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Recent advances in type 2 diabetes genetics have culminated in the discovery and confirmation of multiple risk variants. Two important and largely unanswered questions are whether this information can be used to identify individuals most susceptible to the adverse consequences of sedentary behavior and to predict their response to lifestyle intervention; such evidence would be mechanistically informative and provide a rationale for targeting genetically susceptible subgroups of the population.
Gene × physical activity interactions were assessed for 17 polymorphisms in a prospective population-based cohort of initially nondiabetic middle-aged adults. Outcomes were 1) impaired glucose regulation (IGR) versus normal glucose regulation determined with either fasting or 2-h plasma glucose concentrations (n = 16,003), 2) glucose intolerance (in mmol/l, n = 8,860), or 3) incident type 2 diabetes (n = 2,063 events).
Tests of gene × physical activity interactions on IGR risk for 3 of the 17 polymorphisms were nominally statistically significant:CDKN2A/B rs10811661 (Pinteraction = 0.015), HNF1B rs4430796 (Pinteraction = 0.026), and PPARG rs1801282 (Pinteraction = 0.04). Consistent interactions were observed for the CDKN2A/B (Pinteraction = 0.013) and HNF1B (Pinteraction = 0.0009) variants on 2-h glucose concentrations. Where type 2 diabetes was the outcome, only one statistically significant interaction effect was observed, and this was for the HNF1B rs4430796 variant (Pinteraction = 0.0004). The interaction effects for HNF1B on IGR risk and incident diabetes remained significant after correction for multiple testing (Pinteraction = 0.015 and 0.0068, respectively).
Our observations suggest that the genetic predisposition to hyperglycemia is partially dependent on a person's lifestyle.
Recent advances in high-throughput genotyping methods have facilitated the discovery and confirmation (1–7) of multiple common genetic risk factors for type 2 diabetes. The notion of using genetic information for disease prevention is predicated on the assumption that genetic risk can be offset with drug or lifestyle intervention. Thus, studies that explore this possibility are integral to the process of translating the results of genetic association studies into preventive practice. Furthermore, because interaction effects modify the extent to which genetic risk is conveyed, with the risk varying in magnitude across the spectrum of environmental exposure, information on gene × environment interactions may help improve the sensitivity and specificity of genetic prediction models (8).
In a recent report from the Diabetes Prevention Program, 10 of the previously associated type 2 diabetes risk polymorphisms were assessed in the context of a clinical trial of drug or lifestyle intervention for diabetes risk reduction (9). In that study, a single nucleotide polymorphism (SNP) at the cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) locus (rs10811661) was shown to modify the effects of lifestyle intervention on diabetes risk reduction, such that the lifestyle intervention slowed the progression to type 2 diabetes to a greater extent in those carrying the previously reported high-risk genotype at rs10811661 compared with those with the lower-risk genotypes.
In the current study, we aimed to determine whether the effects of 17 previously associated type 2 diabetes gene variants on the risk of impaired glucose regulation (IGR) or incident type 2 diabetes are modified by physical activity. The study was undertaken in an ethnically homogeneous prospective population-based cohort study of ~16,000 initially nondiabetic middle-aged adults from Sweden.
A preventive case-finding program called the Malmö Preventive Project was started in 1974 at the Department of Preventive Medicine, University Hospital, Malmö, Sweden. The aim was to screen large strata of the adult population in the southern Swedish city of Malmö to find individuals at high risk of developing chronic diseases who might benefit from preventive interventions. Participants were invited to participate in a general health screening that included a physical examination, a self-administered questionnaire on lifestyle habits, and fasting and postchallenge blood sampling for laboratory tests. Between 1974 and 1992, a total of 22,444 male and 10,902 female subjects attended the screening program, with an overall attendance rate of 71%. All participants gave written informed consent to take part in the study, which was approved by the local research ethics committees of Lund University and Malmö University Hospital.
