Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Public Health

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Risk factors of impaired fasting glucose and type 2 diabetes in Yaoundé, Cameroon: a cross sectional study

  • Clement Nyuyki Kufe1, 2Email author,
  • Kerstin Klipstein-Grobusch1, 3,
  • Fezeu Leopold4,
  • Felix Assah2,
  • George Ngufor2,
  • George Mbeh2,
  • Vivian Nchanchou Mbanya2, 5 and
  • Jean Claude Mbanya2
BMC Public Health201515:59

https://doi.org/10.1186/s12889-015-1413-2

Received: 19 May 2014

Accepted: 14 January 2015

Published: 31 January 2015

Abstract

Background

The prevalence of diabetes is increasing worldwide, particularly in low and middle income countries, where treatment and control are often unavailable and inaccessible. Information on risk factors at local and regional levels is of utmost importance for tailored prevention programmes to curb the rise in diabetes.

The current study was undertaken to investigate the prevalence of Impaired Fasting Glucose (IFG)/Type 2 Diabetes (T2D) and its risk factors in the adult population in Biyem-Assi-Yaoundé, Cameroon.

Methods

Information on cardiovascular risk factors using the WHO STEPwise approach was obtained for 1623 men and women aged 25 years and older of the CAMBoD Project in Biyem-Assi, Yaoundé, Cameroon. T2D was defined as fasting capillary glucose (FCG) ≥ 7.0 mmol/l and/or being on diabetes medication, IFG/T2D as FCG ≥ 6.1 mmol/l and/or being on diabetes medication. Descriptive statistics and multivariate logistic regression analyses were used to describe prevalence of IFG/T2D, prevalence of risk factors for IFG/T2D and to investigate the association of risk factors with prevalence of IFG/T2D.

Results

Prevalence of T2D and of IFG/T2D was 3.3% and 5.7%. Prevalence of hypertension, obesity and abdominal obesity (elevated waist circumference) was 26.6%, 28.4% and 34.9%, respectively. Age and abdominal obesity were significantly associated with IFG/T2D in multivariate logistic regression.

Conclusion

For successful primary prevention of T2D in the general population in Cameroon tailored efforts to address obesity, particularly abdominal obesity, and associated life-style factors are warranted.

Keywords

DiabetesImpaired fasting glucosePreventionNon-communicable diseasesLow and middle income countries

Background

Prevalence of type 2 diabetes (T2D) in both developed and developing countries is expected to increase dramatically from 246 million people (6.0%) aged 20 to 79 in 2007 [1] to 382 million in 2013 and to a projected 592 million people by 2035 [2]. About 175 million people with diabetes are undiagnosed. Diabetes caused 5.1 million deaths in 2013 and impact considerably on people under the age of 60 years. In Africa, 76.0% of deaths due to diabetes affected this age group [2]. The burden of T2D is particularly high in low and middle income countries [1-4]; for Africa prevalence of T2D was 4.5% in 2011, 5.7% in 2013 and is projected to increase to 6.0% by 2035 with the highest projected increase (109.6%) in the number of adults aged 20-79 years [2]. For Cameroon prevalence of T2D was 0.8% in 2003, projections for 2030 estimate diabetes prevalence to be 4.8% [5,6]. The rapid urbanisation in sub-Saharan Africa coupled with sedentary lifestyle and increased adiposity in urban areas is a major determinant in the increasing prevalence of diabetes and cardiovascular diseases. Urban residence is associated with a more than twice increased risk of diabetes or IFG [4]. IFG identifies risk state for future diabetes, cardiovascular disease and premature mortality. Determining IFG in asymptomatic individuals allows for prevention strategies to be implemented that can delay or prevent onset of T2D. Overall a prevalence of 5.0% and more for IFG has been observed, varying across different age groups and between populations [5]. Age-adjusted prevalence of IFG in World Health Organisation’s African region was 9.7% in 2011 as compared to global estimates of 6.5%. [5].

T2D is responsible for major contemporary causes of morbidity and mortality including cardiovascular diseases (CVD), retinopathies, nephropathies and neuropathies. Fifty percent of people living with T2D die of cardiovascular disease [2]. Most of T2D cases could be controlled and prevented via lifestyle modification. Factors that affect the onset of T2D are well known, such as age, ethnicity and modifiable risk factors like obesity, physical inactivity, dietary intake, tobacco and excessive alcohol consumption [6,7], though their prevalence and thus prevention potential may differ according to geographical and cultural context. IFG indicates above normal fasting blood glucose levels but lower than threshold for diagnosis of diabetes. IFG is a strong risk marker for the development of diabetes and associated with increased CVD risk [8]. Previous studies showed that during the five years preceding a diagnosis of T2D, 60.0% of people showed IFG or Impaired Glucose Tolerance (IGT) [9]. Screening for diabetes is not different from screening for IFG and the same risk factors associated with diabetes are associated with IFG [10]. Thus targeting individuals with IFG/T2D for prevention and control is of major importance to delay and/or prevent the onset of diabetes and complications which are a major cause of morbidity and mortality and highly related to duration, chronic levels of glycaemia and other risk factors.

The current study was undertaken to investigate the prevalence of modifiable risk factors and their association with IFG/T2D to guide primary prevention and control of IFG/T2D in an urban population in Cameroon.

