Summary
In this study, the coexistence of joint and respiratory disorders, with or without the prescription of corticosteroids, NSAIDs, or both, was associated with poorer glucose control compared to no comorbidity. This is meaningful in relation to joint problems because these reduce physical activity that is considered a cornerstone of optimal diabetes control.1,23 Additionally, joint problems tend to be unstable over time, resulting in irregular increases and decreases of the type and dosage of drugs taken, which make stable glycaemia control more difficult to achieve.
Of interest is the large proportion (>80%) of patients who had joint disorders. These findings are in line with those of other recent studies about the association between diabetes and various musculoskeletal manifestations.24 In addition, the findings that respiratory disorders are associated with poor glycaemic control are in agreement with several findings in literature.25–27 There was no effect of anaemia, depression, or malignancy.
Strengths and limitations
The strengths of this study lay in its large sample size and the laboratory-measured response with complete data. Additionally, a retrospective cohort design was used; this is a very strong design in epidemiology, in which robust data are collected and stored over time. A large amount of data that had been collected and stored in the past was analysed using a cohort design; by doing this, information collected tempore non suspecto could be used, avoiding the main risks of bias, especially selection and recall bias, that often occur in retrospective studies.
Weaknesses were also noted. No details were available about each comorbidity and comedication, which could include disorders or drugs with different characteristics. The categorisation employed in this study was a compromise between specificity and practicality. Only date of diagnosis or first prescription was recorded. Severity of the comorbidities are potential confounders,28 although use of corticosteroids, for example, may be a proxy for disease severity in respiratory and joint disorders.
The effect of five comorbidities and three groups of medication, which to the authors' knowledge is more than has ever been done before, were examined. However, more chronic diseases and more groups of medication that may also influence glycaemic control exist; if related to the predictors used, these might confound these results. Smoking status and other lifestyle behaviours that influence glycaemic control were insufficiently captured and, therefore, not used in the analysis. In addition, although a generic measure of the outcome status of the patients would have been helpful, such information is not available in the Intego database.
The derivation of the response used in this study is susceptible to random variation in the HbA1c levels at the end of the study. To check for the possible effect of this phenomenon, these data were initially recalculated, taking into consideration only an increase in HbA1c level larger than the standard deviation of the overall mean as a worse outcome. In addition, only a difference between the initial and the last observation of >1 unit of HbA1c was considered as evidence of elevated HbA1c. As the results from modelling did not differ a great deal, the initial response was maintained. In an alternative approach, a repeated-measures analysis on each subject, rather than the change in HbA1c level from the initial to the last available level, was performed. Two techniques taken into consideration were the linear mixed models for continuous HbA1c level and the generalised estimating equations for the dichotomised HbA1c level, based on the 7% stable cut-off point. Both analyses gave largely similar results to those reported in this article.
A further point to note is that some of the patients had been diagnosed with diabetes before the start of the registration period. This could bias the results as incident and prevalent cases were considered together so the second year of follow-up was considered the initial year into the study for each patient and the response was derived in a similar fashion. In this way, only patients who had had diabetes for at least 1 year were considered. There were no significant changes in results.
Differences between treatment groups in age at baseline and sex subgroups were significant, as was the absence of any comorbidity or comedication. However, this has no real influence on the study conclusions as all analyses were adjusted for treatment group, age, and sex.
This study did not find any association between depressive disorders and worsening HbA1c levels: a finding that contradicts those of several other studies that did find such an association.29,30 In addition, a recent trial showed that, in patients who were simultaneously treated for both type 2 diabetes and depression, medication compliance, blood-sugar, and depression levels significantly improved, compared with patients receiving usual care.31 A possible explanation is that coded depressive disorders in primary care may differ from the diagnosis of major depression in patients selected for prospective cohort studies.
Comparison with existing literature
No effect was found relating to malignancy or anaemia; this could be a result of complex mechanisms either increasing (for example, some drug treatment) or decreasing (for example, weight loss) HbA1 levels in interactions with routine diabetes care.
Few studies have reported on the effect of comedication. Faul et al32 did not find a significant impact of corticosteroid use on changes in HbA1c levels, despite suggesting careful monitoring of blood glucose levels for patients initiating use of corticosteroids. It seems sensible that, although starting corticosteroids or a change in dosage can lead to short periods of increased glycaemia, chronic corticosteroid use is easily controlled by adapting the treatment prescription. These results also pointed to treatment with NSAIDs being associated with an increased risk of elevated HbA1c levels. It is interesting to note that the comorbidities and NSAID treatments seemed to have an independent effect without meaningful interaction; in fact, the effect of each of them separately seemed to have a magnitude that was, more or less, similar; this was not cumulative when the treatments were combined. This may be important as the population under consideration is ageing and, hence, most patients with diabetes are likely to be prescribed more than one medication for a chronic condition.
Implications for research and practice
This study once more confirms the need for comprehensive guidelines to effectively care for patients with multiple diseases; these guidelines should be as generic as possible but could include adapted metabolic targets discussed on the basis of patient-specific treatment goals. The findings of this study could add to the body of evidence underpinning such new guidelines.
This study was performed in a population of patients with type 2 diabetes. However, in the future, a generic set-up must be developed to study all combinations of chronic diseases and chronic drug therapy. Ongoing databases of routinely collected information — such as the General Practice Research Database (UK), Intego (Belgium), RegistratieNet Huisartsgeneeskunde (The Netherlands) — seem to be a good framework for such studies. However, for most chronic disorders it may be difficult to identify good-quality outcome indicators that are routinely recorded in these databases.
