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‐
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.