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Prevalence of dyslipidemia associated with complications in diabetic patients: a nationwide study in Thailand

  • Ploypun NarindrarangkuraEmail author
  • William Bosl
  • Ram Rangsin
  • Panadda Hatthachote
Open Access
Research
  • 341 Downloads

Abstract

Background

Dyslipidemia is an important modifiable risk factor for cardiovascular disease. It is diagnosed by the presence of an abnormal lipid profile, primarily with elevated levels of plasma cholesterol, triglyceride, or both, or reduced levels of high-density lipoprotein cholesterol. However, some studies have reported increased risk of ischemic stroke with elevated low-density lipoprotein cholesterol (LDL-C) levels and increased risk of cardiovascular mortality independent of LDL-C levels in type 2 diabetes mellitus (T2DM) patients.

Methods

In this cross-sectional study, data were included for Thai adults with diabetes from the Diabetes Mellitus/ Hypertension (DM/HT) study, 2010–2014 (data was collected by the Medical Research Network of the Consortium of Thai Medical Schools). The target population comprised T2DM patients who were treated at a hospital for more than 12 months. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated to determine factors associated with dyslipidemia.

Results

In total, 140,557 participants (average age, 60 years) were enrolled, with a dyslipidemia prevalence of 88.9% in the cohort. The factors associated with dyslipidemia included female sex (aOR: 1.47, 95% CI: 1.38–1.56); age < 50 years (aOR: 1.16, 95% CI: 1.10–1.22); waist circumference ≥ 90 cm in males and ≥ 80 cm in females (aOR: 1.23, 95% CI: 1.16–1.31); treatment at a primary care unit (aOR: 1.28, 95% CI: 1.23–1.33); and a history of unknown stroke (aOR: 1.10, 95% CI: 1.02–1.19), coronary revascularization (aOR: 0.85, 95% CI: 0.79–0.91), diabetic nephropathy (aOR: 1.06, 95% CI: 1.01–1.12), or renal insufficiency (aOR: 1.08, 95% CI: 1.02–1.13).

Conclusions

Dyslipidemia is prevalent among Thai T2DMpatients and is associated with gender; age; obesity; central obesity; treatment at a primary care unit; and a history of unknown stroke, coronary revascularization, diabetic nephropathy, and renal insufficiency. Our study results will help increase the awareness of healthcare providers regarding dyslipidemia in diabetic patients. To reduce cardiovascular risk, healthcare professionals should provide regular follow-up and proper advice and ensure primary prevention of vascular complications. Improved education and increased self-awareness regarding the need to change behaviors and regular intake of medication would help decrease dyslipidemia prevalence among diabetic patients.

Keywords

Prevalence Dyslipidemia Diabetes T2DM Complications Thailand 

Abbreviations

aOR

adjusted odds ratio

BMI

body mass index

CI

confidence interval

HDL-C

high-density lipoprotein cholesterol

LDL-C

low-density lipoprotein cholesterol

T2DM

type 2 diabetes mellitus

Background

Dyslipidemia is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes mellitus (T2DM) [1], but it is modifiable by lifestyle changes and medication [2]. The disorder is characterized by an abnormal lipid profile, which can include elevated levels of plasma cholesterol, triglycerides, or both, or reduced levels of high-density lipoprotein cholesterol (HDL-C) [3, 4]. According to the National Health and Nutrition Examination Survey 2003–2006, lipid abnormalities were present in 53% of US adults [5]. Compared with other middle-income Asian countries, unawareness regarding hypercholesterolemia in Thailand was reportedly the highest at 78% in 2004, with low levels of treatment and control [6]. This was compounded by the results of a national survey in 2009, which found that dyslipidemia was present in 66.5% of the Thai population [7].

Dyslipidemia is one of the risk factors for vascular complications in diabetic patients because it increases free fatty acid flux secondary to insulin resistance and aggravated by increased inflammatory adipokine levels [8]. According to the Framingham Heart Study, in diabetic patients, the prevalence rates for high cholesterol levels were 13% in males and 24% in females, and these rates for high plasma triglyceride levels were 19% in males and 17% in females [9]. A cross-sectional, multicenter, hospital-based diabetes registry conducted in Thailand, covering the year 2003, showed that more than 80% diabetic patients had dyslipidemia, but only 40% patients who received lipid-lowering medications achieved the target low-density lipoprotein cholesterol (LDL-C) levels [10]. In Thailand, socioeconomic development and lifestyle changes such as consumption of Western-style diets, reduced physical activity, and changes in type of work have contributed to the increased dyslipidemia prevalence among the population.

