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Paraoxonase-2 Gene (PON2) G148 Variant Associated with Elevated Fasting Plasma Glucose in Noninsulin-Dependent Diabetes Mellitus¹

Department of Medicine, St. Michael’s Hospital (R.A.H., P.W.C.); Department of Genetics, The Hospital for Sick Children (S.W.S., L.-C.T.); Samuel Lunenfeld Research Institute and Mount Sinai Hospital (A.J.G.H., B.Z.), University of Toronto, Toronto; and Thames Valley Family Practice Research Unit, University of Western Ontario (S.B.H.), London, Canada

Address all correspondence and requests for reprints to: Robert A. Hegele, M.D., DNA Research Laboratory, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario, Canada M5B 1W8. E-mail: robert.hegele{at}utoronto.ca

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Defining the genetic determinants of NIDDM requires evidence from several complementary approaches, including both linkage and association analyses using both discrete phenotypes and intermediate quantitative traits. We tested for association between common genomic variation in three genes that map to chromosome 7q21-q22 and quantitative traits related to NIDDM in a sample of Oji-Cree. We found that a common genomic variation in codon 148 (alanine or glycine) of the paraoxonase-2 gene (PON2) demonstrated a significant association with a variation in fasting plasma glucose (P < 0.0001). Furthermore, we found a significant association between a variation in fasting plasma glucose and the interaction term comprised of a PON2 codon 148 genetic variation and the presence of noninsulin-dependent diabetes mellitus (NIDDM; P < 0.0001). We then analyzed subjects according to PON2 genotype and NIDDM status. In subjects with NIDDM, the PON2 codon 148 G/G homozygotes had significantly higher mean fasting plasma glucose than subjects with the other two genotypes (P < 0.0001). However, in non-NIDDM subjects, there was no difference in mean fasting plasma glucose among any of the genotypes. There was no association of the PON2 genotype with NIDDM itself, with impaired glucose tolerance, or with other quantitative traits related to NIDDM in this sample. These findings suggest that 1) the PON2 G148 gene variant worsens glycemia in subjects with NIDDM; 2) defining the physiological role of the PON2 gene product would be worthwhile; and 3) genetic factors can modify the severity of clinical phenotypes in subjects with NIDDM.

	Introduction

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ALTHOUGH noninsulin-dependent diabetes mellitus (NIDDM) is acknowledged to have a genetic component, the identification of susceptibility genes to date has proven to be difficult (1, 2). Various complementary strategies, including both linkage and association analysis, are probably needed to dissect a genetic component for NIDDM (2). To date, linkage studies have provided most of the promising leads for causative genes in NIDDM (3, 4). However, common NIDDM is genetically heterogeneous, and association analysis might also be useful for identifying susceptibility or modifier genes (2, 5). Furthermore, Ghosh and Schork have argued that analysis of the genetic determinants of intermediate traits related to NIDDM, such as fasting plasma glucose, may be useful in identifying NIDDM susceptibility genes (2).

Genome-wide scanning to identify NIDDM susceptibility genes in the Pima Indians found linkages of DNA markers at 7q21.3-q22.1 with both quantitative traits related to glucose uptake and storage and possibly with NIDDM itself (6). Within this region are the genes encoding paraoxonase (PON1) (7), a paraoxonase-like protein (PON2) (8), and an isoenzyme of pyruvate dehydrogenase kinase (PDK4) (9). Serum paraoxonase is associated with high density lipoprotein and hydrolyzes a number of organophosphates (10). In contrast to PON1, which is mainly expressed in the liver, PON2 is expressed in a variety of tissues, including the pancreas (8). Although the physiological role of the PON2 gene product is unknown, its tissue distribution suggests a role that unique from that of paraoxonase (8). The PDK4 product modulates the activity of the mitochondrial pyruvate dehydrogenase complex, which catalyzes the first step in mitochondrial glucose oxidation; thus, PDK4 is also a reasonable candidate gene for metabolic phenotypes (9).

