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.
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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.
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%).
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.
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.
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.
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.
References
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.
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 790735
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.
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.
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).
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.
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.
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.
Secko D. Inexpensive CD4 counting for the developing world. CMA J. 2005;173(5):478.
Bentwich Z. CD4 Measurements in patients with HIV: are they feasible for poor settings? PLoS Med. 2005;2(7):e214.
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).
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.
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).
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.
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.
Bateman C. Drug stock-outs: inept supply-chain management and corruption. S Afr Med J. 2013;103(9):600–2.
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).
Thairu L, Katzenstein D, Israelski D. Operational challenges in delivering CD4 diagnostics in sub-Saharan Africa. AIDS Care. 2011;23(7):814–21.
Chip Lab. Rapid, label-free CD4 testing using a smartphone compatible device. The Royal Society of Chemistry. 2017;17:2910–9.
CMA Media Inc. Inexpensive CD4 counting for the developing world. JAMC. 2005;175(3):478.
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).
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.
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.
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.
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.
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.
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.
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.
Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: A bad idea. Stat Med. 2006;25(1):127–41.
Lawrence Erlbaum Associates. Continuous and discrete variables. Journal of Consumer Psychology. 2001;10(1&2):37–533.
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).
Weston R, Marett B. HIV infection pathology and disease progression. Clinical Pharmacist. 2009;1:387.
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.
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.
Gie Liem D, Miremadi F, Keast RSJ. Reducing sodium in foods: the effect on flavor. Nutrients. 2011;3:694–711.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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 19854
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.
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.
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.
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.
Sivaram M, White A, Radcliffe K. Eosinophilia: clinical significance in HIV-infected individuals. Int J STD AIDS. 2012;23(9):635–8.
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.
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.
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.
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).
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
Sun J, and Reddy, C.K. Big Data Analytics for Healthcare. SIAM International Conference on Data Mining; Austin, TX.2013.
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.
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).
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.
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.
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.
Cohen AJ, Steigbigel RT. Eosinophilia in patients infected with human immunodeficiency virus. J Infect Dis. 1996;174:615–8.
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.
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.
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.
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.
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.
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.
James AG. Understanding Blood Tests. In: The James, editor. Cancer Hospital and Richard J. Solove Research Institute.2017.
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.
NAM. CD4, viral load & other tests. UK2012.
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.
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.
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.
Siracusa MC, Kim BS, Spergel JM, Artis D. Basophils and allergic inflammation. J Allergy Clin Immunol. 2013;132(4):789–98.
The Johns Hopkins Lupus Center. Blood Chemistry Panel America2017 [Available from: https://www.hopkinslupus.org/lupus-tests/screening-laboratory-tests/blood-chemistry-panel/.
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.
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.
Whitfield J. Gamma glutamyl transferase. Crit Rev Clin Lab Sci 2001 Aug;38(4):. 2001;38(4):263-355.
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.
Arya SS, Kumar PK. Folate: sources, production and bioavailability. Agro Food Industry Hi Tech. 2012;23(4):23–7.
Dieticians of Canada. Food Sources of Folate. In: Canadian Nutrient File Health Department, editor. Canada: Dieticians of Canada; 2014.
Junqueira L, Carneiro J, Kelley R. Basic Histology. A Lange Medical Book. 7th ed: Appleton and Lange; 2006.
Jensen FB, Fago A, Weber RE. Hemoglobin structure and function. Fish Physiology. 1998;17:1–40.
Wintrobe M, Greer J. Wintrobe’s Clinical Hematology. Philadelphia.: Lippincott Williams & Wilkins, ; 2009.
Arika W, Nyamai D, Musila M, Ngugi M, Njagi E. Hematological markers of in vivo toxicity. Journal of Hematology & Thromboembolic Diseases. 2016;4(2).
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.
Spectrum. Total protein: Biuret Reagent: Egyptian Company for Biotechnology (S.A.E); 2007.
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.
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Tinarwo, P., Zewotir, T., Yende-Zuma, N. et al. An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa. Infect Dis Ther 8, 269–284 (2019). https://doi.org/10.1007/s40121-019-0235-4
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DOI: https://doi.org/10.1007/s40121-019-0235-4