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The Effect of Comorbidity on Glycemic Control and Systolic Blood Pressure in Type 2 Diabetes: A Cohort Study with 5 Year Follow-Up in Primary Care.

Luijks H, Biermans M, Bor H, van Weel C, Lagro-Janssen T, de Grauw W, Schermer T - PLoS ONE (2015)

Bottom Line: In subgroup effect analyses we tested if potential differences were modified by age, sex, socioeconomic status, and BMI.Patients with cardiovascular diseases had sustained elevated levels of SBP (p = 0.014).Effect modification by socioeconomic status was observed in some comorbidity subgroups.

View Article: PubMed Central - PubMed

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

ABSTRACT

Aims: To explore the longitudinal effect of chronic comorbid diseases on glycemic control (HbA1C) and systolic blood pressure (SBP) in type 2 diabetes patients.

Methods: In a representative primary care cohort of patients with newly diagnosed type 2 diabetes in The Netherlands (n = 610), we tested differences in the five year trend of HbA1C and SBP according to comorbidity profiles. In a mixed model analysis technique we corrected for relevant covariates. Influence of comorbidity (a chronic disease already present when diabetes was diagnosed) was tested as total number of comorbid diseases, and as presence of specific disease groups, i.e. cardiovascular, mental, and musculoskeletal disease, malignancies, and COPD. In subgroup effect analyses we tested if potential differences were modified by age, sex, socioeconomic status, and BMI.

Results: The number of comorbid diseases significantly influenced the SBP trend, with highest values after five years for diabetes patients without comorbidity (p = 0.005). The number of diseases did not influence the HbA1C trend (p = 0.075). Comorbid musculoskeletal disease resulted in lower HbA1C at the time of diabetes diagnosis, but in higher values after five years (p = 0.044). Patients with cardiovascular diseases had sustained elevated levels of SBP (p = 0.014). Effect modification by socioeconomic status was observed in some comorbidity subgroups.

Conclusions: Presence of comorbidity in type 2 diabetes patients affected the long-term course of HbA1C and SBP in this primary care cohort. Numbers and types of comorbidity showed differential effects: not the simple sum of diseases, but specific types of comorbid disease had a negative influence on long-term diabetes control parameters. The complex interactions between comorbidity, diabetes control and effect modifiers require further investigation and may help to personalize treatment goals.

No MeSH data available.


Related in: MedlinePlus

Effect of number of comorbid diseases on five year HbA1C trend (Panel A, p 0.075) and on five year SBP trend (Panel B, p 0.005*).The reference category is male sex, low SES, median age, median BMI. Beta-coefficients (slopes for graph lines): Panel A (% HbA1C per year): 0 diseases: +0.0175; 1–2 diseases: -0,0225; ≥3 diseases: +0,0714. Panel B (mmHg per year): 0 diseases: +0,728; 1–2 diseases: -0,324; ≥3 diseases: +0,249. Abbreviations: BMI, Body Mass Index. SBP, systolic blood pressure. SES, socio-economic status.
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pone.0138662.g002: Effect of number of comorbid diseases on five year HbA1C trend (Panel A, p 0.075) and on five year SBP trend (Panel B, p 0.005*).The reference category is male sex, low SES, median age, median BMI. Beta-coefficients (slopes for graph lines): Panel A (% HbA1C per year): 0 diseases: +0.0175; 1–2 diseases: -0,0225; ≥3 diseases: +0,0714. Panel B (mmHg per year): 0 diseases: +0,728; 1–2 diseases: -0,324; ≥3 diseases: +0,249. Abbreviations: BMI, Body Mass Index. SBP, systolic blood pressure. SES, socio-economic status.

Mentions: Figs 2 and 3 present the direction of effects graphically. The lines represent the predicted values for HbA1C or SBP over five years from the mixed models. Corresponding p-values indicate the statistical significance of the difference between their slopes. Absence and presence of comorbid disease are determined on the date of the diabetes diagnosis. We define the (theoretical) combination of specific patient characteristics (e.g., sex, age, SES) as ‘reference category’. In the graphic presentation, graph lines represent HbA1C or SBP trends for subjects from this ‘reference category’. The corresponding additional effects tables (Tables 2 and 3) contain 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) for other covariates included in the model. These values are not time dependent and apply to any of the comorbidity groups displayed in the corresponding Figure. Example: Fig 3 Panel A shows predicted HbA1C time trends for patients with and without comorbid musculoskeletal disease (mixed model results). The reference category for these graph lines includes male sex. Table 3 (with additional effects for Fig 3, Panel A) 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 musculoskeletal disease and 0.04 should be added to the red line for female patients with musculoskeletal disease. The p-value of 0.69 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. For Figs 2–4, the number of patients with complete contribution up to and including a specific year after the diabetes diagnosis (follow-up ≥ x years) was as follows: after 0 years: 610, after 1 year: 554, after 2 years: 484, after 3 years: 430, after 4 years: 379, after 5 years: 342. Based on the distribution of age and BMI values of the patients who contributed to the analysis, limits for ‘low’, ‘intermediate’, and ‘high’ values of age were 54, 64 and 72 years, and for BMI, these were 26.0, 28.5 and 31.8 kg/m2. High BMI was associated with increased HbA1C and SBP values and higher age with increased SBP values. In the analysis of the effect of selected comorbidity we corrected for presence of other selected comorbidity and found, for instance, that baseline cardiovascular disease increased SBP, and comorbid mental disease decreased both HbA1C and SBP.


