Content uploaded by Juan Maria Luco
Author content
All content in this area was uploaded by Juan Maria Luco on Sep 24, 2020
Content may be subject to copyright.
Hydroxychloroquine as Post-Exposure Prophylaxis for Covid-19: Why simple
data analysis can lead to the wrong conclusions from well-designed studies
Juan M. Luco, PhD
Department of Chemistry, Faculty of Chemistry, Biochemistry and Pharmacy, National
University of San Luis, Chacabuco and Pedernera 5700 San Luis, Argentina.
Author to whom correspondence should be addressed: Prof. Dr. Juan M. Luco, Facultad de
Química-Bioquímica-Farmacia, Universidad Nacional de San Luis, Chacabuco y Pedernera, 5700,
San Luis, Argentina. E-mail: jmluco@unsl.edu.ar, (TW: @JML21071664).
ABSTRACT
Researchers of the University of Minnesota Medical School reported the first prospective
randomized placebo-controlled trial (RCT) in evaluating the role of hydroxychloroquine
(HCQ) as post-exposure prophylaxis (PEP) against COVID‐19. The trial's primary result
reported by the authors was that, within four days after moderate or high-risk exposure to
Covid-19, HCQ did not show benefit over placebo to prevent illnesses compatible with
Covid-19 or confirmed infection (P=0.351, Fisher exact test). In this re-analysis, we show
why the authors’ oversimplified analysis led to an incorrect conclusion from the data.
We re-analyzed the dataset by applying multiple correspondence analysis (MCA) and
hierarchical cluster analysis (HCA), which are noise reduction methods used in large data
sets. We used the same primary outcome measures as the authors (incidence of COVID-
19-compatible disease by day 14) and the same statistical test that the authors used, such
as the two-sided Fisher's exact test and others. The results obtained indicate that the
individuals' age is a determining factor in the chemopreventive efficacy exerted by HCQ.
Thus, in contradiction to the original authors' conclusions, the full data set's risk analysis
shows that HCQ exhibits a chemopreventive effect for the group of subjects of ≤ 50 yrs
that does not reach significance (P= 0.083). However, not considering the analysis of the
moderate-risk exposure group, we confirm that the high-risk exposure group (N=719)
demonstrates a significant effect of HCQ in the under 50 age group (p=0.025). We also
show, using MCA and the Mantel test, systematic differences between the treatment and
placebo groups in their clinical characteristics, specifically asthma, and other-
comorbidities which act as confounders that add noise to the data, such that the genuine
effect of the drug is not seen in a standard analysis. After correcting these differences, the
risk analysis showed that HCQ is also useful as a prophylactic agent for people over 50
years of age. This study, therefore, provides evidence of the necessity for higher-order
analytics (such as MCA) in the presence of large data sets that include unknown
confounders. In this case, it shows that the published conclusion of the group – that HCQ
does not prevent COVID-type infective symptoms – was fundamentally flawed and should
be reconsidered.
KEYWORDS: COVID‐19, Post-exposure prophylaxis (PEP), Hydroxychloroquine (HCQ),
Pattern Recognition, Multiple correspondence analysis (MCA), Mantel's permutation test.
INTRODUCTION
The coronavirus disease 19 (COVID-19) is a global epidemic with high morbidity and
mortality, caused by a novel enveloped, RNA, beta-coronavirus. This was first reported
from Wuhan-China in late 2019, by the end of March 2020, and spread worldwide [1,2].
Currently, several drugs and vaccines are being tested for their potential activity against
COVID-19. Several mini and comprehensive reviews are available, offering differing
perspectives on its effectiveness and potential drawbacks [3-5].
Hydroxychloroquine (HCQ) and chloroquine (CQ), also known as antimalarial drugs, are
widely used to treat systemic autoimmune diseases and have been increasingly
recognized in many other diseases in addition to malaria. Thus, they have shown to
display various biological activities, such as anti-inflammatory, antithrombotic, antiviral,
and antineoplastic activities [6-8]. Concerning their antiviral properties, very recently,
Pastick et al. [9] reported an overview of HCQ and CQ and their pharmacology and the
possible mechanisms of action against SARS-CoV-2. Although there is no clinically
approved antiviral drug available against COVID-19, up to date, HCQ is perhaps the
therapeutic option more studied than any other potential COVID-19 drug [10]. Several
authors have addressed the clinical effectiveness and incidence of HCQ toxicity when
administrated to prevent and treat COVID-19. On this and other topics, a comprehensive
study has been reported recently [11].
