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  • br Introduction In addition to glucose which is the

    2022-01-25


    Introduction In addition to glucose, which is the foremost stimulator of insulin secretion in beta-cells, other nutrients such as free fatty acids (also called non-esterified fatty acids, NEFA) contribute to glucose-stimulated insulin secretion (GSIS). Glucose triggers the processes leading to insulin secretion by its metabolism within beta-cells [1], [2]. NEFA also act as fuels in beta-cells and they are able to modulate insulin secretion by influencing intracellular metabolism [3]. It was not until 2003, the discovery of the role of a previously orphaned receptor, the G-protein-coupled receptor 40 (GPR40), now called free fatty Berberine Sulfate receptor 1 (FFAR1), that an additional important coupling mechanism between NEFA and insulin secretion was revealed [4]. The acute increase of insulin secretion after raising NEFA levels was shown to be ∼50% in healthy volunteers [5], [6], and the FFAR1-pathway is thought to be involved in at least 50% of NEFA-mediated insulin secretion [7]. FFAR1-agonists have been developed to enhance GSIS, some of which could be candidates for a new class of antidiabetic drugs [8]. We recently demonstrated that FFAR1-agonism may be protective against beta-cell apoptosis and provided evidence that common variation near the FFAR1 gene modulates insulin secretion dependent on free-fatty acid levels [9]. Recent data from an investigation conducted with beta-cell cultures and animals suggest that FFAR1 gene expression is modulated by stimulation of peroxisome proliferator-activated receptor gamma (PPARG) [10]. Intriguingly, in 2001 we had demonstrated effects of the PPARG Pro12Ala single nucleotide polymorphism (SNP) on insulin secretion in an elevated NEFA milieu. In this investigation, carriers of the minor allele of the Pro12Ala polymorphism (rs1805192) had lower insulin secretion during hyperglycemic clamp studies conducted with a concomitant intravenous lipid infusion, but no difference was seen between the genotypes without increasing NEFA [11]. Given these data supporting a crosstalk between PPARG and FFAR1 signaling, we set out to look for evidence of a PPARG × FFAR1 interaction in humans. For this purpose, we analyzed insulin secretion effects of interactions between previously described tagging SNPs in FFAR1 [9] and the aforementioned diabetes-related SNP in PPARG, also accounting for NEFA levels.
    Methods
    Results As expected, PPARG genotypes alone did not impact insulin secretion in the study population (a table on the genotype-dependent distribution of key cohort phenotypes is provided as Supplementary Table 1). To test the hypothesis that there is an interaction between genetic variation in PPARG and FFAR1 on insulin secretion, we analyzed gene × gene interactions between rs1805192 in PPARG and 7 tagging SNPs of known frequent variants in FFAR1. Since the modulatory effect of PPARG on insulin secretion manifested only in a high-NEFA environment [11] and FFAR1 effects on insulin secretion were seen only in interaction with fasting NEFA [9], we additionally adjusted for fasting NEFA and added interaction terms of fasting NEFA with the FFAR1 SNPs and the PPARG SNP to the models. We adjusted insulin secretion as determined by the insulinogenic index for sex, age, insulin sensitivity, fasting NEFA, PPARG Pro12Ala genotype, FFAR1 genotypes and 4 interaction terms: between FFAR1 and PPARG genotypes, between PPARG genotype and NEFA level, between FFAR1 genotype and NEFA and, finally, a 3-way interaction term with PPARG × NEFA × FFAR1-SNP. We found a significant gene × gene interaction between the FFAR1 SNP rs10422744 and the PPARG Pro12Ala SNP (p = 0.005) with a concomitant nominally significant interaction between NEFA level and the PPARG SNP (p = 0.03). Additionally, a nominally significant interaction was found between rs12462800 and PPARG (p = 0.01) with a concomitant PPARG × NEFA interaction (p = 0.03). The 3-way interaction term did not reach statistical significance, although the p-value of 0.0592 suggested a trend for nominal significance in the case of rs12462800. Detailed results of the interaction tests are provided in Supplementary Table 2. An additional adjustment for obesity by adding BMI to the interaction models did not relevantly change the results (data not shown). Alternatively using DI as outcome variable, the gene × gene interaction term had a p-value of 0.01 and 0.001 for rs12462800 and rs10422744, respectively.