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Predictors of HbA1c over 4 years in people with type 2 diabetes starting insulin therapies: The CREDIT study
Diabetes Research and Clinical Practice, Volume 108, Issue 3, June 2015, Pages 432 - 440
- We identified factors predictive of HbA1c in people with T2DM starting insulin.
- Multivariable backward regression analysis selected variables associated with HbA1c.
- HbA1c over 4 years was mainly determined by the HbA1c before starting therapy.
- At 4 years, current insulin dose was the characteristic most strongly associated with concurrent HbA1c.
To identify factors associated with glucose control, as measured by HbA1c over 4 years, in people with type 2 diabetes starting insulin therapy.
CREDIT, an observational cohort study, collected data semi-annually over 4 years, on people with type 2 diabetes starting any insulin, in 311 centres in 12 countries; 2803 people had data on HbA1c during follow-up. Multivariable backward regression analysis selected characteristics associated with glycaemic control from a limited number of candidate variables.
Before starting insulin therapy, HbA1c was 9.3% (78 mmol/mol) and decreased to 7.6% (60 mmol/mol) after 1 year, and changed little after that. Insulin dose increased from 0.21 U/kg to 0.36 U/kg at 1 year, and then by 0.10 U/kg over the next 3 years. Body weight increased by 2.0 kg in the first year and increased little thereafter. Poorer glycaemic control over the 4 years was mainly determined by the HbA1c before starting therapy, after accounting for the other statistically significant associated variables in multivariable analysis: higher BMI, younger age, longer diabetes duration, more glucose-lowering drugs, using basal insulin alone, higher insulin dose and female sex. At 4 years, a higher current insulin dose was the characteristic most strongly associated with a higher concurrent HbA1c.
HbA1c at the start of insulin therapy was the characteristic most predictive of later HbA1c, after accounting for other variables associated with HbA1c. This may provide some justification for earlier insulin introduction to improve glucose control to target.
Keywords: Glycaemic control, Insulin therapy, Observational, Predictors, Type 2 diabetes.
Blood glucose control is well recognised as a factor associated with microvascular complications in people with type 2 diabetes, and there is some evidence that it may slow the progression of cardiovascular diseases  and . People with type 2 diabetes can often maintain adequate glycaemic control with appropriate lifestyle and oral glucose-lowering agents, but timely introduction of insulin therapy is indicated when glycaemic control is no longer maintained
Clinical trials have provided invaluable information about glycaemic control following therapy by differing insulin therapies. However, the people in clinical trials are highly selected according to the trial protocol, advised by the more motivated clinicians who agree to participate in trials, while participants are closely monitored in line with a trial protocol. There is less published information about the outcomes of insulin therapy in current clinical practice, and it is often only very short term
The Cardiovascular Risk Evaluation in people with Type 2 Diabetes on Insulin Therapy (CREDIT) study was designed in 2006
2. Subjects, materials and methods
2.1. Study design and participants
The CREDIT study design and selection of the people with type 2 diabetes have been reported previously
The study was approved by recruiting centre ethics committees, and informed written consent was obtained from each participant. The study was conducted according to the principles of the Declaration of Helsinki.
The principal outcome measure for this report is the average 1–4 year HbA1c, the average at years 1, 2, 3 and 4 after starting insulin; the HbA1c data used for the 1 year time point was the blood sample with a date closest to 1 year, within the time window 9–18 months after inclusion in the study; similar windows were used for the 2, 3 and 4-year HbA1c values. The average of the available yearly values was then used.
The secondary outcome measure is the HbA1c at year 4, at the end of the study, after 4 years of insulin therapy.
The main variables studied at the start of insulin therapy, as predictors of the two HbA1c outcome measures, were: sex, age, physical activity (recorded as some physical activity or none), smoking status (currently smokes or stopped <1 year; never smoked or stopped ≥1 year), family history of premature cardiovascular disease, micro- or macrovascular disease, other comorbidities, body mass index (BMI), duration of diabetes, HbA1c (normalised according to local laboratory values), number of oral glucose-lowering drugs, diagnosis of high blood pressure, number of blood pressure-lowering drugs, insulin regimen (basal alone, basal and short acting, short acting alone, premix insulin alone, other), total dose of insulin. At the end of 4 years the following additional variables were included as potential explanatory factors for HbA1c at 4 years: micro- and/or macrovascular disease or other comorbidities during follow-up; number of oral glucose-lowering drugs, insulin regimen and total insulin dose at 4 years; change in weight over 4 years; and symptomatic hypoglycaemia in the 6 months prior to the 4-year evaluation.
