Abstract
Aims/Introduction
Medication adherence, which is decreased by a poor understanding of medications, has a close association with blood glucose level in patients with type 2 diabetes. However, a relationship between the understanding of antidiabetic medication and blood glucose level in patients with type 2 diabetes is unclear. Here, we aimed to investigate the relationship between the understanding of antidiabetic medication and blood glucose level in patients with type 2 diabetes.
Materials and Methods
Lifestyle factors were evaluated by a questionnaire method, in the present cross‐sectional study. Poor understanding of antidiabetic medication (PUAD) was defined as a discrepancy between the answer and the actual use of oral antidiabetic medication on the questionnaire. Poor blood glucose level was defined as hemoglobin A1c ≥8%. To investigate the impact of PUAD on poor blood glucose level, propensity‐score matching analysis was used to remove the bias of confounding variables, including sex, age, log (duration of diabetes +1), body mass index, number of oral antidiabetic medications, smoking status, alcohol drinking, exercise, nephropathy, neuropathy, oral antidiabetic medications and insulin.
Results
Among 479 patients, 40 patients (8.4%) were categorized into the PUAD group. The hemoglobin A1c of patients with PUAD was higher than that of patients without (7.5 [1.3] vs 7.2 [0.9]%, P = 0.041). In the propensity‐matched 74 patients, PUAD was associated with poor blood glucose level (odds ratio 5.45, 95% confidence interval 1.54–25.8, P = 0.007) by logistic regression analysis.
Conclusion
A poor understanding of antidiabetic medication is associated with poor blood glucose level in patients with type 2 diabetes.
Keywords: Blood glucose, Medication adherence, Patient medication knowledge
Introduction
Currently, the number of patients with diabetes is increasing worldwide, and it is well known that diabetes give rise to various complications1. To prevent these complications, maintaining a good blood glucose level is required2, 3. Both exercise and diet therapies are the main therapies; however, it is difficult to achieve a good blood glucose level with these therapies only in patients with type 2 diabetes4. Thus, to achieve a good blood glucose level, many patients with type 2 diabetes receive a medication therapy4. Regardless of these various approaches, many patients cannot achieve a good blood glucose level5.
One of the reasons why many patients cannot achieve a good blood glucose level is poor medication adherence6. The risk of diabetic complications was reduced by proper use of antidiabetic medications, and medication adherence reinforces the effect of medications7. In fact, medication adherence affects the blood glucose level4, 8, 9, 10.
In contrast, it is reported that an understanding of medications prescribed for patients is associated with their medication adherence11. Although some previous studies reported the relationship between medication adherence and blood glucose level, no previous studies reported the relationship between the understanding of antidiabetic medications and blood glucose level in patients with type 2 diabetes12. Thus, we examined the relationship between the understanding of antidiabetic medications and blood glucose level in patients with type 2 diabetes at the baseline date of the KAMOGAWA‐DM cohort study.
Methods
Patients and study design
The KAMOGAWA‐DM cohort study, which was started to elucidate the natural history of patients with diabetes from 2014, is an ongoing cohort study of patients with diabetes12. We gave informed consent to all patients for this cohort study, and we recorded patients’ medical data into a database after eliminating personal identification information. In this cross‐sectional study, we enrolled the outpatients at the Kyoto Prefectural University of Medicine (Kyoto, Japan), from January 2014 to January 2016. Patients who used steroid treatment, with active malignancy, with severe renal dysfunction (estimated glomerular filtration rate <30 mL/min/1.73 m2)13, after a renal transplantation, after a liver transplantation and with missing data of covariates were excluded from the present study. We also excluded patients who attended a hospital for psychosis, because they could not take medication regularly; and patients whose answer for insulin use showed a discrepancy to actual use in the questionnaire, because their answer was not credible. In addition, we excluded the patients with anemia whose hemoglobin concentration was <11 g/dL14, because anemia affects hemoglobin A1c (HbA1c) levels. Approval for this study was obtained from the Ethics Committee of Kyoto Prefectural University of Medicine (No. RBMR‐E‐466‐1).