Methods

Study population

A population-based survey to evaluate the burden of diabetes was conducted in Biyem-Assi, Yaoundé, Cameroon, in 2007 as part of the Cameroon Burden of Diabetes Project (CAMBoD), a multidisciplinary programme contributing to surveillance, prevention and control of diabetes in urban areas [11]. Biyem Assi health district had 9.7% of health facilities of the centre region, Republic of Cameroon, which comprised 25.1% of health facilities of the country in 2011 [12]. Biyem Assi, an urban area, with most inhabitants being government or private sector workers earning a salary, business men and women or students, has previously been shown to represent the urban population in Yaoundé [13]. Based on a census enlisting all adults aged 25 years and above living in the CAMoD study area in Biyem-Assi, Yaoundé participants were invited to participate in the 2007 survey. Multilevel systematic cluster sampling stratified by age group was used, a cluster being a household. The first household was selected randomly. Individual age group sampling was done by which every age group was represented thus avoiding having the sample size of the most populated age group within a limited spatial surface. Survey participation rate was 82.5% (n = 2062). Participants without the main dependent variable (FCG) were excluded from the analyses resulting in an analytic study sample of 1623 study participants.

Data collection and management

Teams of four trained interviewers, each including a medical doctor, collected information on participants’ self-reported behavioural and lifestyle factors for chronic diseases, blood pressure, anthropometry measurements and blood samples for biochemical analysis from September to October 2007 using standardized procedures. For quality control purposes calibrated instruments were used and adherence of trained interviewers to standardised procedures was monitored.

Participants were asked to fast overnight. For measurement of fasting capillary glucose (FCG), study participants, who had not eaten or drank anything (except plain water) for at least eight hours were visited at home between 5:30 am and 9:00 am the next day. Assessment of IFG/T2D was based on fasting capillary glucose measurements with HemoCue®B-Glucose photometer, a validated instrument shown to give consistent readings as compared to other instruments [14]. The participant’s middle or ring finger, not bearing a ring, was cleaned with an alcohol swab and pricked at the side with a lancet. Blood was collected with a microcuvette, placed into a cuvette holder and results displayed immediately [15]. HemoCue®B-Glucose photometers were controlled every morning. Every sixth consecutive participant screened negative was administered an oral glucose tolerance test to ascertain reliability of results for FCG. IFG/T2D was defined as fasting capillary glucose (FCG) ≥6.1 mmol/l and/or on diabetes medication (insulin and/or anti-diabetic drugs) [8], participants with FCG between 6.1 mmol/l and 6.9 mmol/l were considered to have IFG, and those with FCG ≥7.0 mmol/l (126 mg/dl) and/or on diabetes medication (insulin and/or anti-diabetic drugs) were considered to have diabetes.

The World Health Organisation (WHO) STEPwise approach, a standardized questionnaire for chronic disease risk factor surveillance [16] including questions on diabetes and hypertension status (if diabetic or hypertensive and whether on diabetes or hypertensive medication (insulin and/or oral remedy)), smoking habits, alcohol intake, dietary intake and physical activity was applied by interviewers trained in the standardized administration of questionnaires in both English and French, the official languages in Cameroon. Participants previously on anti-diabetes medication and with FCG < 6.1 mmol/l and, participants informed by a health professional that s/he was diabetic but on local anti-diabetic remedy were excluded. The instruments for the survey were pretested and revised if indicated prior to the commencement of the survey.

Systolic (SBP) and diastolic (DBP) blood pressure was measured using fully automated calibrated Omron M3 machines with the participant being seated for at least five minutes, legs uncrossed and the left arm resting on a table prior to taking measurements. Blood pressure was measured thrice, the mean of the second and third measurement was considered for this study. Participants with mean SBP ≥ 140 and/or DBP ≥ 90 mmHg and/or on hypertensive medication were considered to be hypertensive [17].

Anthropometric measurements, including weight (to the nearest 0.1 kg, SECA scales), height (to the nearest 0.1 cm, locally made wood stadiometers), waist and hip circumferences (to the nearest 0.1 cm, flexible inelastic fibreglass meter band), were performed using standard methods. Waist circumference was measured over light clothing at the level of the midpoint between the inferior margin of the last rib and the crest of the ilium in the mid-axillary plane. Hip circumference was measured at the level of the greater trochanter of the femur, -around the buttocks through the symphysis pubis.

Obesity and central obesity were assessed by body mass index (BMI), waist-to-hip ratio (WHR) and waist circumference (WC). BMI was calculated as weight (kg) divided by squared height (m2) and used to define normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2) and obesity (BMI ≥ 30 kg/m2) [18,19]. WHR was calculated as waist (cm) divided by hip (cm) circumference. Presence of abdominal obesity was considered in case of elevated WHR (>1.0 for men and >0.85 for women, respectively) or elevated waist circumference (WC) (>102 cm for men and 88 cm for women, respectively) [20-22].

Using the WHO STEPwise approach vigorous physical activity was defined as sustained activity that results in a significant increase in heart and breathing rate for at least ten minutes at a time such as forestry, digging construction, farm work or sporting activity. Moderate intensity physical activity was defined as activity that results in breathing somewhat harder than normal for at least ten minutes such as cleaning, washing, planting and harvesting crops, weaving, woodwork, brisk or quick walking or carrying of light loads. Low intensity physical activity was defined as involving mostly sitting or standing with walking for not more than ten minutes [23].

In addition, information on socioeconomic and demographic data, data on past and current medical history of diabetes and its risk factors (tobacco smoking, alcohol consumption, dietary habits, and physical activity) was obtained from all study participants.

