SciELO - Scientific Electronic Library Online

vol.36 issue3Comparison of the dietary intake amongst women in the late postoperative period after Roux-en-Y gastric bypass with the bariatric food pyramidRelationship of anthropometric indexes and indicators of body composition by arm anthropometry on hospitalized pediatric patients author indexsubject indexarticles search
Home Pagealphabetic serial listing  


Services on Demand




Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google


Nutrición Hospitalaria

On-line version ISSN 1699-5198Print version ISSN 0212-1611

Nutr. Hosp. vol.36 n.3 Madrid May./Jun. 2019  Epub Feb 17, 2020 

Trabajos Originales

INSIG2 gene polymorphism is associated with higher blood pressure and triglyceride levels in Brazilian obese subjects

El polimorfismo del gen INSIG2 se asocia con una mayor presión sanguínea y niveles de triglicéridos en sujetos obesos brasileños

Carolina F Nicoletti1  , Raquel G Azevedo1  , Marcela A S Pinhel1  , Heitor B P Delfino1  , Carla B Nonino1 

1Department of Internal Medicine. Ribeirão Preto Medical School. University of São Paulo. Ribeirão Preto, São Paulo. Brazil.



this study aimed to evaluate the association between polymorphisms of INSIG, PCSK9 and FTO genes with anthropometric, biochemical characteristics and presence of metabolic syndrome in patients with severe obesity.

Material and methods:

the present study enrolled 150 patients with grade II or III obesity, who were submitted to nutritional assessment, blood pressure measurement and peripheral blood collection. INSIG2 (rs75666605), PCSK9 (rs505151), and FTO (rs9939609) polymorphisms were genotyped using TaqMan Pre-Designed SNP Genotyping Assays probes in real time polymerase chain reaction (PCR). The experimental data are processed in SPSS Statistics 22.0 (p < 0.05).


in this study, 72.2% of obese subjects had metabolic syndrome (MS). There was a higher prevalence of AA (86.9%), CG (51.1%) and AT (46.2%) genotypes for the PCSK9, INSIG2 and FTO polymorphisms, respectively. There was no association of these polymorphisms with the prevalence of MS (p > 0.05). On the other hand, individuals with at least one variant allele (G) for the INSIG2 gene had higher triglycerides levels, systolic and diastolic blood pressure (p < 0.05).


the polymorphism rs7566605 of the INSIG2 gene is associated with higher triglycerides levels and blood pressure values, which are also considered as risk factors for the development of MS.

Key words: Obesity; Metabolic syndrome; Polymorphism; INSIG2; PCSK9; FTO



este estudio tuvo como objetivo evaluar la asociación entre polimorfismos de los genes INSIG, PCSK9 y FTO con las características antropométricas, bioquímicas y la presencia de síndrome metabólico (SM) en pacientes con obesidad grave.

Material y métodos:

el presente estudio incluyó 150 pacientes con obesidad de grado II o III, que fueron sometidos a evaluación nutricional, medición de la presión arterial y extracción de sangre periférica. Los polimorfismos INSIG2 (rs75666605), PCSK9 (rs505151) y FTO (rs9939609) fueron genotipados utilizando sondas TaqMan Pre-Designed SNP Genotyping Assays en la reacción en cadena de la polimerasa en tiempo real (PCR). Los datos experimentales se procesan en SPSS Statistics 22.0 (p < 0,05).


en este estudio, el 72,2% de los sujetos obesos tenían síndrome metabólico (EM). Hubo una mayor prevalencia de genotipos AA (86,9%), CG (51,1%) y AT (46,2%) para los polimorfismos PCSK9, INSIG2 y FTO, respectivamente. No hubo asociación de estos polimorfismos con la prevalencia de SM (p > 0,05). Por otro lado, los individuos con al menos una variante de alelo (G) para el gen INSIG2 tenían niveles más altos de triglicéridos, presión arterial sistólica y diastólica (p < 0,05).


el polimorfismo rs7566605 del gen INSIG2 se asocia con niveles más altos de triglicéridos y valores de presión arterial, que también se consideran factores de riesgo para el desarrollo del síndrome metabólico.

