Research Article Open Access
Prevalence of Dyslipidemia and The Associated Factors Among Type 2 Diabetes Patients in Turbo Sub-County kenya
Sang Victor Kiplagat1*, Kaduka Lydia2, Kamano Jemimah3 and Makworo Drusilla1
1College of Health Sciences, Institute of Tropical Medicine and Infectious Diseases, Jomo Kenyatta University of Agriculture and Technology.
2Centre for Public Health Research, Kenya Medical and Research Institute (KEMRI).
3College of Health Sciences, Department of Medicine, Moi University
*Corresponding author: Sang Victor Kiplagat, B. Sc, M. Sc Public Health Student/Microbiologist, College of Health Sciences, Institute of Tropical Medicine and Infectious Diseases, Jomo Kenyatta University of Agriculture and Technology (JKUAT).P.O Box 62000-00200, Nairobi, Kenya. Tel: +254728217607; E-mail: @
Received: November 30, 2017; Accepted: December 13, 2017; Published: December 21, 2017
Citation: Sang V. K, Kaduka L., Kamano J., Makworo D. (2017) Prevalence of Dyslipidemia and The Associated Factors among Type 2 Diabetes Patients in Turbo Sub-County, Kenya. J Endocrinol Diab 4(5): 1-9. DOI: 10.15226/2374-6890/4/5/00190
Abstract
Background: A large number of deaths worldwide are attributed to non-communicable diseases (NCDs). Diabetes, an important NCD, contributes to this large mortality mainly through cardiovascular complications. Cardiovascular disease in diabetes is caused by multiple co-morbid conditions; key of which is dyslipidemia.

Objectives: This study aimed to determine prevalence of dyslipidemiaand its associated factors among patients with type 2 diabetes mellitus attending Chronic Disease Management clinics (CDM) in Turbo sub-county, Kenya.

Methodology: This was a cross sectional study conducted between 2015 and 2016 at Huruma County hospital and Turbo health centre CDM clinics. Data was collected from 208 randomly selected fasting participants using: structured questionnaires; laboratory investigations (lipid profile and fasting blood sugar); and health records. Data was analyzed using SAS 9.2. All variables at p ≤ 0.2 level of significance in the univariate analysis were included in the multivariate model. Using backward elimination criteria, variables that had a p value of < 0.05 were retained.

Results: A total of 179 out of 208 (86.1%) patients had dyslipidemia. Employment status [OR 3.1; (95% CI 1.3-7.5); p=0.01], BMI [OR 2.7; (95% CI 1.3-5.9); p=0.0007], FBS [OR 3.4; (95% CI 1.6-7.1); p=0.001] and physical activity [OR 4.8; (95% CI 1.1-21.2); p=0.04] were significantly associated with dyslipidemia. Surprisingly, age and being hypertensive were not associated with occurrence of dyslipidemia although the condition was more prevalent in elderly patients and those with elevated blood pressure.

Conclusion: There is a high prevalence of dyslipidemia amongst patients with T2DM in the two CDM clinics studied. Employment status, BMI, FBS and physical activity are important factors associated with dyslipidemia in these patients.There is need to prioritize research driven control and management of dyslipidemia, diabetes and related CVD risk factors plus more vigorous patient education on importance of physical activity. This should be done at both the national level and county level with government and society playing the role.Given the failure to show any association of dyslipidemia with historical CVD risk factors such as age and blood pressure, it is imperative that screening for lipids be done in all diabetes patients routinely.

Keywords: Dyslipidemia ; Type 2 Diabetes;

