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Journal of Clinical Endocrinology & Metabolism , doi:10.1210/jc.2004-2471
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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 5 2642-2647
Copyright © 2005 by The Endocrine Society

Genetic and Environmental Effects on Fasting and Postchallenge Plasma Glucose and Serum Insulin Values in Finnish Twins

Shuichi Katoh1, Mikko Lehtovirta1, Jaakko Kaprio, Valma Harjutsalo, Markku Koskenvuo, Johan Eriksson, Naoko Tajima and Jaakko Tuomilehto

Diabetes and Genetic Epidemiology Unit (S.K., V.H., J.E., J.T.), Department of Epidemiology and Health Promotion, and Department of Mental Health and Alcohol Research (J.K.), National Public Health Institute, FIN-00300 Helsinki, Finland; Division of Diabetes and Endocrinology (S.K., N.T.), Department of Internal Medicine, Jikei University School of Medicine, 105-8461 Tokyo, Japan; Department of Medicine (M.L.), Helsinki University Hospital, FIN-00029 HUS Helsinki, Finland; Department of Public Health (J.K., J.T., S.K., J.E.), University of Helsinki, Helsinki FI-00014, Finland; Department of Public Health (M.K.), University of Turku, FI-20520 Turku, Finland; and South Ostrobotnia Central Hospital (J.T.), FIN-60101 Seinäjoki, Finland

Address all correspondence and requests for reprints to: Jaakko Kaprio, M.D., Ph.D., Department of Public Health, P.O. Box 41 (Mannerheimintie 172), University of Helsinki, FIN-00014 Helsinki, Finland. E-mail: jaakko.kaprio{at}helsinki.fi.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
The aim of this study was to evaluate genetic and environmental effects on plasma glucose, insulin secretion, and resistance in Finnish twins. Altogether 151 randomly selected twin pairs were examined by the oral glucose tolerance test; 66 twin pairs were monozygotic and 85 like-sexed dizygotic. We estimated the intraclass correlation coefficients and variance components of genetic and environmental effects on waist circumference, plasma glucose, and serum insulin. For fasting insulin, the proportion of total variation accounted for by additive genetic effects (A) and nonshared environmental effects (E) were 43 and 57%, respectively. As to postchallenge insulin and waist circumference, A effects were stronger in female twins (51 and 70%, respectively) than male twins in whom no significant evidence for genetic variance was found. Of the variation in fasting glucose, A and E effects accounted for 45 and 55%, respectively. Of the variation in postchallenge glucose, E effects had a greater role (65%), compared with A effects (35%); A effects on pre- and postchallenge insulin levels were highly correlated (genetic correlation coefficient = 0.81). In conclusion, additive genetic effects are important for the insulin secretion, whereas nonshared environmental effects contribute strongly to peripheral insulin resistance.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
TWIN AND FAMILY studies have been conducted to study the contribution of genetic influences to levels and distributions of insulin secretion and action in the population at large and illuminate the role of genetic influences in the pathogenesis of diabetes mellitus and cardiovascular diseases. The greater concordance for type 2 diabetes among monozygotic (MZ) compared with dizygotic (DZ) twin pairs has been suggested to indicate an influence of diabetogenic genetic factors (1, 2, 3). Several twin studies have also demonstrated significant heritable influences on glucose and insulin values (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25).

There is no clear consensus on the degree to which genes account for the variance of insulin sensitivity and secretion. Both methodologies to measure traits and the selection of study subjects influence the results. A study with 209 middle-aged Dutch twin pairs estimated that heritability (h2), the proportion of the total variance accounted for by genes, was 20–25% for fasting insulin, which is a proxy for insulin resistance (13). Danish researchers studied the insulin and glucose responses for oral glucose tolerance test (OGTT) in 303 twin pairs and concluded that only 19% of the variance in fasting insulin could be attributed to genetic factors (20). The former of these two studies included a bivariate variance component analysis of fasting glucose and insulin values; no postchallenge data, however, were available. The latter of the studies involved both fasting and postchallenge concentrations of glucose and insulin, but no bivariate analysis based on these values was performed.

We, on the other hand, have shown, by applying metabolic techniques more precise than OGTT to a limited number of twin pairs, that polygenic factors accounted for more than 55% of the variance in insulin secretion (iv glucose tolerance test) and 37% of the variance in glucose uptake (euglycemic hyperinsulinemic clamp) (19).

