Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Incidence and clinical outcomes of diabetes mellitus in HIV-infected adults in Thailand: a retrospective cohort study

  • 1, 2,
  • 3, 4, 5,
  • 6,
  • 1, 7,
  • 8,
  • 9,
  • 3, 4, 5, 10,
  • 11,
  • 3 and
  • 6Email authorView ORCID ID profile
BMC Public Health201818:1079

https://doi.org/10.1186/s12889-018-5967-7

  • Received: 19 January 2018
  • Accepted: 14 August 2018
  • Published:
Open Peer Review reports

Abstract

Background

Since 2005, Thailand has scaled up one of the largest antiretroviral treatment (ART) programs in South East Asia. Although diabetes mellitus (DM) incidence is increasing in low and middle-income countries, its burden and contributing factors in the HIV infected population are not well known.

Methods

Using the Thai National AIDS Program data over a period of 8-years, we identified patients diagnosed with DM based on the following records: 1) fasting plasma glucose equal to or greater than 126 mg/dl following the 2013 American Diabetes Association criteria or 2) diagnosis codes E11-E14 of the 2010 WHO International Classification of Diseases, or 3) anti-diabetic drugs. Incidence was the number of new cases divided by that of person-years of follow-up (PYFU). Competing risks survival regression, treating death without DM as a competing event, was used to identify factors associated with DM. The risk of death in patients diagnosed with DM was estimated using Cox regression models.

Results

Data of 763,666 PYFU from 199,707 patients (54.2% male; median age 36.2 years at registration with the program) were available and 8383 cases were diagnosed with DM, resulting in an incidence rate of 11.0 per 1000 PYFU. New DM diagnosis was more likely in men (adjusted sub-distribution hazard ratio 1.2), older patients (compared to patients 18 to 34 years old: 1.8 for 35 to 44; 3.0 for 45 to 59; 3.8 for ≥60), and if ART was initiated (1.3). In 2014, 1313 (16.6%) of 7905 diabetic patients had DM complications (11.5% microvascular complications and 6.9% macrovascular complications). Patients diagnosed with DM were at higher risk of death compared to the others.

Conclusions

DM incidence was higher in this Thailand cohort of HIV infected adults than in the general population. Risk factors were similar to those in the general population, in addition to starting ART.

Keywords

  • HIV infection
  • Antiretroviral treatment
  • Diabetes mellitus
  • Incidence
  • Diabetic complications

Background

The burden of diabetes prevalence is rising especially in low and middle-income countries [1]. In 2017, the International Diabetes Federation estimated that 425 million people worldwide, or 8.8% of adults 20–79 years, were affected by diabetes type 1 and type 2 (8.3% in Thailand) and that half (50.0%) of all people 20–79 years with diabetes are unware of their disease [2].

Known risks factors of diabetes mellitus (DM) include older age, male sex, family history of DM, alcohol use, adiposity, dyslipidemia, hypercholesterolemia, and hypertriglyceridemia [1]. Indeed, hyperlipidemia, insulin resistance and lipodystrophy are commonly observed in HIV-infected patients on antiretroviral treatment (ART) and several studies have suggested that HIV infection and/or antiretroviral drugs may increase the risk of DM [36]. The Thai national AIDS program database, which includes data on DM incidence, related mortality and prevalence of related complications, provided a unique opportunity to assess the importance of DM in a South-East Asian HIV infected adult population.

Since Fiscal Year (FY) 2005 (October 1, 2004 to September 30, 2005), Thailand has provided free health services, including ART and laboratory monitoring to HIV-infected patients through the Thailand National AIDS Program (NAP), under the National Health Security Office (NHSO). At each visit, or at the end of each month for hospitalizations, data from HIV-infected patients covered by these schemes are entered into the NAP database. We estimated the incidence of DM diagnosis, investigated associated risk factors and clinical outcomes using the data from adults registered with the NAP under the Universal Coverage Scheme (UCS), which covers about three-quarters of the Thai population [7].

Methods

This study was a retrospective cohort study and an analysis of secondary data of the NHSO. A total of 199,707 HIV infected adults (≥18 years old) with no history of DM registered between FY2007 to FY2013 and follow up until September 30,2014 (end of FY2014) for care with the NAP and received care under UCS in 1035 hospitals throughout Thailand.

We extracted the following patient characteristics from the NHSO database: sex, date of birth, weight, height, fasting plasma glucose (FPG), triglycerides, hepatitis C virus infection, absolute CD4 cell count (including nadir) and date of ART initiation [7]. A patient not showing up for at least 7 months after last visit was considered lost to follow up (LTFU). The number of PYFU was calculated from date of NAP registration (baseline) to censoring date, i.e. 7 months after last visit date, date of death (within 7 months after last visit), date of first DM diagnosis, or September 30, 2014, which ever occurred first.

