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Int. J. Environ. Res. Public Health 2022, 19, 10362. https://doi.org/10.3390/ijerph191610362 www.mdpi.com/journal/ijerph
Article
Lowered Quality of Life in Long COVID Is Predicted by
Affective Symptoms, Chronic Fatigue Syndrome, Inflammation
and Neuroimmunotoxic Pathways
Michael Maes 1,2,3,4,*, Haneen Tahseen Al-Rubaye 5, Abbas F. Almulla 1,6, Dhurgham Shihab Al-Hadrawi 7,
Kristina Stoyanova 2,3, Marta Kubera 8 and Hussein Kadhem Al-Hakeim 9
1 Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
2 Department of Psychiatry, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
3 Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria
4 School of Medicine, Barwon Health, IMPACT, The Institute for Mental and Physical Health and Clinical
Translation, Deakin University, Geelong 3217, Australia
5 College of Medical laboratory Techniques, Imam Ja’afar Al-Sadiq University, Najaf 54001, Iraq
6 Medical Laboratory Technology Department, College of Medical Technology, The Islamic University,
Najaf 54001, Iraq
7 Al-Najaf Center for Cardiac Surgery and Transcatheter Therapy, Najaf 54001, Iraq
8 Laboratory of Immunoendocrinology, Department of Experimental Neuroendocrinology, Maj Institute of
Pharmacology, Polish Academy of Sciences, 12 Smetna St., 31-343 Krakow, Poland
9 Department of Chemistry, College of Science, University of Kufa, Kufa 54002, Iraq
* Correspondence: dr.michaelmaes@hotmail.com
Abstract: The physio-affective phenome of Long COVID-19 is predicted by (a) immune-inflamma-
tory biomarkers of the acute infectious phase, including peak body temperature (PBT) and oxygen
saturation (SpO2), and (b) the subsequent activation of immune and oxidative stress pathways dur-
ing Long COVID. The purpose of this study was to delineate the effects of PBT and SpO2 during
acute infection, as well as the increased neurotoxicity on the physical, psychological, social and en-
vironmental domains of health-related quality of life (HR-QoL) in people with Long COVID. We
recruited 86 participants with Long COVID and 39 normal controls, assessed the WHO-QoL-BREF
(World Health Organization Quality of Life Instrument-Abridged Version, Geneva, Switzerland)
and the physio-affective phenome of Long COVID (comprising depression, anxiety and fibromyal-
gia-fatigue rating scales) and measured PBT and SpO2 during acute infection, and neurotoxicity
(NT, comprising serum interleukin (IL)-1β, IL-18 and caspase-1, advanced oxidation protein prod-
ucts and myeloperoxidase, calcium and insulin resistance) in Long COVID. We found that 70.3% of
the variance in HR-QoL was explained by the regression on the physio-affective phenome, lowered
calcium and increased NT, whilst 61.5% of the variance in the physio-affective phenome was ex-
plained by calcium, NT, increased PBT, lowered SpO2, female sex and vaccination with Astra-
Zeneca and Pfizer. The effects of PBT and SpO2 on lowered HR-QoL were mediated by increased
NT and lowered calcium yielding increased severity of the physio-affective phenome which largely
affects HR-QoL. In conclusion, lowered HR-Qol in Long COVID is largely predicted by the severity
of neuro-immune and neuro-oxidative pathways during acute and Long COVID.
Keywords: Long COVID; depression; myalgic encephalomyelitis/chronic fatigue syndrome;
depression; neuro-immune; inflammation; psychiatry
1. Introduction
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syn-
drome coronavirus 2 (SARS-CoV-2) is still spreading around the world. Most people with
COVID-19 have mild clinical symptoms, but some people may experience acute
Citation: Maes, M.; Al-Rubaye, H.T.;
Almulla, A.F.; Al-Hadrawi, D.S.;
Stoyanova, K.; Kubera, M.;
Al-Hakeim, H.K. Lowered Quality
of Life in Long COVID Is Predicted
by Affective Symptoms, Chronic
Fatigue Syndrome, inflammation
and Neuroimmunotoxic Pathways.
Int. J. Environ. Res. Public Health 2022,
19, 10362. https://doi.org/10.3390/
ijerph191610362
Academic Editors: Paul B.
Tchounwou and Lonnie R. Snowden
Received: 26 July 2022
Accepted: 17 August 2022
Published: 19 August 2022
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
tional affiliations.
Copyright: © 2022 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).
Int. J. Environ. Res. Public Health 2022, 19, 10362 2 of 21
respiratory distress or even severe acute respiratory syndrome (SARS), which can lead to
multiorgan failure and death, especially in older adults and people with comorbid condi-
tions such as hypertension, obesity, and type 2 diabetes mellitus (T2DM) [1,2]. Ten to
twenty percent of COVID-19 patients will have Long COVID symptoms within weeks to
months of recovery, whether they experienced symptoms or were asymptomatic during
the acute stage of the illness [3,4]. Following recovery from the acute phase, many people
with Long COVID experience a range of mental symptoms, such as sleep disturbances,
affective symptoms (low mood and anxiety), chronic fatigue, neurocognitive impair-
ments, as well as somatic manifestations such as muscle pain, muscle tension, autonomic
symptoms, gastro-intestinal symptoms, headache, and a flu-like malaise [5–11].
In recent studies, we discovered that (a) acute and Long COVID are both character-
ized by concurrent elevations in fatigue and physiosomatic (previously called psychoso-
matic) symptoms (pain symptoms, malaise, muscle tension, somatic anxiety, gastrointes-
tinal anxiety, genitourinary anxiety, somatic sensory, cardiovascular, autonomous and
genitourinary symptoms), key depression (depressed mood, guilt-related feelings, sui-
cidal ideation, loss of interest), and key anxiety (anxious mood, tension, fears, anxiety be-
havior at interview) symptoms; and (b) that in both conditions a validated and replicable
single latent vector could be extracted from these depression, anxiety, fatigue and physi-
osomatic symptoms [7,8,12,13]. This indicates that a single latent trait drives these multi-
ple neuro-psychiatric symptoms which, therefore, are all manifestations of the common
core dubbed the “physio-affective phenome” of both acute and Long COVID [7,8,12,13].
The pathogenesis of acute COVID-19 involves the entry of SARS-CoV-2 into host res-
piratory epithelial cells followed by viral replication and translation in the cytoplasm and
infection of adjacent cells of the host cells [14–21]. These processes are accompanied by
the activation of immune-inflammatory pathways and, at times, lung injuries, pneumo-
nia, and excessive inflammatory responses, which can progress into disseminated intra-
vascular coagulation and multisystem failure [14–21].
The physio-affective phenome in acute and long COVID-19 is substantially predicted
by immune-inflammatory pathways. First, abnormalities from chest computerized to-
mography scans (CCTAs), lower oxygen saturation in peripheral blood (SpO2), increased
peak body temperature (PBT), and higher levels of immune-proinflammatory mediators
are all strongly associated with the physio-affective phenome in acute COVID [12,13]. Im-
portantly, during acute infection, lower SpO2 and higher PBT are reliable indicators of the
intensity of the immune-inflammatory response, and they may even be able to predict the
development of critical COVID-19 and higher mortality [13,22]. Second, indices of in-
creased nitro-oxidative and immune-inflammatory processes, such as C-reactive protein
(CRP), myeloperoxidase (MPO), nitric oxide, lipid and protein oxidation, activation of the
nucleotide-binding domain, leucine-rich repeat and pyrin domain-containing protein 3
(NLRP3) inflammasome, and lowered antioxidant defenses, such as lowered glutathione
peroxidase (Gpx) and zinc levels, and lowered serum calcium are also strongly predictive
of the physio-affective phenome of Long COVID [7,8]. The physio-affective phenome of
long COVID is, therefore, substantially predicted by the infectious-immune-inflammatory
pathways during acute SARS-CoV-2 infection, and these effects are mediated via im-
mune-inflammatory and nitro-oxidative pathways, according to our findings [7,8]. It
should be stressed that also in major depression (MDD), bipolar disorder (BD), and
chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), the same pathways and
biomarkers are substantially associated with the severity of these disorders [23–25].
