help button home button Endocrine Society JCEM
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Submit a related Letter to the Editor
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Copyright Permission
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Hasstedt, S. J.
Right arrow Articles by Elbein, S. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hasstedt, S. J.
Right arrow Articles by Elbein, S. C.
The Journal of Clinical Endocrinology & Metabolism Vol. 86, No. 2 536-541
Copyright © 2001 by The Endocrine Society

From the Clinical Research Centers

Effect of the Peroxisome Proliferator-Activated Receptor-{gamma}2 Pro12Ala Variant on Obesity, Glucose Homeostasis, and Blood Pressure in Members of Familial Type 2 Diabetic Kindreds1

Sandra J. Hasstedt, Qian-Fang Ren, Kui Teng and Steven C. Elbein2

Department of Human Genetics, University of Utah (S.J.H.), Salt Lake City, Utah 84112-5330; and Division of Endocrinology, Department of Medicine, Central Arkansas Veterans Healthcare System and University of Arkansas for Medical Sciences (Q.-F.R., K.T., S.C.E.), Little Rock, Arkansas 72205

Address all correspondence and requests for reprints to: Steven C. Elbein, M.D., Endocrinology 111J/LR, 4300 West 7th Street, Little Rock, Arkansas 72205. E-mail: elbeinstevenc{at}exchange.uams.edu


   Abstract
Top
 Abstract
Introduction
 Experimental Subjects
 Materials and Methods
 Results
 Discussion
 References
 
The Pro12Ala (P12A) variant of exon B of the peroxisome proliferator-activated receptor {gamma}2 (PPAR{gamma}) been variably associated with obesity, insulin sensitivity, diabetes, and dyslipidemia, but its role in insulin resistance-associated traits remains uncertain. We tested the hypothesis that this variant is associated with the insulin resistance syndrome by genotyping 619 members of 52 familial type 2 diabetes kindreds. A subset of 124 family members underwent iv glucose tolerance tests and minimal model determination of insulin sensitivity. We estimated the frequency of the A12 allele as 0.12, within the range observed in random Caucasian samples. We were unable to demonstrate any effect on direct measures of insulin sensitivity, and no trait was linked to markers near PPAR{gamma} on chromosome 3q. However, body mass index, serum total cholesterol levels, triglyceride levels, systolic and diastolic blood pressures, and glucose concentration showed at least a trend to association (P < 0.1) when tested separately for a family-based association. When these 6 traits were included in a multivariate analysis, body mass index, systolic and diastolic blood pressures, triglyceride levels, and glucose concentration remained significantly associated with the P12A variant (P < 0.05), whereas the effect of P12A on liability for diabetes was not significant. The predicted means for each trait and each genotype suggested that the P12A variant acted most like a recessive mutation, with the major effect among homozygous individuals who comprise only 1–2% of the population. We confirm an association of the P12A variant in traits commonly ascribed to the insulin resistance syndrome, but not with direct measures of insulin sensitivity. The tendency for this variant to act in a recessive manner with effects on multiple traits may explain the inconsistent associations noted in previous studies.


   Introduction
Top
 Abstract
 Introduction
Experimental Subjects
 Materials and Methods
 Results
 Discussion
 References
 
INSULIN RESISTANCE IS a well established component of the pathogenesis of type 2 diabetes mellitus (T2DM) (1), and in prospective studies insulin resistance both precedes and predicts the onset of T2DM (2). Furthermore, insulin resistance is prevalent among family members of T2DM patients who are at risk for future diabetes (3) and is heritable (4, 5, 6). Despite the apparently pivotal role for inherited defects in insulin action in the pathogenesis of diabetes, candidate genes in the pathways of insulin signaling and insulin action, including glycogen synthesis, have not been widely implicated in diabetes susceptibility or inherited disorders of insulin sensitivity.

The peroxisome proliferator-activated receptors (PPARs) are members of the nuclear hormone receptor subfamily of transcription factors. The PPAR{gamma} subtype is involved in adipocyte differentiation and is the target for the thiazolidinedione class of antidiabetic drugs (7), which appears to act primarily by increasing peripheral insulin sensitivity. PPAR{gamma} comprises two isoforms, PPAR{gamma}1 and PPAR{gamma}2, which differ by 84 nucleotides and 28 amino acids at the 5'-end of the gene. Both isoforms are expressed in adipocytes, but PPAR{gamma}2 expression is largely limited to adipocytes, appears to have 5- to 10-fold increased ligand-independent activation compared with PPAR{gamma}1, and may be the more important regulator of adipocyte differentiation and energy storage (7).

In recent studies mice heterozygous for inactivation of the PPAR{gamma} gene showed improved insulin sensitivity of both liver and peripheral tissues compared with wild-type mice, suggesting that reduced PPAR{gamma} expression improved insulin sensitivity (8). In contrast, recently described naturally occurring human substitutions P467L and V290M, which inhibit PPAR{gamma} trans-activation in a dominant negative fashion, cause insulin resistance, hypertension, hypertriglyceridemia, and early-onset diabetes (9). Other mutations of PPAR{gamma} (Pro115Gln) appear to cause severe obesity, consistent with the key role proposed for PPAR{gamma} in adipocyte differentiation (10). Thus, rare human variants and experimental models support a key role for PPAR{gamma} in the modulation of insulin sensitivity, obesity, hypertension, and triglyceride (TG) levels.

