Clinical and genetic determinants of progression of type 2 diabetes: A DIRECT Study

Abstract

Objective

The rate at which diabetes progresses following diagnosis of type 2 diabetes is highly variable between individuals.

Research Design and Methods

We studied 5250 patients with type 2 diabetes using comprehensive electronic medical records on all patients in Tayside, Scotland from 1992 onwards. We investigated the association of clinical, biochemical and genetic factors with the risk of progression of type 2 diabetes from diagnosis to requirement for insulin treatment (defined as insulin treatment or HbA1c ≥8.5%/69 mmol/mol treated with two or more non-insulin diabetes therapies).

Results

Risk of progression was associated with both low and high BMI. In an analysis stratified by BMI and HbA1c at diagnosis, faster progression was independently associated with younger age at diagnosis, higher log triacylglyceride concentrations (Hazard Ratio (HR) 1.28 per mmol/L (95% CI 1.15-1.42)) and lower HDL concentrations (HR 0.70 per mmol/L (95% CI 0.55-0.87)). A high genetic risk score derived from 61 diabetes risk variants was associated with a younger age of diagnosis, a younger age at starting insulin, but was not associated with the progression rate from diabetes to requirement for insulin treatment.

Conclusions

Increased triacylglyceride and low HDL are independently associated with increased rate of progression of diabetes. The genetic factors that predispose to diabetes are different from those that cause rapid progression of diabetes suggesting a difference in biological process that needs further investigation.

The clinical course following diagnosis of diabetes is highly variable. Some patients have a rapid deterioration in glycaemia requiring early insulin treatment; others can be treated with oral agents for in excess of 20 years. It is important to gain insight into what factors are associated with progression of diabetes, as understanding the biological mechanisms may aid development of therapies specifically aimed at delaying diabetes progression, and understanding the characteristics of those who progress rapidly or slowly may aid in management of patients with type 2 diabetes.

It is generally accepted that there is a physiological continuum between pre-diabetes and diabetes, with progression to diabetes being caused by progressive loss of beta-cell secretory capacity (1), and glycemic deterioration of diabetes due to ongoing loss of function (2, 3). This may suggest a common biological process for diabetes risk and diabetes progression.

Diabetes risk factors have been extensively studied (summarized in (4)) and include clinical characteristics (e.g., age, sex, ethnicity, family history, BMI), glucose and biochemical parameters associated with insulin resistance and inflammation (e.g. HDL, triacylglyceride concentrations (TG), high-sensitivity C-reactive protein and inflammatory cytokines (5, 6)). In addition, genetic association studies have identified over 65 diabetes risk variants (7), however these provide little predictive utility over traditional clinical risk factors (8, 9). Where the physiological impact of these variants is known the majority of the risk variants impact on beta-cell function (10).

A few previous studies have investigated factors associated with rate of diabetes progression (3, 11-16). The consensus of these studies is that a low BMI, young age at diagnosis, and low beta-cell function are associated with faster progression of diabetes (either to failure of monotherapy or progression to insulin). UKPDS 25 reported that presence of ‘positive’ GAD antibody concentrations was also associated with faster progression to insulin (12). Beyond this, the mechanisms driving glycemic deterioration once diabetes is established remain unknown. No studies have investigated biomarkers of insulin resistance and inflammation and none have explored the effect of genetic variation on rates of diabetes progression.

Using a large, contemporary, population-based cohort in northeast, Scotland with extensive longitudinal follow up and DNA biobanked we tested the hypothesis that clinical available ‘insulin resistance’ biomarkers (low HDL, high LDL, low TG, high alanine transaminase (ALT), high BMI) were associated with rapid progression of diabetes. We further hypothesized that the genetic variants associated with diabetes risk were associated with increased rates of diabetes progression.