Lowered health-related quality of life (HR-QoL) is another characteristic of Long
COVID. For instance, 90 days after the acute phase, patients exhibiting symptoms of Long
COVID (such as fatigue, sadness, myalgia, joint pain, dyspnea, anxiety, and vertigo) had
significantly poorer SF-36 ratings for physical function, vitality, mental health, and phys-
ical health [26]. Another study found that HR-QoL was substantially lower 8.1 (±3.2)
months after the acute episode [27]. In both systematic review and meta-analysis studies,
HR-QoL was considerably decreased in Long COVID-19 patients especially in those with
Int. J. Environ. Res. Public Health 2022, 19, 10362 3 of 21
fatigue and admission to critical care units, with up to 37% (95% confidence interval: 18
to 60%) of the patients exhibiting decreased HR-QoL [28–30]. In neuropsychiatric disor-
ders, including affective disorders and schizophrenia, lowered HR-QoL is substantially
linked with the severity of depression, chronic fatigue, anxiety, and physiosomatic symp-
toms, as well as immune-inflammatory and nitro-oxidative pathways [31–33]. However,
there are no data indicating whether the immune-inflammatory response during the acute
phase and/or the subsequent immunological and nitro-oxidative pathways of Long
COVID may explain the decreased HR-QoL.
Hence, the present study was performed to delineate the effects of the inflammatory
responses during the acute phase (assessed using PBT and SpO2), and indicants of in-
creased neurotoxicity during Long COVID (including activation of the NLRP3 and oxida-
tive pathways, insulin resistance and lowered serum calcium) on four different domains
of HR-QoL (physical, psychological, social and environmental) and whether the effects of
the acute infectious phase on the Long COVID physio-affective phenome are mediated by
increased neurotoxicity, insulin resistance and lowered calcium.
2. Methods
2.1. Participants
Prior to participating in our study, we obtained written informed consent from all
participants. The institutional ethics board and the Najaf Health Directorate-Training and
Human Development Center approved our research, which were numbered 8241/2021
and 18378/2021, respectively. The current study was designed and carried out in accord-
ance with Iraqi and international ethical and privacy laws, including the World Medical
Association’s Declaration of Helsinki, The Belmont Report, the Council for International
Organizations of Medical Sciences (CIOMS) Guideline, and the International Conference
on Harmonization of Good Clinical Practice; our institutional review board follows the
International Guidelines for Human Research Safety; and our institutional review board
adheres to the International Guidelines for Human Research Safety (ICH-GCP).
In this study, we recruited 86 participants with Long COVID and 39 normal controls
from September to the end of December 2021. Long COVID was diagnosed using the
World Health Organization (WHO) criteria [3]. These criteria used here are as follows: (a)
the subjects should have a confirmed infection with COVID-19, (b) patients’ daily life ac-
tivities should be influenced by at least two symptoms, namely fatigue, memory or con-
centration impairment, achy muscles, absence of smell or taste senses, affective symp-
toms, and cognitive impairment, (c) symptoms should last for at least two months, and
(d) symptoms should persist beyond the acute phase or become apparent 2–3 months
later. The current study comprises (a) a case-control study comparing Long COVID pa-
tients with healthy controls, and (b) a retrospective inception and single cohort study
which included inflammatory measures during the acute phase of Long COVID some
months earlier.
In the recruited individuals (all staff members) with Long COVID, the diagnosis of
acute COVID-19 infection was made by specialists in the fields of clinical pathology and
virology based on the following criteria: (a) positive IgM antibody reactivity against
SARS-CoV-2, (b) positive test results of reverse transcription real-time polymerase chain
reaction (rRT-PCR), (c) clinical signs of severe infection, such as loss of the senses of smell
and taste, shortness of breath, coughing and fever, and (d) being quarantined, hospital-
ized and treated for COVID-19 infection in one of the official Iraqi COVID centers includ-
ing Middle Euphrates Center for Cancer; Al-Sader Medical City of Najaf; Al-Najaf Teach-
ing Hospital, Al-Hakeem General Hospital, Hasan Halos Al-Hatmy Hospital for Trans-
mitted Diseases, and Imam Sajjad Hospital. During Long COVID, the subjects showed a
negative PCR test and were free of any symptoms of acute COVID-19, such as dry cough,
sore throat, shortness of breath, fever, night sweats, or chills.
Int. J. Environ. Res. Public Health 2022, 19, 10362 4 of 21
The controls were selected from the same group of staff members employees as the
Long COVID participants and matched to the latter in terms of age, gender and BMI. A
Hamilton Depression Rating Scale (HAMD) [34] score of <9 was the criterion for partici-
pation in the control group, which included about 33% of individuals who reported minor
mental symptoms such as low mood and anxiety as a consequence of their social isolation
and lack of social ties. Controls were only included if they had a negative rRT-PCR test
result and had never shown clinical symptoms of COVID-19 infection.
Long COVID and control individuals with a life-time history of major depressive
disorder, bipolar disorder, generalized anxiety disorder, dysthymia, and panic disorder
as well as schizophrenia, psycho-organic syndrome, and substance use disorders (except
tobacco use disorder or TUD) were not included in the study. We also excluded Long
COVID and control individuals with a lifetime history of neurodegenerative and neuroin-
flammatory diseases such as Parkinson’s or Alzheimer’s disease, chronic fatigue syn-
drome, multiple sclerosis, stroke or systemic (auto-)immune diseases such as type 1 dia-
betes mellitus, psoriasis, inflammatory bowel disease, rheumatoid arthritis, systemic lu-
pus erythematosus. Subjects with hepatic or renal disease, pregnant women, and lactating
people were also excluded from the study.
