AJTCCM VOL. 30 NO. 3 2024 113
ORIGINAL RESEARCH: ARTICLES
Background. ere is a paucity of evidence on the impact of mild COVID‑19 on the respiratory system, particularly in non‑healthcare‑
seeking individuals.
Objectives. To investigate the eects of mild COVID‑19 on respiratory function and to identify indicators of decreased lung function.
Methods. We conducted a cross‑sectional study in 175 non‑healthcare‑seeking individuals with confirmed acute SARS‑CoV‑2
infection who did not require hospitalisation. Participants were divided into three groups: those who had pulmonary function tests
(PFTs) within 6months, between 6 and 12months, and between 12 and 24months after infection. Each participant underwent
spirometry, measurement of the diffusing capacity of the lungs for carbon monoxide (DLCO), a 6‑minute walking distance test
(6MWD) and plethysmography.
Results. e mean age of the participants was 44.3 years, and the mean body mass index (BMI) 32.7 kg/m2. Forty‑six participants had PFTs
within 6months, 64 between 6 and 12months, and 65 between 12 and 24months. Lower than expected DLCO was the most commonly
detected abnormality (57%). Spirometry anomalies were noted in 23%, 10% showing an obstructive impairment and 13% a restrictive
impairment, conrmed by a total lung capacity <80%. An increased BMI was the only variable that was signicantly and independently
linearly associated with lower than predicted (<80%) forced vital capacity, forced expiratory volume in the 1st second, DLCO and 6MWD.
Conclusion. DLCO was low in a considerable proportion of non‑healthcare‑seeking individuals 2 years aer mild COVID‑19. A high BMI
was found to be signicantly and independently associated with lower than predicted PFT results and 6MWD.
Keywords. Body mass index, carbon monoxide, diusion, mild COVID‑19 , pulmonary function tests.
Afr J Thoracic Crit Care Med 2024;30(3):e1629. https://doi.org/10.7196/AJTCCM.2024.v30i3.1629
e impact of mild COVID‑19 on medium‑term
respiratoryfunction
J van Heerden,1,2 MSc Med Physiol, BTech Pulmonol, BTech Crit Care ;
H Strijdom,2 MB ChB, BMedSc, PhD;
A Parker,3 MB ChB, MMed (Int), FCP (SA), Cert (ID) SA, PhD;
B W Allwood,1 MB ChB, FCP (SA), MPH, Cert Pulmonology (SA), PhD;
U Lalla,1 MB ChB, MMed (Int), FCP (SA), MRCP (UK), Cert Critical Care (SA) Phys; C J Lombard,4,5,6 MSc, PhD;
C F N Koegelenberg,1 MB ChB, MMed (Int), FCP (SA), FRCP (UK), Cert Pulmonology (SA), PhD
1 Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town,
South Africa
2 Centre for Cardiometabolic Research in Africa, Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences,
Stellenbosch University, Cape Town, South Africa
3 Division of Infectious Diseases, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town,
South Africa
4 Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa
5 Biostatistics Unit, South African Medical Research Council, Cape Town, South Africa
6 Department of Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa
Corresponding author: J van Heerden (jacques.vanheerden@westerncape.gov.za)
Study synopsis
What the study adds. We found that pulmonary function, particularly diusing capacity, was lower than predicted in a signicant proportion
of non‑healthcare‑seeking individuals up to 2 years aer mild COVID‑19. A high body mass index (BMI) was found to be signicantly and
independently associated with decreased lung function.
Implications of the ndings. ere is a paucity of evidence on the medium‑term eects of mild COVID‑19 on the respiratory system in
non‑healthcare‑seeking individuals. We investigated the medium‑term eects of mild COVID‑19 on the respiratory system, showed lower
than predicted lung function, and identied one independent predictor, BMI. Even individuals classied as having ‘mild’ COVID‑19 could
have medium‑term respiratory sequelae.
114 AJTCCM VOL. 30 NO. 3 2024
ORIGINAL RESEARCH: ARTICLES
e World Health Organization classied COVID‑19 as a pandemic
on 11 March 2020.[1] Early evidence suggests that the respiratory
system is the primary target of the SARS‑CoV‑2 virus. Severe injury
to the alveolar epithelial and endothelial cells with subsequent
broproliferation is regarded as a major underlying pathophysiological
mechanism of COVID‑19 , suggesting that chronic and alveolar
remodelling may develop, resulting in lung brosis and potential long‑
term impairment.[2]
Mounting evidence suggests that individuals with severe COVID‑19
(hospitalised during the disease) develop medium‑term impaired
lung function with persistent respiratory symptoms.[3] In addition,
the use of corticosteroid therapy was identied to improve recovery
of lung function between 6 and 12months in some individuals with
organising pneumonia.[4]
Currently, most studies focus on the effects of COVID‑19 on
individuals who were hospitalised during infection and/or required
supplementary oxygen therapy.[3,5‑8] ere is a paucity of evidence
on the medium‑term eects of mild COVID‑19 , especially in non‑
healthcare‑seeking individuals. We therefore aimed to assess the
medium‑term eects of mild COVID‑19 on the respiratory system
and to identify predictors of lower than expected lung function.
