2Department of Internal Medicine Kilimanjaro Christian Medical Centre, PO BOX 3010 Moshi Tanzania
3Kilimanjaro Clinical Research Institute, PO BOX 2236, Moshi Tanzania
4Better Human Health Foundation, PO BOX 1348, Moshi Tanzania
5Imagedoctors Organization, PO BOX 16341, Arusha Tanzania
Dyslipidemia is a disorder which arises as a result of abnormalities in the plasma lipoproteins. The lipid abnormalities in diabetes include quantitative changes which occur due to an increase of glucose for very low density lipoprotein (VLDL) synthesis and decrease in lipoprotein lipase activity leading to decrease of VLDL from peripheral circulation, increase in low density lipoprotein-C (LDL-C) levels and decrease in high density lipoprotein C (HDLC) levels due to increase in hepatic activity decrease in VLDL clearance. Qualitative changes consists of increase of triglyceride (TG), LDL-C and HDL-C, non-enzymatic glycation of LDL and non-enzymatic glycation of high density lipoprotein (HDL) [4].
Due to the abnormalities in lipoproteins, diabetes mellitus is associated with cardiovascular and cerebrovascular morbidity and mortality worldwide [5]. Dyslipidemia affects approximately 70% to 97% of people with diabetes [6]. World Health Organization (WHO) in 2002 reported that dyslipidemia accounted for 18% of ischemic heart disease, 56% of stroke and over 4million deaths per year globally [7].
The prevalence of dyslipidemia is terrifyingly high within the African continent. The trend has been seen to increase, in Nigeria, the prevalence ranged from 82.6% to 90.7% from 2008 to 2011 respectively [8, 9, 10]. In South Africa the prevalence is over 90% [11]. In Tanzania, the prevalence of diabetic dyslipidemia was 95% in 2007 [12]. This is mainly due to the adopted western diet, sedentary lifestyle as well as physical inactivity resulting to obesity [13]. By determining the prevalence of abnormal lipid profile levels among the diabetic patients, it will provide the need to aggressively manage dyslipidemia among diabetic patients.
Anthropometric measurements, including weight, height and waist circumference were measured with the subjects wearing light clothing and no shoes. Body Mass Index (kg/m2) was calculated as weight (kilograms) divided by squared height in (meters). The study populations were classified as underweight BMI < 18.5 kg/m2, normal BMI 18.5 ≥BMI < 25 kg/m2 and overweight BMI≥25 kg/m2 and obese BMI ≥30 kg/m2 [14].Central obesity was considered as measurements above 102cm and 88cm in men and women respectively. Blood pressure was measured twice for each patient after at least 5 minutes of rest, by the use of a standardized mercury sphygmomanometer. Glycosylated hemoglobin test was done on the spot using the A1CNOW PLUS kits from the Bayer manufacturer and the results were ready in 5 minutes. Poor glycemic control was defined as glycosylated hemoglobin as >7%.
After an overnight fast, approximately 4mls of venous blood was obtained from each patient for lipid profile (TC, TG, LDL-C and HDL-C) analysis. The samples were analyzed using the chemical analyzer COBAS INTEGRA 400 Plus serial NO 397672 in the main Kilimanjaro Christian Medical Centre (KCMC) laboratory. Dyslipidemia was taken as derangement in any of the lipid components: TC> 5.2 mmol/l, LDL-C >2.6 mmol/l, HDL-C < 1.1 mmol/l for males, < 1.38 mmol/l for females and TG > 1.7 mmol/l [15].Data analysis was done using Statistical Package of Social Sciences (SPSS) version16. Analysis of variance (ANOVA), t-test and chi-square test were used. Regression analysis was used to determine the predictors of dyslipidemia. A 95% confidence was used for the determination of significance of probabilities, and p-value < 0.05 was regarded as statistically significant. Patients had to sign the consent form before enrolled in the study. Those found with dyslipidemia were started on lipid lowering agents and advised on the lifestyle changes. Original data and supporting material are available upon request from the publishers.