With the ongoing pandemic, prophylaxis is a particularly critical factor in breaking the
spread and rapid rate of increase of SARS-CoV-2 infection, especially in patients at risk of
severe forms. Pre-exposure (PrEP) and post-exposure (PEP) prophylaxes are both required
components as public health measures, and the safety and efficacy of prophylactic use of
HCQ have been reviewed recently [11]. Researchers of the University of Minnesota
Medical School reported the first prospective randomized placebo-controlled trial (RCT) in
evaluating the role of HCQ as PEP against COVID‐19 [12]. The trial was conducted on 821
people recruited for the study. The participants were identified as moderate or high risk of
contracting COVID-19, based on time, distance, and protection systems at the time of
close contact with someone with confirmed Covid-19. The trial's primary result reported
by Boulware et al. [12] was that, within four days after moderate or high-risk exposure to
Covid-19, HCQ did not show benefit over placebo to prevent illnesses compatible with
Covid-19 or confirmed infection. However, the authors' conclusion about the
ineffectiveness of HCQ for the prevention of Covid-19 has been subject to numerous
criticisms. On the one hand, some critics were addressed on limitations in the study's
experimental design, as pointed by Cohen and others [13]. On the other hand, according
to Watanabe and others [11, 14], perhaps the critics most important is referred to as the
fact that HCQ will be useful as post-exposure prophylaxis only when it is used in the
shortest possible time (0-2 days) after exposure.
Pattern Recognition (PR) methods, also referred to as chemometrics or multivariate
statistics, are commonly used in rational drug design [15-17], and in general, in areas such
as the analysis of clinical data as well as in the biomedical and biology fields, among others
[18-19]. The PR term describes any mathematical or statistical method that may be used
to detect or reveal patterns in data, which is deemed to be particularly advantageous
when dealing with complex systems since PR-methods considers the behavior of multiple
variables simultaneously providing useful information that would not get with only an
evaluation between two variables. Thus, by applying several chemometric approaches,
systematic information can be extracted from a diversified dataset.
Considering the criticisms mentioned above raised on Boulware's study, the present work
aimed to re-analyze the Minnesota-study data by applying multivariate methods such as
multiple correspondence analysis, principal component, and hierarchical cluster an alysis.
It is relatively frequent that the differences between the original trial studies and the
reanalysis occurred due to different statistical or analytical methods, or ways of defining
outcomes or handling missing data [20]. Thus, in the present study, the post-application
analysis of chemometric techniques mentioned before was based on the same outcome
primary as the authors defined in the original work (incidence of Covid-19 disease by day
14) and the same statistical test that the authors used, such as the two-sided Fisher's
exact test and others.
DATA SET AND METHODS
The de-identified dataset understudy was obtained from the authors through the site
www.covidpep.umn.edu. Before carrying out any new analysis, the dataset was checked
by performing several analyzes repeated in the same way described in the study's
published report. For clarity in presentation, Table 1 summarizes participants'
demographic and clinical characteristics at baseline used in the present study. For details
of trial design, characteristics of the participants, enrollment, assignment of interventions,
and outcomes, see the original study (ref 12).
Table 1 Participant’s demographic and clinical characteristics at baseline used in this study
Hydroxychloroquine
N = 414
Placebo
N = 407
Column
Difference
Demographic characteristics
(Count)
(Count)
(Count)
Gender
Male
192
197
-5
Female
218
206
+12
Not Answer
4
4
0
Age (yrs)
[18-50]
310
316
-6
[51-89]
104
91
+13
Weight (lb)
weight <170
233
222
+11
weight >170
181
185
-4
Clinical characteristics
Hypertension
51
48
+3
Diabetes
12
16
-4
Asthma
31
31
0
Other-Comorbidities
25
31
-6
The data matrix was constructed by variables in the columns and individuals in the rows.
The demographic variables considered in this study were age (AG) and weight (WT), both
continuous variables, and the gender (sex) nominal variable. Concerning the clinical
variables, and according to the chronic health conditions of participants at the time of
enrollment such as reported in the original study, the following variables were assessed:
hypertension (P), diabetes (D), asthma (A), and one defined by the authors in the original
work, so-called other-comorbidities (OT), which included all others chronic health
conditions of participants in addition to those previously mentioned. The others variables
assessed were treatment and no-treatment (placebo) with HCQ (labeled as HCQ-1 and
HCQ-2, respectively), and the primary outcome (labeled with positive or negative signs)
such as defined by the authors in the original work: incidence of Covid-19 disease by day
14 based on PCR-confirmed (20/107) or based on symptom-based criteria (103/107). The
clinical, interventional, and gender variables are categorical or nominal and comprise
several levels, where each of these levels is coded as a dichotomous variable. This fact can
be illustrated with the gender (F vs. M) variable, one nominal with two levels where a
male respondent's pattern will be '1' and '0' for a female.
