Skip to content

Advertisement

  • Review
  • Open Access

Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017

^Deceased
Cardiovascular Diabetology201817:83

https://doi.org/10.1186/s12933-018-0728-6

  • Received: 20 November 2017
  • Accepted: 28 March 2018
  • Published:

Abstract

Background

Cardiovascular disease (CVD) is a common comorbidity in type 2 diabetes (T2DM). CVD’s prevalence has been growing over time.

Purpose

To estimate the current prevalence of CVD among adults with T2DM by reviewing literature published within the last 10 years (2007–March 2017).

Methods

We searched Medline, Embase, and proceedings of major scientific meetings for original research documenting the prevalence of CVD in T2DM. CVD included stroke, myocardial infarction, angina pectoris, heart failure, ischemic heart disease, cardiovascular disease, coronary heart disease, atherosclerosis, and cardiovascular death. No restrictions were placed on country of origin or publication language. Two reviewers independently searched for articles and extracted data, adjudicating results through consensus. Data were summarized descriptively. Risk of bias was examined by applying the STROBE checklist.

Results

We analyzed data from 57 articles with 4,549,481 persons having T2DM. Europe produced the most articles (46%), followed by the Western Pacific/China (21%), and North America (13%). Overall in 4,549,481 persons with T2DM, 52.0% were male, 47.0% were obese, aged 63.6 ± 6.9 years old, with T2DM duration of 10.4 ± 3.7 years. CVD affected 32.2% overall (53 studies, N = 4,289,140); 29.1% had atherosclerosis (4 studies, N = 1153), 21.2% had coronary heart disease (42 articles, N = 3,833,200), 14.9% heart failure (14 studies, N = 601,154), 14.6% angina (4 studies, N = 354,743), 10.0% myocardial infarction (13 studies, N = 3,518,833) and 7.6% stroke (39 studies, N = 3,901,505). CVD was the cause of death in 9.9% of T2DM patients (representing 50.3% of all deaths). Risk of bias was low; 80 ± 12% of STROBE checklist items were adequately addressed.

Conclusions

Globally, overall CVD affects approximately 32.2% of all persons with T2DM. CVD is a major cause of mortality among people with T2DM, accounting for approximately half of all deaths over the study period. Coronary artery disease and stroke were the major contributors.

Keywords

  • Cardiovascular disease
  • Type 2 diabetes
  • Prevalence
  • Stroke
  • Ischemic heart disease
  • Myocardial infarction
  • Angina

Background

The International Diabetes Federation (IDF) estimates that worldwide, 415 million people have diabetes, 91% of whom have type 2 diabetes mellitus (T2DM) [1]. People with diabetes comprise 8.8% of the world’s population, and IDF predicts that the number of cases of diabetes will rise to 642 million by 2040 [1]. The prevalence of T2DM has been steadily increasing over time. Using data from the Framingham Heart Study, Abraham et al. [2] noted that the overall annualized incidence rates of the disease per 1000 persons increased from 3.0 in the 1970s to 5.5 in the first decade of the 2000s. That change represented an increase in the incidence of T2DM of 83.3% and was higher in males than females by a factor of 1.61.

Cardiovascular disease (CVD) is a major cause of death and disability among people with diabetes [1, 3]. Adults with diabetes historically have a higher prevalence rate of CVD than adults without diabetes [4], and the risk of CVD increases continuously with rising fasting plasma glucose levels, even before reaching levels sufficient for a diabetes diagnosis [5].

T2DM reduces life expectancy by as much as 10 years, and the main cause of death for patients with T2DM is CVD [1, 3]. Furthermore, people with T2DM are disproportionately affected by CVD compared with non-diabetic subjects [6]. Haffner et al. [6] reported death rates due to cardiovascular causes over a 7-year period in patients with and without T2DM. In persons with T2DM, the death rates were 15.4% for those with no prior myocardial infarction (MI) and 42.0% in patients having a history of MI. In contrast, patients who did not have T2DM, the death rates due to cardiovascular causes were 2.1 and 15.9%, respectively.

In the Framingham Heart Study, Fox [7] reported that, along with the increasing T2DM prevalence, the attributable risk of CVD due to T2DM increased from 5.4% in the period 1952–1974 to 8.7% in the period 1975 and 1998. In a longitudinal study of 881 patients with T2DM over 10 years, van Hateren et al. [8] indicated that the hazard ratio for death due to CVD was constantly increasing each year. Thus, an increasing burden of diabetes will likely be followed by an increasing burden of CVD.

Given the clinical burden that CVD complications have on T2DM patients, there has been an increased focus on the joint management of T2DM and CVD. Good glycemic control remains the main foundation for managing T2DM. Although the importance of intensive glycemic control for protection against microvascular complications and CVD in people with T1DM is well established [9, 10], its role for reducing cardiovascular risk has not been established as clearly in people with T2DM [1113]. Hence, the most effective approach for prevention of macrovascular complications appears to be multifactorial risk factor reduction (glycemic control, smoking cessation, diet, exercise, aggressive blood pressure control, treatment of dyslipidemia).

As a result, diabetes treatment guidelines have been updated to provide guidance on how to prevent and manage the onset of CVD [14]. Furthermore, there is increasing pressure from regulatory agencies that antidiabetic treatments demonstrate cardiovascular safety and benefits, especially for major cardiovascular events such as cardiovascular mortality, non-fatal MI, and stroke [15, 16]. Following these regulatory requirements, several cardiovascular outcomes trials (CVOT) have been completed, which demonstrate that certain anti-diabetic treatments are associated with a lower risk of CVD [1720].

The increased focus on adequately treating patients with both CVD and T2DM requires that we have updated prevalence rates of CVD among patients with T2DM. This is especially needed to inform clinical and policy level decision-making by healthcare providers, healthcare policy decision-makers, and health economic analysts. Reviews have been published on the epidemiology of type 1 diabetes (T1DM), and CVD [21], pre-diabetes and the risk of CVD [22], or reviews have focused on specific countries [23]. However, there is no recent global review on the prevalence of CVD among adults with T2DM. Therefore, the objective of this systematic literature review was to quantitatively summarize rates of prevalence of CVD in adults with T2DM in studies published during the past 10 years.

Although CVD is an umbrella term that includes coronary artery disease (CAD), cerebrovascular disease (CBV), and peripheral vascular disease, the focus of this review was on CVD outcomes that are relevant to major cardiovascular events. Therefore, the review specifically focused on the prevalence of CAD and CBV. CAD has many synonyms, including ischemic heart disease, coronary heart disease (CHD), atherosclerotic heart disease, and atherosclerotic CVD. Conditions within this category are stable angina pectoris, unstable angina pectoris, MI (also known as heart attack), and sudden cardiac death (SCD). CBV comprises mainly stroke (intracerebral hemorrhage, cerebral infarction, cerebral arterial disease), but also may include transient ischemic attacks.

Methods

This review was undertaken in adherence to the PRISMA Statement for systematic reviews [24].

Eligibility criteria

Criteria for eligibility were guided by the PICO reporting system (which describes the participants, interventions, comparisons, and outcome[s] of the systematic review), together with the specification of the type of study design (PICOS), from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [24].

Participants

Included in this research were adult patients ≥ 18 years old who had been diagnosed with T2DM.

Interventions

Not applicable in this research.

Comparisons

Prevalence rates of CVD between males and females, and between obese and non-obese patients were compared. It was acknowledged that, in the literature, authors often used different terms or combinations of terms to describe their patients. The aim was to be all-inclusive in order to capture all relevant patient populations. Broad definitions of acceptable diseases were CVD, CAD, CHD, ischemic heart disease (IHD), congestive heart failure (CHF), or CBV. Specific conditions of interest included stroke, MI/heart attack, angina pectoris, heart failure, and atherosclerosis as well as cardiovascular or cardiac death.

Excluded were other forms of CVD including peripheral artery disease (PAD), rheumatic heart disease, cardiac dysrhythmias (e.g., atrial or ventricular fibrillation), or requirement for surgery such as coronary artery bypass grafting (CABG)/coronary revascularization. Also excluded were intermediate states such as hypertension or metabolic syndrome or studies of carotid intima-media thickness (CIMT).

Outcome[s]

The outcome of interest was the prevalence of each of these diseases/outcomes, then aggregated by continent/IDF Region, by country, and by the country’s economic status.

Study design

The primary focus was on prevalence studies and cross-sectional surveys, including database studies or patient chart reviews. Incidence studies were accepted only if they provided population-based baseline and follow-up data. Included were peer-reviewed studies published in any language. Both published articles and abstracts from scientific meetings were eligible. However, any published studies from clinical trial programs or individual pharmaceutical products were excluded.

Information sources and search strategy

The search was undertaken between February 15 and March 6, 2017. Databases searched included Medline and Embase between January 2007 and March 2017. In addition, PubMed was searched from 2014 to identify articles that were “ahead of print” yet fully available. Evidence presented at selected conferences during the last 5 years were accessed, including the Annual Meetings of the International Society Pharmacoeconomic Outcomes and Research (ISPOR), American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD) and American Association of Clinical Endocrinologists (AACE). Keywords linked to MeSH terms specific to each database were used in the search including prevalence, OR epidemiology, AND acute coronary syndrome, OR cardiovascular disease, OR cardiovascular death, OR non-fatal myocardial infarction, OR non-fatal stroke, OR obesity AND type 2 diabetes mellitus. Other keywords were cerebrovascular disease, cerebral arterial disease, intracerebral hemorrhage, cerebral infarction, coronary artery disease, ischemic heart disease, atherosclerotic heart disease, coronary heart disease, angina pectoris. Identified articles and previous reviews were hand searched for articles that may have included data useful to this search.

Article identification and selection

Two reviewers independently searched Medline, Embase and the proceedings of major scientific meetings for suitable papers. Results were compared and adjudicated through consensus discussion. A third reviewer checked all results for quality assurance.

Data collection

Data extracted from articles included information concerning the publication, the patients involved, and outcomes of interest. Publication items included the first author, year of publication, the country in which the data were collected, and date of data collection. Patient data collected included the number of patients screened, percentages of males and females, average age, duration of T2DM, the proportion with obesity (or average body mass index (BMI) ± SD). Outcome data consisted of the numbers and percentages of patients having each cardiovascular outcome, overall and separately for males and females, where available. The same procedure (two independent reviewers plus a third judge) was followed for data collection as for article selection.

Data analysis

Data were analyzed descriptively, with sums, averages, and medians, and ranges reported. The primary outcome was the estimate of prevalence rates of CVD in patients with T2DM. No overall quantitative synthesis was undertaken. Weighted averages were calculated for individual countries and IDF regions. For patient characteristics, we calculated simple averages and medians across studies. Due to a single study with a sample size of more than three million people, which skewed the data, we calculated weighted averages for patient characteristics with and without that study. It should be noted that averages were based on the studies that reported the outcome, which may then represent a subgroup of the entire pool of studies.

