G6PC2 rs560887 Gene Variant is Associated with Fasting Blood Glucose in the Admixed Mexican Population

Rodrigo Fernández-Pons1, Paula Costa-Urrutia1, Jacqueline Solares-Tlapechco1, Julio Granados2, Martha E. Rodríguez-Arellano1, *
1 Genomic Medicine Laboratory, Hospital Regional Lic. Adolfo López Mateos, ISSSTE, 1321 Universidad Avenue, Florida, Álvaro Obregón, Mexico City, 01030, Mexico
2 Division of Immunogenetics, Department of Transplants, National Institute of Medical Sciences and Nutrition Salvador Zubirán, 15 Vasco de Quiroga Avenue, Mexico City, 14080, Mexico

© 2020 Fernández-Pons et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at Genomic Medicine Laboratory, Hospital Regional Lic. Adolfo López Mateos, ISSSTE, 1321 Universidad Avenue, Florida, Álvaro Obregón, Mexico City, 01030, Mexico; e-mail: marthaeunicer@yahoo.com.mx



In Mexico, type 2 diabetes prevalence is 13.7%, which has a huge impact on Mexican public health. There is an urgent need to focus on the prevention of pre-diabetes to decrease the likelihood of type 2 diabetes onset. Gene variants predisposed to increase Fasting Blood Glucose (FBG) and glycosylated hemoglobin (HbA1c) levels could be helpful for prevention purposes. This study aimed to analyze the association of the G6PC2 rs560887 variant with pre-diabetes in a Mexican-Mestizo population.


A cross-sectional case-control study was performed in 960 Mexican Mestizos participants. The association of G6PC2 rs560887 with pre-diabetes was analyzed by logistic regression and with Fasting Blood Glucose (FBG) and glycosylated hemoglobin (HbA1c) by linear regression.


The G6PC2 rs560887 variant was significantly associated with FBG (β -1.80, p=0.03), but not with HbA1c or the presence of pre-diabetes.


The G6PC2 rs560887 loci could be a potential early marker of type 2 diabetes.

Keywords: Pre-diabetes, Fasting blood glucose, Glycosylated haemoglobin, G6PC2 rs560887, Mexican-mestizo, Glycolysis.


Pre-diabetes (pre-DM) is a reversible condition characterized by high blood glucose and/or glycosylated hemoglobin (HbA1c), but not high enough to diagnose T2D (glucose>126 mg/dl, HbA1c≥6.5%) [1, 2]. About 70% of patients with pre-DM will progress to type-2 diabetes (T2D) [2, 3], a disease whose prevalence in Mexico is 13.7% [4]. Thus, there is an urgent need to focus on the treatment and prevention of pre-DM to decrease the likelihood of T2D onset.

In Mexicans, there are few genes that have been associated with elevated glucose levels: PPARG (Pro12Ala), ASCL1 (rs9997745), PPARGC1A (rs8192678), and ADIPOQ (rs82 2396) [5-7]. Hence, identifying additional loci influen- cing glucose or HbA1c levels may improve the understanding of the pathogenesis of T2D and contribute to its prevention.

In this framework, the G6PC2 gene encodes an islet-specific glucose-6-phosphatase catalytic subunit, that hydrolyzes glucose 6-phosphate; thus, increasing glycolysis and decreasing glycolytic flux [8]. G6pc2- null mice exhibit a mild metabolic phenotype with decreased blood glucose levels but unchanged insulin, glucagon, glycerol, and cholesterol concentrations. In addition, several human related gene variants conferred a decreased G6PC2 protein expression in mice [8].

The G6PC2 rs560887 is an intron variant that has been largely associated with T2D and elevated fasting blood glucose (FBG) in Americans (including Hispanics), Europeans, and Asians [8-13]. An extensive meta-analysis also showed the association of not only this variant but also rs16856187 and rs573225 with FBG [14]. In addition, the G6PC2 rs560887 has been associated with FBG in Mexican children [15]; however, it has not been tested in the adult population. This study aimed to analyze the association of the G6PC2 rs560887 variant with the susceptibility to pre-DM and with FBG and HbAc1 in Mexican Mestizo adults.