Data analysis

Means and standard deviations (SD) were used to describe continuous variables. Logistic regression was used to investigate the association of demographic and anthropometric characteristics, blood pressure and lifestyle risk factors to IFG/T2D. Sex, marital status and variables significantly associated (two sided p-value < 0.05) with IFG/T2D in univariate analyses were subsequently included in multivariate logistic regression analyses. Results were reported as Odds Ratios (OR) with corresponding 95% confidence intervals (95% CI). Statistical correlations for the presence of IFG/T2D were assessed between WHR and BMI, BMI and waist circumference, and between sex and markers of central obesity and BMI. Separate models were considered in case of correlation between variables. We carried out direct age standardisation using World Health Organisation New World Population as reference [24]. Regression analysis took into consideration stratification, proportional weights and clustering for participants with main dependent variable having complete data. All statistical analyses were adjusted for the cluster random sampling design by use of the statistical software package STATA/IC (version 11.1).

Ethical considerations

Prior to study inclusion participants gave informed consent by either signing or thumb printing on the informed consent form, a copy of which was given to the participant. Information obtained from individuals and households and results of the physical and biochemical examinations were kept confidential. All participants received individual feedback on the results of their examinations and were referred where necessary to Biyem-Assi hospital–Yaoundé for appropriate follow up. Data were anonymized for statistical analysis. Ethical clearance for the CAMBoD study was obtained from the Cameroon National Ethics Committee; Yaoundé, Cameroon; ethical clearance for analysis of the 2007 survey was given by the Human Research Ethics Committee (Medical) of the University of the Witwatersrand, Johannesburg, South Africa.

Results

Demographic and socio-economic characteristics of study participants

The study sample comprised 1623 participants, 666 men (41.1%) and 957 women (58.9%). Average age was 39.7 (SD 12.9) years at the time of the survey in 2007. Demographic characteristics, i.e. marital status, educational attainment and information on occupation are given in Table 1.
Table 1

Description of study participants according to IFG/T2D status, Yaoundé, Cameroon, 2007

Variable

All

IFG/T2D

Non IFG/T2D

P-value*

(n = 1623)

(n = 93)

(n = 1530)

 
 

N

%

n

%

N

%

 

Gender

       

Male

666

41.0

55

45.1

611

40.7

 

Female

957

59.0

67

54.9

890

59.3

0.345

Age group (years)

       

25-24

768

47.3

22

18.0

746

49.7

 

35-44

278

17.1

22

18.0

256

17.1

 

45-54

337

20.8

38

31.2

299

19.9

 

55-64

170

10.5

34

27.9

136

9.1

 

65+

70

4.3

6

4.9

64

4.3

<0.001

Marital status

       

Married

852

52.5

84

68.9

768

51.2

 

Single

584

36.0

20

16.4

564

37.6

 

Others

187

11.5

18

14.8

169

11.3

<0.001

Occupation (n = 1526)

       

Civil servant/Private sector

591

38.7

51

46.4

540

38.1

 

Self-employed/unpaid subsistence farming

259

16.9

18

16.4

241

17.0

 

Student

197

12.9

4

3.6

193

13.6

 

Retired/House keeper

288

18.9

30

27.3

258

18.2

 

Unemployed

191

12.5

7

6.3

184

12.9

<0.001

Highest level of education (n = 1607)

       

Never went to school to completed primary school

366

22.8

31

25.6

335

22.5

 

Completed secondary school

405

25.2

30

24.8

375

25.2

 

Completed high school

391

24.3

27

22.3

364

24.5

 

Completed university

445

27.7

33

27.3

412

27.7

0.873

Weight status

       

Underweight to normal

593

36.5

30

24.6

563

37.5

 

Overweight

562

34.6

42

34.4

520

34.6

 

Obese

468

28.8

50

41.0

418

27.9

0.003

Waist-to-hip ratio

       

<0.90(men) or <0.85(women)

1029

63.4

48

39.3

981

65.4

 

>0.90(men) or >0.85(women)

594

36.6

74

60.7

520

34.6

<0.001

Waist circumference

       

≤94(men) ≤ 80 cm(women)

743

45.8

40

32.8

703

46.8

 

94 > & < 102(men)80 > &88 cm (women)

313

19.3

18

14.8

295

19.7

 

≥102 cm(men) ≥ 88 cm(women)

567

34.9

64

52.4

503

33.5

<0.001

Hypertension

       

Yes

432

26.6

51

41.8

381

25.4

 

No

1191

73.4

71

48.2

1120

74.6

<0.001

Smoking (n = 1 607)

       

Never

1219

75.9

91

75.8

1128

75.9

 

Former

266

16.5

19

15.8

247

16.6

 

Current

122

7.6

10

8.3

112

7.5

0.935

Alcohol consumption

       

Never

269

16.7

19

15.6

250

16.7

 

Former

293

18.2

23

18.9

270

18.1

 

Current

1051

65.1

80

65.5

971

65.1

0.935

General physical activity

       

None

628

38.6

37

30.3

591

39.4

 

Low intensity

837

51.6

73

59.8

764

50.9

 

Moderate activity

87

5.4

4

3.3

83

5.5

 

Vigorous activity

71

4.4

8

6.6

63

4.2

0.084

Walking/cycling for at least 10 minutes (n = 1 609)

       

Yes

1339

83.2

96

80.7

1 243

83.4

 