The objective for the current study was to test whether our observations (19) on the heritability of insulin sensitivity and insulin secretion could be replicated when the number of twin pairs was doubled and a more clinical study approach (OGTT) applied. The size of the study population also allowed for the evaluation of sex-associated differences of h2 estimates as well as the study of possible genetic covariance between any of the traits measured both in the fasting and postchallenge state of OGTT.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
A total of 156 twin pairs were included in the present study independent of their diabetic status. The mean age of these patients was 60 yr (range 50–74). The twins were residents of Helsinki and the surrounding area, and about 210 twin pairs of known zygosity were identified randomly from the Finnish Twin Cohort (24), and 156 twin pairs participated in an OGTT. The response rate was about 74%. An institutional human research committee had approved the investigations, and written informed consent was obtained from all subjects. Twin zygosity was determined by examining the responses of both members of each pair to two questions on the similarity of appearance at primary school age; about 7% of pairs are left unclassified by this method (26). This method is very accurate in a validation study using genetic markers (26). Some twin pairs were excluded from this study because of taking antidiabetic drugs (n = 4) or unclear zygosity (n = 1). The final study sample consisted of 151 twin pairs. Among these 302 individuals, there were 24 (nine MZ), 54 (23 MZ), and 14 (six MZ) subjects with impaired fasting glucose, impaired glucose tolerance, and diabetes, respectively, following World Health Organization criteria (27) (Table 1Go). Among these pairs, there were one DZ pair concordant for impaired fasting glucose, one MZ, and four DZ pairs concordant for impaired glucose tolerance and a single MZ pair concordant for type 2 diabetes.


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  		TABLE 1. Statistical description of MZ and DZ twins in fasting and postchallenge status

 
Measurements

Height and weight were recorded with light indoor clothes and body mass index (BMI) was calculated as weight/height2 (kilograms per square meter). Waist circumference (to the nearest centimeter without clothes) was measured with a nonelastic soft tape midway between the lowest rib and the iliac crest while subjects were standing. Hip circumference (to the nearest cm without clothes) was measured over the widest part of the gluteal region. The waist to hip ratio (WHR) was also calculated.

A standard 75-g OGTT was carried out. Blood samples for the analysis of serum insulin (Ins) and plasma glucose (Gluc) were drawn in the fasting state (Ins_0, Gluc_0), and 2 h after the oral glucose load (Ins_120, Gluc_120), respectively.

Analytical methods

Serum insulin was measured using RIA (Pharmacia, Uppsala, Sweden). Plasma glucose was measured by the glucose oxidase method (Glucose Analyzer II, Beckman Instruments, Fullerton, CA, and Cobas Mira Plus analyzer, Roche Instrument Center, Rotkreuz, Switzerland).

Statistical methods

The homeostasis model insulin resistance index (HOMA-R) [fasting glucose (millimoles per liter) x fasting insulin (milliunits per liter)/22.5] and ß-cell function (HOMA-BETA) [fasting insulin (milliunits per liter) x 20/(fasting glucose (millimoles per liter) – 3.5)] were calculated as suggested by Matthews et al. (28). For the calculation of phenotypic correlations between variables, all except waist (square root) and HOMA-BETA values (reciprocal of the value) were log transformed. The descriptive statistics and phenotypic correlations were calculated using STATA 7-software (Stata Corp. LP, College Station, TX), which also enables the computation of correct SE and P values in a clustered population (i.e. by taking into account that the study sample consisted of twin pairs).

For the estimation of variance components, log-transformed values were used in all variables except glucose, which fitted best as such. Intraclass correlations, i.e. within twin pair-correlation coefficients (intraclass r), were calculated to first test for the existence of a genetic variance component for each trait; higher MZ than DZ correlations indicate that genetic effects may exist. Intraclass correlations were computed using the Twinan software (29).

After that, standard univariate analyses were carried out using Mx (30), a custom-built program for the modeling of genetically informative data (http://www.vcu.edu/mx/). Finally, bivariate analyses were performed to study the degree of correlation of the latent genetic and environmental factors influencing both Ins_0 and Ins_120. Variance components were estimated by applying the maximum likelihood method to fit the models to the raw data, using age and sex as covariates. Models were fitted to the data, assuming that variation in the phenotype was attributable to a combination of a nonshared environmental component (E), a shared environmental component (C), an additive genetic component (A), and genetic effects due to dominance (D). The estimates of the variance components were calculated as the proportion of variance divided by the total variance. The Akaike’s information criterion value was used to assess the goodness of fit of the model (31). The model with the lowest Akaike’s information criterion value reflects both the goodness of fit of a model and its parsimony, i.e. the ability to account for the observed data with few parameters. The superiority of alternative, hierarchically nested models was tested by hierarchic {chi}2 tests because the difference between the value for the reduced model and that of the full model ({delta} {chi}2) is {chi}2 distributed, with degrees of freedom (df) equal to the differences in df of the models to be compared. In the case in which covariance structure was different between men and women, analyses were done separately for men and women (32). The proportion of A in relation to total variance (percent) was called heritability, and expressed as h2. Heritability is a population-specific characteristic that has no interpretation on the level of the individual or family. To interpret the results of model fitting, a comparison of the trait intraclass correlation (intraclass r, between twin pair members) coefficient values between MZ and DZ twins was applied (33).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Descriptive statistics and phenotypic correlations