Palabras clave: Obesidad; Síndrome metabólico; Polimorfismo; INSIG2; PCSK9; FTO


Metabolic syndrome (MS) is a term used to characterize cardiovascular risk factors and is characterized by the association of various abnormalities, such as central obesity, dyslipidemia, type 2 diabetes mellitus and systemic arterial hypertension (SAH). There are different definitions for diagnostic criteria, all using combinations of risk factors 18,25. MS frequency is significantly associated with favorable socioeconomic conditions, sedentary lifestyle, hypercaloric diets and high body mass index (BMI) 20. The global prevalence of MS varies depending on the region, urban or rural environment and characteristics of the population studied, like sex, age, race and ethnicity 16.

Considering that obesity is one of the risk factors for MS, the literature has pointed out the important role of genetic susceptibility for this disease development 23, including gene interactions and gene-environment interactions 19. Recent studies show the existence of several single nucleotide polymorphisms (SNPs) located in different genes involved in lipid metabolism and insulin resistance, which play a role in obesity development 22.

In this context, the proprotein convertase subtilisin/kexin 9 (PCSK9) gene encodes the glycoprotein PCSK9, which plays an important role in the cholesterol metabolism. Its expression modulates the number of low-density lipoprotein (LDLR) receptors 28 and affects the circulating levels of low-density lipoprotein cholesterol (LDL-cholesterol), the main carrier of cholesterol in humans 11. The polymorphism E670G (rs505151) of the PCSK9 gene promotes increase of PCSK9 activity (gain of function), leading to reduction of LDLR and consequently, raise in plasma LDL-cholesterol, contributing to hypercholesterolemia 11,28.

Another gene that regulates cholesterol synthesis is the insulin-induced gene 2 (INSIG2), which encodes the INSIG2 membrane protein 8. INSIG2 inhibits sterol synthesis in the liver when the cells have an adequate supply of these (29). Deregulation of this process may affect lipid metabolism and increase insulin resistance 3, which are closely related to MS 4. Some studies have shown an association between the polymorphism rs7566605 of INSIG2 gene and obesity 13,29.

Furthermore, the fat mass and obesity-associated gene (FTO) encode an important protein whose exact physiological function remains unknown. Studies indicate that this gene plays a role in the nervous and cardiovascular systems and has a strong association with BMI, obesity and type 2 diabetes development 30,31. The FTO gene has several know SNPs, many of them related to BMI, with emphasis in rs9939609. In this case, the A allele is directly related to a greater accumulation of body fat, especially in homozygous form (AA).

In line with this, the objective of the present study was to evaluate the association between polymorphisms of INSIG2, PCSK9 and FTO genes with anthropometric, biochemical characteristics and presence of metabolic syndrome in patients with severe obesity.



This is a cross-sectional study, which enrolled individuals of an ethnic mixed population 27, aged between 18 and 60 years, with grade II or III obesity. The sampling procedure is fully described elsewhere and anthropometric and biochemical data of the studied population were already published by our group 21. Pregnant and lactating individuals, patients on hypocaloric diets and who underwent bariatric surgery were excluded. The study was conducted after approval by the Ethics Committee of the institution and according to the Declaration of Helsinki. All participants provided a written informed consent.

Questions about personal history of chronic diseases such as diabetes mellitus, systemic arterial hypertension, cardiovascular diseases and obesity were collected from medical records.


For anthropometric evaluation, the following indicators were used: weight (kg), height (m), BMI (kg/m2) and abdominal circumference (AC). According to Nicoletti et al. (2016), patients were weighed in a Filizola® scale-type digital scale, with a capacity of 300 kg and an accuracy of 0.2 kg. For height measurement, a vertical rod with a graduation of 0.5 cm was used. BMI was obtained using the formula: BMI = P/A2, where P is the weight in kilograms and A is the height in meters. AC was measured by passing an inextensible metric tape with graduation of 0.1 mm on the largest circumference.

