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Exploring the impact of chronic obstructive pulmonary disease (COPD) on diabetes control in diabetes patients: a prospective observational study in general practice.

Luijks HD, de Grauw WJ, Bor JH, van Weel C, Lagro-Janssen AL, Biermans MC, Schermer TR - NPJ Prim Care Respir Med (2015)

Bottom Line: In subgroup effect analyses, we tested whether potential differences between diabetes patients with/without COPD were modified by age, sex, socio-economic status (SES) and body mass index (BMI).We analysed 610 diabetes patients.Further research is needed.

View Article: PubMed Central - PubMed

Affiliation: Department of Primary and Community Care, Radboud university medical center, Nijmegen, The Netherlands.

ABSTRACT

Background: Little is known about the association between COPD and diabetes control parameters.

Aims: To explore the association between comorbid COPD and longitudinal glycaemic control (HbA1C) and systolic blood pressure (SBP) in a primary care cohort of diabetes patients.

Methods: This is a prospective cohort study of type 2 diabetes patients in the Netherlands. In a mixed model analysis, we tested differences in the 5-year longitudinal development of HbA1C and SBP according to COPD comorbidity (present/absent). We corrected for relevant covariates. In subgroup effect analyses, we tested whether potential differences between diabetes patients with/without COPD were modified by age, sex, socio-economic status (SES) and body mass index (BMI).

Results: We analysed 610 diabetes patients. A total of 63 patients (10.3%) had comorbid COPD. The presence of COPD was not significantly associated with the longitudinal development of HbA1C (P=0.54) or SBP (P=0.33), but subgroup effect analyses showed significant effect modification by SES (P<0.01) and BMI (P=0.03) on SBP. Diabetes patients without COPD had a flat SBP trend over time, with higher values in patients with a high BMI. For diabetes patients with COPD, SBP gradually increased over time in the middle- and high-SES groups, and it decreased over time in those in the low-SES group.

Conclusions: The longitudinal development of HbA1C was not significantly associated with comorbid COPD in diabetes patients. The course of SBP in diabetes patients with COPD is significantly associated with SES (not BMI) in contrast to those without COPD. Comorbid COPD was associated with longitudinal diabetes control parameters, but it has complex interactions with other patient characteristics. Further research is needed.

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Related in: MedlinePlus

Mixed model results (no subgroup effect analysis): longitudinal HbA1C (a) and SBP (b) outcomes of diabetes patients with and without comorbid COPD. Comorbid diseases: absence and presence are assessed on the date of diabetes diagnosis. Number (n) of cases with completed longitudinal analysis (no missing data on any of the variables included in the mixed model throughout): 582. Cases with missing values for BMI: n=23, cases with missing values for SES: n=5. *P-values <0.05. Age and BMI categories: based on the distribution of age and BMI values of patients contributing to the analyses, limits for ‘low’, ‘intermediate’ and ‘high’ values were 54, 64 and 72 years for age, and 26.0, 28.5 and 31.8 kg/m2 for BMI, respectively. Graphs for ‘reference categories’: in the graphic presentation, graph lines represent HbA1C or SBP courses for specific patient variables—for example, a male patient from the low-SES group with a specific age and BMI. We define the (theoretical) combination of the patient characteristics ‘male sex, low SES, median age, median BMI and absence of other comorbidity’ as ‘reference category’. The ‘Additional effects table’ below each graph contains information needed to construct lines of predicted outcomes, based on the mixed model results, for other subjects than the ‘reference category’. It shows the additional effects (to be added to the graphs displayed above) for other covariates included in the model. These values are not time dependent and not dependent on the absence or presence of COPD. Example: HbA1C courses over time for patients with and without comorbid COPD are shown in a. The ‘Additional effects table’ shows an additional effect of +0.04 (% HbA1C) for female sex. This means 0.04 should be added to the blue line for female patients without COPD and 0.04 should be added to the red line for female patients with COPD. The P-value of 0.70 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. BMI, body mass index; COPD, chronic obstructive pulmonary disease; SBP, systolic blood pressure; SES, socio-economic status.
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fig2: Mixed model results (no subgroup effect analysis): longitudinal HbA1C (a) and SBP (b) outcomes of diabetes patients with and without comorbid COPD. Comorbid diseases: absence and presence are assessed on the date of diabetes diagnosis. Number (n) of cases with completed longitudinal analysis (no missing data on any of the variables included in the mixed model throughout): 582. Cases with missing values for BMI: n=23, cases with missing values for SES: n=5. *P-values <0.05. Age and BMI categories: based on the distribution of age and BMI values of patients contributing to the analyses, limits for ‘low’, ‘intermediate’ and ‘high’ values were 54, 64 and 72 years for age, and 26.0, 28.5 and 31.8 kg/m2 for BMI, respectively. Graphs for ‘reference categories’: in the graphic presentation, graph lines represent HbA1C or SBP courses for specific patient variables—for example, a male patient from the low-SES group with a specific age and BMI. We define the (theoretical) combination of the patient characteristics ‘male sex, low SES, median age, median BMI and absence of other comorbidity’ as ‘reference category’. The ‘Additional effects table’ below each graph contains information needed to construct lines of predicted outcomes, based on the mixed model results, for other subjects than the ‘reference category’. It shows the additional effects (to be added to the graphs displayed above) for other covariates included in the model. These values are not time dependent and not dependent on the absence or presence of COPD. Example: HbA1C courses over time for patients with and without comorbid COPD are shown in a. The ‘Additional effects table’ shows an additional effect of +0.04 (% HbA1C) for female sex. This means 0.04 should be added to the blue line for female patients without COPD and 0.04 should be added to the red line for female patients with COPD. The P-value of 0.70 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. BMI, body mass index; COPD, chronic obstructive pulmonary disease; SBP, systolic blood pressure; SES, socio-economic status.