Of the 119 diabetic patients, 78 (65.55%) were hypertensive and in Eleven (9.2%) patients reported to be smokers and alcohol intake was significant in 34 (28.57%) of the patients. The body mass index (BMI) ranged from 18.8- 45.1 with mean (±SD) of 27.9 (±5.0). About 31 (26%) of the patients had a normal BMI, 56(47%) were overweight and 32(27%) were obese. Among the females, the BMI ranged from18.8- 45.1 with mean (±SD) of 29.4 (±5.5), and in males, the BMI ranged from19.3- 35.5 with mean (±SD) of 26 (±3.8).-Majority of the patients 85 (63.0%) were physically inactive. Among the 119 patients, 86 (63.7%) were on oral hypoglycemic drugs, 34 (25.2%) on insulin only, 9 (6.7%) both insulin and oral hypoglycemic drugs while only 6 (4.4%) were on diet control (Table 1).
Diabetic dyslipidemia was found in (94.1%) patients. The pattern of lipid abnormalities according to sex, age, BMI and central obesity are presented in (Table 2). High TG, high LDL-C, high TC and low HDL-C exhibited an increasing trend in the proportion of patients with dyslipidemia by the BMI and the differences were statistically significant (p< 0.05). A similar trend was observed in the patients with central obesity compared to those without. The difference was statistically significant for high TC and low HDL-C and the overall dyslipidemia (p< 0.05).
The following risk factors namely female sex, age above 50- years, BMI (overweight and obese), poor glycemic control, central obesity and physical inactivity were associated with diabetic dyslipidemia among diabetic patients attending the diabetes clinic, the p-values were statistically significant (Table 3). Other variables namely duration of diabetes mellitus, the type of diabetes mellitus, smoking habits and hypertension were not statistically significant in the association of dyslipidemia.
By multivariate logistic regression analysis, significant predictor for dyslipidemia among the diabetic patients was overweight and obesity (BMI >25kg/m2), with p-value of 0.040, OR (95%CI) 0.2 (0.1-0.9).
Variable |
Attribute |
No. (%) |
Sex: |
Females |
59(49.6) |
Males |
60 (50.4) |
|
Age (years): |
Mean (±SD, range) |
58.1 (±12.2, 27-83) |
Females |
54.3 (±13.1, 27-83) |
|
Males |
62.4(±9.6, 35-79) |
|
Younger than 50 |
30 (22.2) |
|
50 - 70 |
86 (63.7) |
|
Older than 70 |
19 (14.1) |
|
Type II DM |
Type II |
119 (88.1) |
Duration of DM (years): |
Mean (SD, range) |
9.4 (6.6, 0-28) |
Up to 5 |
44 (32.6) |
|
More than 5 |
91 (67.4) |
|
Glycemic control |
Good <7% |
53 (39.3) |
Poor >7% |
82 (60.7) |
|
Compliance on treatment: |
Good |
77 (65) |
Poor |
42 (35) |
|
Hypertension |
Yes |
78 (65.5) |
Smoking |
Yes |
11 (9.2) |
Alcohol Intake |
Yes |
34 (28.6) |
Body Mass Index (kg/m2) |
Mean (±SD, Range) |
27.9 |
18.5 - 24.9 |
31 (26) |
|
25.0 - 29.9 |
56 (47) |
|
30 or above |
32 (27) |
|
Physical activity |
Inactive |
85 (71.43.) |
Active |
34 (28.57) |
Variable |
Total |
High TG |
High LDL |
High TC |
Low HDL |
Overall |
No. (%) |
No. (%) |
No. (%) |
No. (%) |
No. (%) |
||
Sex |
||||||
Female |
59 |
34 (47.2) |
52 (72.2) |
36(56.2) |
41 (70.8) |
64 (88.9) |
Male |
60 |
30 (47.6) |
34 (54.0) |
23(47.9) |
34 (63.9) |
48 (76.2) |
p-value |
0.963 |
0.028 |
0.382 |
0.728 |
0.05 |
|
Age (years) |
||||||
< 50 |
22 |
12 (40.0) |
14 (46.7) |
9 (30.0) |
15 (50.0) |
21 (70.0) |
50 or older |
97 |
52 (49.5) |
72 (68.6) |
50(47.6) |
60 (57.1) |
91 (86.7) |
p-value |
0.357 |
0.028 |
0.086 |
0.487 |
0.032 |
|
Body mass index (kg/m2) |
||||||
Normal |
31 |
12 (30.8) |
13 (33.3) |
10(25.6) |
13 (33.3) |
24 (61.5) |
Overweight/obese |
88 |
52 (54.2) |
73 (76.0) |
49(51.0) |
62 (64.6) |
88 (91.7) |
p-value |
0.014 |
<0.001 |
0.007 |
0.001 |
<0.001 |
|
Waist circumference |
||||||
Central obesity |
68 |
41 (53.9) |
54 (71.1) |
39(51.3) |
52 (68.4) |
70 (92.1) |
No central obesity |
51 |
23 (39.0) |
32 (55.9) |
20 (33.9) |
23 (39.0) |
42 (71.2) |
p-value |
0.084 |
0.069 |
0.043 |
0.001 |
0.001 |
Variable |