In contrast to rare PPAR{gamma} variants, Yen et al. (11) described a common variant, Pro12Ala (P12A), in an alternatively spliced exon B of the PPAR{gamma}2 isoform. This variant is present in most populations. In population-based studies, Beamer (12) subsequently reported an association of the P12A allele with higher body mass index (BMI), higher waist to hip ratio, higher waist circumference, and possibly altered lipid profile. Deeb et al. (13), on the other hand, reported that the same allele was associated with improved insulin sensitivity, lower insulin levels, and lower BMI in middle-aged Finnish men and women and found a similar trend in elderly Finns and Japanese Americans. The P12A variant appears to have reduced trans-activation capacity (14). Meirhaeghe et al. (15) suggested that P12A altered the relationship between leptin and adiposity. Subsequent studies of the P12A variant have found variable effects on obesity, lipids, and glucose homeostasis. Several studies were unable to replicate the association with these traits (16). Valve et al. (17) found no association with obesity, but a higher BMI among obese women with the variant. Ek et al. (18) found a variable effect on BMI depending on the subject’s level of obesity. Knoblauch et al. (19) found both linkage to and association with high density lipoprotein cholesterol levels, low density lipoprotein cholesterol concentration, and BMI in healthy, nonobese dizygotic twins. Koch et al. (20) found no effect in offspring of diabetic subjects, but reported an association of the P12A allele with improved insulin sensitivity in obese subjects. Thus, the role of this variant is unclear and controversial. We are unaware of published studies that have used a family-based association strategy in familial T2DM to address these questions.

We used a multivariate, family-based likelihood approach to test the role of the PPAR{gamma}2 P12A variant on quantitative traits related to obesity, glucose homeostasis, lipids, and blood pressure. In the present study we examined 619 nondiabetic members of 52 families that were ascertained for at least 2 siblings with T2DM. Using this approach, we confirm that the common P12A variant of PPAR{gamma}2 impacts traits commonly associated with the insulin resistance syndrome in members of families with a strong history of T2DM.


   Experimental Subjects
Top
 Abstract
 Introduction
 Experimental Subjects
Materials and Methods
 Results
 Discussion
 References
 
Families were ascertained for at least 2 siblings who were diagnosed with T2DM before age 65 yr. At most, 1 parent was known to have T2DM. All subjects were of Northern European ancestry. All available parents and siblings of the index sibling pair were studied, as were any available offspring of diabetic siblings. Details of the study population have been described previously (21). The study population comprised 619 individuals for whom genotypic data were available, including 355 nondiabetic individuals, 239 diabetic individuals, and 25 individuals who were not tested or had probable type 1 diabetes and were thus considered of unknown diagnosis. Each participant in the study gave informed consent under a protocol approved by the institutional review board of the University of Utah Health Sciences Center (Salt Lake City, UT). All clinical studies were performed at the University of Utah General Clinical Research Center.


   Materials and Methods
Top
 Abstract
 Introduction
 Experimental Subjects
 Materials and Methods
Results
 Discussion
 References
 
Phenotypic analysis

All nondiabetic family members underwent a standard 75-g oral glucose tolerance test after an overnight fast. Both insulin and glucose were measured at baseline, 30, 60, 90, and 120 min using a protocol previously described (22). Weight was measured on a digital scale, and height was measured by a wall-mounted stadiometer. Waist circumference was determined as the mean of 3 measurements at the umbilicus, and hip as the mean of 3 measurements at the greatest diameter (22). Systolic and diastolic blood pressure were determined as the mean of three measures at 30-s intervals with the subject sitting quietly and the cuff deflated between measures (22). All subjects had fasting levels of total cholesterol (TC) and TG determined. Insulin and glucose levels at 30 min and waist and hip measures were not available for many subjects tested early in the study. A subset of 124 individuals underwent frequently sampled iv glucose tolerance testing as described previously (23), with determination of insulin sensitivity, acute insulin response to glucose, and disposition index.

Quantitative indices

Insulin was measured by RIA in one of two laboratories, as described previously (22, 24). Glucose was measured by the glucose oxidase method. Lipids were measured by standard methods. Leptin was measured in a subset of families by RIA (Amgen, Inc. Thousand Oaks, CA). BMI was calculated as weight (kilograms)/height (meters)2; the waist/hip ratio was calculated as the mean waist circumference divided by the mean hip circumference (22). Insulin sensitivity was determined by minimal model calculation of the insulin sensitivity index (SI) from iv glucose tolerance test data (22). For all other nondiabetic individuals, insulin sensitivity was determined from the homeostatic model assessment (HOMA) as the product of baseline (fasting) insulin and glucose (25).