To update ourselves and fill the gap of knowledge, we aimed to determine dyslipidemia prevalence among diabetic patients in Thailand and to assess the association between dyslipidemia and diabetes-related complications.

Methods

Study design

In this cross-sectional study, we used secondary data from multicenter, hospital-based diabetes and hypertension registries for the period 2010–2014. This data was collected by the Medical Research Network of the Consortium of Thai Medical Schools, which is responsible for collaboration and networking between medical and public health research in Thailand. We then performed a quantitative analysis to determine dyslipidemia prevalence and associated factors among diabetic patients. The study was reviewed and approved by the Institutional Review Board of the Royal Thai Army Medical Department (S058 h/60_Exp).

Study population

We used secondary data collected from diabetic patients treated at public Thailand Ministry of Public Health hospitals in Thailand and patients treated at private clinics in Bangkok who participated in the Thailand National Health Security Office program for more than 12 months. Diabetic patients aged ≥18 years who provided signed informed consent were included in the study.

Data collection

Secondary data were sourced from a database named “An Assessment on Quality of Care among Patients Diagnosed with Type 2 Diabetes and Hypertension Visiting Ministry of Public Health and Bangkok Metropolitan Administration Hospitals in Thailand” (referred to as the Thailand DM/HT study database), for the period 2010–2014. The following participant characteristics were assessed: gender, region, age, body mass index (BMI), waist circumference, health scheme, treatment setting, occupation, and smoking status. We also collected data on lipid profile, fasting plasma glucose, and glycosylated hemoglobin (HbA1c) levels to determine the associated factors. Finally, data on the following complications were included: cerebrovascular accident, cerebral infarction, ischemic stroke, hemorrhagic stroke, unknown stroke, cerebral hemorrhage, transient ischemic attack, angina pectoris, congestive heart failure, myocardial infarction, coronary revascularization, peripheral arterial disease, atrial fibrillation, diabetic retinopathy, diabetic nephropathy, renal insufficiency, and neuropathy.

Dyslipidemia was defined as total cholesterol ≥200 mg/dL, LDL-C ≥ 100 mg/dL, triglycerides ≥150 mg/dL, or HDL-C ≤ 40 mg/dL in males and ≤ 50 mg/dL in females [4, 11]. Furthermore, we separated patients according to six abnormal lipid profiles: pure hypercholesterolemia, pure hypertriglyceridemia, mixed hyperlipidemias, high non-HDL-C, high LDL-C, and low HDL-C.

Statistical analysis

Secondary data were analyzed using RStudio (Version 1.1.383). The association between variables and dyslipidemia in T2DM Thai patients was assessed using Χ2 or Fisher’s exact tests with 95% confidence intervals (CIs). Univariate and multivariate analyses were performed using linear regression modeling and binary logistic regression modeling to determine the independent risk factors for dyslipidemia, with adjusted odds ratios (aORs) and 95% CIs.

Results

The participants’ characteristics are shown in Table 1. Of the 140,557 participants, 97,627 (69.47%) were female, and the average age was 60 years (four-fifth patients were aged > 50 years). Approximately half of the participants were overweight or obese and 70% had truncal obesity, but most (88.53%) were nonsmokers. Nearly half of the patients received treatment at a primary care unit and most (74.37%) used the universal health coverage system. Dyslipidemia prevalence was 88.92%, and the main type of abnormal lipid profile was a low HDL-C (59.59%) (Table 2). The mean lipid levels among T2DM patients are summarized in Table 3, which shows that there were no significant differences regarding lipid levels among the patients per year. On separating the mean lipid, fasting blood sugar, and HbA1c levels by gender, age, BMI, waist circumference, and smoking subgroups, we found that female sex and obesity were associated with higher mean levels in each lipid profile category (Table 4).
Table 1

Diabetic patients’ characteristic and dyslipidemia prevalence (N = 140,557)

 

n

%

Dyslipidemia

   

n

%

Gender

 Male

42,902

30.53

26,551

84.62

 Female

97,627

69.47

65,107

90.80

Region

 North

28,984

20.62

19,893

89.11

 Central

32,611

23.2

18,170

85.85

 East

12,753

9.07

7160

85.96

 North-East

47,756

33.98

32,892

91.74

 South

9757

6.94

7135

87.40

 South Boundary

8696

6.19

6408

88.51

Age (years)*

60.31 ± 10.79 (18–99)

