Association of genetic variants and behavioral factors with the risk of metabolic syndrome in Pakistanis

Biologia ◽  
2022 ◽  
Author(s):  
Adil Anwar Bhatti ◽  
Sobia Rana
2008 ◽  
Vol 6 (3) ◽  
pp. 209-214 ◽  
Author(s):  
Naresh Ranjith ◽  
Rosemary J. Pegoraro ◽  
Datshana P. Naidoo ◽  
Rebecca Shanmugam ◽  
Lee Rom

2018 ◽  
Vol 59 (13) ◽  
pp. 2028-2039 ◽  
Author(s):  
Peri H. Fenwick ◽  
Khursheed Jeejeebhoy ◽  
Rupinder Dhaliwal ◽  
Dawna Royall ◽  
Paula Brauer ◽  
...  

2020 ◽  
Vol 9 (8) ◽  
pp. 2510
Author(s):  
Katerina Pavelcova ◽  
Jana Bohata ◽  
Marketa Pavlikova ◽  
Eliska Bubenikova ◽  
Karel Pavelka ◽  
...  

Urate transporters, which are located in the kidneys, significantly affect the level of uric acid in the body. We looked at genetic variants of genes encoding the major reabsorption proteins GLUT9 (SLC2A9) and URAT1 (SLC22A12) and their association with hyperuricemia and gout. In a cohort of 250 individuals with primary hyperuricemia and gout, we used direct sequencing to examine the SLC22A12 and SLC2A9 genes. Identified variants were evaluated in relation to clinical data, biochemical parameters, metabolic syndrome criteria, and our previous analysis of the major secretory urate transporter ABCG2. We detected seven nonsynonymous variants of SLC2A9. There were no nonsynonymous variants of SLC22A12. Eleven variants of SLC2A9 and two variants of SLC22A12 were significantly more common in our cohort than in the European population (p = 0), while variants p.V282I and c.1002+78A>G had a low frequency in our cohort (p = 0). Since the association between variants and the level of uric acid was not demonstrated, the influence of variants on the development of hyperuricemia and gout should be evaluated with caution. However, consistent with the findings of other studies, our data suggest that p.V282I and c.1002+78A>G (SLC2A9) reduce the risk of gout, while p.N82N (SLC22A12) increases the risk.


2011 ◽  
Vol 12 (11) ◽  
pp. 952-967 ◽  
Author(s):  
C. M. Povel ◽  
J. M. A. Boer ◽  
E. Reiling ◽  
E. J. M. Feskens

2015 ◽  
Vol 14 (1) ◽  
pp. 2518-2526 ◽  
Author(s):  
Y.L. Chen ◽  
D. Pei ◽  
Y.J. Hung ◽  
C.H. Lee ◽  
F.C. Hsiao ◽  
...  

2009 ◽  
Vol 158 (2) ◽  
pp. 257-262.e1 ◽  
Author(s):  
Alessandra C. Goulart ◽  
Kathryn M. Rexrode ◽  
Suzanne Cheng ◽  
Lynda Rose ◽  
Julie E. Buring ◽  
...  

2009 ◽  
Vol 206 (2) ◽  
pp. 486-493 ◽  
Author(s):  
Mitsutoshi Oguri ◽  
Kimihiko Kato ◽  
Kiyoshi Yokoi ◽  
Tatsuo Itoh ◽  
Tetsuro Yoshida ◽  
...  

2020 ◽  
Author(s):  
Johanna Seddon ◽  
Rafael Widjajahakim ◽  
Bernard Rosner

IMPORTANCE Genes and lifestyle factors influence progression to advanced age-related macular degeneration (AAMD). However, the impact of genetic and behavioral factors on age when this transition occurs has not been evaluated prospectively. OBJECTIVE To determine whether genetic and environmental factors are associated with age of progression to AAMD and to quantify the effect on age. DESIGN, SETTING, AND PARTICIPANTS Longitudinal progression to AAMD was based on the severity scale in the Age-Related Eye Disease Study database. Progression was defined as an eye that transitioned from non-advanced dry AMD without any evidence of geographic atrophy (GA) (levels 1-8) to any GA or evidence of neovascularization (NV) or both (levels ≥9) during 13 years follow up. Genotypes were determined from DNA samples. MAIN OUTCOME AND MEASURES A stepwise selection of genetic variants with the eye as the unit of analysis, using age as the time scale, yielded 11 genetic variants associated with overall progression, adjusting for sex, education, smoking history, BMI, baseline severity scale, and AREDS treatment. Multivariate analysis was also performed to calculate the effect of genetic and behavioral factors on age of progression. RESULTS Among 5421 eyes, 1206 progressed. Genetic variants associated with progression to AAMD were in the complement, immune, inflammatory, lipid, extracellular matrix, DNA repair and protein binding pathways. Three of these variants were significantly associated with earlier age of progression, adjusting for other covariates: CFH R1210C (P=0.019) with 4.7 years earlier age at progression among carriers of this mutation, C3 K155Q (P=0.011) with 2.44 years earlier for carriers, and ARMS2/HTRA1 A69S (P=0.012) with 0.67 years earlier per allele. Subjects who were smokers (P<.001) or had high BMI (P=0.006) also had an earlier age at progression (4.1 years and 1.4 years, respectively). CONCLUSIONS Carriers of rare variants in the complement pathway and a common risk allele in ARMS2/HTRA1 develop advanced AMD at an earlier age, and unhealthy behaviors including smoking and higher body mass index lead to earlier age of progression to AAMD.


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