The risk of bias was explored by applying the checklist from the STROBE initiative [25]. They have produced a validated checklist of items that should be addressed in reports of observational studies. There are 22 main items, each of which addresses an issue of research design and is presented in a list of recommendations. Items are scored as dichotomously as acceptable or not acceptable.

Results

Included studies

The flowchart in Fig. 1 depicts the article selection process. We initially identified 1539 papers that appeared to be suitable, but after examining them systematically, 57 studies were accepted. Three articles each presented two different sets of results [2628]; therefore, there are 60 sets of analyses within these 57 articles. Table 1 lists these studies along with their descriptive variables. There were 51 full articles and six abstracts presented at scientific meetings. Data collectively represent more than 4.5 million people with T2DM from around the globe.
Fig. 1
Fig. 1

Flowchart of article selection. The flowchart depicts the article selection process

Table 1

Overview of studies in the analysis

Author (year)

Country

Patients

% obese or reported BMI

% males

Age (years)

Diabetes duration (years)

Follow-up (years)

Time of data collection

Alaboud (2016) [41]

Saudi Arabia

748

64.3%

42.4%

57.9

13.3

NR

Apr–Jun 2014

Alonso-Moran (2014) [42]

Spain

134,421

NR

54.0%

NR

NR

NR

2007–2011

Alwakeel (2008) [30]

Saudi Arabia

1952

44.8%

48.3%

58.4

10.4

7.9

Jan 1989–Jan 2004

Bhatti (2016) [26]a

India

1522

BMI = 26.7 ± 4.4

58.3%

58.1

7.2

NR

2011–2014

Boonman-de Winter (2012) [38]

Netherlands

581

28.1%

53.4%

71.6

5.5

NR

Feb 2009–Mar 2010

Cardoso (2008) [31]

Brazil

471

NR

34.2%

60.5

9.3

4.8

1994–1996, 2001

Carnethon (2010) [32]

USA

919

BMI = 28.2 ± 4.9

53.4%

72.8

NR

11.3

(1989/92–93) through 2005

Carrasco-Sánchez (2014) [43]

Spain

490

BMI = 31.4 ± 14.23

44.3%

76.6

NR

NR

2008–2011

Cheng (2014) [86]

China

2834

91.6%

51.8%

58.5

7.0

NR

Aug 2011–Mar 2012

Collier (2015) [27]a

Scotland

7385

51.0%

NR

64.3

NR

NR

NR

  

6032

57.5%

NR

66.4

NR

NR

NR

Cortez-Dias (2010) [87]

Portugal

3215

45.1%

38.4%

58.1

NR

NR

Apr 2006–Nov 2007

Daghash (2007) [88]

Qatar

180

BMI = 30.35 ± 4.9

43.0%

51.3

NR

NR

May–Oct 2004

Doucet (2016) [89]

France

987

BMI = 29.7 ± 5.2

47.9%

77.1

17.8

NR

Jun 2009–Jul 2010

Eeg-Olofsson (2010) [33]

Sweden

18,334

BMI = 28.8 ± 5

56.7%

64.0

8.0

5.6

1997–1998–2003

Farrell (2014) [90]

Ireland

309

NR

NR

NR

NR

NR

NR

Fu (2010) [91]

Spain, France, UK, Norway, Finland, Germany, Poland

1942

52.9%

64.4%

64.5

6.2

2.8

Jun 2006–Feb 2007

Giallauria (2015) [92]

Italy

475

NR

74%

69.7

NR

NR

Jan 28–Feb 10, 2008

Glogner (2014) [40]

Sweden

83,021

BMI = 28.9 ± 5.04

55.3%

65.8

7.6

7.2

Enrolled: 1998–2003; through 2009

Gobardhan (2017) [93]

Netherlands

318

53.0%

50.9%

52.3

11.0

10.0

NR

Gondim (2016) [94]

Brazil

66

BMI = 27.17 ± 4.62

43.9%

64.6

NR

NR

NR

Hermans (2016) [95]

Belgium

711

BMI = 29.5 ± 5.8

66%

67.0

16.0

NR

NR

Hunt (2014) [96]

USA

1030

BMI = 33.6

23.5%

52.7

10.5

NR

1995–2003

Jackson (2012) [97]

Scotland

216,652

NR

53.6%

≥40

NR

4.5

2001–2007

Jurado (2009) [98]

Spain

307

44.9%

61.6%

59.6

8.5

NR

Nov 2001–Dec 2002

Kucharska-Newton (2010) [99]

USA

209

BMI = 31.0 ± 6.0

43.5%

55.5

NR

NR

1987–1989–2001

Kwon (2014) [100]

Korea

59

NR

59.3%

64.5

NR

13.0

Korea

Lin (2013) [45]

USA

162,332

NR

NR

≥18

≥2

≥2

USA

Liu (2015) [101]

China

21,072

NR

53.9%

63.7

NR

NR

China

Luo (2014) [102]

China

4836

BMI: 24.3

57.6%

64.9

NR

1.0

China

MacDonald (2011) [103]

247 countries

669

BMI = 31.2 ± 4.6

51.7%

58.8

7.2

2.0

247 countries

Malik (2015) [34]

Scotland

121,523

BMI = 31.7 ± 6.6

52.0%

63.0

4.2

4.8

Scotland

Mansour (2013) [48]

Iraq

1079

33.8%

58.8%

56.3

7.4

NR

Iraq

Mazza (2007) [104]

Italy

581

30.1%

34.8%

74.3

20.3

12.0

Italy

Menghua (2014) [49]

China

240

NR

NR

NR

NR

NR

China

Menzaghi (2014) [36]

Italy

2094

BMI = 29.1 ± 5.3

51.3%

61.9

10.4

12.9

Italy

Mody (2007) [105]

USA

4816

NR

34.4%

50.9

NR

NR

USA

Mundet (2012) [106]

Spain

4298

BMI = 29.34 ± 4.84

48.2%

67.4

8.4

10.0

Spain

Narksawat (2013) [107]

Thailand

1505

32.2%

30.4%

63.3

NR

NR

Thailand

Norhammar (2016) [50]

Sweden

352,436

NR

56.1%

67.1

NR

NR

Sweden

Penno (2015) [108]

Italy

11,538

34.5%

52.9%

65.5

12.5

NR

Italy

Rodriguez-Poncelas (2014) [109]

Spain

1141

46.5%

60.6%

66.8

9.1

NR

Feb–Jul 2011

Rossi (2011) [51]

Italy

5181

BMI = 29.8 ± 5.0

58.4%

64.4

10.0

2.3

(Jan 2006–Nov 2007), 2009

Salinero-Fort (2016) [35]

Spain

3407

BMI = 30.1 ± 4.9

50.3%

69.0

9.1

5.0

2007 (2008–2012)

Senthil (2014) [69]

India

134

NR

72.1%

NR

NR

NR

NR

Shestakova (2016) [29]

Russian Federation

3,060,517

NR

28.3%

NR

NR

1.0

2014–2015

Soetedjo (2014) [110]

Indonesia

400

56.8%

43.8%

57.7

10.3

NR

Dec 2013–Jun 2014

Song (2009) [46]

UK

2733

BMI = 33.4 ± 6.7

NR

64.2

12.7

NR

2008

Suh (2008) [111]

USA

608

51.4%

44.82%

73.2

12.9

10.0

1999–2004

Tamba (2013) [37]

Cameroon

132

30.0%

56%

58.0

12.0

6.0

2000–2009

Tan (2016) [28]a

Australia

793

54.8%

50.9%

67.2

8.0

15.0

2008–2011

  

65

35.4%

56.9%

61.1

10.0

15.0

 

Utrera-Lagunas (2013) [112]

Mexico

160

33.8%

45.0%

69.2

18.3

NR

Feb 2011–Jan 2012

Vinagre (2012) [113]

Spain

286,791

45.4%

53.7%

68.2

6.5

NR

2009

Wentworth (2012) [39]

Australia

711

>50%

55.1%

53.0

11.4

NR

1998–2011

Wong (2012) [114]

USA

889

NR

46.2%

60.6

13.3

NR

2003–2006

Yan (2015) [115]

Hong Kong

10,952

63.6%

56.1%

58.2

7.0

NR

Nov 2007–Jul 2012

Yang (2015) [47]

Korea

595

BMI = 24.29 ± 3.15

58.32%

64.9

13.6

NR

2006–2010

Zekry (2012) [116]

Switzerland

83

BMI = 27.2 ± 4.9

36.1%

84.2

NR

4.0

Jan 2004–Dec 2005

57 studies

Total

4549,481

      
 

Median

1030

45.4%

52.0%

64.3

10.0

6.0

 
 

Average

77,110

47.0%

50.5%

63.6

10.4

7.3

 
 

SD (range)

(59–3,060,517)

14.7%

10.3%

6.9

3.7

4.5

 

BMI body mass index, NR not reported, SD standard deviation

aStudy reports two separate analyses within the same paper; thus, there are 60 studies in 57 articles

In Table 2, results are presented geographically according to the classification system used by the IDF [1]. Studies from 25 countries were represented in this review: Australia, Belgium, Brazil, Cameroon, China, France, India, Indonesia, Iraq, Ireland, Italy, Korea, Mexico, Netherlands, Portugal, Qatar, Russian Federation, Saudi Arabia, Scotland, Spain, Sweden, Switzerland, Thailand, UK and USA. Details are provided in Table 3. Three areas were responsible for generating 80% of the studies. Europe produced most articles (46%), followed by the Western Pacific/China (21%), and North America (13%). The other 20% were from the rest of the world. There were no discernible patterns differentiating prevalence rates between countries, based on income status. Part of the problem is that there are few studies in low- and middle-income countries and none from those in the lowest income level.
Table 2

Geographic distribution of prevalence studies of cardiovascular disease in type 2 diabetes mellitus

Region

Populationa (millions)

Studies

N

Stroke (%)

MI

Angina

Heart failure

Atherosclerosis

CAD

CVD (%)

Africa

441

1

132

5.0

NR

NR

NR

NR

23.6%

28.6

Europe

660

29b

4,327,503

7.2

10.0%

14.6%

19.0%

33.0%

15.4%

30.0

Middle East and North Africa

387

4

3959

7.1

11.4%

NR

NR

NR

27.4%

26.9

North America and Caribbean

344

8

170,963

10.9

13.6%

17.2%

29.5%

NR

20.1%

46.0

South and Central America

315

2

537

5.5

NR

NR

4.2%

NR

22.6%

27.5

Southeast Asia

926

3b

1656

3.1

NR

NR

NR

NR

29.4%

42.5

Western Pacific (includes China)