Mentions: After correction for covariates, comorbid COPD was not significantly associated with the course of HbA1C (P=0.54) or SBP (P=0.33) values over time in the initial analyses. Figure 2 shows the time trends for patients with and without comorbid COPD and the additional effects of covariates. The figure footnotes provide information for the definition of the ‘reference category’.


Exploring the impact of chronic obstructive pulmonary disease (COPD) on diabetes control in diabetes patients: a prospective observational study in general practice.

Luijks HD, de Grauw WJ, Bor JH, van Weel C, Lagro-Janssen AL, Biermans MC, Schermer TR - NPJ Prim Care Respir Med (2015)

Mixed model results (no subgroup effect analysis): longitudinal HbA1C (a) and SBP (b) outcomes of diabetes patients with and without comorbid COPD. Comorbid diseases: absence and presence are assessed on the date of diabetes diagnosis. Number (n) of cases with completed longitudinal analysis (no missing data on any of the variables included in the mixed model throughout): 582. Cases with missing values for BMI: n=23, cases with missing values for SES: n=5. *P-values <0.05. Age and BMI categories: based on the distribution of age and BMI values of patients contributing to the analyses, limits for ‘low’, ‘intermediate’ and ‘high’ values were 54, 64 and 72 years for age, and 26.0, 28.5 and 31.8 kg/m2 for BMI, respectively. Graphs for ‘reference categories’: in the graphic presentation, graph lines represent HbA1C or SBP courses for specific patient variables—for example, a male patient from the low-SES group with a specific age and BMI. We define the (theoretical) combination of the patient characteristics ‘male sex, low SES, median age, median BMI and absence of other comorbidity’ as ‘reference category’. The ‘Additional effects table’ below each graph contains information needed to construct lines of predicted outcomes, based on the mixed model results, for other subjects than the ‘reference category’. It shows the additional effects (to be added to the graphs displayed above) for other covariates included in the model. These values are not time dependent and not dependent on the absence or presence of COPD. Example: HbA1C courses over time for patients with and without comorbid COPD are shown in a. The ‘Additional effects table’ shows an additional effect of +0.04 (% HbA1C) for female sex. This means 0.04 should be added to the blue line for female patients without COPD and 0.04 should be added to the red line for female patients with COPD. The P-value of 0.70 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. BMI, body mass index; COPD, chronic obstructive pulmonary disease; SBP, systolic blood pressure; SES, socio-economic status.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4532160&req=5