This is a cross-sectional and case-control study consisting of 960 volunteers over 45 years old, non-related, all with Mexican-Mestizo ancestry (at least three not related grandparents born in Mexico), and enrolled in the Cohort for the Study of Complex Diseases [Cohorte para el Estudio de Enfermedades Complejas], carried out at the Hospital Regional Lic. Adolfo López Mateos of the Institute for Social Security for State Workers [Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, ISSSTE]. This study started in 2013 in response to the need for establishing a prospective metabolic epidemiologic study. Participants are ISSSTE right holders who are monitored annually regarding clinical, anthropometric, and biochemical parameters. Participants had no diabetes or cardiovascular disease.

Weight, height, and body mass index (BMI, kg/m2) were measured. Venous blood samples were drawn after a 10-hour fast to measure FBG, Hb1Ac, and triglycerides. The concentrations were measured in an automated analyzer (Miura 200, Italy) and expressed as mg/dL, except for HbA1c, which was expressed as a percentage.

Genomic DNA was extracted from 500 µL of whole blood, using an automated system InviGenius® and the DNA Mini Kit InviMag Blood (STRATEC Molecular GmbH, Berlin, Germany). For genotyping, a pre-designed 5’ exonuclease TaqMan assay was used on a 7500 series Real-Time PCR system, according to manufacturer’s instructions (Applied Biosystems, Foster City, CA, USA).

2.1. Data Analysis

Descriptive results are presented as means and standard deviations. Those variables were compared between cases and controls by Student’s t-test. The Hardy-Weinberg equilibrium was assessed by using the exact test. Glucose and Hb1Ac mean pairwise comparisons were performed between genotypes by the Mann-Whitney test using the XLSTAT software (Addinsoft 2020, XLSTAT statistical and data analysis solution, New York, USA, https://www.xlstat.com).

The association between the rs560887 variant was evaluated by using logistic and linear regression models. For logistic regression models, participants were classified into two groups (cases and controls) according to their glucose levels: i) pre-DM (case group n=435) when FBG >100 mg/dl, and ii) control group when glucose ˂100 mg/dl (n=525); or by their Hb1Ac levels: i) pre-DM (case group n=99) when fasting Hb1Ac 35.7%, and ii) control group when Hb1Ac ˂5.7% (n=861) [2]. For linear regression models, FBG and Hb1Ac were considered as continuous outcome variables. Regression models were adjusted by age, sex, BMI, and triglycerides, under the Additive/Dominant/Recessive model conducted with PLINK 1.7 software version [16] (http://pngu.mgh.harvard. edu/purcell/plink, Harvard University, Cambridge, MA, USA).

The Quanto software version 1.2.4 (http://biostats.usc.edu/ Quanto.html, University of Southern California, Los Angeles, CA, USA) was used to perform a power analysis considering prevalence of the disease to be 13.7% [4], and means and standard deviations for glucose (99 + 10.9 mg/dl) and HbA1c (4.8+0.9%) for the case-control and continuous outcome designs, respectively. Power analysis was done for gene only, under additive, dominant and recessive inheritance models using the SNPs allele frequency reported in this study (0.09), obtaining power of 80% to detect 0.6 ≤ OR ≤0.7 OR≥1.4 and β≥1.5.

All participants signed informed consent. This study was approved by the Research, Ethics, and Biosafety Committees of the HRLALM (registration number 236.2011).


The study included a total of 960 individuals (359 men and 601 women). The descriptive results are shown in Table 1. All pairwise comparisons between cases and controls resulted in statistically significantly differences (p<0.05), except for BMI and triglycerides when pre-DM classification was based on HbA1c (Table 1). No departure from Hardy-Weinberg equilibrium was found, being 0.09, the G6PC2 rs560887 minor allele frequency for the T allele. Comparisons of genotypic frequency are shown in Table 2.