Questionnaire and measurements
Lifestyle factors were assessed by a questionnaire survey, including “Do you take antidiabetic medications?”. Current treatments data, including medications for diabetes and total number of oral medications, were gathered from medical records. In regard to the exercise status, we defined the patients who did any sports once a week regularly as regular exercisers15. In regard to alcohol, we defined patients who drank alcohol daily as alcohol drinkers. In regard to smoking, patients were categorized into three groups; never‐smoker, ex‐smoker or current smoker16. We defined a poor understanding of antidiabetic medication (PUAD) as the answer to the question “Do you take antidiabetic medications?” being inconsistent with the actual prescriptions on the questionnaire (Figure S1).
Body mass index was calculated as weight in kilograms divided by height in meters squared. We collect the venous blood after an overnight fast, and we checked the levels of several factors, including total cholesterol, high‐density lipoprotein cholesterol, triglycerides, fasting plasma glucose, gamma‐glutamyltransferase, aspartate aminotransferase, alanine aminotransferase, uric acid and creatinine. Glomerular filtration rate was approximated by the Japanese Society of Nephrology equation: estimated glomerular filtration rate (mL/min/1.73 m2) = 194 × serum creatinine−1.094 × age−0.287 (×0.739 for women)17. The National Glycohemoglobin Standardization Program unit was used for HbA1c, and we defined HbA1c ≥8% (63 mmol/mol) as a poor blood glucose level18.
We assessed the relationship between the poor understanding of antidiabetic medication and the prevalence of microvascular complications, which included diabetic nephropathy and diabetic neuropathy. As for diabetic nephropathy, we divided patients into three groups according to the urinary albumin excretion level: <30 mg/g, 30–300 mg/g and <300 mg/g creatinine19. In regard to diabetic neuropathy, we judged the presence of diabetic nephropathy by assessing whether the attending physician had diagnosed neuropathy in medical record (Figure 1).
Figure 1.
Inclusion and exclusion flow.
We also examined the diabetic medications and categorized them as follows: insulin secretagogues, incretin‐based therapy, insulin sensitizers, nutrient load reducers and insulin. We categorized sulfonylureas and glinides into insulin secretagogues, dipeptidyl peptidase‐4 inhibitors and glucagon‐like peptide‐1 receptor agonist into incretin‐based therapy, pioglitazone and metformin into insulin sensitizers, and α‐glucosidase inhibitors and sodium‐glucose cotransporter inhibitors into nutrient load reducers.
Statistical analysis
Statistical analyses were carried out using JMP version 12.0 software (SAS Institute, Cary, NC, USA), and P < 0.05 was defined as significant statistically. We calculated the mean, median and frequencies of variables. Continuous variables were shown as the mean (standard deviation [SD]), and if the variables were skewed, as the median (interquartile range). Student's t‐test or the Mann–Whitney U‐test were used to evaluate statistical significance of differences between groups. Category variables were shown as the number. The χ2‐test was used to evaluate statistical significance of differences between groups.
Univariate logistic regression analyses were carried out to evaluate the effect of various factors, including PUAD, on poor blood glucose level.
In the present study, 94 (19.6%) patients had a poor blood glucose level. Because this number of patients might be small for statistical analysis, we used propensity scores, which preserved statistical power by reducing the covariates into a single variable. For the assessment of the propensity score, the dependent variable was the PUAD. The propensity score was evaluated using multivariable logistic regression models that included the following parameters: sex, age, log (duration of diabetes +1), body mass index, number of oral antidiabetic medications, smoking status, alcohol drinking, exercise, nephropathy, neuropathy, insulin secretagogues, incretin‐based therapy, insulin sensitizers, nutrient load reducers and insulin.
The c‐statistic for the propensity score model was 0.80, which shows an acceptable discrimination. Then, 1:1 matching on the propensity score was carried out using nearest neighbor matching with a maximum caliper of 0.05 of the propensity score. Finally, 74 patients were selected for the propensity‐matched population, and we calculated the odds ratio for poor blood glucose level by logistic regression analysis.
Results
A total of 638 patients (398 men and 240 women) were enrolled into the present cross‐sectional study. Then, 159 patients (93 men and 66 women) were excluded. Thus, 479 patients (305 men and 174 women) were selected for this study.