The systolic and diastolic blood pressure (BP) was measured using the indirect method with an auscultatory technique using a mercury column sphygmomanometer, with cuffs appropriate for obese individuals. The procedure was performed with the patient after five minutes of rest in a quiet environment, without having practiced physical exercises for at least 60 minutes and without having smoked in the 30 minutes before the procedure.


Plasma total cholesterol (TC), low-density lipoprotein (LDL-cholesterol) and high-density lipoprotein (HDL-cholesterol), triglycerides (TG) and glycemia were analyzed in blood samples after 12 hours of fasting. As a protocol of the service, the dosages of TC, HDL-cholesterol and TG were performed by automated colorimetric method. LDL-cholesterol values were calculated by the Friedwald formula: LDL-cholesterol = TC - (HDL-cholesterol + TG/5), with TG values lower than 400 mg/dl 9.


MS was defined according to the criteria of the I Brazilian Guidelines for the Diagnosis and Treatment of Metabolic Syndrome, which uses diagnostic standards similar to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP ATP III). In this case, MS was defined by a group of cardiovascular risk factors, characterized by at least three of the following components: a) abdominal circumference > 88 cm; b) fasting glycemia > 100 mg/dl; c) triglycerides > 150 mg/dl; d) HDL cholesterol < 50 mg/dl; and e) increased blood pressure: systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg.


DNA was extracted from peripheral blood samples using a GE Healthcare kit according to manufacturer's protocol. The genotypic determination was performed by the allele discrimination method in real time PCR (polymerase chain reaction), which allows the analysis of the variants in a specific segment of DNA. The single nucleotide polymorphisms rs505151, rs75666605 and rs9939609 were genotyped using TaqMan(tm) Pre-Designed SNP Genotyping Assays probes (Applied Biosystems, Foster City, CA), as specified by the manufacturer.


Data were presented in mean and standard deviation. The Kolmogorov-Smirnov test was used to verify the data normality. The Hardy-Weinberg equilibrium calculations for the polymorphisms were performed by applying the Chi-square test. The t-test for independent samples was used to compare the phenotypic variables among the genotypes of each polymorphism. To analyze the dominant model (DM), individuals with at least one variant allele were grouped and compared with those of the reference genotype. For the recessive model (RM) analysis, individuals with at least one wild-type allele were grouped and compared with those with both variant alleles. The associations of continuous variables with genotypes were analyzed with general linear models controlling for sex and age. Statistical significance was set at 5%, all analyzes being performed in Statistical Package for Social Science software (SPSS version 22.0; Inc. Chicago, IL).


For this study, 150 severe obese subjects (80% females) with mean age of 47.2 ± 10.4 years were selected. The means of systolic and diastolic pressure were 122.2 ± 15.4 mmHg and 78.1 ± 10.0 mmHg, respectively. It was identified that 94.7% of patients presented grade III obesity, 73.3% systemic arterial hypertension, 25% high glycemic levels and 26.4% hypertriglyceridemia. Indeed, abdominal obesity was present in all patients. Thus, taking into account the criteria established by the NCEP, 72.7% of the patients selected for this study present metabolic syndrome.

Genotypic distributions of the polymorphisms rs505151 (X2 = 12.33, p = 0.92), rs7566605 (X2 = 1.72, p = 0.64) and rs9939609 (X2 = 0.82, p = 0.49) exhibited the Hardy-Weinberg equilibrium. The allelic and genotype frequencies of the polymorphisms are presented in Table 1. The genotypes AA homozygous for PCSK9 gene, CG heterozygote for INSIG2 gene prevailed. On the other hand, there was a similar distribution between the AA and TT genotypes for FTO gene.

Table I. Genotypic and allelic frequency of polymorphisms assessed in severe obese patients evaluated in the study (n = 150) 

n: number of individuals.