2.2. Clinical Assessments
A senior psychiatrist conducted a semi-structured interview 3–4 months after recov-
ery from acute COVID-19 to obtain the socio-demographic and clinical characteristics of
all patients and the same psychiatrist also assessed the controls using the same interview
and rating scales. On the same day as the semi-structured interview, all subjects com-
pleted the WHO-QoL-BREF (World Health Organization Quality of Life Instrument-
Abridged Version) [35]. This scale rates 26 items across four categories of HR-QoL: (1)
Domain 1 or physical health: energy, sleep, fatigue, pain, discomfort, work capacity, ac-
tivities of daily living, medication dependence, and mobility; (2) Domain 2 or psycholog-
ical health: self-esteem, body image, learning, thinking, concentration, memory, negative
and positive feelings, and beliefs (spirituality-religion-personal); (3) Domain 3 or social
relationships: social support, sexual activity, and personal relationships; and (4) Domain
4 or environment: physical safety and security. We calculated the raw scores for the four
domains using the WHO-QoL-BREF criteria. The psychiatrist evaluated the severity of
several symptom domains including depression severity using the HAMD [34] and the
Beck Depression Inventory-II (BDI-II) [36], chronic fatigue and fibromyalgia using the Fi-
bro-fatigue (FF) scale [37], and anxiety severity using the Hamilton Anxiety Rating Scale
(HAMA, Monheim, Germany) [38]. Furthermore, we used the rating scale items to create
subdomain severity scores of the major symptoms. We divided the HAMD into two sub-
domains: pure depressive symptoms (pure HAMD), which is the sum of sad mood, feel-
ings of guilt, suicidal thoughts, and loss of interest, and physiosom HAMD (physiosom
HAMD), which is the sum of somatic anxiety, gastrointestinal (GIS) anxiety, genitourinary
anxiety, and hypochondriasis. Similarly, two subdomains of the HAMA were computed:
pure anxiety symptoms (pure HAMA), which were defined as the sum of anxious mood,
tension, fears, anxiety, and anxious behavior during the interview, and physiosomatic
HAMA symptoms (physiosom HAMA), which were defined as the sum of somatic sen-
sory, cardiovascular, GIS, genitourinary, and autonomic symptoms. Furthermore, after
removing items from the FF scale that represented cognitive and affective symptoms, a
pure physiosomatic FF (pure FF) score was calculated as the sum of muscular pain, muscle
tension, fatigue, autonomous symptoms, gastro-intestinal symptoms, headache, and a flu-
like malaise. We also computed the sum of all pure depressive BDI-II (pure BDI) symp-
toms, excluding physiosomatic symptoms, such as sadness, discouragement about the fu-
ture, feeling a failure, dissatisfaction, feeling guilty, feeling punished, being disappointed
in oneself, being critical of oneself, suicidal ideation, crying, loss of interest, difficulty mak-
ing decisions, and work inhibition. In previous studies [7,8,12,13], the physio-affective
phenome of Long COVID was defined as the first factor extracted from pure FF and BDI,
Int. J. Environ. Res. Public Health 2022, 19, 10362 5 of 21
as well as pure and physiosom HAMA and HAMD scores. We also recorded the vaccina-
tions that the subjects received (AstraZeneca, Pfizer, and Sinopharm) as well as the treat-
ments during the acute phase, namely dexamethasone, ceftriaxone (antibiotic of the ceph-
alosporin third generation), azithromycin (antibiotic), enoxaparin sodium (anticoagulant)
and bromhexine (mucolytic drug). TUD was diagnosed using the Diagnostic and Statisti-
cal Manual of mental Disorders, 5th edition. Body mass index (BMI) was calculated by
dividing weight in kilograms by height in meters squared.
2.3. Biomarker Assessments
The biomarkers of the acute phase of infection were peak body temperature (PBT)
and the lowest SpO2 values. Patients’ records were used to acquire PBT and the lowest
SpO2 values that were recorded while they were quarantined or hospitalized for the acute
infection. Both the electronic oximeter (Shenzhen Jumper Medical Equipment Co. Ltd.,
Shenzhen, China) and a sublingual digital thermometer with beep sound were used by a
qualified paramedical professional. A composite score based on lowered SpO2 but in-
creased PBT was calculated by subtracting the z transformed SpO2 (z SpO2) values from
z PBT values (dubbed as the TO2 index).
During Long COVID, 3–4 months after the acute phase, five milliliters of venous
blood were taken in the early morning hours of 7.30–9.00 a.m., and immediately injected
into serum tubes after the subjects had fasted overnight. We did not use any blood that
had been hemolyzed, lipemic, or icteric. After 10 min of incubation at room temperature,
all tubes were centrifuged at 3000× g rpm. For biochemical testing, we prepared three ali-
quots of serum and kept them at −70 °C in Eppendorf tubes. Agappe, Diagnostics Ltd.
(Cham, Switzerland), provided ready-to-use kits for the spectrophotometric measurement
of total serum calcium. Serum levels of IL-1β, IL-18, IL-10, caspase-1, MPO and advanced
oxidation protein products (AOPP) (a marker of protein oxidation) were measured using
kits from Nanjing Pars Biochem (Nanjing, China). Glucose was measured spectrophoto-
metrically by a kit supplied by Biolabo® (Maizy, France) and insulin using a commercial
ELISA sandwich kit from DRG® International Inc. (Springfield, NJ, USA). All inter-assay
coefficients of variations of all analytes were <10%. Using the biomarker concentrations,
we calculated z unit-based weighted composite scores including oxidative stress as z MPO
+ z AOPP (dubbed oxidative stress toxicity or OSTOX), NLRP3 inflammasome as z IL-1β
+ z IL-18 + z caspase 1 (dubbed NLRP3), and OSTOX+NLRP3 as zIL-1β + zIL-18 + z caspase
1 + z MPO + z AOPP. Insulin resistance was computed as z insulin + z glucose (dubbed
zIR) and added to OSTOX+NLRP3 was denoted as neurotoxicity (NT). Finally, we also
combined the biomarkers of acute inflammation (PBT and SpO2) together with the NT
biomarkers into one composite (dubbed NT+TO2) computed as z PBT − zSpO2 + zIL-1β +
zIL-18 + z caspase 1 + z MPO + z AOPP + zIR.
2.4. Statistics
Analysis of variance (ANOVA) and contingency tables were used to explore differ-
ences in scale or nominal variables between the research groups. The association between
scale variables was checked using Pearson product-moment coefficients. This study em-
ployed univariate general linear models (GLMs) to assess how clinical rating scale scores
and COVID biomarkers are associated with newly formed categories based on WHO-Qol
data while accounting for variables such as age, gender, sex and BMI. Fisher’s protected
Least Significant Difference (LSD) test was used to examine multiple groups mean differ-
ences (LSD) (protected means that the omnibus test should be significant prior to using
the LSD test). K-means and two-step cluster analysis were applied to the 4 WHO-QoL
domains to delineate novel clusters of subjects based on their WHO-QoL scores. The ac-
curacy of the cluster solution was evaluated employing the silhouette cohesion and sepa-
ration measure (>0.5 is deemed adequate). Multiple regression analysis was used to ex-
amine the potential of clinical rating scales and biomarkers of acute and Long COVID to
predict WHO-QoL scores, while allowing for the effects of age, sex, education, BMI,
Int. J. Environ. Res. Public Health 2022, 19, 10362 6 of 21
treatments and vaccinations. We also used a forward stepwise automatic regression
method with 0.05 and 0.06 p-values for inclusion and omission in the final regression
model. We calculated the standardized beta-coefficients and t-statistics (with exact p-
value) for each of the explanatory variables in the final recession models, and we also
calculated F statistics (and p values) and total variance (R2 or partial eta squared, also used
as effect size) explained by the model. Collinearity and multicollinearity were also evalu-
ated using the variance inflation factor (cut off >4), tolerance (<0.25) and the condition
index and variance proportions in the collinearity diagnostics table. Where needed, we
grouped predictors in composites to solve collinearity problems or to reduce the number
of features, for example, using the TO2 and NT indices. The White and modified Breusch-
Pagan tests were used to verify the heteroskedasticity. All statistical analyses were con-
ducted using IBM SPSS version 28 (IBM, Chicago, IL, USA). All tests are two-tailed, with
a significance level of p =0.05.
To investigate the causative relationships between the inflammatory response of
acute SARS-CoV-2 infection (the TO2 index), the biomarkers of Long COVID, the physio-
affective phenome of Long COVID and HR-QOL, a method known as partial least squares
(PLS) path analysis was utilized [24]. Our model assumes that the effects of the input var-
iables (infection and TO2 index) on HR-QoL are partially mediated by the path from bi-
omarkers of Long COVID (NT and calcium) to the physio-affective phenome. In addition,
we added sex, age, BMI, and vaccination as additional input variables. All the variables
that were used as input (e.g., sex, vaccination, TO2 index) and the long COVID biomarkers
were entered as single indicators, whilst the physio-affective phenome and HR-QoL do-
main scores were entered as latent vectors. We derived a first latent vector from the values
of pure and physiosom HAMA and HAMD, as well as pure FF and BDI (dubbed the
physio-affective phenome), and a second latent vector from the 4 WHO-QoL raw scores
(dubbed the HR-QoL latent vector). Only when the inner and inner models satisfied the
following prespecified quality criteria was complete PLS path analysis carried out: (a) the
output latent vectors demonstrate high construct and convergence validity as indicated
by average variance extracted (AVE) > 0.5, rho A > 0.8, Cronbach’s alpha > 0.7, and com-
posite reliability > 0.8, (b) all loadings on both extracted latent vectors are >0.6 at p < 0.001,
(c) the overall model fit namely the standardized root square residual (SRMR) value is
<0.08, (d) Confirmatory Tetrad Analysis (CTA) demonstrates that both latent vectors are
not mis-specified as reflective models, (e) blindfolding demonstrates that the construct’s
cross-validated redundancies are adequate, and (f) the model’s prediction performance is
satisfactory as measured by PLS Predict. If all the above-mentioned model quality data
meets the predetermined criteria, we run a complete PLS path analysis with 5000 boot-
strap samples, produce the path coefficients (with exact p-values) and in addition, com-
pute the specific indirect and total indirect (that is mediated) effects as well as the total
effects.
