Advertisement

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa

  • Partson TinarwoEmail author
  • Temesgen Zewotir
  • Nonhlanhla Yende-Zuma
  • Nigel J. Garrett
  • Delia North
Open Access
Original Research

Abstract

Introduction

Past endeavours to deal with the obstacle of expensive Cluster of Difference 4 (CD4+) count diagnostics in resource-limited settings have left a long trail of suggested continuous CD4+ count clinical covariates that turned out to be a potentially important integral part of the human immunodeficiency virus (HIV) treatment process during disease progression. However, an evaluation to determine the strongest candidates among these CD4+ count covariates has not been well documented.

Methods

The Centre for the AIDS Programme of Research in South Africa (CAPRISA) initially enrolled HIV-negative (phase 1) patients into different study cohorts. The patients who seroconverted (237) during follow-up care were enrolled again into a post-HIV infection cohort where they were further followed up with weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3–12 months (phase 3: early infection) and quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5). The CD4+ count and 46 covariates were repeatedly measured at each phase of the HIV disease progression. A multilevel partial least squares approach was applied as a variable reduction technique to determine the strongest CD4+ count covariates.

Results

Only 18 of the 46 investigated clinical attributes were the strongest CD4+ count covariates and the top 8 were positively and independently associated with the CD4+ count. Besides the confirmatory lymphocytes, these were basophils, albumin, haematocrit, alkaline phosphatase (ALP), mean corpuscular volume (MCV), platelets, potassium and monocytes. Overall, electrolytes, proteins and red blood cells were the dominant categories for the strongest covariates.

Conclusion

Only a few of the many previously suggested continuous CD4+ count clinical covariates showed the potential to become an important integral part of the treatment process. Prolonging the pre-treatment period of the HIV disease progression by effectively incorporating and managing the covariates for long-term influence on the CD4+ cell response has the potential to delay challenges associated with ART side effects.

Keywords

CD4+ count Continuous clinical covariates HIV disease progression Multilevel partial least squares Prospective cohort studies Variable reduction 

Introduction

The Cluster of Difference 4 (CD4+) count is the most common indicator of health status and immune function of patients infected with the human immunodeficiency virus (HIV) [1]. Several CD4+ count covariates from different clinical platforms have been investigated in HIV-positive patients. The quest for understanding the behavioural patterns of the CD4+ count covariates has been due to different reasons ranging from their potential use as either cost-effective CD4+ count surrogates [2, 3, 4] or predictors [5, 6, 7] to pre-treatment assessment and monitoring of therapy in HIV-positive patients [8]. Such endeavours to keep abreast of the health status of HIV-positive patients in the absence of the CD4+ count were triggered by high costs of the CD4+ count diagnostic devices in the past [9, 10], making them not easily accessible to resource-limited settings in the developing world [11] where the health facilities are usually overburdened [12]. The challenge was exacerbated by operational and logistical issues [13, 14] in the supply of essential medicine for the patients [15, 16, 17] including frequent instrument breakdown and poor manufacturer maintenance of CD4+ count diagnostics [18]. Recently, obtaining the CD4+ count has become extraordinarily inexpensive [19, 20, 21] and, in the contemporary era of antiretroviral therapy (ART), the monitoring and restoration of patient’s CD4+ count to acceptable levels are now relatively easy [22] and have led to improved patient survival periods [23]. Despite the breakthrough in ART, recommendations have been made to suggest other factors that influence long-term CD4+ cell response in conjunction with the therapy [24]. As such, previous studies have been inclined more towards social, demographic and other categorical factors [25, 26, 27], which suffer from information loss due to their grouping nature [28, 29]. On the other hand, the “richer” continuous clinical covariates are more sensitive to sources of variation [30] in the CD4+ count and better capable of capturing and explaining realistic behavioural patterns of the CD4+ cell response in the face of the rapidly mutating [31] HIV that is known to attack the CD4+ cells [32]. The suggestion of the CD4+ count surrogates, predictors and pre-treatment assessment options in the past turned out to have the potential to be manipulated as drivers for influencing long-term CD4+ cell response in HIV-positive patients. For example, a close follow-up on sodium has been reported to improve outcomes [33] as it positively influences the CD4+ count and its early management was found to be a contributing factor to the survival rates of HIV-positive patients [34]. Among other CD4+ count clinical covariates, sodium and calcium levels are affected by dietary conditions [35, 36], which can become a potentially important integral part of the HIV treatment process during disease progression. Other blood chemistry components have also been suggested [5, 7, 33, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52] including CD4+ count covariates from other clinical platforms such as the full blood count [3, 5, 53, 54, 55, 56, 57], lipids [58, 59, 60], sugar [61, 62, 63] and clinical examination measurements [2, 6, 64, 65, 66, 67, 68, 69, 70, 71]. It then stands to reason that endeavours to deal with the obstacle of expensive CD4+ count diagnostic devices in the past left a long trail of suggested continuous CD4+ count clinical covariates that have potential to be an important integral part of the treatment process during HIV disease progression. The list of such potentially manageable continuous CD4+ count clinical covariates has also grown over the past few years owing to the tremendously high volume of patient electronic health records that are being stored at a faster pace and relatively cheaper than in the past [72]. However, an evaluation to determine the strongest candidates of these continuous CD4+ count clinical covariates during HIV disease progression has not been well documented.

This bioinformatics study aimed to pool and evaluate the previously and independently suggested continuous CD4+ count clinical covariates to give an insight on the strongest drivers of the long-term CD4+ cell response in HIV-positive patients during the disease progression. Our goal was to shed more light on the possibilities of integrating and managing the continuous CD4+ count clinical covariates in the HIV treatment process. For example, ART is a major milestone in HIV treatment [73] but it is associated with side effects that can lead patients into challenging situations [74, 75]. Hence, the realisation of managing this continuous clinical covariate influence on the CD4+ cell response would potentially prolong the pre-treatment period and increase the likelihood of delaying patients in experiencing ART-related issues at an early stage of the disease progression. Some of the statistical tools previously used to assess the CD4+ count and covariate associations either were limited or suffered from information loss, for example, analysis of variance (ANOVA) [51, 76, 77], confidence intervals [64], t-tests [58, 59, 60], non-parametric tests [33, 38], chi-square tests [61, 78], linear regression [65, 79], sensitivity, specificity and positive prediction [2, 8] and correlation analysis [63, 66, 80]. As such, we also sought to pave the way for other areas such as predictive modelling with streamlined influential clinical covariates that are richer in information preserved in their continuous nature to explain the CD4+ count variation. We evaluated available measurements of the continuous CD4+ count clinical covariates routinely collected at the Centre for the AIDS Programme of Research in South Africa (CAPRISA).