Abbreviations: AMPATH: Academic Model Providing Access To Healthcare; BMI: Body mass index; BP: Blood pressure; CDM: Chronic diseases management; CVDs: Cardiovascular diseases; HbA1c: Glycated hemoglobin; HDL-C: High density lipoprotein cholesterol; JKUAT: Jomo Kenyatta University of Agriculture and Technology; LDL-C: Low density lipoprotein cholesterol; MET: Metabolic equivalent; MI: Myocardial infarction; MOH-K: Ministry of Health-Kenya; MTRH: Moi Teaching and Referral Hospital; NCDs: Non-Communicable Diseases; T2DM: Type 2 diabetes mellitus; TC: Total cholesterol; TG: Triglycerides; WHO: World Health Organization
Introduction
A large number of deaths worldwide are attributed to non-communicable diseases (NCDs) [1]. Diabetes, an important NCD, contributes to this large mortality mainly through cardiovascular complications [2, 3]. Type 2 diabetes mellitus (T2DM) is the most common form of diabetes and makes up about 90% of global diabetes cases, with the other 10% due primarily to type 1 diabetes mellitus and gestational diabetes. The burden of diabetes in the world is estimated to be 9% among adults aged 18 years and above [4]. A healthy diet, regular physical activity, maintaining normal body weight and avoiding tobacco use can prevent or delay the onset of type 2 diabetes [5]. Cardiovascular disease in Diabetes is caused by multiple co-morbid conditions; key of which is Dyslipidemia. Other cardiovascular diseases that include coronary heart diseases, stroke, and peripheral vascular diseases account for the majority of deaths in diabetic patients [6]. It is noted that most people with diabetes do not die of causes uniquely related to diabetes, but to cardiovascular complications that are caused by risk factors including Dyslipidemia. Dyslipidemia is defined as an abnormal lipid profile characterized by high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), low high-density lipoprotein cholesterol (HDL-C) and high triglycerides (TG). For diabetic patients the targets are: LDL < 100mg/dl (2.6mmol/l), HDL > 40mg/dl (1.02mmol/l) and TG < 150mg/dl (1.7mmol/l) [7].

Coronary artery disease, especially myocardial infarction (MI) is also among the leading causes of morbidity and mortality worldwide [8]. The World Health Organization estimated that Dyslipidemia is associated with more than half of the global cases of ischaemic heart disease and more than 4 million deaths annually [9]. Dyslipidemia has emerged as an important cardiovascular risk factor in sub-Saharan Africa. Research shows that high cholesterol level (≥3.8 mmol/l) accounted for 59% of ischemic heart disease and 29% of ischemic stroke burden in adults age 30 and over. Dyslipidemia, especially elevated cholesterol has been shown to vary across regions in sub-Saharan Africa [10]. According to Kenya STEPWise Survey for NCDs Risk Factors 2015 report [11], mortality attributed to CVD in Kenya is reported to be 6.1% to 8%. The latest Kenya STEPWise Survey for NCDs Risk Factors (2015) report, estimated the prevalence of elevated TC to be 1.5% of Kenya’s population while low HDL levels were 50% and 60% for males and females respectively. Despite this, Dyslipidemia levels among T2DM patients attending primary care centers in Kenya are still unclear. A previous hospital based study found a 70% prevalence of elevated levels of TC and TG requiring therapeutic intervention amongst T2DM patients with no obvious chronic complications [12]. A recently concluded study in Tanzania indicates a prevalence of 83% amongst T2DM patients with BMI reported to be an important predictor [13]. To date diabetes management in primary setting has focused on glycemic control at the detriment of holistic management of all CVD risk factors in these patients. Additionally, there is a gap in data on prevalence of Dyslipidemia among T2DM patients in primary care settings in the country. Identification of predictors of Dyslipidemia in T2DM would be an important first step in designing a locally appropriate way to address the CVD risk factor.

We set out to determine the prevalence of Dyslipidemia and describe the associated factors among type 2 diabetes patients attending Chronic Disease Management clinics in Turbo sub-county, Kenya.
Methods
Study design and setting
This was a cross sectional study conducted at Turbo health center and Huruma county hospital CDM clinics in Turbo sub-county which is one of the six sub-counties in Uasin Gishu County. Turbo health center is located 34 km north-west of Eldoret town along the Eldoret-Webuye road and serves the rural population of Turbo sub-county. It currently serves diabetes patients in its Comprehensive Care Center (CCC). Huruma County hospital is 6.5km north-north-west of Eldoret town along Eldoret-Kitale road and serves the urban population. These are public facilities managed by Ministry of Health with same AMPATH support for its CCC; to offer both HIV and diabetes care. They are among the earliest in the country to be supported by AMPATH on MOH partnership organization for HIV care and later diabetes and hypertension care in level 3.
Recruitment of participants
The study population was adult diabetes patients attending the CDM clinics. Inclusion criteria were adults over 35 years of age who had confirmed diabetes. Exclusion criterion was all patients diagnosed with diabetes mellitus before the age of 35. Patients attending the two CDM clinics and met inclusion criteria were approached for recruitment.
Data collection
Data was collected from September 2015 to December 2015 at the two CDM clinics.