2.5. Avoiding Bias
The retrospective identification of exposure biomarkers (SpO2 and PBT) was per-
formed by chart reviewers who assessed patient records and were blinded from the out-
come data. The target study population (Long COVID) was well defined as described
above and we selected individuals who showed clinical signs of Long COVID coupled
with a negative rRT-PCR and had suffered from confirmed (by rRT-PCR and symptoms)
acute COVID-19 infection some months earlier. Interviewer bias was minimized because
the senior psychiatrist interviewer was blinded from the exposure data (medical records)
and the outcome (medical diagnoses of Long COVID and HR-QoL data). Bias from mis-
classification is excluded because exposure (acute infection) and outcome diagnosis (Long
COVID) are based on laboratory and well-defined clinical data. Statistical analyses were
controlled for diverse confounders including age, sex, education, and tobacco use. As re-
ported, there were no conflicts of interest, and the study was not funded. Figure 1 shows
the flow of the subjects from recruitment to statistical analysis. Electronic Supplementary
Int. J. Environ. Res. Public Health 2022, 19, 10362 7 of 21
File Table S1 shows the baseline characteristics of patients and controls indicating that
there were no significant differences in age, sex, body mass index, education, marital sta-
tus, residency, tobacco use disorder and vaccination status between the groups.
Figure 1. Flow chart depicting the flow of the participants from recruitment to statistical analysis.
3. Results
3.1. Results of Cluster Analysis
In order to retrieve clusters of participants based on the four WHO-QoL domain
scores, we performed cluster analysis whereby K-means showed the best solution with
three clusters: a first cluster (n = 42) with normal WHO-QoL domain scores, a second clus-
ter (n = 37) with moderately reduced WHO-QoL values, and a third cluster with very low
WHO-QoL domain scores. The silhouette measure of cohesion and separation was good
with a value of 0.6. Table 1 shows the scores on the four WHO-QoL domains as well as
the first latent vector extracted from the four domains after covarying for age, sex, educa-
tion, and treatments. There was a strong association between WHO-QoL groups and the
diagnosis of acute COVID infection. There were no significant differences in age, sex, BMI,
marital status, education, rural/urban ratio, TUD and vaccination among the three clus-
ters. This table also shows the treatments during the acute phase of illness.
Int. J. Environ. Res. Public Health 2022, 19, 10362 8 of 21
Table 1. Socio-demographic and clinical variables in healthy controls (HC) and subjects with Long
COVID divided into those with normal, moderately low and very low health-related quality of life
(QoL) scores as measured with the Health Organization Quality of Life Instrument-Abridged Ver-
sion (WHO-QoL).
Parameter
Normal WHO-QoL
A
n = 42
Moderate Low
WHO-QoL B n = 37
Very Low
WHO-QoL C n = 46
F/χ2
df
p
WHO-QoL, physical *
27.46 ± 0.66 B,C
21.60 ± 0.51 A,C
16.83 ± 0.49 A,B
66.83
2/114
<0.001
WHO-QoL, psychological *
25.70 ± 0.57 B,C
21.40 ± 0.43 A,C
16.43 ± 0.42 A,B
77.50
2/114
<0.001
WHO-QoL, social *
11.92 ± 0.43
10.58 ± 0.33
10.56 ± 0.32
2.50
2/114
0.086
WHO-QoL, environment *
33.60 ± 0.71 B,C
26.78 ± 0.54 A,C
22.89 ± 0.53 A,B
54.03
2/114
<0.001
PC 4 WHO-QoL domains *
1.121 ± 0.087
−0.124 ± 0.066
−0.924 ± 0.064
135.31
2/114
<0.001
HC/Long COVID
38/4
1/36
0/46
FFHE
<0.001
Age (years)
28.0 ± 7.4
29.3 ± 6.5
27.9 ± 5.9
0.35
2/127
0.706
Female/Male ratio
19/23
15/22
20/26
0.18
2
0.914
BMI (kg/m2)
25.84 ± 4.08
25.83 ± 3.53
26.21 ± 5.23
0.05
2/127
0.950
Education (years)
15.0 ±1.2 B,C
15.8 ± 1.9 A,C
15.6 ± 1.7 A,B
9.99
2/127
<0.001
Married/Single (No/Yes)
19/23
21/31
15/21
0.23
2
0.901
Rural/Urban (No/Yes)
8/34
8/29
7/39
0.58
2
0.749
TUD (No/Yes)
29/13
24/13
32/14
2.40
2
0.887
Vaccination A/Pf/S
9/23/10
5/23/9
15/23/8
4.46
4
0.347
Dexamethasone (No/Yes)
39/3
24/13
23/23
19.17
2
<0.001
Ceftriaxone (No/Yes)
41/1
18/19
16/30
38.94
2
<0.001
Azithromycine (No/Yes)
38/4
17/20
25/21
19.87
2
<0.001
Enoxaparin sodium
(No/Yes)
38/4
4/33
8/38
67.52
2
<0.001
Bromhexine (No/Yes)
39/3
10/27
8/38
57.71
2
<0.001
Data are shown as mean (SD) (except: * shown as estimated marginal means and SE after adjusting
for confounders) or as ratios. F: results of analysis of variance; χ2: results of analysis of contingency
tables; FFHE: Fisher-Freeman-Halton Exact test. A, B, C: Pairwise comparison among group means.
BMI: body mass index, TUD: tobacco use disorder, vaccination A/Pf/S: AstraZeneca, Pfizer and Si-
nopharm.
3.2. The Physio-Affective Phenome Scores and Biomarkers in WHO-QoL Clusters
Table 2 shows the physio-affective phenome scores and biomarkers in the WHO-QoL
clusters. We found that all rating scale scores, either total scores or the subdomains (except
physiosom HAMA) were significantly different between the three clusters and increased
from the normal QoL to the moderately low QoL to the very low QoL cluster. The physi-
osom HAMA score was significantly higher in both the moderate and very low clusters
as compared with the normal QoL cluster. PBT, TO2 index, OSTOX+NLRP3, and NT+TO2
were significantly different between the three clusters and increased from the normal
WHO-QoL to the moderately low WHO-QoL to the very low WHO-QoL cluster. SpO2
values were significantly lower in those with moderate and very low WHO-QoL scores as
compared with those with normal WHO-QoL, whilst OSTOX, NLRP3, and NT were sig-
nificantly increased in the moderate and very low WHO-QoL groups.
Int. J. Environ. Res. Public Health 2022, 19, 10362 9 of 21
Table 2. Neuropsychiatric rating scale scores and biomarkers in healthy controls (HC) and subjects
with Long COVID divided into those with normal, moderately low and very low health-related
quality of life (QoL) scores as measured with the Health Organization Quality of Life Instrument-
Abridged Version (WHO-QoL).