  

  < 50

22,626

16.11

15,055

91.12

  ≥ 50

117,853

83.89

76,562

88.50

Body mass index (kg/m2) **

  < 18.50

4736

3.6

2853

82.86

 18.5–22.9

33,806

25.69

21,918

87.65

 23.0–24.9

27,105

20.6

18,135

89.77

 25.0–29.9

47,290

35.93

31,632

90.19

  ≥ 30.0

18,671

14.19

12,466

89.83

Waist circumference (cm)

 Male < 90, Female < 80

12,233

28.76

8131

84.55

 Male ≥90, Female ≥80

30,309

71.24

21,469

89.29

Health scheme

 Universal Health Coverage

103,628

74.37

68,172

90.24

 Government

23,788

17.07

15,177

83.43

 Social Security

4499

3.23

2812

86.07

 State Enterprise Officer

501

0.36

314

83.96

 Cash

638

0.46

334

85.64

 Other

6278

4.51

4101

92.12

Hospital setting

 Tertiary Care Hospital

40,040

30.04

27,294

87.58

 Secondary Care Hospital

30,460

22.85

19,334

87.25

 Primary Care Unit

62,806

47.11

42,051

91.02

Occupation

 Agriculture

54,245

39.14

37,305

91.45

 Non-agriculture

72,707

52.46

46,393

87.74

 Unemployment

7557

5.45

4847

83.87

 Others

4081

2.94

2106

85.23

Smoking

 Non-smoker

108,340

88.53

72,923

89.27

 Ex-smoker

8608

7.03

5561

86.15

 Current smoker

5434

4.44

3595

87.05

*Mean ± SD (min–max)

**WHO/IASO/IOTF. The Asia-Pacific perspective: Redefining obesity and its treatment. Health Communications Australia: Melbourne, 2000

Table 2

Prevalence of abnormal lipid profile among T2DM Thai patients in 2010–2014

 

n

%

Dyslipidemia*

91,658

88.92

 Pure hypercholesterolemia

39,738

35.05

 Pure hypertriglyceridemia

59,151

49.94

 Mixed hyperlipidemias

25,201

21.72

 High non-HDL

55,255

53.41

 High LDL

66,508

56.54

 Low HDL

63,914

59.59

*Defined by total cholesterol ≥200 mg/dL, LDL ≥ 100 mg/dL, TG ≥ 150 mg/dL, or HDL ≤ 40 mg/dL in males and ≤ 50 mg/dL in females

Table 3

Mean ± SD for lipid profile among T2DM Thai patients in 2010–2014

 

2010

2011

2012

2013

2014

Total

p for trend

Total cholesterol

188.62 ± 51.19

187.8 ± 45.47

187.44 ± 45.65

187.56 ± 44.5

188.59 ± 46.53

188 ± 46.57

0.8065

Triglyceride

177.76 ± 118.67

174.46 ± 93.48

175.04 ± 104.47

175.34 ± 111.52

175.62 ± 111.91

175.57 ± 108.23

0.7071

HDL

46.06 ± 27.39

45.14 ± 12.46

45.82 ± 13.02

46.74 ± 13.88

47.20 ± 15.53

46.25 ± 16.87

0.2597

LDL

110.61 ± 42.67

109.33 ± 36.77

109.62 ± 37.47

108.61 ± 37.29

109.33 ± 37.95

109.45 ± 38.3

0.5661

Table 4

Mean lipid and fasting blood sugar levels among Thai patients with T2DM in 2010–2014

 

Total Cholesterol

Triglyceride

HDL

LDL

FBS

HbA1c

Gender

 Male

181.68

174.92

43.8

105.96

150.9

7.85

 Female

190.78

175.86

47.32

110.97

153.76

8.08

Age (years)