No

270

16.8

23

19.3

247

16.6

0.440

Activity during recreation/leisure (n = 1614)

       

None

89

5.5

2

1.7

87

5.8

 

Low intensity

1394

86.4

105

88.2

1 289

86.2

 

Moderate activity

42

2.6

4

3.4

38

2.5

 

Vigorous activity

89

5.5

8

6.7

81

5.4

0.251

Fruit consumption/week (n = 1 471)

       

0/day

299

20.3

26

22.0

273

20.2

 

1-2 days

252

17.1

29

24.6

223

16.5

 

3-4 days

731

49.7

44

37.3

687

50.8

 

5-7 days

189

12.9

19

16.1

170

12.5

0.025

Vegetable consumption/week(n = 1 555)

       

1-2 days

183

11.8

19

16.1

164

11.4

 

3-4 days

407

26.1

37

31.4

370

25.8

 

5-7 days

965

62.1

62

52.5

903

62.8

0.074

*Chi-square test for comparison of distribution of variable categories for IFG/T2D versus non IFG/T2D.

Prevalence of IFG/Type 2 diabetes, hypertension and obesity

Prevalence of T2D was 3.3% (n = 54), 4.2% (n = 28) among men and 2.7% (n = 26) among women. Figures for IFG/T2D were 5.7% (n = 93), respectively 6.8% (n = 45) in men and 5.0% (n = 48) in women. Mean FCG was 5.4 mmol/l. IFG/T2D prevalence increased with age (Table 2). Thirty-one (57.4%) of the 54 respondents considered to have T2D reported diabetes treatment; 42.6% were newly diagnosed cases. Diabetes control was poor: 42.0% of diabetics on treatment had FCG ≥ 6.1 mmol/l, 29.0% had FCG ≥ 7.0 mmol/l.
Table 2

Percentage description of type 2 diabetes (T2D), Impaired fasting glucose (IFG)/T2D, hypertension, waist-hip-ratio, waist circumference and body mass index by age group and by gender among 1623 study participants in Yaoundé, Cameroon, 2007

  

Men (n = 666)

 

Women (n = 957)

  

25-34

35-44

45-54

55-64

>65

p-value7

 

25-34

35-44

45-54

55-64

>65

p-value7

N

666

317

100

116

100

33

 

957

451

178

221

70

37

 

Variable

N (%)

N (%)

N (%)

N (%)

N (%)

N (%)

 

N (%)

N (%)

N (%)

N (%)

N (%)

N (%)

 

T2D1

28 (4.2)

3 (10.7)

1 (3.6)

8 (28.6)

14 (50.0)

2 (7.1)

<0.001

26 (2.7)

4 (15.4)

4 (15.4)

10 (38.5)

6 (23.1)

2 (7.7)

<0.001

IFG/T2D2

45 (6.8)

5 (11.1)

8 (17.8)

9 (20.0)

21 (46.7)

2 (4.4)

<0.001

48 (5.0)

9 (18.8)

8 (16.7)

20 (41.7)

8 (16.6)

3 (6.2)

<0.001

Hypertension3

203 (30.5)

37 (18.2)

21 (10.3)

58 (28.6)

59 (29.1)

28 (13.8)

<0.001

229 (23.9)

35 (15.3)

24 (10.5)

101 (44.1)

43 (18.8)

26 (11.4)

<0.001

Weight status4

              

Normal weight

314 (47.2)

195 (62.1)

38 (12.1)

33 (10.5)

32 (10.2)

16 (5.1)

 

279 (29.2)

185 (66.3)

38 (13.6)

28 (10.0)

15 (5.4)

13 (4.7)

 

Overweight

243 (36.5)

95 (39.1)

41 (16.9)

54 (22.2)

44 (18.1)

9 (3.7)

 

319 (33.3)

157 (49.2)

60 (18.8)

71 (22.3)

22 (6.9)

9 (2.8)

 

Obese

109 (16.4)

27 (24.8)

21 (19.3)

29 (26.6)

24 (22.0)

8 (7.3)

<0.001

359 (37.5)

109 (30.4)

80 (22.3)

122 (33.9)

33 (9.2)

15 (4.2)

<0.001

Waist-hip-ratio5

              

Normal

414 (62.2)

280 (67.6)

58 (14.0)

40 (9.7)

29 (7.0)

7 (1.7)

 

615 (64.3)

340 (55.3)

118 (19.2)

116 (18.9)

29 (4.7)

12 (1.9)

 

Elevated

252 (37.8)

37 (14.7)

42 (16.7)

76 (30.2)

71 (28.2)

26 (10.3)

<0.001

342 (35.7)

111 (32.5)

60 (17.5)

105 (30.7)

41 (12.0)

25 (7.3)

<0.001

Waist circumference6

              

Normal

485 (72.8)

287 (59.2)

64 (13.2)

65 (13.4)

52 (10.7)

17 (3.5)

 

258 (27.0)

185 (71.7)

40 (15.5)

21 (8.1)

8 (3.1)

4 (1.6)

 

Increased risk

98 (14.7)

23 (23.5)

17 (17.4)

23 (23.5)

28 (28.6)

7 (7.1)

 

215 (22.5)

113 (52.6)

33 (15.4)

43 (20.0)

15 (7.0)

11 (5.1)

 

Elevated

83 (12.5)

7 (8.4)

19 (22.9)

28 (33.7)

20 (24.1)

9 (10.8)

<0.001

484 (50.6)

153 (31.6)

105 (21.7)

157 (32.4)

47 (9.7)

22 (4.6)

<0.001

1FCG > =7.0 mmol/l and /or on diabetes medication.