Univariate analysis |
Multivariate analysis |
||
OR (95% CI) |
p-value |
OR (95% CI) |
p-value |
|
Sex: |
0.4 (0.2-1.0) |
0.05 |
0.4 (0.1-1.7) |
0.233 |
Age (years): |
0.4 (0.1-1.0) |
0.032 |
0.5 (0.1-2.2) |
0.326 |
Body mass index (kg/m2) |
0.1 (0.1-0.4) |
<0.001 |
0.2 (0.1-0.9) |
0.04 |
Glycemic control |
3.0 (1.2-7.3) |
0.02 |
1.0 (0.2-3.6) |
0.927 |
Waist Circumference |
0.2 (0.1-1.0) |
0.001 |
0.1 (0.2-2.5) |
0.525 |
Physical activity |
3.3 (1.1-10.4) |
0.032 |
1.0 (0.2-3.2) |
0.695 |
This study showed a high prevalence of dyslipidemia with the commonest lipid abnormality being elevated LDL-C (64%) followed by low HDL-C (56%). Similar findings were also noted in the Third US National Health and Nutritional Examination Survey and the Behavioral Risk factors Surveillance System where majority DM patients had LDL-C of 58% [19] in Gaborone [20] and in Ghana [21]. Reasoning to this is that within the African ethnicity, African-Americans, it has been observed that the elevated LDL-C levels are a common subclass of dyslipidemia compared with other subclasses [16].
Different observations from other studies where total cholesterol to be the common subclass in dyslipidemia. In Dar es Salaam, Chattanda observed that among the subclasses of dyslipidemia, 95% had high triglycerides [12]. In Kenya, over 70% of the study participants had high total cholesterol levels [22] as well as in Jordan [23]. The observed differences in the subclasses of dyslipidemia could be explained by the difference in ethnicity where Indians who tend to have elevated levels of total cholesterol, presented in the diabetic clinics. Majority of Indians reside in the urban regions within the big cities in Africa. In this case, the different cut off levels in classifying dyslipidemia could have also contributed to the difference. The high frequency of LDL-C among the female patients over 50 years, was over 90% in this study. It can be explained by the fact that over half of the women involved in our study were in the post-menopausal years who tend to lose the protective effect of estrogen on lipid metabolism. Thus this leads to higher prevalence of LDL-C in women compared to men, also mostly observed within the black race. On the other hand, Chattanda did not find any difference between males and females [12]; this could have been due to similar lifestyles in the study population and the racial differences. This is comparable to a study done in Pakistan, where they found that females above 60 years were more likely to have dyslipidemia [24].
The increase in BMI (;25.0kg/m2) was an independent determinant of dyslipidemia among the diabetic patients in this study, which was strongly associated with the prevalence of dyslipidemia. Majority of the patients also had central obesity over 90%. Similarly in Gaborone [20] and South-East Nigeria [10] showed that overweight and obesity was strongly associated with elevated plasma lipid levels. A report in the National Health and Nutrition Examination Survey, 1999-2004, showed that the prevalence of dyslipidemia substantially increases with increased BMI [14]. Despite the gender differences within our study population, it was clearly seen that an increase in BMI among diabetic patients is the strongest factor in the development of dyslipidemia. Physical inactivity mostly observed among the elderly patients due to arthritis could be another contributory factor of overweight and obesity as well. In contrast to the study done in Dar es Salaam, where poor glycemic control was the sole determinant leading to the development of dyslipidemia [12]. The difference could be explained by the fact that most of the diabetic patients were on medications, mostly oral hypoglycemic drugs, majority having a good compliance henceforth, such treatment reduces the blood glucose level and most probably alters the overall diabetic dyslipidemia pathophysiology.
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