Methods
Study design and participants
We conducted a cross‑sectional study at Tygerberg Hospital, a 1 380‑
bed tertiary hospital in Cape Town, South Africa (SA). Hospital sta
who had previously tested positive for SARS‑CoV‑2 on a reverse
transcription polymerase chain reaction test and who had never
experienced severe COVID‑19 were invited to participate in the study.
Data collection started on 7 May 2021 and ended on 2 September
2022. Mild COVID‑19 was dened as not requiring hospitalisation or
any form of supplementary oxygen during the course of the disease.
[9] e date on which the specimen that tested positive was obtained
was used to dene day zero. Participants were stratied into three
groups based on how long aer SARS‑CoV‑2 infection pulmonary
function tests (PFTs) were performed: within 6months, between
6and 12months, and between 12 and 24months.
Basic demographic and clinical data
Demographic information gathered on the day of testing included
age (years), sex at birth (dened as male or female), and ethnicity
(self‑reported). Smoking status, defined as current smoker,
non‑smoker and ex‑smoker (smoking cessation >6months prior to
the day of testing), was documented in all participants. Comorbidities,
specifically diabetes mellitus, hypertension, chronic obstructive
pulmonary disease, asthma, other respiratory diseases, active or
previous tuberculosis and ischaemic heart disease, were recorded.
Given that the participants were co‑workers, HIV status was not
documented.
Participants were asked if they had experienced any of the following
symptoms during their COVID‑19 disease: fever, cough, tiredness/
fatigue, muscle or body aches, sore throat, nasal congestion or a runny
nose, nausea or vomiting, diarrhoea, headache, loss of sense of taste or
smell, diculty breathing or shortness of breath, or chest pain.
All participants were weighed on the day of testing (wearing only
light clothing), and their heights were measured up to an accuracy of
0.5 cm (no shoes). Body mass index (BMI) was calculated as weight
(kg) divided by height (m) squared.
Pulmonary function testing
All PFTs were performed by a qualified pulmonary clinical
technologist. Testing was conducted using a Jaeger MasterScreen
CareFusion system V5.32.0.5 CD‑Version 5.72.1.77 (Jaeger, Germany).
Testing was done in accordance with the current American oracic
Society (ATS) and European Respiratory Society guidelines,[10,11] and
the Global Lung Initiative 2012 (GLI 2012) reference equations were
used.[12] e GLI 2012 reference equation model is routinely adjusted
for height, age and sex at birth.[12] As per SA data and guidelines, black
African and mixed ethnicity was labelled as ‘other’, white as ‘white’ and
Indian as ‘Southeast Asian’.[13]
Routine spirometry was performed, as well as forced vital capacity
(FVC), forced expiratory volume in the 1st second (FEV1) and FEV1/
FVC ratio. e diusing capacity of the lungs for carbon monoxide
(DLCO) was measured in mL/min/mmHg with a single breath‑hold
technique, as per the current ATS guidelines.[10,11] DLCO was considered
to be lower than expected when diffusion capacity was <80% of
predicted. Finally, plethysmography was performed according to the
current ATS guidelines.[14]
The main outcome data analysed were FVC, FEV1 and DLCO.
All measurements were expressed and analysed as percentage
predicted, using the GLI 2012 reference equations.[12,13] Spirometric
results were categorised as normal, obstructive (with or without
a reduced FVC), restrictive or mixed.[15] Where spirometry was
suggestive of restriction or mixed impairment, the total lung capacity
(TLC) and other parameters obtained from plethysmography were
used to categorise the results. Restrictive impairment was conrmed
with a TLC <80% of predicted.[14]
Six‑minute walking distance
e 6‑minute walking distance (6MWD) was conducted in accordance
with the current ATS guidelines.[16] e test was performed on a 30 m
at indoor surface. A Nihon Covidien forehead pulse oximeter device,
model number PVM‑2703 (Covidien, USA), was used for recording of
oxygen saturation. e 6MWD was expressed in absolute values (m).