Table 2, Table 3 and Table 4 show the anthropometric and metabolic variables for DM and RM for polymorphisms of PCSK9, FTO and INSIG2 gene, respectively. No difference was observed between the possible genotypes for PCSK9 and FTO gene. However, there was a difference between systolic and diastolic BP and triglycerides levels among the possible genotypes for INSIG2 gene, showing that individuals with at least one variant allele (G) had higher BP and triglycerides values. Linear regression analyses confirm the association between the polymorphism of INSIG2 gene and this variables, even when adjusted for gender and age (Table 5). However, none of the polymorphisms studied was directly related to the risk of MS developing in patients with severe obesity (Fig. 1).

Table II. Anthropometric and metabolic variables for genotypes of the rs505151 polymorphism in the PCSK9 gene in subjects with severe obesity (n = 150) 

BMI: body mass index; AC: abdominal circumference; BP: blood pressure; LDL-cholesterol: low density lipoprotein; HDL-cholesterol: high density lipoprotein.

Table III. Anthropometric and metabolic variables for genotypes of the rs9939609 polymorphism of FTO gene in severe obese subjects evaluated in the study (n = 150) 

BMI: body mass index; AC: abdominal circumference; BP: blood pressure; LDL-cholesterol: low density lipoprotein; HDL-cholesterol: high density lipoprotein

Table IV. Anthropometric and metabolic variables for genotypes of polymorphism rs7566605 of INSIG2 gene in severe obese evaluated in this study (n = 150) 

BMI: body mass index; AC: abdominal circumference; BP: blood pressure; LDL-cholesterol: low density lipoprotein; HDL-cholesterol: high density lipoprotein.


Table V. Multiple linear regression analysis, adjusted for sex and anthropometric variables, showing the contribution of the polymorphism rs 7566605 in the INSIG2 gene in the concentrations of triglycerides, systolic and diastolic pressure in subjects with severe obesity (n = 150) 

AC: abdominal circumference; CI: confidence interval.

Figure 1. Odds ratio (95% CI) of the association between polymorphisms rs505151 of PCSK9 gene, rs7566605 of INSIG2 gene and rs9939609 of FTO gene and the metabolic syndrome (MD: dominant model; MR: recessive model) 


No direct relationship was found between the presence of polymorphisms rs505151 of PCSK9 gene, rs7566605 of INSIG2 gene and rs9939609 of FTO gene and the presence of MS. However, it was observed that rs7566605 of INSIG2 gene showed a significant relationship with three parameters analyzed: systolic blood pressure, diastolic blood pressure and triglyceride levels.

Although our findings do not show a direct relationship between PCSK9 polymorphism and BMI or abdominal obesity, some cases have already been well described in the literature 28,32. Moreover, a Chinese study showed an association between the variant allele and HDL-cholesterol and LDL-cholesterol concentrations 2. Evidence supports that in patients with the polymorphism, the expression of the protein was higher, and consequently the circulating LDL-cholesterol concentrations were increased 15. Furthermore, a study investigating the effect of this polymorphism on the risk of cardiovascular diseases (CVD) and stroke showed that in patients with a higher risk of CVD who had the variant allele, LDL-cholesterol concentrations were significantly higher 28.

Regarding the polymorphism of the INSIG2 gene, the literature is still controversial. Our findings corroborate previous studies that did not evidence associations of polymorphism with BMI or waist circumference 1. In contrast, Oki et al. (2009) evidenced that CC genotype is a protective genetic factor against the progression of hypercholesterolemia. The polymorphism of INSIG2 gene does not inhibit fatty acid synthesis, leading to an exaggerated production of cholesterol, fatty acids, triglycerides and phospholipids 3. Other studies found no association between polymorphism and severe obesity, or with phenotypes associated with hypertension 5,13. Thus, the present study is a pioneer to show association between blood pressure levels and this polymorphism. A possible mechanism of increased systolic and diastolic blood pressure would be the fact that the polymorphism increases the production of cholesterol and may contribute for atherosclerosis development, consequently raising the individual's blood pressure. As for the biochemical and metabolic parameters, the wild homozygous form of the INSIG2 (CC) gene was related to the reduction of waist/hip ratio and glycated hemoglobin, which may lead to an improvement in glucose intolerance 6.