1600

12b

44,062

11.4

NR

NR

4.3%

26.0%

23.6%

33.6

Multiple countries

NR

1

669

1.9

3.9%

9.9%

0.7%

NR

NR

16.4

Totalc

4673

60

4,549,481

7.6

10.0%

14.6%

14.9%

29.1%

21.2%

32.2

CAD coronary artery disease, CVD cardiovascular disease (includes all complications), MI myocardial infarction, NR not reported

aAdults aged 20–79. Source: IDF Atlas 2015 [1]

bA study reports two separate analyses within the same paper; thus, there are 60 studies in 57 articles

cPrevalence rates weighted by inverse variance

Table 3

Number of studies and cardiovascular outcomes reported, by country

Country

Income statusa

Studies

Patients

Stroke

MI

Angina

CHF

Atherosclerosis

CAD

CVD (%)

Australia

High

3b

1569

7.7%

NR

NR

NR

NR

21.8%

29.4

Belgium

High

1

711

8.0%

NR

NR

NR

NR

22.0%

30.0

Brazil

Upper middle

2

537

5.5%

NR

NR

4.2%

NR

22.6%

27.5

Cameroon

Low middle

1

132

5.0%

NR

NR

NR

NR

23.6%

28.6

China

Upper middle

5

39,934

15.2%

NR

NR

4.3%

30.5%

17.0%

28.4

France

High

1

987

15.3%

NR

NR

9.1%

NR

29.5%

53.9

India

Low middle

3b

1656

3.1%

NR

NR

NR

NR

29.4%

42.6

Indonesia

Low middle

1

400

10.8%

NR

NR

NR

NR

28.8%

39.6

Iraq

Upper middle

1

1079

NR

NR

NR

NR

NR

NR

16.0

Ireland

High

1

309

5.2%

NR

NR

NR

NR

17.8%

23.0

Italy

High

5

19,869

2.4%

6.2%

NR

NR

NR

11.1%

14.8

Korea

High

2

654

20.3%

NR

NR

NR

21.5%

37.0%

47.2

Mexico

Upper middle

1

160

NR

NR

NR

57.5%

NR

NR

57.5

Netherlands

High

3

899

6.1%

NR

NR

30.6%

33.0%

28.2%

44.5

Portugal

High

1

3215

5.0%

NR

NR

NR

NR

12.0%

17.0

Qatar

High

1

180

NR

NR

NR

NR

NR

31.7%

31.7

Russian Federation

Upper middle

1

3,060,517

4.2%

3.4%

NR

NR

NR

13.4%

21.0

Saudi Arabia

High

2

2700

7.1%

11.4%

NR

NR

NR

23.1%

30.0

Scotland

High

3b

351,592

NR

NR

NR

NR

NR

19.7%

19.7

Spain

High

7

430,855

8.0%

20.2%

NR

30.3%

NR

13.6%

29.8

Sweden

High

3

453,791

10.3%

9.3%

14.6%

9.5%

NR

NR

31.3

Switzerland

High

1

83

15.7%

NR

NR

NR

NR

33.7%

49.4

Thailand

Upper middle

1

1505

2.5%

NR

NR

NR

NR

NR

2.5

UK

High

1

2733

7.9%

NR

NR

NR

NR

34.5%

42.4

USA

High

7

170,803

10.9%

13.6%

17.2%

15.5%

NR

28.1%

44.0

16 countries

High

42

1,440,950

9.3%

12.1%

15.9%

19.0%

27.3%

24.3%

33.6

6 countries

Upper middle

11

3,103,732

6.9%

3.4%

 

22.0%

30.5%

17.7%

25.5

3 countries

Low middle

5

2188

6.3%

    

27.3%

36.9

0 countries

Low

0

0

       

25 countries

Overall

58

4,546,870c

8.4%

10.7%

15.9%

20.1%

28.3%

23.7%

32.1

CAD coronary artery disease (also reported as coronary heart disease or ischemic heart disease), CHF congestive heart failure, CVD cardiovascular disease, MI myocardial infarction, NR not reported

aWorld Bank status [117] based on Gross National Income per capita per annum (in USD) as of 01 July, 2016: Low < $1025; Low middle = $1026–$4035; Upper middle = $4036–$12,475; High > $12,476. Country status extracted from World Bank Database [118]

bA study reports two separate analyses within the same paper. In total there are 58 studies across 55 articles

cStudy evaluated patients from multiple countries and therefore is not included in this table. Thus, there are 4,546,870 patients across 55 studies

Patient characteristics

In the 57 individual studies, data from 4.5 million people with T2DM were presented with nearly 3.1 million people coming from a single Russian study by Shestakova [29]. As presented in Table 1, using a simple average across studies, the average age was 63.6 ± 6.9 (median = 64.3 years; weighted average = 66.3 ± 6.9 years). The weighted average proportion of persons with obesity was 46.3 ± 15.0%, with a simple average of 47.0 ± 14.7% (median = 45.4%), defined as a BMI ≥ 30 kg/m2. The mean percentage of males across the studies was 50.5 ± 10.3% (median = 52.0%); the weighted average of the proportion of males was 36.0 ± 10.0%, including the study by Shestakova [29], and 54.1 ± 9.9% excluding that study. The patients had T2DM for an average duration of 10.4 ± 3.7 years (median = 10.0 years; weighted average = 6.6 ± 3.7 years). Among the 23 studies that reported duration of follow-up, the average was 7.3% ± 4.5 years (median = 6.0 years; weighted average = 5.2 ± 4.3 years).

Prevalence rates of cardiovascular comorbidities are summarized in Table 4 for all patients as well as separately for males and females. In studies reporting gender-specific prevalence rates, males had higher prevalence rates than females for all outcomes except overall CVD, where both sexes had an overall prevalence rate of approximately 27%. Overall, in studies that presented prevalence rates for males and females combined, the prevalence of CVD among persons with T2DM was 32.2%. CAD and atherosclerosis were the most prevalent CVD comorbidities, with prevalence rates of 21.2 and 29.1%, respectively, whereas stroke was the least prevalent with a prevalence rate of 7.6%. It is unclear why people with T2DM have different susceptibilities to these diseases. An explanation for the high prevalence rate for atherosclerosis could be that it is an artifact of patient selection. In the studies that examined atherosclerosis, most patients were enrolled if they had high-risk scores for atherosclerosis, resulting in a very high rate of disease detection.
Table 4

Summary of prevalence rates of cardiovascular comorbidities in persons with type 2 diabetes

Sex

Cardiovascular outcome

Studies

N

Ratea (%)

95% confidence interval (%)

Both

Stroke

39

3,901,505

7.6

6.6–8.6

 

Myocardial infarction

13

3,518,833

10.0

7.5–12.5

 

Angina pectoris

4

354,743

14.6

12.0–17.3

 

Heart failure

14

601,154

14.9

13.0–16.7

 

Atherosclerosis

4

1153

29.1

21.7–36.4

 

Coronary artery disease

42

3,833,200

21.2

20.3–22.2

 

Cardiovascular disease (any)

53

4,289,140

32.2

30.0–34.4

Malesb

Stroke

10

232,525

6.7

6.0–7.3

 

Myocardial infarction

2

1170

11.9

4.3–19.5

 

Angina pectoris

1

454

21.1

16.3–26.9

 

Heart failure

4

73,361

25.3

11.4–39.2

 

Coronary artery disease

9

237,367

18.7

16.5–20.8

 

Cardiovascular disease

16

241,406

27.6

25.3–29.9

Femalesb

Stroke

10

202,348

5.9

5.1–6.7

 

Myocardial infarction

2

1812

9.8

3.5–16.0

 

Angina pectoris

1

803

17.4

15.0–20.2

 

Heart failure

4

62,690

24.0

11.2–36.8

 

Coronary artery disease

10

205,493

14.3

12.4–16.1

 

Cardiovascular disease

16

209,153

27.2

22.7–31.7

aWeighted by inverse variance

bNo studies reported atherosclerosis for males or females; only in the aggregate. Rates for males and females do not sum to the total as not all studies reported all outcomes

CVD mortality among patients with T2DM

Table 5 presents the data regarding the rates of mortality associated with CVD in persons with T2DM. The weighted average of death rates from the eight studies with 3,208,557 patients with T2DM was 9.9% (95% CI 8.6–11.3%) [2936]. There were 6.3% who died due to CAD and another 1.5% from CBV. Comparing patients with both T2DM and CVD with patients having neither T2DM nor CVD, the odds ratio for death was 4.56 (95% CI 3.53–5.89) [32]. Using a weighted average from seven studies (N = 86,557) [2935], the calculated deaths due to CVD comprised 50.3% (95% CI 37.0–63.7%) of all deaths in patients with T2DM. The major contributors were CAD, which was responsible for 29.7% (95% CI 25.1–34.4%) and stroke/CBV for 11.0% (95% CI 8.8–13.3%).
Table 5

Mortality associated with cardiovascular disease in persons with type 2 diabetes

Disease

Author (year)

Data collection period

Patients

n

All deaths

%

CVD Deaths

% Death rate

% CVD proportion of all deaths

CVD

Alwalkeel (2008) [30]

Jan 1989–Jan 2004

T2DM adults

1952

161

8.20%

97

5.00%

60.20%

CVD

Cardoso (2008) [31]

1994–96 to 2001

T2DM adults

471

121

25.70%

44

9.30%

36.40%

CVD

Carnethon (2010) [32]

1989–93 to 2005

T2DM only

659

468

71.00%

211

32.0%

45.10%

CVD

Carnethon (2010) [32]

1989–93 to 2005

CVD only

868

620

71.40%

304

35.0%

49.00%

CVD

Carnethon (2010) [32]

1989–93 to 2005

T2DM + CVD

260

219

84.20%

129

49.60%

58.90%

CVD

Carnethon (2010) [32]

1989–93 to 2005

No T2DM or CVD

3997

2095

52.40%

710

17.8%

33.90%

CVD

Eeg-Olofsson (2010) [33]

1997–98 to 2003

T2DM adults

18,334

1902

10.40%

1456

7.90%

76.60%

CVD

Malik (2015) [34]

2005–2011

T2DM adults

121,523

17,637

14.50%

3722

3.10%

21.10%

CVD

Menzaghi (2014) [36]

2001–2008

GHS study—males

242

NR

42

17.40%

CVD

Menzaghi (2014) [36]

2001–2008

GHS study—females

117

NR

16

13.70%

CVD

Menzaghi (2014) [36]

1993–1999

HPFS study—males

833

NR

146

17.50%

CVD

Menzaghi (2014) [36]

1976–1990

NMS study—females

902

NR

144

16.00%

CVD

Salinero-Fort (2016) [35]

2007–08 to 2012

T2DM adults

2442

203

8.30%

96

3.90%

47.30%

CVD

Salinero-Fort (2016) [35]

2007–08 to 2012

T2DM adults + kidney disease

965

221

22.90%

124

12.80%

56.10%

CVD

Shestakova (2016) [29]

2015

T2DM adults

3,060,516

66,093

2.20%

30,560

1.00%

46.20%

 

All CVD death

 