2FCG > =6.1 mmol/l and /or on diabetes medication.

3SBP ≥ 140 and /or DBP ≥ 90 mmHg and / or on hypertensive medication.

4Normal weight (18.5Kg/m2 ≤ BMI < 25Kg/m2), overweight (25Kg/m2 ≤ BMI < 30Kg/m2) and obese (BMI ≥ 30Kg/m2).

5Normal waist-hip-ratio < 1.0 for men and <0.85 for women and elevated waist-hip-ratio >1.0 for men and >0.85 for women.

6Normal waist circumference < 94 cm for men and <80 cm for women, increased risk (94 cm > WC < 102 cm) for men and (80 cm > WC < 88 cm) for women and elevated ≥102 cm for men and ≥ 88 cm for women.

Prevalence of hypertension was 26.6% and significantly higher in older age groups (Table 2). Of the 432 participants considered to be hypertensive, 93 (21.5%) participants reported receiving treatment and 45 (10.4%) had controlled blood pressure. The majority of study participants were overweight (34.5%) or obese (28.4%). Average BMI of the study population was 27.6 kg/m2 (SD 5.4)) and was lower in men (25.9 (SD4.1) kg/m2 than women (28.7 (SD 5.8) kg/m2). Women were more often obese (37.0%) than men (16.1%); obesity was most prevalent in the age group 45–54 years. More than a third of the study participants (36.6%) showed elevated WHR, slightly more pronounced in men (37.8%) than women (35.7%). Elevated waist circumference was present in 34.9% of the study participants and was four times as high in women (50.6%) as in men (12.5%). For both parameters of central obesity a significant relation with age was observed.

Prevalence of lifestyle factors – tobacco consumption, alcohol consumption, physical activity and dietary habits

Most survey respondents were non-smokers (75.3%), 7.7% reported to be current smokers, the majority of them being men (89.1%). Prevalence of current smoking was most pronounced in the age group 35–44 years, older age groups tended to report less current smoking. On average current smokers started smoking at 21.2 (SD 5.9) years. Less than a third (28.%) of those who reported to have smoked previously indicated to have stopped because of health related problems; on average they reported to have quit smoking at age 34.7 (SD 11.1) years.

Alcohol consumption was common with 65.5% of participants reporting current alcoholic beverage consumption such as beer, wine, spirit or local brews as palm wine, corn beer, “bilibili”, “arki” or “afofo” and was most prominent in those aged 25–34 years (men 91.2%, women 78.5%). Men more often reported current alcohol consumption than women and were less often abstainers. Beer was the most consumed alcoholic drink, specifically in men (70.9%, women 49.8%) followed by consumption of wine, palm wine, whisky, “bilibili” or corn beer and “arki”.

Most respondents (61.5%) reported physical activity, mostly low intensity physical activity (51.8%). Vigorous physical activity was only reported by 4.3% of the participants. In general, intense physical activities were reported more often by younger age groups and more often by men than women (data not shown). For leisure/recreational activities most study participants (85.9%) reported light activities, vigorous intensity was only reported by 6.1% of the study participants.

On average fruit consumption was reported on 3 days/week, vegetable consumption on 2.4 days/week; less than 2.5% of respondents reported any vegetable consumption, whereas 13.4% did not report any fruit consumption. Older women reported highest fruit consumption.

Risk factors for IFG/T2D

Diabetic participants were often older, obese, hypertensive, had elevated waist circumference and WHR, and were less often single and more often retired (Table 1). Age was significantly associated with IFG/T2D (Table 3). Being obese was associated with increased likelihood of IFG/T2D when compared to normal weight participants (OR: 2.24, 95% CI: 1.33–3.78). Both markers of abdominal obesity were associated with IFG/T2D, OR for elevated WHR was 2.91 (95% CI: 1.99–4.25), OR for high waist circumference was 2.23 (95% CI: 1.45–3.46). Hypertension (OR = 2.11, 95% CI: 1.42–3.13) was also associated with IGF/T2D (Table 3).
Table 3

Risk factors for IFG/T2D in study participants aged 25 years and older in Yaoundé, Cameroon, 2007

Factors

Univariate logistic regression

Multivariate logistic regression

 

OR

95% CI

P

p for trend

OR

95% CI

p

p for trend

Gender

        

Female

1

   

1

   

Male

1.19

0.82-1.74

0.352

0.352

1.23

0.73-2.07

0.443

0.332

Age Group

        

25-34

1

   

1

   

35-44

2.91

1.59-5.31

0.000

 

2.65

1.27-5.53

0.009

 

45-54

4.31

2.53-7.32

0.000

 

3.04

1.50-6.14

0.002

 

55-64

8.48

4.67-15.37

0.000

 

5.69

2.52-12.83

0.000

 

65+

3.18

1.28-7.90

0.013

<0.001

1.88

0.60-5.86

0.275

<0.001

Marital status

        

Married

1

       

Single

0.32

0.19-0.54

0.000

     

Others

0.97

0.50-1.88

0.937

0.119

    

Weight status

        

Normal

1

   

1

   

Overweight

1.52

0.90-2.55

0.117

 

0.92

0.47-1.80

0.810

 