Genotypic analysis

Genotype at position 12 in exon B of the PPAR{gamma}2 isoform was determined in 580 members of 52 families. Genotypes were unambiguously inferred for an additional 39 family members. Enzymatic amplification was performed using Taq polymerase and primers GCCAATTCAAGCCCAGTC (forward) and GATATGTTGCAGACAGTGTATCAGTGAAGGAATCGCTTTCCG (reverse), with annealing temperature of 66 C for 10 cycles and 62 C for 20 cycles. The amplification product was digested with 10 U BstUI (New England Biolabs, Inc., Gaithersburg, MD) for 4 h at 60 C, separated on 2% agarose (1:2, NuSieve-Seakem, FMC Bioproducts, Rockland, ME), and detected with ethidium bromide staining (11, 26).

Statistical analysis

The areas under the insulin and glucose curves were estimated using the trapezoidal rule, including only individuals for whom measurements were available at 0, 30, 60, 90, and 120 min. Homeostatic model of insulin resistance (HOMA-IR) was computed as the product of fasting insulin and fasting glucose. A surrogate measure of insulin secretion was computed as (60 min insulin - fasting insulin)/60 min glucose, because this measure was available for all individuals and showed the best correlation of available measures with both the widely used {Delta}30 min insulin/{Delta}30 min glucose and the acute insulin response to iv glucose (unpublished data; r = 0.70 and 0.42, respectively). Values for insulin area, HOMA-IR, insulin secretion, leptin, TG, and cholesterol were natural logarithm transformed to normality. Each quantitative variable or transformed variable was adjusted for gender and age; insulin area, HOMA-IR, and insulin secretion were also adjusted for the laboratories in which the insulin measurements were made.

The effect of the P12A variant on each trait of interest was evaluated using likelihood analysis (27). We used this likelihood analysis to estimate the allele frequency and to test for an effect of the P12A variant on each of the quantitative variables related to obesity, glucose homeostasis, or the insulin resistance syndrome and on liability to T2DM. We first performed a bivariate analysis of T2DM and each quantitative variable (trait) individually (Table 1Go). Then we performed a multivariate analysis including T2DM and the six quantitative traits for which the initial bivariate analysis supported an effect of the P12A variant. By including T2DM in the model, we could account for age of onset and gender while evaluating PPAR{gamma} genotype effects on T2DM liability. For each bivariate and multivariate model, we tested the null hypothesis of no genetic effect of the P12A variant by comparing the likelihood of a submodel in which parameters were set to remove a genetic effect to a general model in which all parameters were maximized.


View this table:
[in this window]
[in a new window]
  		Table 1. Traits tested, sample size, and significance for bivariate analysis of PPAR{gamma} P12A variant on each trait

 
The model parameters included dominance (di), displacement (ti), heritability (h2i), and correlation ({rho}ij), where i and j designate traits that include both the adjusted quantitative variables under study and the liability to T2DM. The displacement is the difference between the trait means for the two homozygous genotypes, expressed as within-genotype SD. The dominance is the ratio of the difference in trait means between the heterozygous individuals and individuals homozygous for the P12/P12 genotype to the difference in trait means between the two homozygous genotypes. The heritability is the proportion of the within-genotype variance due to polygenes. The correlation is between the within-genotype variation for two variables (traits). This model is similar to one that we described previously (23).

For each trait, we tested the hypothesis of no genetic effect of PPAR{gamma} by setting both dominance (di) and displacement (ti) to 0 for that trait. We then compared the maximized likelihood with these constraints to the likelihood of the general model in which no constraints were imposed. When no P12A homozygotes were available for a trait, the dominance was fixed at 1, and only the displacement was tested; thus, if no P12A homozygotes were observed for a trait, the test had only 1 df. Otherwise, both di and ti were estimated, and the test had 2 df. For traits in which diabetic pedigree members were excluded from the analysis, these individuals were considered to be unknown (missing values) for that quantitative trait. For the multivariate analysis, we sequentially tested each of the six traits included in the model by setting dominance and displacement to 0 for that trait while allowing these parameters to vary for all other traits. As in the bivariate analysis, each of the six submodels was then compared with the most general model in which all trait parameters were maximized.

We used the Pedigree Analysis Package (28) to calculate the likelihood; likelihoods were maximized using the GEMINI program (29). We estimated each parameter as the value that maximized the likelihood. Hypotheses were tested for each trait by computing {chi}2 statistics as twice the natural logarithm of the ratio of the maximized likelihood of the general model (all parameters estimated) to the maximized likelihood of the submodel for that trait (displacement and dominance fixed at 0 for that trait). Because we tested families ascertained for two diabetic siblings, we corrected our calculated likelihood estimate for this ascertainment by dividing it by the probability that we would observe a sibling pair diagnosed with T2DM in the general population under that analytical model.

We incorporated T2DM into our multivariate model by assuming that an unmeasurable quantitative liability variable underlies T2DM susceptibility (30). That liability was distributed as a mixture of three multivariate normal densities (31). The area for each of these three curves was determined by the PPAR{gamma} genotype, and genotype frequencies were determined from allele frequencies under the assumption of Hardy-Weinberg equilibrium. The total liability for each age bracket was taken from the age-, gender-, and obesity-specific figures of Melton et al. (32). The population incidence of T2DM at any age was thus determined by the sum of the areas of each of the three genotype-determined curves at that age. Individuals at younger ages had correspondingly higher liabilities.