Table 1Go shows the number of twin pairs and the summary of descriptive statistics. The proportion of female subjects was 58% among MZ and 61% among DZ pairs, respectively. There were no significant differences in means or variances between individuals from MZ and DZ pairs in study variables, except for Ins_120, which was lower in female (P = 0.04) MZ pairs, compared with DZ pairs. Also, both systolic (P = 0.01) and diastolic (P = 0.01) blood pressure mean values were higher in individuals from MZ pairs, compared with DZ pairs.

The phenotypic correlations among BMI, waist, Gluc_0, Gluc_120, Ins_0, Ins_120, HOMA-R, and HOMA-BETA are shown in Table 2Go. All correlations were statistically significant at the level of P < 0.01, with the exception of that between HOMA-BETA and Gluc_120 (P = 0.91).


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  		TABLE 2. Phenotypic correlation coefficients (upper value) between each parameter and the number of twins (lower value)

 
Sex was used as a covariate in the association between BMI and waist and between waist and Gluc_0, Gluc_120, Ins_0, Ins_120, HOMA-R or HOMA-BETA, respectively, when it was added to the linear regression model together with age; sex was statistically significant in the association between Gluc_0 and Gluc_120, Ins_0, Ins_120, HOMA-R, or HOMA-BETA, respectively.

The correlation coefficient between waist and WHR was 0.82, and the paired correlation coefficients of WHR with other traits were almost identical with the corresponding correlations between waist and other traits (data not shown). Therefore, only waist was used in the intraclass r and variance component analyses described below.

Intrapair correlations

All intrapair correlation coefficients (r) were significant, and higher, in MZ than DZ twins, with the exception of Ins_120 in male twins (MZ intraclass r = 0.24, N.S.; DZ 0.48, P = 0.002) and male and female twins together (MZ intraclass r = 0.32, P = 0.006; DZ 0.40, P < 0.001), respectively. For BMI, female waist, Ins_0 (male and female twins together), and HOMA-R, the MZ intraclass r was more than twice the DZ value. The MZ intraclass r was less than twice the DZ intraclass r in male waist and Ins_120 in both sexes as well as waist, Gluc_0, Gluc_120, and HOMA-BETA in men and women together (Table 3Go).


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  		TABLE 3. Intrapair correlation coefficients (r) among MZ and DZ twins

 
Variance component models

Univariate analysis. Table 4Go shows the best-fitting models for each trait, based on the univariate analysis of the variance components. The model with component E (unique and error variance) alone could be rejected in all traits (data not shown). The AE model was the best fitting for BMI, waist female, waist overall, Ins_0, female Ins_120, Gluc_0, Gluc_120, HOMA-R, and HOMA-BETA. The CE model fitted best for male waist and Ins_120 in men and overall. The h2 estimates were 68% for both BMI and for waist when male and female twins were analyzed together. In female twins, the h2 for waist was 70%. Whereas the corresponding estimate could not be estimated for male twins, in which the best-fitting model was CE. The h2 for Ins_0 was 43% in male and 42% in female twins, respectively. When the most parsimonious model was used for Ins_120, only female h2 (51%) could be estimated. The h2 were 45% for Gluc_0, 35% for Gluc_120, 42% for HOMA-R, and 38% for HOMA-BETA, respectively.


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  		TABLE 4. Variance components and 95% confidence interval (95% CI) of the best-fitting model for body composition and OGTT-based glucose and insulin parameters

 
Bivariate analysis. Only insulin values (Ins_0 and Ins_120) lent themselves to a bivariate variance component analysis. To test whether Ins_0 and Ins_120 shared any A-, C-, or E-influencing factors in common, bivariate genetic analyses were carried out. According to likelihood ratio tests, either common genetic influence alone (for {chi}2 (CE) – {chi}2 (ACE) = 828.034 – 827.200 = 0.834, difference in df = 1; P = 0.361) or common environmental influence alone (for {chi}2 (AE) – {chi}2 (ACE) = 828.932 – 827.200 = 1.731, difference in df = 1; P = 0.188) could be discarded but not both of them together (for {chi}2 (E) – {chi}2 (ACE) = 844.163 – 827.200 = 16.963 with difference in df = 2; P < 0.0001). However, testing E alone against the AE model (for {chi}2 (E) – {chi}2 (AE) = 844.163 – 829.304 = 14.859 with difference in df = 1; P = 0.00012) disclosed that Ins_0 and Ins_120 were influenced by a shared genetic factor. Using this AE model as the final model, the genetic correlation coefficient between the latent polygenetic factors, influencing the variances of both fasting and 2-h (postchallenge) insulin values, was estimated to be 0.812 [95% confidence interval (CI) 0.591–0.990] and the correlation between the unique environmental influences 0.540 (95% CI 0.364–0.680), respectively.