In the present work, the analysis of data matrix was carried out by using the following
statistical methods: multiple correspondence analysis, principal component, and
hierarchical cluster analysis. Principal component analysis (PCA) is a method of orthogonal
projection commonly used to express multivariate data with fewer dimensions. These new
dimensions, so-called principal components, are linear combinations of the original
variables. PCA's primary objectives are to evaluate the underlying dimensionality
(complexity) of the data and get an overview of the data's dominant patterns or significant
trends. The other method here used was multiple correspondence analysis (MCA). It is a
powerful exploratory multivariate approach for the graphical and numerical analysis of a
data matrix, which is based on the use of chi-squared metrics. MCA is also a dimensional
reduction technique, and can conceptually be considered a technique analog to principal
components analysis but applied for categorical variables. Thus, as in PCA, the factorial
axes are ranked by their order of importance in accounting for the system's total inertia
(variance). Factorial maps are then drawn by plotting any two of these orthogonal axes
and displaying the projections of the row and column points. In the case of continuous
variables (quantitative data), the MCA analysis can also be performed, but prior to the
discretization of such variables. A crucial feature of MCA is the possibility to assess the
relationships between the variables and study the associations between the categories by
means to analyze the generated multidimensional maps [21]. Finally, a hierarchical cluster
analysis (HCA) was used in the present work. The HCA method explores the organization
of variables or observations in groups and among groups depicting a hierarchy. HCA's
result is usually presented in a diagram, so-called dendrogram, which is a plot that shows
the hierarchical relationship between objects (variables or observations). Thus, this
method was applied to obtained MCA maps, where the hierarchical grouping of
categorical variables was performed according to Ward's minimum variance method [22].
The MCA, PCA, and HCA analysis were performed by using the Minitab 17.0 version and
Statgraphics-centurion 18.0 version software packages. Mantel's test was performed by
XLSTAT 2020 software.
RESULTS AND DISCUSSION
Exploratory analysis using principal component analysis (PCA)
To reveal the dominant patterns and possible groupings in the complete dataset (N = 821),
a PCA was carried out based on the correlation matrix of the age, weight, and gender
demographic variables. The first principal component (PC1) accounted for 45.9 % and the
second principal component (PC2) for 33% of the variance in variables considered. In Fig. 1
the two-dimensional scatterplot of the loadings is displayed.
Fig. 1. Loading plot of PCA model based on PC1 and PC2 components
The loading plot shows that the PCA model's first dimension mostly reflects individuals'
weight and sex, both unrelated to each other and with an opposite linkage. In contrast,
the age of individuals dominates the second dimension. In Figure 2, the score plot shows
the projection of all the observations (individuals) onto space spanned by the PC1 and PC2
components. The PCA model's interpretation can be facilitated by simultaneously looking
at both plots shown in Fig.1 and 2.
Fig. 2. Score plot of the PCA model based on PC1 and PC2 components. (A) The black squares
represent individuals (observations) older than 50 years of age. (B) The 107 individuals
(observations) assigned positive for COVID-19 are highlighted. Group treated with HCQ (HCQ-1)
are denoted with black circles. Red squares characterize the placebo group (HCQ-2).
On analyzing the graphs A and B showed in Fig. 2, in an initial look, one notes a data
structure relatively homogeneous concerning the demographic variables. However, a
close examination of these graphs reveals differences in the effect of HCQ among the
group of people under and over 50 years of age. The authors' subgroup analyses in the
original work confirm this (see table S6, appendix of ref. 12). The authors perform an
absolute risk difference analysis for three age-subgroups: 18-35 yrs (P= 0.108), 36-50 yrs
(P= 0.387), and > 50 yrs (P= 0.125). However, if one performs a risk analysis considering
only two age-subgroups; that is, a group of ≤ 50 yrs and other group of >50 yrs, finds
statistical evidence at a > 90% confidence level for the group of subjects of ≤ 50 yrs (P=
0.083), that HCQ show benefit over placebo to prevent illnesses compatible with Covid-19.
Further, if one performs the same age-subgroup analysis (≤ 50 and > 50 yrs) but for the
high-risk exposure group (N = 719), one finds statistical evidence again, but this time at a >
95% confidence level for the group of subjects of ≤ 50 yrs (P= 0.025). Taking into account
the symptom-based criteria used by Boulware et al. in the assignment of subjects as illness
compatible with Covid-19, this last finding is particularly important because of the lower
degree of expected error in the COVID-19 illness assignment method for the high-risk
exposure group. Table 2 summarizes these results.