  < 50

188.63

183.49

45.83

110.28

166.13

8.49

  ≥ 50

187.89

174.06

46.32

109.29

150.37

7.92

Body mass index (kg/m2) **

  < 30

187.88

175.9

46.07

109.39

152.58

8.02

  ≥ 30

189.5

177.85

46.61

110.48

156.51

8.07

Waist circumference (cm)**

 Male < 90, Female < 80

184.1

165.28

47.15

106.49

154.2

7.98

 Male ≥90, Female ≥80

189.56

181.45

46.72

109.71

156.19

8.06

Smoking

 Current smoker

184.33

185.87

43.72

106.92

156.52

7.96

 Ex-smoker

181.87

175.61

43.5

105.52

151.84

7.94

 Non-smoker

188.66

174.85

46.64

109.83

153.05

8.01

**WHO/IASO/IOTF. The Asia-Pacific perspective: Redefining obesity and its treatment. Health Communications Australia: Melbourne, 2000

Univariate and multivariate analyses were performed to determine the factors associated with lipid profiles and fasting plasma glucose levels (Table 5). Linear regression modeling revealed that total cholesterol (aOR: 1.07, 95% CI: 1.05–1.08), triglycerides (aOR: 1.06, 95% CI: 1.05–1.06), and LDL-C (aOR: 1.05, 95% CI: 1.04–1.07) were associated with fasting plasma glucose levels. To determine the factors associated with dyslipidemia, logistic regression modeling was used with univariate and multivariate analyses. As shown in Table 6, the independent factors associated with dyslipidemia were gender, age, waist circumference, unknown stroke, coronary revascularization, diabetic nephropathy, and renal insufficiency.
Table 5

Univariate and multivariate of lipid profile and fasting plasma glucose in T2DM Thai patients

 

Total

Fasting plasma glucose level (mg/dl) *

Crude odds ratio

95% CI

p-value

Adjusted odds ratio

95% CI

p-value

Total cholesterol

102,792

152.8 ± 53.6 (1–835)

1.15 (1.14–0.16)

< 0.001

1.07 (1.05–1.08)

< 0.001

Triglyceride

107,376

152.6 ± 53.4 (1–835)

1.07 (1.06–1.07)

< 0.001

1.06 (1.05–1.06)

< 0.001

HDL

97,450

152.7 ± 53.6 (1–835)

0.99 (0.97–1.01)

0.155

  

LDL

106,699

152.5 ± 53.2 (1–835)

1.13 (1.13–1.14)

< 0.001

1.05 (1.04–1.07)

< 0.001

Non-HDL

96,598

152.7 ± 53.5 (1–835)

1.16 (1.15–1.17)

< 0.001

  

*Mean ± SD (Min–Max)

Linear Regression; After adjusted for HDL and non-HDL level

Table 6

Univariate and multivariate analysis of associated factors and dyslipidemia in T2DM Thai patients

 

Total

Dyslipidemia

Adjusted odds ratio

95% CI

p-value

n

%

Gender

 Male

42,902

26,551

84.62

1

 

 Female

97,627

65,107

90.80

1.47 (1.38–1.56)

< 0.001

Age (years)*

60.31 ± 10.79 (18–99)

    

  ≥ 50

117,853

76,562

88.50

1

 

  < 50

22,626

15,055

91.12

1.16 (1.10–1.22)

< 0.001

Body mass index (kg/m2) **

  < 18.50

4736

2853

82.86

1

 

 18.5–22.9

33,806

21,918

87.65

1.97 (1.80–2.15)

< 0.001

 23.0–24.9

27,105

18,135

89.77

1.51 (1.39–1.64)

< 0.001

 25.0–29.9

47,290

31,632

90.19

1.92 (1.77–2.09)

< 0.001

  ≥ 30.0

18,671

12,466

89.83

2.03 (1.87–2.20)

< 0.001

Waist circumference (cm)

 Male < 90, Female < 80

12,233

8131

84.55

1

 

 Male ≥90, Female ≥80

30,309

21,469

89.29

1.23 (1.16–1.31)

< 0.001

Hospital setting

 Tertiary Care Hospital

40,040

27,294

87.58

1

 

 Secondary Care Hospital

30,460

19,334

87.25

0.99 (0.94–1.03)

0.583

 Primary Care Unit

62,806

42,051

91.02

1.28 (1.23–1.33)

< 0.001

Smoking

 Non-smoker

108,340

72,923

89.27

1

 

 Ex-smoker

8608

5561

86.15

1.12 (1.05–1.20)

< 0.001

 Current smoker

5434

3595

87.05

1.23 (1.13–1.33)

< 0.001

Complications

 Unknown stroke

  No

63,781

42,384

87.86

1

 

  Yes

641

399

85.81

1.1 (1.02–1.19)