Research Design and Methods

We performed an observational study using data from the Genetics of Diabetes Audit and Research (GoDARTS) database, which has been described previously (17, 18). In brief, since October 1997, all patients with type 2 diabetes have been invited to give written informed consent to DNA collection as part of the Wellcome Trust United Kingdom Type 2 Diabetes case control collection. To date, nearly 10,000 patients with type 2 diabetes have participated in this GoDARTS study. All anonymised clinical information on these patients can be obtained from SCI-Diabetes (an Electronic Medical Record for all patients with diabetes in Scotland) and linked to all biochemistry records and prescription encashments from 1992 onwards, giving a comprehensive longitudinal record of diabetes therapy and glycemic control. The GoDARTS study has been approved by Tayside Committee on Medical Research Ethics and informed consent was obtained from all patients (REC reference 053/04).

Study population

To be eligible for the study, patients had to have been diagnosed with diabetes after 1st January 1994, to ensure sufficient prescribing information to accurately define time to insulin. In addition, patients were required to have a baseline HbA1c and BMI measurement. To minimize inclusion of patients with type 1 diabetes, patients were included if they had a clinical diagnosis of type 2 diabetes diagnosed after the age of 35 years with no progression onto insulin treatment within one year of diabetes diagnosis. From a total of 9636 patients with diabetes in the GoDARTS study, 5250 (54%) met the criterion for inclusion into the study. All patients were white European. Detailed sample selection from this observational cohort can be found in supplementary figure 1.

This cohort and research question were studied as part of the DIabetes REsearCh on patient sTratification (DIRECT) study – an EU FP7 Innovative Medicines Initiative (see www.direct-diabetes.org).

Measurement of Diabetes Progression (time to insulin event)

To avoid bias due to insulin inertia (16, 19), whereby there is a delay in commencing insulin after it is clinically indicated due to reluctance by the patient or the clinician, we used a composite endpoint for ‘requirement of insulin treatment’. This time to insulin outcome was reached in 1169 patients, defined as the period from diagnosis to a clinical endpoint of the earlier of either (i) starting sustained (more than six months duration) insulin treatment (339 patients) or (ii) clinical requirement of insulin treatment as indicated by two or more HbA1c measurements greater than 8.5%/69 mmol/mol more than three months apart when on two or more non-insulin diabetes therapies (880 patients). Patients who did not go on to insulin treatment were right censored in the event of death, moving away from the study area or reaching the study end of December 31st, 2009. The mean (sd) follow up was 8.5 (4.3) years.

Clinical Covariates

The following clinical variables were included as covariates: age at diagnosis of diabetes, calendar year of diagnosis, sex, BMI category (grouped by every 2 kg/m² between 22kg/m² and 44kg/m²); smoking status (ever vs. never); social class (derived from Scottish Index of Multiple Deprivation (SIMD); A lower score represents higher deprivation). In addition, the following baseline biochemistry parameters were included: HbA1c, HDL, LDL, TG, HDL and ALT. All biochemical baseline parameters were the closest measure to diagnosis within 12 months prior to diagnosis or the first measure after diagnosis. All biochemistry measurements except HDL were log transformed to fit a normal distribution.

Genetic covariates

We used a weighted Genetic Risk Score (GRS) that covers 61 established type 2 diabetes risk variants to represent an individual’s genetic susceptibility. The SNPs were selected from the latest DIAGRAM publication that reported 65 type 2 diabetes loci (7). Genotypes of the GoDARTS cohort were available from two sources: 1) Affymetrix 6.0 SNP genotyping array data on 3714 patients; 2) and CardioMetaboChip data on 3064 patients and 4114 controls. A proxy SNP with r²>0.6 (according to HapMap CEU panel) was selected where the index SNP was not genotyped. Four SNPs with poor proxies were dropped. All the remaining 61 SNPs passed routine GWAS genotyping quality control (call rate>98% and in Hardy-Weinberg equilibrium p>0.001) and their details are shown in supplementary table 1.