Table 2. Analysis of risk on effects of HCQ as post-exposure prophylaxis for COVID-19
Participants
Age in years
Treatment
with HCQ
Placebo
Test and CI for
Risk Difference
P Value
(two-tailed
test)
Complete Dataset
(N = 821)
N N Events
N N Events
≤ 50
310 37
316 53
-0.048(-0.094, -0.002)a
0.083(c)
> 50
104 12
91 5
0.060(-0.004, 0.125)a
0.125
High-Risk Exposure
Group (N = 719)
≤ 50
275 31
272 49
-0.067(-0.126, -0.008)b
0.025(d)
> 50
90 12
82 5
0.072 (-0.015, 0.160)b
0.104
(a) 90% confidence level, (b) 95% confidence level, (c) P = 0.088 (Fisher’s exact test), (d) P = 0.029
(Fisher’s exact test)
In light of these results, several important considerations must be highlighted. First and
foremost, taking into account that the COVID-19 pandemic has and will continue to impact
economics and public life profoundly, the fact that HCQ exhibits a chemopreventive effect
on the population of 50 or less than 50 years of age is of vital importance. Evidence of this
is a report dated August 14, 2020, from CDC and the U.S. Department of Health and
Human Services that summarizes the pandemic's dramatic effects on the U.S. population's
mental health. Strikingly, the most affected population was the youngest population. For
example, people among 18 to 24 yrs (25.5% of respondents) and 25 to 44 yrs (16.0% of
respondents) seriously considered suicide in the past 30 days, as can be observed in table
1 of that report, among others adverse mental health outcomes [23].
The other aspect to highlight is about the age-subgroup analysis performed in the present
study, which suggests the influence of sample size on the P-value, an issue that an
editorial of Nature has recently appraised [24]. This fact becomes evident by carrying out
a comparative analysis. For example, the two age-groups (18-35 and 36-50 yrs) that
Boulware et al. analyzed in the original work correspond to a sample size of 296 and 330
individuals (observations). Now, if both groups are analyzed as a single group, as done in
the present work, the sample size increases to 626 subjects, and the P-value decreases to
the point of being statistically significant at a > 90% confidence level. However, it is
essential to note that the assumption of sample size's influence about the decrease of P -
value is valid only when the observed differences between treatment and control groups
respond to a causal origin, as in this case, and not at the random source. Following this
line of analysis, it is also clear that incorporating the group of people over 50 years of age
implies an increase of sample size from 626 to 821. However, the P-value now does not
decrease but grows and is not statistically significant at a > 90% confidence level. Although
it is not clear which are the latent factor (s) that explain this change in the effect of HCQ
for this age-group, it is likely related to several chronic health conditions present in older
people. Thus, to obtain insights into the impact of HCQ for this age-group, a multiple
correspondence analysis (MCA) was performed by using, in addition to demographic
variables, several clinical and interventional variables.
Multiple correspondence analysis (MCA)
A first multiple correspondence analysis (MCA-1) was carried out using the data matrix's
demographic and clinical variables. The aim of not including interventional variables in this
initial exploratory analysis was to evaluate at baseline of demographic and clinical
variables and the association and grouping patterns. As previously mentioned, PCA
handles continuous variables, whereas MCA handles categorical variables. Thus, the age
(AG) and weight (WT) variables were discretized in two categories: between ≤ 50 yrs and >
50 yrs for age and between < 170 lbs and > 170 lbs for weight. The interval selected for
the age variable was discussed before, whereas the mean value was the weight variable's
criteria. Concerning the demographic and dichotomous variable, gender (labeled as sex-1
and sex-2, M vs. F), eight participants (rows in data array) were excluded from analysis
since they did not respond to the quiz on gender. Consequently, the dataset used in all
MCA was with an N = 813. The clinical variables included in the MCA were hypertension
(P), diabetes (D), asthma (A), and other-comorbidities (OT). Such variables were labeled as
follows: P1 or P0, D1 or D0, A1 or A0, and OT1 or OT0, respectively, where '1' and '0'
indicate the presence or absence of a particular condition. The scree plot was used to
determine the number of factors to retain in the analysis. In Fig. 3 are represented the
results of MCA-1 based on the indicator matrix for the first 3 dimensions.
Fig. 3. Multiple correspondence analysis maps for projections of demographic and clinical
variables on the first three dimensions. See text for details of categorical variables.
