NAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects - PubMed (original) (raw)

. 2018 Jun 15;27(12):2214-2223.

doi: 10.1093/hmg/ddy124.

Sylvain Sebert 1 2 3, Peter Würtz 4 5, Antti J Kangas 5, Pasi Soininen 5 6, Terho Lehtimäki 7, Mika Kähönen 8, Jorma Viikari 9, Minna Männikkö 10, Mika Ala-Korpela 1 2 6 11 12 13 14, Olli T Raitakari 15 16, Johannes Kettunen 1 2 11 12

Affiliations

NAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects

Eeva Sliz et al. Hum Mol Genet. 2018.

Abstract

Fatty liver has been associated with unfavourable metabolic changes in circulation. To provide insights in fatty liver-related metabolic deviations, we compared metabolic association profile of fatty liver versus metabolic association profiles of genotypes increasing the risk of non-alcoholic fatty liver disease (NAFLD). The cross-sectional associations of ultrasound-ascertained fatty liver with 123 metabolic measures were determined in 1810 (Nfatty liver = 338) individuals aged 34-49 years from The Cardiovascular Risk in Young Finns Study. The association profiles of NAFLD-risk alleles in PNPLA3, TM6SF2, GCKR, and LYPLAL1 with the corresponding metabolic measures were obtained from a publicly available metabolomics GWAS including up to 24 925 Europeans. The risk alleles showed different metabolic effects: PNPLA3 rs738409-G, the strongest genetic NAFLD risk factor, did not associate with metabolic changes. Metabolic effects of GCKR rs1260326-T were comparable in many respects to the fatty liver associations. Metabolic effects of LYPLAL1 rs12137855-C were similar, but statistically less robust, to the effects of GCKR rs1260326-T. TM6SF2 rs58542926-T displayed opposite metabolic effects when compared with the fatty liver associations. The metabolic effects of the risk alleles highlight heterogeneity of the molecular pathways leading to fatty liver and suggest that the fatty liver-related changes in the circulating lipids and metabolites may vary depending on the underlying pathophysiological mechanism. Despite the robust cross-sectional associations on population level, the present results showing neutral or cardioprotective metabolic effects for some of the NAFLD risk alleles advocate that hepatic lipid accumulation by itself may not increase the level of circulating lipids or other metabolites.

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Figures

Figure 1.

Figure 1.

Cross-sectional associations of fatty liver with lipoprotein particle subfraction concentrations, lipoprotein particle diameter, apolipoproteins, triglycerides, fatty acids, fatty acid saturation, beta-oxidation, glycolysis, amino acid-related metabolites and inflammation marker GlycA, and the corresponding associations with four NAFLD risk alleles. Cross-sectional associations were determined in 1, 810 adults aged 34–49 years of whom 338 were diagnosed with ultrasound-based fatty liver. The metabolic phenotypes were adjusted for age, sex, and 10 first genetic principal components prior to analysis. Genetic effects of the NAFLD risk alleles GCKR rs1260326-T, LYPLAL1 rs12137855-C, PNPLA3 rs738409-G and TM6SF2 rs58542926-T were acquired from a metabolomics GWAS including up to 24 925 Europeans (19). Genetic effect estimates were scaled with respect to the NAFLD risk associated with the corresponding locus (12). VLDL, very low-density lipoprotein; IDL, intermediate density lipoprotein; HDL, high density lipoprotein; PUFA, polyunsaturated fatty acids; MUFA, monounsaturated fatty acids; GlycA, glycoprotein acetylation.

Figure 2.

Figure 2.

The overall match between the metabolic effects of the NAFLD risk alleles and fatty liver. The black dashed line shows the linear fit between metabolic changes associated with fatty liver and PNPLA3 rs738409-G (A), TM6SF2 rs58542926-T (B), GCKR rs1260326-T (C) and LYPLAL1 rs12137855-C (D). The grey area indicates the 95% confidence interval for the line. _R_2 is a measure of goodness of fit.

Figure 3.

Figure 3.

Relation of the studied NAFLD risk alleles to the main pathways in hepatic triglyceride partitioning. Liver converts carbohydrates to lipids in de novo lipogenesis. Newly synthesized fatty acids enter to the hepatic fatty acid pool which is also supplied by dietary fats and circulating free fatty acids derived mostly from adipose tissue lipolysis or lipoprotein lipase spillover (1,25). Fatty acids can be partitioned to oxidative pathway or esterified to triglycerides that can be stored in hepatic lipid droplets or used for VLDL production to be secreted from the liver (1,25). The hepatic fatty acid pool is located upstream from the lipid storage and secretion pathways, and thus abundance in hepatic fatty acids can contribute to both development of fatty liver and increased production of VLDL. In line with this, GCKR rs1260326-T that enhances the lipogenic pathway by providing more substrates for fatty acid biosynthesis (24) increases risk of fatty liver (12,13) and raises concentrations of all the apoB containing lipoproteins and lipids in these particles while circulating glucose level is marginally reduced (Fig. 1). LYPLAL1 may be functioning on the same hepatic glucose metabolism and lipogenesis related pathway, as the metabolic effects of GCKR rs1260326-T and LYPLAL1 rs12137855-C are highly similar (Supplementary Material, Fig. S4). On the contrary, TM6SF2 rs58542926-T impairs the secretory pathway leading to lipid accumulation into the liver (14) and reduction in levels of circulating lipids and lipoproteins (Fig. 1). PNPLA3 rs738409-G, in turn, enhances triglyceride accumulation to the storage pool by diminishing triglyceride hydrolysis to fatty acids (41,42), but does not directly contribute to VLDL secretion, and thus conveys no major consequences to circulation (Fig. 1).

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