Table 1 shows the clinical characteristics of the overall study patients according to the presence of PUAD. The mean age and median of duration of diabetes (interquartile range) were 68.4 years (SD 10.4 years) and 12 years (SD 7–20 years), respectively. The mean HbA1c was 7.2% (SD 1.0%; 55.3 mmol/mol [SD 10.6 mmol/mol]), and 94 patients (20%) were categorized as having a poor blood glucose level. A total of 281 patients (59%) had hypertension, and 425 patients (89%) use medication for diabetes. The mean total number of oral medications and the number of oral antidiabetic medications were 5.4 (SD 3.3) and 1.7 (SD 1.2), respectively. In addition, 40 patients (8.4%) were categorized into the PUAD group. The HbA1c of patients with PUAD was higher than that of patients without (7.5% [SD 1.3%] vs 7.2% [SD 0.9%], P = 0.041 [58.5 mmol/mol (SD 14.0 mmol/mol) vs 55.0 mmol/mol (SD 10.2 mmol/mol)]). The number of antidiabetic medications of patients with PUAD was lower than that of patients without (1.1 [SD 1.0] vs 1.7 [SD 1.2], P = 0.001).
Table 1.
Clinical characteristics of overall study patients according to the presence of a poor understanding of antidiabetic medication
| Total | Poor understanding of antidiabetic medication (−) | Poor understanding of antidiabetic medication (+) | P | |
|---|---|---|---|---|
| n | 479 | 439 | 40 | – |
| Men/women (n) | 305/174 | 271/168 | 34/6 | 0.003 |
| Age (years) | 68.4 (10.4) | 68.7 (10.1) | 65.5 (13.1) | 0.063 |
| Duration of diabetes (years) | 12 (7–20) | 12 (7–20) | 15 (6–20) | 0.282 |
| Body mass index (kg/m2) | 23.6 (3.7) | 23.6 (3.7) | 23.9 (3.3) | 0.579 |
| Hypertension (−/+) | 198/281 | 183/256 | 15/25 | 0.607 |
| Hemoglobin A1c (%) | 7.2 (1.0) | 7.2 (0.9) | 7.5 (1.3) | 0.041 |
| Hemoglobin A1c (mmol/mol) | 55.3 (10.6) | 55.0 (10.2) | 58.5 (14.0) | 0.041 |
| Poor blood glucose level (−/+) | 385/94 | 358/81 | 27/13 | 0.032 |
| Fasting plasma glucose (mmol/L) | 8.0 (2.3) | 7.9 (2.2) | 8.3 (3.8) | 0.278 |
| Total cholesterol (mmol/L) | 4.7 (0.9) | 4.8 (0.9) | 4.4 (0.8) | 0.025 |
| Triglycerides (mmol/L) | 1.2 (0.8–1.7) | 1.2 (0.8–1.7) | 1.3 (0.8–1.8) | 0.839 |
| HDL cholesterol (mmol/L) | 1.5 (0.4) | 1.6 (0.4) | 1.4 (0.4) | 0.049 |
| Aspartate aminotransferase (IU/L) | 25.4 (12.7) | 25.5 (13.0) | 24.2 (10.0) | 0.521 |
| Alanine aminotransferase (IU/L) | 24.0 (17.4) | 24.0 (17.7) | 23.4 (14.4) | 0.829 |
| Gamma‐glutamyltransferase (IU/L) | 26 (18–41) | 25 (18–41) | 30 (21–37) | 0.408 |
| Creatinine (μmol/L) | 70.4 (20.9) | 70.0 (20.9) | 73.8 (21.0) | 0.279 |
| eGFR (mL/min/1.73 m2) | 72.3 (18.6) | 72.1 (18.5) | 74.1 (19.1) | 0.514 |
| Uric acid (μmol/L) | 315 (77) | 316 (77) | 306 (79) | 0.460 |
| Exercise (−/+) | 221/258 | 203/236 | 18/22 | 0.880 |
| Never‐/ex‐/current‐smoker (n) | 187/225/67 | 172/208/59 | 15/17/8 | 0.512 |
| Alcohol drinking (−/+) | 360/119 | 333/106 | 27/13 | 0.242 |
| Nephropathy (UAE <30/30–300/<300 mg/g creatinine) | 300/123/56 | 275/116/48 | 25/7/8 | 0.156 |
| Neuropathy (−/+) | 343/136 | 319/120 | 24/16 | 0.089 |
| Medication usage for diabetes (−/+) | 54/425 | 47/392 | 7/33 | 0.193 |
| Total no. oral medication | 5.4 (3.3) | 5.4 (3.3) | 5.6 (4.0) | 0.667 |
| No. oral antidiabetic medication | 1.7 (1.2) | 1.7 (1.2) | 1.1 (1.0) | 0.001 |
| No. oral medication other than antidiabetic medication | 3.7 (3.1) | 3.6 (3.1) | 4.5 (3.7) | 0.097 |
| Insulin (−/+) | 387/92 | 356/83 | 31/9 | 0.581 |
| Insulin secretagogues (−/+) | 230/249 | 202/237 | 28/12 | 0.004 |
| Incretin‐based therapy (−/+) | 170/309 | 152/287 | 18/22 | 0.189 |
| Insulin sensitizers (−/+) | 290/189 | 261/178 | 29/11 | 0.106 |
| Nutrient load reducers (−/+) | 399/80 | 364/75 | 35/5 | 0.457 |
Data are expressed as mean (standard deviation), median (interquartile range) or number. Poor blood glucose level was defined as hemoglobin A1c (HbA1c) ≥8.0%. The difference between groups was analyzed by Student's t‐test, Mann–Whitney U‐test or the χ2‐test. eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; UAE, urinary albumin excretion.