Data collected from all participants who met the inclusion criteria included socio-demographic; age, sex, level of education, occupation/employment status, monthly income and residence. Clinical data collected include blood pressure (BP), fasting blood sugar (FBS) weight and height which were measured using Omron M2 intelligence automatic blood pressure monitor, accu-chekperfoma, seca weighing scale and height stature meter 2M respectively.

Other clinical data recorded were duration since DM diagnosis, personal and family history of cardiac disease. Behavioral/practice data included physical activity levels, sedentary behavior; determined using GPAQ self-administered questionnaire level i.e. Metabolic Equivalent Minutes per week (METmis/week). Clinic attendance, alcohol consumption, cigarette smoking, adherence to medication and dietary advice were also collected.

Laboratory investigations were done at Moi Teaching and Referral Hospital (MTRH) to determine fasting lipid profile using COBAS Integra 400plus. All participants were given a unique code number alongside their questionnaire for easy identification and tracking on the clinical and laboratory results. Data was entered into a Microsoft Excel database. Data cleaning was done at the end of data collection analysis.
Statistical analyses
• Dependent variable: was Dyslipidemia which was defined as; Total cholesterol >5.2mmol/dl (200mg/dl), and or increased LDL cholesterol >2.6mmol/L (130mg/dl), and or decreased HDL cholesterol < 0.9 mmol/l (35mg/dl) and or triglycerides >1.7mmol/l (150mg/dl) [6].

• Independent variables: were age, sex, level of education, employment status, monthly income, marital status, residence, BP, FBS, BMI, physical activity (METmins/week), sedentary behavior, clinic attendance, alcohol consumption, cigarette smoking, family and personal cardiac history.

Analyses were done using SAS 9.2. Descriptive statistics were done to describe distribution of Dyslipidemia against all variables. Univariate analysis was done to assess for association with Dyslipidemia, and factors found to be associated by the way of p ≤ 0.2 were subjected to multivariate analysis. Using backward elimination criteria, those variables that attained p< 0.05 on the multivariate logistic regression were considered statistically significant.

Comparisons of occurrence of Dyslipidemia were done between males and females.
Results
Participants’ characteristics
Socio-demographic and economic characteristics: The mean age of the participants was 58.4years (sd =11.4) The majority (64%) were female, had attained primary level education or below (62%) , were of low social-economic status as 66% earned a monthly income of Kshs < 15,000 (66%) and lived in rural areas (71%) (Table 1).
Table 1: Socio-demographic and economic characteristics of T2DM patients in Turbo Sub-County

Participant Characteristics

Unit (s)

No. (%) N=208

Age group

35-49

40 (19)

50-64

106 (51)

65+

62 (30)

Gender

Male

75 (36)

Female

133 (64)

Level of Education

Non e

34 (16)

Primary

95 (46)

Secondary

62 (30)

College & University

17 (8)

Occupation

Unemployed

37 (18)

Business person

56 (27)

Farmer

79 (38)

Employed

23 (11)

Retired

13 (6)

Monthly Income (Kshs)

≤15,000

138 (66)

>15,001

70 (34)

Marital status

Single

17 (8)

Married

157 (75)

Previously married

34 (17)

Residence

Urban

60 (29)

Rural

148 (71)

Clinical characteristics:
Prevalence of Dyslipidemia and other CVD risk factors: Among all participants, majority (86%) had Dyslipidemia, (61%) had been diagnosed with diabetes mellitus within the past 5 years, had hypertension (67%). Overweight and obesity was noted in 65%; while fasting blood sugar was suboptimal (FBS above the recommended 7.0mmol/L) in 75% of them. Despite the significant history of cardiac disease in 51% of the participants, and high rates of multiple CVD risk factors in the study, 63% reported only fair or poor medications’ adherence (Table 2a).