The weighted GRS was constructed by summing up the number of risk increasing alleles carried by each person at each SNP weighted by the logarithm of the allelic odds ratio of the SNP as reported in the published meta analyses (7). Missing genotypes were imputed with twice the population allele frequency of the risk increasing allele. As such the GRS created from the 61 SNPs has a possible range of 0 to 10.543. Instead of reporting an “averaged allelic effect” that is specific to the composite SNPs for GRS construction (20), here we reported a SNP independent “per GRS unit effect”. Results from analysis of the “averaged allelic effect” GRS are generally not comparable between studies because the initial type 2 diabetes genes discovered tend to have larger allelic effect than those from the bigger more recent studies. In contrast, our method of GRS construction can be readily extended to include any number of SNPs as the number of known type 2 diabetes variants increases, and the effect estimate of GRS defined as such are comparable regardless of the number of SNPs involved. This is particularly useful when comparing multiple scores derived from different subgroups of SNPs. Using this approach each unit of the GRS corresponds to an expected type 2 diabetes genetic risk increase of 2.72. In keeping with this, the GRS score showed a per unit risk increase of 2.68 (95% CI 2.40 to 2.88) in 6230 cases and 3866 controls from the GoDARTS study (data not shown).

To further dissect the type 2 diabetes risk genes, we developed two sub-scores: a beta-cell function GRS from 16 SNPs and an insulin resistance GRS from 7 SNPs, based upon what is known about the impact of these SNPs on glucose/insulin traits from the latest MAGIC publication (SNPs marked in supplementary table 1) (21).

Statistical Analysis

Our primary analysis utilized the Cox proportional hazards regression model (coxph in R (http://www.r-project.org/) for time to requirement for insulin treatment. When HbA1c at diagnosis was included as a continuous covariate, proportional hazard assumptions were not met. Baseline HbA1c was subsequently stratified into three groups to allow for a different hazard function in each group (HbA1c <7%/53mmol/mol, 7-9%/53-75mmol/mol, >9%/75mmol/mol). The relationship between BMI and rate of progression to insulin was non-linear, and was categorized into groups by an increment of 2kg/m². Thus BMI and baseline HbA1c categories were included as strata variables to allow different baseline hazard functions for each BMI and baseline HbA1c subgroup while the other covariates are assumed to have the same effect across strata. A high level of missing data exist in baseline LDL and ALT measurements and they were excluded from the phenotype model given the high collinearity between them and TG and HDL. The phenotypic model was stratified by the categorical groups for HbA1c and BMI, and included all the other clinical covariates. To assess the impact of the diabetes GRS we included the GRS as a covariate to the clinical phenotype model. For both models a p<0.05 was considered significant.

Results

Clinical Phenotype

The characteristics of the patients included in the study are shown in table 1, along with the univariate association of each clinical variable with progression to requirement for insulin treatment. Year of diagnosis was an important predictor, showing clear change in practice over time, with slower progression to insulin treatment in those diagnosed more recently. Univariately, there was an increased risk of progression to insulin treatment in those with a higher baseline TG, LDL and ALT, and a lower baseline HDL. The BMI distribution is presented in figure 1, and shows an increased rate of progression to insulin in those with a low and a high BMI, relative to the lowest risk group with a BMI between 24 and 26kg/m². For example, compared to those with a BMI between 24 and 26kg/m² those with a BMI<24kg/m² have a HR of 1.35 (95% CI 1.00 - 1.79) and those with a BMI>30kg/m² have a HR of 1.30 (95% CI 1.06 - 1.58). As would be expected the HbA1c at diagnosis of diabetes is a major determinant of the risk of progression to insulin treatment particularly where the composite endpoint relies upon the HbA1c result. Compared to the group with baseline HbA1c<7%/53mmol/mol, those with 7%/53mmol/mol, <HbA1c<9%/75mmol/mol, had a HR of 1.98 (95% CI 1.71- 2.3) and those with HbA1c>9%/75mmol/mol, had a HR of 3.22 (95% CI 2.78 - 3.72) (data not shown).

Table 1. Characteristics of the patients and their univariate associations with diabetes progression.