Methods

The Study Design

The CAPRISA 002 enrolled 245 HIV-negative (phase 1: pre- HIV infection) female sex workers into an Acute Infection study. The establishment of the acute infection study, cohort screening and seroconverts; routine evaluation procedures; CAPRISA-participant interaction and data management have been previously documented [81]. The study protocol and informed consent documents were reviewed and approved by the local ethics committees of the University of KwaZulu-Natal, the University of Cape Town, the University of the Witwatersrand in Johannesburg and the Prevention Sciences Review Committee (PSRC) of the Division of AIDS (DAIDS, National Institutes of Health, USA). The study was also performed in accordance with the Helsinki Declaration of 1964 and its later amendments. The consent forms were translated into vernacular language, isiZulu, and written informed consent was obtained at each stage of the study. All minors under the age of 18 years were excluded from the study as part of the screening procedure. The HIV-negative cohort was followed up and upon HIV infection they were further followed up with weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3–12 months (phase 3: early infection) and quarterly visits thereafter (phase 4: established infection) until ART initiation (phase 5). Eventually 27 seroconversions were recorded. In addition to the 27 seroconverts, 210 more patients who seroconverted from other CAPRISA studies were also enrolled and similarly followed up post infection from the acute to ART phase. Figure 1 summarises how the total sample size of 237 seroconverts for this study was obtained.
Fig. 1

Study design. The HIV-negative cohort screening involved 775 voluntary potential candidates of which 462 were already HIV positive and 313 initially eligible. Of the 313 HIV-negative patients, only 245 were enrolled and the rest excluded for various reasons according to the eligibility criteria. Eventually 27 out of the 245 seroconverted were enrolled into follow-up care. Seroconverts from other CAPRISA studies (210) were also included into the follow-up care that resulted in a total of 237 patients for this study

Data

Four time points prior to each phase transition were selected, which resulted in a total of 16 repeated measurements being investigated for each patient. The baseline (Phase 1) repeated measurements were scarce; hence, this study focused on phases 2 to 5 only. The CD4+ count covariates include: full blood count, lipids, sugar, blood chemistry and clinical examination. Several of these variables have been studied as potential covariates for the CD4+ count but mostly contested in isolation or within a small group of barely just under five variables confined within their respective clinical platforms. All the data sets from the different clinical platforms were pooled into a single data set.

Statistical Analysis

All the analysis was performed in the open-source R software, version 3.5.0. Firstly, a descriptive summary of the repeated measurements was provided using the function stat.desc in the pastecs library. Secondly, redundant features among the covariates were investigated using correlation analysis that dropped off the covariates with the highest mean absolute correlation using the findCorrelation function. Thirdly and last, this was then followed by the partial least squares (PLS) approach to model building with the application of the spls function in the mixOmics library, which is capable of handling the complex structure of repeated measurements. The package incorporates a design matrix to account for variation in the multilevel structure of the longitudinal data. PLS handles multicollinearity and a very large number of variables in longitudinal data. It ranks the covariates from strongest to weakest allowing variable selection and consequently dimension reduction. Since the PLS is a multidimensional analysis technique, graphical displays of the results were vital to comprehensively visualise the variable selection process with the aid of the instrumental R libraries: the ggplot2 and ggrepel.

Results

Descriptive Statistics

Table 1 shows that throughout the follow-up care, the minimum and maximum CD4+ counts recorded were 45 and 1395 cells/mm3, respectively. During the follow-up period, at least 50% of the CD4+ count repeated measurements were above 539 cells/mm3 and averaging 571.14 ± 238.45 cells/mm3 with an overall variation of 41.75% around the cohort average. The greatest variation in the covariates was observed in eosinophils (101.11%), basophils (75.74%) and gamma glutamyl transferase (64.20%).
Table 1

A descriptive summary of the investigated variables

  

Variable

Min

Median

Mean

Max

SD

CV (%)

Full blood count

Red blood cells

CD4 count (cells/mm3)

45.00

539.00

571.14

1395.00

238.45

41.75

Red blood cell count

2.69

4.23

4.28

5.55

0.49

11.48

Haemoglobin(Hb)

7.20

12.20

12.28

16.20

1.59

12.96

Haematocrit

0.23

0.37

0.37

0.48

0.04

11.18

MCV

64.20

86.90

87.11

108.00

6.99

8.02

MCH

19.40

28.70

28.78

37.20

2.88

10.02

MCHC

29.10

33.00

33.02

37.10

1.34

4.05

RDW

11.10

14.30

14.68

22.40

1.87

12.71

Platelet

43.00

287.00

295.13

591.00

81.01

27.45

White

blood cells

Leucocytes

1.85

4.98

5.31

11.90

1.73

32.62

Neutrophils

0.59

2.43

2.78

7.79

1.33

47.84

Lymphocytes

0.37

1.79

1.90

4.42

0.74

38.78

Monocytes

0.07

0.39

0.41

1.01

0.15

37.13

Eosinophils

0.00

0.10

0.15

0.80

0.15

101.11

Basophils

0.00

0.02

0.02

0.09

0.02

75.74

Lipids

 

Cholesterol

1.10

3.80

3.84

7.00

0.87

22.73

LDL

0.60

2.30

2.31

4.80

0.71

30.73

Triglycerides

0.00

0.90

0.97

2.80

0.46

47.20

  

Glucose

1.00

2.00

1.62

3.00

0.67

40.96

Blood chemistry

Liver function

ALT(GPT)

4.00

19.00

21.22

64.00

9.43

44.43

AST(GOT)

12.50

25.00

27.30

72.00

10.06

36.86

Bilirubin

0.00

7.00

7.32

20.00

3.35

45.71

Alkaline phosphatase

28.00

66.00

70.52

162.00

21.52

30.51

Gamma glutamyl transferase

4.00

19.00

23.58

94.00

15.14

64.20

Electrolytes

Calcium

1.93

2.28

2.29

2.67

0.11

4.67

Chloride

95.00

105.00

105.14

115.00

2.83

2.69

Magnesium

0.58

0.82

0.82

1.09

0.08

9.63

Potassium

0.65

2.92

3.19

6.00

0.82

25.73

Sodium

129.00

137.00

136.90

145.00

2.35

1.72

Protein

Protein

56.00

83.00

84.16

99.00

7.84

9.32

Albumin

21.00

38.00

37.90

52.00

4.51

11.89

Lactate dehydrogenase

27.00

440.00

438.31

1076.00

169.58

38.69

Iron and vitamins

Iron (Fe) (mcg/dl)