Behaviour and practices: Majority (99%) of the participants reported having received dietary advice on the management of their illness. However, 38% did not always adhere to the dietary advice they had received. Fortunately, only a minority consumed alcohol (10%) or smoked tobacco (9%). Additionally, 77% achieved physical activity levels (≥600 Met mins/week) albeit, over half of the participants also spent more than 3 hours on sedentary behavior. Almost half (46%) had an adherence index of 5, which implies that patients only reported one or more but not all of the following: always strictly takes medication, takes the right amount of medicine, takes medication as prescribed by the doctor, visits his doctor as scheduled and follows his doctor’s or nurse’s advice (Table 2b).
Univariate analyses of factors associated with Dyslipidemia :
On univariate analysis, sex, employment status, marital status, level of education, BMI, BP, FBS, physical activity and adherence to medication were significantly associated with Dyslipidemia (attained a p ≤ 0.2) (Table 3).
Multivariate analyses of factors associated with Dyslipidemia:
Multivariate logistic regression was performed for variables that attained p≤0.2 on univariate section. Employment status [OR 3.1; (95% CI 1.3-7.5); p=0.01], BMI [OR 2.7; (95% CI 1.3-5.9); p=0.0007], FBS [OR 3.4; (95% CI 1.6-7.1); p=0.001] and physical activity [OR 4.8; (95% CI 1.1-21.2); p=0.04] remained significantly associated with Dyslipidemia (Table 4).
Discussions
This study found out 86% of the patients with diabetes in the primary care setting had dyslipidemia. This was similar to findings in India where 86% and 89% dyslipidemia prevalence were reported [14, 15]. The current study prevalence was lower than findings in Tanzania 95% [16] and Pakistan 94% [17] but higher compared to those done in Nigeria 74% [18]. This difference may be due to the variation in cut-offs for dyslipidemia in these different studies. And difference in stage of urbanization in the various settings. [19]. A third of participants had insufficient amount of physical activity which is similar to previous findings [20]. Dyslipidemia was more prevalent in females than in males which is consistent with a study in the Middle East that found females to be more dyslipidemic [21]. Despite short period since diagnosis of diabetes, majority had dyslipidemia and multiple CVD risk. This finding was similar to previous study that found that T2DM patients compared with non-diabetic people have increased cardiovascular risk [22]. Part of this is associated with a higher prevalence of other cardiovascular risk factors like obesity and hypertension [23].

Employment status, BMI, fasting, FBS and insufficient physical activity (MET mins/week< 600) were important factors associated with occurrence of dyslipidemia. Working was found to be significantly associated with dyslipidemia. This is in concordance with a previous study that found dyslipidemia to be associated with occupation [24] which may have been from lack
Table 2a: Clinical characteristics: Prevalence of dyslipidemia and other CVD risk factors among T2DM patients in Turbo Sub-County

Participant Characteristics

Unit (s)

No. (%) N=208

Male
N=75 (36%)

Female
N=133 (64%)

P value

Dyslipidemia

Present

179 (86)

60 (33)

119 (67)

0.05*

Absent

29 (14)

15 (52)

34 (48)

Blood pressure

Normal

68 (33)

23 (34)

45 (66)

0.64

Elevated

140 (67)

52 (37)

88 (63)

BMI category

Underweight

3 (1)

1 (33)

2 (67)

0.39

Normal weight

70 (34)

30 (43)

40 (57)

Overweight

81 (39)

29 (36)

52 (64)

Obese

54 (26)

15 (28)

39 (72)

Fasting Blood Sugar

<7mmol/L

51 (25)

22 (43)

29 (57)

0.22

≥7mmo/L

157 (75)

53 (34)

104 (66)

Family history of cardiac disease

Present

81 (39)

35 (43)

46 (57)

0.09

Absent

127 (61)

40 (32)

87 (68)

Personal history of cardiac disease

Present

106 (51)

37 (35)

69 (65)

Absent

102 (49)

38 (37)

64 (63)

Duration since DM diagnosis

1-4 years

127 (61)

47 (37)

80 (63)

0.92

5-9 years

42 (20)

15 (36)

27 (64)

10+  years

39 (19)

13 (33)

26 (67)

||Normal refers to “systolic < 140mmHG & Diastolic < 90 mmHg) & not on anti-hypertensive medication” while Elevated refers to Systolic ≥140mmHG or Diastolic ≥90mmHg or on anti-hypertensive’s
*significance as p< 0.05
Table 2b: Behavior and practices of T2DM patients in Turbo Sub-County

Participant Characteristics

Unit (s)

No. (%) N=208

Received dietary management advice

Yes

207 (99)

No

1 (1)

Adhere to dietary advice

Always

130 (62)

Not always

78 (38)

Alcohol consumption

No

188 (90)

Did but stopped

17 (8)

Yes

3 (2)

Smoke(d) tobacco

No

190 (91)

Did but stopped

14 (7)