   Results
Top
 Abstract
 Introduction
 Experimental Subjects
 Materials and Methods
 Results
Discussion
 References
 
The sample contained 452 Pro12/Pro12 homozygous individuals, 118 Pro12/Ala12 heterozygous individuals, and 10 Ala12/Ala12 homozygous individuals. In addition, the genotypes of 39 pedigree members (36 homozygous members, 3 heterozygous members) could be inferred from the known genotypes for a total of 619 genotypes available for analysis. Allowing for relationships among pedigree members, we estimated the frequency of the P12A allele in this sample as 12.1 ± 1.7%, consistent with estimates of 11–15% in samples of unrelated Caucasians (11, 12, 16, 17). Therefore, the frequency among individuals ascertained for a strong family history of T2DM was not increased above that in the general population.

We tested each of the quantitative variables related to glucose tolerance, insulin sensitivity, obesity (BMI, waist circumference, waist/hip ratio, leptin), and the insulin resistance syndrome (cholesterol, TG, blood pressure) for linkage to marker D3S1263, which is located only 1.5 Mb from PPAR{gamma} (33). No trait showed significant linkage using the variance component approach implemented in the SOLAR algorithm (34) (data not shown). The highest LOD score obtained was 0.88 for TG levels. The absence of support for linkage is consistent with a small effect size for the PPAR{gamma} gene.

Direct measures of insulin secretion as acute insulin response to glucose (AIRg), insulin sensitivity as insulin sensitivity index (SI), and ß-cell compensation to insulin resistance (disposition index = SI x AIRg) were available for 124 family members who underwent iv glucose tolerance testing (23). No significant effect of the P12A variant was found for any of these variables using either the multivariate model described above (Materials and Methods) or a mixed effect model that included family membership as a random factor (35).

Each trait in Table 1Go was then tested in a bivariate analysis. Variables that attained significance at P < 0.1 were subsequently included in the multivariate analysis (Table 2Go). BMI, systolic blood pressure, diastolic blood pressure, TG, and glucose area under the curve retained significance in the multivariate model, whereas TC was no longer significant when all variables were included in the model. These results suggested that P12A may act indirectly on TC through correlated variables, thus explaining the weak effect noted in the bivariate analysis. In support of this possibility we found a correlation between TC and TG in the full sample of 0.34 (P < 0.001).


View this table:
[in this window]
[in a new window]
  		Table 2. Genotype means and {chi}2 values for effect of PPAR{gamma} P12A variant on each trait in the multivariate analysis

 
Although we did not find an increase in the overall frequency of the P12A in this population, the proportion of individuals with T2DM increased with the number of P12A alleles, from 39% in P12 homozygous individuals to 45% in P12/A12 heterozygous individuals and 70% among A12 homozygous individuals. Nonetheless, the effect of P12A on liability to T2DM in the model was not significant ({chi}2(4) = 2.40; P > 0.05). Our inability to detect a significant effect on diabetes liability may have resulted from the small number of A12 homozygous individuals.


   Discussion
Top
 Abstract
 Introduction
 Experimental Subjects
 Materials and Methods
 Results
 Discussion
References
 
The P12A allele appears to have reduced trans-activation efficiency (13) and reduced ability to stimulate adipogenesis in response to thiazolidinedione activation (14). Nonetheless, effects in association studies in man have been variable, both in the ability to detect an effect on obesity and glucose homeostasis and in the direction of that effect. More severe defects in PPAR{gamma} activity suggest that reduced PPAR{gamma} function results in glucose intolerance, hypertension, insulin resistance, and hypertriglyceridemia (9). On the other hand, the Pro115Gln mutation appears to inhibit phosphorylation at Ser114, increases PPAR{gamma} activity, leads to fat accumulation in vitro, and is associated with obesity in carriers of the mutation. Furthermore, mice heterozygous for a null allele of PPAR{gamma} show increased, rather than reduced, insulin sensitivity (8). Thus, even in rare human mutations, in vitro experiments, and experimental mouse models, the phenotypic effects of PPAR{gamma} variants are difficult to predict. Complex interactions may explain these variable results, including the apparently variable effects of the more common P12A variant despite in vitro evidence for reduced activity of the P12A allele.

