Established Secondary Regulators of Plasma HDL Cholesterol
There are a large number of other factors that affect HDL cholesterol concentration through modulating the lipolysis of plasma triglycerides [reviewed in Oldoni et al. (2014)]. Figure 2 only illustrates the major players. Most of these regulate the activity of lipoprotein lipase (LPL), the sole enzyme capable of breaking down triglycerides (packaged in chylomicrons and very-low-density lipoproteins
Fig. 2 A schematic presentation of factors that affect HDL through roles in the conversion and remodeling of HDL or through effects on triglyceride/glucose metabolism. The genes in the white boxes encode for established factors of HDL conversion/remodeling or plasma TG hydrolysis. The genes in the blue boxes encode for less established (new) factors. Abbreviations (proteins encoded by the gene names): ANGPTL angiopoietin-like protein, TRIB1 tribbles homolog 1, TTC39B tetratricopeptide repeat domain 39B, PPP1R3B glycogen-targeting PP1 subunit G(L), GALNT2 ppGalNAc-T2, GCKR glucokinase (hexokinase 4) regulator
(VLDL)) in the circulation. These include genes that encode for LPL's cofactor apoC-II (APOCII) as well as apoA-V (APOAV) and inhibitors of the LPL reaction (APOCIII, ANGPTL3, ANGPTL4). More recently, also GPI-anchored HDL-binding protein 1 (GPIHBP1) was shown to affect HDL concentration through ultimately its effect on LPL activity. In addition, hepatic lipase (encoded by LIPC) and endothelial lipase (LIPG) also exert marked effects on HDL cholesterol concentration mainly through modulating HDL phospholipids and HDL triglycerides, respectively.
So far, studies of over 40 genes have provided solid evidence that their products affect plasma HDL cholesterol concentration. With so many established genes, one may expect these accounting for the estimated 50 % heritability of this trait. This appears however not to be the case when GWA data are analyzed with the current statistical methods and datasets: The most recent of meta-analysis indicates that common genetic variation can only explain 12 % variation of HDL cholesterol levels while in this study, both variations in established loci as well as newly identiﬁed loci (n ¼ 46) were taken into account (Willer et al. 2013). However, in these calculations, gene–gene and gene–environment interactions are not for. In addition, the estimated impact of gene variation on the phenotype is based on the presence and frequency of such variations and these are not constant factors over the genome.
From a different angle, candidate gene resequencing studies in individuals with very high or low HDL cholesterol (selected from the general population) have shown that apparent functional mutations are only found in a few percent of the cases (Cohen et al. 2004; Haase et al. 2012). Also in individuals that were referred to the clinic because of extreme levels of HDL cholesterol, resequencing studies of candidate genes only provided satisfying clues in a minority of the subjects studied (Candini et al. 2010; Holleboom et al. 2011a, b; Kiss et al. 2007). It may be noted, however, that most studies conducted thus far focused only on APOAI, LCAT, and ABCA1 leaving ample room for large-effect variants in other genes. Another study focused on the origin of high HDL cholesterol levels through sequencing CETP, LIPG, and GALNT, showed an enrichment of rare coding and splicing mutations in 171 probands (Tietjen et al. 2012). A second study conducted a search for mutations in 197 lipid-related genes in 80 individuals with extreme HDL cholesterol phenotypes. The outcome was that multiple mutations in different genes combined could be responsible for extreme low or high HDL cholesterol levels (Motazacker et al. 2013). Although a polygenic origin of a complex trait like HDL cholesterol level appears logical, especially in view of similar studies for plasma triglycerides (Johansen et al. 2010) or LDL cholesterol (Talmud et al. 2013), this needs to be conﬁrmed. Larger comprehensive resequencing efforts are warranted to study to what extent large-impact mutations in established and candidate genes can explain HDL cholesterol concentration in plasma and how such mutations relate to CVD risk.