Open Access

Nutritional status and its effect on treatment outcome among HIV infected clients receiving HAART in Ethiopia: a cohort study

AIDS Research and Therapy201613:32

https://doi.org/10.1186/s12981-016-0116-9

Received: 11 December 2015

Accepted: 8 September 2016

Published: 22 September 2016

Abstract

Purposes

The aim of this study was to determine the effects of nutritional status at the start of highly active anti-retroviral therapy on treatment outcomes among HIV/AIDS patients taking HAART at Jimma University Specialized Hospital.

Methods

We performed a retrospective cohort study involving 340 adults who started highly active anti-retroviral therapy. The patients have been clinically followed for 2 years. Data were extracted from paper based medical charts by trained data collectors from January 30 to February 28, 2014 using data collection format. We entered data into Epi data version 3.1 and then exported to SPSS for windows version 21. Predictors of CD4 change were identified using multivariable linear regression model. Time to an event (death) was estimated by Kaplan–Meier and predictors of mortality were identified by Cox proportional hazard model.

Results

Out of 340 patients, 42 patients died during the follow-up. Twenty-five (59.5 %) deaths were from malnourished group. Age, baseline CD4, sex, baseline HAART and marital status were significant predictors of immunologic recovery at different time points. Malnutrition was associated with lower CD4 recovery and greater hazard of death.

Conclusions

Malnutrition tends to decrease CD4 recovery and predisposes patient to early death.

Keywords

Malnutrition CD4 Death Survival Ethiopia

Background

Treatment of HIV-infected patients with highly active antiretroviral therapy (ART) leads to immune reconstitution as shown by increases in CD4 lymphocyte counts, decreased risk of opportunistic infections and improved survival [1, 2]. However, all patients do not have an optimal response to therapy. Some patients have slow and incomplete recovery of immune function and remain at greater risk of developing opportunistic infections and death than those who show more rapid immune reconstitution [3]. Patients may die with an undetectable viral load and adequate CD4 count recovery [2]. Therefore, adjunctive treatments that accelerate the recovery of immune function or that address other related causes of mortality may provide additional gains in survival in patients with HIV starting HAART.

Even though, previous studies showed malnutrition was independent predictor of death in patients taking HAART [48] in different countries, there were conflicting results on impact of malnutrition at HAART initiation on immunologic recovery at different time periods after HAART initiation, some studies showed malnutrition does not prevent an excellent response to HAART [9] while, other suggest poor immunological response [10].

However, no previous study had holistically examined the impact on survival, CD4 recovery and occurrence of opportunistic infections of malnutrition at the time of starting HAART. Furthermore, there were few studies in Africa and no study done in Ethiopia that examined effect of malnutrition at the initiation of HAART on treatment outcome. It is possible that malnutrition may impair the immune response to HAART, prolong the period during which patients are at risk of opportunistic infection and directly or indirectly increasing the risk of death. Malnutrition may therefore, represent a potentially reversible cause of increased mortality in patients who are initiating ART.

Methods

Study design and participants

We conducted retrospective cohort study at Jimma University Specialized Hospital, the only teaching and referral hospital with bed capacity of 450 in the South Western part of the country providing specialized health service for approximately 9000 inpatients and 80,000 outpatients each. The ART clinic of the hospital started providing service to people living with HIV/AIDS (PLWA) in 2002. Since establishment the clinic had 3700 patients following care and treatment [11]. The primary data was collected from September 11, 2006 to September 10, 2011. Data was extracted from the medical record from January 30 to February 28, 2014. The sample size was calculated by single proportion formula used for cohort studies, which assumes proportion of mortality in malnourished group to be 61.8 % and proportion of mortality in well-nourished group to be 46.8 with 95 % confidence interval, 80 % power and 1:1 ratio of unexposed versus exposed. The sample size calculated was 340 patients; one hundred seventy (170) patients in both malnourished and well-nourished groups. The medical records of adult patients who started HAART between September 2006 and September 2011 were isolated. The isolated medical charts were categorized into malnourished and well-nourished groups based on their BMI at the start of HAART. Malnutrition was defined as a BMI <18.5, while BMI ≥18.5 was defined as a well-nourished as per WHO criteria. All patients whose age was greater than 14 were included in the study. Pregnant women’s (BMI and nutrient metabolism vary during pregnancy), patients with incomplete data on weight, height and outcome variables, transferred-out during follow-up were excluded from the study. The data were collected by clinically trained data collectors using data collection tool adapted from national ART clinic intake form, ART follow up form and anti-retroviral drugs and patient information sheet. The endpoint of this study was death. The patients were followed until the occurrence of event (death) or until 2 years (end of study). Patients who were alive at the end of study were censored. The survival time was calculated in days using date of starting treatment and date of an event or date censored.