Covariate	Mean [standard deviation] or N	Hazard Ratio [95% CI]	P	n
Females vs. males	2877:2373	1.09 [0.97,1.22]	0.15	5250
Smokers vs. non smokers	3999:1251	1.06 [0.93,1.22]	0.38	5250
Year of diagnosis (year)	2002 [1999,2004]*	0.90 [0.88,0.92]	<0.001	5250
Age at diagnosis (year)	61.8 [10.8]	0.96 [0.95, 0.96]	<0.001	5250
Social class (per 1 SIMD unit from most deprived to most affluent)†	2.84 [1.44]	0.93 [0.89, 0.96]	<0.001	5191
Body Mass Index (kg/m2)	31.3 [5.9]	NA‡	NA	5250
Baseline HbA1c (%/mmol/mol)	7.86[2.05]/62	NA ‡	NA	5250
Baseline HDL (mmol/L)	1.2 [0.33]	0.39 [0.32, 0.48]	<0.001	5222
Baseline LDL (mmol/L)§	2.35 [0.85]	1.34 [1.06, 1.71]	0.02	4306
Baseline TG (mmol/L)§	2.77 [2.51]	1.77 [1.61, 1.95]	<0.001	5114
Baseline ALT (mmol/L)§	32.3 [17.0]	1.41 [1.24, 1.61]	<0.001	4504

^*year of diagnosis shows the median and quartile

^†coded from 1(most deprived) to 5(most affluent);

^‡covariates treated as stratification factors;

^§log transformed

Figure 1.

BMI distribution and its effect on progression to insulin requirement. Distribution of BMI in the population studied is shown on primary axis. For each BMI band the Hazard Rate ratio is shown relative to the BMI band 24-26 kg/m², error bars are the 95% confidence intervals for the hazard rate ratio.

Table 2 shows the full clinical phenotypic model. Within the BMI and HbA1c strata, a younger age of diagnosis of diabetes, an earlier year of diagnosis, lower HDL and higher TG were independently associated with a faster rate of progression to insulin treatment.

Table 2. Adjusted Cox proportional hazards model for diabetes progression (from diagnosis of diabetes to requirement of insulin treatment).

Covariate	Hazard Ratio [95% CI]	P
Age at diagnosis (per 1 year)	0.96[0.95,0.97]	<0.001
Year of diagnosis (per 1 year)	0.91[0.89,0.94]	<0.001
Baseline TG (per 1 mmol/L)*	1.28[1.15,1.42]	<0.001
Females vs. males	1.19[1.05,1.36]	0.008
Baseline HDL (per 1 mmol/L)	0.70[0.55,0.87]	0.002
Smokers vs. non smokers	1.11[0.95,1.29]	0.20
Social class (per 1 SIMD unit from most deprived to most affluent)	0.96[0.92,1.01]	0.09

^*log transformed. Analysis was stratified by HbA1c at diagnosis and BMI category.

Sensitivity Analysis

To assess the impact of using a combined endpoint rather than actual prescribed insulin use as the endpoint, we carried out the phenotypic modeling again using the single endpoint of sustained insulin treatment (supplementary table 2a and 2b); 723 patients eventually reached this endpoint. The results of this model were largely unchanged. The only additional significant effect seen was in the univariate analysis where female sex was associated with earlier insulin treatment. As there was no detectable association when the composite endpoint was used, our inference is that we had successfully controlled for the insulin inertia effect by using the composite endpoint, which our data would suggest is more commonly seen in men than women.

Diabetes genetic risk factors

We added the type 2 diabetes GRS to the clinical phenotypic model reported in table 2 and found no significant association with time to requirement for insulin treatment (HR 0.89, 95% CI 0.78 - 1.17) (data for other covariates in the full model not shown). A univariate analysis of the GRS also revealed no association (HR 1.02 per unit GRS, 95% CI 0.88 - 1.18) (data for other covariates in the full model not shown).

We then examined the effect of the type 2 diabetes GRS on age at diagnosis of diabetes and age at requirement for insulin treatment. The results of a multiple linear regression with adjustment for BMI are shown in figure 2. Each unit of type 2 diabetes GRS was associated with being 2.43 (95% CI 1.1 - 3.8) years younger at diagnosis and 2.15 (95% CI 0.71 - 3.19) years younger at requirement for insulin treatment; as before there was no effect of the GRS on the interval between diagnosis and insulin requirement (0.28 years per GRS unit 95% CI -0.12 - 0.69). Thus a greater genetic risk of diabetes is associated with a younger age at diagnosis of diabetes and subsequent younger age at insulin treatment, but not the time between diagnosis and insulin treatment, when compared to those with lower genetic risk of diabetes.

