Figures
Abstract
Background and Aim
The first genome-wide association study on birth weight was recently published and the most significant associated birth weight lowering variant was the rs900400 C-allele located near LEKR1 and CCNL1. We aimed to replicate the association with birth weight in the Danish Inter99 study and furthermore to evaluate associations between rs900400 and indices of insulin secretion and insulin sensitivity obtained by oral glucose tolerance tests in adults from the Danish Inter99 study and the Finnish, Metabolic Syndrome in Men (METSIM) sample.
Methods
For 4,744 of 6,784 Inter99 participants, midwife journals were traced through the Danish State Archives and association of rs900400 with birth weight was examined. Associations between rs900400 and fasting serum insulin, fasting plasma glucose, insulinogenic index, homeostasis model assessment of insulin resistance (HOMA-IR) and disposition index were studied in 5,484 Danish and 6,915 Finnish non-diabetic individuals and combined in meta-analyses.
Results
The C-allele of rs900400 was associated with a 22.1 g lower birth weight ([−41.3;−3.0], P = 0.024) per allele. Moreover, in combined analyses of the Danish Inter99 study and the Finnish METSIM study we found that the birth weight lowering allele was associated with increased insulin release measured by the insulinogenic index (β = 2.25% [0.59; 3.91], P = 0.008) and with an increased disposition index (β = 1.76% [0.04; 3.49], P = 0.05).
Conclusion
The birth weight lowering effect of the C-allele of rs900400 located near LEKR1 and CCNL1 was replicated in the Danish population. Furthermore the C-allele was associated with increased insulin response following oral glucose stimulation in a meta-analysis based on Danish and Finnish non-diabetic individuals.
Citation: Andersson EA, Harder MN, Pilgaard K, Pisinger C, Stančáková A, Kuusisto J, et al. (2011) The Birth Weight Lowering C-Allele of rs900400 Near LEKR1 and CCNL1 Associates with Elevated Insulin Release following an Oral Glucose Challenge. PLoS ONE 6(11): e27096. https://doi.org/10.1371/journal.pone.0027096
Editor: Michael Nicholas Weedon, Peninsula College of Medicine and Dentistry, -University of Exeter, United Kingdom
Received: July 19, 2011; Accepted: October 10, 2011; Published: November 4, 2011
Copyright: © 2011 Andersson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The study was supported by grants from the Lundbeck Foundation Centre of Applied Medical Genomics for Personalized Disease Prediction, Prevention and Care (LuCAMP). The Inter99 was initiated by T. Jørgensen (principal investigator), K. Borch-Johnsen (co-principal investigator), H. Ibsen and T. F. Thomsen. The Steering Committee comprises the former two and C. Pisinger. The study was financially supported by research grants from the Danish Strategic Research Council, Hagedorn Research Institute, the Novo Nordisk Foundation Center for Basic Metabolic Research, the PhD School of Molecular Metabolism University of Southern Denmark and the Copenhagen Graduate School of Health Sciences. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal's policy and have the following conflicts; KF and DRW are employed by Steno Diabetes Center A/S, a research hospital working in the Danish National Health Service and owned by Novo Nordisk A/S and own shares in Novo Nordisk A/S. OP is employed by Hagedorn Research Institute, which is a basic research facility of Novo Nordisk and have employee shares in Novo Nordisk. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
Introduction
Birth weight is a crude measure of the entire period of pre-natal growth. It is a complex trait influenced by multiple intrauterine factors as well as the genetic disposition of both the fetus and the mother. It is well known that low birth weight is associated with adult-onset metabolic diseases including type 2 diabetes (T2D) [1], [2] and it has been hypothesized that this link is due to foetal growth restriction being detrimental for the natural organ development [3]. Besides, recent data provided proof-of-concept for the idea that the relationship between low birth weight and risk of T2D to some extent may be explained by common genetic disposition to both traits (the fetal insulin hypothesis) [4]. This hypothesis is based on insulin being an important fetal growth factor, and thus genetic variation impairing insulin secretion or action may result in both reduced fetal growth and risk of T2D. Thus, lower birth weight was found among carriers of T2D risk alleles in or near HHEX-IDE, CDKAL1 and ADCY5 [5], [6], [7], [8], [9], however only the latter was confirmed at genome-wide significance. Risk alleles in HHEX-IDE and CDKAL1 confer increased risk of T2D due to lower insulin secretion [10], [11] and the birth weight lowering effect of these two alleles suggests that insulin secretion is diminished already in pre-natal life. This may as well be the case for the ADCY5 risk allele, but the exact mechanism by which this variant predisposes to both low birth weight and T2D is yet to be determined [12]. However, several T2D risk alleles affecting adult insulin secretion have not been found to have an effect on birth weight. Variants in the FTO and TCF7L2 loci have been tested in large cohorts [13], [14], but it cannot be excluded that for other variants the lack of association with birth weight might be due to missing power.