Statistical analysis
The descriptive statistics for demographic, anthropometric, lung
function and 6MWD test variables were calculated for the three
COVID time groups and consisted of means, standard deviations,
frequencies and percentages. e nature of the association between
the lung function measurement and BMI was investigated using a
non‑parametric locally weighted scatterplot smoothing regression
function model in each of the COVID‑19 time groups. e association
between lung function and the 6MWD test and BMI was evaluated
using linear regression models with BMI, age, sex and smoking status
as the covariates. Regression coecients were reported with 95%
condence intervals. e 6MWD was not normally distributed, so
a quantile regression model was used. Stata 17 statistical soware
(StataCorp, USA) was used, with a signicance level of 5%. Pearson’s
χ2 test and Fisher’s exact test were performed to test for signicant
associations between the COVID‑19 time groups and categorical
demographic and clinical factors.
AJTCCM VOL. 30 NO. 3 2024 115
ORIGINAL RESEARCH: ARTICLES
Ethical considerations
The study was conducted in accordance with the Declaration of
Helsinki (as revised in 2013), and was approved by the Health Research
Ethics Committee of Stellenbosch University (ref. no. S21/03/004_
COVID‑19; project ID 21796). Informed consent was obtained from
all individual participants.
Results
Baseline demographics and patient characteristics
Across the three time groups, 46, 64 and 65 participants performed
PFTs and the 6MWD test. Baseline characteristics and demographics
are summarised in Table1. Most of the participants were female
(n=140; 80%), never‑smokers (n=140; 80%) and of mixed ethnicity
(n=121; 69%). The mean (SD) age was 44.28 (11.40) years.
Asignicant dierence in mean BMI was observed between the time
groups (p=0.023). e mean BMI was similar in the 6‑12months
group (33.40 kg/m2) and the 12‑24months group (33.71 kg/m2), but
lower in the <6months group (30.14 kg/m2). A total of 47% of the
participants reported pre‑existing comorbidities. e most common
comorbidity was hypertension (33%), followed by diabetes mellitus
(13%) and asthma (11%).
Headache was the most common symptom reported as having
been experienced during the time of SARS‑CoV‑2 infection (75%),
followed by tiredness/fatigue (74%), muscle or body aches (62%), sore
throat (58%), loss of sense of taste or smell (57%), fever (56%) and
cough (56%) (Fig.1).
e SARS‑CoV‑2 variant at the time of primary infection was not
known, because molecular strain typing was not routinely performed
in our setting. However, 23 (13%) of the participants tested positive
during a wave driven by the Alpha variant, 96 (55%) during a wave
driven by the Beta variant, 38 (22%) during a wave when the Delta
variant was predominantly present, and 18 (10%) when the Omicron
variant was the dominant circulating strain.
Pulmonary function testing and 6‑minute walking
distance
e PFT results and 6MWD measurements in the population as a
whole and in the dierent time groups are summarised in Table2.
Spirometry was normal in 35 (76%) of participants in the <6months
group and in 48 (74%) in the 12‑24months group, compared with
52 (82%) in the 6‑12months group. Spirometry was abnormal in
40 (23%) of the total study population. Obstructive impairment was
consistent between the three time groups, aecting 5 (11%), 6 9%)
and 6 (9%) participants, respectively. Restrictive impairment was
more common in the 12‑24months group (n=11; 17%) compared
with the 6‑12months group (n=6; 9%) and the <6months group
(n=6; 13%). Mean FVC and FEV1 were highest in the 6‑12months
group (94.35% and 94.12%, respectively). ere was no signicant
dierence between the three time groups for FVC (p=0.134), FEV1
(p=0.140) or 6MWD (p=0.9081).
Of the participants, 99 had a DLCO lower than predicted
(Table2). Inthe time groups, 59% in the <6months group, 47% in
Table1. Baseline characteristics and demographics of the study population as a whole and in the dierent COVID‑19 time groups
Characteristic
Total
(N=175), n (%)*
<6months
(n=46), n (%)*
6‑12months
(n=64), n (%)*
12‑24months
(n=65), n (%)*
Basic demographics
Age (years), mean (SD) 44.28 (11.40) 43.02 (12.32) 45.61 (10.60) 43.86 (11.55)
Male 35 (20) 12 (26) 7 (11) 16 (25)
Female 140 (80) 34 (74) 57 (89) 49 (75)
Height (cm), mean (SD) 163.76 (8.04) 164.94 (7.95) 162.79 (8.05) 163.88 (8.08)
Weight (kg), mean (SD) 87.41 (19.63) 91.98 (19.06) 88.19 (18.32) 90.48 (20.75)
BMI (kg/m2), mean (SD) 32.66 (7.29) 30.14 (6.50) 33.40 (7.31) 33.71 (7.51)
Population group
Black African 23 (13) 4 (9) 8 (12) 11 (17)
White 29 (17) 11 (24) 12 (19) 6 (9)
Mixed ethnicity 121 (69) 30 (65) 44 (69) 47 (72)
Indian 2 (1) 1 (2) 0 1 (2)
Smoking status
Active smoker 22 (13) 7 (15) 8 (13) 7 (11)
Ex‑smoker 13 (7) 5 (11) 3 (5) 5 (8)
Comorbidities
Diabetes mellitus 23 (13) 6 (13) 7 (11) 10 (15)
Hypertension 58 (33) 12 (26) 25 (39) 21 (32)
Asthma 20 (11%) 7 (15) 8 (13) 5 (8)
Tuberculosis†10 (6) 2 (4) 4 (6) 4 (6)
Other 7 (4) 1 (2) 2 (3) 4 (6)
SD = standard deviation; BMI = body mass index.