Like others, we found no evidence that the P12A variant plays a role in susceptibility to T2DM. First, we observed no evidence of linkage in this region to T2DM (23). Second, we observed no increase in the frequency of the P12A allele above randomly selected Caucasian individuals. Finally, we observed no significant effect on diabetes liability in our multivariate model. Nonetheless, the prevalence of diabetes was increased among P12A homozygous individuals, and P12A was associated with a significant effect on glucose area. A larger study with more P12A homozygous individuals might detect a significant effect, but other large studies likewise found no association of P12A with diabetes (16, 36). We were also unable to demonstrate an effect on insulin sensitivity, measured directly using the minimal model or using the surrogate measures fasting insulin and HOMA-IR. We may have lacked the power to detect an effect in the subset of individuals who underwent iv glucose tolerance testing, and surrogate measures show a correlation of only r = 0.6 with direct measures of insulin sensitivity (37). Deeb (13) suggested improved insulin sensitivity with this variant, and Koch et al. (20) suggested improved insulin sensitivity among obese subjects. However, both results appear inconsistent with the observation that the P12A allele has reduced trans-activation in vitro. In contrast, PPAR{gamma} agonists improve insulin sensitivity by activating PPAR{gamma} (7). This paradox may result from an interaction of the PPAR{gamma} variant and obesity (18). Alternatively, P12A could be in linkage disequilibrium with an undiscovered variant elsewhere in the gene, and both the strength and the direction of that association might vary among populations.

Although we could not demonstrate an effect of the P12A variant on insulin sensitivity, we found an effect of the P12A variant on several traits that are associated with the insulin resistance syndrome: BMI, blood pressure, TG levels, and glucose tolerance as manifest by an increased glucose area. In this study the P12A variant acted more like a recessive than a dominant mutation; mean values for P12/A12 heterozygous individuals were closer to those for P12 homozygous individuals than to A12/A12 homozygous individuals. Unfortunately, our sample contained only 10 A12 homozygous individuals, and of these 10 the 7 diagnosed with T2DM were excluded from many analyses. A primary effect in these rare A12 homozygous individuals might explain our failure to find significant effects in other variables as well as the failure of other studies (16) to find an effect of the P12A allele or to find an effect in a different direction (13). Nevertheless, the effect of P12A was not completely recessive, as heterozygous individuals had trait values elevated over those of P12 homozygous individuals in our population. Indeed, the impact of P12A on glucose area was apparent without inclusion of any homozygous A12 individuals.

Of all the variables tested, the P12A genotype had the greatest effect on TG levels. Correspondingly, significance was also highest for TG despite the reduced sample size that resulted from excluding pedigree members diagnosed with T2DM from this analysis. TC exhibited a similar trend, but did not achieve significance. This effect may have resulted from a correlation with TG ({rho} = 0.35), rather than any direct effect of P12A on TC. Other investigators also reported an effect of P12A on TG. Knoblauch et al. (19) found both linkage of TG to the PPAR{gamma} gene and an association of TG with the specific P12A allele in healthy, nonobese siblings. Beamer et al. (12) found increased TG among P12A carriers, but only in obese men. TG levels were also elevated by severe dominant negative mutations in PPAR{gamma} (9). However, others were unable to find any effect on TG (26). The P12A allele also significantly increased both systolic and diastolic blood pressure. Hypertension is part of the insulin resistance syndrome in Caucasians (38) and was reported by Barroso et al. (9) among carriers of dominant negative PPAR{gamma} mutations. Furthermore, thiazolidinediones lower blood pressure in Japanese T2DM subjects (39). However, others found no significant association of P12A with blood pressure (26).

We found an effect of P12A on BMI that was of similar magnitude to the effect on blood pressure. The P12A allele has been reported previously to increase BMI (12) and to increase BMI among obese Caucasian subjects (17). However, others reported either decreased BMI (13, 18) or no effect on obesity (19, 26, 36). In contrast with some studies (12), we found no effect of the P12A allele on either waist circumference or waist to hip ratio. However, this measure was not available in all family members, and our power was thus more limited than for BMI.

We previously reported (40) evidence of two recessive obesity loci in a subset of these pedigrees that increased the mean BMI in homozygous individuals to 32 kg/m2 and 39 kg/m2, respectively. PPAR{gamma} does not appear to represent either of these loci. In contrast to the predicted recessive obesity loci, the P12A allele increased the mean BMI to only 27.5 kg/m2 for heterozygous individuals and 30 kg/m2 for A12 homozygous individuals. Furthermore, using the two locus recessive model, we rejected linkage to microsatellite marker D3S1263 at 1% recombination fraction, with LOD scores of -1.37 for the moderate obesity locus and -6.67 for the extreme obesity locus. D3S1263 is only 1.5 Mb from PPAR{gamma} (33). We also rejected linkage of BMI to this region using the relatively model-independent variance components method. In contrast, Knoblauch et al. (19) did find linkage of the PPAR{gamma} gene to BMI in normal siblings.

Another PPAR{gamma} variant, a C to T substitution in exon 6, showed similar frequency and effects on BMI as P12A in some studies (17). Because this variant is in strong linkage disequilibrium with P12A (17), is synonymous (i.e. does not alter an amino acid), and thus is probably silent, we tested only the putative functional variant.

Deeb et al. (13) found that the association of P12A with insulin sensitivity disappeared when corrected for BMI. We similarly found that some apparent effects disappeared when viewed in a multivariate analysis. Thus, despite well demonstrated PPAR{gamma} effects on insulin sensitivity, the primary effect of this variant indeed may be on body weight.