Table 2 shows the unadjusted odds ratios of various factors for poor blood glucose levels for the overall patient cohort. PUAD (odds ratio 2.13, 95% confidence interval 1.02–4.23, P = 0.044) was associated with poor blood glucose level.
Table 2.
Unadjusted odds ratios for poor blood glucose level in overall patients
| Odds ratio (95% CI) | P | |
|---|---|---|
| Sex | 1.13 (0.71–1.84) | 0.606 |
| Age | 0.98 (0.96–1.00) | 0.035 |
| Log (duration of diabetes +1) | 4.58 (2.12–10.36) | <0.001 |
| Body mass index | 1.09 (1.03–1.16) | 0.005 |
| No. oral antidiabetic medication | 1.54 (1.27–1.88) | <0.001 |
| Smoking status | ||
| Never‐smoker | Ref | – |
| Ex‐smoker | 0.90 (0.55–1.47) | 0.673 |
| Current‐smoker | 1.04 (0.51–2.03) | 0.921 |
| Alcohol drinking | 0.84 (0.48–1.42) | 0.527 |
| Exercise | 0.60 (0.38–0.94) | 0.026 |
| Insulin | 2.28 (1.35–3.79) | 0.002 |
| Insulin secretagogues | 1.73 (1.09–2.78) | 0.019 |
| Incretin based therapy | 2.19 (1.32–3.79) | 0.002 |
| Insulin sensitizers | 1.81 (1.15–2.85) | 0.011 |
| Nutrient load reducers | 2.17 (1.25–3.70) | 0.006 |
| Poor understanding of antidiabetic medication | 2.13 (1.02–4.23) | 0.044 |
Odds ratios given with 95% confidence intervals (CI) express the risk associated with 1‐unit increase in each continuous variable. As for smoking status, never‐smoker was used as reference group. Log, logarithm.
Table 3 shows clinical characteristics of propensity‐matched 74 patients (62 men and 12 women) according to the presence of PUAD. The mean age (SD) and median of duration of diabetes (interquartile range) were 66.3 (13.1) years and 15 (7–23) years, respectively. Clinical characteristics were not different between groups without the ratio of poor blood glucose level (32% (case/n = 12/37) in patients with PUAD vs 8% (case/n = 3/37) in patients without, P = 0.009). In addition, PUAD was associated with poor blood glucose level (odds ratio: 5.45, 95% confidence interval 1.54–25.8, P = 0.007) by logistic regression analysis.
Table 3.