Patients with T2DM

3,208,557

86,557a

42.3%a

36,576a

9.9%a

50.3%a

CAD

Cardoso (2008) [31]

1994–1996 to 2001

T2DM adults

471

121

25.70%

30

6.40%

24.80%

CAD

Carnethon (2010) [32]

1989–1993 to 2005

T2DM only

659

468

71.00%

132

20.00%

28.20%

CAD

Carnethon (2010) [32]

1989–1993 to 2005

CVD only

868

620

71.40%

213

24.50%

34.40%

CAD

Carnethon (2010) [32]

1989–1993 to 2005

T2DM + CVD

260

219

84.20%

111

42.70%

50.70%

CAD

Carnethon (2010) [32]

1989–1993 to 2005

No T2DM or CVD

3997

2095

52.40%

425

10.60%

20.30%

CAD

Jackson (2012) [97]

2001–2007

Male diabetics

116,145

22,033

19.00%

6000

5.20%

27.20%

CAD

Jackson (2012) [97]

2001–2007

Male non-diabetics

2,433,748

  

36,801

1.50%

 

CAD

Jackson (2012) [97]

2001–2007

Female diabetics

100,507

20,571

20.50%

4554

4.50%

22.10%

CAD

Jackson (2012) [97]

2001–2007

Female non-diabetics

2,630,482

  

32,449

1.20%

 
 

All CAD death

 

Patients with T2DM

218,462a

42,944a

24.9%a

10,695a

6.3%a

29.7%a

CHF

Mazza (2007) [104]

1983–1985 to 1997

Male diabetics

202

22

10.90%

CHF

Mazza (2007) [104]

1983–1985 to 1997

Female diabetics

379

29

7.70%

CHF

Mazza (2007) [104]

1983–1985 to 1997

All

581

369

63.50%

13.80%

CHF

Shestakova (2016) [29]

2015

T2DM adults

3,060,516

66,093

2.20%

18,963

0.60%

28.70%

 

All CHF deaths

 

Patients with T2DM

3,061,097a

66,093a

2.2%a

1,9104a

6.1%a

28.7%a

MI

Shestakova (2016) [29]

2015

T2DM adults

3,060,516

66,093

2.20%

3393

0.10%

5.10%

SCD

Kucharska-Newton (2010) [99]

1987–89 to 2001

T2DM adults

1550

NR

69

4.50%

SCD

Kucharska-Newton (2010) [99]

1987–89 to 2001

No T2DM

12,428

NR

140

1.10%

Stroke

Jackson (2012) [97]

2001–2007

Male diabetics

116,145

22,033

19.00%

1942

1.70%

8.80%

Stroke

Jackson (2012) [97]

2001–2007

Male non-diabetics

2,433,748

  

13,191

0.50%

 

Stroke

Jackson (2012) [97]

2001–2007

Female diabetics

100,507

20,571

20.50%

2436

2.40%

11.80%

Stroke

Jackson (2012) [97]

2001–2007

Female non-diabetics

2,630,482

  

23,632

0.90%

 

Stroke

Shestakova (2016) [29]

2015

T2DM adults

3,060,516

66,093

2.20%

8204

0.30%

12.40%

 

All CBV deaths

 

Patients with T2DM

327,168a

108,697a

3.30%

12,582a

1.50%

11.0%a

CAD coronary artery disease (variously reported as coronary heart disease or ischemic heart disease), CBV cerebrovascular disease, CHF congestive heart failure (also reported simply as heart failure), CVD cardiovascular disease, MI myocardial infarction, NR not reported, SCD sudden cardiac death, T2DM type 2 diabetes mellitus

aWeighted average of rates taken only for patients with T2DM and where complete outcomes were reported; thus, the number does not represent the sum of all of the numbers in the column above it

CVD among obese vs. non-obese people with T2DM

About half of the patients included in this analysis had obesity. Three-quarters of the included studies reported on patients’ BMI or the percent of patients with obesity. While the definitions and BMI cut-off points of obesity varied across studies, the most commonly used definition of obesity was a BMI ≥ 30 kg/m2, which was employed by 16 studies (43% of those providing a definition).

Five papers reported prevalence rates of CVD according to obesity status, and all of them found a positive relationship between obesity and increased prevalence rates of CVD [26, 3740]. Using logistic regression to control for multiple factors, Bhatti et al. [26] found a positive correlation between obesity and CAD (P = 0.021). Tamba et al. [37] reported positive correlations between obesity and both CAD (r = 0.3, P < 0.001) and stroke (r = 0.5, P < 0.001). Boonman-de Winter et al. [38] quantified the relationship between BMI and heart failure. The prevalence rate of heart failure was 38.7% (95% CI 31.2–46.1%) in patients with a BMI ≥ 30 kg/m2 and 23.4% (95% CI 19.4–27.5%) in those with a BMI < 30 kg/m2, which represents a 65% increase due to obesity.

Two studies explored the relationship between increasing BMI and risk of CVD [39, 40]. According to Wentworth et al. [39], for CAD in both males and females, the prevalence rate of CAD increased with each successive increase in BMI, with a five-fold increase between the lowest and highest categories [< 25 (normal), 25–30 kg/m2 (overweight), 30–35 kg/m2 (mild obesity), 35–40 kg/m2 (moderate obesity) and > 40 kg/m2 (severe obesity)]. The difference was that prevalence rates in males were about double those for females in every BMI category. For the outcome stroke/transient ischemic attack (TIA) in males, only the highest category (BMI > 40) had elevated prevalence rates, which were about double those for the lowest category (BMI < 25). For females, prevalence rates of stroke/TIA increased in those who were overweight and had mild or moderate obesity but decreased for those with severe obesity. Finally, Glogner et al. [40] had quite different results. They reported a steady increase in prevalence rates of MI from 6.86% in those with a BMI < 20–9.33% in patients who were overweight (BMI 25–30), a 36% increase. However, MI prevalence rates declined thereafter with each increasing category of obesity. The highest category (BMI ≥ 40) had a prevalence rate of 5.01%, which was 27% lower than those in the lowest category (BMI < 20). Thus, patterns vary quite widely, and studies often examined different outcomes.

Risk of bias in included studies

In the assessment of risk for bias, the studies addressed 80% of the STROBE checklist items (i.e., research design and data presentation), on average. The mean was 80 ± 12%, and the median was 81%, with a range of 54–100%. The two items that were addressed by 100% of the articles were reporting of outcome data and reporting of outcomes. The two items addressed the least were the statement of funding (56%) and indicating the study design with a commonly used term in the title or abstract (60%).

Discussion

In this systematic review of 4,549,481 persons with T2DM, we estimated the overall prevalence of CVD at 32.2%. The most frequent type of CVD reported was CAD (21.2%) and lowest was stroke (7.6%). Males had higher rates of prevalent disease than females. CVD was responsible for 50.3% of all deaths in T2DM patients over the period of the review. Along with diabetes, cardiovascular disease is associated with several risk factors, obesity, and age. We, therefore, evaluated the association between age and obesity among patients with CVD and T2DM in the selected articles.

Age as a risk factor for CVD

Age is a well-known risk factor for CVD. Out of the 57 articles, thirteen (25%) reported on the relationship between age and CVD and the results were quite mixed. Nine studies identified a significant relationship between age and CVD [30, 38, 4147], but only two presented results across multiple age categories [38, 42]. Alonso-Moran [42] found that the odds ratio for IHD, stroke, heart failure and MI all increased sequentially with each increase in 5-year age category as compared with the age group 35–39 used as a reference. All of these individual outcomes achieved statistical significance (P < 0.001). Boonman-de Winter et al. [38] similarly reported a sequential increase in prevalence rates of heart failure for all patients in 5-year age categories from 60 to 64 to > 80 years of age. Other authors reported that older patients had higher prevalence rates than younger patients, but provided few details on age categories [30, 41, 43, 45, 46]. On the other hand, four studies reported no differences between age categories [26, 34, 48, 49]. Three other studies used age as a covariate in a logistic regression with no further details [28, 50, 51]. Therefore, few studies have quantified the effect of age on CVD prevalence rates among people with T2DM.

Obesity as a risk factor for CVD

Obesity has long been established as an independent risk factor for CVD [7, 52], and is associated with CAD [53, 54], atherosclerosis [51], and cardiac death [55, 56]. Furthermore, it has been shown that overweight and obesity are highly prevalent in T2DM patients with high CV risk and that BMI and waist circumference are related to major cardiometabolic risk factors such as hypertension and elevated low-density lipoprotein cholesterol (LDL-C) [57].

Obesity is usually defined by body mass index (BMI, calculated as body weight in kg divided by the square of height in meters), with the World Health Organization (WHO) classifying adults with a BMI 30 kg/m2 as obese [58]. However, BMI as a measure to stratify patients with obesity has limitations and does not account for the wide variation in body fat distribution nor the quality of fat, and may not account for associated health risk in different individuals and populations [58]. This has been shown to be true for South Asian populations [59]. In a study from Raji et al. [60] noted that compared with Caucasians, Asian Indians had significantly greater total abdominal and visceral fat matched with Caucasians of the same age, gender, and BMI, meaning that this population has an increased CVD risk. Besides, there is a weaker association between increasing BMI and T2DM in Asian populations compared with Caucasians due to the risk for T2DM begins increasing at comparatively normal BMI in Asian populations [61].

Seven of the included studies evaluated the relationship between obesity and/or BMI and CVD risk. Five of the studies included in this review identified a positive relationship between obesity and increased prevalence rates of CVD [26, 3740]. One of these studies [26] used lower BMI cut-off points to account for Asian populations in accordance with WHO recommendations on BMI for Asian populations [62] and evaluated abdominal adiposity with waist circumference measurements to determine the prevalence of obesity. Overall, the studies found a positive relationship between increasing BMI and CVD; except in one study [39], where women with severe obesity had a reduced prevalence of stroke. While the authors do not explain the reduced prevalence of stroke/TIA, it may be explained by differences in vascular risk markers in men, such as pre-existing ischemic heart disease, age, and smoking [63]. Furthermore, the presence of gonadal steroids, most notably estrogen, may lend a protective effect against stroke/TIA in women and it has been shown that adiposity is associated with increased levels of estrogen [64].

Although obesity is identified as a risk factor for CVD, it is associated with a paradox in that mortality is lower in patients who are overweight or obese than in those whose BMI is normal or underweight [65]. Lee et al. [66] reported that obesity provided a survival benefit to patients with heart failure who did not have comorbid diabetes, but not in patients who did have concomitant diabetes. In contrast, a group led by Abi Khalil [67] examined a cohort of 2492 T2DM patients in seven countries in the Middle East, Gulf region, with acute heart failure. They reported that BMI was inversely correlated with the risk of mortality, with severe obesity associated with less mortality risk.

It is clear that the relationship between obesity and the risk of CVD and CVD-related deaths requires further exploration to identify these mechanisms and relationships.