To our knowledge, the current study is the first to examine the effect of PPAR{gamma} in family members at high risk for T2DM. The study of a large number of members of only 52 families required us to account for both nonindependence of the observations and the ascertainment bias of the families. The likelihood analysis used here provides for these corrections, whereas an ANOVA (data not shown), which cannot correct for these biases, provided much lower levels of significance. In this analysis the greatest effect was among the relatively few A12 homozygous individuals. Because of the small number of such individuals, we chose not to subdivide the population further according to obesity or gender to test for differences. Nonetheless, we show that among individuals in whom the insulin resistance syndrome is prevalent and who are at considerably increased risk for T2DM, the P12A allele contributes to the genetic susceptibility to increased BMI, TG, blood pressure, and glucose. One would expect a similar finding in families at lower risk for T2DM, because we were unable to demonstrate a significant impact of P12A on T2DM liability. However, P12A might be interacting with an as yet unidentified risk factor for diabetes that segregates in high risk families, and such interactions may help explain the inconsistent strength and direction of the associations of this variant with the traits of the insulin resistance syndrome.


   Acknowledgments
 
We thank Kim Wegner and Cindy Miles for subject ascertainment and Teresa Maxwell for sample preparation.


   Footnotes
 
1 This work was supported by the Department of Veterans Affairs and Grants DK-36311 (to S.C.E.) and HD-17463 (to S.J.H.). Many of the subjects studied were ascertained and sampled under a Genetics of NIDDM (GENNID) grant from the American Diabetes Association. Sampling was also supported by the General Clinical Research Center of the University of Utah under Public Health Service Grant MO1-RR-00064 from the National Center for Research Resources and University of Arkansas General Clinical Research Center Informatics Core Grant M01-RR-14288. Back

2 Formerly at Division of Endocrinology, Metabolism and Diabetes, University of Utah. Back

Received April 27, 2000.

Revised September 13, 2000.

Accepted October 26, 2000.


   References
Top
 Abstract
 Introduction
 Experimental Subjects
 Materials and Methods
 Results
 Discussion
 References
 