Figure 2.

Type 2 diabetes GRS association with different time spans in diabetes progression. The linear regressions were adjusted for BMI at diagnosis.

In a secondary analysis we defined two sub-GRS scores: a beta-cell function GRS (beta-cell GRS) and an insulin resistance GRS. When included in the phenotypic model, we found no association of either the beta-cell GRS (HR 1.10 per unit GRS, 95% CI 0.90 - 1.34) or insulin resistance GRS (HR 1.16 per unit GRS, 95% CI 0.77 - 1.73) on progression to insulin treatment after diabetes diagnosis (data for other covariates in the full model not shown). Each unit increase of the beta-cell GRS was associated with being 1.23 (95% CI −0.66 – 3.12) years younger at diagnosis and 1.0 (95% CI −0.97 – 2.97) years younger at requirement for insulin treatment; each unit increase of the insulin resistance GRS was associated with being 4.62 (95% CI 0.7 – 8.5) years younger at diagnosis and 3.69 (95% CI −0.37 – 7.74) years younger at requirement for insulin treatment. In an exploratory analysis, we assessed the impact of each variant on time to insulin treatment (supplementary figure 2, supplementary table 1). No individual variants achieved significance for progression from diagnosis to requirement for insulin treatment after correction for multiple testing.

Conclusions

In this large, population-based study spanning the last 15 years, we have identified a number of clinical parameters that are associated with progression of diabetes through to insulin requirement. Firstly, we show for the first time that high TG and low HDL are independently associated with progression beyond diabetes through to requirement of insulin treatment. We secondly confirm the finding of previous studies that at diagnosis, young age, poor glycemic control and low BMI are associated with rapid progression to requirement of insulin treatment (5, 6). However, we report a U-shaped curve for BMI, with the lowest rate of progression at a BMI of between 24-26kg/m². Thirdly, we show that established type 2 diabetes associated genetic variants are associated with a younger onset of diabetes and a younger age at insulin treatment but are not associated with diabetes progression.

A low HDL and high TG in the phenotypic model (and high ALT and high BMI when analysed univariately) are associated with more rapid progression of diabetes. These results are consistent with our hypothesis that these parameters that drive progression to diabetes are associated with progression of diabetes after diagnosis. As measures of insulin at diagnosis are not routinely collected, it is not possible to determine whether the lipid and ALT changes are simply a marker of insulin resistance, with ‘insulin resistance’ being the driver of progression, or whether these are having a primary impact (e.g., lipotoxicity) on beta-cell decline. It would be interesting to assess the effect of insulin sensitivity at diagnosis of diabetes on progression in a prospective collection as our data suggest that progression is associated with a more insulin resistant phenotype (low HDL, high TG, high BMI).

The clinical phenotypic model that we have developed is largely consistent with previous publications. Whilst some studies report that low BMI is associated with progression (13), others do not (15). Indeed, when we consider only a linear relationship between BMI and progression the data do not show an association of BMI with progression to insulin requirement. The U-shaped curve fits with our understanding of the biology of diabetes. Those who are normal weight at diagnosis (i.e., slim for someone with type 2 diabetes) are likely to be beta-cell deficient and progress rapidly to insulin requirement, as shown in UKPDS 26 (13). In contrast those who are particularly adipose are likely to be markedly insulin resistant and have other factors that drive progression (e.g., lipotoxicity).

Surprisingly we did not find a significant effect of type 2 diabetes risk variants on progression of diabetes. This lack of an association suggests that the biological factors captured by the diabetes risk variants do not play a large part in the biological mechanisms that result in progression of diabetes after diagnosis, and there are certainly biological mechanisms that may explain this difference, e.g. gluco or lipotoxicity driving progression after development of hyperglycaemia (22). Alternative explanations for the lack of association of the GRS with diabetes progression could be lack of power to detect an effect, and the presence of confounding factors that cannot be fully adjusted for in our model.