Statistical methods

The data was cleaned, edited and completed using Epi data version 3.1 and exported to SPSS for window version 21 for analysis. The main analysis in this study was linear regression for CD4 recovery and survival analysis for death. Variables with p value of <0.25 in bivariate analysis and BMI were fitted to multivariable cox-proportional analysis model to identify independent predictors of CD4 change at all time points. Kaplan–Meier survival analysis was done to estimate the survival time. Log-rank test was used to compare the KM curves for two or more categories of patients on HAART. Predictors of event (death) over a period of time t, was analyzed by Cox proportional hazard model. The level of significance was set at p value less than 0.05 and 95 % confidence intervals (CI) were used throughout. Multi-collinerity was checked for independent variables before fitting into multivariable analysis.

Results and discussion

Results

Baseline characteristics

We followed all patients for 2 years. There were a total of 42 deaths during the follow-up period. Twenty-five (59.5 %) deaths were from malnourished group. The mean age of the study participants was 34 [IQR 26–38] years. The median age was 30 years and 200 (58.8 %) patients were females. Mean baseline BMI was 1.50 [IQR 1.0–2.00] kg/m2. The median baseline CD4 count was 144.5 [IQR 89–209] cells/mm3 (Tables 1, 2).
Table 1

Baseline demographic characteristics and associated mortality and opportunistic infections of 340 HIV-infected patients in Jimma University Specialized Hospital, from January 2006 to December 2011

Characteristics

Number of patients (%)

Number of deaths (%)

Number of OI (%)

Sex

 Male

140 (41.2)

19 (45.2)

38 (45.8)

 Female

200 (58.8)

23 (54.8)

45 (54.2)

BMI

 <18.5

170 (50)

25 (59.5)

55 (66.3)

 ≥18.5

170 (50)

17 (40.5)

28 (33.7)

Religion

 Orthodox

203 (59.7)

29 (69)

44 (53)

 Muslim

100 (29.4)

13 (31)

26 (31.3)

 Protestant

31 (9.1)

0

11 (13.3)

 Catholic

2 (0.6)

0

1 (1.2)

 Others

2 (0.6)

0

0

Age (years)

 <30

174 (51.2)

21 (50.0)

46 (55.4)

 30–39

94 (27.6)

11 (26.2)

21 (25.3)

 40–49

57 (16.8)

6 (14.3)

12 (14.5)

 >50

15 (4.4)

4 (9.5)

4 (4.8)

Marital status

 Single

43 (12.6)

7 (16.7)

10 (12.0)

 Married

116 (34.1)

16 (38.1)

36 (43.4)

 Widowed

22 (6.5)

1 (2.4)

5 (6.0)

 Divorced

44 (12.9)

8 (19.0)

9 (10.8)

Occupation

 Gov’t employee

98 (28.8)

14 (33.3)

19 (22.9)

 Merchant

5 (1.5)

0 (0)

2 (2.4)

 Unemployed

189 (55.6)

25 (59.5)

51 (61.4)

 Private org

37 (10.9)

2 (4.8)

10 (12.0)

 NGO’s

5 (1.5)

1 (2.4)

0 (0)

Educational status

 Not educated

69 (20.3)

4 (9.5)

24 (28.9)

 Primary

122 (35.9)

17 (40.5)