The first genome-wide association (GWA) study on birth weight demonstrated that rs9883204 located in ADCY5 and rs900400 located near LEKR1 and CCNL1 were associated with a decrease in birth weight by 0.063 and 0.086 z-score units per allele, respectively [5].
By investigating published GWA-databases from the GIANT and MAGIC consortia the authors found that, while the ADCY5 locus was related to T2D, rs900400 was not associated with either T2D, height, BMI, or fasting glyceamic traits [5]. So far, genetic variants in or near LEKR1 and CCNL1 have not been reported to be associated with adult metabolic phenotypes. Due to limited knowledge about this novel signal, we aimed to confirm the association between rs900400 and birth weight in the Danish population and furthermore to evaluate associations between rs900400 and five indices of insulin release and insulin sensitivity obtained from an oral glucose tolerance test in adults of the Danish Inter99 population and the Finnish Metabolic Syndrome in Men (METSIM) study as well as in combined analyses.
Materials and Methods
Study population
Inter99.
Ethics statement: All participants gave written informed consent and the protocol was in accordance with the Helsinki Declaration, and approved by Copenhagen County ethic committee (ClinicalTrials.gov NCT00289237).
Individuals examined in the present study were from the Danish Inter99 study, which at baseline comprised 6,784 individuals living in the region of Copenhagen. The Inter99 study is a population-based, randomised non-pharmacological intervention study of prevention of ischemic heart disease conducted at the Research Centre for Prevention and Health in Glostrup, Denmark (www.inter99.dk) [15], [16]. For 4,744 participants, midwife journals were traced through the Danish State Archives. These journals contained information on mother's age, parity and marital status as well as birth weight, length at birth and prematurity of the newborn [17]. Ponderal index was calculated as birth weight (kg) / birth length (m)3. Birth weight characteristics of participants included in the study can be seen in Table 1. Information about maternal diabetes status (yes/no/unknown) was obtained by a questionnaire during the baseline visit in 1999–2001. The age of onset of maternal diabetes was not registered. Term birth was defined by a gestational week between 37 and 41. Preterm singleton deliveries (n = 446) and individuals born from multiple pregnancies (n = 85) were excluded. The final number of individuals included in the analyses of birth weight was 4,210.
Association between the rs900400 genotype and quantitative diabetes-related traits were studied in 5,484 non-diabetic individuals from the population-based Inter99 sample of Danish individuals aged 30–60 years. All participants had an oral glucose tolerance test (OGTT). Included in the analysis were 4,329 individuals with normal glucose tolerance (NGT), 489 individuals with impaired fasting glycaemia (IFG) and 666 individuals with impaired glucose tolerance (IGT). Patients in the Inter99 sample with diabetes were not included in the present analysis of quantitative traits. All individuals were Danes by self report.
METSIM.
Ethics statement: Written informed consent has been obtained from each participant after the purpose and the risks/benefits of the study have been explained. The study was approved by the Ethics Committee of the University of Kuopio and Kuopio University Hospital, and it was in accordance with the Helsinki Declaration.
Individuals from the population-based cross-sectional Metabolic Syndrome in Men (METSIM) study comprise 10,197 men aged 45 to 70 years. The men have been randomly selected from the population register of the town of Kuopio in eastern Finland (population 95,000). Every participant had a 1-day outpatient visit to the Clinical Research Unit at the University of Kuopio, including an interview on the history of previous diseases and current drug treatment and an evaluation of glucose tolerance and cardiovascular risk factors. No birth weight data were available for the participants. Associations between the rs900400 genotype and quantitative diabetes related traits were studied in 6,915 non-diabetic individuals. Of these participants, 4,638 had normal glucose tolerance, 1,206 isolated impaired fasting glucose (IFG), 630 isolated impaired glucose tolerance (IGT), 441 a combination of IFG and IGT according to WHO 1997 criteria [18].
Biochemical measures
Inter99.
Blood samples were drawn after an overnight fast followed by an OGTT. Plasma glucose was analyzed by a glucose oxidase method (Granutest; Merck, Darmstadt, Germany). Serum insulin ((excluding des-31,32) and intact proinsulin) was measured using the AutoDELFIA insulin kit (Perkin-Elmer, Wallac, Turku, Finland).
METSIM.
