Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals

Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10−8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.

www.nature.com/scientificreports www.nature.com/scientificreports/ Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10 −8 ) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
Type 2 diabetes mellitus (T2D), a disease characterized by persistent hyperglycemia, is a common and heritable complex disease affecting the health of millions of people worldwide 1 . Estimates from the World Health Organization indicate that 8.5% of the adult population had T2D in 2016, and this prevalence has been steadily increasing during the last three decades 2 .
Prospective epidemiological studies have demonstrated that the risk of T2D starts even in the normal fasting glucose range and exponentially increases in pre-diabetic ranges [3][4][5][6][7] . Relevant physiological perturbations producing a slow utilization of fasting glucose are likely to be present at stages of the disease as early as a decade before diagnosis 8 . The etiological causes of early glycemic perturbations are likely to be triggered by environmental and lifestyle factors 9,10 , but the precise biological mechanisms underpinning why people differently progress to hyperglycemia are unknown.
Recent large-scale genetic association meta-analyses have uncovered genetic variants cross-sectionally associated with T2D and related glycemic traits [11][12][13][14][15][16] . However, prospective data for genetic variant association discovery are scarce and findings have been inconsistent [17][18][19] . In this study, we conducted the largest genome-wide association study (GWAS) to date to identify genetic variants associated with fasting glucose changes over up to 14 years in 13,807 non-diabetic participants of European descent from nine cohorts.

Results
We included a total of 13,807 participants of European descent and free of diabetes at baseline and during the entire follow-up period with repeated fasting glucose levels measured at least at two time points over up to 14 years from nine cohorts. Characteristics of the study sample and follow-up, phenotype, and genotype information are presented in Table S1. The participants' average age at baseline for each cohort ranged from 41 to 70 years old. The follow-up time varied by cohort, on average ranging from 5 to 25 years. The slope of fasting glucose was calculated based on available fasting glucose measurements during the follow-up and then the slopes were inverse normal transformed within each cohort, for harmonization. We refer to this transformed slope as fasting glucose change over time. At baseline, cohort-specific average fasting glucose ranged from 4.9 to 6.2 mmol/L in men and 4.8 to 5.9 mmol/L in women, respectively. Our primary analysis included all available participants. In an exploratory analysis, we investigated whether stratifying our sample by cohorts with long-term follow-up [≥10 years] or short-term follow-up [<10 years] identified pertinent signals.
In a genome-wide association meta-analysis we did not find evidence of genetic variants associated with fasting glucose change over time at genome-wide significance level (P < 5 × 10 −8 ) (Supplemental Figs 1-3) nor evidence of inflated signals  in the primary analysis including the entire sample or the sensitivity analysis. For the analysis with all samples, the most significant association with fasting glucose changes over time was an intronic variant at the ODZ4 locus (rs7114256; P = 8.78 × 10 −7 , Table 1, Fig. 1). There were five other suggestively associated (P < 5 × 10 −6 ) variants for fasting glucose changes over time in three loci whose closest reference genes including ALLC (rs606243), NUDT12 (rs17496593, rs17496653, rs17562893) and ODZ4 (rs7103693) (  www.nature.com/scientificreports www.nature.com/scientificreports/ Tables 5-6). These included four loci whose closest reference genes were SNX16, BEGFA, GATA3 and CDKAL1 from short follow-up analysis with sample size up to 8,195 and ten loci whose closest reference genes were HCRTR2, WRN, SEPT9, SLC35B3, FAM84A, GRM8, MPP6, BAMBI, SSB, and C8orf31 from long follow-up analysis with sample size up to 3,669.
We conducted a power analysis with a sample size of 13,807 at the genome-wide significant threshold (5 × 10 −8 ) to detect a genetic variant explaining at least 0.05% to 0.5% of the variation in the fasting glucose change over time. The results showed that our study has 80% power to detect genetic variants, which explain at  www.nature.com/scientificreports www.nature.com/scientificreports/ least 0.28% of variation in change of fasting glucose over time (Fig. 2). This is equivalent to detect the genetic variants with minor allele frequency of 0.05 or 0.25 whose minimum effect corresponds to 0.17 or 0.09 standard deviation unit difference in the change of fasting glucose over time, respectively.