1.00

11.00

11.59

34.45

6.09

52.52

Folate (nmol/l)

1.15

13.65

15.69

49.28

8.47

53.99

Vitamin B12 (ng/ml)

0.00

274.00

296.56

829.75

126.54

42.67

 

Urea (mmol/l)

1.10

3.30

3.42

6.80

0.95

27.87

Clinical

examination

Physical

BP(systolic) (mmHg)

74.00

118.00

118.64

168.00

13.81

11.64

BP(diastolic) (mmHg)

46.00

74.50

75.14

109.00

9.67

12.87

Pulse (bpm)

48.00

80.00

81.03

118.00

10.08

12.44

Axillary temperature (oC)

34.30

36.25

36.24

37.90

0.49

1.36

Anthropometric

Waist circumference (cm)

31.00

84.00

86.99

145.00

16.25

18.69

Hip circumference(cm)

64.12

106.00

107.99

160.00

14.58

13.50

Arm(right) circumference (cm)

13.50

29.00

29.87

47.00

5.35

17.92

Triceps skin fold (mm)

5.00

25.00

27.29

61.00

10.53

38.60

Height (m)

1.31

1.57

1.58

1.81

0.08

5.14

Weight (kg)

38.50

69.00

73.66

150.00

20.96

28.46

Body mass index (BMI) (kg/m2)

16.00

27.53

29.52

61.10

8.10

27.45

Redundant Feature Selection

Table 2 shows that haemoglobin (Hb), mean corpuscular haemoglobin (MCH), leucocytes, cholesterol, hip circumference, weight (kg) and body mass index (BMI) were highly correlated with the other covariates. The anthropometric measurements were the most highly correlated among themselves but the BMI, although marked as a redundant feature, was intuitively included in the second stage of variable reduction using the PLS.
Table 2

Redundant features: highly correlated (r > 0.75) covariates of the CD4 count

 

Hba

MCHa

Leucocytesa

Cholesterola

Hip

circumferencea

Weight

(kg) a

BMIb

Haematocrit

0.9499

      

MCV

 

0.9211

     

Neutrophils

  

0.8464

    

LDL

   

0.8636

   

Waist circumference

    

0.7580

0.8158

0.7810

Hip circumference

     

0.8884

0.8414

Arm(right) circumference

    

0.7867

0.8006

0.7925

Weight(kg)a

      

0.9149

Hb, haemoglobin, MCH, mean corpuscular haemoglobin, MCV, mean corpuscular volume, LDL, low-density lipoproteins, BMI, body mass index

aDropped redundant feature

bIntuitively included redundant feature in the PLS variable reduction

Variable Selection

The optimal principal component (Fig S1) explained 68.95% of the variance in the response (CD4+ count) and the variable selection simultaneously considered both the variable importance in projection (VIP) and regression coefficients (see Fig S2 for details). We presented all three VIP cut-off points where a cut-off point of 1.5 can be considered as a strict selection, 1.0 as moderate and 0.8 as lenient. A stricter variable selection process selected two covariates, the moderate (13) and lenient (18), of the 40 non-redundant features available for our study. We developed an interest in all the 18 strongest covariates as selected by the lenient cut-off point.

Figure 2 provides a list of all 40 covariates from the strongest to the weakest significance as well as their behavioural patterns in the predictive power (coefficients), component construction (loadings) and independent association (correlation) with the CD4+ count together with the associated p values. The covariate loadings and regression coefficients indicated more or less the same effects in component construction and predictive power, respectively. Among the significant covariates, folate, magnesium, calcium and sodium had the highest reducing effect on the CD4+ count, whereas alkaline phosphatase (ALP), mean corpuscular volume (MCV) and lactate dehydrogenase (LDH) corresponded to an increased CD4+ count. In this study, the lymphocytes had the highest direct independent positive correlation with the CD4+ (r = 0.5421, p < 0.0001) followed by haematocrit (r = 0.2337, p < 0.0001). On the other hand, protein had the highest negative correlation (r = − 0.1740, p < 0.0001) with the CD4+ count followed by folate (r = − 0.1530, p < 0.0001). The results showed that the top 8 of the 18 selected covariates were positively and independently associated with the CD4+ count. Of all the investigated 40 non-redundant covariates, red blood cell distribution width (RDW), pulse, urea, alanine aminotransferase (glutamate pyruvate transaminase) ALT(GPT) and axillary temperature were the least important.
Fig. 2

Variable importance. Also shown are the related loadings, standardised regression coefficients and correlations of each covariate with the response variable (CD4 count)

A look at the significant variables by clinical category (Fig. 3) revealed that there was no significant variable selected from lipids, physical examination and anthropometric measurements. Folate was the only significant variable in its category and similarly alkaline phosphatase only among the liver function indicators. The PLS suggested chloride and RDW as the only insignificant CD4+ count covariates among the electrolytes and red blood cells, respectively. Given the lymphocytes, basophils and monocytes, the significant covariates within the white blood cells group, the lymphocytes were dominantly significant. Generally, most of the significant CD4+ count covariates were selected from electrolytes, proteins and red blood cells. The data for all the variable selection plots are given in File S1.
Fig. 3

Variable importance by clinical category. The broken red horizontal lines divide the major groups. From the top, the groups are clinical examination, blood chemistry, sugar, lipids and full blood count. The horizontal broken grey lines divide the subgroups within the major groups