A detailed description of the case ascertainment and other follow-up procedures is given elsewhere (10,11). Briefly, all known prevalent cases of type 2 diabetes were excluded from the cohort. Incident diabetes (n = 2,063 events) was ascertained using information collected from hospital records detailing a clinical diagnosis of type 2 diabetes or a fasting plasma glucose value >7.0 mmol/l at one of the Malmö Preventive Project follow-up examinations.
The clinical examination included measurements of height (m) and weight (kg) as previously described (11). Blood samples were drawn at 0, 40, and 120 min of an oral glucose tolerance test for measurements of blood glucose using a hexokinase method, the details of which have previously been described in detail (12). BMI was calculated as the weight in kilograms divided by height in meters squared (kg/m2). IGR was defined as an elevated 2-h glucose concentration of 7.8–11.0 mmol/l (or an elevated fasting value of 5.6–6.9 mmol/l if the 2-h value was unavailable), whereas normal glucose regulation (NGR) was defined as a 2-h glucose concentration <7.8 mmol/l (or a fasting value <5.6 mmol/l if the 2-h value was unavailable).
Physical activity was assessed using a self-administered computer-based questionnaire. The current study spanned a number of years, and during that time the questions used to assess physical activity were changed. The different questions used throughout the survey are shown below. Therefore, we constructed a variable where individuals were classified as “physically active” if they had answered “yes” when responding to one of the following questions used to assess physical activity throughout the duration of the survey:1) Do you walk or cycle to and from work?2) Do you walk or cycle for recreation during weekdays?3) Do you walk or cycle for recreation during weekend days?4) Do you undertake at least 3 h per week of structured physical exercise?5) Do you walk to work or do yard work? or 6) Do you perform light structured physical exercise each week? Conversely, those who answered “no” to these questions were classified as “physically inactive.” In this report, the physical activity variable was associated with glucose concentrations and diabetes incidence (see the results section) in a manner consistent with prior studies using objective assessment methods (13), indicating that the questionnaire used here correctly classifies individuals into physically active and inactive subgroups.
All polymorphisms have previously been examined for association with incident type 2 diabetes in this cohort (10,14,15). Data on the association between MTNR1B (rs10830963) and fasting glucose also have previously been reported (15). A Plasmid Maxi Kit (Qiagen) was used to extract DNA from white blood cells. For the rs7903146, rs1801282, rs5219, rs7754840, and rs10811661 variants, genotyping was performed using the matrix-assisted laser desorption–ionization time-of-flight mass spectrometry method on a MassARRAY platform (Sequenom). For rs4430796, rs4402960, rs10010131, rs1111875, rs864745, rs12779790, rs7961581, rs7578597, rs4607103, rs10923931, and rs10830963, genotyping was performed using an allelic discrimination assay-by-design approach on an ABI 7900 (Applied Biosystems). The rs13266634 variant was genotyped using an allele-specific assay (KASPar; KBioscience). The genotyping success rate and accuracy (11% of samples were regenotyped using Sequenom) exceeded 95 and 98.7%, respectively. As previously described, the concordance rates for the vast majority of SNPs exceeded 99% (10,15). All variants fulfilled Hardy-Weinberg expectations (P≥ 0.001).