CVD-related mortality in T2DM

In persons with T2DM, CVD is responsible for at least half of the mortality, as previously mentioned. Among the specific diseases within that term, CAD was most lethal, followed by stroke. Similar results have been demonstrated with other models. In an incidence-based study, Straka et al. [68] followed 29,863 patients (5501 with T2DM and 24,362 without T2DM) over a 1-year period. Four of the incident cardiovascular outcomes they reported were significantly higher in those with T2DM. Patients with T2DM had 10% greater risk of CAD, 53% of MI, 58% of stroke, and 112% increased risk of heart failure. Therefore, T2DM is a substantial risk factor for CVD and its consequences.

CVD prevalence rates across regions and countries

As this was a global review, studies from across the world were included. Given the variation in which diabetes and its macrovascular complications are treated and managed across countries and income levels, it is relevant to look at prevalence rates across regions and countries. However, almost half (46.0%) of the research was produced in Europe, and very little information was obtained from the less developed regions of the world such as Africa, Latin America, and the Asian subcontinent.

As shown in Table 2, the regions with the highest prevalence of overall CVD were North America and Caribbean (46.0%; N = 4,327,503), Southeast Asia (42.5%, N = 537) and Western Pacific (including China) (33.6%; N = 44,062). Southeast Asia stands out with a higher prevalence of CAD (29.4%) compared with other regions. The prevalence of CAD in this region is driven by one study from India [69], which specifically investigated the pattern of CAD in 134 symptomatic T2DM patients in India. However, epidemiological studies on people of South Asian origin have shown an increased likelihood of developing CAD that is up to two times higher than in Caucasians [70]. The higher risk is due to both pathophysiological and life course-related risk factors [70].

The summaries across countries and regions provide an overview of the geographic spread of research but should be interpreted with caution given the limited number of studies for some of the regions and countries. Figure 2 illustrates the distribution of studies across regions and countries and clearly shows that few studies exist for several regions. For example, only one study from one country in the African region was identified and therefore should not be seen to represent findings for the region’s entire T2DM population.
Fig. 2
Fig. 2

Distribution of studies across countries and regions. The figure illustrates the global distribution of studies across countries and regions

Treatment of both T2DM and CVD vary greatly between and within countries, and although much of the CVD risk in T2DM can be associated with the long-term complications of T2DM, there has been growing interest to determine whether certain antidiabetic drugs influence this risk. For example, sulfonylureas which are the second most commonly used antidiabetic drug after metformin, have been shown to be associated with an increased risk of cardiovascular events and mortality [71]. Newer antidiabetic drugs have been shown to lower the risk of CVD in T2DM patients [1720]; however, these drugs are often intended to be used as second- to third-line treatments and many years may pass before patients can benefit.

Temporal trends in CVD risk assessment and management in T2DM

Encouragingly, CVD mortality is declining in high-income countries among the general population due to reductions in cardiovascular risk factors as well as to recent advances in prevention, treatment, and management [72]. This trend has also been observed in people with T2DM in some countries. Jung et al. [73] estimated trends in CVD in people with and without T2DM in South Korea using data from the national health insurance system. The results show a significant reduction in CVD risk among people with T2DM brought on by improvements in the care and management of patients. However, in many developing countries where the burden of T2DM is rapidly rising and lifestyle patterns changing an increase in CV risk factors among people with T2DM can be expected [3]. A study from China analyzed the relationship between lifestyle behaviors and multiple CV risk factors in 25,454 people with T2DM [74]. The researchers found that unhealthy lifestyles were common, especially among those who are non-elderly, and above-college educated. Furthermore, it was found that an unhealthy lifestyle was associated with poor blood, blood lipid, and blood pressure control. Decreasing the impact of T2DM and CVD in developing countries will require interventions aimed at changing risky lifestyle behaviors.

Screening people with T2DM for CV risk is an important strategy for reducing mortality and CVD events. A study from Denmark [75] found that a single round of diabetes screening and cardiovascular risk assessment in middle-aged adults in general practice was associated with a significant reduction in risk of all-cause mortality and CVD events in people with T2DM. The same researchers found that population-based stepwise screening for T2DM and CVD among all middle-aged adults was not associated with a reduction in mortality or CV events. Therefore, the benefits of population-based screening are limited in this context [76]. Kesall et al. [77] found that targeting specific occupational and industry groups with health checks could help identify individuals at high risk of both T2DM and CVD. In a study of 500,000 members of the Australian working population, they found that high T2DM and CVD risk was increased significantly in many occupational groups and industries.

Recent research points to an increasingly better understanding of the markers for identifying high CVD risk in people with T2DM. Li et al. [78] found that the combined application of carotid and lower extremity ultrasonography may be helpful to identify patients with T2DM who have a higher CVD risk. In a study of 2830 hospitalized patients with T2DM, they found that the concomitant presence of carotid and lower extremity atherosclerosis further increases the risk of CVD in patients with T2DM, compared with those who had either carotid or lower limb atherosclerosis and those without atherosclerosis. A study by Mohammedi et al. [79] found that major peripheral arterial disease (PAD) presenting as lower-extremity ulceration or amputation and peripheral revascularization is associated with increased risk of death and CV events in people with T2DM. The researchers conclude that screening for PAD along with active management are crucial for prevention of CVD in people with T2DM. In addition, coronary artery calcium (CAC) assessments have been found to significantly improve the risk classification for CHD and atherosclerotic CVD events in people with T2DM—regardless of the duration of diabetes [80]. Thus, a CAC assessment can be a useful tool for classifying people with T2DM into lower- or higher-risk groups for long-term CVD risk.

Lipid profile has long been considered among the most important risk factors for CVD in T2DM, and several trials have confirmed that lowering low-density lipoprotein cholesterol (LDL-C) via statins in T2DM was effective in reducing the risk of CVD [81, 82]. It is also well known that statins also have a triglyceride-lowering effect [83]. In a cross-sectional study of 223,612 patients with T2DM in China, researchers found that although lower triglyceride was associated with reduced CVD risk in the short-term, it was associated with increased risk in the long-term [84]. This paradox could mean that low triglyceride is not necessarily associated with good clinical outcomes in all people with T2DM and that there are subgroup associations with CVD in patients with different durations of T2DM. Furthermore, Clua-Espuny et al. [85] suggest that the relative importance of risk factors wanes in complex chronic patients with T2DM with advancing age. In a cohort study of almost 3500 complex chronic patients above the age of 80 of whom 53% had diabetes and a high prevalence of associated classical risk factors, the researchers found that all-cause mortality was more affected by aging factors than by specific complications of diabetes. The authors make the recommendation that, for these patients, the care strategy may need to be redefined and adapted to comorbidities and functional autonomy rather than being focused on treatment outcomes.

Limitations

As with all literature reviews, we were limited by the availability of the literature and the validity and quality of the articles. Some of the results appeared only in abstract form, and many were not subsequently published as full articles within the time horizon of this review. Abstracts had space limitations, restricting the amount of information they could present. As well, we noted that there was often incomplete reporting or selective reporting of specific outcomes of interest.

Furthermore, the findings of this literature review are limited to a select patient population. Specifically, in this research, we accepted only data from adults aged 18 or older. Therefore, our results may not apply to children or adolescents. As well, we dealt only with T2DM; therefore, outcomes may not apply to T1DM or secondary diabetes such as that associated with hemochromatosis or pancreatitis.

This study was also challenged by the fact that CVD and its associated conditions are described differently across the literature. For example, CHD was used interchangeably with CAD or ischemic heart disease. We made every effort to standardize definitions and to group like with like. Furthermore, the types of CVD conditions evaluated varied across articles. Some articles focused on a single outcome, whereas others focused on several outcomes. As a result, the calculated prevalence rates may represent underestimates, as not all studies reported all outcomes.

The types of studies included would have also impacted the overall results of this study. First, we analyzed only prevalence studies; incidence studies would have different results due to their different perspective. Second, the studies varied both in the method of data collection (e.g., national databases versus clinic records) and the length of time over which they collected data. It is plausible that time-period over which studies were conducted could have impacted the observed prevalence rate of CVD. For example, health status, lifestyle, and treatments have varied over time, which could impact the prevalence rates in the studies using older data.

Overall, it is possible that the prevalence estimates for CVD presented in this article overestimate the prevalence of CVD among patients with T2DM. First, studies in the medical literature tend to include a sicker population compared to the general T2DM population; therefore, due to self-selection bias, the sample may not be representative of the broader T2DM population and thus lead to an overestimate of the prevalence of CVD. Second, some of the studies included T2DM patients with an existing CVD diagnosis; therefore, the overall estimate of CVD within these studies could be higher compared to the broader T2DM population.

Finally, only 25 countries were represented in this analysis. Noticeably absent were such countries as Germany, Canada, and Denmark, which all have excellent electronic health data, yet no research studies have been published from them. Very little has appeared from Africa, the Asian subcontinent or Latin America. More studies from these areas would be welcome. While the scope of this study was to evaluate evidence from peer-reviewed literature, an alternative approach to estimating the prevalence of CVD among patients with T2DM could be to analyze data within existing registries.

Conclusions

This is the first systematic review to synthesize global prevalence rates of CVD, including stroke, MI, angina, heart failure, atherosclerosis and CAD among people with T2DM. The results show that CVD is a major cause of comorbidity and death among patients with T2DM with CAD having the highest prevalence. There is a paucity of research studies investigating both the prevalence of CVD and risk factors such as obesity among people with T2DM. Given the large burden that CVD exerts on healthcare systems, patients and families around the world, more evidence is needed, ideally in the form of registry studies, to more accurately quantify the global prevalence of CVD among people with T2DM.

Declarations

Authors’ contributions

TRE, AA, CL and UHP made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; been involved in drafting the manuscript or revising it critically for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements

This research was funded by Novo Nordisk A/S, Bagsværd, Denmark.

TRE, AA, and CL all received consulting fees for undertaking this project.

Competing interests

TRE, AA, and CL all received consulting fees for undertaking this project. UHP is an employee of Novo Nordisk A/S.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

This research was funded by Novo Nordisk A/S, Bagsværd, Denmark.