The Effect of Comorbidity on Glycemic Control and Systolic Blood Pressure in Type 2 Diabetes: A Cohort Study with 5 Year Follow-Up in Primary Care.

Luijks H, Biermans M, Bor H, van Weel C, Lagro-Janssen T, de Grauw W, Schermer T - PLoS ONE (2015)

Effect of number of comorbid diseases on five year HbA1C trend (Panel A, p 0.075) and on five year SBP trend (Panel B, p 0.005*).The reference category is male sex, low SES, median age, median BMI. Beta-coefficients (slopes for graph lines): Panel A (% HbA1C per year): 0 diseases: +0.0175; 1–2 diseases: -0,0225; ≥3 diseases: +0,0714. Panel B (mmHg per year): 0 diseases: +0,728; 1–2 diseases: -0,324; ≥3 diseases: +0,249. Abbreviations: BMI, Body Mass Index. SBP, systolic blood pressure. SES, socio-economic status.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4591264&req=5

pone.0138662.g002: Effect of number of comorbid diseases on five year HbA1C trend (Panel A, p 0.075) and on five year SBP trend (Panel B, p 0.005*).The reference category is male sex, low SES, median age, median BMI. Beta-coefficients (slopes for graph lines): Panel A (% HbA1C per year): 0 diseases: +0.0175; 1–2 diseases: -0,0225; ≥3 diseases: +0,0714. Panel B (mmHg per year): 0 diseases: +0,728; 1–2 diseases: -0,324; ≥3 diseases: +0,249. Abbreviations: BMI, Body Mass Index. SBP, systolic blood pressure. SES, socio-economic status.
Mentions: Figs 2 and 3 present the direction of effects graphically. The lines represent the predicted values for HbA1C or SBP over five years from the mixed models. Corresponding p-values indicate the statistical significance of the difference between their slopes. Absence and presence of comorbid disease are determined on the date of the diabetes diagnosis. We define the (theoretical) combination of specific patient characteristics (e.g., sex, age, SES) as ‘reference category’. In the graphic presentation, graph lines represent HbA1C or SBP trends for subjects from this ‘reference category’. The corresponding additional effects tables (Tables 2 and 3) contain 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) for other covariates included in the model. These values are not time dependent and apply to any of the comorbidity groups displayed in the corresponding Figure. Example: Fig 3 Panel A shows predicted HbA1C time trends for patients with and without comorbid musculoskeletal disease (mixed model results). The reference category for these graph lines includes male sex. Table 3 (with additional effects for Fig 3, Panel A) 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 musculoskeletal disease and 0.04 should be added to the red line for female patients with musculoskeletal disease. The p-value of 0.69 shows that this additional effect of sex on HbA1C in this analysis is not statistically significant. For Figs 2–4, the number of patients with complete contribution up to and including a specific year after the diabetes diagnosis (follow-up ≥ x years) was as follows: after 0 years: 610, after 1 year: 554, after 2 years: 484, after 3 years: 430, after 4 years: 379, after 5 years: 342. Based on the distribution of age and BMI values of the patients who contributed to the analysis, limits for ‘low’, ‘intermediate’, and ‘high’ values of age were 54, 64 and 72 years, and for BMI, these were 26.0, 28.5 and 31.8 kg/m2. High BMI was associated with increased HbA1C and SBP values and higher age with increased SBP values. In the analysis of the effect of selected comorbidity we corrected for presence of other selected comorbidity and found, for instance, that baseline cardiovascular disease increased SBP, and comorbid mental disease decreased both HbA1C and SBP.

Bottom Line: In subgroup effect analyses we tested if potential differences were modified by age, sex, socioeconomic status, and BMI.Patients with cardiovascular diseases had sustained elevated levels of SBP (p = 0.014).Effect modification by socioeconomic status was observed in some comorbidity subgroups.

View Article: PubMed Central - PubMed

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

ABSTRACT

Aims: To explore the longitudinal effect of chronic comorbid diseases on glycemic control (HbA1C) and systolic blood pressure (SBP) in type 2 diabetes patients.

Methods: In a representative primary care cohort of patients with newly diagnosed type 2 diabetes in The Netherlands (n = 610), we tested differences in the five year trend of HbA1C and SBP according to comorbidity profiles. In a mixed model analysis technique we corrected for relevant covariates. Influence of comorbidity (a chronic disease already present when diabetes was diagnosed) was tested as total number of comorbid diseases, and as presence of specific disease groups, i.e. cardiovascular, mental, and musculoskeletal disease, malignancies, and COPD. In subgroup effect analyses we tested if potential differences were modified by age, sex, socioeconomic status, and BMI.

Results: The number of comorbid diseases significantly influenced the SBP trend, with highest values after five years for diabetes patients without comorbidity (p = 0.005). The number of diseases did not influence the HbA1C trend (p = 0.075). Comorbid musculoskeletal disease resulted in lower HbA1C at the time of diabetes diagnosis, but in higher values after five years (p = 0.044). Patients with cardiovascular diseases had sustained elevated levels of SBP (p = 0.014). Effect modification by socioeconomic status was observed in some comorbidity subgroups.

Conclusions: Presence of comorbidity in type 2 diabetes patients affected the long-term course of HbA1C and SBP in this primary care cohort. Numbers and types of comorbidity showed differential effects: not the simple sum of diseases, but specific types of comorbid disease had a negative influence on long-term diabetes control parameters. The complex interactions between comorbidity, diabetes control and effect modifiers require further investigation and may help to personalize treatment goals.

No MeSH data available.


Related in: MedlinePlus