2.2. Statistical analyses
SAS statistical programme version 9.2 (Cary, NC, USA) was used for all analyses.
The characteristics of participants are described by percentages and by medians (quartile 1, quartile 4), geometric means and percentiles. Those with and without the two HbA1c outcome measures were compared by chi-square and Student t-tests. The percentage (95% confidence interval [CI]) achieving an HbA1c target of 7.0% at each year is shown graphically.
Both HbA1c outcome measures were loge transformed for statistical analysis, as they were right-skewed. The functional form of the continuous predictive variables was determined in linear models adjusted for region (six regions – Eastern Europe: Croatia, Ukraine and Russia; Southern Europe: Italy, Portugal, Spain; France; Northern Europe: United Kingdom, Finland, Germany; Japan; Canada) and for random centre effects (to allow for physician characteristics), by comparing the Akaike Information Criterion for models with the predictive variables in linear, loge and restricted cubic spline forms; all continuous variables (excepting change in weight) were loge transformed.
Univariate analyses of variables predicting the two HbA1c outcome measures used linear models, adjusted for region, as a fixed factor and recruiting centres as random effects. For the multivariable models, variables were selected by a backwards procedure, limited to effects significant at the conventional 0.05 level. For the HbA1c outcome measure at 4 years, an additional multivariable model included variables already selected at starting insulin, along with those measured at 4 years listed above, using a similar backwards selection procedure.
Predictors of the two HbA1c outcome measures above the 7.0% units HbA1c target were studied using univariate and multivariable mixed effects logistic regression models and backwards variable selection methods, similar to those above.
3.1. Baseline characteristics of people followed for glycaemic control
Of the 3060 people with type 2 diabetes who were started on insulin therapy and included in the CREDIT study, 2803 had HbA1c levels recorded at and after entry (principal outcome: average 1–4 year HbA1c), of whom 2212 had HbA1c at year 4 (secondary outcome) (Supplementary Fig. S1). The people with the principal HbA1c outcome available had similar baseline characteristics to those who were not followed, except for small but statistically significant differences: more physically active, fewer smoked, more micro- or macro-vascular disease, and more high blood pressure; for the secondary HbA1c outcome measure, those studied were more often women, more physically active, fewer smoked, more family history of premature CVD, more microvascular disease, fewer comorbidities, and more treated with short-acting insulin (23% vs. 18%). The people studied were distributed across 12 countries (Supplementary Table S1), with Russia and Japan recruiting the largest numbers of people.
The characteristics of the populations with the principal and secondary HbA1c outcomes were very similar (
3.2. Characteristics over the 4 years after starting insulin therapy
The principal HbA1c outcome measure had a skewed distribution, with a geometric mean of 7.6% (60 mmol/mol), an upper quartile of 8.2% (66 mmol/mol), and 4.4% of people had an HbA1c >10.0% (86 mmol/mol) (Supplementary Fig. S2). After 1 year, HbA1c was 7.6% (60 mmol/mol) and was fairly constant thereafter, being 7.5% (58 mmol/mol) at year 4
The insulin dose increased from 0.21 U/kg (geometric mean) at starting insulin to 0.36, 0.40, 0.43, and 0.46 U/kg at years 1–4, respectively
3.3. Characteristics associated with the average 1–4 year HbA1c, the principal outcome measure
The baseline characteristics associated with a higher HbA1c outcome (Table 2 and Table 3) were: absence of other comorbidities (other than micro- or macrovascular complications, neither of which showed an association), more glucose-lowering drugs, a younger age, a higher BMI and a higher baseline HbA1c (all P < 0.0001); other variables associated with a higher HbA1c outcome were female sex, a family history of premature CVD, the insulin regimen (in particular basal insulin alone and premix insulin alone) and a longer duration of diabetes (all P < 0.01).
Geometric means (95% CI)
Univariate regression coefficients (SE)a and b for continuous variables associated with the principal outcome: average 1–4 year HbA1c and the secondary outcome: HbA1c at year 4, according to continuous characteristics at baseline and 4 years or during follow-up (adjusted for region and centre). The CREDIT study.