We wished to determine whether common genomic changes affecting the amino acid sequence at codon 148 (AG) predicted from the PON2 gene and within the PDK4 gene promoter were associated with NIDDM-related phenotypes in Oji-Cree. We tested for association between the common genomic variation of PON2 and PDK4 and quantitative intermediate phenotypes of NIDDM. We also evaluated the association between these quantitative traits and a common sequence variant in another gene on chromosome 7q, namely the codon 192 RQ variant of paraoxonase (encoded by PON1) (7, 10), and a common sequence variant in a gene on chromosome 4q, namely the codon 54 AT variant in codon 54 of intestinal fatty acid binding protein (encoded by FABP2) (11).

	Subjects and Methods

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Study subjects

The community of Sandy Lake, Ontario, is located about 2000 km northwest of Toronto, in the subarctic boreal forest of central Canada. The community is isolated and is accessible only by air during most of the year. Most members of the community speak both English and Oji-Cree, a member of the Algonkian family of languages (12). Historically, the ancestors of the contemporary residents of this region lived a nomadic, hunting-gathering subsistence typical of other Algonkian-speaking peoples of the northeastern subarctic. Since the development of the reservation and residential school systems, the lifestyle has changed radically from physically active to sedentary. The primary source of food has changed from wildlife with supplementation by roots and berries to processed foods high in animal fats.

Seven hundred and twenty-eight members (72% of the total population) of this community aged 10 yr and above participated in the Sandy Lake Health and Diabetes Project (12). Assessments included a questionnaire to assess medical history, including a previous diagnosis of NIDDM. Body mass index (BMI) was defined as weight (kilograms)/height (meters)². The project was approved by the University of Toronto ethics review committee.

Biochemical analyses

Plasma samples were obtained with informed consent. Exclusion criteria included an inadequate blood sample available for all biochemical and/or genetic determinations. Volunteers provided plasma samples after fasting overnight for 8–12 h. Blood was centrifuged at 2000 rpm for 30 min, and the plasma was stored at -70 C. Concentrations of fasting glucose were determined as previously described (12). Concentrations of fasting plasma insulin were determined by RIA (Pharmacia, Piscataway, NJ). A standard 75-g oral glucose tolerance test (OGTT; Glucodex, Rougier, Chambly, Canada) was administered, and a second blood sample was collected after 120 min for plasma glucose determination. Volunteers were excluded from the OGTT if they had physician-diagnosed diabetes and were currently receiving treatment with insulin and/or oral hypoglycemic agents or if they had a fasting blood glucose level exceeding 11.1 mmol/L. Volunteers who were pregnant at the time of recruitment had their OGTT deferred until 3 months postpartum. NIDDM and impaired glucose tolerance (IGT) were diagnosed using established criteria (13, 14).

The genotypes for FABP2 codon 54 and PON1 codon 192 were determined as previously described (15, 16). The genotype for PON2 codon 148 was determined using 0.5 µg genomic DNA amplified in a mismatch PCR reaction using primers PON2–148-5' (5'-AGT GGA AAT TTT TAA ATT TGA AGC AG-3') and PON2–148-3' (5'-TTG TTT GCA AAT GCT GGG GAT-3'). The resulting 130-bp fragment was digested with BsoFI, which created two smaller fragments for an amplimer derived from a PON2 allele encoding A148 and a single 130-bp fragment for an amplimer derived from a PON2 allele encoding G148. These fragments were resolved on 4% agarose gels. The genotype for the PDK4 promoter sequence was determined using primers PDK4-pro-5' (5'-CCT CCG AGT TGT AAA CAA GG-3') and PDK4-pro-3' (5'-AAC GCG TCC TGA ACT CCA G-3'). Two single base substitutions, a CT and a TC, respectively, at positions -208 and -153 nucleotides relative to the transcription start site of PDK4, were included in this fragment and could be simultaneously detected after digestion with MspI. There were three combinations of MspI fragments in the Sandy Lake Oji-Cree; these genotype combinations were treated as effective haplotypes. Haplotype A contained the -208 nucleotide MspI site but lacked the -153 nucleotide MspI site; haplotype B contained both MspI sites; and haplotype C lacked both MspI sites. We did not observe any alleles that contained the fourth possible haplotype, namely the absence and presence, respectively, of the -208 and -153 nucleotide MspI sites. The fragments were resolved on 10% polyacrylamide gels.