The association between the FTO gene and obesity in humans is already well described 7,13. However, the evidence showing the role of this polymorphism in the development of type 2 diabetes mellitus is weak. Li et al. (2008) did not find associations between the gene and waist circumference and fasting glycemia. On the other hand, a recent study by Guclu-Geyik et al. (2016) found an association between polymorphism and MS in men. The mechanism by which the FTO gene leads to the development of obesity is not fully understood. The polymorphism of FTO gene was related to obesity, but not to MS; nonetheless, a predisposition to an increased risk of obesity may trigger other variables such as increased AC, altered fasting glycemia and dyslipidemia.

Thus, as a main finding, despite not being directly associated with the prevalence of MS, the presence of the variant allele of INSIG2 polymorphism could increase the chances of developing the disease. It should be emphasized that our sample size is limited and our analysis was performed in a population of mixed ethnic origin. Low power may have been responsible for lack of significant associations among severe obese individuals, and reaching significance for some of the potential interactions. Moreover, although adjustment by sex and age was performed, residual confounding such as physical active and dietary intake cannot be totally ruled out.


Polymorphisms of PCSK9, INSG2 and FTO genes are not associated with the MS prevalence; however, the polymorphism of INSIG2 gene is associated with higher concentrations of triglycerides and blood pressure values, which are also considered as risk factors for the development of the disease.


This work was supported by grants #2015/20513-8 from the São Paulo Research Foundation (FAPESP).


1. Apalasamy YD, Moy FM, Rampal S, Bulgiba A, Mohamed Z. Genetic associations of the INSIG2 rs7566605 polymorphism with obesity-related metabolic traits in Malaysian Malays. Genet Mol Res 2014;13(3):4904-10. DOI: 10.4238/2014.July.4.4 [ Links ]

2. Aung LHH, Yin RX, Miao L, Hu XJ, Yan TT, Cao XL, et al. The proprotein convertase subtilisin/kexin type 9 gene E670G polymorphism and serum lipid levels in the Guangxi Bai Ku Yao and Han populations. Lipids Health Dis 2011;10:5. DOI: 10.1186/1476-511X-10-5 [ Links ]

3. Baylin A, Deka R, Tuitele J, Viali S, Weeks DE, McGarvey ST. INSIG2 variants, dietary patterns and metabolic risk in Samoa. Eur J Clin Nutr 2013;67(1):101-7. DOI:10.1038/ejcn.2012.124 [ Links ]

4. Bhowmik B, Afsana F, Siddiquee T, Munir SB, Sheikh F, Wright E, et al. Comparison of the prevalence of metabolic syndrome and its association with diabetes and cardiovascular disease in the rural population of Bangladesh using the modified National Cholesterol Education Program Expert Panel Adult Treatment Panel III and International Diabetes Federation definitions. J Diabetes Investig 2015;6(3):280-8. DOI: 10.1111/jdi.12268 [ Links ]

5. Bressler J, Fornage M, Hanis CL, Kao WH, Lewis CE, McPherson R, et al. The INSIG2 rs7566605 genetic variant does not play a major role in obesity in a sample of 24,722 individuals from four cohorts. BMC Med Genet 2009;10:56. DOI: 10.1186/1471-2350-10-56 [ Links ]

6. Burgdörfer E, Korenkov M, Jonas D, Weise D, Haaf T, Zechner U, et al. FTO and INSIG2 genotyping combined with metabolic and anthropometric phenotyping of morbidly obese patients. Mol Syndromol 2013;4(6):273-9. DOI: 10.1159/000353563 [ Links ]

7. Fang H, Li Y, Du S, Hu X, Zhang Q, Liu A, et al. Variant rs9939609 in the FTO gene is associated with BMI among Chinese children. BMC Med Genet 2010;11:136. DOI: 10.1186/1471-2350-11-136 [ Links ]

