Influence of PPARA, RXRA, NR1I2 and NR1I3 gene polymorphisms on the lipid-lowering efficacy and safety of statin therapy

Lima, Luciana Otero; Bruxel, Estela M.; Hutz, Mara H.; Sand, Cézar Roberto Van der; Sand, Luiz Carlos Van der; Ferreira, Maria Elvira Wagner; Pires, Renan Canibal; Fiegenbaum, Marilu; Almeida, Silvana

doi:10.1590/s0004-27302013000700003

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…A previous study has reported that dietary magnesium could reduce lipid accumulation in yellow catfish through PPARα pathway. Some other studies indicated that fenofibrate, a drug used for the treatment of hyperlipidemia, could promote the cellular cholesterol efflux by phos-phorylating PPARα [37, 38]. Earlier studies would fit nicely with the present results regarding the downregulation of SR-A and CD36 and upregulation of ABCA-1 and ABCG-1 by NXT, accompanied by increased PPARα protein expression.…”

Section: Discussionsupporting

confidence: 88%

Naoxintong Retards Atherosclerosis by Inhibiting Foam Cell Formation Through Activating Pparα Pathway

Zeng

Shi

Zhao

et al. 2019

CMM

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Background: We recently reported that Naoxintong (NXT), a China Food and Drug Administration (FDA)-approved cardiac medicine, could reduce the plaque size, but the underlying mechanism remains elusive now. Objective: In this study, we investigated the effects of NXT on foam cell accumulation both in vivo and in vitro and explored related mechanisms. Method: THP-1 cells and bone marrow-derived macrophages were incubated with oxidized low-density lipoprotein (ox-LDL) with/without Naoxintong. ApoE-/- mice fed an atherogenic diet were administered to receive NXT for eight weeks. Macrophage-derived foam cell formation in plaques was measured by immunohistochemical staining. Expression of proteins was evaluated by Western blot. Lentivirus was used to knockdown PPARα in THP-1 cells. Results: After NXT treatment, foam cell accumulation was significantly reduced in atherosclerotic plaques. Further investigation revealed that oxidized low-density lipoprotein (ox-LDL) uptake was significantly decreased and expression of scavenger receptor class A (SR-A) and class B (SR-B and CD36) was significantly downregulated post-NXT treatment. On the other hand, NXT increased cholesterol efflux and upregulated ATP-binding cassette (ABC) transporters (ABCA-1 and ABCG-1) in macrophages. Above beneficial effects of NXT were partly abolished after lentiviral knockdown of PPARα. Conclusion: Our findings suggest that NXT could retard atherosclerosis by inhibiting foam cell formation through reducing ox-LDL uptake and enhancing cholesterol efflux and above beneficial effects are partly mediated through PPARα pathway.

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Section: Discussionsupporting

confidence: 88%

Naoxintong Retards Atherosclerosis by Inhibiting Foam Cell Formation Through Activating Pparα Pathway

Zeng

Shi

Zhao

et al. 2019

CMM

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“…We also observed from the literatures that a number of genes that appeared in the list of interacted genes with five traits had major genetic effects with single trait. Many reports showed that CETP , LIPC and LIPG were associated with HDL and LDL [ 60 – 62 ] and that MTHRR had known main effects for LDL [ 63 ] and blood pressure [ 64 ], NR1I3 for lipid metabolism [ 65 ], PLTP for LDL [ 66 ],[ 67 ], FOXO1 for LDL [ 68 ] and hypertension [ 69 ], SMAD9 for hypertension [ 70 ], and CSMD1 for SBP [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

et al. 2016

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To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

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“…NR1I2 (c.-1663T>C, c.-22-7659T>C), NR1I3 (c.540C>T, c.238+1099A>G), PPARA (c.484C>G) and RXRA (Indel -/A) did not influence lipid-lowering response to these statins. NR1I3 c.540TT genotype carriers had lower risk for statin-induced liver or muscle ADR, but the other variants were not associated with safety of statins (Lima et al, 2013).…”

Section: Drug Metabolizing Enzymes and Transportersmentioning

confidence: 89%

Pharmacogenetic implications in the management of metabolic diseases in Brazilian populations

Hirata

Cerda

Genvigir

et al. 2018

Braz. J. Pharm. Sci.

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Dyslipidemia, diabetes, obesity and hypertension are common metabolic diseases. In the last decades, unhealthy lifestyle and aging have leads to an increased incidence of these diseases, increasing morbidity and mortality by cardiovascular causes. The treatment of metabolic diseases includes lifestyle interventions as healthy diet and physical exercise, as well as pharmacological interventions. Several drugs are available for the management of metabolic diseases including among others lipidlowering antidiabetics and antihypertensive drugs. Variability in response to these drugs is influenced by both genetic and non-genetic factors. Polymorphisms in genes related to drug pharmacokinetics and pharmacodynamics have been shown to influence drug efficacy and safety. This review is focused on pharmacogenetic studies related to the management of metabolic diseases in samples of the Brazilian population. Associations of variants in drug metabolizing enzymes and transporters, drug target and metabolism-related genes with the efficacy and safety of lipid-lowering, antidiabetic and antihypertensive drugs are described. Most pharmacogenetic studies in Brazil have focused in pharmacological response to a small group of drugs, as statins and some antihypertensives, while there are almost no studies on antidiabetic and antiobesity drugs. Some studies reported significant associations of gene polymorphisms with drug response confirming previous data from other populations, whereas other works did not replicate, which may relay on the genetic admixture of our population. In conclusion, further studies are necessary considering larger sample sizes, new unexplored drugs and more genetic variants to obtain stronger conclusions to explore clinical applications of pharmacogenetic studies in our population.

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Influence of PPARA, RXRA, NR1I2 and NR1I3 gene polymorphisms on the lipid-lowering efficacy and safety of statin therapy

Cited by 11 publications

References 30 publications

Naoxintong Retards Atherosclerosis by Inhibiting Foam Cell Formation Through Activating Pparα Pathway

Naoxintong Retards Atherosclerosis by Inhibiting Foam Cell Formation Through Activating Pparα Pathway

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

Pharmacogenetic implications in the management of metabolic diseases in Brazilian populations

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