Yes

4  (2)

Received physical activity advice

Yes

208 (100)

Physical activity (MET mins/week)

≥ 600

160 (77)

<600

48 (23)

Sedentary behaviour

≥3 hours/day sitting/reclining

112 (54)

<3 hours/day sitting/reclining

96 (46)

Level of adherence to medication

Always

77 (37)

Adherence index

4.0-5.0

95 (46)

1.0-3.0

113 (54)

Clinic Attendance

Always

146 (70)

||an adherence index of 5 refers to a patient who always strictly takes his medication, takes the right amount of medicine, takes medication as prescribed by the doctor, visits his doctor as scheduled and follows his doctor’s or nurse’s advice. A lower adherence index refers to one or more (but not all) of the above 5 combinations
Table 3: Univariate analyses of factors associated with dyslipidemia among T2DM patients in Turbo Sub-County

Variables

Participant Characteristics

No. (%) N=208

Dyslipidemia¥
N=179 (86)
N(%)

COR (CI)

P values

Socio-demographic and economic

Age group

 35-49

 40 (19)

33 (83)

Ref

0.8

 50-64

106 (51)

92 (86)

1.3 (0.5-3.7)

 65+

62 (30)

54 (87)

1.4 (0.5-4.3)

Gender

Male

75 (36)

60 (80)

Ref

0.05

Female

133 (64)

119 (89)

2.1 (1.0-4.7)

Level of Education

Primary and below

129 (62)

115 (89)

Ref

0.2

Secondary and above

79 (3811)

64 (81)

0.5 (0.2-1.1)

Employment status **

Working

159 (76)

140 (89)

2.2 (0.9-5.0)

0.06

Not working

50 (24)

39 (78)

Ref

Monthly Income in Kshs

≤15,000

138 (66)

118 (86)

0.8 (0.4-2.0)

0.7

>15,001

70 (34)

61 (87)

Ref

Marital status

Single

17 (8)

14 (82)

Ref

0.1

Married

157 (75)

132 (84)

1.1 (0.3-4.2)

Previously married

34 (17)

33 (97)

7.1 (0.7-73.9)

Residence

Urban

60 (29)

53 (88)

1.3 (0.5-3.3)

0.5

Rural

148 (71)

126 (85)

Ref

Clinical

DM Duration

1-4 years

127 (61)

111 (87)

Ref

0.6

5-9 years

42 (20)

34 (81)

0.6 (0.2-1.7)

10+ years

39 (19)

34 (87)

1.6 (0.5-5.3)

Blood pressure

Normal BP

68 (33)

59 (86)

Ref

0.06

Elevated BP

140 (67)

120 (86)

0.9 (0.4-2.0)

BMI***

Underweight Normal

83 (38)

60 (82)

Ref

0.003

Overweight & Obese

135 (65)

119 (88)

1.4 (2.5-5.0)

Fasting blood sugar

<7 mmol/L

51 (25)

41 (80)

Ref

0.2

≥7mmol/L

157 (75)

138 (88)

3.3 (0.7-10.0)

Family history of cardiac disease

Present

81 (39)

71 (88)

1.2 (0.5-2.8)

0.6

Absent

127 (61)

108 (85)

Ref

Personal Cardiac history

Present

106 (51)

91 (86)

0.9 (0.4-2.1)

0.9

Absent

102 (49)

88 (86)

Ref

Behavior and practices

Adhered to dietary advice

Not always

78 (38)

66 (85)

1.2 (0.5-2.7)

0.6

Always

130 (62)

113 (87)

Ref

Alcohol consumption

None

188 (90)

160 (85)

Ref

0.5

Yes

3 (2)

3 (100)

-

Did but stopped

17 (8)

16 (94)

2.8 (0.4-21.9)

Smoke tobacco

No

190 (91)

162 (85)

Ref

0.5

Yes but stopped

14 (7)

13 (93)

2.2 (0.3-17.9)

Yes

4  (2)

4 (100)

-

Sedentary behavior (sitting)

≥3 hours/day

112 (54)

101 (90)

2.1 (0.9-4.7)

0.06

<3 hours/day

96 (46)

78 (81)

Ref

Physical activity (met mins/week)

≥ 600

150 (77)

133 (83)

Ref

0.03

<600

48 (23)

46 (96)

4.7 (1.1-20.4)

Clinic Attendance

Not Always

62 (30)