8. Fornage M, Papanicolaou G, Lewis CE, Boerwinkle E, Siscovick DS. Common INSIG2 polymorphisms are associated with age-related changes in body size and high-density lipoprotein cholesterol from young adulthood to middle age. Metabolism 2010;59(8):1084-91. DOI: 10.1016/j.metabol.2009.11.005 [ Links ]

9. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18(6):499-502. [ Links ]

10. Guclu-Geyik F, Onat A, Yuzbasiogullari AB, Coban N, Can G, Lehtimäki T, et al. Risk of obesity and metabolic syndrome associated with FTO gene variants discloses clinically relevant gender difference among Turks. Mol Biol Rep 2016;43(6):485-94. DOI: 10.1007/s11033-016-3992-0 [ Links ]

11. Guo YL, Zhang W, Li JJ. PCSK9 and lipid lowering drugs. Atheroscler Suppl 2015;18:21-7. DOI: 10.1016/j.cca.2014.07.008 [ Links ]

12. Hinney A, Herrfurth N, Schonnop L, Volckmar AL. Genetic and epigenetic mechanisms in obesity. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2015;58(2):154-8. DOI: 10.1007/s00103-014-2094-1 [ Links ]

13. Hotta K, Nakamura M, Nakata Y, Matsuo T, Kamohara S, Kotani K, et al. INSIG2 gene rs7566605 polymorphism is associated with severe obesity in Japanese. J Hum Genet 2008;53(9):857-62. DOI: 10.1007/s10038-008-0317-8 [ Links ]

14. Hubacek JA, Kuthanova L, Bohuslavova R, Adámková V, Lánská V, Meitinger T, et al. INSIG2 promoter variant, obesity markers and lipid parameters - No association in a large Slavonic Caucasian population Sample. Folia Biol (Praha) 2010;56(3):131-4. [ Links ]

15. Ibarretxe D, Girona J, Plana N, Cabré A, Ferré R, Amigó N, et al. Circulating PCSK9 in patients with type 2 diabetes and related metabolic disorders. Clin Investig Arterioscler 2016;28(2):71-8. DOI: 10.1016/j.arteri.2015.11.001 [ Links ]

16. Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract 2014;2014:943162. DOI: 10.1155/2014/943162 [ Links ]

17. Li HX, Wu Y, Loos RJF, Hu FB, Liu Y, Wang J, et al. Variants in the fat mass- and obesity-associated (FTO) gene are not associated with obesity in a Chinese Han population. Diabetes 2008;57(1):264-8. DOI: 10.2337/db07-1130 [ Links ]

18. Martínez-Hernández A, Córdova EJ, Rosillo-Salazar O, García-Ortiz H, Contreras-Cubas C, Islas-Andrade S, et al. Association of HMOX1 and NQO1 polymorphisms with metabolic syndrome components. PLoS One 2015;10(5):e0123313. DOI: 10.1371/journal.pone.0123313 [ Links ]

19. Moleres A, Milagro FI, Marcos A, González-Zorzano E, Campoy C, Garagorri JM, et al. Common variants in genes related to lipid and energy metabolism are associated with weight loss after an intervention in overweight/obese adolescents. Nutr Hosp 2014;30(1):75-83. DOI: 10.3305/nh.2014.30.1.7542 [ Links ]

20. Moreira GC, Cipullo JP, Ciorlia LAS, Cesarino CB, Vilela-Martin JS. Prevalence of metabolic syndrome: association with risk factors and cardiovascular complications in an urban population. PLoS One 2014;9(9):e105056. DOI: 10.1371/journal.pone.0105056 [ Links ]

21. Nicoletti CF, De Oliveira APRP, Brochado MJF, Oliveira BAP, Pinhel MA, Marchini JS, et al. UCP1 -3826 A&gt;G polymorphism affects weight, fat mass, and risk of type 2 diabetes mellitus in grade III obese patients. Nutrition 2016;32:83-7. DOI: 10.1016/j.nut.2015.07.016 [ Links ]