For this study, DM was considered diagnosed at the first date of at least two of the following records: 1) FPG ≥126 mg/dl following the 2013 American Diabetes Association criteria [8] or 2) confirmed diagnosis codes E11-E14 (which excludes type-1 diabetes mellitus) of the 2010 WHO International Classification of Diseases (ICD-10) [9], or 3) confirmed receipt of anti-diabetic drugs [8]. Hypertriglyceridemia was defined by triglycerides ≥200 mg/dl [4]. DM incidence rates were estimated by the number of new diagnoses divided by the total number of PYFU. The 95% confidence intervals (CIs) were calculated using the quadratic approximation to the Poisson log likelihood [10].

We fitted competing risks survival regression models (Fine-Gray) [1113], treating death without DM as a competing event (except in case of death after DM diagnosis), to assess the association of new DM diagnosis occurrence with sex, baseline age (categories: 18–34, 35–44, 45–59 or ≥ 60 years [14]) and Body Mass Index (BMI) (≥25 or < 25 kg/m2); and baseline and time-updated triglycerides (< 200 or ≥ 200 mg/dl), baseline absolute CD4 cell count (< 200 or ≥ 200 cells/mm3), and time-updated ART initiation (yes/no) [4, 14]. All models were adjusted for the existence of at least a previous record of FPG (dichotomous, time-updated variable) and time-updated absolute CD4 < 200 cells/mm3. We imputed missing data by multiple imputation with chained equations (MICE procedure, Stata Corp, College Station, Texas, USA) based on logistic regression for binary variables if they were ≤ 20% missing data (time-updated absolute CD4 values if there was a missing data more than 9 months after the previous known value) [15]. We conducted multivariable analyses using a backward selection approach starting with factors significantly associated with time to DM in the univariable analysis (p ≤ 0.20), excluding variables with more than 20% missing values (baseline BMI, baseline and time-updated triglycerides, baseline absolute CD4 data) and sensitivity analyses [16] were conducted including each of these variables. The cumulative incidence function (CIF) at time t was defined as the probability of a new diagnosis from baseline using Gray’s sub-distribution hazard technique [17, 18].

A Poisson distribution was assumed to calculate the 95% confidence intervals (CIs) of prevalence. We used the 2010 WHO ICD-10 [9] codes for microvascular diabetic complications: ophthalmic complications code E11-E14 with additional code “.3”, diabetic retinopathy codes H36.0*, renal complications codes E11-E14 with additional external cause code “.2” or code N18, neurological complications codes E11-E14 with additional code “.4” (diabetic amyotrophy, autonomic neuropathy, mononeuropathy, polyneuropathy and autonomic), and macrovascular diabetic complications: ischaemic heart diseases (ICH) codes I00-I25, cerebrovascular disease (CVD) codes I60-I69, peripheral circulatory complication codes E11-E14 with additional code“.5” (diabetic gangrene, peripheral angiopathy or diabetic ulcer), and amputation (The 2010 WHO ICD-9-CM procedure code 84.11, 84.12, 84.14, 84.15 and 84.17 [19]).

We analyzed the interaction between time-updated ART initiation and time-updated DM for the risk of death by year using a constant proportional hazard in univariable and multivariable Cox’s survival regression analyses (p ≤ 0.05). We tested the proportional hazards assumption of each model based on Schoenfeld residuals [20] and considered time-updated DM diagnosis as a time-varying variable [21, 22].

Analyses were performed using Stata software, version 13.1 (Stata Corp, College Station, Texas, USA). Patient identifiers were encrypted by NHSO prior data management and analysis. The analysis plan was approved by the Ethical Committee of the Faculty of Medicine, Chiang Mai University, Thailand on March 18, 2014 (114/2014, Research ID: COM-2557-02140).

Results

Study population and follow up

All 199,707 HIV-infected adults (54.2% male) with no history of DM, registered with the NAP through the UCS from FY2007 to FY2013, were included in the analysis. Baseline median age was 36.2 years (IQR 30.5 to 42.6), BMI 20.2 kg/m2 (IQR 18.2 to 22.4), absolute CD4 cell count 120 cells/mm3 (IQR 33 to 324) and nadir CD4 cell count 119 cells/mm3 (IQR 33 to 323); and 1408 of 199,707 (0.7%) had a history of hepatitis C infection. Of the 11,763 patients with available records, 2511 (21.4%) had triglycerides ≥200 mg/dl.

Data from a total of 763,666 PYFU were available from FY2007 to FY2014 (median follow-up time was 3.8 years [IQR 1.6 to 6.1]); 15,114 patients (7.6%) were lost-to-follow up, 8383 (4.2%) were diagnosed with DM and 44,062 (22.1%) died (Fig. 1). 116,394 patients (58.3%) had at least one FPG measure. ART was initiated in 152,664 patients (76.4%) and the median duration from registration to ART initiation was 1.3 months (IQR 0.7 to 4.9). Of note, 24,539 (55.7%) of the patients who died had not initiated ART.
Fig. 1
Fig. 1

Study population for the analysis of incidence of type-2 diabetes mellitus (DM) in the National AIDS Program in Thailand