Blood samples were drawn after 12 h of fasting followed by an OGTT. Plasma glucose was measured from plasma (FC-mixture: NaF/ Na-Citrate/ EDTA-Na2) by Enzymatic photometric test, Glucose hexokinase. Reagent: Konelab System Reagents, Glucose (HK), Thermo Fisher Scientific, Vantaa, Finland. Instrumentation: Konelab 20XTi Clinical Chemistry Analyzer, Thermo Fisher Scientific, Vantaa, Finland. Insulin was measured in mU/l units (converted to pmol/l multiplying by 6) from plasma (EDTA) by Immunoassay, luminometric measurement. Reagent: ADVIA Centaur Insulin IRI, no 02230141, Siemens Medical Solutions Diagnostics, Tarrytown, NY, USA. Instrumentation: Siemens ADVIA Centaur®, Siemens Medical Solutions Diagnostics, Tarrytown, NY, USA.
Indices of insulin release and insulin sensitivity for both study samples.
Oral glucose-stimulated insulin release was reported as the insulinogenic index. The insulinogenic index was calculated as (serum insulin at 30 minutes [pmol/l]-fasting serum insulin [pmol/l]) / plasma glucose at 30 minutes (mmol/l). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as ((fasting plasma glucose (mmol/l) * fasting serum insulin (pmol/l)) / 135) for the Inter99 cohort and as ((fasting plasma glucose (mmol/l) * fasting plasma insulin (µU/l))/ 22.5) in the METSIM cohort. In order to construct an OGTT-based disposition index, we divided insulinogenic index with the reciprocal of HOMA-IR (Insulinogenic index/HOMA-IR). Clinical characteristics of the Inter99 and METSIM individuals are shown in Table 1.
Genotyping
Statistical analysis
Inter99.
All statistical analyses were performed using RGui version 2.8.1 (available at http://www.r-project.org). The associations of rs900400 with birth weight, length at birth and ponderal index in the Inter99 population were calculated using linear regression models adjusted for sex, maternal diabetes (yes vs. no/NA) and parity (0, 1, 2, 3 or ≥4). Z-scores were calculated as [value-mean]/standard deviation (based on the mean and SD birth weight in the included Inter99 sample).
METSIM.
Statistical analyses were performed using SPSS version 17 (SPSS, Chicago, IL). Associations with quantitative variables during an OGTT were performed as described below.
Both studies.
Associations of rs900400 with indices of insulin release and insulin sensitivity were performed by linear regression models adjusted for age or for age and BMI (the analyses were also adjusted for sex in the Inter99 sample). Effect sizes are given as actual values or percentage (%) if the trait was natural logarithmically (ln) transformed. Only additive genetic models were considered assuming a constant change per risk allele and a P-value <0.05 was considered significant.
Fixed effect meta-analyses (up to n = 12,394) were performed using effect size estimates and standard errors (SE) derived from linear regression analyses from the Inter99 (adjusted for age and sex) and the METSIM (adjusted for age) populations. The weight of the two studies in the meta-analyses was estimated using inverse variance assuming fixed effects. Heterogeneity was measured by Q-statistics.
Statistical power was estimated using 1000 simulations. We used the empirical variance of the observed traits in the Danish Inter99 cohort to simulate phenotypes from a normal distribution, so that variance across genotypes is drawn from the estimated variance.
In the combined analyses, we have estimated the effect sizes that we have more than 80% statistical power to detect (P<0.05) assuming a minor allele frequency of 40% resembling the HapMap-CEU frequency of rs900400 (Table S1).
Results
In 4,210 individuals from the Danish Inter99 population the C-allele of rs900400 near LEKR1 and CCNL1 was associated with 22.1 g lower birth weight per allele (95% CI −41.3;−3.0, P = 0.024). A trend towards a lower ponderal index was also observed (β = −0.08 [−0.18;0.01], P = 0.094) for carriers of the C-allele, whereas length at birth was not associated with rs900400 (P = 0.230) (Table 2).