*Except where otherwise indicated.
†Active or previous tuberculosis.
116 AJTCCM VOL. 30 NO. 3 2024
ORIGINAL RESEARCH: ARTICLES
the 6 ‑12months group and 65% in the
12‑24months group had impairment. For
DLCO, we found a statistically significant
dierence overall (p=0.005) between the time
groups, with the <6months group having a
signicantly lower mean DLCO compared with
the other groups (p=0.003).
We observed a greater mean 6MWD in the
12‑24months group (486.21 m) compared
with the 6‑12months group (475.66 m). e
longest mean distance walked was observed in
the <6months group (498.54 m).
Factors associated with impaired
pulmonary function
Linear regression models adjusted for BMI,
age, sex and smoking status were used to
determine predictors for impaired lung
function and low 6MWD (Tables 3‑6). BMI
had a signicant negative slope independent
of time for each of FVC, FEV1, DLCO and
6MWD (p<0.001, p=0.001, p=0.007 and
p<0.001, respectively). Sex had no inuence
on FVC, FEV1 or 6MWD, but females had
a significantly lower DLCO compared with
males (p=0.001). Age and smoking status
were not associated with lower than predicted
lung function or 6MWD.
Discussion
We found that pulmonary function,
particularly DLCO, was lower than predicted
in a signicant proportion of non‑healthcare‑
seeking individuals with a previous history
of mild COVID‑19 at all time points, even
2 years aer the illness. Ahigher BMI was
found to be an independent risk factor for
lower than predicted PFT results and 6MWD.
To our knowledge, this is one of the rst
studies to investigate the medium‑term
pulmonary eects of mild COVID‑19. We
demonstrated that the DLCO was lower
than predicted in almost two‑thirds of the
participants, and that restrictive impairment
was present in almost 20% of participants
12 ‑ 224 months after so‑called ‘mild’
COVID‑19. Of note is that these were non‑
healthcare‑seeking individuals, and there was
a statistically signicant association between
a higher BMI and impaired lung function.
The expiratory reserve volume and
functional residual capacity are most aected
by obesity, with a lesser reduction in the TLC
and residual volume. Only moderate changes
have been reported when investigating the
eects of obesity during spirometry.[17] Mild
reductions in both FVC and FEV1 have been
reported, with no significant changes in
FEV1/FVC. The most common finding on
Headache
Participants, n
140
120
100
80
60
40
20
0
132
Tiredness/fatigue
130
Muscle or body aches
109
Sore throat
101
Loss of taste and smell
100
Fever
98
Cough
98
Nasal congestion
or runny nose
78
Diculty breathing or
shortness of breath
77
Chest pain
38
Nausea and vomiting
34
Diarrhoea
21
Symptom
Fig.1. Symptoms reported as having been experienced during the time of SARS-CoV-2 infection
(N=175 participants).
Table2. Pulmonary function test and 6MWD results for the study population as a whole and in the dierent COVID‑19 time
groups
Variable
Total
(N=175), n (%)*
<6months
(n=46), n (%)*
6‑12months
(n=64), n (%)*
12‑24months
(n=65), n (%)* p‑value†
FVC (% predicted),
mean(SD) (range)
91.58 (14.54)
(54.5‑128.2)
91.14 (13.76)
(60.4‑117)
94.35 (15.26)
(62.2‑128.2)
89.17 (14.09)
(54.5‑117.9)
0.134
FEV1 (% predicted),
mean(SD) (range)
91.09 (15.31)
(26.8‑132.1)
89.35 (15.59)
(26.8‑113.7)
94.12 (14.78)
(51.8‑125.2)
89.36 (15.42)
(41.7‑132.1)
0.140
Normal spirometry 135 (77) 35 (76) 52 (82) 48 (74) 0.784
Obstructive impairment 17 (10) 5 (11) 6 (9) 6 (9)
Restrictive impairment 23 (13) 6 (13) 6 (9) 11 (17)
DLCO (% predicted),
mean (SD) (range)
79.12 (12.56)
(43.1‑109.6)
77.22 (12.85)
(43.1‑107.9)
82.08 (12.27)
(57.4‑106.1)
77.57 (12.29)
(56.5‑109.6)
0.005
DLCO normal 76 (43) 19 (41) 34 (53) 23 (35) 0.100
DLCO mild impairment 92 (53) 24 (52) 29 (45) 39 (60)
DLCO moderate impairment 7 (4) 3 (7) 1 (2) 3 (5)
6MWD (m),
mean (SD) (range)
487.27 (69.77)
(284‑683)
498.54 (76.77)
(350‑683)
475.66 (71.27)
(284‑628)
486.21 (64.47)
(334‑646)
0.9081
FVC = forced vital capacity; FEV1 = forced expiratory volume in 1st second; DLCO = diusing capacity of the lungs for carbon monoxide; 6MWD = 6‑minute walking distance.