Variables
Normal WHO-QoL A
n = 42
Moderate Lower
WHO-QoL B n = 52
Very Low
WHO-QoL C n = 36
F (df =
2/122)
p
Total FF score
11.0 ± 4.1 B,C
20.4 ± 10.1 A,C
36.0 ± 12.1 A,B
78.42
<0.001
Total HAMA score
7.9 ± 3.9 B,C
13.8 ± 6.6 A,C
19.7 ± 8.5 A,B
34.26
<0.001
Total BDI-II score
9.1 ± 4.1 B,C
20.3 ± 5.8 A,C
28.9 ± 6.4 A,B
140.46
<0.001
Total HAMD score
6.4 ± 3.7 B,C
14.5 ± 4.8 A,C
18.8 ± 4.5 A,B
90.23
<0.001
Pure FF
−0.867 ± 0.385 B,C
−0.079 ± 0.746 A,C
0.855 ± 0.849 A,B
68.31
<0.001
Pure HAMD
−0.987 ± 0.395 B,C
0.136 ± 0.636 A,C
0.792 ± 0.851 A,B
80.21
<0.001
Physiosom HMD
−0.862 ± 0.672 B,C
0.247 ± 0.949 A,C
0.588 ± 0.726 A,B
40.35
<0.001
Pure HAMA
−0.547 ± 0.766 B,C
−0.084 ± 0.853 A,C
0.568 ± 1.012 A,B
17.53
<0.001
Physiosom HAMA
−0.517 ± 0.564 B,C
0.002 ± 0.958 A
0.470 ± 1.120 A
12.73
<0.001
Pure BDI
−0.998 ± 0.605 B,C
0.209 ± 0.663 A,C
0.743 ± 0.735 A,B
76.11
<0.001
PC Physio-affective phenome
−0.963 ± 0.368 B,C
0.0498 ± 0.706 A,C
0.839 ± 0.804 A,B
82.90
<0.001
Peak body temperature
37.07 (0.78) B,C
38.30 (0.74) A,C
38.75 (0.93) A,B
47.85
<0.001
Lowest SpO2 (%)
94.86 ± 1.96 B,C
91.62 ± 3.59 A
90.37 ± 4.29 A
19.46
<0.001
TO2 index (zBT-zSpO2 in z
scores)
−0.880 ± 0.586 B,C
0.218 ± 0.759 A,C
0.628 ± 0.903 A,B
44.71
<0.001
NLRP3 (z scores)
−0.406 ± 0.945 B,C
0.030 ± 0.833 A
0.347 ± 1.052 A
6.85
0.002
OSTOX (z scores)
−0.380 ± 1.018 B,C
0.140 ± 0.478 A
0.269 ± 1.057 A
5.45
0.005
OSTOX+NLRP3 (z scores)
−0.527 ± 0.880 B,C
0.088 ± 0.794 A,C
0.492 ± 0.905 A,B
15.23
<0.001
zIR (z scores)
−0.426 ± 0.678 B,C
0.307 ± 1.112 A
0.142 ± 1.040 A
6.57
0.002
OSTOX+NLRP3+IR (NT)
−0.625 ± 0.892 B,C
0.188 ± 0.741 A
0.419 ± 1.010 A
16.04
<0.001
NT+TO2 (z scores)
−0.857 ± 0.795 B,C
0.240 ± 0.677 A,C
0.589 ± 0.851 A,B
39.76
<0.001
Data are shown as mean (SD) or as ratios. F: results of analysis of variance. A, B, C: Pairwise compari-
son among group means. BDI: Beck Depression Inventory; FF: Fibro-Fatigue scale, HAMD: Hamil-
ton Depression rating Scale; NLRP3: index comprising interleukin-1β, IL-18 and caspase-1, ad-
vanced oxidation protein products and myeloperoxidase and insulin resistance (IR), OSTOX: index
reflecting oxidative toxicity, IR: insulin resistance index.
3.3. Associations of the Physio-Affective Phenome with WHO-QoL Scores
Table 3 shows the results of multiple regression analyses with the WHO-QoL scores
as dependent variables and the depression, anxiety, and FF scales as explanatory varia-
bles. Model#1 shows that 76.7% of the variance in the first latent vector extracted from the
four subdomains was explained by the regression on pure BDI and FF and total HAMD
scores (all inversely associated). Model#2 shows that 75.0% of the variance in the physical
domain was explained by 4 predictors, namely pure FF and BDI, total HAMD and male
sex. Up to 68.8% of the variance in the psychological domain (Model#3) was explained by
pure BDI and FF (inversely associated). A smaller part of the variance (16.7%) of the social
component was explained by the total HAMD score only, whilst 58.4% of the variance in
the environmental component (Model#5) was explained by pure BDI and FF scores. Fig-
ure 2 shows the partial regression of the social subdomain of the WHO-QoL on the total
BDI-II score.
Int. J. Environ. Res. Public Health 2022, 19, 10362 10 of 21
Figure 2. Partial regression of the social subdomain of the World Health Organization Quality of
Life I(WHO-QoL) Instrument-Abridged Version score on the total Beck Depression Inventory (BDI-
II) score.
Table 3. Results of multiple regression analyses with the health-related quality of life (QoL) scores
as measured with the Health Organization Quality of Life Instrument-Abridged Version (WHO-
QoL) domain scores as dependent variables and physio-affective scores as explanatory variables.
Dependent Variables
Explanatory
Variables
B
t
p
F
Model
df
p
R2
PC_WHO-QoL
domains
Model#1
132.74
2/121
<0.001
0.767
Pure BDI
−0.476
−6.96
<0.001
Pure FF
−0.292
−5.04
<0.001
Total HAMD
−0.238
−3.02
0.003
WHO-QoL physical
Model#2
90.00
3/120
<0.001
0.750
Pure FF
−0.523
−8.63
<0.001
Pure BDI
−0.254
−3.56
<0.001
Sex
−0.125
−2.72
0.007
Total HAMD
−0.221
−2.69
0.008
WHO-QoL
psychological
Model#3
133.17
2/122
<0.001
0.686
Pure BDI
−0.619
−10.41
<0.001
Total FF
−0.316
−5.31
<0.001
WHO-QoL social
Model#4
24.61
1/123
<0.001
0.167
Total HAMD
−0.408
−4.96
<0.001
WHO-QoL
environmental
Model#5
85.53
2/122
<0.001
0.584
Pure BDI
−0.586
−8.78
<0.001
Pure FF
−0.283
−4.24
<0.001
BDI: Beck Depression Inventory; FF: Fibro-Fatigue scale, HAMD: Hamilton Depression Rating
Scale.
3.4. Associations of the Biomarkers with WHO-QoL Scores
Table 4 shows the results of multiple regression analyses with the WHO-QoL scores
as dependent variables and the biomarkers as explanatory variables. Model#1 shows that
59.0% of the variance in the overall WHO-QoL score (first PC extracted from the four do-
mains) was explained by PBT and the NT+TO2 index (both inversely) and calcium (posi-
tively). The physical subdomain was explained (57.9% in Model#2) by PBT and NT
Int. J. Environ. Res. Public Health 2022, 19, 10362 11 of 21
(inversely) and calcium (positively). Figure 3 shows the partial regression of the physical
domain score on PBT. Model#3 shows that 39.9% of the variance in the psychological do-
main was predicted by NT+TO2 (inversely) and calcium (positively). Figure 4 shows the
partial regression of the psychological domain score on the NT+TO2 index. Up to 26.1%
of social WHO-QoL score was explained by the cumulative effects of calcium (positively)
and NT (inversely). Model#5 shows that 48.6% of the variance in the environmental WHO-
QoL domain was predicted by PBT and NT+TO2 (inversely) and calcium (positively). The
same table also shows that the physio-affective phenome score was significantly associ-
ated with female sex, PBT and NT (positively associated) and calcium (inversely associ-
ated).
Figure 3. Partial regression of the physical subdomain of the World Health Organization Quality of
Life I(WHO-QoL) Instrument-Abridged Version score on peak body temperature during the acute
phase of illness.