In the Inter99 study, we found that the birth weight lowering C-allele was associated with increased insulin release as estimated by the insulinogenic index (β = 3.3% [1.0;5.6], P = 0.005) as well as increased disposition index (β = 3.6% [1.0;6.2], P = 0.007) (Table 3). Moreover, we found a lower fasting plasma glucose level (−0.02 mmol/l [−0.04;−0.002], P = 0.021) for carriers of the birth weight lowering allele in the Inter99 cohort. These observations, except the findings for fasting plasma glucose, were also observed in the subgroup of individuals with normal glucose tolerance in Inter99 (data not shown). The C-allele of rs900400 was not significantly associated with any of the five traits in the METSIM population (table 3). However, the directions of the associations with insulinogenic index and disposition index were similar to what was observed in the Inter99 sample although the effect size estimates were considerably smaller in the METSIM population. Additional adjustment for BMI in all analyses did not change the level of significance. Fixed effect meta-analyses of data from the Inter99 and METSIM studies were performed for all five metabolic traits (fasting serum insulin, fasting plasma glucose, insulinogenic index, HOMA-IR and disposition index) including up to 12,394 individuals. In combined meta-analyses the C-allele of rs900400 was associated with increased insulin response to an oral glucose load estimated by the insulinogenic index (β = 2.25% [0.59; 3.91], P = 0.008, figure 1 and table 4) and with an increased disposition index in C-allele carriers (β = 1.76% [0.04; 3.49], P = 0.05, table 4). No other traits were associated with rs900400 in the meta-analyses (table 4). No heterogeneity was observed in any of the meta-analyses (P>0.05).
Effect size estimates and standard errors are combined in a meta-analysis using the inverse variance method. The black diamond represents the combined change in insulinogenic index per C-allele. Effect size estimate (β) and P-value are presented for the combined analysis with 95% confidence interval in square brackets.
Discussion
We confirm the association of rs900400 C-allele near LEKR1 and CCNL1 with lower birth weight in the Danish Inter99 study sample. The same allele was found by Freathy et al. to be associated with a lower ponderal index in 21,515 European individuals [5]. We also observe a trend towards a lower ponderal index, although this trait does not reach statistical significance in our cohort.
Our confidence interval is just overlapping with the estimated effect size reported by Freathy et al, indicating that statistically the effect sizes are similar in the two cohorts. However, the effect size estimates of birth weight and ponderal index in the present study are slightly lower than the combined effects reported by Freathy et al [5]. This could be explained by general population differences and slight deviations in linkage equilibrium among the populations, assuming that this polymorphism is not the causative variant. Moreover, we were not able to adjust for gestational age also impeding the accuracy of birth weight as a measure of growth conditions. However, in the GWA meta-analysis three studies without information about gestational age did not introduce heterogeneity in the overall analyses suggesting that this variant is not strongly associated with gestational age [5].
Surprisingly, in meta-analyses based on the Danish Inter99 and Finnish METSIM studies we found an increased oral glucose stimulated insulin release measured by the insulinogenic index and the disposition index in carriers of the birth weight lowering C-allele. The disposition index could be considered as an estimate of the ability of the pancreatic beta cells to respond appropriately to the level of insulin sensitivity. If these data are upheld in larger study samples, it can be suggested that the increased insulin release represents a primary metabolic feature in these individuals. This finding may appear in contrast to what may have been expected according to the fetal insulin hypothesis [4] and based on recent observations for birth weight lowering variants in or near CDKAL1, HHEX-IDE and ADCY5 [5]–[9]. Nevertheless, the variant near LEKR1 and CCNL1 was not associated with impaired β-cell function or insulin action demonstrating that genetic variants conferring low birth weight may not necessarily be related to an adverse glycemic phenotype. Adult determinants of insulin secretion and insulin action may however not reflect insulin regulation in fetal life. In this study the adult insulin secretion was measured as a response to an oral glucose challenge, which triggers insulin secretion through the gut and not through the umbilical cord like in the uterus. It could also be speculated that the increased secretion may somehow be related to compensatory post-natal mechanisms. Furthermore, it has been seen before that a mutation can cause opposite effects on insulin secretion in early versus later life, since a HNF4A mutation causes hyperinsulimea in utero and then later causes diabetes in adulthood [19]. Obviously further genetic epidemiological and metabolic studies in representative study samples are needed to verify the findings. In order to achieve genome-wide significance with 80% statistical power, sample sizes of >56,000 individuals are needed.
In our study, associations to five metabolic traits have been examined. Although some traits are correlated, we recognize the possibility of the associations being false positive findings due to lack of correction for multiple testing. However, if a stringent Bonferroni correction for five independent tests were performed, the association with the insulinogenic index observed in the combined analysis still remains statistically significant.
In conclusion, we confirm the association with lower birth weight for the C-allele of rs900400 located near LEKR1 and CCNL1 and we demonstrate a novel association with increased insulin response for carriers of the birth weight lowering allele. The validity of the present findings needs to be tested in future studies.
Supporting Information
Table S1.