Obese

2.24

1.33-3.78

0.002

0.002

1.14

0.56-2.34

0.704

0.503

Waist-hip ratio

        

<1.0 (men) or <0.85 (women)

1

   

1

   

>1.0 (men) or >0.85 (women)

2.91

1.99-4.25

0.000

<0.001

2.21

1.22-4.01

0.009

0.008

Waist circumference in cm

        

≤94 (men) or ≤80 (women)

1

   

1

   

94 > & < 102 (men) or 80 > & < 88 (women)

1.07

0.59-1.96

0.821

 

0.71

0.33-1.53

  

≥102 (men) or ≥88 (women)

2.23

1.45-3.46

0.000

<0.001

1.08

0.48-2.47

0.850

0.798

Prevalence of hypertension

        

No

1

   

1

   

Yes

2.11

1.42-3.13

0.000

<0.001

1.58

0.92-2.72

0.100

0.145

Smoking

        

Never

1

       

Former

0.95

0.57-1.60

0.857

     

Current

1.11

0.56-2.19

0.771

0.888

    

Alcohol consumption

        

Never drank

1

       

Former

1.12

0.57-2.21

0.743

     

Current

1.08

0.65-1.81

0.758

0.820

    

General physical activity

        

None

1

       

Low intensity

1.53

0.96-2.42

0.073

     

Moderate activity

0.77

0.26-2.29

0.637

     

Vigorous activity

2.03

0.88-4.69

0.098

0.111

    

Gender and all variables significantly associated with IFG/T2D in univariate models were included in multivariate logistic regression models to further analyse the association with IFG/T2D. The two markers of abdominal obesity were considered in separate multivariate logistic regression analysis due to their high correlation. Both markers were highly associated with IFG/T2D in multivariate adjusted models. Hosmer–Lemeshow goodness-of-fit test confirmed the appropriateness of the two multivariate adjusted models.

The factors that maintained their statistical significance after multivariate adjustment were age (35–44 years: OR = 2.63, 95% CI: 1.26–5.51, p = 0.010; 45–54 years: OR = 3.05, 95% CI: 1.50–6.22, p < 0.002 and 55–64 years: OR = 5.88, 95% CI: 2.59–13.37, p < 0.001) and elevated WHR (OR = 2.21, 95% CI: 1.22–4.01, p = 0.009) (Table 3).

Discussion and conclusions

The current study assessed the prevalence of and the risk factor profile for IFG/T2D in the general adult population in Biyem-Assi, Yaoundé, Cameroon and observed age and central obesity to be the most important risk factors associated with prevalence of IFG/T2D.

An increase in the prevalence of diabetes in this urban area was observed from 2.0% in 1999 [4] to 3.3% in 2007. Regional and comparative prevalence in Africa was 4.9% and 5.7% respectively and ranged from 4.4% to 7.0% in 2013 depending on the country’s economic development [25]. A higher urban prevalence for diabetes was observed in Tanzania for Asian Indians in 1991 (9.1%) and South Africa for Native Africans in 1993 (8.0%) [4].

In Zambia, the combined prevalence of impaired glucose levels or diabetes in 2011 was 4.0% (impaired glucose levels or diabetes defined as fasting glucose ≥ 5.51 mmol/l) [26], higher than for this study most likely due to a lower cut off point for IFG levels. A similar study in China using the same cut off points for IFG and T2D as in our study showed a higher prevalence of diagnosed diabetes, undiagnosed diabetes and IFG in adults respectively; 1.3%, 4.2% and 7.3% [27], possibly in part due to increased levels of physical inactivity, high levels of alcohol consumption and poor dietary habits. Beneficial effects of lifestyle behaviour to prevent or delay the onset of diabetes and other health related benefits have been demonstrated [28-30]. Prevalence estimates observed in the current study were lower than estimates for diabetes mellitus (5.7%) and IFG (4.5%) reported by a systematic review and meta-analysis in sub-Saharan Africa published in 2013 [31]. Disparities in prevalence are probably in part due to differences in health determinants across sub-Saharan Africa [31]. IFG is a risk state prior to diabetes and ideally diabetes prevention strategies identify and focus on individuals at pre-diabetes state. Newly diagnosed cases represented 42.6% in our study in contrast to more than 70% of newly diagnosed cases reported for surveys [4]. This difference can be most likely attributed to on-going sensitisation campaigns in the study area to curb diabetes [12]. The observed associations of age and central obesity with IFG/T2D in this Cameroonian population are in accordance with previous international observations [4-6], in particular central obesity was observed to be a good predictor of IFG/T2D. Waist circumference has previously been shown in a Cameroonian population to be positively associated to all obesity related abnormalities [32].

We observed no association between fruit and vegetable consumption and IFG/T2D, despite previous reports on an inverse association of these low energy density, low glycaemic load, and high fibre and micronutrient content foods with risk of developing IFG/T2D [33,34]. Our results may in part be explained by poor quantification of the type and amounts of fruits and vegetables consumed with the interviewer-administered questionnaire and with seasonal availability of these produce on the local market.

Likewise, we observed no associations for current and former smokers with IFG/T2D, nor an inverse association for moderate alcohol intake or physical activity with IFG/T2D [35-38] pointing towards methodological challenges of the WHO-STEPS tool applied for assessment of risk factors in this population in Cameroon. These may entail for example for physical activity, difficulties in interpretation of the physical activity questions, e.g. perception of categories of physical activity on the one side and on the other side the notion that some leisure/recreational physical activities in Cameroon are rather considered to be social occasions coupled with entertainment including alcohol and food consumption potentially offsetting the beneficial effects that accrue from physical activity.