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)
Leslie Dan Faculty of Pharmacy, University of Toronto, Barrie, Canada
(2)
Last Mile, Holte, Denmark
(3)
Novo Nordisk A/S, Søborg, Denmark

References

  1. International Diabetes Federation. idf diabetes atlas. 7th ed. Brussels: International Diabetes Federation; 2015.Google Scholar
  2. Abraham TM, Pencina KM, Pencina MJ, Fox CS. Trends in diabetes incidence: the Framingham heart study. Diab Care. 2015;38:482–7.View ArticleGoogle Scholar
  3. International Diabetes Federation. Diabetes and cardiovascular disease. Brussels: International Diabetes Federation; 2016. p. 1–144.Google Scholar
  4. Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Angelantonio D, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. emerging risk factors collaboration. Lancet. 2010;375:2215–22.PubMedView ArticleGoogle Scholar
  5. Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, Kaptoge S, Whitlock G, Qiao Q, Lewington S. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS ONE. 2013;8:e65174.PubMedPubMed CentralView ArticleGoogle Scholar
  6. Haffner SM, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N Engl J Med. 1998;339:229–34.PubMedView ArticleGoogle Scholar
  7. Fox CS, Pencina MJ, Wilson PW, Paynter NP, Vasan RS, D’Agostino RB. Lifetime risk of cardiovascular disease among individuals with and without diabetes stratified by obesity status in the Framingham heart study. Diab Care. 2008;31:1582–4.View ArticleGoogle Scholar
  8. Van Hateren KJ, Landman GW, Kleefstra N, Logtenberg SJ, Groenier KH, Kamper AM, Houweling ST, Bilo HJ. The lipid profile and mortality risk in elderly type 2 diabetic patients: a 10-year follow-up study (ZODIAC-13). PLoS ONE. 2009;4:e8464.PubMedPubMed CentralView ArticleGoogle Scholar
  9. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329:977–86.View ArticleGoogle Scholar
  10. Nathan DM, Cleary PA, Backlund JY, Genuth SM, Lachin JM, Orchard TJ, Raskin P, Zinman B. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353:2643–53.PubMedView ArticleGoogle Scholar
  11. ADVANCE Collaborative Group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358:2560–72.View ArticleGoogle Scholar
  12. UK Prospective Diabetes Study. (UKPDS) Group: intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK prospective diabetes study (UKPDS) group. Lancet. 1998;352:837–53.View ArticleGoogle Scholar
  13. Action to Control Cardiovascular Risk in Diabetes Study. Group: effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358:2545–59.View ArticleGoogle Scholar
  14. Rydén L, Grant PJ, Anker SD, Berne C, Cosentino F, Danchin N, Deaton C, Escaned J, Hammes H-P, Huikuri H. ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD-summary. Diab Vasc Dis Res. 2014;11:133–73.PubMedView ArticleGoogle Scholar
  15. Schnell O, Rydén L, Standl E, Ceriello A. Updates on cardiovascular outcome trials in diabetes. Cardiovasc Diabetol. 2017;16:128.PubMedPubMed CentralView ArticleGoogle Scholar
  16. Food and Drug Administration: Guidance for industry: diabetes mellitus—evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes. US Department of Health and Human Services. 2008.Google Scholar
  17. Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, Lingvay I, Rosenstock J, Seufert J, Warren ML. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2016;375:1834–44.PubMedView ArticleGoogle Scholar
  18. Marso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA, Nissen SE, Pocock S, Poulter NR, Ravn LS. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;2016:311–22.View ArticleGoogle Scholar
  19. Sanon VP, Patel S, Sanon S, Rodriguez R, Pham SV, Chilton R. Differential cardiovascular profiles of sodium-glucose cotransporter 2 inhibitors: critical evaluation of empagliflozin. Ther Clin Risk Manag. 2017;13:603.PubMedPubMed CentralView ArticleGoogle Scholar
  20. Trujillo JM, Nuffer WA. Impact of sodium–glucose cotransporter 2 inhibitors on nonglycemic outcomes in patients with type 2 diabetes. Pharmacotherapy. 2017;37:481–91.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Schnell O, Cappuccio F, Genovese S, Standl E, Valensi P, Ceriello A. Type 1 diabetes and cardiovascular disease. Cardiovasc Diabetol. 2013;12:156.PubMedPubMed CentralView ArticleGoogle Scholar
  22. Ford ES, Zhao G, Li C. Pre-diabetes and the risk for cardiovascular disease. J Am Coll Cardiol. 2010;55:1310–7.PubMedView ArticleGoogle Scholar
  23. Saquib N, Saquib J, Ahmed T, Khanam MA, Cullen MR. Cardiovascular diseases and type 2 diabetes in Bangladesh: a systematic review and meta-analysis of studies between 1995 and 2010. BMC Public Health. 2012;12:434.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Moher D, Liberati A, Tetzlaff J, Altman DG. Group. P: preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–9.PubMedView ArticleGoogle Scholar
  25. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, Initiative S. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014;12:1495–9.View ArticleGoogle Scholar
  26. Bhatti GK, Bhadada SK, Vijayvergiya R, Mastana SS, Bhatti JS. Metabolic syndrome and risk of major coronary events among the urban diabetic patients: North Indian Diabetes and cardiovascular disease study—NIDCVD-2. J Diab Complications. 2016;30:72–8.View ArticleGoogle Scholar
  27. Collier A, Ghosh S, Hair M, Waugh N. Impact of socioeconomic status and gender on glycaemic control, cardiovascular risk factors and diabetes complications in type 1 and 2 diabetes: a population based analysis from a Scottish region. Diab Metab. 2015;41:145–51.View ArticleGoogle Scholar
  28. Tan ED, Davis WA, Davis TM. Changes in characteristics and management of Asian and Anglo-Celts with type 2 diabetes over a 15-year period in an urban Australian community: the fremantle diabetes study. J Diab. 2016;8:139–47.View ArticleGoogle Scholar
  29. Shestakova M: Dynamics in prevalence of diabetes, diabetic complications and quality of diabetes care in Russian Federation in 2014–2015 by data of national diabetes register. In: Conference: 52nd annual meeting of the european association for the study of diabetes, EASD. 2016. p. 309.Google Scholar
  30. Alwakeel JS, Sulimani R, Al-Asaad H, Al-Harbi A, Tarif N, Al-Suwaida A, Al-Mohaya S, Isnani AC, Alam A, Hammad D. Diabetes complications in 1952 type 2 diabetes mellitus patients managed in a single institution in Saudi Arabia. Ann Saudi Med. 2008;28:260–6.PubMedView ArticlePubMed CentralGoogle Scholar
  31. Cardoso C, Salles G. Gross proteinuria is a strong risk predictor for cardiovascular mortality in Brazilian type 2 diabetic patients. Braz J Med Biol Res. 2008;41:674–80.PubMedView ArticleGoogle Scholar
  32. Carnethon MR, Biggs ML, Barzilay J, Kuller LH, Mozaffarian D, Mukamal K, Smith NL, Siscovick D. Diabetes and coronary heart disease as risk factors for mortality in older adults. Am J Med. 2010;123:556.PubMedPubMed CentralView ArticleGoogle Scholar
  33. Eeg-Olofsson K, Cederholm J, Nilsson PM, Zethelius B, Svensson AM, Gudbjörnsdóttir S, Eliasson B. New aspects of HbA1c as a risk factor for cardiovascular diseases in type 2 diabetes: an observational study from the Swedish National Diabetes Register (NDR). J Intern Med. 2010;268:471–82.PubMedView ArticleGoogle Scholar
  34. Malik MO, Govan L, Petrie JR, Ghouri N, Leese G, Fischbacher C, Colhoun H, Philip S, Wild S, McCrimmon R, et al. Ethnicity and risk of cardiovascular disease (CVD): 4.8 year follow-up of patients with type 2 diabetes living in Scotland. Diabetologia. 2015;58:716–25.PubMedView ArticleGoogle Scholar
  35. Salinero-Fort MA, Andres-Rebollo FJ, Burgos-Lunar C, Abanades-Herranz JC, Carrillo-de-Santa-Pau E, Chico-Moraleja RM, Jimenez-Garcia R, Lopez-de-Andres A, Gomez-Campelo P. Cardiovascular and all-cause mortality in patients with type 2 diabetes mellitus in the MADIABETES Cohort Study: association with chronic kidney disease. J Diab Complications. 2016;30:227–36.View ArticleGoogle Scholar
  36. Menzaghi C, Xu M, Salvemini L, De Bonis C, Palladino G, Huang T, Copetti M, Zheng Y, Li Y, Fini G. Circulating adiponectin and cardiovascular mortality in patients with type 2 diabetes mellitus: evidence of sexual dimorphism. Cardiovasc Diabetol. 2014;13:130.PubMedPubMed CentralView ArticleGoogle Scholar
  37. Tamba SM, Ewane ME, Bonny A, Muisi CN, Nana E, Ellong A, Mvogo CE, Mandengue SH. Micro and macrovascular complications of diabetes mellitus in Cameroon: risk factors and effect of diabetic check-up-a monocentric observational study. Pan African Med J. 2013;15:141.View ArticleGoogle Scholar
  38. Boonman-de Winter LJ, Rutten FH, Cramer MJ, Landman MJ, Liem AH, Rutten GE, Hoes AW. High prevalence of previously unknown heart failure and left ventricular dysfunction in patients with type 2 diabetes. Diabetologia. 2012;55:2154–62.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Wentworth JM, Fourlanos S, Colman PG. Body mass index correlates with ischemic heart disease and albuminuria in long-standing type 2 diabetes. Diab Res Clin Pract. 2012;97:57–62.View ArticleGoogle Scholar
  40. Glogner S, Rosengren A, Olsson M, Gudbjörnsdottir S, Svensson AM, Lind M. The association between BMI and hospitalization for heart failure in 83,021 persons with Type 2 diabetes: a population-based study from the Swedish National Diabetes Registry. Diab Med. 2014;31:586–94.View ArticleGoogle Scholar
  41. Alaboud AF, Tourkmani AM, Alharbi TJ, Alobikan AH, Abdelhay O, Al Batal SM, Alkashan HI, Mohammed UY. Microvascular and macrovascular complications of type 2 diabetic mellitus in Central, Kingdom of Saudi Arabia. Saudi Med J. 2016;37:1408–11.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Alonso-Moran E, Orueta JF, Fraile Esteban JI, Arteagoitia Axpe JM, Marques Gonzalez ML, Toro Polanco N, Ezkurra Loiola P, Gaztambide S, Nuno-Solinis R. The prevalence of diabetes-related complications and multimorbidity in the population with type 2 diabetes mellitus in the Basque Country. BMC Public Health. 1059;2014:14.Google Scholar