Statistical analysis

SAS (version 6.11) was used for all statistical comparisons (17). Quantitative variables were log transformed and subjected to analysis of normality as previously described (18). ANOVAs were performed using the general linear models procedure to determine the sources of variation for log fasting plasma glucose and insulin and log plasma glucose 2 h after a standard glucose load. Adult subjects, aged 18 yr and older, were analyzed. F tests were computed from the type III sums of squares (17). This form of sums of squares is applicable to unbalanced study designs and adjusts the level of significance to account for other independent variables included in the model. Independent variables for each ANOVA were sex, age, the log of BMI, the presence of previously diagnosed or newly diagnosed NIDDM, and genotypes of FABP2 codon 54, PON1 codon 192, PON2 codon 148, and the PDK4 promoter. We had previously tested for association between the FABP2 codon 54 polymorphism with fasting plasma insulin and NIDDM status and had found no association. However, we had not previously tested for association between the FABP2 codon 54 polymorphism and fasting plasma glucose or plasma glucose 2 h after a standard glucose load. Interaction terms composed of the presence of the NIDDM variable and the significantly associated genotype variables were included in the ANOVA. As recommended by others (2, 19), the nominal P value for a significant association was taken to be less than 0.01. When a significant genotype-phenotype was identified, the mean values for the trait were compared between genotypic classes using pairwise comparisons of least squares means (17).

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Clinical attributes of sample

Sufficient DNA and phenotypic information were obtained for analysis from 523 subjects, aged 18 yr and older, of whom 298 (57%) were women. The mean ± SD for the age and BMI were, respectively, 35.8 ± 14.6 yr and 28.1 ± 5.3 kg/m². One hundred and twenty-four subjects were classified as having NIDDM; 54 subjects were classified as having newly diagnosed NIDDM based upon the OGTT, and 70 subjects had been previously diagnosed with NIDDM. Of the subjects with NIDDM, 6 were taking insulin, 30 were taking oral hypoglycemic agents, and the remainder were controlled by diet alone. Normal subjects and those with IGT were classified as not having NIDDM (non-NIDDM).

Allele and genotype frequencies

The frequencies of the FABP2 T54, PON1 Q192, and PON2 G148 alleles were 0.14, 0.22, and 0.27, respectively. The frequency of the FABP2 T54 allele was the same in this population as we had reported previously. The frequencies of the A, B, and C haplotypes of PDK4 were 0.732, 0.266, and 0.002, respectively. Genotype frequencies of FABP2, PON1, and PON2 and diploid haplotype frequencies of PDK4 did not deviate from those predicted by the Hardy-Weinberg law in this study sample (all P > 0.10).

Phenotype-genotype associations

Transformation using the natural logarithm for each variable resulted in a distribution that was not significantly different from normal. One ANOVA was performed for each of log fasting plasma glucose, log 2 h postglucose challenge plasma glucose, and log fasting plasma insulin level. There were no significant genotype-phenotype associations for log 2 h postglucose challenge plasma glucose and log fasting plasma insulin levels. However, there were two significant genotype-phenotype associations for log fasting plasma glucose. The first was between PON2 codon 148 genotype and log fasting plasma glucose, and the second was between FABP2 codon 54 genotype and log fasting plasma glucose (Table 1). There was also a highly significant association between log fasting plasma glucose and the interaction term comprised of NIDDM status and PON2 genotype, and there was a suggestion of an association between log fasting plasma glucose and the interaction term comprised of NIDDM status and FABP2 genotype (Table 1). Log fasting plasma glucose was also significantly associated with both NIDDM status and log BMI in adults (Table 1). There were no significant phenotype-genotype associations for any quantitative trait when normal adults and adults with IGT were analyzed separately (data not shown). There were no significant genotype-phenotype associations with the presence of NIDDM or IGT (data not shown). All significant associations were unaffected by including medication use as a covariate (data not shown).