Statistical analyses were conducted using SAS software (version 9.1; SAS Institute, Cary, NC). Hardy-Weinberg equilibrium was assessed using Haploview version 4.0 (http://www.broad.mit.edu/mpg/haploview). Statistical power was calculated with Quanto (version 1.2.3) (16) using parameters described in the legend of Table 3. Logistic regression adjusted for age, sex, and BMI was used to determine the strength of the relationship between physical activity and IGR. Differences between means were calculated using independent sample, two-sided Student's t tests to establish whether age, BMI, and glucose levels differed between active and inactive individuals. A likelihood ratio test with 1 degree of freedom was used to calculate the difference for categorical traits. For models where glucose tolerance was expressed as a binary variable (i.e., NGR vs. IGR), logistic regression was used, including interaction terms for genotype × physical activity. Generalized linear models were used to test associations and interactions (also incorporating an interaction term for genotype × physical activity) for each of the gene variants and with 2-h glucose concentrations as the dependent variable. Cox proportional hazards regression models were used to test main genetic effects (genes and physical activity separately) and gene × physical activity interactions on type 2 diabetes incidence. A time-dependent interaction term was fitted to the model to ensure that the proportionality assumption of Cox models was fulfilled, which it was (Pinteraction > 0.05). All regression models were adjusted for age, sex, and BMI and assumed an additive mode of genetic inheritance. Adjustments for multiple statistical comparisons across the 17 polymorphisms were made for each of the three dependent variables using the Holm procedure (17). This procedure requires that the probability statistics for each hypothesis test is placed in a rank-ordered list, with the highest P value appearing first. The total number of P values within this list represents the denominator. The least significant nominal P value is divided by 1, the second least significant by 2, and so on until all P values within the list have been corrected. This correction method was applied to each dependent variable separately.
Participant characteristics for 16,003 initially nondiabetic middle-aged whites are shown in Table 1. To indirectly quantify the validity of the physical activity assessment instrument, we tested the association between physical activity and IGR or diabetes risk after adjustment for age, sex, and BMI. In these models, physical inactivity conveyed a 1.15-fold increased risk (95% CI 1.05–1.26, P = 0.002) of IGR and a 1.41-fold increased risk (1.29–1.57, P < 0.0001) of type 2 diabetes.
The following section describes the results for the models focusing on main genetic effects and gene × physical activity interactions. Unless otherwise stated, all P values are unadjusted for multiple comparisons.
The associations between each of the polymorphisms with IGR risk and 2-h glucose concentrations are shown in Table 2, ranked by the P value for the test of gene × physical activity interaction. SLC30A8 (rs13266634), TCF7L2 (rs7903146), CDKAL1 (rs7754840), NOTCH2 (rs10923931), KCNJ11 (rs5219), IGFBP2 (rs4402960), JAZF1 (rs864745), HHEX (rs1111875), MTNR1B (rs10830963), and TSPAN8 (rs7961581) all showed evidence for association with IGR risk and/or 2-h glucose concentrations in directions consistent with previous reports. No statistical association was observed between the CDKN2A/B rs10811661 variant and 2-h glucose (β = 0.02 mmol/l per copy of the major allele, P = 0.43) or the risk of having IGR (vs. NGR; odds ratio [OR] 1.08 per copy of the major allele, 95% CI 0.99–1.15, P = 0.08). Similarly, no statistical association was observed for the HNF1B rs4430796 variant and 2-h glucose (β = 0.02 mmol/l per copy of the minor allele, P = 0.65) or IGR risk (OR 1.02 per copy of the minor allele, 95% CI 0.97–1.08, P = 0.36). The associations between each of the gene variants and type 2 diabetes have previously been described in detail (10,15).
Statistical power calculations are shown in Table 3 for all variants. As shown in Table 2, no statistical evidence of interaction emerged for the majority of the polymorphisms tested. The remaining section focuses on the variants for which the uncorrected interaction terms were statistically significant (Pinteraction < 0.05) for at least one of the three outcomes of interest in this report.
The gene × physical activity interaction terms for HNF1B (rs4430796) on IGR risk and on 2-h glucose concentrations were both statistically significant (Pinteraction = 0.026 and 0.0009, respectively), and the latter remained significant after correction for multiple hypothesis testing (Pinteraction = 0.015). Although not statistically significant, the minor A allele at HNF1B rs4430796 tended to be associated with lower risk of IGR (OR 0.92 per allele, 95% CI 0.82–1.03, P = 0.13) in physically inactive individuals (n = 3,335) and with increased risk (1.06, 1.00–1.12, P = 0.066) in physically active individuals (n = 12,015). In a concordant manner, the minor A allele was associated with lower 2-h glucose levels (β = −0.13 mmol/l per allele, P = 0.005) in physically inactive individuals (n = 1,619), with a contrasting effect (0.04, P = 0.056) in physically active individuals (n = 6,981) (Fig. 1). The Cox proportional hazards regression model testing the gene × baseline physical activity interaction term on type 2 diabetes as the outcome yielded an uncorrected P value of 0.0004 (corrected Pinteraction = 0.0068). The nature of the interaction reflected those observed in the glucose regulation models, where the minor allele was associated with decreased risk of diabetes in sedentary individuals (hazard rate ratio [HRR] = 0.85; 95% CI 0.74–0.96; P = 0.011), with a contrasting effect in physically active individuals (HRR = 1.10; 95% CI 1.03–1.18; P = 0.007). To illustrate this interaction, cumulative incidence curves were plotted stratified by genotype and level of physical activity (see Fig. 2).