fig2: Mixed model results (no subgroup effect analysis): longitudinal HbA1C (a) and SBP (b) outcomes of diabetes patients with and without comorbid COPD. Comorbid diseases: absence and presence are assessed on the date of diabetes diagnosis. Number (n) of cases with completed longitudinal analysis (no missing data on any of the variables included in the mixed model throughout): 582. Cases with missing values for BMI: n=23, cases with missing values for SES: n=5. *P-values <0.05. Age and BMI categories: based on the distribution of age and BMI values of patients contributing to the analyses, limits for ‘low’, ‘intermediate’ and ‘high’ values were 54, 64 and 72 years for age, and 26.0, 28.5 and 31.8 kg/m2 for BMI, respectively. Graphs for ‘reference categories’: in the graphic presentation, graph lines represent HbA1C or SBP courses for specific patient variables—for example, a male patient from the low-SES group with a specific age and BMI. We define the (theoretical) combination of the patient characteristics ‘male sex, low SES, median age, median BMI and absence of other comorbidity’ as ‘reference category’. The ‘Additional effects table’ below each graph contains information needed to construct lines of predicted outcomes, based on the mixed model results, for other subjects than the ‘reference category’. It shows the additional effects (to be added to the graphs displayed above) for other covariates included in the model. These values are not time dependent and not dependent on the absence or presence of COPD. Example: HbA1C courses over time for patients with and without comorbid COPD are shown in a. The ‘Additional effects table’ shows an additional effect of +0.04 (% HbA1C) for female sex. This means 0.04 should be added to the blue line for female patients without COPD and 0.04 should be added to the red line for female patients with COPD. The P-value of 0.70 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. BMI, body mass index; COPD, chronic obstructive pulmonary disease; SBP, systolic blood pressure; SES, socio-economic status.
Mentions: After correction for covariates, comorbid COPD was not significantly associated with the course of HbA1C (P=0.54) or SBP (P=0.33) values over time in the initial analyses. Figure 2 shows the time trends for patients with and without comorbid COPD and the additional effects of covariates. The figure footnotes provide information for the definition of the ‘reference category’.

Bottom Line: In subgroup effect analyses, we tested whether potential differences between diabetes patients with/without COPD were modified by age, sex, socio-economic status (SES) and body mass index (BMI).We analysed 610 diabetes patients.Further research is needed.

View Article: PubMed Central - PubMed

Affiliation: Department of Primary and Community Care, Radboud university medical center, Nijmegen, The Netherlands.

ABSTRACT

Background: Little is known about the association between COPD and diabetes control parameters.

Aims: To explore the association between comorbid COPD and longitudinal glycaemic control (HbA1C) and systolic blood pressure (SBP) in a primary care cohort of diabetes patients.

Methods: This is a prospective cohort study of type 2 diabetes patients in the Netherlands. In a mixed model analysis, we tested differences in the 5-year longitudinal development of HbA1C and SBP according to COPD comorbidity (present/absent). We corrected for relevant covariates. In subgroup effect analyses, we tested whether potential differences between diabetes patients with/without COPD were modified by age, sex, socio-economic status (SES) and body mass index (BMI).

Results: We analysed 610 diabetes patients. A total of 63 patients (10.3%) had comorbid COPD. The presence of COPD was not significantly associated with the longitudinal development of HbA1C (P=0.54) or SBP (P=0.33), but subgroup effect analyses showed significant effect modification by SES (P<0.01) and BMI (P=0.03) on SBP. Diabetes patients without COPD had a flat SBP trend over time, with higher values in patients with a high BMI. For diabetes patients with COPD, SBP gradually increased over time in the middle- and high-SES groups, and it decreased over time in those in the low-SES group.

Conclusions: The longitudinal development of HbA1C was not significantly associated with comorbid COPD in diabetes patients. The course of SBP in diabetes patients with COPD is significantly associated with SES (not BMI) in contrast to those without COPD. Comorbid COPD was associated with longitudinal diabetes control parameters, but it has complex interactions with other patient characteristics. Further research is needed.

Show MeSH
Related in: MedlinePlus