Patients diagnosed with DM

Over the study period, 8383 patients were diagnosed with DM leading to an estimated cumulative incidence of DM of 11.0 per 1000 PYFU (CI 10.7 to 11.2). The incidence rate of DM was 10.8 per 1000 PYFU during the first year, reached 13.0 per 1000 PYFU during the third year and tended to decrease thereafter. The incidence was 12.5 per 1000 PYFU in men (CI 12.2 to 12.9) and 9.5 per 1000 PYFU in women (CI 9.1 to 9.8) (p < 0.001). Male sex, age above 35 years, baseline BMI ≥25 kg/m2, triglycerides ≥200 mg/dl, absolute CD4 cell count < 200 cells/mm3 and hepatitis C infection were associated with a higher incidence rate of DM (Table 1). The estimated cumulative incidence of DM was 3.8% (CI 3.7 to 3.9) at 4 years follow-up. It was about 3.4% (CI 3.3 to 3.5) in women and 4.1% (CI 4.0 to 4.3) in men; 2.0% (CI 1.9 to 2.1) in patients aged 18–34 years, 4.2% (CI 4.0 to 4.3) in patients aged 35–44 years, 7.0% (CI 6.7 to 7.3) in patients aged 45–59 years and 9.4% (CI 8.5 to 10.5) in patients aged ≥60 years (Fig. 2).
Table 1

Incidence of diabetes mellitus according to patient baseline characteristics

Baseline characteristics

Category

Total

Number of patients with diabetes mellitus

PYFU

Incidence per 1000 PYFU

95% CI

P value*

Total

 

199,707

8383

763,666

10.98

10.74–11.21

 

Fiscal year of registration

2007

41,537

2852

248,385

11.48

11.07–11.91

< 0.001

2008

34,100

1729

165,818

10.43

9.95–10.93

 

2009

28,872

1343

119,249

11.26

10.68–11.88

 

2010

26,026

916

89,424

10.24

9.60–10.93

 

2011

24,270

699

66,880

10.45

9.70–11.26

 

2012

22,861

443

46,229

9.58

8.73–10.52

 

2013

22,041

401

27,682

14.49

13.14–15.98

 

Sex

Female

91,407

3604

381,552

9.45

9.14–9·76

< 0.001

Male

108,300

4779

382,114

12.51

12.16–12.87

 

Age (years)

18–34

87,948

2076

354,196

5.86

5.61–6.12

< 0.001

35–44

74,610

3521

288,739

12.19

11.80–12.60

 

45–59

33,154

2428

110,286

22.02

21.16–22.91

 

≥60

3995

358

10,444

34.28

30.90–38.02

 

History of hepatitis C infection

No

198,299

8327

760,649

10.95

10.71–11·18

< 0.001

Yes

1408

56

3017

18.56

14.28–24.12

 

Variables with more than 20% missing data

 Body mass index (kg/m2)

< 25

59,249

2154

218,284

9.87

9.46–10.29

< 0.001

≥25

7296

509

29,241

17.41

15.96–18.99

 

 Triglycerides (mg/dl)

< 200

9252

289

30,352

9.52

8.48–10.69

0.0021

≥200

2511

111

8280

13.41

11.13–16.15

 

 Absolute CD4 cell count (cells/mm3)

≥200

36,877

1283

144,795

8.86

8.39–9.36

< 0.001

< 200

58,570

2114

183,322

11.53

11.05–12.03

 

 Nadir CD4 cell count (cells/mm3)

≥200

36,877

1279

144,522

8.85

8.38–9.35

< 0.001

< 200

58,671

2118

183,721

11.53

11.05–12.03

 

Abbreviations: CI confidence interval, PYFU Person-years of follow-up

*Chi-square test

Fig. 2
Fig. 2

The estimate cumulative incidence function of diabetes mellitus in HIV-infected adults (a) overall (b) by sex and (c) by age at baseline. Legend text: (a) Cumulative incidence, 95% Confidence Interval (b) Man, Woman (c) > = 60 year, 45–59 year, 35–44 years, 18–34 year, 95% Confidence Interval

Table 2 provides the results of the univariable and multivariable analyses using competing risks survival models. In the univariable analyses, DM diagnosis was associated with male sex, older age at baseline, higher BMI at baseline, hypertriglyceridemia at baseline and time-updated, and time-updated ART initiation (all p ≤ 0.05). In the multivariable analysis, DM diagnosis was associated with male sex, age above 35 years at baseline, and time-updated ART initiation. The sensitivity analyses restricted to cases with available baseline BMI and hypertriglyceridemia data showed similar associations with the factors identified in the main analyses (Additional file 1: Table S1).
Table 2

Univariable and multivariable competing risk regression analyses of potential risk factors for diabetes mellitus (treating death without diabetes mellitus as a competing event)

Variables

Category

Univariable

Multivariable

 

SHR

[95% CI]

P value

aSHR

[95% CI]

P value

Sex

Female

1

Reference

 

1

Reference

 

Male

1.22

1.17–1.27

< 0.001

1.13

1.08–1.18

< 0.001

Age (years) at baseline

18–34

1

Reference

 

1

Reference

 