Discussion
We tested whether common genetic variants were associated with fasting glucose change over time in a GWAS including 13,807 initially non-diabetic participants from nine cohorts of European descent with repeated fasting glucose measures over up to 14 years, We found three suggestive associated variants at sub genome significance level (near ODZ4, ALLC, and NUDT12), and eight nominally associated previously known T2D-glycemia GWAS loci (CDKN2A/B, GRB10, PDX1, TMC6, CDH3, ABO, PDX1, and HK1) but none reached genome-wide significance for association or survived adjustment for multiple testing. We have placed the GWAS results data sets from this analysis on line at the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) website (https://www.magicinvestigators.org) and T2D knowledge portal (http://www.type2diabetesgenetics.org) to provide a genomic resource to further combine with futures studies and evaluate shared genetic links with T2D and other metabolic risk traits.
To date, more than 120 genetic loci have been identified to be associated with cross-sectional glycemic outcomes in successive waves of large-scale genetic association studies 12,14,15 . These risk alleles are associated with glycemic phenotypes and predict incident T2D when aggregated into a genetic risk score 12,20,21 . However, findings from our study do not support that single common risk alleles have a substantive impact on longitudinal fasting glucose changes. If there are effects on glucose changes, they are likely to be small. This observation is in agreement with evidence from other intermediate phenotypes such as genetic variants associated with deterioration of lipid levels [22][23][24][25] , lung function 26,27 or change in BMI 28-31 , where modest effects have been attributed to genetic factors on longitudinal trait changes, relative to single cross sectional trait measures. Regarding glycemic trajectories, no single genetic variants associated with glucose deterioration over time were detected in previous studies of European descent indivuals 17 or in Han individuals 19 , although Han Chinese carrying a higher number of T2D increasing-risk variants showed a greater increase in FG over time compared with those carrying a lower number of T2D increasing-risk variants. One small single-cohort study identified five genomic regions associated at genome-wide significance with longitudinal change in fasting glucose (GCKR, G6PC2, GCK, SLC30A8, MTNR1B), but the study included diabetic individuals taking hypoglycemic medications, which almost certainly introduced confounding into genotype -glucose change associations 18 .
Our results may highlight the importance of environmental determinants of glycemic deterioration. Nevertheless, genetic determinants of changes in glycemia may remain relevant for people with rapid transition from pre-diabetes to diabetes, which the design of our study was not able to capture. A potential future strategy to identify these loci, if they exist, would be to focus on pre-diabetic individuals who progress to T2D and adjust for the effects of all known variants affecting cross-sectional blood glucose inter-individual variability 32 . A limitation to our study is that the meta-analysis only involved European participants, so the results may not be generalizable to other ancestry groups. The power of our study was relatively limited even though this is the largest existing meta-analysis of fasting glucose change. In addition, we identified challenges posed by phenotypic heterogeneity, e.g. different follow-up duration or different numbers of longitudinal data points. Larger sample sizes from new cohorts will be key to help confirm or refute the current findings. An alternative approach, if data are available in the future, would be to study longitudinal glycemia in large and homogeneous populations, with more homogeneous phenotypes especially with more consistent number of follow-up visits and similar follow-up duration to gain more statistical power.
In summary, a large GWAS did not identify common genetic variation genome-wide significantly associated with fasting glucose change over time. Such genetic effects, if present, are likely small. The data have been deposited as a public genetic epidemiological resource to aid the hunt for genetic determinants of T2D and its relevant physiology.