Clinical characteristics of propensity‐matched patients according to the presence of a poor understanding of antidiabetic medication
| Poor understanding of antidiabetic medication (−) | Poor understanding of antidiabetic medication (+) | P | |
|---|---|---|---|
| n | 37 | 37 | – |
| Men/women (n) | 31/6 | 31/6 | 1.0 |
| Age (years) | 67.1 (13.3) | 65.5 (13.1) | 0.600 |
| Duration of diabetes (years) | 14 (7–25) | 15 (6–20) | 0.992 |
| Body mass index (kg/m2) | 24.3 (4.8) | 24.0 (3.4) | 0.732 |
| Hypertension (−/+) | 13/24 | 14/23 | 0.809 |
| Poor blood glucose level (−/+) | 34/3 | 25/12 | 0.009 |
| Fasting plasma glucose (mmol/L) | 7.9 (2.3) | 8.3 (3.9) | 0.576 |
| Total cholesterol (mmol/L) | 4.8 (1.2) | 4.5 (0.8) | 0.172 |
| Triglycerides (mmol/L) | 1.2 (0.8–1.7) | 1.2 (0.8–1.8) | 0.347 |
| HDL cholesterol (mmol/L) | 1.5 (0.4) | 1.4 (0.4) | 0.421 |
| Aspartate aminotransferase (IU/L) | 22 (20–27) | 22 (18–28) | 0.539 |
| Alanine aminotransferase (IU/L) | 18 (15–28) | 19 (14–27) | 0.916 |
| Gamma‐glutamyltransferase (IU/L) | 29 (18–43) | 30 (21–38) | 0.864 |
| Creatinine (μmol/L) | 79.9 (28.7) | 72.5 (21.0) | 0.209 |
| eGFR (mL/min/1.73 m2) | 70.6 (25.0) | 75.3 (19.1) | 0.366 |
| Uric acid (μmol/L) | 324 (92) | 303 (80) | 0.309 |
| Exercise (−/+) | 17/20 | 17/20 | 1.0 |
| Never‐/ex‐/current‐smoker (n) | 11/16/10 | 13/17/7 | 0.696 |
| Alcohol drinking (−/+) | 26/11 | 25/12 | 0.802 |
| Nephropathy (UAE <30/30–300/<300 mg/g) | 22/9/6 | 25/6/6 | 0.673 |
| Neuropathy (−/+) | 24/13 | 23/14 | 0.809 |
| Medication use for diabetes (−/+) | 5/32 | 6/31 | 0.744 |
| Total no. oral medication | 5.1 (3.6) | 5.8 (4.1) | 0.401 |
| No. oral antidiabetic medication | 1.1 (0.9) | 1.2 (1.0) | 0.712 |
| No. oral medication other than antidiabetic medication | 4.0 (3.5) | 4.6 (3.8) | 0.431 |
| Insulin (−/+) | 27/10 | 29/8 | 0.588 |
| Insulin secretagogues (−/+) | 30/7 | 25/12 | 0.183 |
| Incretin based therapy (−/+) | 15/22 | 16/21 | 0.814 |
| Insulin sensitizers (−/+) | 28/9 | 27/10 | 0.790 |
| Nutrient load reducers (−/+) | 33/4 | 32/5 | 0.722 |
Data are expressed as mean (standard deviation), median (interquartile range) or number. The difference between groups was analyzed by Student's t‐test, Mann–Whitney U‐test or the χ2‐test. The propensity score was evaluated using multivariable logistic regression models that include the following parameters: sex, age, log (duration of diabetes +1), body mass index, number of oral antidiabetic medications, smoking status, alcohol drinking, exercise, nephropathy, neuropathy, insulin, insulin secretagogues, incretin‐based therapy, insulin sensitizers and nutrient load reducers. eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; UAE, urinary albumin excretion.
Discussion
The present study shows that PUAD was associated with poor blood glucose levels in patients with type 2 diabetes. The proportion of patients with PUAD was 8.4% in this study. It was reported that half of the patients, who suffered chronic disease, had low adherence20. In addition, it was reported that 15–33% of patients with diabetes did not take prescribed antidiabetic medications, and that of the patients with poor blood glucose level, three‐fifths of patients did not take prescribed antidiabetic medications5, 21. Medication adherence consisted of many factors, such as an understanding of medications, duration of disease, age, polypharmacy, tolerability and cost4, 22. Although the relationship between medication adherence and glycemic control was reported in the past6, no previous studies clarified the relationship between an understanding of antidiabetic medications and blood glucose level. This is the first study to clarify the relationship between an understanding of antidiabetic medications and blood glucose level in patients with type 2 diabetes.
Low medication adherence was reported to result in poor glycemic control6. Medication adherence consisted of many factors, including an understanding of medications22. Poor understanding of medications was correlated with low knowledge of each disease23. As for diabetes, it was reported that an understanding of medications, through having knowledge of diabetes24, affects medication adherence, which is associated with blood glucose level.