35–44

1.88

1.78–1.99

< 0.001

1.83

1.73–1.93

< 0.001

45–59

3.17

2.99–3.37

< 0.001

3.1

2.92–3.29

< 0.001

≥60

4.41

3.93–4.94

< 0.001

4.32

3.85–4.84

< 0.001

History of hepatitis C infection at baseline

No

1

Reference

 

1

Reference

 

Yes

1.24

0.95–1.62

0.106

1.09

0.84–1.43

0.515

Time-updated antiretroviral treatment initiation

No

1

Reference

 

1

Reference

 

Yes

1.26

1.19–1.35

< 0.001

1.26

1.18–1.34

< 0.001

Baseline body mass index (kg/m2) (n = 66,529)

< 25

1

Reference

    

≥25

1.97

1.78–2.17

< 0.001

   

Baseline triglycerides (mg/dl) (n = 11,762)

< 200

1

Reference

    

≥200

1.41

1.14–1.76

0.002

   

Baseline absolute CD4 cell count (cells/mm3) (n = 95,432)

≥200

1

Reference

    

< 200

1.04

0.96–1.12

0.33

   

Nadir CD4 cell count (cells/mm3) (n = 95,476)

≥200

1

Reference

    

< 200

0.89

0.83–0.96

0.002

   

Abbreviations: CI confidence interval, n number of patients with available data, SHR sub-distribution hazard ratio, aSHR adjusted sub-distribution hazard ratio

Note: impute time-updated CD4 (18.0% of values); Univariable and multivariable analysis were adjusting for previous fasting plasma glucose measurement and time-updated absolute CD4 < 200 cells/mm3

Prevalence of diabetes mellitus and diabetes-related complications

Of 8383 DM patients, the median follow-up time since DM diagnosis until end of study was 2.7 years (IQR 1.2 to 4.3). In the last year of the study (FY2014), of 157,980 alive patients on follow-up, 7905 patients (5.0%, CI 4.9 to 5.1) had DM. It was higher in men and increased with age. Of 7905, 1313 (16.6%, CI 15.7 to 17.5) patients had DM-related complications. The overall prevalence of microvascular complications was 11.5% (CI 10.8 to 12.3); renal (nephropathy) 7.8% (CI 7.2 to 8.4), ophthalmic 3.1% (CI 2.7 to 3.5) (diabetic retinopathy 0.6% [CI 0.5 to 0.8]), and neurological (neuropathy) 2.1% (CI 1.8 to 2.4). The overall prevalence of macrovascular complications was 6.9% (CI 6.3 to 7.5); ischemic heart diseases 3.1% (CI 2.7 to 3.5), cerebrovascular disease 2.7% (CI 2.3 to 3.1) and peripheral circulatory 1.6% (CI 1.3 to 1.9) (amputation 0.3% [0.2 to 0.5]) (see Table 3).
Table 3

Prevalence of diabetes related complications among HIV-infected adults with diabetes during last year of follow-up (Fiscal Year 2014)

Type of complications

Category

Sub category

Number of patients with complications

Total

P-valuea

Annual prevalence (%)

95% CI

MICROVASCULAR OR MACROVASCULAR

 

1313

7905

 

16.61

15.72–17.53

 

Sex

0.144

  
  

Female

596

3444

 

17.31

15.94–18.75

  

Male

717

4461

 

16.07

14.92–17.29

 

Age in years

   

< 0.001

  
  

18–34

81

770

 

10.52

8.35–13.07

  

35–44

321

2901

 

11.07

9.89–12.34

  

45–59

658

3543

 

18.57

17.18–20.05

  

≥60

253

691

 

36.61

32.24–41.41

MICROVASCULAR

  

911

7905

 

11.52

10.79–12.30

 Ophthalmic

  

246

7905

 

3.11

2.74–3.53

 

Sex

   

0.122

  
  

Female

119

3444

 

3.46

2.86–4.13

  

Male

127

4461

 

2.84

2.37–3.39

 

Age in years

   

0.004

  
  

18–34

25

770

 

3.25

2.10–4.79

  

35–44

71

2901

 

2.45

1.91–3.09

  

45–59

115

3543

 

3.25

2.68–3.90

  

≥60

35

691

 

5.07

3.53–7.04

Diabetic retinopathy

 

49

7905

 

0.62

0.46–0.82

 

Sex

   

0.443

  
  

Female

24

3444

 

0.7

0.45–1.04

  

Male

25

4461

 

0.56

0.36–0.83

 

Age in years

   

0.072

  
  

18–34

2

770

 

0.26

0.03–0.94

  

35–44

13

2901

 

0.45

0.24–0.77

  

45–59

26

3543

 

0.73

0.48–1.08

  

≥60

8

691

 

1.16

0.50–2.28

 Renal

  

615

7905

 

7.78

7.18–8.42

 

Sex

   

0.174

  
  

Female

284

3444

 

8.25

7.31–9.26

  

Male

331

4461

 

7.42

6.64–8.26

 

Age in years

   

< 0.001

  
  

18–34

24

770

 

3.12

2.00–4.64

  

35–44

149

2901

 

5.14

4.34–6.03

  