Methods and Materials
Study sample. We recruited in total 13,807 individuals of European descent free from T2D at baseline and during the entire follow-up period with repeated fasting glucose measurements at two or more time points from nine cohorts representing three continents (America, Europe and Australia). The participating cohorts include the Bogalusa Heart Study (BHS), the CoLaus study (COLAUS), the Data from the Epidemiological Study on the Insulin Resistance Syndrome study (DESIR), the Erasmus Rotterdam Gezondheid Onderzoek study (ERGO), Framingham Heart Study (FHS), the Helsinki Birth Cohort Study (HBCS), Cooperative Health Research in the Region of Augsburg (KORA), Prevention of Renal and Vascular End-stage Disease study (PREVEND), and the National Institute on Aging (NIA) SardiNIA Study (SARDINIA). The ethnicity information for each individual was based on questionnaires or assessed using genetic data (principal component analysis). Ethnic outliers detected by principal component analysis for European ethnicity were excluded from further analysis. Diabetes was defined as a fasting glucose level >7 mmol/l, or use of glucose lowering medication. The study conformed to the Declaration of Helsinki guidelines. Institutional Review Board and/or oversight committees approved the study in each participating cohort and all participants provided written informed consent (See Supplemental Text).
Genotyping, imputation and quality control. Genotyping was conducted as specified in the Table S1.
Each study imputed their genotype to ~2.5 million Phase 2 HapMap CEU SNPs with imputation software, either IMPUTE or MACH 33,34 . We applied a quality control filter by removing SNPs with a minor allele frequency less than 1% and those with an imputation quality threshold proper_info < 0.4 for cohorts using IMPUTE and r 2 > 0.3 for cohorts using MACH. We used imputed allelic dosage in our association analysis.
www.nature.com/scientificreports www.nature.com/scientificreports/ Phenotype. To calculate longitudinal fasting glucose slopes we used repeated FG measurements available in the longitudinal cohort studies. To harmonize FG measures, FG measures obtained from whole blood were converted to plasma levels by using a coefficient of 1.13 (FG in mmol/l). We then modelled the association for each individual between FG and duration of time between baseline measure and each follow-up measure. The resulting beta coefficients (slopes) were then pooled and inverse normal transformed. The transformed slopes were used as the trait 'fasting glucose change' for the genetic association analysis.

Association Analysis and Meta-analysis.
For each participating cohort, we conducted genome-wide association analysis with transformed longitudinal fasting glucose changes adjusting for age at baseline, sex, and principal components of genetic variation to account for population stratification using linear regression with additive genetic effects for cohorts with unrelated samples. We performed mixed-effect model analysis with random effect to account for sample relatedness for cohorts with related samples. We then conducted inverse variance weighted meta-analysis of cohort-specific association results using METAL 35 . In an exploratory analysis, we stratified our analyses by study follow-up time and classified each cohort as having a long follow-up time (≥10 years) or a short follow-up time (<10 years). We applied genomic control correction to control type I error 36 . SNPs with a meta-analysis p-value ≤ 5 × 10 −8 were considered to be genome-wide significant.

Interrogation of Published Loci for Type 2 Diabetes Related Traits.
We tested the hypothesis that longitudinal fasting glucose slopes would be associated with previously-identified GWAS variants for T2D (128 SNPs) 15 , fasting glucose (32 SNPs) 13 , and HbA1c (60 SNPs) 16 . If the previously reported most-associated SNP was unavailable in the present analysis, we used a proxy SNP (LD r 2 > 0.8) if available. After proxy searches we evaluated 172 loci, including 82 for T2D, 58 for HbA1c and 32 for FG. The Bonferroni corrected p-value threshold for these look-ups was set at 0.0003 (0.05/172).

Post-hoc power calculation.
We conducted a post-hoc power analysis using Quanto software to investigate the power to detect 0.05% to 0.5% percent variation in phenotype explained by a genetic variant with a sample size of 13,807 at the genome-wide significant threshold (5 × 10 −8 ).

Data Availability
We have publicly deposited the summary results statistics on line at the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) website (https://www.magicinvestigators.org) and T2D knowledge portal (http://www.type2diabetesgenetics.org).