*Except where otherwise indicated.
†Adjusted p‑value from the linear/quantile regression models. For spirometry and DLCO impairment levels, p‑values were obtained using the χ2 and Fisher’s exact tests.
AJTCCM VOL. 30 NO. 3 2024 117
ORIGINAL RESEARCH: ARTICLES
analysis of the eect of obesity on the DLCO was results within the
normal range. However, there is evidence suggesting that an increased
BMI tends to lead to an increase in DLCO.[17]
Exercise capacity is profoundly decreased in obesity owing to
mechanical factors. Some researchers have shown that despite
having the same BMI, greater total body fat was observed in females
compared with males. e distribution of fat diers, with males
accumulating adipose tissue in the abdominal area as opposed to
in the lower extremities in females, aecting physical function.
Individuals with increased BMI adapt for their greater body mass
by slowing down walking velocity.[18] In any type of physical activity,
being overweight or obese is associated with increased physical
limitations.[19,20]
Lower than predicted DLCO and spirometry anomalies have been
reported in individuals who had suered from severe COVID‑19.[21]
Zhang etal.[4] found an improvement in FVC between 6months and
Table5. Linear regression coecients of modelling DLCO (% predicted) on the covariates COVID‑19 time groups, BMI, age,
sexand smoking status
Covariate Coecient p‑value 95% CI
6‑12months 7.04 0.003 2.39‑11.69
12‑24months 1.54 0.510 –3.07‑6.15
BMI –0.35 0.007 –0.61‑–0.10
Age 0.01 0.943 –0.16‑0.17
Sex female –8.04 0.001 –12.67‑–3.42
Smoking status –2.77 0.327 –8.35‑2.80
DLCO = diusing capacity of the lungs for carbon monoxide; BMI = body mass index; CI = condence interval.
Table3. Linear regression coecients of modelling FVC (% predicted) on the covariates COVID‑19 time groups, BMI, age,
sexand smoking status
Covariate Coecient p‑value 95% CI
6‑12months 4.61 0.099 –0.87‑10.09
12‑24months 0.16 0.954 –5.27‑5.58
BMI –0.56 <0.001 –0.86‑–0.26
Age –0.06 0.550 –0.25‑0.13
Sex female 5.04 0.069 –0.40‑10.49
Smoking status 2.02 0.544 –4.54‑ 8.58
FVC = forced vital capacity; BMI = body mass index; CI = condence interval.
Table6. Quantile regression coecients of modelling 6MWD on the covariates COVID time groups, BMI, age, sex and smoking
status
Covariate Coecient ‑value 95% CI
6‑12months –2.77 0.862 –34.10‑28.56
12‑24months –6.75 0.669 –37.85‑24.34
BMI –4.13 <0.001 –5.88‑–2.38
Age –0.47 0.399 –1.58‑0.63
Sex female –2.57 0.873 –34.23‑29.08
Smoking status –21.70 0.256 –59.27‑15.87
6MWD = 6‑minute walking distance; BMI = body mass index; CI = condence interval.
Table4. Linear regression coecients of modelling FEV1 (% predicted) on the covariates COVID‑19 time groups, BMI, age,
sexand smoking status
Covariate Coecient p‑value 95% CI
6‑12months 5.59 0.061 –0.26‑11.43
12‑24months 1.73 0.555 –4.05‑7.52
BMI –0.54 <0.001 –0.86‑–0.21
Age 0.02 0.815 –0.18‑0.23
Sex female 4.93 0.095 –0.88‑10.75
Smoking status –0.92 0.797 –7.92‑6.09
FEV1 = forced expiratory volume in 1st second; BMI = body mass index; CI = condence interval.