As shown in Fig.3, the first three principal factorial axes, describe a substantial proportion
(65.29%) of the total inertia (variance) in the data matrix. The relative positions of the
category points in these maps indicate certain similarity or association levels be tween the
categories. On analyzing the graph of F1 vs. F2, one observes two major groups, first
characterized by the clinical categories P1, D1, A1, and OT1 along with the demographic
category AG > 50 yrs, indicating that chronic health conditions such as hypertension,
diabetes, asthma, and other-comorbidities are associated with the people over 50 years.
These observations are in line with the results obtained from several population-based
studies regarding age-related chronic diseases, which provide evidence that comorbidities
are typically more common in older age groups. A comprehensive study on this topic
corresponds to a recent review by Marengoni et al. [25]. The other group was formed
between the AG ≤ 50 yrs category and the demographic types gender (sex) and weight
(WT), along with the clustering around the origin of P0, D0, A0, and OT0 clinical
categories. In this last group, also are observed associations between the categories WT>
120 and sex-1 (male) and WT <120 and sex-2 (female). Several studies on the association
between gender and weight have been reported [26]. On the other hand, on the F2-F3
graph analysis, the A1 and OT1 location shows they are the farthest from the origin,
clustered together far-right. This strong association observed between asthma and the
OT1 variable suggests that in the population analyzed in the present study, the
participants with asthma also had other comorbidities. The association and the impact of
comorbidities on asthma have been recently reviewed by Rogliani et al. [27].
Whether or not all associations or interrelationships discussed above influence the
effectiveness of HCQ as a chemopreventive agent will be discussed later. As shown in Fig.
4, a hierarchical cluster analysis (HCA) using Ward's method was applied to information
extracted by the first three principal factorial axes. The categories' observed grouping
summarizes and confirms the performed previous analysis of the maps obtained using
MCA-1.
Fig. 4. Dendrogram of Hierarchical Cluster Analysis (HCA) on the first three MCA-1 dimensions,
showing the grouping clinical and demographic categories according to Ward’s minimum variance
method.
A second multiple correspondence analysis (MCA-2) was performed, including, in addition
to the clinical and demographic variables, the following dichotomous variables: the
treatment and no-treatment (placebo) with HCQ (labeled as HCQ-1 and HCQ-2,
respectively), and the primary outcome labeled with positive or negative signs. In Fig. 5
are represented the results of MCA-2 based on the indicator matrix for the first three
dimensions.
Fig. 5. Multiple correspondence analysis maps for projections of demographic, clinical, and
interventional variables on the first three dimensions. See text for details of categorical variables.
Basing on the eigenvalues and according to the scree plot, four factors were retained for
the analysis. The first factor accounted for 25.40% of the data matrix variance, the second
for 15.94 %, the third for 11.48%, and the fourth for 11.13% of the variance. Altogether,
the factors extracted accounted for about 64% of the variance in the matrix data. On
analyzing the F1 vs. F2 relationship in Fig. 5, it is clear that the clustering pattern of
categories P1, D1, A1, OT1, and AG > 50 yrs is similar to that observed in the MC1 map
shown in Fig. 4. In contrast, the relationship between F3 vs. F2 showed in Fig. 5 presents a
different association pattern compared to the one observed in Fig. 4. This difference arises
in the third factorial axis (F3), which is mostly loaded by the interventional categories HCQ
and the primary endpoint, and therefore, a separate discussion should be devoted. This
Factor contains information that clearly discriminates (negative vs. positive coordinates
along the F3 axis) between the HCQ-1 and negative primary endpoint (-) group and the
other group formed by HCQ-2 and positive primary endpoint (+). In other words, this F3
vs. F2 map contains information explicitly expressed by the association between the
interventional variables, thus, suggesting that positive COVID-19 subjects correspond or
are more associated with the placebo group, and vice versa. Considering the four-
dimensional nature of the developed MCA-2 model, a hierarchical cluster analysis (HCA)
using Ward's method was applied to the first four principal factorial axes. The
corresponding dendrogram is shown in Fig. 6.
Fig. 6. Dendrogram of Hierarchical Cluster Analysis (HCA) on the first four MCA-2 dimensions,
showing the grouping clinical, demographic, and interventional categories according to Ward’s
minimum variance method.