Table 1. Descriptive results from cases and controls. BMI (Body Mass Index), FBG (Fasting Blood Glucose), Glycosylated Haemoglobin (HbA1c), Standard Deviation (SD).
   -    Pre-DM Classification Based on FBG    -    -    Pre-DM Classification Based on Hb1Ac    -    -
   -    Cases (n=435)    Controls (n=525)    P- value    Cases (n=99)    Controls (n=861)    P- value
   -    Mean (SD)    Mean (SD)    Mean (SD)    Mean (SD)    -
   Age    51 (6.9)    48 (7.4)    0.0001    52 (7.0)    49 (7.3)    0.0001
   BMI (kg/m2)    29.1 (4.7)    28.1 (4.7)    0.001    29.4 (4.6)    28.5 (4.4)    0.1
   HbA1c (%)    5.0 (0.9)    4.6 (0.7)    0.0001    6.2 (1.04)    4.6 (0.7)    0.0001
   Glucose (mg/dl)    108.6 (6.7)    91.1 (6.3)    0.0001    104.9 (11.7)    98.3 (10.7)    0.0001
   Triglycerides (mg/dl)    200.3 (141.3)    165.6 (82.5)    0.0001    184.3(101.0)    181.0 (151.3)    0.77
Table 2. Genotype frequencies of G6PC2 rs560887. Number of (N) and percentages of participants by genotype. Mean (Standard deviation) of by genotype. Hb1Ac (glycosylated haemoglobin). * p-value (P) between CC vs. CT, and CC vs. CT+TT.
   -    CC    CT    TT    CT+TT    P
   N    787 (82.0%)    165 (17.2%)    8 (0.8%)    173(18%)
   Glucose    99.4 (10.8)    97.0 (11.1)    98.0 (8.4)    97.2 (11.1)    0.02*
   Hb1Ac    4.8 (0.8)    4.7(1.1)    4.8 (0.4)    4.7(1.1)    0.15
Table 3. Logistic and linear regression models for the association Fasting blood glucose (FBG) or glycosylated haemoglobin (Hb1Ac). FBG and Hb1Ac were used to classify individuals as pre-DM or controls for the logistic regression models, and they were considered as continuous for the linear regression models. Odds ratio (OR), and estimates (β), 95% confidence interval (CI 95%), additive model (ADD), dominant model (DOM), recessive model (REC).
   -    -    FBG    -    -    -    Hb1Ac    -    -    -
   Model    Allele    OR (CI 95%)    P    β (CI 95%)    P    OR (CI 95%)    P    β (CI 95%)    P
   ADD    T    0.78 (0.56, 1.08)    0.14    -1.592 (-3.19,0.01)    0.05    0.76 (0.43, 1.33)    0.3442    -0.05(-0.17,0.079)    0.46
   DOM    T    0.77 (0.54, 1.08)    0.14    -1.80 (-3.52,-0.09)    0.03    0.78 (0.43, 1.42)    0.4259    -0.05(-0.20,0.08)    0.41
   REC    T    0.761 (0.17, 3.39)    0.72    -0.46 (-7.69, 6.76)    0.90    0.001 (0,2)    0.9982    0.04(-0.52,0.60)    0.88

Higher FBG was significantly associated with the T allele under the dominant inheritance model (β=-1.80 p=0.03, Table 3). None of the other models provided statistically significant associations (Table 3).


In this study, the association between the G6PC2 rs560887 common gene variant and pre-DM, FBG, and HbA1c, in 960 Mexican-Mestizos enrolled in the CPEEC, was evaluated.

It was found that mean BMI was above the standard cut-off for overweight (25kg/m2) for both cases and controls (Table 1). Mean triglycerides were also above the level considered normal (150 mg/dl), suggesting that even those subjects classified as controls are at risk of developing pre-DM.

It was also found that G6PC2 rs560887 was associated only with FBG as a continuous variable, but not with HbA1c or pre-DM (either when the pre-DM classification was based on FBG or HbA1c). The rs560887 T allele seemed to confer protection against high FBG (C/T reported in this study). This is in line with other studies carried out in Europeans; the A allele (complimentary G/A) conferred protection against high FBG in 654 subjects and the results were replicated in other 9,353 subjects [13]. Besides, the G allele was related to the risk of having high FBG in Europeans [12, 17], African Americans, Hispanics [11], and American Indians [12].

A limitation of this study is that it has not enough statistical power to detect a near cero OR (OR≈0.8) or a small β (β<1.5). This could be the reason why a significant statistical association could not be found between G6PC2 rs560887 and pre-diabetes or Hb1Ac. This means that small effects cannot be discarded before considering larger sample size.

In Mexico, the rs560887 G allele was associated with higher FBG in children [15], suggesting that age could not necessarily be an additional contributor to this association. Additionally, and in agreement with these results, variations in GCK and G6PC2 genes have effects on both FBG and insulin secretion in Mexican Americans [18].


Although replications studies in the Mexican population are needed, this fact makes the G6PC2 rs560887 gene variant a potential early marker of type 2 diabetes in clinical practice.