0.011

 Coronary revascularization

  No

66,791

44,764

88.16

1

 

  Yes

301

188

82.10

0.85 (0.79–0.91)

< 0.001

 Diabetic nephropathy

  No

72,468

49,062

88.21

1

 

  Yes

9325

6443

90.00

1.06 (1.01–1.12)

0.031

 Renal insufficiency

  No

68,058

45,879

88.03

1

 

  Yes

12,957

8780

90.22

1.08 (1.02–1.13)

0.005

Multiple logistic regression; using Multivariate Imputation by Chained Equations, Both direction

After adjusted for cerebrovascular accident, cerebral infarction, hemorrhagic stroke, myocardial infarction, and neuropathy

We obtained several noteworthy results. Females had 1.47-time increased odds of having dyslipidemia over males (p < 0.001), while patients aged < 50 years had a 1.16-time increased odds over those aged > 50 years (p < 0.001). Patients with a higher BMI had higher odds of having a disease, with obesity being associated with 2.03-time increased odds over underweight (p < 0.001). Men with a waist circumference ≥ 90 cm and women with a waist circumference ≥ 80 cm had 1.23-time increased odds of having dyslipidemia (p < 0.001). Interestingly, receiving treatment at a primary care unit was associated with 1.28-time increased odds of having dyslipidemia over receiving treatment at a tertiary care unit (p < 0.001). Being a current or ex-smoker was associated with 1.23-time (p < 0.001) or 1.12-time (p < 0.001) increased odds of having dyslipidemia, respectively. Furthermore, we found associations between dyslipidemia and complications, notably for a history of stroke (aOR 1.1, p = 0.011) and coronary revascularization (aOR 0.85, p < 0.001) and for patients with diabetic nephropathy (aOR 1.06, p = 0.031) and renal insufficiency (aOR 1.08, p = 0.005).

Discussion

In this study, based on the revised National Cholesterol Education Program-Adult Treatment Panel III criteria, we aimed to determine dyslipidemia prevalence among patients visiting hospitals for diabetes throughout Thailand. The last comparable study was conducted between April and December 2003 only in the diabetes clinics of tertiary care hospitals. Therefore, we anticipated that this study would update our knowledge regarding dyslipidemia and the associated outcomes among T2DM patients in Thailand.

There was an obvious high dyslipidemia prevalence among Thai patients with diabetes (88.9%), which had remained largely unchanged compared with that reported in 2003 (80%) [10, 12]. In a similar report from China, dyslipidemia prevalence was 67.1% among Chinese T2DM patients [13]. Likewise, according to the United States National Health and Nutritional Examination Survey for 1999–2000, the prevalence of LDL-C > 100 mg/dL was 71% among males and 78.9% among females with diabetes, indicating that dyslipidemia control was inadequate based on the American Diabetes Association guidelines [14]. The reasons for high dyslipidemia prevalence in these patients can be explained in several ways.

Healthcare system as well as appropriate patient education plays an important role in disease control. According to the Thai report in 2003, medications were not taken by 30% diabetic patients who fulfilled the criteria for receiving lipid-lowering medications, and only 40.1% of those who took their medications achieved the target LDL levels of < 100 mg/dL [11]. Furthermore, it is noteworthy that patients covered by government-supported health plan were less likely to receive lipid-lowering medications compared with those covered by private health plans. The impact of shifting toward a modern lifestyle should not be underestimated, with developing countries increasingly adopting Western-style diets that include high-calorie foods with increased carbohydrate, fat, and red meat content and low fiber content. These dietary changes correlate with a rapid growth in the prevalence of obesity, metabolic syndrome, and T2DM [15]. The Thai government, therefore, launched a plan named “Thailand Healthy Lifestyle Strategy 2011–2020 Plan” that aims to decrease the prevalence of and complications, disability, mortality, and cost of illness associated with major diseases, including diabetes, hypertension, ischemic heart disease, stroke, and cancer [16]. However, despite a good initiation process, this plan has not resulted in any significant changes in the prevalence of the abovementioned non-communicable diseases [17].

When we focused on the subtypes of dyslipidemia, the major types found were low HDL levels (59.6%), followed by high LDL (56.5%) and non-HDL (53.4%) levels and pure hypertriglyceridemia (49.9%), with lower rates of pure hypercholesterolemia (35.1%) and mixed hyperlipidemias (21.7%). Similar to these results, studies in China and other Asian countries have shown hypertriglyceridemia and low HDL-C levels to be the major types of dyslipidemia [18, 19, 20, 21, 22]. Conversely, in the US population, the major type of abnormal lipid profile is high LDL levels (27%) [5].

