ABSTRACT: Type 2 diabetes mellitus (T2DM) has been linked to a state of pre-clinical chronic inflammation resulting from abnormalities in the innate immune pathway. Serum levels of pro-inflammatory cytokines and acute-phase proteins, collectively known as 'inflammatory network', are elevated in the pre-, or early, stages of T2DM and increase with disease progression. Genetic variation can affect the innate immune response to certain environmental factors, and may, therefore, determine an individual's lifetime risk of disease.We conducted a cross-sectional study in 6,720 subjects from the Twins UK Registry to evaluate the association between 18 single nucleotide polymorphisms (SNPs) in five genes (TLR4, IL1A, IL6, TNFA, and CRP) along the innate immunity-related inflammatory pathway and biomarkers of predisposition to T2DM [fasting insulin and glucose, HDL- and LDL- cholesterols, triglycerides (TGs), amyloid-A, sensitive C-reactive protein (sCRP) and vitamin D binding protein (VDBP) and body mass index (BMI)].Of 18 the SNPs examined for their association with nine metabolic phenotypes of interest, six were significantly associated with five metabolic phenotypes (Bonferroni correction, P ? 0.0027). Fasting insulin was associated with SNPs in IL6 and TNFA, serum HDL-C with variants of TNFA and CRP and serum sCRP level with SNPs in CRP. Cross-correlation analysis among the different metabolic factors related to risk of T2DM showed several significant associations. For example, BMI was directly correlated with glucose (r = 0.11), insulin (r = 0.15), sCRP (r = 0.23), LDL-C (r = 0.067) and TGs (r = 0.18) but inversely with HDL-C (r = -0.14). sCRP was also positively correlated (P < 0.0001) with insulin (r = 0.17), amyloid-A (r = 0.39), TGs (r = 0.26), and VDBP (r = 0.36) but inversely with HDL-C (r = -0.12).Genetic variants in the innate immunity pathway and its related inflammatory cascade is associated with some metabolic risk factors for T2DM; an observation that may provide a rationale for further studying their role as biomarkers for disease early risk prediction.