25 (30.1)

 Secondary

106 (31.2)

14 (33.3)

21 (25.3)

 Tertiary

42 (12.4)

7 (1.7)

12 (14.4)

Table 2

Baseline clinical characteristics and associated mortality and opportunistic infections of HIV-infected patients in Jimma University Specialized Hospital, between January 2006 and December 2011

Characteristics

Number of patients (%)

Number of deaths (%)

Number of OI (%)

WHO clinical stage

 Stage I

65 (19.1)

6 (14.3)

15 (18.1)

 Stage II

95 (27.9)

9 (21.4)

15 (18.1)

 Stage III

144 (42.4)

17 (40.5)

44 (53.0)

 Stage IV

36 (10.6)

10 (23.8)

9 (10.8)

HAART regimen

 Stavudine based

219 (64.4)

19 (45.2)

60 (72.3)

 Zidovudine based

72 (21.2)

12 (28.6)

18 (21.7)

 Tenofovir based

49 (14.4)

11 (26.2)

5 (6.0)

Base line CD4

 >350

5 (1.5)

0 (0.0)

2 (2.4)

 200–350

94 (27.6)

7 (16.7)

19 (22.9)

 100–199

139 (40.9)

15 (35.7)

37 (44.6)

 <100

102 (30.0)

20 (47.6)

25 (30.1)

Risky behaviour

 Yes

120 (35.3)

14 (33.3)

58 (69.9)

 No

220 (64.7)

28 (67.7)

25 (30.1)

Cotrimoxazole prophylaxis

 Yes

330 (97.1)

42 (100.0)

81 (97.6)

 No

10 (2.9)

0 (0.0)

2 (2.4)

Fluconazole prophylaxis

 Yes

20 (5.9)

8 (19.1)

5 (6.0)

 No

320 (94.1)

34 (80.9)

78 (94.0)

INH prophylaxis

 Yes

62 (18.2)

7 (16.7)

20 (24.1)

 No

278 (81.8)

35 (83.3)

63 (75.9)

Immunologic recovery

Immunologic recovery at 6 months
Age was the only independent predictor of immunologic recovery. For 1 year increase in the age of the patient, CD4 decreases by 3.40 cells/mm3 (p = 0.003). CD4 count of malnourished group decreases by 12.40 cells/mm3 (p = 0.500) compared to well-nourished group (Table 3).
Table 3

Multivariate predictors of CD4 change at 6 months in HIV-infected patients at Jimma University Specialized Hospital, January 2006 to December 2011

Variables

Number

B [95 % CI]

p value

Age

340

−3.4 [−5.5, −1.2]

0.003

Marital status

 Single

43

13.7 [−61.7, 89.2]

0.720

 Married

116

57.2 [−4.2, 118.7]

0.068

 Divorced

44

3.5 [−66.1, 73.1]

0.922

 Widowed

22

Reference

 

BMI

 <18.5

170

−12.4 [−48.8, 23.9]

0.5

 ≥18.5

170

Reference

 

Baseline co morbidity

 Yes

20

25.6 [−40.3, 91.4]

0.445

 No

320

Reference

 

CD4 count

 <100

5

126.9 [−13.8, 267.6]

0.077

 100–199

94

150.1 [10.2, 290.0]

0.036

 200–350

139

107.2 [−34.3, 248.7]

0.137

 >350

102

References

 
Immunologic recovery at 12 months
Age and baseline CD4 count were independent predictors of immunologic recovery. For 1 year increase in the age of the patient CD4 decreases by 1.91 cells/mm3 (p = 0.005). CD4 count of patients with baseline CD4 count in the range of 100–199 increases by 201.29 cells/mm3 (p = 0.047) compared to patients with baseline CD4 count greater than 350. CD4 count of malnourished patients decreases by 21.5 cells/mm3 (p = 0.321) compared with well-nourished group at 12 months (Table 4).
Table 4

Multivariate predictors of CD4 change at 12 months in HIV-infected individuals at Jimma University Specialized Hospital, January 2006 to December 2011

