Recent research has demonstrated that Type 2 Diabetes (T2D) risk is influenced by a number of common polymorphisms, including MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146. Knowledge of the a Show more
Recent research has demonstrated that Type 2 Diabetes (T2D) risk is influenced by a number of common polymorphisms, including MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146. Knowledge of the association between these single nucleotide polymorphisms (SNPs) and body weight changes in different forms of prediabetes treatment is still limited. The aim of this study was to investigate the association of polymorphisms within the MC4R, PPARG, and TCF7L2 genes on the risk of carbohydrate metabolism disorders and body composition changes in overweight or obese patients with early carbohydrate metabolism disorders. From 327 patients, a subgroup of 81 prediabetic female patients (48.7 ± 14.8 years) of Eastern European descent participated in a 3-month study comprised of diet therapy or diet therapy accompanied with metformin treatment. Bioelectrical impedance analysis and genotyping of MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146 polymorphisms were performed. The MC4R CC and TCF7L2 TT genotypes were associated with increased risk of T2D (OR = 1.46, p = 0.05 and OR = 2.47, p = 0.006, respectively). PPARG CC homozygotes experienced increased weight loss; however, no additional improvements were experienced with the addition of metformin. MC4R TT homozygotes who took metformin alongside dietary intervention experienced increased weight loss and reductions in fat mass (p < 0.05). We have shown that the obesity-protective alleles (MC4R T and PPARG C) were positively associated with weight loss efficiency. Furthermore, we confirmed the previous association of the MC4R C and TCF7L2 T alleles with T2D risk. Show less
Finding biomarkers that provide shared link between disease severity, drug-induced pharmacodynamic effects and response status in human trials can provide number of values for patient benefits: elucid Show more
Finding biomarkers that provide shared link between disease severity, drug-induced pharmacodynamic effects and response status in human trials can provide number of values for patient benefits: elucidating current therapeutic mechanism-of-action, and, back-translating to fast-track development of next-generation therapeutics. Both opportunities are predicated on proactive generation of human molecular profiles that capture longitudinal trajectories before and after pharmacological intervention. Here, we present the largest plasma proteomic biomarker dataset available to-date and the corresponding analyses from placebo-controlled Phase III clinical trials of the phosphodiesterase type 4 inhibitor apremilast in psoriasis (PSOR), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from 526 subjects overall. Using approximately 150 plasma analytes tracked across three time points, we identified IL-17A and KLK-7 as biomarkers for disease severity and apremilast pharmacodynamic effect in psoriasis patients. Combined decline rate of KLK-7, PEDF, MDC and ANGPTL4 by Week 16 represented biomarkers for the responder subgroup, shedding insights into therapeutic mechanisms. In ankylosing spondylitis patients, IL-6 and LRG-1 were identified as biomarkers with concordance to disease severity. Apremilast-induced LRG-1 increase was consistent with the overall lack of efficacy in ankylosing spondylitis. Taken together, these findings expanded the mechanistic knowledge base of apremilast and provided translational foundations to accelerate future efforts including compound differentiation, combination, and repurposing. Show less