We utilized the GRS to maximize our power for this analysis and to test the hypothesis that the diabetes genetic risk factors when considered together are associated with progression of diabetes. Given the effect sizes of these variants on pre-diabetes risk we would not anticipate a large clinical effect on diabetes progression. However, for a GRS element that confers diabetes risk of OR=1.5 which is similar to the effect size of rs7903146 in TCF7L2, the current study has 80% statistical power to detect an increased diabetes progression rate of HR=1.09 at α=0.05 level. As such we were well powered to detect even a modest effect of the GRS. It is important to acknowledge that the use of the GRS makes an assumption that all gene variants that contribute to diabetes risk also contribute to progression. To explore heterogeneity in the effect of the diabetes risk variants on rate of diabetes progression we carried out a single variant analysis. This analysis was underpowered and should only be considered exploratory, but it did not reveal any one diabetes risk variant that was associated with progression of diabetes. A much larger, multi-centre analysis would be required to explore the impact of individual diabetes risk variants on diabetes progression.

We have used an observational data set to identify a large number of patients followed up for a sufficient length of time to enable a study of progression from diagnosis to requirement of insulin treatment. There are no prospective studies available that are in any way comparable in size and duration of follow up. However, the use of observational data does restrict analysis to biomarkers collected in routine clinical care. Therefore pancreatic autoantibody titres, and insulin or HOMA derived measures could not be included in the analysis. To avoid inclusion of patients with type 1 diabetes, we excluded patients who were diagnosed under the age of 35 or progressed onto insulin within one year, however patients with slower onset LADA could have been included, and this may account for the more rapid progression in the non-obese patients. There are further limitations to this study that should be acknowledged: Firstly, as this is a consented biobank, there is potential for bias in those included in the study, however approximately half of the population with type 2 diabetes in the region are included so the study cohort should be reasonably representative; secondly, the use of clinical data relies on patients engaging in clinical care and remaining in the region, however, due to the free healthcare system, the comprehensive coverage of all patients by the primary care system and the static population this should add minimal bias; finally, only 56% of the available population were included in the study, largely due to incomplete data in those excluded, thus the study cohort may not be completely representative of the population.

Another important consideration that we cannot include in this analysis is the behavioural factors that may impact on rate of progression. Unlike prospective studies where activity, diet and adherence can be assessed, these data are not available in routine health records and so cannot be incorporated into the model. The inclusion of the covariates for social deprivation score, smoking status and baseline BMI, may partly capture the effect of diet and lifestyle on progression, but it is possible that the lack of contribution of the type 2 diabetes genetic risk score reflects masking of the biological effect by these, and other, unmeasured variables.

The lack of an association of the total type 2 diabetes GRS, and in particular a GRS derived from known beta-cell genes, needs some consideration as a low beta-cell function at diagnosis is strongly associated with progression to insulin (13). These variants are well established to impact on beta-cell function when assessed in normal individuals without diabetes (21, 23). If beta-cell function could be measured at exactly the point at which diabetes develops, the beta-cell function would be the same for all individuals for a given level of insulin resistance. Therefore a genetic defect in insulin secretion would result in an earlier age at developing diabetes, as we see in our data, but the insulin secretion, if measured at development of diabetes, should not differ between individuals with different genotypes, and should therefore not impact on progression rate of diabetes. This is also consistent with cross sectional analyses that report that diabetes risk variants are associated with earlier use of insulin treatment (24, 25); this should not be misinterpreted as a greater progression to insulin from diagnosis of diabetes.

In summary, we report that type 2 diabetes risk genetic variants result in a younger age at diagnosis of diabetes, and as result of this a younger age at which insulin is required but in a well powered analysis are not associated with the rate at which diabetes progresses following diagnosis of diabetes. Our results suggest that the genetic factors that predispose to diabetes are different from those that cause progression of diabetes, which may be mediated by other mechanisms such as glucolipotoxicity, endoplasmic reticulum and oxidative stress (22). Our findings that increased rate of progression of diabetes is associated with obesity, with low HDL and high TG would certainly support this conjecture. Further genetic studies such as a sufficiently powered GWAS may help elucidate these mechanisms.