118 AJTCCM VOL. 30 NO. 3 2024
ORIGINAL RESEARCH: ARTICLES
1 year aer infection, followed by a decline between 1 and 2 years.
Furthermore, they reported a greater decline in individuals who had
been moderately to severely ill compared with those who had been
critically ill.
In addition, although the available data reporting on the eects
of COVID‑19 by means of PFTs provide insightful results, the
medium‑ to long‑term effects of COVID‑19 on the pulmonary
system are still poorly understood. Most of the available studies
performed PFTs within 3months aer COVID‑19 infection.[1,5‑7,22]
Furthermore, the studies only reported ndings of PFTs at that
specific time point. There are currently only a few studies that
performed PFTs at dierent time intervals. Wu etal.[8] reported PFTs
at 3months, 6months and 12months aer infection. Bretas etal.[3]
reported PFTs at 45 days and 6months aer infection. Both these
studies only reported ndings on participants who were hospitalised
during the time of infection. Wu etal.[8] reported PFTs up to 1 year
aer infection. A report on patients who had survived severe acute
respiratory syndrome (SARS) caused by coronavirus infection
recommended investigations beyond 1 year to further explore the
morbidity of SARS patients.[23] Apart from reporting on ndings in
participants who were not hospitalised during the time of infection,
the novelty of our study is further enhanced by providing data on
PFTs beyond 1 year aer infection.
e statistical association of a higher BMI with lower than expected
PFT results was an unexpected but not unexplained nding. Obesity is
well established as a risk factor for severe COVID‑19 and for mortality
from COVID‑19.[24,25] Obesity now also appears to be emerging as a
risk factor for post‑acute sequelae of COVID‑19 (PASC) or ‘long
COVID’.[26,27] The SARS‑CoV‑2 virus enters a variety of cell types,
including bronchial epithelial cells and adipocytes, by binding to
angiotensin‑converting enzyme 2 (ACE‑2) receptors.[28] In obesity
there is upregulation of ACE‑2 receptors, and these receptors are more
abundant in obese than non‑obese individuals.[29] Aer direct infection
of the adipocyte,[30,31] there is probably viral replication with activation
of the immune response driven by adipocytes. There is now also
evidence of SARS‑CoV‑2 persistence in various anatomical tissues,[32]
but it is unclear whether this persistent viral infection predisposes to
PASC. Obese patients also take longer to clear SARS‑CoV‑2, and there
is prolonged viral shedding in obesity.[29] However, persistent SARS‑
CoV‑2 infection of adipose tissue has not as yet been demonstrated.
One of the major strengths of this study is that we invited non‑
healthcare‑seeking individuals who had confirmed SARS‑CoV‑2
infections (as many of them were merely screened as part of our
institution’s infection prevention and control measures). We also included
participants who were infected 12‑24months prior to enrolment.
Limitations include the fact that the participants had an overall
higher than normal BMI, and there may have been recall bias as
far as symptoms were concerned. Moreover, there may be some
selection bias, as those with mild residual post‑COVID symptoms
were more likely to participate. e nature of the study precluded
aformal sample size estimation, as all personnel were invited over
a rather extended period of time. Moreover, we could not predene
which parameter or what degree of change would have dictated the
sample size (as it was unknown at the time), which may have made
it impossible to be certain of negative ndings and therefore limits
generalisability. e association between BMI and 6MWD may be
related to obesity and deconditioning. Participants did not have
baseline PFTs, and some may have had decreased values caused by
other pathologies. e cross‑sectional nature of our study was also
a limitation, and future research should be longitudinal to measure
progression/regression of pulmonary function. Furthermore,
we acknowledge that the addition of a control group would have
allowed us to draw better conclusions as to whether the lower than
predicted lung function was mediated solely by COVID‑19 or some
participants had pre‑existing lower function before COVID‑19.
However, it must be mentioned that evidence suggests that up to
50% of individuals who had COVID‑19 were asymptomatic, making
it challenging to add a control group.[33,34]
Conclusion
Pulmonary function, particularly DLCO, was lower than predicted in
a signicant proportion of non‑healthcare‑seeking individuals at all
time points, even 2 years aer mild COVID‑19. A high BMI was found
to be associated with lower than predicted PFT results and 6MWD.
Even individuals classied as having ‘mild’ COVID‑19 could therefore
have medium‑term respiratory sequalae.
Declaration. BWL and CFNK are members of the editorial board. e
research for this study was done in partial fullment of the requirements
for JvH’s MSc in Medical Physiology degree at Stellenbosch University.