45–59

305

3543

 

8.61

7.67–9.63

  

≥60

137

691

 

19.83

16.65–23.44

 Neurological

  

163

7905

 

2.06

1.76–2.40

 

Sex

   

< 0.001

  
  

Female

101

3444

 

2.93

2.39–356

  

Male

62

4461

 

1.39

1.07–1.78

 

Age in years

   

< 0.001

  
  

18–34

4

770

 

0.52

0.14–1.33

  

35–44

32

2901

 

1.1

0.75–1.56

  

45–59

86

3543

 

2.43

1.94–3.00

  

≥60

41

691

 

5.93

4.26–8.05

MACROVASCULAR

 

545

7905

 

6.89

6.33–7.50

 Ischaemic heart diseases

 

243

7905

 

3.07

2.70–3.49

 

Sex

   

0.681

  
  

Female

109

3444

 

3.16

2.60–3.82

  

Male

134

4461

 

3

2.52–3.56

 

Age in years

   

< 0.001

  
  

18–34

14

770

 

1.82

0.99–3.05

  

35–44

45

2901

 

1.55

1.13–2.08

  

45–59

116

3543

 

3.27

2.71–3.93

  

≥60

68

691

 

9.84

7.64–12.48

 Cerebrovascular disease

 

212

7905

 

2.68

2.33–3.07

 

Sex

   

0.002

  
  

Female

70

3444

 

2.03

1.58–2.57

  

Male

142

4461

 

3.18

2.68–3.75

 

Age in years

   

< 0.001

  
  

18–34

10

770

 

1.3

0.62–2.39

  

35–44

45

2901

 

1.55

1.13–2.08

  

45–59

109

3543

 

3.08

2.53–3.71

  

≥60

48

691

 

6.95

5.12–9.21

 Peripheral circulatory

  

123

7905

 

1.56

1.29–1.86

 

Sex

   

0.174

  
  

Female

61

3444

 

1.77

1.35–2.28

  

Male

62

4461

 

1.39

1.07–1.78

 

Age in years

   

< 0.001

  
  

18–34

10

770

 

1.3

0.62–2.39

  

35–44

24

2901

 

0.83

0.53–1.23

  

45–59

70

3543

 

1.98

1.54–2.50

  

≥60

19

691

 

2.75

1.66–4.29

Amputation

  

25

7905

 

0.32

0.20–0.47

 

Sex

   

0.719

  
  

Female

10

3444

 

0.29

0.14–0.53

  

Male

15

4461

 

0.34

0.19–0.55

 

Age in years

   

0.214

  
  

18–34

4

770

 

0.52

0.14–1.33

  

35–44

5

2901

 

0.17

0.06–0.40

  

45–59

12

3543

 

0.34

0.18–0.59

  

≥60

4

691

 

0.58

0.16–1.48

aFisher’s exact test

Diabetes mellitus and mortality

During the study period, from a total of 199,707 patients, 46,252 died, including those who died after a diagnosis of DM. During the first year of follow-up, patients not diagnosed with DM had a higher risk of death than those with DM diagnosis, adjusting for sex, age, time-updated absolute CD4 cell count and time-updated ART initiation, with a strong interaction between time-updated DM and ART initiation (aHR 9.3, CI 6.3 to 13.7 in patients not receiving ART; aHR 2.6, CI 1.7 to 4.0 in patients receiving ART). In the following 3 years, the risk of death was similar in those with and without DM diagnosis (aHR 1.0, CI 1.0 to 1.1) but tended to increase thereafter in patients with DM (5th to 8th year: aHR 1.2, CI 1.0 to 1.5, p = 0.05).

Discussion

The incidence of new DM diagnosis during the follow up of 199,707 of HIV infected adults over 8 years in Thailand (763,666 PYFU) was 11.0 per 1000 PYFU with slight variations over time. The study population included 45.8% females and was relatively young (median 36.2 years), with a lean baseline body shape (median 20.2 kg/m2) and a low absolute CD4 cell count (median 120 cells/mm3). In multivariable analyses, risk factors associated with DM diagnosis were male sex and older age, which are known risk factors in the general population. In addition, initiation of antiretroviral treatment was associated with DM, as previous reported [14, 2325].

Our overall DM incidence estimate was similar to or slightly lower than that reported in several studies conducted in population with similar characteristics (sex, age, CD4, ART): 11.35 per 1000 PYFU in a study of 6816 HIV-infected patients registered with the South Carolina Medicaid system [3] in the USA (43% women, median age 39.0 years, and 80.4% on ART); 14.1 per 1000 PYFU (95% CI, 11.6 to 17.0) in the French Aproco-Pilote cohort [6] (78.5% men, median age 37.0 years, median CD4 cell count 280 cells/mm3, all on ART, men 14.6 per 1000 PYFU versus women 12.6 per 1000 PYFU); 13.1 per 1000 PYFU in the National Taiwan University Hospital study [26] (86.0% men, median age 34.0 years, median CD4 cell count 92 cells/mm3, all patients on ART). The incidence rate of DM and the risk factors for DM in our study were similar to 13.7 per 1000 PYFU, the pooled incidence rate estimated in a meta-analysis [27].