Furthermore, we found multiple factors to be associated with dyslipidemia among Thai diabetic patients. Interestingly, age < 50 years was significantly associated with dyslipidemia in diabetic patients, which could be attributed to work pressure and a lack of physical activity [2]. As expected, obesity was significantly associated with dyslipidemia in T2DM patients. In a comparable study from India, it was reported that T2DM pa tients who had obesity along with hypertriglyceridemia and high LDL-C and non-HDL-C levels exhibited poorly controlled HbA1c levels compared with a group with normal lipid profiles [23]. Moreover, several reports have shown a significant influence of lipid levels on HbA1c levels and cardiovascular complications, possibly as a consequence of increased insulin resistance [24, 25, 26].

In Thailand, hospitals are classified into three categories: regional hospitals (tertiary care), general hospitals (secondary care), and community hospitals (primary care). We found that treatment at a primary care unit was significantly associated with dyslipidemia in T2DM patients. This finding may reflect the fact that primary care units are located at the district level and are limited to providing only primary care treatment. Patients with uncomplicated diseases are referred back to primary care units (which have no specialists) to receive regular medications. General practitioners might, therefore, underdiagnose the complications because of lack of experience and resources (including some laboratory tests). Furthermore, primary care units have limited available drug options and often lack access to advanced or novel therapies. A survey of primary care settings in Thailand showed that necessary and routine aspects of diabetic care were not regularly performed by healthcare providers [27].

Researchers have consistently found an association between dyslipidemia and smoking [28, 29, 30]. Our results also confirmed that current smoking was significantly related to dyslipidemia prevalence in diabetic patients. In a community-based prospective cohort of American Indians, it was shown that hypertriglyceridemia and low HDL-C levels among diabetic patients were associated with a 2.13-fold greater hazard ratio for stroke (95% CI, 1.06–4.29) [31].

We also found clear associations between certain vascular complications and dyslipidemia in Thai T2DM patients. For instance, a significant association was found between dyslipidemia and stroke. The mechanism underlying the increased cardiovascular morbidity and mortality could be explained by factors such as increased leptin, adipocyte dysregulation, increased insulin resistance, and increased C-reactive protein levels [32]. We also showed a significant association between dyslipidemia and renal diseases (diabetic nephropathy and renal insufficiency), consistent with other studies reporting a significant association of dyslipidemia and microalbuminuria with the risk of cardiovascular and kidney complications in diabetic patients [33]. However, a history of coronary revascularization was found to be protective against dyslipidemia, probably because of the closer attention paid to these patients for the control of hypertension, cholesterol, and HbA1c, as well as assistance with smoking cessation [34]. Dyslipidemia is a risk factor that can be modified with lifestyle interventions, and each of the abovementioned vascular complications can be targeted by primary prevention as well as by appropriate secondary prevention.

Conclusion

In summary, based on secondary data from hospitals throughout Thailand, we showed high dyslipidemia prevalence in Thai diabetic patients. Dyslipidemia in this patient population was associated with gender, age, obesity, central obesity, treatment at a primary care unit, and several vascular complications. Our findings should raise awareness regarding the need for healthcare providers to offer better management of dyslipidemia in Thai diabetic patients. Indeed, regular follow-up, proper advice, and greater attention to the primary prevention of vascular complications could reduce the risk of cardiovascular diseases. Specific efforts to educate patients and increase their awareness regarding the need to change behaviors and regularly take medication would be a positive step toward decreasing dyslipidemia prevalence in diabetic patients.

Notes

Acknowledgments

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and analyzed during this study are available from the corresponding author on reasonable request.

Authors’ contributions

PN analyzed and interpreted secondary data of Thai diabetic patients. PN was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was reviewed and approved by the Institutional Review Board of Royal Thai Army Medical Department (S058 h/60_Exp).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Military and Community MedicinePhramongkutklao College of MedicineBangkokThailand
  2. 2.Health Informatics ProgramSchool of Nursing and Health Professions of the University of San FranciscoSan FranciscoUSA
  3. 3.Department of PhysiologyPhramongkutklao College of MedicineBangkokThailand

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