It was reported that the rate of exercise therapy of patients was affected by the understanding of the knowledge of diabetes, including diabetic medications25. In the present study, among patients with type 2 diabetes, there was no significant difference between the ratio of regular exercise for patients with PUAD and without (55% [case/n = 22/40] vs 54% [case/n = 236/439], P = 0.880). Exercise therapy was affected by environmental factors, including family support26. There is a possibility that these environmental factors might lead to a similarity in the habit of regular exercise between patients with a poor understanding of medication and those with a good understanding.
Generally, poor glycemic control is positively associated with microvascular complications of diabetes1. In the present study, there were no significant differences in the prevalence of diabetic nephropathy between patients with and without PUAD (P = 0.156). As for diabetic neuropathy, the ratio of diabetic neuropathy in patients with PUAD tended to be higher than that in patients without PUAD (P = 0.089). It was reported that microvascular complications of diabetes were associated with glycemic control, age, blood pressure and duration of diabetes1, 27. In the present study, there were no significant differences in age, the prevalence of hypertension and the duration of diabetes between patients with and without PUAD, although glycemic control among patients with PUAD was higher than that of patients without. This might result in there being no significant difference in the prevalence of diabetic microangiopathy between patients with and without PUAD.
Furthermore, we showed the relationship between the number of oral antidiabetic medications and poor blood glucose level, which was almost same as previous studies28, 29. In contrast, in the present study, the glucose level of patients with PUAD was higher than that of patients without, although patients with PUAD took fewer antidiabetic medications than patients without PUAD. One of the reasons is that PUAD itself causes poor medication adherence23.
Previous studies showed that there is a relationship between PUAD and the education of medical staff about antidiabetic medication23. Thus, education of medical staff might improve the blood glucose level of patients with PUAD through the improvement of PUAD30.
There are some limitations that should be mentioned. First, because of the cross‐sectional nature of the present study, the causal relationship between PUAD and poor blood glucose level was unclear. Second, we did not evaluate cognitive function. Thus, we cannot deny the possibility that an understanding of antidiabetic medication might have been affected by cognitive function. In fact, previous studies have shown that cognitive impairment and memory problems could play an important role in medication adherence31, 32. However, in the present study, age and the duration of diabetes, which were generally associated with cognitive function in patients with type 2 diabetes33, were not significantly different between patients with PUAD and without. Thus, the influence of cognitive function might be small. Third, we did not evaluate the number of unused medicines. Therefore, increased unused medicine led by low medication adherence might have resulted in poor blood glucose level. However, unused medicine is also related to PUAD, because medication adherence is affected by an understanding of medications30. Fourth, only Japanese patients were included in the present study. Therefore, whether these results can be generalized to non‐Japanese patients with type 2 diabetes is uncertain. Fifth, as for the questionnaire used in the present study, there are no gold standard methods for such a questionnaire method in any languages, including Japanese.
In conclusion, the present cross‐sectional study shows, for the first time, that a poor understanding of antidiabetic medication is associated with poor blood glucose levels in patients with type 2 diabetes. In clinical practice of diabetes, we should consider the understanding of antidiabetic medications.
Disclosure
Yoshitaka Hashimoto received grants support from the Fuji Foundation, Japan Society for the Promotion of Science, Asahi Kasei Pharma and MSD K.K. outside the submitted work. Masahiro Yamazaki received honoraria from AstraZeneca plc outside the submitted work. Michiaki Fukui reports grants from Japan Society for the Promotion of Science, AstraZeneca plc, Astellas Pharma Inc., Nippon Boehringer Ingelheim Co., Ltd., Daiichi Sankyo Co., Ltd., Eli Lilly Japan K.K., Kyowa Hakko Kirin Company Ltd., Kissei Pharmaceutical Co., Ltd., MSD K.K., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Pharma Ltd., Sanwa Kagaku Kenkyusho Co., Ltd., Sanofi K.K., Ono Pharmaceutical Co., Ltd. and Takeda Pharmaceutical Co., Ltd. outside the submitted work. The sponsors were not involved in the study design; in the collection, analysis, interpretation of data; in the writing of this manuscript; or in the decision to submit the article for publication. The authors, their immediate families and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article. The authors declare that although they are affiliated with a department that is supported financially by a pharmaceutical company, the authors received no current funding for this study and this does not alter their adherence to all the journal policies on sharing data and materials. The other authors declare no conflict of interest.