  1. DeFronzo RA. 1997 Pathogenesis of type 2 diabetes: metabolic and molecular implications for identifying diabetes genes. Diabetes Rev. 5:177–270.
  2. Warram JH, Martin BC, Krolewski AS. 1990 Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the offspring of diabetic parents. Ann Intern Med. 113:909–915.
  3. Haffner SM, Stern MP, Hazuda HP, Mitchell BD, Patterson JK. 1988 Increased insulin concentrations in nondiabetic offspring of diabetic parents. N Engl J Med. 319:1297–1301.[Abstract]
  4. Schumacher MC, Maxwell TM, Wu LL, Hunt SC, Williams RR, Elbein SC. 1992 Dyslipidemias among normoglycemic members of familial NIDDM pedigrees. Diabetes Care. 15:1285–1289.[Abstract]
  5. Bogardus C, Lillioja S, Nyomba BL, et al. 1989 Distribution of in vivo insulin action in Pima Indians as a mixture of three normal distributions. Diabetes. 38:1423–1432.[Abstract]
  6. Mitchell BD, Kammerer CM, O’Connell P, et al. 1995 Evidence for linkage of postchallenge insulin levels with intestinal fatty acid-binding protein (FABP2) in Mexican-Americans. Diabetes. 44:1046–1053.[Abstract]
  7. Spiegelman BM. 1998 PPAR-{gamma}: adipogenic regulator and thiazolidinedione receptor. Diabetes. 47:507–514.[Abstract]
  8. Miles PD, Barak Y, He W, Evans RM, Olefsky JM. 2000 Improved insulin-sensitivity in mice heterozygous for PPAR-gamma deficiency. J Clin Invest. 105:287–292.[Medline]
  9. Barroso I, Gurnell M, Crowley VE, et al. 1999 Dominant negative mutations in human PPAR{gamma} associated with severe insulin resistance, diabetes mellitus and hypertension. Nature. 402:880–883.[Medline]
  10. Ristow M, Muller-Wieland D, Pfeiffer A, Krone W, Kahn CR. 1998 Obesity associated with a mutation in a genetic regulator of adipocyte differentiation. N Engl J Med. 339:953–959.[Abstract/Free Full Text]
  11. Yen CJ, Beamer BA, Negri C, et al. 1997 Molecular scanning of the human peroxisome proliferator activated receptor {gamma} (hPPAR{gamma}) gene in diabetic Caucasians: identification of a Pro12Ala PPAR{gamma}2 missense mutation. Biochem Biophys Res Commun. 241:270–274.[CrossRef][Medline]
  12. Beamer BA, Yen CJ, Andersen RE, et al. 1998 Association of the Pro12Ala variant in the peroxisome proliferator-activated receptor-{gamma}2 gene with obesity in two Caucasian populations. Diabetes. 47:1806–1808.[Medline]
  13. Deeb SS, Fajas L, Nemoto M, et al. 1998 A Pro12Ala substitution in PPAR{gamma}2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet. 20:284–287.[CrossRef][Medline]
  14. Masugi J, Tamori Y, Mori H, Koike T, Kasuga M. 2000 Inhibitory effect of a proline-to-alanine substitution at codon 12 of peroxisome proliferator-activated receptor-{gamma}2 on thiazolidinedione-induced adipogenesis. Biochem Biophys Res Commun. 268:178–182.[CrossRef][Medline]
  15. Meirhaeghe A, Fajas L, Helbecque N, et al. 1998 A genetic polymorphism of the peroxisome proliferator-activated receptor {gamma} gene influences plasma leptin levels in obese humans. Hum Mol Genet. 7:435–440.[Abstract/Free Full Text]
  16. Ringel J, Engeli S, Distler A, Sharma AM. 1999 Pro12Ala missense mutation of the peroxisome proliferator activated receptor gamma and diabetes mellitus. Biochem Biophys Res Commun. 254:450–453.[CrossRef][Medline]
  17. Valve R, Sivenius K, Miettinen R, et al. 1999 Two polymorphisms in the peroxisome proliferator-activated receptor-{gamma} gene are associated with severe overweight among obese women. J Clin Endocrinol Metab. 84:3708–3712.[Abstract/Free Full Text]
  18. Ek J, Urhammer SA, Sorensen TI, Andersen T, Auwerx J, Pedersen O. 1999 Homozygosity of the Pro12Ala variant of the peroxisome proliferation-activated receptor-{gamma}2 (PPAR-{gamma}2): divergent modulating effects on body mass index in obese and lean Caucasian men. Diabetologia. 42:892–895.[CrossRef][Medline]
  19. Knoblauch H, Busjahn A, Muller-Myhsok B, et al. 1999 Peroxisome proliferator-activated receptor {gamma} gene locus is related to body mass index and lipid values in healthy nonobese subjects. Arterioscler Thromb Vasc Biol. 19:2940–2944.[Abstract/Free Full Text]
  20. Koch M, Rett K, Maerker E, et al. 1999 The PPAR{gamma}2 amino acid polymorphism Pro 12 Ala is prevalent in offspring of type II diabetic patients and is associated to increased insulin sensitivity in a subgroup of obese subjects. Diabetologia. 42:758–762.[CrossRef][Medline]
  21. Elbein SC, Hoffman MD. 1996 Role of mitochondrial DNA tRNA leucine and glucagon receptor missense mutations in Utah white diabetic patients. Diabetes Care. 19:507–508.[Abstract]
  22. Raffel LJ, Robbins DC, Norris JM, et al. 1996 The GENNID Study. A resource for mapping the genes that cause NIDDM. Diabetes Care. 19:864–872.[Abstract]
  23. Elbein SC, Hasstedt SJ, Wegner K, Kahn SE. 1999 Heritability of pancreatic ß-cell function among nondiabetic members of Caucasian familial type 2 diabetes kindreds. J Clin Endocrinol Metab. 84:1398–1403.[Abstract/Free Full Text]
  24. Schumacher MC, Hasstedt SJ, Hunt SC, Williams RR, Elbein SC. 1992 Major gene effect for insulin levels in familial NIDDM pedigrees. Diabetes. 41:416–423.[Abstract]
  25. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. 1985 Homeostasis model assessment: insulin resistance and ß-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 28:412–419.[CrossRef][Medline]
  26. Mori Y, Kim-Motoyama H, Katakura T, et al. 1998 Effect of the Pro12Ala variant of the human peroxisome proliferator-activated receptor {gamma}2 gene on adiposity, fat distribution, and insulin sensitivity in Japanese men. Biochem Biophys Res Commun. 251:195–198.[CrossRef][Medline]
  27. Elston RC, Stewart J. 1971 A general model for the genetic analysis of pedigree data. Hum Hered. 21:523–542.[CrossRef][Medline]
  28. Hasstedt SJ. 1994 PAP: pedigree analysis package, rev 4. Salt Lake City: University of Utah.
  29. Lalouel JM, Department of Medical Biophysics and Computing, University of Utah. 1979 GEMINI: a computer program for optimization of general nonlinear functions. Tech Rep 14. Salt Lake City: University of Utah.
  30. Morton NE, MacLean CJ. 1974 Analysis of family resemblance. III. Complex segregation of quantitative traits. Am J Hum Genet. 26:489–503.[Medline]
  31. Hasstedt SJ. 1993 A variance components/major locus likelihood approximation for quantitative, polychotomous, and multivariate data. Genet Epidemiol. 10:145–158.[CrossRef][Medline]
  32. Melton LJI, Palumbo PJ, Chu C-P. 1983 Incidence of diabetes mellitus by clinical type. Diabetes Care. 6:75–86.[Abstract]
  33. Beamer BA, Negri C, Yen CJ, et al. 1997 Chromosomal localization and partial genomic structure of the human peroxisome proliferator activated receptor-{gamma} (hPPAR{gamma}) gene. Biochem Biophys Res Commun. 233:756–759.[CrossRef][Medline]
  34. Almasy L, Blangero J. 1998 Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 62:1198–1211.[CrossRef][Medline]
  35. Allison DB, Heo M, Kaplan N, Martin ER. 1999 Sibling-based tests of linkage and association for quantitative traits. Am J Hum Genet. 64:1754–1763.[CrossRef][Medline]
  36. Mancini FP, Vaccaro O, Sabatino L, et al. 1999 Pro12Ala substitution in the peroxisome proliferator-activated receptor-gamma2 is not associated with type 2 diabetes. Diabetes. 48:1466–1468.[Abstract]
  37. Yeni-Komshian H, Carantoni M, Abbasi F, Reaven GM. 2000 Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care. 23:171–175.[Abstract]
  38. Reaven GM. 1988 Banting lecture 1988: role of insulin resistance in human disease. Diabetes. 37:1595–1607.[Abstract]
  39. Ogihara T, Rakugi H, Ikegami H, Mikami H, Masuo K. 1995 Enhancement of insulin sensitivity by troglitazone lowers blood pressure in diabetic hypertensives. Am J Hypertens. 8:316–320.[CrossRef][Medline]
  40. Hasstedt SJ, Hoffman M, Leppert MF, Elbein SC. 1997 Recessive inheritance of obesity in familial non-insulin-dependent diabetes mellitus, and lack of linkage to nine candidate genes. Am J Hum Genet. 61:668–677.[Medline]