Some issues should be in mind to overview obtained results by using the PCA, MCA, and
HCA chemometric approaches. First and foremost, the individuals' age is a determining
factor in the chemopreventive efficacy exerted by HCQ, which is demonstrated by the
results shown in Table 2. Second, the statistical techniques here used are basically
exploratory methods, and therefore they do not provide statistical significance of the
displayed clustering patterns. However, admitting this, the associations between the
categories revealed by all MCA-maps strongly suggest two things: in the first place, for the
studied population sample, participants over 50 years of age presented at the time of
enrollment several age-related chronic diseases, which could be one of the factors for the
Categories
Distance
SEX-1
WT>170
SEX-2
WT<170
HCQ-2
HCQ-1
OT0
A0
-
D0
P0
AG<50
OT1
A1
+
D1
P1
AG>50
6.59
4.40
2.20
0.00
Dendrogram with Ward Linkage and Euclidean Distance
Factors F1 F2 F3 F4 (63.94%)
ineffectiveness of HCQ for this age population group. However, this remains an open
issue, as discussed later. Secondly, the association between the placebo group (HCQ-2)
and the group corresponding to positive COVID-19 subjects shown in the F3 vs. F2 map of
MCA-2 (Fig.5), again suggests the effectiveness of HCQ as a chemopreventive agent.
Finally, as previously mentioned, the MCA maps showed a distinctive behavior of A1 and
OT1 concerning the rest of the clinical categories. Consequently, to assess these variables'
possible effect on the homogeneity/heterogeneity of the population under study, a
comparative analysis between the data matrix of treatment and placebo groups was
performed.
HCQ-Treatment and placebo matrix comparison
One of the RCTs' distinguishing characteristics is that both the treatment and control
groups do not present systematic differences about all baseline and on-treatment
variables that could influence the outcome, except for the study treatment. For example,
if groups are not comparable to key demographic factors, then between-group differences
in treatment outcomes cannot be attributed solely to the intervention study. The
technique usually used to avoid systematic differences between treatment and control
groups and eliminate or minimize the influence of confounding variables is the so-called
randomization. Thus, bearing in mind the observed atypical behavior of A1 and OT1 versus
the rest of the clinical categories according to results obtained from the MCA maps, a
comparative study between the treatment and control groups was performed to assess
the possible confounding effect of these variables. The study was performed by using the
Mantel's permutation test, which is based on calculating the Pearson correlation
coefficient between two (dis)similarity or distance matrices, and then a randomization
procedure or a parametric approximation is applied to evaluate whether the observed
correlation is different from random [28]. The procedure's basic assumption carried out in
this study is that the MCA-eigenvectors matrix of two similar samples (e.g., treatment and
placebo) should explain similar amounts of variance in these samples. Thus, the procedure
applied can be expressed as follows: first, a separate MCA was performed for both the
treatment group (N=410) and the placebo group (N=403), obtaining the corresponding
matrices of the principal coordinates (eigenvectors). The MCA applied to each group was
carried out using the demographic (AG, WT, Sex) and clinical (P, D, A. OT) variables and
extracting the maximum number of principal coordinates (seven in this case) to account
for the data matrix's 100% variance. The next step consisted of translating these
eigenvector matrices into the corresponding distance matrices to finally apply the Mantel
test to evaluate the association between such distance matrices. The following figure
shows the results obtained after applying the Mantel test:
Fig. 7. Mantel test for correlation between MCA-eigenvectors distance matrix of the treatment
group and placebo group. The labels “Matrix HCQ-1” and “Matrix HCQ-2” correspond to the MCA-
eigenvectors distance matrix of the treatment group (N=410) and placebo group (N=403),
respectively.
As shown in Fig. 7A, the Mantel test revealed a modest but statistically significant
correlation between both the treatment and placebo distance matrices (r = 0.639, P =
0.002). However, a close examination of this graph shows that three data points have
values that significantly deviate from the other data points, causing the low correlation
coefficient observed between both matrices. On the other hand, Fig 7B shows the graphs
after applying the Mantel test to the distance matrices of both the treatment and placebo
eigenvector matrices but obtained separately for the demographic and clinical variables.
The procedure followed was exactly the one mentioned above: extraction of the
maximum number of MCA-principal coordinates to obtain 100% of the data matrix
variance, followed by translating into the corresponding distance matrices and finally
applying the Mantel test. Looking at both graphs in Fig. 7B, it is evident that the low
obtained correlation between both matrices showed in Fig. 7A is due to the total absence
of correlation between the matrices based only on clinical variables and not on those
based on demographic variables, which showed an almost perfect correlation. The
identification of the anomalous data points shown in Fig. 7A reveals that they correspond
to the intercorrelations between the first, third, and fourth principal coordinates of the
treatment and placebo matrices. Fig 8 shows the relationships between these
coordinates; that is, the first and third MCA-principal coordinates of both the treatment
and placebo matrices. The obtained graphs clearly reveal that finally, the categories A1
and OT1 are the responsible for the anomalous behavior previously mentioned.
Fig. 8. Simple regressions between first and third MCA-principal coordinates of both the treatment
and placebo matrices, displaying at 95% confidence intervals (equations not shown).