  43. Carrasco-Sánchez FJ, Gomez-Huelgas R, Formiga F, Conde-Martel A, Trullàs JC, Bettencourt P, Arévalo-Lorido JC, Pérez-Barquero MM. Association between type-2 diabetes mellitus and post-discharge outcomes in heart failure patients: findings from the RICA registry. Diabetes Res Clin Pract. 2014;104:410–9.PubMedView ArticleGoogle Scholar
  44. Gregg EW, Cheng YJ, Saydah S, Cowie C, Garfield S, Geiss L, Barker L. Trends in death rates among US adults with and without diabetes between 1997 and 2006. Diab Care. 2012;35:1252–7.View ArticleGoogle Scholar
  45. Lin PJ, Cohen JT, Kent WA, Neumann PJ. Patterns of comorbidity clusters among adults with diabetes. Value Health. 2013;16:A155–6.View ArticleGoogle Scholar
  46. Song S, Hardisty C. Early onset type 2 diabetes mellitus: a harbinger for complications in later years—clinical observation from a secondary care cohort. QJM. 2009;102:799–806.PubMedView ArticleGoogle Scholar
  47. Yang HK, Kang B, Lee S-H, Yoon K-H, Hwang B-H, Chang K, Han K, Kang G, Cho JH. Association between hemoglobin A1c variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes. J Diab Complications. 2015;29:776–82.View ArticleGoogle Scholar
  48. Mansour AA, Ajeel NA. Atherosclerotic cardiovascular disease among patients with type 2 diabetes in Basrah. World J Diab. 2013;4:82.View ArticleGoogle Scholar
  49. Menghua Z. GW25-e1447 clinical significance of multislice coronary CT angiography in asymptomatic patients with type 2 diabetes mellitus. J Am Coll Cardiol. 2014;64:C227.View ArticleGoogle Scholar
  50. Norhammar A, Bodegard J, Nystrom T, Thuresson M, Eriksson JW, Nathanson D. Incidence, prevalence and mortality of type 2 diabetes requiring glucose-lowering treatment, and associated risks of cardiovascular complications: a nationwide study in Sweden, 2006–2013. Diabetologia. 2016;59:1692–701.PubMedView ArticleGoogle Scholar
  51. Rossi MC, Lucisano G, Comaschi M, Coscelli C, Cucinotta D, Di Blasi P, Bader G, Pellegrini F, Valentini U, Vespasiani G. Quality of diabetes care predicts the development of cardiovascular events: results of the AMD-QUASAR study. Diab Care. 2011;34:347–52.View ArticleGoogle Scholar
  52. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart study. Circulation. 1983;67:968–77.PubMedView ArticleGoogle Scholar
  53. Rabkin SW, Mathewson FA, Hsu PH. Relation of body weight to development of ischemic heart disease in a cohort of young North American men after a 26 year observation period: the Manitoba study. Am J Cardiol. 1977;39:452–8.PubMedView ArticleGoogle Scholar
  54. Garcia-Labbe D, Ruka E, Bertrand OF, Voisine P, Costerousse O, Poirier P. Obesity and coronary artery disease: evaluation and treatment. Can J Cardiol. 2015;31:184–94.PubMedView ArticleGoogle Scholar
  55. Plourde B, Sarrazin JF, Nault I, Poirier P. Sudden cardiac death and obesity. Exp Rev Cardiovasc Ther. 2014;12:1099–110.View ArticleGoogle Scholar
  56. Engeland A, Bjorge T, Sogaard AJ, Tverdal A. Body mass index in adolescence in relation to total mortality: 32-year follow-up of 227,000 Norwegian boys and girls. Am J Epidemiol. 2003;157:517–23.PubMedView ArticleGoogle Scholar
  57. Masmiquel L, Leiter L, Vidal J, Bain S, Petrie J, Franek E, Raz I, Comlekci A, Jacob S. Gaal Lv: LEADER 5: prevalence and cardiometabolic impact of obesity in cardiovascular high-risk patients with type 2 diabetes mellitus: baseline global data from the LEADER trial. Cardiovasc Diabetol. 2016;15:29.PubMedPubMed CentralView ArticleGoogle Scholar
  58. World Health Organization. Obesity and Overweight. Factsheet No 311. Geneva: World Health Organization; 2017.Google Scholar
  59. Gujral UP, Pradeepa R, Weber MB, Narayan KM, Mohan V. Type 2 diabetes in South Asians: similarities and differences with white Caucasian and other populations. Ann N Y Acad Sci. 2013;1281:51–63.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Raji A, Seely EW, Arky RA, Simonson DC. Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians. J Clin Endocrinol Metab. 2001;86:5366–71.PubMedView ArticleGoogle Scholar
  61. Huxley R, James W, Barzi F, Patel J, Lear S, Suriyawongpaisal P, Janus E, Caterson I, Zimmet P, Prabhakaran D. Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes Rev. 2008;9:53–61.PubMedView ArticleGoogle Scholar
  62. World Health Organization Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157.View ArticleGoogle Scholar
  63. Koellhoffer EC, McCullough LD. The effects of estrogen in ischemic stroke. Transl Stroke Res. 2013;4:390–401.PubMedView ArticleGoogle Scholar
  64. Meseguer A, Puche C, Cabero A. Sex steroid biosynthesis in white adipose tissue. Horm Metab Res. 2002;34:731–6.PubMedView ArticleGoogle Scholar
  65. Hartrumpf M, Kuehnel RU, Albes JM. The obesity paradox is still there: a risk analysis of over 15,000 cardiosurgical patients based on body mass index. Interact Cardiovasc Thorac Surg. 2017;25:18–24.PubMedView ArticleGoogle Scholar
  66. Lee KS, Moser DK, Lennie TA, Pelter MM, Nesbitt T, Southard JA, Dracup K. Obesity paradox: comparison of heart failure patients with and without comorbid diabetes. Am J Crit Care. 2017;26:140–8.PubMedView ArticleGoogle Scholar
  67. Abi Khalil C, Sulaiman K, Singh R, Jayyousi A, Asaad N, AlHabib KF, Alsheikh-Ali A, Al-Jarallah M, Bulbanat B, Al Mahmeed W, et al. BMI is inversely correlated to the risk of mortality in patients with type 2 diabetes hospitalized for acute heart failure: findings from the Gulf aCute heArt failuRE (Gulf-CARE) registry. Int J Cardiol. 2017;5273(0116):34386–8.Google Scholar
  68. Straka RJ, Liu LZ, Girase PS, DeLorenzo A, Chapman RH. Incremental cardiovascular costs and resource use associated with diabetes: an assessment of 29,863 patients in the US managed-care setting. Cardiovasc Diabetol. 2009;8:53.PubMedPubMed CentralView ArticleGoogle Scholar
  69. Senthil AN, Ravishankar G, Ravi MS, Meenakshi K, Muthu Kumar D, Swaminathan N, Paul J, Venkatesan S. Pattern of coronary artery disease in symptomatic Type 2 diabetic subjects in the contemporary era and the difference from past studies. Indian Heart J. 2014;66:S46.View ArticleGoogle Scholar
  70. Nair M, Prabhakaran D. Why do South Asians have high risk for CAD? Global Heart. 2012;7:307–14.PubMedView ArticleGoogle Scholar
  71. Azoulay L, Suissa S. Sulfonylureas and the risks of cardiovascular events and death: a methodological meta-regression analysis of the observational studies. Diab Care. 2017;40:706–14.View ArticleGoogle Scholar
  72. World Health Organization. Global status report on noncommunicable diseases. Geneva: World Health Organization; 2014. p. 1–302.Google Scholar
  73. Jung CH, Chung JO, Han K, Ko S-H, Ko KS, Park J-Y. Improved trends in cardiovascular complications among subjects with type 2 diabetes in Korea: a nationwide study (2006–2013). Cardiovasc Diabetol. 2017;16:1.PubMedPubMed CentralView ArticleGoogle Scholar
  74. Huang Y, Li J, Zhu X, Sun J, Ji L, Hu D, Pan C, Tan W, Jiang S, Tao X. Relationship between healthy lifestyle behaviors and cardiovascular risk factors in Chinese patients with type 2 diabetes mellitus: a subanalysis of the CCMR-3B STUDY. Acta Diabetol. 2017;54:569–79.PubMedView ArticleGoogle Scholar
  75. Simmons RK, Griffin SJ, Lauritzen T, Sandbæk A. Effect of screening for type 2 diabetes on risk of cardiovascular disease and mortality: a controlled trial among 139,075 individuals diagnosed with diabetes in Denmark between 2001 and 2009. Diabetologia. 2017;60:2192–9.PubMedView ArticlePubMed CentralGoogle Scholar
  76. Simmons RK, Griffin SJ, Witte DR, Borch-Johnsen K, Lauritzen T, Sandbæk A. Effect of population screening for type 2 diabetes and cardiovascular risk factors on mortality rate and cardiovascular events: a controlled trial among 1,912,392 Danish adults. Diabetologia. 2017;60:2183–91.PubMedView ArticlePubMed CentralGoogle Scholar
  77. Kelsall HL, Fernando PHS, Gwini SM, Sim MR. Cardiovascular disease and type 2 diabetes risk across occupational groups and industry in a statewide study of an Australian working population. J Occup Environ Med. 2018;60:286–94.PubMedView ArticleGoogle Scholar
  78. Li M-F, Zhao C-C, Li T-T, Tu Y-F, Lu J-X, Zhang R, Chen M-Y, Bao Y-Q, Li L-X, Jia W-P. The coexistence of carotid and lower extremity atherosclerosis further increases cardio-cerebrovascular risk in type 2 diabetes. Cardiovasc Diabetol. 2016;15:43.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Mohammedi K, Woodward M, Marre M, Colagiuri S, Cooper M, Harrap S, Mancia G, Poulter N, Williams B, Zoungas S. Comparative effects of microvascular and macrovascular disease on the risk of major outcomes in patients with type 2 diabetes. Cardiovasc Diabetol. 2017;16:95.PubMedPubMed CentralView ArticleGoogle Scholar
  80. Malik S, Zhao Y, Budoff M, Nasir K, Blumenthal RS, Bertoni AG, Wong ND. Coronary artery calcium score for long-term risk classification in individuals with type 2 diabetes and metabolic syndrome from the multi-ethnic study of atherosclerosis. JAMA Cardiol. 2017;2:1332–40.PubMedPubMed CentralView ArticleGoogle Scholar
  81. Cannon CP, Braunwald E, McCabe CH, Rader DJ, Rouleau JL, Belder R, Joyal SV, Hill KA, Pfeffer MA, Skene AM. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med. 2004;350:1495–504.PubMedView ArticleGoogle Scholar
  82. Shepherd J, Barter P, Carmena R, Deedwania P, Fruchart J-C, Haffner S, Hsia J, Breazna A, LaRosa J, Grundy S. Effect of lowering LDL cholesterol substantially below currently recommended levels in patients with coronary heart disease and diabetes: the treating to new targets (TNT) study. Diab Care. 2006;29:1220–6.View ArticleGoogle Scholar