56 (90)

1.7 (0.7-4.5)

0.21

Always

146 (70)

123 (84)

Ref

Medication adherence level

Not always

131 (63)

116 (88)

1.7 (0.8-3.8)

0.2

Always

77 (37)

63 (82)

Ref

Adherence index

4.0-5.0

95 (46)

80 (84)

Ref

0.5

1.0-3.0

113 (54)

99 (88)

0.8 (0.3-1.7)

** not working included retired and unemployed, working included Self employed includes Business persons and farmers
***Normal and underweight category includes 3 patients who were underweight and none of them had dyslipidemia
an adherence index of 5 refers to a patient who always strictly takes his medication, takes the right amount of medicine, takes medication as prescribed by the doctor, visits his doctor as scheduled and follows his doctor’s or nurse’s advice. A lower adherence index refers to one or more (but not all) of the above 5 combinations
Table 4: Multivariate analyses of factors associated with dyslipidemia among T2DM patients in Turbo Sub-County

 

Participant Characteristics

Dyslipidemia/Total
179/208 (86.1%)
n/N (%)

COR
(CI)

P value

AOR
(95% CI)

P value

Socio-demographic & economic

Age group

 35-49

 33/40 (83)

Ref

0.8

 

 

 50-64

92/106 (86)

1.3 (0.5-3.7)

 

 

 65+

54/62 (13)

1.4 (0.5-4.3)

 

 

Sex

Male

60/75 (80)

Ref

0.05*

Ref

0.11

Female

119/133 (89)

2.1 (1.0-4.7)

 

 1.9 (0.9-4.6)

Level of Education

Primary and below

115/129 (89)

Ref

0.1

 

 

Secondary and above

64/79 (81)

0.5 (0.2-1.1)

 

 

Employment status **

Working

140/159 (89)

2.2 (0.9-5.0)

0.06

3.1 (1.3-7.5)

0.01*

Not working

39/50 (78)

Ref

 

Ref

Monthly Income (Kshs)

≤15,000

118/138 (86)

0.8 (0.4-2.0)

0.7

 

 

>15,001

61/70(87)

Ref

 

 

Marital status

Single

14/17 (82)

Ref

0.1*

Ref

0.2

Married

132/157 (84)

1.1 (0.3-4.2)

 

1.4 (0.3-5.3)

Previously married

33/34 (97)

7.1 (0.7-73.9)

 

9.2 (0.8-102.0)

Residence

Urban

53/60 (88)

1.3 (0.5-3.3)

0.5

 

 

Rural

126/148 (85)

Ref

 

 

Clinical

DM Duration

1-4 years

111/127 (87)

Ref

0.6

 

 

5-9 years

34/42 (81)

0.6 (0.2-1.7)

 

 

10+ years

34/39 (87)

1.6 (0.5-5.3)

 

 

Blood pressure

Normotensive

59/68 (87)

ref

0.06

 

 

Hypertensive

120/140 (86)

0.9 (0.4-2.0)

 

 

BMI***

Normal &Underweight

60/83 (72)

Ref

0.003*

Ref

0.0007*

Overweight & Obese

119/135 (88)

1.4 (2.5-5.0)

 

2.7 (1.3-5.9)

Fasting blood sugar

<7 mmol/L

41/61 (67)

Ref

0.0003*

Ref

0.001*

≥7mmol/L

138/157 (88)

3.3 (0.7-10.0)

 

3.4 (1.6-7.1)

Family history of cardiac disease

Present

71/81 (88)

1.2 (0.5-2.8)

0.6

 

 

Absent

108/127 (85)

Ref

 

 

Personal history of cardiac disease

Present

91/106 (86)

0.9 (0.4-2.1)

0.9

 

 

Absent

88/102 (86)

Ref

 

 

Behavioral

Physical activity(met mins/week)

≥ 600

133/160 (83)

Ref

0.03

Ref

0.04*

<600

46/48 (96)

4.7 (1.1-20.4)

 

4.8 (1.1-21.2)

Clinic attendance

Not always

56/62 (90)

1.7 (0.7-4.5)

0.21

 

 

Always

123/146 (84)

Ref

 

 

Adhere to dietary advice

Not always

66/78 (85)

1.2 (0.5-2.7)

0.6

 

 

Always

113/130 (87)

Ref

 

 

Level of adherence to medication

Not always

116/131 (89)