Discussion

In the present study, we evaluated a list of continuous CD4+ count clinical covariates that were available at CAPRISA to determine the strongest candidates that can potentially become an important integral part of the HIV treatment process. The HIV targets and kills CD4+ cells resulting in the CD4+ count being an important outcome indicator for the patient’s health status. ART is known to supress the viral load and consequently an increased number of CD4+ cells are spared giving rise to an improved immune system [73]. Hence, during the HIV treatment phase, ART is a major determinant of the CD4+ count distribution. The intention of this study was to select the continuous clinical covariates that contributed to the greatest variation in the CD4+ count from an overall perspective throughout the post-HIV period including ART. We used the PLS approach to achieve this and variable reduction is possible [82] given the long list of covariates under study. The PLS also handles the variation in the multilevel structure of the data. The evaluated covariates were already known to be associated with the CD4+ count based on other statistical methods that were limited in some way or suffered from information loss due to grouping and details given in the introduction section. The predictive nature of the selected continuous covariates was beyond the scope of this work as our focus was on variable selection yet paving the way for such areas as predictive modelling with streamlined and richer continuous CD4+ count clinical covariates. In this discussion we provided a brief summary of the functions of the selected and strongest 18 (out of 46) covariates according to our PLS model to point out the direction for future studies on the feasibility of incorporating them in the HIV treatment process to influence long-term CD4+ cell response especially in an attempt to prolong the pre-treatment period and hence the likelihood of delaying the patients from experiencing the ART side effects, although the covariates can still be influential in the long-term CD4+ cell response during therapy as previously reported [33, 34]. On our list of selected continuous clinical covariates, the lymphocytes were the strongest, as expected, because the CD4+ cells are a T cell type [83] whereas the lymphocytes are either B or T cells [4, 56, 84]. Our results also showed the lymphocytes to have the highest independent positive correlation with the CD4+ count (r =0.5421, p < 0.0001). Hence, efforts to improve the CD4+ cell response seem to be similar to those for the lymphocytes and the results obtained hereby serve to give an assurance of the effectiveness of our statistical methodology. In light of the other selected variables, our results showed the need to pay much attention to the white blood cells (basophils and monocytes) and platelet count. Basophils and monocytes control damage to body tissues and inflammation and fight pathogens, respectively [84]. Platelet count measures the blood clotting condition [84, 85, 86, 87]. Although they are the least abundant leucocytes [88], our study has found basophils to explain the greatest variation in the CD4+ count following the lymphocytes. However, the direct contact between human basophils and CD4+ T cells is known to mediate viral trans-infection of T cells through the formation of viral synapses [89, 90]. Also, the presence of basophils and other white blood cells in the blood is affected by underlying infection [91]. Areas of potential consideration in the blood chemistry group included potassium, sodium, calcium, magnesium, ALP and folate. Potassium regulates the acid-base chemistry and water balance [92], nerve impulses and heart muscle [84, 85]. Potassium's effect on the CD4+ count is affected by underlying comorbidities [93]. Sodium and calcium regulate the water balance, blood pressure, blood volume, heart rhythm and most importantly the brain and nerve function [84, 85, 92]. Changes in the sodium concentration are known to create an osmotic gradient between the extra- and intracellular fluid in cells [94] suggesting that a proper balance is essential. Magnesium is involved in muscle contractions and protein processing [84], ALP in detecting liver health [1, 95, 96] and folate for cell growth and metabolism [97, 98]. Red blood cells indices [haematocrit, MCV, mean corpuscular haemoglobin concentration (MCHC) and red blood cells] are related to haemoglobin [99], which binds oxygen for transport to tissues and binds tissue carbon dioxide to transport it back for exhalation [100, 101]. The indices indicate the volume, concentration and proportions of red blood cells [101, 102]. Because volume contributes to the haematocrit, dehydration becomes a confounder of the CD4+ count relationship. Details on patient dehydration were not available and this has not been taken into consideration in this study. In line with the red blood cell indices, our results revealed that LDH also needs attention. LDH is a cytosolic enzyme that enables the fulfilment of short-term energy requirements in the absence of sufficient oxygen at the expense of a greater consumption of glucose cells [103]. Proteins (total protein, albumin and LDH) were included in the selected list for the maintenance of normal water distribution between the tissues and blood as well as acid-base balance [104].

It is important to acknowledge that there were some limitations to this study. Several variables that influence the clinical covariates may not have been included, for example, dehydration, underlying infection, comorbidities and patient dietary conditions, especially their effect on the biochemistry covariates. These are potentially important confounders that could have been adjusted. Furthermore, the study findings were limited to adult females. We recommend future studies to consider the effect of gender and age on the strongest CD4+ count covariates during HIV disease progression. Given a large enough sample size, evaluating the clinical covariates for subjects with CD4+ count < 250 cells/mm3 is also recommended owing to the key driver for prophylaxis and surveillance for opportunistic infections related to CD4+ count < 250 cells/mm3.

Conclusion

Only a few of the many clinical attributes routinely collected during the HIV disease progression were found to be strong CD4+ count covariates and mostly from electrolytes, proteins and red blood cells. Prolonging the pre-treatment period of the HIV disease progression by effectively incorporating and managing the covariates for the long-term influence on the CD4+ cell response has the potential to delay the challenges associated with ART side effects. Damage to body tissues and inflammation as indicated by basophils was found to be the strongest CD4+ count covariate to effectively incorporate and manage for long-term influence on the CD4+ cell response. Lipids, physical examination and anthropometric measurements are not worth considering as important drivers of the CD4+ count when monitoring the health status of HIV-infected women during disease progression. There is a possibility of resource optimisation by streamlining the amount of routinely collected information when monitoring the health status of HIV-infected patients during the disease progression using just a few of the clinical attributes that strongly co-vary with the CD4+ count.

Notes

Acknowledgments

We thank the CAPRISA study teams and all participants for their important personal contribution to the availability of the data for the HIV research through their support and participation in the project.

Funding

No funding or sponsorship was received for this study or publication of this article. The article processing charges were funded by the authors.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole and have given their approval for this version to be published.

Disclosures

Partson Tinarwo, Temesgen Zewotir, Nonhlanhla Yende-Zuma, Nigel J. Garrett and Delia North have nothing to disclose.

Compliance with Ethics Guidelines

All procedures performed in studies involving human participants were in accordance with the local ethics committees of the University of KwaZulu-Natal, the University of Cape Town, the University of the Witwatersrand in Johannesburg, and the Prevention Sciences Review Committee (PSRC) of the Division of AIDS (DAIDS, National Institutes of Health, USA). The study was also performed in accordance with the Helsinki Declaration of 1964 and its later amendments. The consent forms were translated into vernacular language, isiZulu, and written informed consent was obtained at each stage of the study. All the minors under the age of 18 years were excluded from the study as part of the screening procedure.

Data Availability

All data generated or analysed during this study are included in this published article as supplementary information files.

Open Access

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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.