Variables

Numbers

B [95 % CI]

p value

Age

340

−1.91 [−3.238, −0.582]

0.005

Sex

 Female

200

Reference

 

 Male

140

−38.67 [−81.973, 4.633]

0.080

BMI

 <18.5

170

−21.54 [−64.197, 21.109]

0.321

 ≥18.5

170

Reference

 

HAART regimen

 Stavudine based

219

25.57 [−34.277, 85.420]

0.401

 Zidovudine based

72

−17.53 [−87.25, 52.190]

0.621

 Tenofovir based

49

Reference

 

Baseline CD4

 <100

5

188.75 [−9.859, 387.359]

0.062

 100–199

94

201.29 [2.916, 399.661]

0.047

 200–350

139

133.33 [−64.785, 81.773]

0.186

 >350

102

Reference

 

WHO stage

 Stage 1

65

Reference

 

 Stage 2

95

−15.96 [−78.139, 46.212]

0.614

 Stage 3

144

24.17 [−33.426, 81.773]

0.179

 Stage 4

36

−63.89 [−157.331, 29.548]

0.409

Educational status

 Not educated

69

Reference

 

 Primary

122

52.75 [−4.906, 110.408]

0.073

 Secondary

106

40.11 [−18.174, 98.400]

0.177

 Tertiary

42

40.113 [−76.277, 83.098]

0.933

Immunologic recovery at 24 months
In multivariate linear regression analysis age, sex, marital status and baseline HAART were significant predictors of immunologic recovery at 24th month. For 1 year increase in age of the patients, CD4 increases by 2 cells/mm3 (p = 0.005). Females had increased CD4 count by 104.3 cells/mm3 compared to males. Married patients had 138.56 cells/mm3 (p = 0.005) higher CD4 count compared to widowed patients. CD4 count of patients who started treatment with Stavudine based regimen increases by 87.49 cells/mm3 (p = 0.036) compared to CD4 count of patients who started with Tenofovir based regimen. Malnourished group has 22.4 cells/mm3 lower increases in CD4 count compared to well-nourished group (Table 5).
Table 5

Multivariate predictors of CD4 change at 24 months in HIV-infected individuals at Jimma University Specialized Hospital, January 2006 to December 2011

Variables

Numbers of patients

B [95 % CI]

p value

Age

340

−2.11 [−3.556, −0.665]

0.005

Sex

 Female

200

Reference

 

 Male

140

−104.31 [−170.702, −37.923]

0.002

Marital status

 Single

43

45.10 [−66.086, 156.294]

0.424

 Married

116

138.56 [42.449, 234.655]

0.005

 Divorced

44

52.47 [−55.935, 160.873]

0.340

 Widowed

22

Reference

 

Baseline HAART

   

 Stavudine based

219

87.49 [5.960, 169.015]

0.036

 Zidovudine based

72

16.31 [−75.057, 107.678]

0.724

 Tenofovir based

49

Reference

 

BMI

 <18.5

170

−22.42 [−81.040, 36.202]

0.451

 ≥18.5

170

Reference

 

Educational level

 Not educated

69

−86.18 [−195.893, 23.534]

0.123

 Elementary

122

12.63 [−89.442, 114.698]

0.807

 Secondary

106

−38.30 [−139.605, 63.006]

0.456

 Tertiary

42

Reference

 

Base line CD4

 <100

5

83.84 [−127.555, 295.233]

0.434

 100–199

94

87.16 [−122.670, 296.992]

0.413

 200–350

139

24.14 [−184.264, 232.534]

0.819

 >350

102

Reference

 
Death
In Kaplan–Meier bivariate cox-proportional analysis survival times significantly differ among groups of baseline CD4 (p = 0.014), WHO clinical stage (p = 0.022), baseline HAART (p = 0.010) and Fluconazole prophylaxis (p = 0.000). Even though, malnourished patients had shorter survival times compared to well-nourished group, it was not statistically significant (p = 0.170) (Fig. 1).
Fig. 1