In summary, the developed Mantel test reveals systematic differences between the
treatment and placebo groups in their clinical characteristics, specifically regarding asthma
and other-comorbidities (A1, OT1). Thus, admitting this fact and considering that clinical
Coord3-HCQ-2 (Placebo)
Coord3-HCQ-1 (Treatment)
210-1-2
3
2
1
0
-1
-2
-3
S 0.572649
R-Sq 72.6%
R-Sq(adj) 70.3%
Regression
95% CI
OT1
OT0
A1
A0
D1
D0
P1
P0
SEX-2
SEX-1
WT>170
WT<170
AG<50
AG>50
Coord1-HCQ-2 (Placebo)
Coord1-HCQ-1(Treatment)
1.00.50.0-0.5- 1.0-1.5-2.0
1
0
-1
-2
-3
S 0.367318
R-Sq 85.3%
R-Sq(adj) 84.1%
Regression
95% CI
OT1
OT0
A1
A0
D1
D0
P1
P0
SEX-2
SEX-1
WT>170
WT<170
AG<50
AG>50
categories A1 and OT1 present a strong association with the variable AG> 50 yrs as shown
in MCA-maps, a risk analysis was performed in order to gain further insight on the
effectiveness of HCQ as a prophylactic agent but excluding of analysis the individuals with
these clinical characteristics (A1 and OT1 rows in data array). Table 3 summarizes these
results.
Table 3. Analysis of risk on effects of HCQ as post-exposure prophylaxis for COVID-19
excluding of analysis the individuals belonging to the A1 and OT1 clinical categories
Participants
Treatment
with HCQ
Placebo
Test and CI for
Risk Difference
P Value
(two-tailed
test)
Complete Dataset
N N Events
N N Events
N = 710
361 40
349 54
-0.044(-0.086, -0.0021)a
0.084c
High-Risk Exposure
Group
N = 620
319 35
301 50
-0.056(-0.111, -0.0021)b
0.042d
(a) 90% confidence level, (b) 95% confidence level, (c) P = 0.0966 (Fisher’s exact test), (d) P = 0.047
(Fisher’s exact test)
The results shown in Table 3 are very encouraging because HCQ also appears to show
effectiveness as a prophylactic agent for people over 50 years of age when the test and
control groups present similar characteristics about all baseline variables. An important
additional support to this finding comes from the review recently published by Chuan
Yang et al. [29], which reported that most studies on using HCQ as a prophylactic agent
showed a beneficial effect supporting their use independent of age population. To this
end, the consistency of results shown in Table 3 provides support added to the obtained
results from MCAs and HCA approaches.
CONCLUSION
Two important consequences emerge from the present report.
Firstly, the obtained results evidence that the individuals' age is a determining factor in
the chemopreventive efficacy exerted by HCQ. Thus, taking into account that the COVID-
19 pandemic has and will continue to impact economics and public life profoundly, the
fact that HCQ exhibits a chemopreventive effect on the population of 50 or less than 50
years of age is of essential importance. Besides, it is important to note that considering
the results obtained by jointly applying the MCA models and the Mantel test, the HCQ also
appears to show effectiveness as a prophylactic agent for people over 50 years of age, but
when the characteristics of subject populations are similar in test and control groups.
These results are in complete agreement with and extend the implications of those
reported by Chuan Yang et al. [29].
Secondly, this study provides evidence for the great potential of the chemometric
approaches for dealing with complex systems since principal component-like methods,
such as MCA, consider the behavior of multiple variables simultaneously providing useful
information that would not get with only an evaluation between two variables.
ACKNOWLEDGMENT
I cordially thank Dr. Samir A Saidi for careful reading of the manuscript and for his friendly
support. The present work was supported by grants from University of San Luis and
CONICET, Argentine.
CONSENT FOR PUBLICATION
Not applicable.
CONFLICT OF INTEREST
The author declares no conflict of interest, financial or otherwise.
REFERENCES
1- Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected
with 2019 novel coronavirus in Wuhan, China. Lancet, 2020, 395:497-506.
2- Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and
epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor
binding. Lancet, 2020, 395:565-74.
3- Amawi H, Deiab GA, Aljabali AAA, Dua K, and Tambuwala MM; COVID-19 pandemic: an
overview of epidemiology, parthenogenesis, diagnostics and potential vaccines and
therapeutics, Ther Deliv. 2020, Apr: 10.4155/tde-2020-0035.
4- Nittari G, Pallotta G, Amenta F, Tayebati SK, Current pharmacological treatments for
SARS-COV-2: A narrative review, European Journal of Pharmacology, 2020, 882, 173328.