The interaction terms for the CDKN2A/B rs10811661 polymorphism and physical activity, with IGR risk or 2-h glucose concentrations as outcomes, were both nominally statistically significant (Pinteraction = 0.015 and 0.013, respectively). Though not statistically significant, the minor allele at rs10811661 tended to be associated with higher risk of IGR (OR 1.10 per allele, 95% CI 0.95–1.28, P = 0.20) in physically inactive individuals (n = 3,468), but it was protective of IGR risk (0.89, 0.82–0.97, P = 0.0075) in active individuals (n = 12,525). Similarly, in physically inactive individuals (n = 1,618), the rs10811661 minor allele tended to be associated with elevated 2-h glucose concentrations (β = 0.12 mmol/l per allele, P = 0.064), whereas in physically active individuals (n = 6,680), a contrasting effect was observed (−0.06, P = 0.070), although neither was statistically significant. No statistical evidence of interaction was observed where type 2 diabetes was the outcome (Pinteraction = 0.57).
The interaction between the PPARG rs1801282 polymorphism and physical activity on IGR risk was nominally statistically significant (Pinteraction = 0.04). In inactive individuals, the minor allele appeared to be protective of IGR (OR 0.88 per allele, 95% CI 0.75–1.02, P = 0.097), whereas this effect was reversed in active individuals (1.05, 0.97–1.15, P = 0.22), albeit neither stratified effect was statistically significant. No evidence of statistical interaction was observed for 2-h glucoseconcentrations (Pinteraction = 0.78) or incident diabetes(Pinteraction = 0.67).
It is possible that the interactions reported above might be driven by interactions between the gene variants and BMI because both physical activity and BMI are related (13). Therefore, we tested age- and sex-adjusted BMI interaction terms for each SNP with the dependent variable being IGR vs. NGR. In these models, only one term, for ADAMTS9 rs4607103, was nominally statistically significant (Pinteraction = 0.024). The BMI interaction terms for the CDKN2A/B rs10811661 (Pinteraction = 0.973), HNF1B rs4430796 (Pinteraction = 0.763), and PPARG rs1801282 (Pinteraction = 0.724) variants did not approach statistical significance. The inclusion or exclusion of BMI as a covariate in the SNP × physical activity interaction models made no material difference to the interaction results, and no novel interaction effects emerged.
A growing number of gene variants have been convincingly shown to influence type 2 diabetes risk (1–3,18,19). In the current study, the risk of glucose intolerance and type 2 diabetes conveyed by one of these variants (HNF1B rs4430796) appeared to be offset in people reporting physically active lifestyles. These putative interactions were observed in ~16,000 people, of whom >2,000 developed type 2 diabetes during a median follow-up period of 24.5 years. Consistent interaction effects emerged in the subgroup of nearly 9,000 individuals in whom 2-h glucose concentrations in response to an oral glucose tolerance test had been recorded. The uncorrected interaction effect for a second variant at CDKN2A/B (rs10811661) was nominally statistically significant when baseline IGR or 2-h glucose was the outcome. The nature of these effects is consistent with those reported in a recent clinical trial of intensive lifestyle intervention (9). However, when type 2 diabetes was the outcome, the gene × physical activity interaction term was not statistically significant in this cohort. A borderline uncorrected statistical interaction on IGR risk for a third variant at the PPARG gene (rs1801282) was also observed, but this was not apparent within the subcohort in which 2-h glucose concentrations were available, nor was an interaction evident when type 2 diabetes was the outcome. The absence of a statistical interaction in the cohort with 2-h glucose concentrations may be a consequence of lower statistical power owing to the smaller sample size, but inadequate power is unlikely to explain the lack of effect where diabetes was the outcome. For this locus, numerous prior reports of gene-lifestyle interaction exist, although most have focused on gene-nutrient interaction effects (20).