Kaplan–Meier plots of nutritional status in HIV infected individuals in cohort of patients at Jimma University specialized hospital, January 2006 to December 2011

In multivariable cox proportional model four variables were found to be significant predictors of mortality. These were Zidovudine based regimen (AHR = 4.182, 95 % CI 1.550, 11.282, p = 0.005), Tenofovir (AHR = 5.156, p = 0.001), Fluconazole prophylaxis (HR = 5.639, 95 % CI 1.811, 17.563, p = 0.003), age above 50 years (AHR = 4.783, p = 0.040) and CD4 greater than 200 (HR = 0.287, 95 % CI 0.097, 0.848, p = 0.002). Malnourished patients were 1.3 times at risk of death compared to well-nourished group [AHR = 1.460, 95 % CI (0.648, 3.287), p = 0.361] (Table 6).
Table 6

Multivariate predictors death in HIV-infected individuals at Jimma University Specialized Hospital, January 2006 to December 2011

Variable

Number of death

HR [95 % CI]

p value

Age

 Below 30

21

1

 

 30–39

11

0.898 [0.357, 2.261]

0.820

 40–49

6

1.047 [0.317, 3.459]

0.940

 Above 50

4

4.783 [1.076, 21.268]

0.040

BMI

 <18.5

25

1.460 [0.648, 3.287]

0.361

 ≥18.5

17

1

 

Marital status

 Single

7

1

 

 Married

16

0.470 [0.173, 1.278]

0.139

 Widowed

1

0.258 [0.028, 2.374]

0.232

 Divorced

8

0.520 [0.154, 1.763]

0.294

CD4 count

 <100

20

1

 

 100–199

15

0.522 [0.217, 1.254]

0.146

 >200

7

0.287 [0.097, 0.848]

0.02

Baseline WHO stage

 Stage I

6

1

 

 Stage II

9

1.540 [0.446, 5.315]

0.495

 Stage III

17

1.501 [0.487, 4.631]

0.480

 Stage IV

10

2.802 [0.657, 11.942]

0.164

Occupation

 Gov’t employee

14

3.608 [0.720, 18.090]

0.119

 Unemployed

25

3.481 [0.740, 16.371]

0.114

 Others

1

1

 

Educational level

 Not educated

4

1

 

 Primary

17

2.293 [0.686, 7.667]

0.178

 Secondary

14

2.376 [0.079, 8.317]

0.176

 Tertiary

7

2.401 [0.494, 11.661]

0.277

Fluconazole prophylaxis

 No

34

1

 

 Yes

8

5.639 [1.811, 17.563]

0.003

Baseline HAART

 Stavudine based

19

1

 

 Zidovudine based

12

4.182 [1.550, 11.286]

0.005

 Tenofovir based

11

5.156 [1.924, 13.819]

0.001

Discussion

There was no significant difference between malnourished and well-nourished patients in terms of CD4 recovery after HAART initiation. This finding is similar with reports by Elizabeth et al. who reported nutritional status at the start of HAART as measured by BMI, FFMI, FMI and skin folds did not predict good post HAART CD4 change at 6, 12 and 24 months in a cohort of HIV infected Rwanda women [12]. There was no significant difference in CD4 recovery between malnourished patients and well-nourished patients after initiation of HAART [12]. Likewise, Brandon et al. showed that baseline BMI does not predict CD4 change at 48 weeks (11 months) but that this baseline BMI did predict CD4 change at 96 and 144 weeks [13]. Lack of longterm outcomes related to baseline BMI in our study may be related to different ethnic background, geographical region, diet, smaller sample size and shorter follow-up period.