Acknowledgements. The authors acknowledge the Pulmonary
Function Laboratory at Tygerberg Hospital and all its technologists and
administrative sta. Furthermore, our results shed new light on the oen‑
hidden sacrices that healthcare workers made for patients during the
COVID‑19 pandemic, at Tygerberg Hospital and worldwide. We cannot
be suciently grateful for their commitment.
Author contributions. JvH: conception and design, administrative
support, provision of study materials or patients, collection and assembly
of data, manuscript writing, nal approval of manuscript. HS: conception
and design, administrative support, provision of study materials or
patients, manuscript writing, nal approval of manuscript. AP: conception
and design, manuscript writing, final approval of manuscript. BWA:
conception and design, manuscript writing, nal approval of manuscript.
UL: conception and design, manuscript writing, final approval of
manuscript. CJL: conception and design, data analysis and interpretation,
manuscript writing, nal approval of manuscript. CFNK: conception and
design, administrative support, provision of study materials or patients,
manuscript writing, final approval of manuscript. The authors are
accountable for all aspects of the work in ensuring that questions related
to the accuracy or integrity of any part of the work are appropriately
investigated and resolved.
Funding.None.
Conicts of interest.None. All authors completed the International
Committee of Medical Journal Editors uniform disclosure form.
1. Liu Y, Kuo R, Shih S. COVID‑19 : e rst documented coronavirus pandemic in
history. Biomed J 2020;43(4):328‑333. https://doi.org/10.1016/j.bj.2020.04.007
2. Shi H, Han X, Jiang N, etal. Radiological ndings from 81 patients with COVID‑19
pneumonia in Wuhan, China: A descriptive study. Lancet Infect Dis 2020;20(4):425‑434.
https://doi.org/10.1016/S1473‑3099(20)30086‑4
3. Bretas DC, Leite AS, Mancuzo EV, etal. Lung function sixmonths after severe
COVID‑19 : Does time, in fact, heal all wounds? Braz J Infect Dis 2022;26(3):102352.
https://doi.org/10.1016/j.bjid.2022.102352
AJTCCM VOL. 30 NO. 3 2024 119
ORIGINAL RESEARCH: ARTICLES
4. Zhang H, Li X, Huang L, etal. Lung function trajectories in covid 19 survivors aer
discharge: A two‑year longitudinal cohort study. EClinicalMedicine 2022;54:101668.
https://doi.org/10.1016/j.eclinm.2022.101668
5. Frija‑Masson J, Debray MP, Gilbert M, etal. Functional characteristics of patients
with SARS‑CoV‑2 pneumonia at 30 days post infection. Eur J Respir Med
2020;56(2):2001754. https://doi.org/10.1183/13993003.01754‑2020
6. Huang Y, Tan C, Wu J, etal. Impact of coronavirus disease 2019 on pulmonary
function in early convalescence phase. Respir Res 2020;21(1):163. https://doi.
org/10.1186/s12931‑020‑01429‑6
7. Zhao Y, Shang YM, Song W, et al. Follow‑up study of the pulmonary function
and related physiological characteristics of COVID‑19 survivors threemonths
after recovery. EClinicalMedicine 2020;25:100463. https://doi.org/10.1016/j.
eclinm.2020.100463
8. Wu X, Liu X, Zhou Y, etal. 3‑month, 6‑month, 9‑month, and 12‑month respiratory
outcomes in patients following COVID‑19 ‑related hospitalisation: A prospective study.
Lancet Respir Med 2021;9(7):747‑754. https://doi.org/10.1016/S2213‑2600(21)00174‑0
9. World Health Organization. Living guidance for clinical management of COVID‑19.
23 November 2021. https://www.who.int/publications/i/item/WHO‑2019‑nCoV‑
clinical‑2021‑2 (accessed June 2022).
10. Graham BL, Steenbruggen I, Miller MR, etal. Standardisation of spirometry 2019
update. An ocial American oracic Society and European Respiratory Society
technical statement. Am J Respir Crit Care Med 2019;200(8):e70‑e88. https://doi.
org/10.1164/rccm.201908‑1590ST
11. Graham BL, Brusasco V, Burgos F, etal. 2017 ERS/ATS standards for single‑breath
carbon monoxide uptake in the lung. Eur J Respir Med 2017;49(1):1600016. https://
doi.org/10.1183/13993003.00016‑2016
12. Quanjer PH, Stanojevic S, Cole TJ, etal.; European Respiratory Society Global Lung
Function Initiative. Multi‑ethnic reference values for spirometry for the 3‑95‑yr age
range: e global lung function 2012 equations. Eur Respir J 2012;40(6):1324‑1343.