Figure 4. Partial regression of the psychological domain of the World Health Organization Quality
of Life I(WHO-QoL) Instrument-Abridged Version score on an index of inflammation (TO2) during
acute infection and neurotoxicity (NT) during Long COVID (NT+TO2).
Int. J. Environ. Res. Public Health 2022, 19, 10362 12 of 21
Table 4. Results of multiple regression analyses with the health-related quality of life (QoL) scores
as measured with the Health Organization Quality of Life Instrument-Abridged Version (WHO-
QoL) domain scores as dependent variables and biomarkers of acute and Long COVID as explana-
tory variables.
Dependent Variables
Explanatory
Variables
B
t
p
F Model
df
p
R2
PC_WHO-QoL
domains
Model#1
57.47
3/120
<0.001
0.590
PBT
−0.290
−3.02
0.003
Calcium
0.302
4.57
<0.001
NT+TO2
−0.329
−3.56
<0.001
WHO-QoL physical
Model#2
54.93
3/120
<0.001
0.579
PBT
−0.465
−6.22
<0.001
Calcium
0.256
3.82
<0.001
NT
−0.241
−3.58
<0.001
WHO-QoL
psychological
Model#3
40.13
2/121
<0.001
0.399
NT+TO2
−0.446
−5.83
<0.001
Calcium
0.305
3.99
<0.001
WHO-QoL social
Model#4
21.33
2/121
<0.001
0.261
Calcium
0.401
5.04
<0.001
NT
−0.251
−3.16
0.002
WHO-QoL
environmental
Model#5
37.87
3/120
<0.001
0.486
PBT
−0.276
−2.57
0.011
Calcium
0.288
3.89
<0.001
NT+TO2
−0.274
−2.65
0.009
PC phenome
Model#6
47.71
4/119
<0.001
0.616
PBT
0.480
6.70
<0.001
Calcium
−0.266
−4.13
<0.001
Female sex
−0.206
−3.61
<0.001
NT
0.223
3.45
<0.001
PC_WHO-QoL 4 domains: principal component extracted from the 4 domains of the WHO-QoL-
BREF (World Health Organization Quality of Life Instrument-Abridged Version) scale, PBT: peak
body temperature, NT: neurotoxicity index comprising interleukin-1β, IL-18 and caspase-1, ad-
vanced oxidation protein products and myeloperoxidase and insulin resistance (IR), TO2: index of
increased PBT and lowered oxygen saturation (SpO2).
Table 5 lists some regression analyses which assessed the effects of being infected
with the SARS-CoV-2 virus, the treatments during the acute phase and the physio-affec-
tive domains of Long COVID on the WHO-QoL domains. We found that 84.8% (Model#1)
of the variance in the overall WHO-QoL score was predicted by infection, pure BDI and
FF (inversely) and treatment with enoxaparin (positively). Model#2 shows that after re-
moval of the affective phenome features, 68.6% of the variance in the physical WHO-QoL
domain score was explained by infection, PBT, ceftriaxone treatment and the Astra vac-
cination (all inversely associated). Model#3 shows that 59.6% of the variance in the envi-
ronmental component was explained by infection (inversely) and treatment with enoxap-
arin (positively).
Int. J. Environ. Res. Public Health 2022, 19, 10362 13 of 21
Table 5. Results of multiple regression analyses with the health-related quality of life (QoL) scores
as measured with the Health Organization Quality of Life Instrument-Abridged Version (WHO-
QoL) domain scores as dependent variables and being infected with the SARS-CoV-2 virus, treat-
ments, vaccinations and clinical data as explanatory variables.
Dependent Variables
Explanatory
Variables
B
t
p
F Model
df
p
R2
PC_WHO-QoL 4
domains
Model#1
167.94
4/120
<0.001
0.848
Acute infection
−0.644
−8.50
<0.001
Pure BDI
−0.354
−6.95
<0.001
Pure FF
−0.274
−6.10
<0.001
Enoxaparin
0.262
4.08
<0.001
WHO-QoL physical
Model#2
65.39
4/120
<0.001
0.686
Acute infection
−0.525
−6.49
<0.001
PBT
−0.238
−3.16
0.002
Ceftriaxone
−0.165
−2.68
0.008
Vaccination A
−0.103
−2.00
0.048
WHO-QoL
environmental
Model#3
89.82
2/122
<0.001
0.596
Acute infection
−1.054
−10.35
<0.001
Enoxaparin
0.379
3.72
<0.001
PC_WHO-QoL 4 domains: principal component extracted from the 4 domains of the WHO-QoL-
BREF (World Health Organization Quality of Life Instrument-Abridged Version) scale, BDI: Beck
Depression Inventory; FF: Fibro-Fatigue scale; PBT: peak body temperature 3.5. Results of PLS Anal-
ysis.
Figure 5 shows the final PLS model which displays only the significant paths and
indicators. With an SRMR of 0.047, the model quality is adequate. We observed adequate
convergence and construct reliability validity values for (a) the HR-QoL latent factor with
AVE = 0.721, composite reliability = 0.910, Cronbach alpha = 0.867, and rho_A = 0.910,
while all loadings were >0.669 at p < 0.001); and (b) the physio-affective phenome, with
AVE = 0.610, composite reliability = 0.904, Cronbach alpha = 0.873, rho_A = 0.887, while
the loadings of the physio-affective phenome variables were >0.704. Blindfolding revealed
acceptable construct redundancies of 0.491 for the HR-QoL latent vector and 0.365 for the
physio-affective phenome latent vector. According to CTA, both latent vectors were cor-
rectly specified as reflective models. According to PLSPredict, all construct indicators had
positive Q2 predict values, indicating that the prediction error was less than the naivest
benchmark. PLS analysis with 5000 bootstraps showed that 70.3% of the variance in HR-
QoL was explained by the regression on the physio-affective phenome, calcium and NT,
whilst 61.5% of the variance in the physio-affective phenome was explained by calcium,
NT, TO2, vaccination (Astra-Zeneca and Pfizer) and female sex. Moreover, TO2 signifi-
cantly affected calcium (inversely) and NT (positively). All five specific indirect effects of
TO2 (and thus also SARS-CoV-2 infection) on HR-QoL were significant yielding highly
significant total indirect (t = −5.03, p < 0.001) and total (t = −13.30, p < 0.001) effects. The
effects of sex (t = 2.66, p = 0.004) and vaccination (t = −2.35, p = 0.009) on HR-QoL were
mediated via effects on the physio-affective phenome. Finally, the NT index yielded not
only direct effects but also specific indirect effects (t = −3.16, p < 0.001) and thus a highly
significant total effect (t = −3.86, p < 0.001).
We have also examined if we could combine the six phenome domains and the four
WHO-QoL domains into one latent vector. We found that indeed one replicable vector
could be extracted from the ten indicators with AVE = 0.593, rho_A = 0.931, with sufficient
loadings (all >0.6 at p < 0.001 except for the social domain which showed a loading of
0.552). Blindfolding revealed an acceptable construct redundancy of 0.379. Complete PLS
analysis performed using 5000 bootstrap samples showed that 65.5% of the variance in
Int. J. Environ. Res. Public Health 2022, 19, 10362 14 of 21
this physio-affective-HR-QoL factor was explained by female sex (p = 0.03), vaccination (p
= 0.012), TO2 (p < 0.001), calcium (p < 0.001) and the neurotoxicity index (p < 0.001).