The multivariable analysis predicting higher average 1–4-year HbA1c, after a backwards selection of baseline characteristics, included eight predictors (
Multivariable linear and logistic regression coefficients (SE)a and b and odds ratios (95% confidence intervals) of variables at the start of insulin therapy associated with the principal outcome: average 1–4 year HbA1c after backwards selection of variables. The CREDIT study.
3.4. Characteristics associated with HbA1c at year 4, the secondary outcome measure
The baseline characteristics associated with a higher HbA1c at year 4 (Table 2 and Table 3) were: younger age, higher BMI and higher baseline HbA1c (all P < 0.0001); fewer non-vascular comorbidities and more glucose-lowering drugs (both P < 0.005). Year 4 characteristics associated with a higher HbA1c were a higher insulin dose, a greater increase in weight, the insulin regimen (in particular basal and short-acting insulin) (all P < 0.0001); microvascular disease and fewer comorbidities during the follow-up, more glucose-lowering drugs, and lack of hypoglycaemia (all P < 0.02).
After backwards selection of variables, and accounting for all other variables included as predictors in the multivariable analysis, baseline HbA1c was again the strongest predictor of HbA1c at year 4, followed by age (both P < 0.0001), then BMI diabetes duration and other glucose-lowering drugs, in order of significance (
In this study of people with type 2 diabetes starting insulin therapy in routine clinical practice, there was a marked improvement in glycaemic control at 1 year, which remained stable over the remaining 3 years of the study. However, at 4 years after starting insulin therapy, only 35% of the participants had an HbA1c below the general glycaemic target of 7.0% (53 mmol/mol), with 13% having an HbA1c ≥9.0% (75 mmol/mol). The main baseline characteristics associated with poorer glycaemic control over the 4 years were higher values of baseline HbA1c, age, BMI, duration of diabetes, number of other glucose-lowering drugs, insulin regimen (specifically basal or premix insulins vs. multiple injection regimens), and finally a higher insulin dose and female sex. For glycaemic control at the end of the 4 years, a higher concomitant insulin dose was the most predictive factor, with more glucose-lowering drugs, weight gain, lack of hypoglycaemia and new microvascular disease over the follow-up also associated with poorer control; baseline HbA1c and age remained predictive.
While at first sight it might seem clinically sensible that in the years after starting insulin therapy, a higher baseline HbA1c should be a co-predictor of HbA1c, along with BMI and the starting insulin dose, these variables were retained in multivariable analysis, so it cannot simply be that they are co-associated with later poor control due to investigators starting insulin later in the more obese, and then at higher doses, in conjunction with more oral agents. Similarly, it is tempting to attribute both high baseline HbA1c and duration of diabetes to a relative deficiency of endogenous insulin secretion; again these characteristics were both retained on multivariable analysis, suggesting that there may not be a single underlying unmeasured explanatory factor for predicting poorer blood glucose control. The independent association of BMI with later HbA1c could reflect a reluctance of clinicians to titrate the insulin dose in the more obese (although a higher concomitant insulin dose at the end of the study was an independent explanatory factor), or perhaps it is simply a marker of difficulty in harnessing self-motivation to improve measures of health status.