1.7 (0.8-3.8)

0.2

1.8 (0.8-4.0

0.1

Always

63/77 (82)

Ref

 

Ref

Adherence index

4.0-5.0

80/95 (84)

Ref

0.5

 

 

1.0-3.0

99/113 (88)

0.8 (0.3-1.7)

 

 

** not working included retired and unemployed, working included Self employed includes Business persons and farmers
***Normal and underweight category includes 3 patients who were underweight and none of them had dyslipidemia
an adherence index of 5 refers to a patient who always strictly takes his medication, takes the right amount of medicine, takes medication as prescribed by the doctor, visits his doctor as scheduled and follows his doctor’s or nurse’s advice. A lower adherence index refers to one or more (but not all) of the above 5 combinations
*significance as p<0.05
of physical activity. Previous studies reported that dyslipidemia occurrence is more prevalent in subjects whose occupation was management/administrative compared to those that doing physical/labour [25], although other studies found no significant association between occupation and dyslipidemia [26]. These differences may have been as a result of confounding factors.

Insufficient physical activity (MET mins/week < 600) was significantly associated with dyslipidemia. This concurs with previous findings that showed a strong dose-response association between exercise intensity and lipids [27]. Physical activity of >600METmins/week is associated with cardiovascular health benefits [28]. In a previous study, intense physical activity was found to be associated with improved lipids [29]. Also intervention study findings showed that increase in physical exercises has the same effect [30, 31]. Sedentary lifestyle has been found to be associated with most cardiovascular risk factors [32].

BMI was also significantly associated with dyslipidemia. This corroborated with previous study that showed excess weight to be associated with increased prevalence of dyslipidemia and metabolic syndrome [33, 34]. Obesity is also a well known determinant of dyslipidemia [35] suggesting that this could add to other existing forces responsible for rising burden of cardiovascular risk factors [36].

FBS was also found to be significantly associated with dyslipidemia. This was in agreement with previous studies in Kuwait [37] in India [38] and in China [39] which found the same association.

Surprisingly, sex, BP and age were not significantly associated with dyslipidemia. The association between sex and dyslipidemia showed no significance although females were more likely to have dyslipidemia compared to males. This was similar to findings in Pakistan [21] and Tanzania[16] that did not find any significant association. However, this contrasted an Ethiopian study [40] that found a significant association. BP showed lack of significance with dyslipidemia but a higher proportion of dyslipidemic patients had elevated BP compared to non-dyslipidemic patients. This therefore joins other studies that try to explain probable existing link between hypertension and abnormal lipids [41, 42]. The study also found that there is unsatisfactory adherence to medication, and to diet despite dietary education but physical activity was generally good. Similar findings of poor diet adherence were reported in India [43] but this was lower compared to findings in US that found 52% followed a meal plan [44]. Regarding physical activity, more than half of T2DM patients were noted to attain required physical activity weekly [45].
Conclusions
The study found a high prevalence of dyslipidemia among T2DM patients attending CDM clinics in Turbo sub-county. This is high earlier on after diagnosis of DM despite good physical activity. Occupation, BMI, FBS and insufficient physical activity are important predictors of dyslipidemia. The alarming burden means that there is need for patient education and practice plan on importance of diet observation, clinical attendance, physical exercises and weight reduction especially to those in occupations that do not involve much physical activity (business persons). There is need to prioritize research driven control and management of dyslipidemia, diabetes and related CVD risk factors. This should be done at both national level and county level with government and society playing the role.
Acknowledgements
This research has been supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through USAID under the terms of Cooperative Agreement No. AID-623-A-12-0001 It is made possible through joint support of the United States Agency for International Development (USAID). The contents of this research are the sole responsibility of AMPATH and do not necessarily reflect the views of USAID or the United States Government.
Ethical considerations
Research proposal development was followed by ethical approval from Institutional Research and Ethics Committee/ Moi university bearing the reference number Ref: 0606. Prior to data collection, permission was sought from AMPATH. The PI was introduced to the facility in charge by the AMPATH program manager officially and to the clinics by the facility in charge. The subjects that met the inclusion criteria were given an information sheet and detailed consent form which they signed voluntarily with assistance from research assistants. The PI had coded the questionnaires to assure anonymity of the subjects hence keeping every detail confidential before the data were safely stored under lock and key. An excel database was created for data entry on a password secured computer.
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