Figure 5. Results of partial least squares (PLS) analysis. Health related quality of life (HR-QoL) is
entered as a latent vector extracted from 4 QoL domains, namely physical, psychological, social and
environmental. The physio-affective phenome of Long COVID is entered as a latent vector extracted
from 6 clinical domains, namely the pure Fibro-Fatigue (PuFF), Hamilton Depression (PuHAMD)
and Anxiety (PuHAMA) rating scale scores, pure Beck Depression Inventory (PuBDI) scores, and
physiosomatic HAMD (PhHAMD) and HAMA (PhHAMA) scores. All other variables were entered
as single indicators, namely sex (men = 1 and women = 0), vaccination (Astra-Zeneca and Pfizer = 1
and Sinopharm = 0), calcium, neurotoxicity (NT, a combination of inflammation + insulin resistance
+ oxidative stress) and TO2 (index of increased peak body temperature and lower oxygen satura-
tion). Shown are path coefficients (with p-values) and loadings (with p-values) on the latent vectors
and variance explained (white figures in blue circles).
4. Discussion
4.1. Lowered HR-Qol in Long COVID
The first major finding of this study is that individuals with Long COVID had signif-
icantly lower total HR-QoL scores as well as physical, psychological, and environmental
QoL scores, but not social QoL scores. These findings support recent systematic review
and meta-analysis studies that show HR-QoL is significantly lower in Long COVID-19
and that 18 to 60% of people with Long COVID have decreased HR-QoL [28–30]. In this
context, we discovered that approximately 55% of people with Long COVID have ex-
tremely low physical, psychological, and environmental QoL scores. Comparing our find-
ings to those obtained in major depressive disorder and bipolar disorder reveals a strong
parallel, except for the social QoL domain, which was significantly decreased in 50% of
MDD/BD patients, whereas Long COVID patients did not show a significant decrease in
social QoL [39]. Thus, 50% of MDD/BD patients have very low levels of the physical do-
main (mean ± SD: 19.3 ± 4.3) compared to 55% of Long COVID patients who show a mean
(SEM) score of 16.8 (0.49). In contrast, the psychological domain was lower in the
MDD/BD patients with the lowest mean ±SD scores, namely (14.7 ± 3.1), versus Long
COVID (mean, SEM: 16.4 ± 0.42). Such disparities may be attributed to the greater impact
of SARS-CoV-2 infection on the physical domain, and affective disorders on the
Int. J. Environ. Res. Public Health 2022, 19, 10362 15 of 21
psychological domain. Both MDD/BD and Long COVID patients had similar environmen-
tal QoL domain scores [39].
4.2. The Physio-Affective Phenome of Long COVID Predict Lowered HR-QoL
The second major finding of this study is that in Long COVID, there were strong
associations between lower HR-QoL scores and higher physio-affective domain scores,
which included depression, fatigue, and physio-somatic symptoms, as well as anxiety. In
fact, the cumulative effects of the pure BDI, pure FF, and total HAMD score explained a
large portion of the variance in total QoL (around 76.7 percent) and domain scores (16.7–
75 percent). As a result, a combination of pure affective, pure physiosomatic symptoms,
and chronic fatigue determines HR-QoL in Long COVID to a large extent. These findings
support previous research that Long COVID patients with affective (such as sadness and
anxiety) and physiosomatic (such as fatigue, myalgia, and joint pain) symptoms have sig-
nificantly lower HR-QoL scores on physical and mental health [26]. In patients suffering
from MDD/BD we previously discovered that the total WHO-QoL score, as well as the
four subdomain scores, were strongly and inversely related to the total HAMD and
HAMA scores [39,40], with the HAMD having a much stronger effect on the total WHO-
QoL scores than the HAMA [40]. In a Thai schizophrenia patient population, we discov-
ered that total FF and HAMA scores explained a large portion of the variance in the total
WHO-QoL score, while the four subdomain scores were inversely related to the HAMA
(physical, social, and environmental) and the FF (psychological and environmental) scores
[41]. Overall, both affective and physiosomatic symptoms, including chronic fatigue, con-
tribute significantly to lower HR-QoL in Long-COVID.
4.3. Lowered HR-Qol in Long COVID Is Predicted by Neuroimmunotoxic and Oxidative
Pathways
The third major finding of this study is that biomarkers of acute and Long COVID
strongly predict lower WHO-QoL scores in Long COVID. Thus, increased PBT and the
TO2 index, reflecting the immune-inflammatory response during the acute infectious
phase (see Introduction), and Long-COVID biomarkers, namely increased neurotoxicity
(due to increased NLRP3, OSTOX, and IR combined), and lowered calcium, strongly pre-
dict all WHO-QoL domain scores. In patients with MDD/BD, lower WHO-QoL scores
were strongly associated with neuro-oxidative toxicity markers such as peroxides,
malondialdehyde, superoxide dismutase, nitric oxide, and AOPPs, as well as lower HDL-
cholesterol and paraoxonase 1, an antioxidant enzyme [39]. Kanchanatawan et al. [41] dis-
covered that the total WHO-QoL score was associated with indices of tryptophan catabo-
lite (TRYCAT) pathway activation with increased production of neurotoxic TRYCATs
such as picolinic acid, xanthurenic acid, and 3-OH-kynurenine in Thai schizophrenia pa-
tients. Al-Musawi et al. [42] discovered a significant inverse relationship in Iraqi schizo-
phrenia patients between total WHO-QoL scores and the pathogenic Thelper-17 (Th-17)
phenotype and the IL-6/IL-23/Th-17 axis, which has major neurotoxic effects. Further-
more, increased levels of IL-1β, IL-6, IL-17, IL-21, IL-22, IL-23, and tumor necrosis factor
(TNF)-α were all found to be inversely related to HR-QoL in these schizophrenia patients.
Overall, it appears that decreased HR-QoL in Long COVID and other neuro-immune dis-
orders is caused, at least in part, by increased toxicity due to neuro-immune and neuro-
oxidative stress pathways.
4.4. The Effects of SARS-CoV-2 Infection on Lowered HR-QoL in Long COVID Are Mediated by
Acute and Chronic Immune-Inflammatory Processes
The fourth major finding of this study is that the effects of SARS-CoV-2 infection and
the severity of the immune-inflammatory response during the acute phase on HR-QoL
were significantly mediated by increased neurotoxicity and decreased calcium (both of
which were determined at least in part by the acute inflammation) and the effects of those
Int. J. Environ. Res. Public Health 2022, 19, 10362 16 of 21
biomarkers on the physio-affective phenome, which in turn affects HR-QoL. As such, this
study discovered multiple mediated causal paths from SARS-CoV-2 infection to activa-
tion of immune-inflammatory pathways, increased neurotoxicity, and decreased calcium
to the phenome, and, as a result, HR-QoL. We previously developed comparable multi-
step mediated models to explain decreased HR-QoL in schizophrenia. Al-Musawi et al.
[42], for example, discovered that the neurotoxic effects of the IL-6/IL-23/Th-17 axis on the
four WHO-QoL domain scores combined with disability scores were significantly and
partially mediated by effects of the neurotoxic axis on the symptomatome of schizophre-
nia. In another study in schizophrenia, we discovered that the effects of neurotoxic TRY-
CATs on WHO-QoL scores were mediated by the schizophrenia phenome, as measured
by key symptoms of schizophrenia and neurocognitive deficits [43].
Furthermore, in the current study, we were able to extract one latent construct from
the four WHO-QoL subdomains and the six domains of the physio-affective core, com-
bining the physio-affective symptomatome and phenomenome (or HR-QoL) data into a
single phenome core. Notably, the TO2 index, NT index, and lower calcium explained
65.5% of the variance in this combined phenome core. Maes et al. [44] combined HAMD
and HAMA scores, as well as the four WHO-QoL domains and Sheehan disability scores,
in MDD/BD patients to create one latent construct that was highly predicted by OSTOX
neurotoxicity and decreased antioxidant activity of the HDL-paraoxonase 1 complex [44].