5- Ortiz-Prado E, Simbaña-Rivera K, Gomez-Barreno L, et al. Clinical, molecular, and
epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease
2019 (COVID-19), a comprehensive literature review, Diagnostic Microbiology and
Infectious Disease, 2020, 98, 115094.
6- Solomon VR and Lee H, Chloroquine and its analogs: A new promise of an old drug for
effective and safe cancer therapies, European Journal of Pharmacology, 2009, 625, 1–
3,220-233.
7- Ben-Zvi I, Kivity S, Langevitz P, Shoenfeld Y, Hydroxychloroquine: From Malaria to
Autoimmunity, Clin Rev Allergy Immunol, 2012; 42(2): 145–153.
8- Hashem AM, Alghamdi BS, et al., Therapeutic use of chloroquine and hydroxy-
chloroquine quine in COVID-19 and other viral infections: A narrative review, 2020, 35,
101735.
9- Pastick KA, Okafor EC, Wang F, et al., Review: Hydroxychloroquine and Chloroquine for
Treatment of SARS-CoV-2 (COVID-19), Open Forum Infectious Diseases, 2020, 7(4),
ofaa130.
10- Hsiehchen et al., Deficiencies in the Designs and Interventions of COVID-19 Clinical
Trials, Med (N Y), 2020, https:// doi.org/10.1016/j.medj.2020.06.007.
11- CovidAnalysis, Early treatment with hydroxychloroquine: a country-based analysis,
2020, https://hcqtrial.com/.
12- Boulware DR, Pullen MF, Bangdiwala AS, et al., A Randomized Trial of
Hydroxychloroquine as Postexposure Prophylaxis for Covid-19, N Engl J Med, 2020,
383(6):517-525.
13- Cohen, M.S., Hydroxychloroquine for the Prevention of Covid-19—Searching for
Evidence, N Engl J Med, 2020; 383:585-586.
14- Watanabe, M., Efficacy of Hydroxychloroquine as Prophylaxis for Covid-19, 2020,
arXiv:2007.09477v2 [q-bio.QM].
15- Brown, N., et al., Artificial intelligence in chemistry and drug design, Journal of
Computer-Aided Molecular Design, 2020, 34:709–715.
16- Luco, J.M., Prediction of the brain-blood distribution of a large set of drugs from
structurally derived descriptors using partial least-squares (PLS) modeling, Journal of
Chemical Information and Computer Sciences, 1999, 39(2), 396-404.
17- Luco, J.M., Marchevsky, E., QSAR studies on blood-brain barrier permeation, Current
Computer-Aided Drug Design, 2006, 2(1), pp. 31-55.
18- Paolanti, M., Frontoni, E., Multidisciplinary Pattern Recognition applications: A review.
Computer Science Review, 2020, 37, 100276.
19- Saidi SA, Independent component analysis of microarray data in the study of
endometrial cancer, Oncogene, 2004, 26;23(39):6677-83.
20- Ebrahim S, Sohani ZN, Montoya L, et al, Reanalyses of Randomized Clinical Trial Data,
JAMA. 2014, 312(10):1024-1032.
21- Michael Greenacre and Jorg Blasius Eds.; In Multiple Correspondence Analysis and
Related Methods, Chapman and Hall/CRC, 2006, ISBN 9781584886280.
22- Ward, J. H. Jr. Hierarchical grouping to optimize an objective function, J. Am. Stat.
Assoc. 1963, 58, 236–244.
23- Czeisler ME et al., Mental Health, Substance Use, and Suicidal Ideation During the
COVID-19 Pandemic—United States, MMWR, 2020, Vol. 69 / No. 32.
24- Amrhein, V., Greenland, S. and McShane, B. Scientists rise up against statistical
significance, Nature, 2019, 567, 305-307. DOI: 10.1038/d41586-019-00857-9.
25- Marengoni A, et al., Aging with multimorbidity: A systematic review of the literature,
Ageing Research Reviews 2011, 10, 430– 439.
26- Chiriboga DE, Gender Differences in Predictors of Body Weight and Body Weight
Change in Healthy Adults, Obesity, 2008, 16, 137–145. doi:10.1038/oby.2007.38.
27- Rogliani P, et al., The impact of comorbidities on severe asthma, Curr Opin Pulm Med,
2020, 26:47–55.
28- Mantel N, The Detection of Disease Clustering and a Generalized Regression Approach,
Cancer Research, 1967, 27(1), 209-220.
29- Chuan Yang A, et al., Hydroxychloroquine and Interferons for the Prophylaxis and Early
Treatment of Covid-19-Current Clinical Advances, J Clin Cell Immunol, 2020, Vol.11 Iss.5
No:596.