Male sex and older age, which are known factors of DM [1] may account for the differences in estimated incidence reported by previous studies of HIV infected patients, for instance 5.0 per 1000 PYFU in a younger population composed of 76% women in Thailand [28], or 26.0 per 1000 PYFU in older HIV-infected men (median 46 years) participating in the US Multicenter AIDS Cohort Study [29]. ART initiation was identified as a risk factor of DM in our study, similarly to that reported in the Data Collection on Adverse events of Anti-HIV Drugs study (relative risk of 1.1 per year of exposure) [5]. It is possible that, even after adjustment for age and treating death as a competing event, improved survival on ART is a confounder for this association, i.e. patients who survive longer are more likely to develop DM. Also, the risk may vary according to specific antiretroviral drugs [28].

The incidence of DM in HIV-infected adults aged 35 to 59 years in our study was 14.9 per 1000 PYFU, higher than in the Thai general adult population (7.8 to 11.4 per 1000 PYFU) [23, 24], suggesting a contribution of HIV infection or ART in the risk of DM.

At the end of study period, the overall estimated prevalence of DM was lower than in the Thai general population (5.0% versus 8.9%) [30]. This may have resulted from several differences between our population and the general population: lower median age, lower BMI, or regular medical follow-up and FPG assessments in our cohort. Also, DM-related complications seemed less prevalent [7, 30, 31], likely for similar reasons. Generally, microvascular complications develop 5 years after diagnosis [32, 33]. However, with a median follow-up of 2.7 years in our study the prevalence of microvascular complications was 11.5% and the most common was diabetes nephropathy. HIV infection or/and antiretroviral drugs may also have affected the kidneys or accelerated microvascular complications.

Our study showed that the risk of death in patients diagnosed DM tended to be higher after 4 years of follow-up, a finding similar to that reported by a previous study in Thailand [34]. Although causes of death were not available, we hypothesized that the increasing risk of death with DM may have been related to cardiovascular and renal complications as well as HIV infection itself.

A limitation of our study is that the NAP database has been primarily designed for overall monitoring of the program with missing data not systematically tracked as in clinical studies. Importantly, the NAP database represents a unique source of information that likely reflects the actual DM burden in the HIV infected population. Also, the lack of systematic FPG assessments in some patients may have led to an underestimation of DM incidence. This issue is now addressed by a specific program [35] which promotes systematic DM screening in at risk patients [14].

Conclusion

HIV infected adults in Thailand had a slightly higher incidence of DM than that estimated in general population with similar common risk factors. Antiretroviral treatment may contribute to the risk of DM but to a lesser extent than the common risk factors of DM.

Abbreviations

aHR: 

Adjusted hazard ratio

ART: 

Antiretroviral treatment

aSHR: 

Adjusted sub-distribution hazard ratio

BMI: 

Body mass index

CI: 

Confidence interval

CIF: 

Cumulative incidence function

DM: 

Diabetes mellitus

FPG: 

Fasting plasma glucose

FY: 

Fiscal year

ICD: 

International classification of diseases

LTFU: 

Lost to follow up

MICE: 

Multiple imputation with chained equations

NAP: 

The Thailand National AIDS Program

NHSO: 

The National Health Security Office

PYFU: 

Person-years of follow-up

SHR: 

Sub-distribution hazard ratio

UCS: 

Universal Coverage Scheme

WHO: 

World Health Organization

Declarations

Acknowledgments

We would like to thank the NHSO, Thailand for providing the data, in particular Winai Sawasdivorn, Jadej Thammatacharee, Yongyuth Pongsupap, Yolsilp Suchonwanich, Sinchai Tawwuttanakidgul, Suchada Chaivooth, Chirod Narkpaichit, Artit Pusamang, Sumitra Daengprasert, Sumalee Hiranmongkholkul, Traithep Fongthong, Kanjana Sirigomon, and Jutatip Thungthong.

Availability of data and materials

According to the agreement of the first author with the NHSO, the datasets analyzed for the study are not publicly available.

Author’s contributions

NP, NK, RC and GJ wrote the proposal and designed the study. NP, AT, NS and JYM analyzed data. NP, GJ, SB, CB and NK interpreted data. NP, NK and NS search literature. NP, TRC, AT, GJ and NK wrote the manuscript, and edited the final draft for publication. NK and GJ supervised the whole study process and edited the final draft for publication. All authors approved the final manuscript of this article prior to submission.

Ethics approval and consent to participate

The analysis plan was approved by the Ethical Committee of the Faculty of Medicine, Chiang Mai University, Thailand on March 18, 2014 (114/2014, Research ID: COM-2557-02140). The use of the database was authorized by the NHSO only for this study. Patient identifiers were encrypted by NHSO prior data management and analysis.

Consent for publication

The use of the data analyzed was authorized by the NHSO exclusively for this paper.

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.

