Human metabolic profiles are stably controlled by genetic and environmental variation

Nicholson, George; Rantalainen, Mattias; Maher, Anthony D.; Li, Jia V.; Malmodin, Daniel; Ahmadi, Kourosh R.; Faber, Johan H.; Hallgrímsdóttir, Ingileif B.; Barrett, Amy; Toft, Henrik; Krestyaninova, Maria; Vīksna, Juris; Neogi, Sudeshna Guha; Dumas, Marc; Sarkans, Ugis; Silverman, Bernard W.; Donnelly, Peter; Nicholson, Jeremy K.; Allen, Maxine; Zondervan, Krina T.; Lindon, John C.; Spector, Tim D.; McCarthy, Mark I.; Holmes, Elaine; Baunsgaard, Dorrit; Holmes, Chris

doi:10.1038/msb.2011.57

Cited by 148 publications

(124 citation statements)

References 64 publications

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“…Therefore, these results suggest a substantial contribution of genetic factors to individual differences in serum metabolite concentrations. Consistent with previous reports of MZ twin correlations and heritability of blood metabolite concentrations (Alul et al, 2013;Kettunen et al, 2012;Nicholson et al, 2011;Shah et al, 2009), there was considerable heterogeneity in the MZ correlations across all metabolites. We observed more variation in MZ correlation estimates among the acylcarnitines, amino acids, and Table S1.…”

Section: Discussionsupporting

confidence: 89%

Familial Resemblance for Serum Metabolite Concentrations

et al. 2013

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Metabolomics is the comprehensive study of metabolites, which are the substrates, intermediate, and end products of cellular metabolism. The heritability of the concentrations of circulating metabolites bears relevance for evaluating their suitability as biomarkers for disease. We report aspects of familial resemblance for the concentrations in human serum of more than 100 metabolites, measured using a targeted metabolomics platform. Age-and sex-corrected monozygotic twin correlations, midparent-offspring regression coefficients, and spouse correlations in subjects from two independent cohorts (Netherlands Twin Register and Leiden Longevity Study) were estimated for each metabolite. In the Netherlands Twin Register subjects, who were largely fasting, we found significant monozygotic twin correlations for 121 out of 123 metabolites. Heritability was confirmed by midparent-offspring regression. For most detected metabolites, the correlations between spouses were considerably lower than those between twins, indicating a contribution of genetic effects to familial resemblance. Remarkably high heritability was observed for free carnitine (monozygotic twin correlation 0.66), for the amino acids serine (monozygotic twin correlation 0.77) and threonine (monozygotic twin correlation 0.64), and for phosphatidylcholine acyl-alkyl C40:3 (monozygotic twin correlation 0.77). For octenoylcarnitine, a consistent point estimate of approximately 0.50 was found for the spouse correlations in the two cohorts as well as for the monozygotic twin correlation, suggesting that familiality for this metabolite is explained by shared environment. We conclude that for the majority of metabolites targeted by the used metabolomics platform, the familial resemblance of serum concentrations is largely genetic. Our results contribute to the knowledge of the heritability of fasting serum metabolite concentrations, which is relevant for biomarker research.

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Section: Discussionsupporting

confidence: 89%

Familial Resemblance for Serum Metabolite Concentrations

et al. 2013

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“…A recent study by Kettunen et al (2012) examined the heritability for amino acids, lipoproteins, lipids and some other metabolites such as glucose and urea in adult serum samples; however, the paper does not investigate acylcarnitines, which are important for fatty acid oxidation. An additional study by Nicholson et al (2011) investigates the genetic and environmental contribution to human metabolic profiles, and the authors report estimations of familial variation for different metabolite measurements; however, they state that their sample size is insufficient for heritability estimations. A few other studies have shown a high degree of heritability in adult hormone measurements, particularly, adult serum levels of TSH where heritability estimates ranged from 32% (Samollow et al, 2004) to 64% (Hansen et al, 2004) to 65% (Panicker et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The heritability of metabolic profiles in newborn twins

et al. 2012

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Identifying genetic and metabolic biomarkers in neonates has the potential to improve diagnosis and treatment of common complex neonatal diseases, and potentially lead to risk assessment and preventative measures for common adulthood illnesses such as diabetes and cardiovascular disease. There is a wealth of information on using fatty acid, amino acid and organic acid metabolite profiles to identify rare inherited congenital diseases through newborn screening, but little is known about these metabolic profiles in the context of the 'healthy' newborn. Recent studies have implicated many of the amino acid and fatty acid metabolites utilized in newborn screening in common complex adult diseases such as cardiovascular disease, insulin resistance and obesity. To determine the heritability of metabolic profiles in newborns, we examined 381 twin pairs obtained from the Iowa Neonatal Metabolic Screening Program. Heritability was estimated using multilevel mixed-effects linear regression adjusting for gestational age, gender, weight and age at time of sample collection. The highest heritability was for short-chain acylcarnitines, specifically C4 (h 2 ¼ 0.66, P ¼ 2 Â 10 À16 ), C4-DC (h 2 ¼ 0.83, Po10 À16 ) and C5 (h 2 ¼ 0.61, P ¼ 1 Â 10 À9 ). Thyroid stimulating hormone (h 2 ¼ 0.58, P ¼ 2 Â 10 À5 ) and immunoreactive trypsinogen (h 2 ¼ 0.52, P ¼ 3 Â 10 À9 ) also have a strong genetic component. This is direct evidence for a strong genetic contribution to the metabolic profile at birth and that newborn screening data can be utilized for studying the genetic regulation of many clinically relevant metabolites.

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“…Metabolomics, the study of small metabolites present in biological samples, is considered a promising exposomic platform since the metabolome is both environmentally and biologically determined, with a twin study estimating that 60 % of variability observed depends on the environment [48] and is the final expression of all biological processes thereby integrating the environment with all of the biochemical interactions (including with the gut microflora) taking place in the body. A few studies have investigated the pregnancy metabolome with the largest so far conducted by Maitre et al [44•] on first trimester urine samples collected from a Greek birth cohort.…”

Section: Internal Pregnancy Exposome Studiesmentioning

confidence: 99%

The Pregnancy Exposome

Robinson

Vrijheid

2015

Curr Envir Health Rpt

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The exposome concept takes a holistic approach facilitated by new and emerging technologies to describe 'the totality of human environmental (i.e. non-genetic) exposures from conception onwards, complementing the genome'. It provides a framework to advance the environmental epidemiology field that has until now focused almost exclusively on single-exposure health effects. The exposome includes an external domain, measured by methods including geo-spatial modelling, questionnaire and biomonitoring of external exposures while the internal domain is commonly assessed through molecular omics platforms. The internal domain, in part, reflects the biological response to the external domain. New statistical frameworks are required to integrate and assess exposome-health effects. The pregnancy period is a key starting point to describe the dynamic exposome, due to its heightened sensitivity and potential lifetime impact. A handful of studies have started to move towards an exposome approach in assessing the effects of the multiple exposures during pregnancy on child development. New research projects are underway to test the exposome approach on a large scale.

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Human metabolic profiles are stably controlled by genetic and environmental variation

Abstract: A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

Cited by 148 publications

References 64 publications

Familial Resemblance for Serum Metabolite Concentrations

Familial Resemblance for Serum Metabolite Concentrations

The heritability of metabolic profiles in newborn twins

The Pregnancy Exposome

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