Supporting information
Figure S1 ¦ Questionnaire used to define a poor understanding of antidiabetic medication (PUAD). The patients checked the check box according to the current treatment for diabetes. If the patients received several treatments, the patient checked several check boxes.
Acknowledgments
We thank all staff members at the Kyoto Prefectural University of Medicine. This research did not receive any funding from agencies in the public, commercial or not‐for‐profit sectors.
J Diabetes Investig 2019; 10: 458–465
References
- 1. Fowler MJ. Microvascular and macrovascular complications of diabetes. Clin Diabetes 2008; 26: 77–82. [Google Scholar]
- 2. American Diabetes Association AD . Standards of medical care in diabetes—2013. Diabetes Care 2013; 36(suppl 1): S11–S66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Holman RR, Paul SK, Bethel MA, et al 10‐year follow‐up of intensive glucose control in type 2 diabetes. N Engl J Med 2008; 359: 1577–1589. [DOI] [PubMed] [Google Scholar]
- 4. García‐Pérez L‐E, Álvarez M, Dilla T, et al Adherence to therapies in patients with type 2 diabetes. Diabetes Ther 2013; 4: 175–194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Juarez DT, Ma C, Kumasaka A, et al Failure to reach target glycated a1c levels among patients with diabetes who are adherent to their antidiabetic medication. Popul Health Manag 2014; 17: 218–223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Polonsky WH, Henry RR. Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors. Patient Prefer Adherence 2016; 10: 1299–1307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Bundrick Harrison L, Lingvay I. Appointment and medication non‐adherence is associated with increased mortality in insulin‐treated type 2 diabetes. Evid Based Med 2013; 18: 112–113. [DOI] [PubMed] [Google Scholar]
- 8. Chen CC, Tseng CH, Cheng SH. Continuity of care, medication adherence, and health care outcomes among patients with newly diagnosed type 2 diabetes: a longitudinal analysis. Med Care 2013; 51: 231–237. [DOI] [PubMed] [Google Scholar]
- 9. Dunkley AJ, Bodicoat DH, Greaves CJ, et al Diabetes prevention in the real world: effectiveness of pragmatic lifestyle interventions for the prevention of type 2 diabetes and of the impact of adherence to guideline recommendations: a systematic review and meta‐analysis. Diabetes Care 2014; 37: 922–933. [DOI] [PubMed] [Google Scholar]
- 10. Mashitani T, Hayashino Y, Okamura S, et al Patient‐reported adherence to insulin regimen is associated with glycemic control among Japanese patients with type 2 diabetes: Diabetes Distress and Care Registry at Tenri (DDCRT 3). Diabetes Res Clin Pract 2013; 100: 189–194. [DOI] [PubMed] [Google Scholar]
- 11. Ownby RL, Hertzog C, Crocco E, et al Factors related to medication adherence in memory disorder clinic patients. Aging Ment Health 2006; 10: 378–385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Sakai R, Hashimoto Y, Ushigome E, et al Late‐night‐dinner is associated with poor glycemic control in people with type 2 diabetes: the KAMOGAWA‐DM cohort study. Endocr J 2018; 65: 395–402. [DOI] [PubMed] [Google Scholar]
- 13. Webster AC, Nagler EV, Morton RL, et al Chronic kidney disease. Lancet 2017; 389: 1238–1252. [DOI] [PubMed] [Google Scholar]
- 14. Cleland JG, Swedberg K, Follath F, et al The EuroHeart Failure survey programme – a survey on the quality of care among patients with heart failure in Europe. Part 1: patient characteristics and diagnosis. Eur Heart J 2003; 24: 442–463. [DOI] [PubMed] [Google Scholar]
- 15. Hashimoto Y, Hamaguchi M, Kojima T, et al Modest alcohol consumption reduces the incidence of fatty liver in men: a population‐based large‐scale cohort study. J Gastroenterol Hepatol 2015; 30: 546–552. [DOI] [PubMed] [Google Scholar]
- 16. Hashimoto Y, Hamaguchi M, Fukuda T, et al Weight gain since age of 20 as risk of metabolic syndrome even in non‐overweight individuals. Endocrine 2017; 58: 253–261. [DOI] [PubMed] [Google Scholar]