We have previously discussed how various neuroimmunotoxic and neuro-oxidative
pathways may contribute to the physio-affective phenome of Long COVID [7,8,12,13],
MDD and BD [44,45], schizophrenia [31,33], and ME/CFS [46]. According to the theory
summarized in this work, activated neuro-immune and neuro-oxidative pathways cause
dysfunctions in peripheral and central neuronal cells as well as central circuits that medi-
ate affection, sleep, pain, cognition, and memory. Recently, we discovered that in MDD,
the effects of peripheral inflammation (as measured by CRP), lower calcium, and insulin
resistance on the physio-affective phenome of depression (as measured by HAMD,
HAMA, and FF scores) were mediated by neuronal injury indicators indicating damage
to astroglial and neuronal (axonal) projections [47]. In patients with unstable angina, we
discovered that activation of immune-inflammatory pathways (CRP and cytokines in-
cluding IL-6) affects the physio-affective phenome of unstable angina, and that these ef-
fects are mediated by increased atherogenicity and insulin resistance [48]. Insulin re-
sistance and lowered calcium in conjunction with immune biomarkers, also predict the
physio-affective phenome in type 2 diabetes [49]. Previously, we discussed the neurotoxic
effects of increased insulin resistance, including increased permeability of the blood-brain
barrier, decreased levels of brain-derived neurotrophic factor, impaired synaptic plasticity
and dendritic spine damage, and decreased hippocampal volume and metabolic activity
in the prefrontal cortex [47]. Lower serum calcium is not only an indicator of an inflam-
matory response [47], but it is also associated with physiosomatic symptoms including
muscle spasms and cramps, neuromuscular irritability, paresthesia, circumoral numb-
ness, neurocognitive and memory impairments, fatigue, and depression and anxiety [50–
52]. Recent meta-analysis findings indicate that low calcium in COVID-19 patients is as-
sociated with increased severity, higher mortality, and more complications [53].
4.5. Additional Explanatory Variables
This study also discovered that female sex and vaccination with AstraZeneca and
Pfizer were linked to increased severity of the Long-COVID physio-affective phenome.
Similar sex effects were observed in another study on Long COVID [13]. Women have
significantly higher rates of anxiety, depression, and chronic fatigue than men [54–56].
Men have more severe acute respiratory syndrome and critical COVID-19 during acute
COVID-19 infection, whereas women have more sickness symptoms and fatigue [20].
Such differences may be explained by the effects of different neuro-immune pathways,
with males having a more activated NLRP3 inflammasome, which leads to severe acute
respiratory syndrome [20], and females having increased cytokine-induced activation of
Int. J. Environ. Res. Public Health 2022, 19, 10362 17 of 21
indoleamine-2,3-dioxygenase, which is associated with acute COVID-19, depression, anx-
iety, and chronic fatigue [57,58].
Previously, we discovered [7,8] that vaccination with Pfizer (mRNA-based) and
AstraZeneca (viral vector with genetically engineered virus), but not with the Sinopharm
(inactivated virus-based) vaccine may exacerbate the physio-affective phenome of Long
COVID. In a few previous studies, SARS-CoV-2 vaccinations were linked to Long COVID-
like symptoms such as anxiety, fatigue, sadness, and deficits in type 1 interferon signaling,
autoimmune responses, increased synthesis of spike protein and T cell activation [59,60].
Furthermore, we found that treatment with enoxaparin during the acute phase may
have a protective effect against the physio-affective phenome, whereas treatment with
ceftriaxone is associated with a worsening of the phenome. While enoxaparin treatment
has anticoagulant and antithrombotic properties, which may help to prevent blood clot-
ting caused by SARS-CoV-2 infection, it also has anti-inflammatory and neuroprotective
properties [61–64]. Treatment with ceftriaxone, a third-generation cephalosporin antibi-
otic, has been linked to gut dysbiosis [65], which has been linked to increases in the physio-
affective phenome of MDD/BD, ME/CFS, and schizophrenia [24,33,46].
4.6. Limitations
This study would have been more interesting if we had also measured other pro-
inflammatory cytokines of the M1 macrophage, Th-1, and Th-2 phenotypes and the IL-
6/IL-23/Th-17 axis, growth factors and TRYCATs during Long COVID, in addition to ad-
ditional assays of oxidative stress (e.g., xanthine oxidase, chlorinative stress biomarkers)
and nitrosylyation. It could be argued that the relatively smaller sample size would render
the parameter estimates of the regression analyses less precise. However, increasing the
number of participants would entail a larger number of plates to assay the biomarkers
and thus an increasing analytical error due to the increasing inter-assay (and inter-plate)
variation which may significantly decrease the overall precision (especially when meas-
uring cytokines at the lower concentration ranges) [66]. The present study was performed
in an Iraqi population and, therefore, may not have sufficient generalizability to other
populations or ethnicities. Therefore, our results deserve to be replicated in other coun-
tries and ethnicities. The strength of this study is that the precision nomothetic approach
allowed to delineate the effects of inflammation during the acute phase of COVID-19 on
the phenome and lowered HR-QoL in Long COVID, and that these effects are mediated
by the NLRP3 and oxidative stress pathways.
5. Conclusions
The severity of the immune-inflammatory response during the acute infection phase,
which generates greater neuroimmunotoxicity and neuro-oxidative toxicity, predicts the
physio-affective phenome and, as a result, reduced HR-QoL in Long COVID. During the
acute phase, treatment with enoxaparin, which has antithrombotic, anti-inflammatory,
and neuroprotective effects, was linked with an improvement in HR-QoL. These results
suggest that interventions throughout the acute and long COVID phases that improve
neuroprotection and inflammation, and target neurotoxicity may be clinically effective in
preventing Long COVID physio-affective symptoms.
Supplementary Materials: The following supporting information can be downloaded at:
www.mdpi.com/article/10.3390/ijerph191610362/s1, Table S1. Socio-demographic data of healthy
controls (HC) and Long COVID patients.
Author Contributions: H.T.A.-R. and D.S.A.-H. collected the blood samples. The measurements of
serum biomarkers were performed by H.T.A.-R. and H.K.A.-H. The statistical analysis was con-
ducted by M.M. M.M. wrote the first draft, and A.F.A., K.S., M.K., H.T.A.-R., D.S.A.-H. and H.K.A.-
H. co-edited the manuscript. All authors have read and agreed to the published version of the man-
uscript.
Funding: This research received no external funding.
Int. J. Environ. Res. Public Health 2022, 19, 10362 18 of 21
Institutional Review Board Statement: The approval of the study was obtained from the institu-
tional ethics board of the University of Kufa (8241/2021) and the Najaf Health Directorate-Training
and Human Development Center (Document 18378/2021). The present study was performed under
Iraqi and foreign ethics and privacy rules besides the following guidelines: World Medical Associ-
ation Declaration of Helsinki, The Belmont Report, CIOMS Guideline, and the International Confer-
ence on Harmonization of Good Clinical Practice; our IRB adheres to the International Guideline for
Human Research Safety (ICH-GCP). We obtained written signed consent from all patients or par-
ents/legal guardians.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study. Written informed consent has been obtained from the participants to publish this paper.
Data Availability Statement: The dataset generated during and/or analyzed during the current
study will be available from the corresponding author (M.M.) upon reasonable request and once the
dataset has been fully exploited by the authors.
Acknowledgments: The authors would like to express their appreciation to the individuals who
worked diligently to compile the data at the Al-Sader Medical City of Najaf, Al-Hakeem General
Hospital, Al-Zahraa Teaching Hospital for Maternity and Pediatrics, Imam Sajjad Hospital, Hassan
Halos Al-Hatmy Hospital for Transmitted Diseases, Middle Euphrates Center Cancer, and Al-Najaf
Center for Cardiac Surgery and Trans Catheter.
Conflicts of Interest: The authors declare no conflict of interest.
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