  83. Stein EA, Lane M, Laskarzewski P. Comparison of statins in hypertriglyceridemia. Am J Cardiol. 1998;81:66B–9B.PubMedView ArticleGoogle Scholar
  84. Ren Y, Ren Q, Lu J, Guo X, Huo X, Ji L, Yang X. Low triglyceride as a marker for increased risk of cardiovascular diseases in patients with long-term type 2 diabetes: a cross-sectional survey in China. Diab Metab Res Rev. 2018;34:e2960.View ArticleGoogle Scholar
  85. Clua-Espuny JL, González-Henares MA, Queralt-Tomas MLL, et al. Mortality and cardiovascular complications in older complex chronic patients with type 2 diabetes. BioMed Res Int. 2017;2017:6078498.PubMedPubMed CentralView ArticleGoogle Scholar
  86. Cheng Y, Zhang H, Chen R, Yang F, Li W, Chen L, Lin S, Liang G, Cai D, Chen H. Cardiometabolic risk profiles associated with chronic complications in overweight and obese type 2 diabetes patients in South China. PLoS ONE. 2014;9:e101289.PubMedPubMed CentralView ArticleGoogle Scholar
  87. Cortez-Dias N, Martins S, Belo A, Fiuza M. em nome dos Investigadores do Estudio VALSIM: prevalência, tratamento e controlo da diabetes mellitus e dos factores de risco associados nos cuidados de saúde primários em Portugal. Rev Port Cardiol. 2010;29:509–37.PubMedGoogle Scholar
  88. Daghash MH, Bener A, Zirie M, Dabdoob W, Al-Hamaq AO, Al-Arabi ZA. Lipoprotein profile in Arabian type 2 diabetic patients. relationship to coronary artery diseases. Int J Cardiol. 2007;121:91–2.PubMedView ArticleGoogle Scholar
  89. Doucet JA, Bauduceau B, Le Floch JP, Verny C. Medical treatments of elderly, French patients with type 2 diabetes: results at inclusion in the GERODIAB Cohort. Fundam Clin Pharmacol. 2016;30:76–81.PubMedView ArticleGoogle Scholar
  90. Farrell C, Moran J. Comparison of comorbidities in patients with pre-diabetes to those with diabetes mellitus type 2. IMJ. 2014;3:107.Google Scholar
  91. Fu AZ, Qiu Y, Radican L, Yin DD, Mavros P. Impact of concurrent macrovascular co-morbidities on healthcare utilization in patients with type 2 diabetes in Europe: a matched study. Diab Obes Metab. 2010;12:631–7.View ArticleGoogle Scholar
  92. Giallauria F, Fattirolli F, Tramarin R, Ambrosetti M, Griffo R, Riccio C, De Feo S, Piepoli MF, Vigorito C. Clinical characteristics and course of patients with diabetes entering cardiac rehabilitation. Diab Res Clin Pract. 2015;107:267–72.View ArticleGoogle Scholar
  93. Gobardhan SN, Dimitriu-Leen AC, van Rosendael AR, van Zwet EW, Roos CJ, Oemrawsingh PV, Kharagjitsingh AV, Jukema JW, Delgado V, Schalij MJ. Prevalence by computed tomographic angiography of coronary plaques in South Asian and white patients with type 2 diabetes mellitus at low and high risk using four cardiovascular risk scores (UKPDS, FRS, ASCVD, and JBS3). Am J Cardiol. 2017;119:705–11.PubMedView ArticleGoogle Scholar
  94. Gondim F, Caribé A, Vasconcelos KF, Segundo AD, Bandeira F. Vitamin D deficiency is associated with severity of acute coronary syndrome in patients with type 2 diabetes and high rates of sun exposure. Clin Med Insights. 2016;9:37.Google Scholar
  95. Hermans MP, Bouenizabila E, Ahn SA, Rousseau MF. How to transform a metabolic syndrome score into an insulin sensitivity value? Diab Metab Res Rev. 2016;32:87–94.View ArticleGoogle Scholar
  96. Hunt KJ, Kistner-Griffin E, Spruill I, Teklehaimanot AA, Garvey WT, Sale M, Fernandes J. Cardiovascular risk in Gullah African Americans with high familial risk of type 2 diabetes mellitus: project SuGAR. South Med J. 2014;107:607–14.PubMedPubMed CentralView ArticleGoogle Scholar
  97. Jackson CA, Jones NR, Walker JJ, Fischbacher CM, Colhoun HM, Leese GP, Lindsay RS, McKnight JA, Morris AD, Petrie JR, et al. Area-based socioeconomic status, type 2 diabetes and cardiovascular mortality in Scotland. Diabetologia. 2012;55:2938–45.PubMedPubMed CentralView ArticleGoogle Scholar
  98. Jurado J, Ybarra J, Solanas P, Caula J, Gich I, Pou JM, Romeo JH. Prevalence of cardiovascular disease and risk factors in a type 2 diabetic population of the North Catalonia diabetes study. J Am Acad Nurse Pract. 2009;21:140–8.PubMedView ArticleGoogle Scholar
  99. Kucharska-Newton AM, Couper DJ, Pankow JS, Prineas RJ, Rea TD, Sotoodehnia N, Chakravarti A, Folsom AR, Siscovick DS, Rosamond WD. Diabetes and the risk of sudden cardiac death, the atherosclerosis risk in communities study. Acta Diabetol. 2010;47(Suppl 1):161–8.PubMedView ArticleGoogle Scholar
  100. Kwon H, Lim J, Shin J, Son J, Lee S, Kim S, Yoo S. A relationship of asymptomatic coronary artery disease and type 2 diabetes in acute ischaemic stroke patients; cerebral angiography and coronary angiography study. Diabetoligia. 2014;57:S26.Google Scholar
  101. Liu X, Liu Y, Lv Y, Li C, Cui Z, Ma J. Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes. BMJ Open. 2015;5:e007362.PubMedPubMed CentralView ArticleGoogle Scholar
  102. Luo Y, Wang X, Wang Y, Wang C, Wang H, Wang D, Liu L, Jia Q, Liu G, Zhao X, et al. Association of glomerular filtration rate with outcomes of acute stroke in type 2 diabetic patients: results from the China National Stroke Registry. Diab Care. 2014;37:173–9.View ArticleGoogle Scholar
  103. MacDonald MR, Petrie MC, Home PD, Komajda M, Jones NP, Beck-Nielsen H, Gomis R, Hanefeld M, Pocock SJ, Curtis PS, McMurray JJ. Incidence and prevalence of unrecognized myocardial infarction in people with diabetes: a substudy of the rosiglitazone evaluated for cardiac outcomes and regulation of glycemia in diabetes (RECORD) study. Diab Care. 2011;34:1394–6.View ArticleGoogle Scholar
  104. Mazza A, Zamboni S, Rizzato E, Pessina AC, Tikhonoff V, Schiavon L, Casiglia E. Serum uric acid shows a J-shaped trend with coronary mortality in non-insulin-dependent diabetic elderly people. The CArdiovascular STudy in the ELderly (CASTEL). Acta Diabetol. 2007;44:99–105.PubMedView ArticleGoogle Scholar
  105. Mody R, Kalsekar I, Kavookjian J, Iyer S, Rajagopalan R, Pawar V. Economic impact of cardiovascular co-morbidity in patients with type 2 diabetes. J Diab Complications. 2007;21:75–83.View ArticleGoogle Scholar
  106. Mundet X, Cano F, Mata-Cases M, Roura P, Franch J, Birules M, Gimbert R, Llusa J, Cos X. Trends in chronic complications of type 2 diabetic patients from Spanish primary health care centres (GEDAPS study): 10 year-implementation of St. Vincent recommendations. Prim Care Diab. 2012;6:11–8.View ArticleGoogle Scholar
  107. Narksawat K, Sujirarat D, Panket P. Combined effects of hypertension and diabetes mellitus with stroke among Thais in the central region of Thailand: a cross-sectional study. J Med Assoc Thai. 2013;5:S1–7.Google Scholar
  108. Penno G, Solini A, Zoppini G, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Gruden G, Lamacchia O, Laviola L. Independent correlates of urinary albumin excretion within the normoalbuminuric range in patients with type 2 diabetes: the renal insufficiency and cardiovascular events (RIACE) Italian multicentre study. Acta Diabetol. 2015;52:971–81.PubMedView ArticleGoogle Scholar
  109. Rodriguez-Poncelas A, Coll-De Tuero G, Turro-Garriga O, Barrot-de la Puente J, Franch-Nadal J, Mundet-Tuduri X, Red GSG. Impact of chronic kidney disease on the prevalence of cardiovascular disease in patients with type 2 diabetes in Spain: PERCEDIME2 study. BMC Nephrol. 2014;15:150.PubMedPubMed CentralView ArticleGoogle Scholar
  110. Soetedjo N, Permana H, Ruslami R, Livia R, Panduru N, Critchley J, Hulscher M, Tack C, Alisjahbana B, van Crevel R. PO035 High prevalence of macrovascular complications and insufficient cardiovascular management in indonesian diabetes patients; a hospital survey. Diab Res Clin Pract. 2014;106:S63.View ArticleGoogle Scholar
  111. Suh DC, Kim CM, Choi IS, Plauschinat CA. Comorbid conditions and glycemic control in elderly patients with type 2 diabetes mellitus, 1988 to 1994 to 1999 to 2004. J Am Geriatr Soc. 2008;56:484–92.PubMedView ArticleGoogle Scholar
  112. Utrera-Lagunas M, Orea-Tejeda A, Castillo-Martinez L, Balderas-Munoz K, Keirns-Davis C, Espinoza-Rosas S, Sanchez-Ortiz NA, Olvera-Mayorga G. Abnormal myocardial perfusion and risk of heart failure in patients with type 2 diabetes mellitus. Exp Clin Cardiol. 2013;18:e44–6.PubMedPubMed CentralGoogle Scholar
  113. Vinagre I, Mata-Cases M, Hermosilla E, Morros R, Fina F, Rosell M, Castell C, Franch-Nadal J, Bolibar B, Mauricio D. Control of glycemia and cardiovascular risk factors in patients with type 2 diabetes in primary care in Catalonia (Spain). Diab Care. 2012;35:774–9.View ArticleGoogle Scholar
  114. Wong K, Glovaci D, Malik S, Franklin SS, Wygant G, Iloeje U, Kan H, Wong ND. Comparison of demographic factors and cardiovascular risk factor control among US adults with type 2 diabetes by insulin treatment classification. J Diab Complications. 2012;26:169–74.View ArticleGoogle Scholar
  115. Yan BP, Zhang Y, Kong AP, Luk AO, Ozaki R, Yeung R, Tong PC, Chan WB, Tsang C-C, Lau K-P. Borderline ankle–brachial index is associated with increased prevalence of micro-and macrovascular complications in type 2 diabetes: a cross-sectional analysis of 12,772 patients from the Joint Asia Diabetes Evaluation Program. Diab Vasc Dis Res. 2015;12:334–41.PubMedView ArticleGoogle Scholar
  116. Zekry D, Frangos E, Graf C, Michel JP, Gold G, Krause KH, Herrmann FR, Vischer UM. Diabetes, comorbidities and increased long-term mortality in older patients admitted for geriatric inpatient care. Diab Metab. 2012;38:149–55.View ArticleGoogle Scholar
  117. World Bank. GNI ranking, atlas method. http://data.worldbank.org/data-catalog/GNI-per-capita-Atlas-and-PPP-table. Accessed 26 Apr 2017.
  118. World Bank. The data blog: New country classifications by income level. https://blogs.worldbank.org/opendata/new-countryclassifications-2016. Accessed 26 Apr 2017.

Copyright

© The Author(s) 2018

Advertisement