Because of the highly significant association between log fasting plasma glucose and the interaction term comprised of NIDDM status and PON2 genotype, we examined NIDDM and non-NIDDM subjects separately. The means ± SDs of log fasting plasma glucose for NIDDM and non-NIDDM subjects are shown in Fig. 1. As expected, the mean log fasting plasma glucose was significantly higher in NIDDM than in non-NIDDM subjects (P < 0.0001). Pairwise comparisons in the NIDDM subjects indicated that the mean log fasting plasma glucose was significantly higher in the PON2 codon 148 G/G homozygotes than in subjects with the other two genotypes (P < 0.0001). Pairwise comparisons in the non-NIDDM subjects indicated that the mean log fasting plasma glucose level was not different between genotype classes. There were no differences in log plasma glucose between genotype classes when IGT and normal subjects were analyzed separately (data not shown).

View larger version (30K): [in this window] [in a new window]   Figure 1. Fasting glucose and PON2 genotype in Sandy Lake adults. Subjects are divided into those with NIDDM and those without NIDDM (non-NIDDM). The number of subjects with each genotype is indicated above each bar. Means and SDs for log fasting plasma glucose are shown. NIDDM subjects had significantly higher mean log fasting plasma glucose than non-NIDDM subjects (P < 0.0001). NIDDM subjects with the PON2 codon 148G/G genotype had significantly higher log fasting plasma glucose levels than all other subjects (P < 0.0001), as indicated by the double asterisk.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The principal novel finding in this study of Oji-Cree from Sandy Lake was that a conservative AG variant of codon 148 of PON2 gene was highly significantly associated with variation in the fasting plasma glucose concentration. In particular, homozygosity for the PON2 G148 allele was highly significantly associated with elevated plasma glucose in adult NIDDM subjects. There was no association of the PON2 genotype with NIDDM itself, with IGT, or with other quantitative traits related to NIDDM in any subset of this sample. There were no significant phenotype-genotype associations of any quantitative phenotype with variations in two other genes on 7q21.3-q22.1, namely PON1 and PDK4.

The absence of associations with the other closely linked genetic markers suggests that the association of a variation in plasma glucose in NIDDM with the PON2 genotype is specific, although the codon 148 change may be in linkage disequilibrium with the actual functional DNA variant within this gene. The association of homozygosity for PON2 G148 with worsened hyperglycemia in NIDDM suggests that the variation in PON2 modulates a quantitative NIDDM-related phenotype, but may not itself predispose to NIDDM.

The paraoxonase-related gene PON2 is located near PON1 and is part of a multigene family that includes PON1 and PON3 (8). This proximity and the similarity in genomic structure of PON1 and PON2 suggests that the two genes might have arisen by a tandem duplication event. PON1 was discovered after biochemical study of paraoxonase (7). Paraoxonase is associated with a subclass of antiatherogenic, high density lipoprotein, and its physiological substrate is unknown (7, 8, 10). Paraoxonase might modify the oxidation of atherogenic lipoproteins (16). The codon 192 polymorphism in PON1 probably underlies interindividual variation in plasma paraoxonase activity and has been associated with variations in metabolic and cardiovascular phenotypes (16, 20, 21). In contrast, PON2 was discovered by a genetic approach (8), and there is presently no information regarding its possible physiological role.