Effect sizes that we have 80% statistical power to detect in the combined analyses with a minor allele frequency of 40% and with a P-value of 0.05 for the five listed traits.
https://doi.org/10.1371/journal.pone.0027096.s001
(DOC)
Acknowledgments
The authors wish to thank A. Forman, T. Lorentzen and M. Stendal for technical assistance and A. Nielsen, P. Sandbeck, G. Lademann and M.M.H. Kristensen for management assistance.
Author Contributions
Conceived and designed the experiments: EAA MNH NG OP TH. Performed the experiments: EAA MNH AS. Analyzed the data: EAA MNH AS NG. Contributed reagents/materials/analysis tools: KP NG KF PP CP TJ DRW ML AV OP TH. Wrote the paper: EAA MNH AS. Discussed and revised the design, results and the paper: EAA MNH NG KP AS JK KF DRW AV OP TH.
References
- 1. Harder T, Rodekamp E, Schellong K, Dudenhausen JW, Plagemann A (2007) Birth weight and subsequent risk of type 2 diabetes: a meta-analysis. Am J Epidemiol 165: 849–857.
- 2. Pilgaard K, Faerch K, Carstensen B, Poulsen P, Pisinger C, et al. (2010) Low birthweight and premature birth are both associated with type 2 diabetes in a random sample of middle-aged Danes. Diabetologia 53: 2526–2530.
- 3. Hales CN, Barker DJ (2001) The thrifty phenotype hypothesis. Br Med Bull 60: 5–20.
- 4. Hattersley AT, Tooke JE (1999) The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet 353: 1789–1792.
- 5.
Freathy RM, Mook-Kanamori DO, Sovio U, Prokopenko I, Timpson NJ, et al. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat Genet.
- 6. Andersson EA, Pilgaard K, Pisinger C, Harder MN, Grarup N, et al. Type 2 diabetes risk alleles near ADCY5, CDKAL1 and HHEX-IDE are associated with reduced birthweight. Diabetologia 53: 1908–1916.
- 7. Freathy RM, Bennett AJ, Ring SM, Shields B, Groves CJ, et al. (2009) Type 2 diabetes risk alleles are associated with reduced size at birth. Diabetes 58: 1428–1433.
- 8. Pulizzi N, Lyssenko V, Jonsson A, Osmond C, Laakso M, et al. (2009) Interaction between prenatal growth and high-risk genotypes in the development of type 2 diabetes. Diabetologia 52: 825–829.
- 9. Zhao J, Li M, Bradfield JP, Wang K, Zhang H, et al. (2009) Examination of type 2 diabetes loci implicates CDKAL1 as a birth weight gene. Diabetes 58: 2414–2418.
- 10. Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, et al. (2007) Studies of association of variants near the HHEX, CDKN2A/B, and IGF2BP2 genes with type 2 diabetes and impaired insulin release in 10,705 Danish subjects: validation and extension of genome-wide association studies. Diabetes 56: 3105–3111.
- 11. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, et al. (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39: 770–775.
- 12. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116.
- 13. Freathy RM, Weedon MN, Bennett A, Hypponen E, Relton CL, et al. (2007) Type 2 diabetes TCF7L2 risk genotypes alter birth weight: a study of 24,053 individuals. American journal of human genetics 80: 1150–1161.
- 14. Kilpelainen TO, den Hoed M, Ong KK, Grontved A, Brage S, et al. (2011) Obesity-susceptibility loci have a limited influence on birth weight: a meta-analysis of up to 28,219 individuals. The American journal of clinical nutrition 93: 851–860.
- 15. Jorgensen T, Borch-Johnsen K, Thomsen TF, Ibsen H, Glumer C, et al. (2003) A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Eur J Cardiovasc Prev Rehabil 10: 377–386.
- 16. Glumer C, Jorgensen T, Borch-Johnsen K (2003) Prevalences of diabetes and impaired glucose regulation in a Danish population: the Inter99 study. Diabetes Care 26: 2335–2340.
- 17.
Pilgaard K, Færch K, Poulsen P, Larsen C, Andersson EA, et al. Impact of size at birth and prematurity on adult anthropometry in 4744 middle-aged Danes ? The Inter99 study. Journal of Developmental Origins of Health and Disease FirstView. pp. 1–10.
- 18. Who (1999) Part 1: Diagnosis and classification of Diabetes Mellitus.Tech. Rep. Ser., no.WHO/NCD/NCS99.2.
- 19. Dusatkova P, Pruhova S, Sumnik Z, Kolouskova S, Obermannova B, et al. (2011) HNF1A mutation presenting with fetal macrosomia and hypoglycemia in childhood prior to onset of overt diabetes. Journal of pediatric endocrinology & metabolism: JPEM 24: 187–189.