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’ Affiliations

(1)
Clinical Epidemiology Program, Faculty of Medicine, Chiang Mai University, 110 Intavaroros Road, Tambon Sripoom, Muang, Chiang Mai, 50200, Thailand
(2)
National Health Security Office (NHSO), Building B 120 Moo 3 Chaengwattana Road, Lak Si District, Bangkok, 10210, Thailand
(3)
Institut de recherche pour le développement (IRD) UMI 174-PHPT, 187/10, Changklan Rd, Changklan, Muang, Chiang Mai, 50100, Thailand
(4)
Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Road, Tambon Sripoom, Muang, Chiang Mai, 50200, Thailand
(5)
Harvard T.H. Chan School of Public Health, Boston, MA, USA
(6)
Division of Endocrinology, Department of Medicine, Faculty of Medicine, Chiang Mai University, 110 Intavaroros Road, Tambon Sripoom, Muang, Chiang Mai, 50200, Thailand
(7)
Department of Surgery, Faculty of Medicine, Chiang Mai University, 110 Intavaroros Road, Tambon Sripoom, Muang, Chiang Mai, 50200, Thailand
(8)
Department of Medicine, Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Road, Ratchathewi, Bangkok, 10400, Thailand
(9)
National Health Security Office Chiang Mai Branch (Region 1), 6 Mahidol road, Suthep, Muang, Chiang Mai, 50200, Thailand
(10)
Department of Molecular & Clinical Pharmacology, University of Liverpool, Liverpool, UK
(11)
INSERM UMR 1135, Equipe ECSTRA, Centre de Recherche Epidémiologie Biostatistique Sorbonne Paris Cité, Université Paris Diderot, Paris, France