This article has been cited by other articles:


Home page
 J. Clin. Endocrinol. Metab.Home page
F. Orio Jr., S. Palomba, T. Cascella, S. Di Biase, D. Labella, T. Russo, S. Savastano, F. Zullo, A. Colao, R. Vettor, et al.
Lack of an Association between Peroxisome Proliferator-Activated Receptor-{gamma} Gene Pro12Ala Polymorphism and Adiponectin Levels in the Polycystic Ovary Syndrome
J. Clin. Endocrinol. Metab., October 1, 2004; 89(10): 5110 - 5115.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Endocrinol. Metab.Home page
T. Temelkova-Kurktschiev, M. Hanefeld, G. Chinetti, C. Zawadzki, S. Haulon, A. Kubaszek, C. Koehler, W. Leonhardt, B. Staels, and M. Laakso
Ala12Ala Genotype of the Peroxisome Proliferator-Activated Receptor {gamma}2 Protects against Atherosclerosis
J. Clin. Endocrinol. Metab., September 1, 2004; 89(9): 4238 - 4242.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Physiol. Endocrinol. Metab.Home page
J. R. Colca, W. G. McDonald, D. J. Waldon, J. W. Leone, J. M. Lull, C. A. Bannow, E. T. Lund, and W. R. Mathews
Identification of a novel mitochondrial protein ("mitoNEET") cross-linked specifically by a thiazolidinedione photoprobe
Am J Physiol Endocrinol Metab, February 1, 2004; 286(2): E252 - E260.
[Abstract] [Full Text] [PDF]

Home page
 J. Clin. Endocrinol. Metab.Home page
F. Orio Jr., G. Matarese, S. Di Biase, S. Palomba, D. Labella, V. Sanna, S. Savastano, F. Zullo, A. Colao, and G. Lombardi
Exon 6 and 2 Peroxisome Proliferator-Activated Receptor-{gamma} Polymorphisms in Polycystic Ovary Syndrome
J. Clin. Endocrinol. Metab., December 1, 2003; 88(12): 5887 - 5892.
[Abstract] [Full Text] [PDF]

Home page
 DiabetesHome page
P. Poulsen, G. Andersen, M. Fenger, T. Hansen, S. M. Echwald, A. Volund, H. Beck-Nielsen, O. Pedersen, and A. Vaag
Impact of Two Common Polymorphisms in the PPAR{gamma} Gene on Glucose Tolerance and Plasma Insulin Profiles in Monozygotic and Dizygotic Twins: Thrifty Genotype, Thrifty Phenotype, or Both?
Diabetes, January 1, 2003; 52(1): 194 - 198.
[Abstract] [Full Text] [PDF]

Home page
 DiabetesHome page
V. I. Lindi, M. I.J. Uusitupa, J. Lindstrom, A. Louheranta, J. G. Eriksson, T. T. Valle, H. Hamalainen, P. Ilanne-Parikka, S. Keinanen-Kiukaanniemi, M. Laakso, et al.
Association of the Pro12Ala Polymorphism in the PPAR-{gamma}2 Gene With 3-Year Incidence of Type 2 Diabetes and Body Weight Change in the Finnish Diabetes Prevention Study
Diabetes, August 1, 2002; 51(8): 2581 - 2586.
[Abstract] [Full Text] [PDF]

Home page
 EndocrinologyHome page
S. C. Elbein
Perspective: The Search for Genes for Type 2 Diabetes in the Post-Genome Era
Endocrinology, June 1, 2002; 143(6): 2012 - 2018.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Submit a related Letter to the Editor
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Copyright Permission
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Hasstedt, S. J.
Right arrow Articles by Elbein, S. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hasstedt, S. J.
Right arrow Articles by Elbein, S. C.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Endocrinology Endocrine Reviews J. Clin. End. & Metab.
Molecular Endocrinology Recent Prog. Horm. Res. All Endocrine Journals