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
By studying 66 MZ and 85 DZ like-sexed twin pairs, we confirmed our previous finding: the variance of insulin sensitivity is mainly controlled for by nongenetic factors. A novel observation of our present study was that according to a bivariate analysis of fasting and 120-min insulin concentrations, the genetic effects influencing the variance of fasting and postchallenge insulin concentrations may have a shared origin. We also observed a sex-specific difference associated with h2 of waist circumference and insulin at 120 min.

We observed a difference in heritability estimates between men and women for visceral fat accumulation, measured as waist circumference, and postchallenge (120-min) insulin levels, both of which are important risk factors for coronary heart disease (34, 35). Although the overall phenotypic correlation coefficient (Pearson’s r) between waist and 120-min insulin was only 0.25 (P < 0.0001) (Table 2Go), it was possible to detect a difference in the respective sex-specific values: 0.09 (P = 0.32) for male and 0.45 (P < 0.0001) for female twins, respectively (data not shown in the results). This observation is in line with that of Van Gaal et al. (36), who reported that the level of insulin after 120 min could be explained up to 40–80% by sex hormones and WHR in women. Fasting serum insulin is mainly cleared by the liver, whereas postchallenge insulin is cleared by several of the hormone’s target organs (i.e. liver, muscle, and adipocytes) (37, 38, 39, 40). Is it possible that visceral fat accumulation affects liver function and postchallenge insulin clearance (36, 41, 42, 43) in women more than in men? A sex-specific genetic difference of abdominal obesity followed by decreased plasma adiponectin levels and increased oxidative stress might correlate with peripheral insulin resistance and postprandial insulin levels (44, 45). But the sex-specific genes have not been clearly identified; therefore, to determine whether there is a sex-specific genetic difference, we would need information on opposite-sex (one male, one female) twin pairs or other sibling pairs of different sexes. So-called sex-limitation models could then be used to test whether such sex-specific effects exist in a future study. For such an assessment, a much larger number of twin pairs would, however, be needed, with opposite-sex twin pairs.

The current h2 for insulin resistance, measured here both by the use of fasting insulin and of HOMA-R, was in line with results obtained in several previous studies (8, 46, 47, 48) and somewhat higher than that estimated by us when a clamp technique was applied (19). This difference may in part be explained by the fact that fasting insulin and HOMA-R represent the fasting state, whereas clamp glucose uptake reflects insulin sensitivity during more or less constant hyperinsulinemia.

Decomposition of variance components of insulin and glucose concentrations during the OGTT has been performed by several research groups (15, 21, 36, 47). Genetic covariance between trait values measured at two separate moments during the OGTT has, however, not been evaluated. The observation of a strong correlation between genetic variance components of fasting and 120-min insulin is in line with a recent study of Finnish families in which the common genetic architecture of type 2 diabetes was identified to include both fasting and 120-min insulin (47). The observations are, furthermore, compatible with findings by Schumacher et al. (49), suggesting a major gene locus contributing to both fasting and 60-min insulin concentrations.

Conclusions

The strong observed input from environmental factors to peripheral insulin resistance justifies strategies that address the importance of lifestyle changes in the prevention of type 2 diabetes.

Whether abdominal obesity and postprandial insulin levels are unequally associated in men and women, and whether this difference is due to a sex-specific genetic covariance between the traits, could be resolved in studies specifically designed for such a purpose.


   Footnotes
 
This work was supported by grants from the Academy of Finland (46558), the Yrjö Jahnsson Foundation, and the GenomEUtwin project (EU contract QLG2-CT-2002-01254). The work of S.K. was also supported by a grant from the Uehara Memorial Foundation in Japan.

First Published Online February 22, 2005

1 S.K. and M.L. share the first authorship Back

Abbreviations: A, Additive genetic component; BMI, body mass index; C, environmental component; CI, confidence interval; df, degrees of freedom; DZ, dizygotic; Gluc, plasma glucose; E, environmental component; h2, heritability; HOMA-BETA, homeostasis model of ß-cell function; HOMA-R, homeostasis model of insulin resistance index; Ins, serum insulin; MZ, monozygotic; OGTT, oral glucose tolerance test; WHR, waist to hip ratio.

Received December 16, 2004.

Accepted February 11, 2005.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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