Influence of multiple apolipoprotein A-I and B genetic variations on insulin resistance and metabolic syndrome in obstructive sleep apnea

Abstract

Background The relationships between apolipoprotein A-I (APOA-I), apolipoprotein B (APOB) with insulin resistance, metabolic syndrome (MetS) are unclear in OSA. We aimed to evaluate whether the multiple single nucleotide polymorphism (SNP) variants of APOA-I and APOB exert a collaborative effect on insulin resistance and MetS in OSA. Methods Initially, 12 APOA-I SNPs and 30 APOB SNPs in 5259 subjects were examined. After strict screening, four APOA-I SNPs and five APOB SNPs in 4007 participants were included. For each participant, the genetic risk score (GRS) was calculated based on the cumulative effect of multiple genetic variants of APOA-I and APOB. Logistic regression analyses were used to evaluate the relationships between APOA-I/APOB genetic polymorphisms, insulin resistance, and MetS in OSA. Results Serum APOB levels increased the risk of insulin resistance and MetS adjusting for age, gender and BMI [odds ratio (OR = 3.168, P < 0.001; OR = 6.098, P < 0.001, respectively]. APOA-I GRS decreased the risk of insulin resistance and MetS after adjustments (OR = 0.917, P = 0.001; OR = 0.870, P < 0.001, respectively). APOB GRS had no association with insulin resistance (OR = 1.364, P = 0.610), and had weak association with MetS after adjustments (OR = 1.072, P = 0.042). In addition, individuals in the top quintile of the APOA-I genetic score distribution had a lower risk of insulin resistance and MetS after adjustments (OR = 0.761, P = 0.007; OR = 0.637, P < 0.001, respectively). Conclusions In patients with OSA, cumulative effects of APOA-I genetic variations decreased the risk of insulin resistance and MetS, whereas multiple APOB genetic variations had no associations with insulin resistance and weak association with MetS.

Genetics of Cholesterol-Related Genes in Metabolic Syndrome: A Review of Current Evidence

“…Replicative studies in other populations in China further supported the role of SNP rs964184 in lipid parameters [ 29 , 43 ]. A study in the obstructive sleep apnea population found a significant inverse association between SNP rs964184 and Apo A1 levels [ 29 ].…”

“…A possible explanation is the association of the SNP rs693 with other alleles in the APOB gene or other genes at different loci, a condition known as linkage disequilibrium [ 21 , 28 , 29 ]. For instance, Li et al (2020) reported negligible associations between the SNP rs693 and multiple APOB genetic variations in a case-control study involving participants with and without obstructive sleep apnea [ 29 ]. Interestingly, positive associations with TC, LDL and Apo B were reported when rs693 was included in a group of Apo B SNPs.…”

The correlation of serum/plasma IGF-1 concentrations with obstructive sleep apnea hypopnea syndrome: A meta-analysis and meta-regression

“…Insulin receptor activity (tyrosine kinase) also decreases simultaneously, leading to structural or functional defects in various enzymes in the glucose metabolic pathway (e.g., glucokinase) and impaired glucose metabolism in the body, which results in insulin resistance and IGF-1 deficiency. Meanwhile, long-term and repeated nocturnal hypoxic stress responses may increase the antagonistic products of insulin in the body, thereby aggravating insulin resistance ( 66 , 67 ). Furthermore, even during the intermittent stages of stressful periods, the catecholamine and cortisol levels in patients with OSAHS are higher than those of individuals in the general population, thereby aggravating insulin resistance and lowering IGF-1 levels ( 68 ).…”

Multiple genetic variations of chronic rhinosinusitis with nasal polyps are associated with respiratory parameters in men with obstructive sleep apnea

“…We enrolled all the CRSwNP SNPs that met the levels of genome-wide significance from a genome-wide association study [ 14 ], including 17 CRSwNP SNPs (rs1888909, rs78757963, rs146597587, rs149206763, rs34210653, rs1391371, rs1837253, rs1444782, rs17718444, rs338598, rs6543124, rs174535, rs1050152, rs8046011, rs28383314, rs62408225, and rs4807542). After screening the variants in our genomic database [ 19 ], rs17718444, rs1050152, rs78757963, rs146597587, rs149206763, rs34210653, and rs1391371 were ruled out because the call rates were < 95%. SNP rs174535 did not meet the Hardy–Weinberg equilibrium and was excluded.…”

“…We used the GRS as a proxy for multiple genetic variants. The CRSwNP GRSs were generated by multiplying the SNPs’ effect size ( ß ) with the total number of risk alleles for each individual [ 19 , 20 ]. The effect size and risk alleles are listed in Table 2 .…”