The results reported in the current study are based on the CAMBoD survey conducted in the adult population of Biyem-Assi, Yaoundé, Cameroon in 2007. In comparison with a survey conducted in the area in 2003 increased prevalence for IFG/T2D ((3.7% (2003) to 4.1% (2007), hypertension (17.1% (2003) to 20.4% (2007)) and obesity (14.6% (2003) to 18.3% (2007); all age-standardized data) were noted. Our study showed that T2D, IFG/T2D, hypertension and adiposity affects particularly people in the age group 45-54 years. Prevalence has increased though less than for any of the top 10 countries in the African region for which increasing prevalence of 6.3% to 15.4% was reported in 2013 [25]. Urban settlements are fast becoming diabetic and obesity epi-centres and increasingly people at younger ages are observed to develop diabetes [2,4,5]. The high burden of diabetes in low and middle income countries especially in sub-Saharan Africa [25,31] calls for increased efforts to address individual lifestyle changes to reduce modifiable IFG/T2D risk factors [28,29], particularly central obesity, to counteract accelerating prevalence rates of IFG/T2D in a population undergoing rapid urbanisation and epidemiologic transition with limited health resources accentuated by negligible health expenditure on diabetes.

Limitations and advantages of the study

The current cross-sectional study was conducted in an urban setting in Yaoundé and may thus not be generalizable to the rural population in Cameroon. Missing information in the survey population may further limit the generalizability of the study results. For definition of pre-diabetes, we used IFG instead of IGT, which considering that FCG may potentially fail to identify previously undiagnosed diabetes, could have introduced misclassification of IFG/T2D. Participants on anti-diabetic medication were considered to be diabetic, however, some may have been prescribed anti-diabetic medication at a pre-diabetes stage, which might have resulted in some misclassification of IFG and T2D.

Despite these limitations and taking into consideration feasibility, reproducibility, acceptability and local context our study provided prevalence data of T2D, IFG/T2D, hypertension and adiposity by sex and age group strata for an urban Cameroonian population. The study seeks to enhance public awareness on the seriousness of diabetes and its complications and the need for prevention strategies especially in people less than 45 years of age.

Declarations

Acknowledgements

We are grateful to World Diabetes Foundation (WDF) who provided funding to Health of Populations in Transition (HoPiT) Research Group Cameroon for the Cameroon Burden of Diabetes (CAMBoD) Project on which this paper is based. We gratefully acknowledge the input of HoPiT staff, data collection and entry staff and research participants in making this study possible.

Financial support to CNK from the MRC/Wits Rural Public Health and Health Transition Research Unit (Agincourt) for completion of a thesis for the degree of MSc (Med) in Epidemiology & Biostatistics at the University of the Witwatersrand, on which this manuscript is based, is greatly acknowledged.

Authors’ Affiliations

(1)
Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand
(2)
Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1
(3)
Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht
(4)
Université Paris 13, Sorbonne Paris Cité, Unité de Recherche en Epidémiologie Nutritionnelle (UREN)/U557 Inserm/U1125 Inra/Cnam/CRNH
(5)
Department of Community Medicine, University of Oslo

