotyped on both Affymetrix and Illumina platforms. Removing SNPs identified by the extent of discordance can reduce the degree of over-dispersion of the HWE test statistic. Sensible use of perturbation analysis in an association study can correctly identify SNPs with problematic genotyping, reducing the number required for visual inspection. SNPs identified by perturbation analysis had lower call and concordance rates, and removal of these SNPs significantly improved the performance for the remaining SNPs.