CD4 count at the initiation of treatment was significantly associated with a change in CD4 at 6, and 12 months when adjusted for other variables. A CD4 count greater than 350 cells/mm3 at baseline was associated with smaller increase in CD4 at 6 and 12 months in multivariable model compared to patients with lower CD4 counts. This finding was also documented in London, UK where lower CD4 count was associated with greater increase in CD4 after 3 month of HAART initiation both in multivariable model [14]. In both our study and Bennett and his colleagues, females were found to have better CD4 recovery compared to males after at least a year of treatment [15]. Finally, we found that younger patients had greater increase in CD4 at 6, 12, and 24 months similar to study in Europe which shows younger age favors CD4 cell restoration [16] and increasing age was risk factors for not achieving CD4 count >200 [17]. Similarly, this finding is in line with the finding from study done in seven African countries which shows older age at HAART initiation was associated with suboptimal CD4 recovery CD4 [18]. Older age was independent predictor of mortality, which was comparable with study done in Johannesburg, South Africa which shows older patients were at greater risk of early death compared to younger patients [19]. Moreover, this finding was in line with study from South Eastern Nigeria and with what Adena et al. reported which shows patients whose age greater than 45 were at greater risk of early death [20].

Malnourished patients were almost two times more likely to die early compared to well-nourished group though it is not significant in Multivariable Cox proportional model. This finding was in line with study done in Zewditu Memorial hospital, Ethiopia which shows patients with BMI of <18.5 kg/m2 at the start of treatment were 1.13 [95 % CI 0.23, 5.43] times at greater risk of death compared to those with BMI >18.5 kg/m2 [21]. This finding was also consistent with the finding from study done in Singapore where patients with BMI less than 18.5 were 1.4 times more likely to die early compared to patients with BMI greater than or equal to 18.5 kg/m2 [12]. Studies in Tanzania and Malawi where malnourished patients were associated with early death compared to well-nourished group [22, 23]. Likewise, Raoul and his colleagues reported low BMI was an independent predictor of death in West Africa [18]. Other studies [1315] also reported BMI as an important predictor of death.

Tenofovir based regimen was associated with highest risk of death compared to Stavudine and Zidovudine based regimen. This is similar with what Kelechi and his colleagues reported [24] but in contrast to the finding South Africa which shows there was no difference in mortality between Tenofovir and Stavudine [19]. Again different ethnic background, geographical region, diet, smaller sample size and shorter follow-up period in our study. Furthermore, single antiretroviral drugs were compared in our study while Kavindhran et al. [19] compared regimens of three or more antiretroviral drugs.

Independent of specific antiretrovirals, Fluconazole prophylaxis was found to be strong predictor of death in our study and indifference to a Uganda study suggesting that this antifungal did not impact mortality under similar conditions [25]. Most patients in our study took fluconazole when CD4 cell counts were <200/ml.

Conclusion

BMI at the start did not predict change in CD4 at any time point after initiation of HAART after adjustment for other variables.

Age of the patients was significant predictor of immunologic outcome at 6, 12, 24 months adjusting for other factors. Baseline CD4 count was significant predictor of CD4 change at 12 months. Sex was significant predictor of immunologic outcome at 24 months after HAART initiation. At 24th month baseline HAART and marital status predicts immunologic outcome.

Age >50 years of age, Tenofovir based regimen, Zidovudine based regimen, taking fluconazole and CD4 <200 were associated with greater risk of death.

Malnutrition at the start of HAART does not predict early death of patients.

Declarations

Authors’ contributions

TB analyzed the data, interpreted the data, and revised it critically for important intellectual content. SH conceived and designed the study, acquired the data, analyzed and interpreted the data, and drafted the manuscript. NH analyzed the data, interpreted the data, and revised it critically for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements

The author would like to express due appreciation for Jimma University Specialized Hospital for allowing us to access patients data. We are also grateful to Jimma University for providing us different reference materials and financial support.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Consent

Since we used medical chart of the patient, there is no need for consent, but we get permission from hospital management to access the data and information were kept confidential. Personal identifiers were not used in the study.

Ethics statement

Ethical clearance was obtained from Ethical Review Board of College of Public Health and Medical Sciences, Jimma University. We obtained permission from Hospital management before starting data collection. We kept information confidential.

Funding

The study was supported by a research grant from Jimma University (Jimma, Ethiopia). The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Clinical Pharmacy Department, School of Pharmacy, Jimma University
(2)
Human Nutrition Department, Jimma University

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