- 17. Matsuo S, Imai E, Horio M, et al Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009; 53: 982–992. [DOI] [PubMed] [Google Scholar]
- 18. Sevick MA, Korytkowski M, Stone RA, et al Biophysiologic outcomes of the Enhancing Adherence in Type 2 Diabetes (ENHANCE) trial. J Acad Nutr Diet 2012; 112: 1147–1157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Zheng M, Lv LL, Ni J, et al Urinary podocyte‐associated mRNA profile in various stages of diabetic nephropathy. PLoS ONE 2011; 6: e20431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Adisa R, Fakeye TO, Fasanmade A. Medication adherence among ambulatory patients with type 2 diabetes in a tertiary healthcare setting in southwestern Nigeria. Pharm Pract (Granada) 2011; 9: 72–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Karter AJ, Parker MM, Moffet HH, et al New prescription medication gaps: a comprehensive measure of adherence to new prescriptions. Health Serv Res 2009; 44: 1640–1661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Kalyango JN, Owino E, Nambuya AP. Non‐adherence to diabetes treatment at Mulago Hospital in Uganda: prevalence and associated factors. Afr Health Sci 2008; 8: 67–73. [PMC free article] [PubMed] [Google Scholar]
- 23. Akabane M, Chiba M, Konishi H, et al Assessment of comprehension levels of inpatients with diabetes mellitus. Educational significance for enhancement of therapeutic outcome. Japanese J Pharm Heal Care Sci 2001; 27: 317–322 (Japanese). [Google Scholar]
- 24. Heisler M, Bouknight RR, Hayward RA, et al The relative importance of physician communication, participatory decision making, and patient understanding in diabetes self‐management. J Gen Intern Med 2002; 17: 243–252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Arakawa S, Watanabe T, Sone H, et al Current situation of diet and exercise therapy in terms of medical consultations in patients with diabetes mellitus in Japan: a nationwide survey. J Japan Diab Soc 2015; 58: 265–278 (Japanese). [Google Scholar]
- 26. Albright A, Franz M, Hornsby G, et al American College of Sports Medicine position stand. Exercise and type 2 diabetes. Med Sci Sports Exerc 2000; 32: 1345–1360. [DOI] [PubMed] [Google Scholar]
- 27. Zoungas S, Woodward M, Li Q, et al Impact of age, age at diagnosis and duration of diabetes on the risk of macrovascular and microvascular complications and death in type 2 diabetes. Diabetologia 2014; 57: 2465–2474. [DOI] [PubMed] [Google Scholar]
- 28. Badedi M, Solan Y, Darraj H, et al Factors associated with long‐term control of type 2 diabetes mellitus. J Diabetes Res 2016; 2016: 2109542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Cramer JA, Pugh MJ. The influence of insulin use on glycemic control: How well do adults follow prescriptions for insulin? Diabetes Care 2005; 28: 78–83. [DOI] [PubMed] [Google Scholar]
- 30. Tsuboi K, Teramachi H, Kuzuya Y, et al Survey of the patients’ consciousness affecting medication adherence. Japanese J Pharm Heal Care Sci 2012; 38: 522–533 (Japanese). [Google Scholar]
- 31. Ganguli M, Rodriguez E, Mulsant B, et al Detection and management of cognitive impairment in primary care: the steel valley seniors survey. J Am Geriatr Soc 2004; 52: 1668–1675. [DOI] [PubMed] [Google Scholar]
- 32. Salas M, In't Veld BA, van der Linden PD, et al Impaired cognitive function and compliance with antihypertensive drugs in elderly: the Rotterdam Study. Clin Pharmacol Ther 2001; 70: 561–566. [DOI] [PubMed] [Google Scholar]
- 33. Chen S, Zuo X, Li Y, et al Ghrelin is a possible new predictor associated with executive function in patients with type 2 diabetes mellitus. J Diabetes Investig 2017; 8: 306–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Supplementary Materials
Figure S1 ¦ Questionnaire used to define a poor understanding of antidiabetic medication (PUAD). The patients checked the check box according to the current treatment for diabetes. If the patients received several treatments, the patient checked several check boxes.