Supplementary material

40121_2019_235_MOESM1_ESM.pdf (166 kb)
Supplementary file1 (PDF 165 kb)

References

  1. 1.
    Beare A, Stockinger H, Zola H, Nicholson I. The CD system of leukocyte surface molecules: monoclonal antibodies to human cell surface antigens. Current Protocols in Immunology. 2008;73(80):A.4A.1-A.4A.73.Google Scholar
  2. 2.
    Kwantwi LB, Tunu BK, Boateng D, Quansah DY. Body mass index, haemoglobin, and total lymphocyte count as a surrogate for CD4 count in resource limited settings. Journal of Biomarkers. 2017;Volume 2017, Article ID 790735Google Scholar
  3. 3.
    Alavi SM, Ahmadi F, Farhad M. Correlation between total lymphocyte count, hemoglobin, hematocrit and CD4 count in HIV/AIDS patients. Acta Medica Iranica. 2009;47(1):1–4.Google Scholar
  4. 4.
    Obirikorang C, Quaye L, Acheampong I. Total lymphocyte count as a surrogate marker for CD4 count in resource-limited settings. BMC Infect Dis. 2012;12:128.PubMedPubMedCentralGoogle Scholar
  5. 5.
    Obirikorang C, Yeboah FA. Blood haemoglobin measurement as a predictive indicator for the progression of HIV/AIDS in resource-limited setting. J Biomed Sci. 2009;16(102).Google Scholar
  6. 6.
    Nzou C, Kambarami RA, Onyango FE, Ndhlovu CE, Chikwasha V. Clinical predictors of low CD4 count among HIV-infected pulmonary tuberculosis clients: A health facility-based survey. S Afr Med J. 2010;100:602–5.PubMedGoogle Scholar
  7. 7.
    Moolla Y, Moolla Z, Reddy T, Magula N. The use of readily available biomarkers to predict CD4 cell counts in HIV-infected individuals. South Afr Fam Pract. 2015;57(5):293–6.Google Scholar
  8. 8.
    Olawumi H, Olatunji P. The value of serum albumin in pretreatment assessment and monitoring of therapy in HIV/AIDS patients. HIV Med. 2006;7:351–5.PubMedGoogle Scholar
  9. 9.
    Secko D. Inexpensive CD4 counting for the developing world. CMA J. 2005;173(5):478.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Bentwich Z. CD4 Measurements in patients with HIV: are they feasible for poor settings? PLoS Med. 2005;2(7):e214.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Manoto SL, Lugongolo M, Govender U, Mthunzi-Kufa P. Point of care diagnostics for HIV in resource limited settings: An Overview. MDPI 2018;54(3).Google Scholar
  12. 12.
    Elsa Z. Healthcare systems in Sub-Saharan Africa: focusing on community-based delivery (CBD) of health services and the development of local research institutes. United Nations Peace and Progress. 2016;3(1):44–9.Google Scholar
  13. 13.
    Kuupiel D, Bawontuo V, Mashamba-Thompson TP. Improving the accessibility and efficiency of point-of-care diagnostics services in low-and middle-Income countries: Lean and Agile Supply Chain Management. Diagnostics 2017;7(58).Google Scholar
  14. 14.
    Leung N-HZ, Chen A, Yadav P, Gallien J. The impact of inventory management on stock-outs of essential drugs in sub-Saharan Africa: secondary analysis of a field experiment in Zambia. PLoS One. 2016;11(15):e0156026.Google Scholar
  15. 15.
    Jeffery A. The NHI proposal risking lives for no good reason. In: South African Institute of Race Relations, editor. South Africa: South African Institute of Race Relations; 2016.Google Scholar
  16. 16.
    Bateman C. Drug stock-outs: inept supply-chain management and corruption. S Afr Med J. 2013;103(9):600–2.PubMedGoogle Scholar
  17. 17.
    Nditunze L, Makuza S, Amoroso CL, Odhiambo J, Ntakirutimana E, Cedro L, et al. Assessment of essential medicines stock-outs at health centers in Burera District in Northern Rwanda. Rwanda Journal Series F: Medicine and Health Sciences 2015;2(1).Google Scholar
  18. 18.
    Thairu L, Katzenstein D, Israelski D. Operational challenges in delivering CD4 diagnostics in sub-Saharan Africa. AIDS Care. 2011;23(7):814–21.PubMedGoogle Scholar
  19. 19.
    Chip Lab. Rapid, label-free CD4 testing using a smartphone compatible device. The Royal Society of Chemistry. 2017;17:2910–9.Google Scholar
  20. 20.
    CMA Media Inc. Inexpensive CD4 counting for the developing world. JAMC. 2005;175(3):478.Google Scholar
  21. 21.
    Manabe YC, Wang Y, Elbireer A, Auerbach B, Castelnuovo B. Evaluation of portable point-of-care CD4 counter with high sensitivity for detecting patients eligible for antiretroviral therapy. PLoS ONE 2012;7(4).Google Scholar
  22. 22.
    Hunt PW, Deeks SG, Rodriguez B, Valdez H, Shade SB, Abrams DI, et al. Continued CD4 cell count increases in HIV-infected adults experiencing 4 years of viral suppression on antiretroviral therapy. AIDS. 2003;17:1907–15.PubMedGoogle Scholar
  23. 23.
    Egger M, Hirschel B, Francioli P, Sudre P, Wirz M, Flepp M, et al. Impact of new antiretroviral combination therapies in HIV infected patients in Switzerland: prospective multicentre study. BMJ. 1997;315:1194.PubMedPubMedCentralGoogle Scholar
  24. 24.
    Smith CJ, Sabin CA, Youle MS, Kinloch-de Loes S, Lampe FC, Madge S, et al. Factors influencing increases in CD4 cell counts of HIV-positive persons receiving long-term highly active antiretroviral therapy. J Infect Dis. 2004;190:1860–8.PubMedGoogle Scholar
  25. 25.
    Yakubu T, Dedu VK, Bampoh PO. Factors affecting CD4 count response in HIV patients within 12 months of treatment: a case study of Tamale Teaching Hospital. American Journal of Medical and Biological Research. 2016;4(4):78–83.Google Scholar
  26. 26.
    Burch LS, Smith CJ, Anderson J, Sherr L, Rodger AJ, O’Connell R, et al. Socioeconomic status and treatment outcomes for individuals with HIV on antiretroviral treatment in the UK: cross-sectional and longitudinal analyses. Lancet Public Health. 2016;1:e26–36.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Bunyasi EW, Coetzee DJ. Relationship between socioeconomic status and HIV infection: findings from a survey in the Free State and Western Cape Provinces of South Africa. BMJ Open. 2017;7:e016232.PubMedPubMedCentralGoogle Scholar