The structural similarities between PON1 and PON2 suggest that the PON2 gene product may have some function in common with PON1 (8). However, the more widespread tissue expression and multiple forms of the PON2 transcript also suggest an independent function for the PON2 gene product (8). Of special relevance to NIDDM is the expression of PON2 in the pancreas (8). Furthermore, PON2 is expressed in cardiac and skeletal muscle (8), suggesting that it could be involved in the peripheral utilization of glucose. The unique structural aspects and tissue distribution of PON2 taken together with our observed genetic association with hy-perglycemia suggest that defining its function would be worthwhile.

There was also a less significant association between a variation in codon 54 of the FABP2 gene and the fasting plasma glucose concentration. This association appeared to be due to elevated plasma glucose levels in NIDDM homozygotes for the FABP2 T54 allele; however, there were only three such subjects. This is consistent with previous observations of worsened diabetic phenotypes in subjects with the FABP2 T54 allele (11). Furthermore, we previously reported an association between the FABP2 T54 allele and increased body mass, but not with NIDDM or fasting plasma insulin (15). Taken together, our results are consistent with a deleterious metabolic impact of the FABP2 T54 allele.

At least one genomic scan, performed in Mexican-Americans, showed no linkage between any chromosome 7q marker and NIDDM (3). However, genomic scanning in Pima Indian sibships with NIDDM (6) has revealed convincing linkages of D7S527, which is close to PON2, with such quantitative phenotypes as glucose uptake, oxidation, and storage rates at physiological and maximally stimulating insulin concentrations. There was less convincing evidence for possible linkages with the plasma insulin concentration and with NIDDM itself in the Pima Indians (6). These results are consistent with our findings that NIDDM per se is not related to genetic variation in PON2. However, there appears to be a relationship between variation in this region and complex quantitative phenotypes related to glucose uptake and/or storage. This may become manifest as a variation in plasma glucose. Also, the metabolic stress of overt NIDDM may force a more extreme deviation in plasma glucose for Oji-Cree individuals homozygous for PON2 G148 or the linked variant for which G148 is a marker. The specific association with plasma glucose and not with insulin in our sample suggests that these intermediate traits are themselves complex and have distinct genetic determinants (2).

In summary, we have observed that homozygosity for the PON2 G148 allele was associated with worsened fasting hyperglycemia in Oji-Cree with NIDDM. This indicates that an association analysis using quantitative intermediate traits provides results complementary to those obtained from linkage studies (6). The PON2 variation was not associated with the presence of NIDDM and thus cannot be interpreted as being causative. However, PON2 may be a modifier gene for a complex quantitative phenotype related to NIDDM. If genetic factors can worsen clinical phenotypes in NIDDM and thus contribute to the spectrum of disease expression, then determination of such genotypes may have an impact on diagnosis and treatment in a particular subject even after the onset of NIDDM.

	Acknowledgments

 
We acknowledge the chief and council of the community of Sandy Lake, the Sandy Lake community surveyors, the Sandy Lake nurses, the staff of the University of Toronto Sioux Lookout Program, the Department of Clinical Epidemiology of the Samuel Lunenfeld Research Institute, Dr. Alexander Logan, Annette Barnie, Fang Sun, Teresa Lippingwell, Joel Napenas, Stefan Sadikian, and Cheri Tully. Dr. Michal Prochazka provided sequence information for the PON2 and PDK4 amplification primers.

	Footnotes

 
¹ This work was supported by grants from the NIH (91-DK-01), the Ontario Ministry of Health (04307), the Heart and Stroke Foundation of Ontario (T2978), and the St. Michael’s Hospital Foundation and by a Canadian Genome Analysis and Technology award (to L.-C.T. and S.W.S.).

² Career Investigator with the Heart and Stroke Foundation of Ontario.

³ Supported by Health Canada through a National Health Research and Development Program Research Training Award.

Received February 5, 1997.

Revised March 24, 1997.

Revised June 3, 1997.

Accepted June 17, 1997.

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