HNF1B (also known as TCF2) encodes a homeobox transcription factor that controls cell proliferation and differentiation in the kidney, pancreas, liver, and genital tract tissues. Rare monogenic forms of maturity-onset diabetes of the young (MODY5) are caused by HNF1B mutations (21). In the initial reports of association between the HNF1B rs4430796 polymorphism and type 2 diabetes (22), the major G allele was associated with decreased risk, as observed in physically active individuals in the current study. However, any beneficial effect of this allele is essentially lost, and possibly reversed, in the physically inactive individuals studied here. FromFig. 2, it is apparent that the initially strong inverse association between physical activity and diabetes incidence diminishes in magnitude with each additional copy of the minor HNF1B allele. This result suggests that the protective effects of physical activity on diabetes risk may be attenuated in carriers of the rs4430796 minor allele. Interestingly, the same variant was associated in the opposite direction with prostate cancer risk in the initial report (22). Thus, it would be important to determine whether gene × physical activity interactions are also relevant for that disease and, if so, how lifestyle intervention influences prostate cancer risk in carriers of the different rs4430796 genotypes. No prior studies to our knowledge have reported evidence of gene × physical activity interactions at the HNF1B rs4430796 locus. Thus, unlike the interactions for CDKN2A/B and PPARG, the prior probability that the HNF1B interaction tests would be statistically significant was low; therefore, that these tests remained statistically significant after correction for multiple testing strengthens the credibility of our observations.
The CDKN2A and CDKN2B genes localize to neighboring genomic regions. Both genes function as tumor supressors via inhibition of cyclin-dependent kinases (23). The CDKN2A/B genes are highly expressed in adipocytes and pancreatic islets (4). The rs10811661 polymorphism, which maps to a region between CDKN2A and CDKN2B, was initially identified through genome-wide association studies of type 2 diabetes risk (1,2,18,19). Several (7,24–27), but not all (28), subsequent studies found strong support for an association with type 2 diabetes at this locus in a range of ethnic groups. Consistent with our observations in physically active individuals, the major T allele was associated with increased risk in prior reports (1,7,18,19,24–27). In one of the follow-up studies (7), the authors reported moderately elevated 2-h glucose concentrations in nondiabetic T allele carriers. The validity of the CDKN2A/B× physical activity interaction observed here is supportive of a recent report from the Diabetes Prevention Program (9), where the effect of the rs10811661 polymorphism on diabetes risk was abolished with intensive lifestyle intervention. These data tend to suggest that whereas they are protected from IGR or type 2 diabetes when physically active, carriers of the minor allele at rs10811661 are, relative to the major allele homozygotes, at increased risk of these conditions when leading sedentary lifestyles.
The third polymorphism for which we observed weak evidence of statistical interaction is PPARG rs1801282 (encoding a proline-to-alanine amino acid substitution at codon 12). Although the data supporting gene × physical activity interactions at the rs1801282 locus are tenuous, the variant is a strong biological and statistical candidate for gene-nutrient (i.e., fatty acids) interactions (20,29). Hence, because dietary patterns and physical activity tend to coalesce within free-living populations, our observation of gene × physical activity interaction may be attributable to dietary correlates of physical activity, rather than direct biological interactions between PPARG and physical activity—unfortunately, measures of dietary fat intake were unavailable for these analyses. Nonetheless, it is important to stress that the statistical evidence of the interaction between PPARG rs1801282 and physical activity is of nominal statistical significance for IGR risk, and no interaction was observed on 2-h glucose concentrations or type 2 diabetes incidence. Therefore, the tentative interaction for IGR risk may be attributable to chance.