The associations of insulin regimen with glucose control over the 4 years, but not at the end of the study, need a more complex explanation. It might be expected that basal insulin alone and premix insulin would be selected for those with earlier and easier to treat blood glucose control, and thus that attained HbA1c might be lower. However, for the CREDIT study, we have already shown that the HbA1c at start of insulin therapy was not a factor associated with the choice between basal insulin and insulin regimens other than premix
Hypoglycaemia might a priori be expected to be a factor explanatory for HbA1c. Clinicians appear to expect lower HbA1c to be associated with more hypoglycaemia, contrary to the findings of the ACCORD study for either intensive or standard therapy
Other publications have addressed this issue in randomised controlled trials of insulin regimens , , and , in retrospective studies from electronic prescriber databases , , and  and in observational studies with features in common with our own , , and . Randomised clinical trials however, are generally restricted to their planned comparison, and are often of short duration. The 4-T study did suggest that insulin regimen had an effect on achieved glucose control at 1 year, with those on basal insulin having worse glucose control but a better hypoglycaemia experience than those on premix or mealtime insulins alone
A previous report from the CREDIT study compared insulin regimens using a propensity score of baseline characteristics to try to overcome the probable confounding factors in the allocation of specific insulin regimens
Our study has its limitations. While the participating physicians were chosen to represent those prescribing insulin in a given country
With regard to the strengths of this study, it was prospective and longitudinal with findings over a 4-year follow-up–much longer than in many other reports. The results were generally consistent from year 1. The physicians were free to choose the insulin regimen and its dose when initiating insulin therapy; as there were no study recommendations on either, it is expected that physicians would have followed normal practice, both when starting insulin and with any changes during follow-up. This differs from the prospective observational studies discussed above, which were limited to specific insulin analogues, and of shorter duration , , and . Our data are those collected and recorded in routine practice, while the large number of participants and participating physicians provide a wide range of clinical practices from the developed world. Accordingly each of the study variables would have a wide range, thus enhancing the chance of showing variables with a statistically significant association (or not) with glycaemic control. However, the large sample size may also mean that small effects that are clinically insignificant, and perhaps clinically undetectable, may be statistically significant. This is the case for weight gain at the end of the study, where a 1 kg weight gain was associated with only a 0.003% unit (0.03 mmol/mol) increase in HbA1c that was statistically significantly in multivariable analysis.
The principal characteristics for glycaemic control after starting insulin therapy thus appear to be a lower starting HbA1c, a shorter duration of diabetes, a lower number of oral glucose-lowering drugs, and a lower insulin dose. Together, these variables seem to point to an advantage in beginning insulin earlier. Some studies have addressed this point directly, and while the advantages of very early use of insulin over oral agents can be disputed, these studies do not suggest much in the way of detriment either  and . Using the CORE Diabetes Model, Goodall et al.
In conclusion, the glycaemic control of these people with type 2 diabetes starting on insulin therapy, improved markedly by the end of the first year, and this level was maintained over the three subsequent years. However, the major determinant of success was a low baseline HbA1c, supported by lower BMI, higher age, lower starting insulin dose, and a shorter duration of diabetes. At the 4-year follow-up the predictive characteristics were similar, but concurrent low insulin dose was highly predictive, as well as a lower number of oral glucose-lowering drugs. Given the impact of HbA1c at starting insulin therapy, and also the duration of diabetes, it would appear that the timing of starting insulin therapy may be crucial in the longer-term maintenance of good glycaemic control.
Conflict of interest
B.B. has served on advisory bureaus for AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Novo Nordisk and Sanofi. F.C.G. is a consultant to Sanofi. N.F. has received research grants and served as consultant to Eli Lilly, Medtronic, Novo Nordisk, Pfizer, and Sanofi and has served on speaker bureaus for Novo Nordisk and Sanofi. M.V. and V.P. are employees of Sanofi. P.D.H, either personally or through institutions with which he is associated, receives funding for research, advisory, and educational activities from most insulin and other glucose-lowering medication manufacturers, including Sanofi, Novo Nordisk, and Eli Lilly.
B.B., N.F., V.P. and P.D.H. contributed to study design, study conduct/data collection, data analysis, and writing. F.C.G. and M.V. contributed to data analysis and writing. B.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have approved the final article. These results were presented orally at the IDF World Diabetes Congress, Melbourne, Australia, December 2013 and as a poster at the French Speaking Society for Diabetes, Paris, France, March 2014.
The CREDIT study was designed by Sanofi together with a study advisory board. Data collection and analysis were funded by Sanofi; interpretation of the data was by the authors, who wrote the article and made the decision to submit for publication. Editorial support was provided by Tom Claus, PhD, of PPSI (a PAREXEL company), funded by Sanofi.
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a INSERM Centre for Research in Epidemiology and Population Health (CESP), U1018, 16 Avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France
b University Paris-Sud, URMS 1018, Villejuif, France
c AtlanStat, Rezé, France
d University College London, UK
e Sanofi, Paris, France
f Newcastle University, Newcastle upon Tyne, UK
⁎ Corresponding author. Tel.: +33 1 45 59 51 61; fax: +33 1 47 26 94 54.
© 2015 Published by Elsevier B.V.