22. Nilsson E, Jansson PA, Perfilyev A, Volkov P, Pedersen M, Svensson MK, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014;63(9):2962-76. DOI: 10.2337/db13-1459 [ Links ]

23. Oliveira JS, Boery RNSO. An integrative review of associations between polymorphic variants and the metabolic syndrome. J Vasc Bras 2018; 17(2):141-147. DOI: 10.1590/1677-5449.007917 [ Links ]

24. Oki K, Yamane K, Kamei N, Asao T, Awaya T, Kohno N, et al. The single nucleotide polymorphism upstream of insuline-induced gene 2 (INSIG2) is associated with the prevalence of hypercholesterolemia, but not with obesity, in Japanese American women. Br J Nutr 2009;101(3):322-7. DOI: 10.1017/S0007114508006557 [ Links ]

25. O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev 2015;16(1):1-12. DOI: 10.1111/obr.12229 [ Links ]

26. Oki K, Yamane K, Kamei N, Asao T, Awaya T, Kohno N, et al. The single nucleotide polymorphism upstream of insuline-induced gene 2 (INSIG2) is associated with the prevalence of hypercholesterolemia, but not with obesity, in Japanese American women. Br J Nutr 2009;101(3):322-7. DOI: 10.1017/S0007114508006557 [ Links ]

27. Pena SD, Bastos-Rodrigues L, Pimenta JR, Bydlowski SP. DNA tests probe the genomic ancestry of Brazilians. Braz J Med Biol Res 2009;42(10):870-6. DOI: 10.1590/S0100-879X2009005000026 [ Links ]

28. Slimani A, Harira Y, Trabelsi I, Joma W, Maatouk F, Hamda KB, et al. Effect of E670G Polymorphism in PCSK9 gene on the risk and severity of coronary heart disease and ischemic stroke in a Tunisian cohort. J Mol Neurosci 2014;53(2):150-7. DOI: 10.1007/s12031-014-0238-2 [ Links ]

29. Xiao-Ying D, Qiu TS. Insulin-induced gene: a new regulator in lipid metabolism. J Peptides 2010;31(11):2145-50. DOI: 10.1016/j.peptides.2010.07.020 [ Links ]

30. Yang Q, Xiao T, Guo J, Su Z. Complex relationship between obesity and the fat mass and obesity locus. Int J Biol Sci 2017;15;13(5):615-29. DOI: 10.7150/ijbs.17051 [ Links ]

31. Yang Y, Liu B, Xia W, Yan J, Liu H, Hu L, et al. FTO genotype and type 2 diabetes mellitus: spatial analysis and meta-analysis of 62 case-control studies from different regions. Genes (Basel) 2017;8(2):70. DOI: 10.3390/genes8020070 [ Links ]

32. Yin RX, Wu DF, Miao L, Htet-Aung LH, Cao XL, Yan TT, et al. Interactions of several single nucleotide polymorphisms and high body mass index on serum lipid traits. Biofactors 2013;39(3):315-25. DOI: 10.1002/biof.1073 [ Links ]

Carolina F. Nicoletti, Raquel G. Azevedo, Marcela A.S. Pinhel, Heitor B.P. Delfino and Carla B. Nonino. INSIG2 gene polymorphism is associated with higher blood pressure and triglyceride levels in Brazilian obese subjects. Nutr Hosp 2019;36(3):604-610.

This work was supported by grants #2015/20513-8 from the São Paulo Research Foundation (FAPESP).

Received: October 22, 2018; Accepted: November 15, 2018

Correspondence: Carla Barbosa Nonino. Laboratory of Nutrigenomic Studies. Ribeirão Preto Medical School (FMRP/USP). Av. Bandeirantes, 3900. 14049-900 Monte Alegre, Ribeirão Preto. São Paulo, Brazil e-mail:


All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki of 1964 and its later amendments or comparable ethical standards.

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License