  28. 28.
    Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using “Optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994;86:829–35.PubMedPubMedCentralGoogle Scholar
  29. 29.
    Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: A bad idea. Stat Med. 2006;25(1):127–41.PubMedGoogle Scholar
  30. 30.
    Lawrence Erlbaum Associates. Continuous and discrete variables. Journal of Consumer Psychology. 2001;10(1&2):37–533.Google Scholar
  31. 31.
    Cuevas JM, Geller R, Garijo R, López-Aldeguer J, Sanjuán R. Extremely high mutation rate of HIV-1 in vivo. PLoS Biology. 2015;13(9).Google Scholar
  32. 32.
    Weston R, Marett B. HIV infection pathology and disease progression. Clinical Pharmacist. 2009;1:387.Google Scholar
  33. 33.
    Braconnier P, Delforge M, Garjau M, Wissing KM, De Wit S. Hyponatremia is a marker of disease severity in HIV-infected patients: a retrospective cohort study. BMC Infectious Diseases 2017;17:98.Google Scholar
  34. 34.
    Xu L, Ye H, Huang F, Yang Z, Zhu B, Xu Y, et al. Moderate/severe hyponatremia increases the risk of death among hospitalized Chinese human immunodeficiency virus/acquired immunodeficiency syndrome patients. PLoS ONE. 2014;9(10):1–9.Google Scholar
  35. 35.
    Gie Liem D, Miremadi F, Keast RSJ. Reducing sodium in foods: the effect on flavor. Nutrients. 2011;3:694–711.Google Scholar
  36. 36.
    Pravina P, Sayaji D, Avinash M. Calcium and its role in human body. International Journal of Research in Pharmaceutical and Biomedical Sciences. 2013;4(2):659–68.Google Scholar
  37. 37.
    Adhikari PM, Chowta MN, Ramapuram JT, Rao SB, Udupa K, Acharya SD. Effect of vitamin B12 and folic acid supplementation on neuropsychiatric symptoms and immune response in HIV-positive patients. J Neurosci Rural Pract. 2016;7(3):362–7.PubMedPubMedCentralGoogle Scholar
  38. 38.
    Semeere AS, Nakanjako D, Ddungu H, Kambugu A, Manabe YC, Colebunders R. Sub-optimal vitamin B-12 levels among ART-naïve HIV-positive individuals in an urban cohort in Uganda. PLoS ONE. 2012;7(7):e40072.PubMedPubMedCentralGoogle Scholar
  39. 39.
    Volberding PA, Levine AM, Dieterich D, Donna Mildvan, Mitsuyasu R, Saag M. Anemia in HIV infection: clinical impact and evidence-based management strategies. Clinical Infectious Diseases 2004;38:1454–63.Google Scholar
  40. 40.
    Butt AA, Michaels S, Greer D, Clark R, Kissinger P, Martin DH. Serum LDH level as a clue to the diagnosis of histoplasmosis. The AIDS Read. 2002;12(7).Google Scholar
  41. 41.
    Butt AA, Michaels S, Kissinger P. The association of serum lactate dehydrogenase level with selected opportunistic infections and HIV progression. International Journal of Infectious Diseases. 2002;6:178–81.PubMedGoogle Scholar
  42. 42.
    Sudfeld CR, Isanaka S, Aboud S, Mugusi FM, Wang M, Chalamilla GE, et al. Association of serum albumin concentration with mortality, morbidity, CD4 T-cell reconstitution among Tanzanians initiating antiretroviral therapy. J Infect Dis. 2013;207:1370–8.PubMedPubMedCentralGoogle Scholar
  43. 43.
    dos Santos ACO, Almeida AMR. Nutritional status and CD4 cell counts in patients with HIV/AIDS receiving antiretroviral therapy. Rev Soc Bras Med Trop. 2013;46(6):698–703.PubMedGoogle Scholar
  44. 44.
    Pralhadrao HS, Kant C, Phepale K, Mali MK, Raghunath. Role of serum albumin level compared to CD4+ cell count as a marker of immunosuppression in HIV infection. Indian Journal of Basic and Applied Medical Research. 2016;5(3):495-502.Google Scholar
  45. 45.
    Voss TG, Fermin CD, Levy JA, Vigh S, Choi B, Garry RF. Alteration of intracellular potassium and sodium concentrations correlates with induction of cytopathic effects by human immunodeficiency virus. J Virol. 1996;70(8):5447–544.PubMedPubMedCentralGoogle Scholar
  46. 46.
    Choi B, Gatti PJ, Haislip AM, Fermin CD, Garry RF. Role of potassium in human immunodeficiency virus production and cytopathic effects. Virology. 1998;247:189–99.PubMedGoogle Scholar
  47. 47.
    Khaidukov SV, Litvinov IS. Calcium homeostasis change in CD4+ T lymphocytes from human peripheral blood during differentiation in vivo. Biochemistry (Moscow). 2005;70(6):692–702.PubMedGoogle Scholar
  48. 48.
    Bani-Sadr F, Lapidus N, Rosenthal E, Gerard L, Foltzer A, Perronne C, et al. Gamma glutamyl transferase elevation in HIV/hepatitis C virus-coinfected patients during interferon-ribavirin combination therapy. J Acquir Immune Defic Syndr 2009;50(4).Google Scholar
  49. 49.
    Fleischbeina E, O’Brienb J, Martelinoc R, Fenstersheibd M. Elevated alkaline phosphatase with raltegravir in a treatment experienced HIV patient. AIDS. 2008;22(17):2401–7.Google Scholar
  50. 50.
    Gomo E, Ndhlovu P, Vennervald B, Nyazema N, Friis H. Enumeration of CD4 and CD8 T-cells in HIV infection in Zimbabwe using a manual immunocytochemical method. Cent Afr J Med. 2001;47(3):64–70.PubMedGoogle Scholar
  51. 51.
    Dusingize JC, Hoover DR, Shi Q, Mutimura E, Rudakemwa E, Ndacyayisenga V, et al. Association of abnormal liver function parameters with HIV serostatus and CD4 count in antiretroviral-naive Rwandan women. Aids Research and Human Retroviruses. 2015;31(7):723–30.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Shiferaw MB, Tulu KT, Zegeye AM, Wubante AA. Liver enzymes abnormalities among highly active antiretroviral therapy experienced and HAART naïve HIV-1 infected patients at Debre Tabor Hospital, Northwest Ethiopia: a comparative cross-sectional study. AIDS Research and Treatment. 2016;Volume 2016, Article ID 19854Google Scholar