T2D  = Type-2 diabetes
HbA1c  = Glycosylated haemoglobin
FBG  = Elevated fasting blood glucose
pre-DM  = Pre-diabetes


This study was approved by the Research, Ethics, and Biosafety Committees of the HRLALM (registration number 236.2011).


Not applicable.


All participants signed informed consent.


This work was supported by ISSSTE: Grant number: 236.2011-E015.


The authors declare no conflicts of interest, financial or otherwise.


The authors would like to thank the laboratory staff for their logistic and technical support.


[1] Tuso P. Prediabetes and lifestyle modification: time to prevent a preventable disease. Perm J 2014; 18(3): 88-93.
[2] Care D, Suppl SS. Diabetes care in the hospital : Standards of medical care in diabetes d 2020. Diabetes Care 2020; 43: 193-202.
[3] Adam G. Tabák, Christian Herder, Wolfgang Rathmann, Eric J. Brunner, FFPH1 and MK. Prediabetes : A high-risk state for developing diabetes. Lancet 2014; 379: 1-19.
[4] Basto-Abreu A, Barrientos-gutiérrez T, Rojas-martínez R, et al. Prevalencia de diabetes y descontrol glucémico en México : Resultados de la Ensanut 2016. Salud publica Mex pública méxico 2020; 62.
[5] Vázquez-Del Mercado M, Guzmán-Ornelas MO, Corona Meraz FI, et al. Alleles are associated with reduction of metabolic risk factors even obesity in a mexican-mestizo population. BioMed Res Int 2015; 2015285491
[6] Costa-Urrutia P, Abud C, Franco-Trecu V, et al. Genetic susceptibility to pre diabetes mellitus and related association with obesity and physical fitness components in Mexican-Mestizos. Prim Care Diabetes 2018; 12(5): 416-24.
[7] Rubio-Chavez LA, Rosales-Gomez RC, Rubio-Chavez KL, et al. The rs822396 polymorphism of the ADIPOQ gene is associated with anthropometric, clinical, and biochemical alterations related to the metabolic syndrome in the mexican population. Metab Syndr Relat Disord 2020; 18(5): 243-50.
[8] Boortz KA, Syring KE, Pound LD, Wang Y, Oeser JK, O’Brien RM. Functional analysis of mouse G6pc1 mutations using a novel in situ assay for Glucose-6-Phosphatase activity and the effect of mutations in conserved human G6PC1 / G6PC2 amino acids on G6PC2 protein expression. PLoS One 2016; 11(9): e0162439.
[9] Bhupathiraju SN, Hu FB. Si and AF. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res 2016; 118(11): 1723-35.
[10] Rose CS, Grarup N, Krarup NT, et al. A variant in the G6PC2/ABCB11 locus is associated with increased fasting plasma glucose, increased basal hepatic glucose production and increased insulin release after oral and intravenous glucose loads. Diabetologia 2009; 52(10): 2122-9.
[11] Demirci FY, Dressen AS, Hamman RF, Bunker CH, Kammerer CM, Kamboh MI. Association of a common G6PC2 variant with fasting plasma glucose levels in non-diabetic individuals. Ann Nutr Metab 2010; 56(1): 59-64.
[12] Fesinmeyer MD, Meigs JB, North KE, et al. Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: Results from the Population Architecture using Genomics and Epidemiology (PAGE) study. BMC Med Genet 2013; 14: 98.
[13] Bouatia-naji N. A polymorphism within the G6PC2. Science (80-) 2014; 1085: 6-10.
[14] Shi Y, Li Y, Wang J, et al. Meta-analyses of the association of G6PC2 allele variants with elevated fasting glucose and type 2 diabetes. PLoS One 2017; 12(7)e0181232
[15] Langlois C, Abadi A, Peralta-romero J, et al. Evaluating the transferability of 15 European-derived fasting plasma glucose SNPs in Mexican children and adolescents 2016; 1-8.
[16] Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81(3): 559-75.
[17] Heni M, Ketterer C, Hart LM, et al. The impact of genetic variation in the G6PC2 gene on insulin secretion depends on Glycemia 2018; 95: 479-84.
[18] Li X, Shu YH, Xiang AH, et al. Additive effects of genetic variation in GCK and G6PC2 on insulin secretion and fasting glucose. Diabetes 2009; 58(12): 2946-53.