This is the first study to our knowledge to assess gene-lifestyle interactions across a wide range of previously associated type 2 diabetes gene variants. It is also the largest study of gene-lifestyle interaction on pre-diabetic and diabetic traits to date and, hence, one of the most well powered to test related hypotheses. To illustrate this point, we calculated the power to detect statistically significant interactions for the loci of interest (Table 3). Although it is very difficult at this stage to estimate the magnitude of interaction effects because so few well-designed interaction studies exist from which to garner this information, a conservative estimate is that they might range from 1.25 to 1.50. AsTable 3 shows for IGR and type 2 diabetes as outcomes, our study was well powered to detect interactions of ≥1.50 per allele.
A limitation of this report, as with all existing large-scale epidemiological investigations, is that physical activity was assessed using questionnaires, which are prone to error and bias. This will have detrimentally affected power to detect interactions owing to a degree of misclassification of the environmental exposure. However, our measure of physical activity is inversely correlated with IGR and type 2 diabetes risk in this cohort, which is consistent with the direction of association reported in clinical trials involving exercise as part of a program of intensive lifestyle intervention (30,31) or epidemiological studies where physical activity was objectively assessed (13), instilling confidence that the questionnaire correctly classified the behavior of physical activity. Moreover, we purposefully studied people free from diabetes at baseline to minimize the extent to which changes in behavior and cognition attributable to disease labeling and treatment might bias the reporting of physical activity levels by study participants. In the future, large studies may emerge where physical activity has been objectively assessed. In this event, it may be possible to model genetic interaction terms where the different components of physical activity are examined. This is an important objective in furthering our understanding of how behavioral and genetic predispositions interact to influence disease risk, but this is beyond the scope of the current study.
Given that our study is likely to be adequately powered to detect realistic interaction effects, the absence of statistical interactions for the majority of variants examined here supports the idea that the most strongly associated SNPs derived from existing type 2 diabetes genome-wide association studies do not, by and large, exert their effects through interactions with physical activity. To some extent, this is unsurprising because existing genome-wide association studies are insensitive to detecting variants with heterogeneous effects, owing to the strict significance thresholds used to determine genome-wide statistical significance. Thus, the most strongly associated variants in genome-wide association studies are likely to be those with the least heterogeneous effects, which by definition are likely to exclude many variants that exert their effects through interactions with environmental (or other genetic) exposures.
In summary, in this study we examined putative gene × physical activity interactions for 17 confirmed type 2 diabetes risk variants on measures of glucose intolerance and incident type 2 diabetes. Our findings, in combination with other previously published observations (9), suggest that impairments in glucose regulation caused by variants at the HNF1B and CDKN2A/B loci may be attenuated by physical activity. In the event that these interactions are independently replicated, they may shed light of the mechanisms through which physical inactivity and genetic variants influence the risk of type 2 diabetes; whether this information will prove useful for the targeted prevention of type 2 diabetes remains to be determined.
This study was funded in part by grants from the Påhlssons Foundation, the Swedish Heart-Lung Foundation, and the Swedish Diabetes Association. E.C.B. was supported by a studentship from the Portuguese Foundation for Science and Technology. P.W.F. was supported in part by funding from Västerbotten's Health Authority (Regional Authority for Clinical Eduction and Research [ALF] strategic appointment 2006-2009). Some of this work was conducted by P.W.F. while a Visiting Scientist at Harvard School of Public Health and the Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School (supported by the Swedish Research Council and the Royal Physiological Society of Lund).
This study was also funded by Novo Nordisk. No other potential conflicts of interest relevant to this article were reported.
We thank those who participated in this study. We are also grateful for the technical assistance of the staff of the Clinical Research Center in Malmö for genotyping assistance and the statistical advice of Kathleen Jablonski.
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