  53. 53.
    Vanisri H, Vadiraja N. Association between Red blood cell parameters and immune status in HIV infected males. Indian Journal of Pathology and Oncology. 2016;3(4):684–9.Google Scholar
  54. 54.
    Vanisri H, Vadiraja N. Relationship between red blood cell parameters and immune status in HIV infected females. Indian Journal of Pathology and Oncology. 2016;3(2):255–9.Google Scholar
  55. 55.
    Leticia OI, Ugochukwu A, Ifeanyi OE, Andrew A, Ifeoma UE. The correlation of values of CD4 count, platelet, PT, APTT, fibrinogen and factor VIII concentrations among HIV positive patients in FMC Owerri. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS). 2014;13(9 Ver II):94-101.Google Scholar
  56. 56.
    Shapiro N, Karras DJ, Leech SH, Heilpern KL. Absolute lymphocyte count as a predictor of CD4 count. Ann Emerg Med. 1998;32(3):323–8.PubMedGoogle Scholar
  57. 57.
    Sivaram M, White A, Radcliffe K. Eosinophilia: clinical significance in HIV-infected individuals. Int J STD AIDS. 2012;23(9):635–8.PubMedGoogle Scholar
  58. 58.
    Iffen TS, Efobi H, Usoro CAO, Udonwa NE. Lipid profile of HIV-positive patients attending university of Calabar Teaching Hospital, Calabar - Nigeria. World Journal of Medical Sciences. 2010;5(4):89–93.Google Scholar
  59. 59.
    Oka F, Naito T, Oike M, Imai R, Saita M, Inui A, et al. Correlation between HIV disease and lipid metabolism in antiretroviral-naïve HIV-infected patients in Japan. J Infect Chemother. 2012;18:17–211.PubMedGoogle Scholar
  60. 60.
    Floris-Moore M, Howard A, Lo Y, Arnsten J, Santoro N, Schoenbaum E. Increased serum lipids are associated with higher CD4 lymphocyte count in HIV-infected women. HIV Medicine. 2006;7:421–30.PubMedGoogle Scholar
  61. 61.
    Misra R, Chandra P, Riechman SE, Long DM, Shinde S, Pownall HJ, et al. Relationship of ethnicity and CD4 Count with glucose metabolism among HIV patients on highly-active antiretroviral therapy (HAART). BMC Endocrine Disorders 2013;13(13).Google Scholar
  62. 62.
    Maganga E, Smart LR, Kalluvya S, Kataraihya JB, Saleh AM, Obeid L, et al. Glucose metabolism disorders, HIV and antiretroviral therapy among Tanzanian adults. PLoS ONE 2015;10(8:e0134410).Google Scholar
  63. 63.
    McKnight TR, Yoshihara HAI, Sitole LJ, Martin JN, Steffensd F, Meyer D. A combined chemometric and quantitative NMR analysis of HIV/AIDS serum discloses metabolic alterations associated with disease status. Mol BioSyst. 2014;10:2889–977.PubMedPubMedCentralGoogle Scholar
  64. 64.
    Dannhauser A, van Staden A, van der Ryst E, Nel M, Marais N, Erasmus E, et al. Nutritional status of HIV-1 seropositive patients in the Free State Province of South Africa: anthropometric and dietary profile. Eur J Clin Nutr. 1999;53:165–73.PubMedGoogle Scholar
  65. 65.
    Dimala CA, Kadia BM, Kemah B-L, Tindong M, Choukem S-P. Association between CD4 cell count and blood pressure and its variation with body mass index categories in HIV-infected patients. International Journal of Hypertension. 2018; Volume 2018, Article ID 1691474.Google Scholar
  66. 66.
    Esposito FM, Coutsoudis A, Visser J, Kindra G. Changes in body composition and other anthropometric measures of female subjects on highly active antiretroviral therapy (HAART): a pilot study in Kwazulu-Natal, South Africa. The Southern African Journal of HIV Medicine. 2008:36–42.Google Scholar
  67. 67.
    Fofana KC. Correlation between nutritional indicators and low CD4 count (<200 cells>/mm3) among HIV positive adults in Kapiri, Zambia [Thesis]: Georgia State University; 2016.Google Scholar
  68. 68.
    Venter E, Gericke G, Bekker P. Nutritional status, quality of life and CD4 cell count of adults living with HIV/AIDS in the Ga-Rankuwa area (South Africa). South African Journal of Clinical Nutrition. 2009;22(3):124–9.Google Scholar
  69. 69.
    Manner IW, Trøseid M, Oektedalen O, Baekken M, Os I. Low nadir CD4 cell count predicts sustained hypertension in HIV-infected individuals. The Journal of Clinical Hypertension. 2013;15(2):101–6.PubMedGoogle Scholar
  70. 70.
    Hsue PY, Hunt PW, Ho JE, Farah HH, Schnell A, Hoh R, et al. Impact of HIV infection on diastolic function and left ventricular mass. Circ Heart Fail. 2010;3(1):132–9.PubMedGoogle Scholar
  71. 71.
    Palacios R, Santos J, Garcı´a A, Castells E, González M, Ruiz J, et al. Impact of highly active antiretroviral therapy on blood pressure in HIV-infected patients. A prospective study in a cohort of naive patients. HIV Medicine 2006;7:10–5.Google Scholar
  72. 72.
    Sun J, and Reddy, C.K. Big Data Analytics for Healthcare. SIAM International Conference on Data Mining; Austin, TX.2013.Google Scholar
  73. 73.
    Meintjes G, Moorhouse MA, Carmona S, Davies N, Dlamini S, van Vuuren C, et al. Adult antiretroviral therapy guidelines 2017. Southern African Journal of HIV Medicine. 2017;18(1):a776.Google Scholar
  74. 74.
    Chen W-T, Shiu C-S, Yang JP, Simoni JM, Fredriksen-Goldsen kI, Szu-Hsien Lee T, et al. Side effects of antiretroviral therapy (ART) are associated with depression in Chinese individuals with HIV: a mixed methods study. AIDS & Clinical Research. 2013;4(6).Google Scholar
  75. 75.
    Lands L. A Practical Guide to HIV Drug Side Effects for People Living with HIV/AIDS. In: Pustil R, editor: The Canadian AIDS Treatment Information Exchange (CATIE); 2006.Google Scholar
  76. 76.
    Opiyo WO, Ng’wena AGM, Ofulla AVO. Liver function markers and associated serum electrolytes changes in HIV patients attending Patient Support Centre Of Jaramogi Oginga Odinga Teaching And Referral Hospital, Kisumu County, Kenya. East African Medical Journal. 2013;90(9):276-87.Google Scholar
  77. 77.