References

  1. Roglic G, World Health Organization, editors. Global report on diabetes. Geneva: World Health Organization; 2016.Google Scholar
  2. International Diabetes Federation. IDF DIABETES ATLAS 2017. 8th ed. 2017. http:// http://diabetesatlas.org/resources/2017-atlas.html. Accessed 17 Aug 2018.
  3. Tripathi A, Liese AD, Jerrell JM, Zhang J, Rizvi AA, Albrecht H, et al. Incidence of diabetes mellitus in a population-based cohort of HIV-infected and non-HIV-infected persons: the impact of clinical and therapeutic factors over time. Diabet Med J Br Diabet Assoc. 2014;31:1185–93. https://doi.org/10.1111/dme.12455.View ArticleGoogle Scholar
  4. Jellinger PS, Smith DA, Mehta AE, Ganda O, Handelsman Y, Rodbard HW, et al. American Association of Clinical Endocrinologists Guidelines for Management of Dyslipidemia and Prevention of atherosclerosis. Endocr Pract. 2012;18(Suppl 1):1–78.View ArticlePubMedGoogle Scholar
  5. De Wit S, Sabin CA, Weber R, Worm SW, Reiss P, Cazanave C, et al. Incidence and risk factors for new-onset diabetes in HIV-infected patients: the data collection on adverse events of anti-HIV drugs (D:a:D) study. Diabetes Care. 2008;31:1224–9. https://doi.org/10.2337/dc07-2013.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Capeau J, Bouteloup V, Katlama C, Bastard J-P, Guiyedi V, Salmon-Ceron D, et al. Ten-year diabetes incidence in 1046 HIV-infected patients started on a combination antiretroviral treatment. AIDS Lond Engl. 2012;26:303–14. https://doi.org/10.1097/QAD.0b013e32834e8776.View ArticleGoogle Scholar
  7. National Health Security Office, Thailand. NHSO Annual Report Fiscal Year 2014. 2015. http://library.nhso.go.th. Accessed 17 Aug 2018.
  8. American Diabetes Association. Standards of medical care in diabetes--2013. Diabetes Care. 2013;36(Suppl 1):S11–66. https://doi.org/10.2337/dc13-S011.View ArticleGoogle Scholar
  9. World Health Organization. International Statistical Classification of Diseases and Related Health Problems. 10th Revision, 2010.Google Scholar
  10. Loader C. Local regression and likelihood. New York: Springer; 1999.Google Scholar
  11. Barnett A, Graves N. Competing risks models and time-dependent covariates. Crit Care Lond Engl. 2008;12:134. https://doi.org/10.1186/cc6840.View ArticleGoogle Scholar
  12. Lim HJ, Zhang X, Dyck R, Osgood N. Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes. BMC Med Res Methodol. 2010;10:97. https://doi.org/10.1186/1471-2288-10-97.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496–509. https://doi.org/10.1080/01621459.1999.10474144.View ArticleGoogle Scholar
  14. Diabetes association of Thailand, Department of Medical Services Ministry of Public Health, The Endocrine Society of Thailand, National Health Security Office. Clinical Practice Guideline for Diabetes 2014. Bangkok: Aroonkarnpim; 2014.Google Scholar
  15. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work?: multiple imputation by chained equations. Int J Methods Psychiatr Res. 2011;20:40–9. https://doi.org/10.1002/mpr.329.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Thabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol. 2013;13 https://doi.org/10.1186/1471-2288-13-92.
  17. Gray RJ. A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988;16:1141–54.View ArticleGoogle Scholar
  18. Allignol A, Schumacher M, Beyersmann J. A note on variance estimation of the Aalen-Johansen estimator of the cumulative incidence function in competing risks, with a view towards left-truncated data. Biom J. 2010;52:126–37. https://doi.org/10.1002/bimj.200900039.View ArticlePubMedGoogle Scholar
  19. World Health Organization. International Classification of Diseases, Classification of Procedures (ICD-9-CM). 9th Revision, 2010.Google Scholar
  20. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–26. https://doi.org/10.1093/biomet/81.3.515.View ArticleGoogle Scholar
  21. Patumanond J. Clinical Statistic: Standara Regression Analysis. Bangkok: Amarin Printing & Publishing Public Company Limited; 2013.Google Scholar
  22. Tang Z, Lan G, Chen YQ, Zhu Q, Yang X, Shen Z, et al. HIV-1 treatment-as-prevention: a cohort study analysis of Serodiscordant couples in rural Southwest China. Medicine (Baltimore). 2015;94:e902. https://doi.org/10.1097/MD.0000000000000902.View ArticleGoogle Scholar
  23. Jiamjarasrangsi W, Aekplakorn W. Incidence and predictors of type 2 diabetes among professional and office workers in Bangkok, Thailand. J Med Assoc of Thailand. 2005;88:1896–904.Google Scholar
  24. Jiamjarasrangsi W, Lohsoonthorn V, Lertmaharit S, Sangwatanaroj S. Incidence and predictors of abnormal fasting plasma glucose among the university hospital employees in Thailand. Diabetes Res Clin Pract. 2008;79:343–9. https://doi.org/10.1016/j.diabres.2007.09.008.View ArticlePubMedGoogle Scholar
  25. Aekplakorn W, Bunnag P, Woodward M, Sritara P, Cheepudomwit S, Yamwong S, et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care. 2006;29:1872–7. https://doi.org/10.2337/dc05-2141.View ArticlePubMedGoogle Scholar
  26. Lo Y-C, Chen M-Y, Sheng W-H, Hsieh S-M, Sun H-Y, Liu W-C, et al. Risk factors for incident diabetes mellitus among HIV-infected patients receiving combination antiretroviral therapy in Taiwan: a case-control study. HIV Med. 2009;10:302–9. https://doi.org/10.1111/j.1468-1293.2008.00687.x.View ArticlePubMedGoogle Scholar
  27. Nansseu JR, Bigna JJ, Kaze AD, Noubiap JJ. Incidence and risk factors for prediabetes and diabetes mellitus among HIV-infected adults on antiretroviral therapy: a systematic review and meta-analysis. Epidemiology. 2018;29:431–41. https://doi.org/10.1097/EDE.0000000000000815.View ArticlePubMedGoogle Scholar
  28. Riyaten P, Salvadori N, Traisathit P, Ngo-Giang-Huong N, Cressey TR, Leenasirimakul P, et al. New-onset diabetes and antiretroviral treatments in HIV-infected adults in Thailand. J Acquir Immune Defic Syndr. 2015;69:453–9. https://doi.org/10.1097/QAI.0000000000000647.View ArticlePubMedGoogle Scholar
  29. Brown TT, Cole SR, Li X, Kingsley LA, Palella FJ, Riddler SA, et al. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med. 2005;165:1179–84. https://doi.org/10.1001/archinte.165.10.1179.View ArticlePubMedGoogle Scholar
  30. Wichai Aekplakorn. Thai National Health Exam Survey 5th, 2014. Health Systems Research Institute: 2016.Google Scholar
  31. Paengsai N, Ratanachopanich T, Waleekhachonloet O, Limwattananon C, Limwattananon S, Thammatacharee N, et al. Access to screening and treatments for diabetic retinopathy under the universal coverage scheme in Thailand. BMC Public Health. 2017;17(Suppl 6:O118:32 of 41) https://doi.org/10.1186/s12889-017-4877-4.
  32. Chawla A, Chawla R, Bhasin GK, Soota K. Profile of adolescent diabetics in north Indian population. J Clin Diabetol. 2014;1:1–3.Google Scholar
  33. Fowler MJ. Microvascular and macrovascular complications of diabetes. Clin Diabetes. 2008;26:77. https://doi.org/10.2337/diaclin.26.2.77.View ArticleGoogle Scholar
  34. Putcharoen O, Wattanachanya L, Sophonphan J, Siwamogsatham S, Sapsirisavat V, Gatechompol S, et al. New-onset diabetes in HIV treated adults from Thailand: predictors, long-term renal and cardiovascular outcomes. AIDS. 2017;31(11):1535–43. https://doi.org/10.1097/QAD.0000000000001496.View ArticlePubMedGoogle Scholar
  35. National Health Security Office (NHSO). Fund Administrative National Health Security Guideline Fiscal Year 2014. 2013. https://www.nhso.go.th/eng/files/userfiles/file/2018/001/NHSO%20Annual%20Report%202014.pdf. Accessed 17 Aug 2018.

Copyright

© The Author(s). 2018

Advertisement