References

  1. World Health Organization Global Program on Evidence for Health Policy. National burden of disease studies, A practical guide, edition 2.0. 2001.Google Scholar
  2. International Diabetes Federation. Diabetes Atlas. 6th ed. 2013.Google Scholar
  3. Hall V, Thomson RW, Henriksen O, Loshe N. Diabetes in Sub Saharan Africa 1999-2011: epidemiology and public health implications. A systematic review. BMC Public Health. 2011;11:564.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Mbanya JC, Motala AA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet. 2010;375(9733):2254–66.View ArticlePubMedGoogle Scholar
  5. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Report of a WHO/IDF consultation. 2006.Google Scholar
  6. International Diabetes Federation. Diabetes Atlas. 3rd ed. 2006.Google Scholar
  7. International Diabetes Federation. Diabetes Atlas. 4th ed. 2009.Google Scholar
  8. Unwin N, Shaw J, Zimmet P, Alberti GMM. International diabetes federation IGT/IFG consensus statement. Diabet Med. 2002;19:708–23.View ArticlePubMedGoogle Scholar
  9. Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, et al. Impaired fasting glucose and impaired glucose tolerance. Implications of care. Diabetes Care. 2007;30(3):753–9.View ArticlePubMedGoogle Scholar
  10. Kamadjeu RM, Edwards R, Atanga JS, Unwin N, Kiawi EC, Mbanya JC. Prevalence, awareness and management of hypertension in Cameroon: findings of the 2003 Cameroon burden of diabetes baseline survey. J Hum Hyperten. 2006;20:91–2.View ArticleGoogle Scholar
  11. Kiawi E, Edwards R, Shu J, Unwin N, Kamadjeu R, Mbanya JC. Knowledge, attitudes, and behavior relating to diabetes and its main risk factors among urban residents in Cameroon: a qualitative survey. Ethn Dis. 2006;16:503–9.PubMedGoogle Scholar
  12. Cartographie des formations sanitaires au Cameroon. Ministry of public health Cameroon 2011. 2011.Google Scholar
  13. Sobngwi E, Mbanya J-CN, Unwin NC, Kengne AP, Fezeu L, Minkoulou EM, et al. Physical activity and its relationship with obesity, hypertension and diabetes in urban and rural Cameroon. Int J Obes. 2002;26:1009–16.View ArticleGoogle Scholar
  14. Ashworth L, Gibb I, Alberti KGMM. HemoCue: evaluation of a portable photometric system for determining glucose in whole blood. Clin Chem. 1992;38:1479–82.PubMedGoogle Scholar
  15. HemoCue™ Hb 201DM System Photometer Operating Manual. Angelhom, Sweden: HemoCue AB; 2005.Google Scholar
  16. World Health Organization. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance. Geneva: World Health Organization; 2005.Google Scholar
  17. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC7 report (published correction appears in JAMA;290[2]:197). JAMA. 2003;289:2560–72.View ArticlePubMedGoogle Scholar
  18. Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 1996;143:228–39.View ArticlePubMedGoogle Scholar
  19. WHO. Physical status: the use and interpretation of anthropometry, Report of a WHO expert committee. WHO technical report series 854. 1995.Google Scholar
  20. WHO. Obesity: preventing and managing the global epidemic, Report of a WHO consultation. WHO technical report series 894. 2000.Google Scholar
  21. Östman J, Britton M, Jonsson E. Treating and preventing obesity. Kga, Weinheim: WILEY-VCH Verlag GmbH & Co; 2004.View ArticleGoogle Scholar
  22. Mamtani MR, Kulkarni HR. Predictive performance of anthropometric indexes of central obesity for the risk of type 2 diabetes. Arch Med Res. 2005;3:581–9.View ArticleGoogle Scholar
  23. Centers for Disease Control and Prevention (CDC). Physical Activity Terms. Available at: http://www.cdc.gov/nccdphp/dnpa/physical/terms/index.htm. Accessed September 15, 2013.
  24. Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJL, Lozano R, Inoue M. Age standardization of rates: a New WHO standard. GPE discussion paper series: No.31. Geneva: World Health Organisation; 2001.Google Scholar
  25. Peer N, Kengne AP, Motala AA, Mbanya JC. Diabetes in the Africa region: an update. Diabetes Res Clin Pract. 2013;103:197–205.View ArticlePubMedGoogle Scholar
  26. Nsakashalo-Senkwe M, Siziya S, Goma FM, Songolo P, Mukonka V, Babaniyi O. Combined prevalence of impaired glucose level or diabetes and its correlates in Lusaka Urban district. Zambia: a population based survey. Int Arch Med. 2011;4:2.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Gu D, Reynolds K, Duan X, Xin X, Chen J, Wu X, et al. Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterAsia). Diabetologia. 2003;46:1190–8.View ArticlePubMedGoogle Scholar
  28. Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. New Engl J Med. 2001;344:1343–50.View ArticlePubMedGoogle Scholar
  29. Knowler WC, Barrett-Connor E, Fowler SE, Hammon RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with life style intervention or metformin. N Engl J Med. 2002;346:393–403.View ArticlePubMedGoogle Scholar
  30. Cowie CC, Rust KF, Byrd-Holt DD, Flegal KM, Engelgau MM, Saydah SH, et al. Prevalence of diabetes and impaired fasting glucose in adults in the US population: national health and nutrition examination survey 1999-2002. Diabetes Care. 2006;29:1263–8.View ArticlePubMedGoogle Scholar
  31. Hilawe EH, Yatsuya H, Kawaguchi L, Aoyama A. Differences by sex in the prevalence of diabetes mellitus, impaired fasting glycemia and impaired glucose tolerance in sub-Saharan Africa: a systematic review and meta-analysis. Bull World Health Organ. 2013;91:671–682D.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Fezeu L, Balkau B, Sobngwi E, Kengne AP, Vol S, Ducimetiere P, et al. Waist circumference and obesity-related abnormalities in French and Cameroonian adults: the role of urbanization and ethnicity. Int J Obes. 2010;34:446–53.View ArticleGoogle Scholar
  33. Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care. 2008;31:1311–7.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Villegas R, Shu XO, Gao YT, Yang, Elasy T, Li H, et al. Vegetable but not fruit consumption reduces the risk of type 2 diabetes in Chinese women. J Nutr. 2008;138:574–80.PubMedPubMed CentralGoogle Scholar
  35. Beulens JW, van der Schouw YT, Bergmann MM, Rohrmann S, Schulze MB, Buijsse B, et al. Alcohol consumption and risk of type 2 diabetes in European men and women: influence of beverage type and body size. The EPIC–inter act study. J Intern Med. 2012;272(4):358–70.View ArticlePubMedGoogle Scholar
  36. Helmrich SP, Ragland DR, Leung RW, Paffenbarger Jr RS. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. N Engl J Med. 1991;325:147–52.View ArticlePubMedGoogle Scholar
  37. Helmrich SP, Ragland DR, Paffenbarger Jr RS. Prevention of non-insulin-dependent diabetes mellitus with physical activity. Med Sci Sports Exerc. 1994;26:824–30.View ArticlePubMedGoogle Scholar
  38. Hu G, Qiao Q, Silventoinen K, Eriksson JG, Jousilahti P, Lindström J, et al. Occupational, commuting, and leisure-time physical activity in relation to risk for type 2 diabetes in middle-aged Finnish men and women. Diabetologia. 2003;46(3):322–9.View ArticlePubMedGoogle Scholar

Copyright

© Kufe et al.; licensee BioMed Central. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

Advertisement