    Adhikari PMR, Chowta MN, Ramapuram JT, Rao SB, Udupa K, Acharya SD. Prevalence of vitamin B12 and folic acid deficiency in HIV-positive patients and its association with neuropsychiatric symptoms and immunological response. Indian Journal of Sexually Transmitted Diseases and AIDS. 2016;37(2):178–84.PubMedPubMedCentralGoogle Scholar
  78. 78.
    Cohen AJ, Steigbigel RT. Eosinophilia in patients infected with human immunodeficiency virus. J Infect Dis. 1996;174:615–8.PubMedGoogle Scholar
  79. 79.
    Fasakin K, Omisakin C, Esan A, Adebara I, Owoseni I, Omoniyi D, et al. Total and CD4+ T-lymphocyte count correlation in newly diagnosed HIV patients in resource-limited setting. Journal of Medical Laboratory and Diagnosis. 2014;5(2):22–8.Google Scholar
  80. 80.
    Sen. LCS, Vyas A, Sanghi LCS, Shanmuganandan CK, Gupta CR, Kapila BK, et al. Correlation of CD4+ T cell count with total lymphocyte count, haemoglobin and erythrocyte sedimentation rate levels in human immunodeficiency virus type-l disease. MJAFI 2011;67(1):15-20.Google Scholar
  81. 81.
    van Loggerenberg F, Mlisana K, Williamson C, Auld SC, Morris L, Gray CM, et al. Establishing a cohort at high risk of HIV infection in South Africa: challenges and experiences of the CAPRISA 002 acute infection study. PLoS ONE. 2008;3(4):e1954.PubMedPubMedCentralGoogle Scholar
  82. 82.
    Maitra S, Yan J. Principal component analysis and partial least squares: two dimension deduction techniques for regression. Casualty Actuarial Society Discussion Paper Program 2008. p. 79-90.Google Scholar
  83. 83.
    Papagno L, Spina CA, Marchant A, Salio M, Rufer N, Little S, et al. Immune activation and CD8+ T-cell differentiation towards senescence in HIV-1 infection. PLoS Biol. 2004;2(2):0173–185.Google Scholar
  84. 84.
    Project Inform. Monitoring HIV Blood Work: A Complete Guide for Monitoring HIV. In: New York State Department of Health, editor. San Francisco, CA 94103 26212007.Google Scholar
  85. 85.
    James AG. Understanding Blood Tests. In: The James, editor. Cancer Hospital and Richard J. Solove Research Institute.2017.Google Scholar
  86. 86.
    National Institutes of Health Clinical Center. Understanding your complete blood count (CBC) and common blood deficiencies. In: National Institutes of Health Clinical Center, editor. Bethesda2015.Google Scholar
  87. 87.
    NAM. CD4, viral load & other tests. UK2012.Google Scholar
  88. 88.
    Min B, Brown MA, LeGros G. Understanding the roles of basophils: breaking dawn. The Journal of Cells, Molecules, Systems and Technologies. 2011;135:192–7.Google Scholar
  89. 89.
    Marone G, Varricchi G, Galdiero MR, Loffredo S, Rivellese F, de Paulis A. Are basophils and mast cells masters in HIV infection? Int Arch Allergy Immunol. 2016;171:158–65.PubMedGoogle Scholar
  90. 90.
    Jiang A-P, Jiang J-F, Guo M-G, Jin Y-M, Li Y-Y, Wanga J-H. Human blood-circulating basophils capture HIV-1 and mediate viral trans-infection of CD4+ T cells. J Virol. 2015;89:8050–62.PubMedPubMedCentralGoogle Scholar
  91. 91.
    Siracusa MC, Kim BS, Spergel JM, Artis D. Basophils and allergic inflammation. J Allergy Clin Immunol. 2013;132(4):789–98.PubMedPubMedCentralGoogle Scholar
  92. 92.
    The Johns Hopkins Lupus Center. Blood Chemistry Panel America2017 [Available from: https://www.hopkinslupus.org/lupus-tests/screening-laboratory-tests/blood-chemistry-panel/.
  93. 93.
    Collins AJ, Pitt B, McGaughey K, Reaven N, Wilson D, Funk S, et al. Association of serum potassium with all-cause mortality in patients with and without heart failure, chronic kidney disease, and/or diabetes. Am J Nephrol. 2017;46:213–21.PubMedPubMedCentralGoogle Scholar
  94. 94.
    Shu Z, Tian Z, Chen J, Ma J, Abudureyimu A, Qian Q, et al. HIV/AIDS-related hyponatremia: an old but still serious problem. Ren Fail. 2018;40(1):68–74.PubMedPubMedCentralGoogle Scholar
  95. 95.
    Whitfield J. Gamma glutamyl transferase. Crit Rev Clin Lab Sci 2001 Aug;38(4):. 2001;38(4):263-355.Google Scholar
  96. 96.
    Patil R, Kamble P, Raghuwanshi U. Serum ALP & GGT levels in HIV positive latients. International Journal of Recent Trends in Science And Technology. 2013;5(3):155–7.Google Scholar
  97. 97.
    Arya SS, Kumar PK. Folate: sources, production and bioavailability. Agro Food Industry Hi Tech. 2012;23(4):23–7.Google Scholar
  98. 98.
    Dieticians of Canada. Food Sources of Folate. In: Canadian Nutrient File Health Department, editor. Canada: Dieticians of Canada; 2014.Google Scholar
  99. 99.
    Junqueira L, Carneiro J, Kelley R. Basic Histology. A Lange Medical Book. 7th ed: Appleton and Lange; 2006.Google Scholar
  100. 100.
    Jensen FB, Fago A, Weber RE. Hemoglobin structure and function. Fish Physiology. 1998;17:1–40.Google Scholar
  101. 101.
    Wintrobe M, Greer J. Wintrobe’s Clinical Hematology. Philadelphia.: Lippincott Williams & Wilkins, ; 2009.Google Scholar
  102. 102.
    Arika W, Nyamai D, Musila M, Ngugi M, Njagi E. Hematological markers of in vivo toxicity. Journal of Hematology & Thromboembolic Diseases. 2016;4(2).Google Scholar
  103. 103.
    Valvona CJ, Fillmore HL, Nunn PB, Pilkington GJ. The regulation and function of lactate dehydrogenase A: therapeutic potential in brain tumor. Brain Pathol. 2016;26:3–17.PubMedGoogle Scholar
  104. 104.
    Spectrum. Total protein: Biuret Reagent: Egyptian Company for Biotechnology (S.A.E); 2007.Google Scholar

Copyright information

© The Author(s) 2019

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and Computer ScienceUniversity of KwaZulu-NatalDurbanSouth Africa
  2. 2.Centre for the AIDS Programme of Research in South Africa (CAPRISA)University of KwaZulu-NatalDurbanSouth Africa

Personalised recommendations