https://doi.org/10.1183/09031936.00080312
13. Masekela R, Koegelenberg CFN, Gray DM. Guidance to the applicability of the Global
Lung Initiative spirometry reference equations for South African populations. S Afr
Med J 2021;111(2):97. https://doi.org/10.7196/SAMJ.2021.v111i2.15439
14. Wanger J, Clausen JL, Coates A, etal. Standardisation of the measurement of lung volumes.
Eur Respir J 2005;26(3):511‑522. https://doi.org/10.1183/09031936.05.00035005
15. Maree DM, Swanepoel RA, Swart F, etal. Position statement for adult and paediatric
spirometry in South Africa: 2022 update. Afr J orac Crit Care Med 2022;28(4):181‑
192. https://doi.org/10.7196/AJTCCM.2022.v28i4.287
16. American Thoracic Society. ATS Statement: Guidelines for the six‑minute walk
test. Am J Respir Crit Care Med 2002;166(1):111‑117. https://doi.org/10.1164/
ajrccm.166.1.at1102
17. Hegewald MJ, DeCato TW. Does obesity aect diusing capacity? Ann Am orac
Soc 2023;20(7):951‑957. https://doi.org/10.1513/AnnalsATS.202304‑308ED
18. De Faria Santarem GC, de Cleva R, Santo MA, etal. Correlation between body
composition and walking capacity in severe obesity. PLoS ONE 2015;10(6):e0130268.
https://doi.org/10.1371/journal.pone.0130268
19. Vásquez E, Batsis JA, Germain CM, Shaw BA. Impact of obesity and physical activity
on functional outcomes in the elderly: Data from NHANES 2005‑2010. J Aging Health
2014;26(6):1032‑1046. https://doi.org/10.1177/0898264314535635
20. Svard A, Lahti J, Roos E, etal. Obesity, change of body mass index and subsequent
physical and mental health functioning: A 12‑year follow‑up study among ageing
employees. BMC Public Health 2017;17(1):744. https://doi.org/10.1186/s12889‑
017‑4768‑8
21. Torres‑Castro R, Vasconcelo‑Castillo L, Alsina‑Restoy X, etal. Respiratory function
in patients post‑infection by COVID‑19 : A systemic review and meta‑analysis.
Pulmonology 2021;27(4):328‑337. https://doi.org/10.1016/j.pulmoe.2020.10.013
22. Mo X, Jian W, Su Z, etal. Abnormal pulmonary function in COVID‑19 patients
at time of hospital discharge. Eur Respir J 2020;55(6):2001217. https://doi.
org/10.1183/13993003.01217‑2020
23. Ong KC, Ng AW, Lee LS, etal. 1‑year pulmonary function and health status in
survivors of severe acute respiratory syndrome. Chest 2005;128(3):1393‑1400. https://
doi.org/10.1378/chest.128.3.1393
24. Parker A, Boloko L, Moolla MS, etal. Clinical features and outcomes of COVID‑19
admissions in a population with a high prevalence of HIV and tuberculosis:
Amulticentre cohort study. BMC Infect Dis 2022;22(1):559. https://doi.org/10.1186/
s12879‑022‑07519‑8
25. Sawadogo W, Tsegaye M, Gizaw A, Adera T. Overweight and obesity as risk factors
for COVID‑19 ‑associated hospitalisations and death: Systematic review and
meta‑analysis. BMJ Nutr Prev Health 2022;5(1):10‑18. https://doi.org/10.1136/
bmjnph‑2021‑000375
26. Mattioli AV, Coppi F, Nasi M, Pinti M, Gallina S. Long COVID: A new challenge for
prevention of obesity in women. Am J Lifestyle Med 2023;17(1):164‑168. https://doi.
org/10.1177/15598276221111054
27. PHOSP‑COVID Collaborative Group. Clinical characteristics with inammation
proling of long COVID and association with 1‑year recovery following hospitalisation
in the UK: A prospective observational study. Lancet Respir Med 2022;10(8):761‑775.
https://doi.org/10.1016/S2213‑2600(22)00127‑8
28. Hamming I, Timens W, Bulthuis ML, Lely AT, Navis GJ, van Goor H. Tissue
distribution of ACE2 protein, the functional receptor for SARS coronavirus: A rst
step in understanding SARS pathogenesis. J Pathol 2004;203(2):631‑637. https://doi.
org/10.1002/path.1570
29. Richter FC, Alrubayyi A, Teijeira Crespo A, Oxford‑Cardi COVID‑19 Literature
Consortium, Hulin‑Curtis S. Impact of obesity and SARS‑CoV‑2 infection:
Implications for host defence – a living review. Oxf Open Immunol 2021;2(1):iqab001.
https://doi.org/10.1093/oxmm/iqab001
30. Basolo A, Poma AM, Bonuccelli D, etal. Adipose tissue in COVID‑19 : Detection of
SARS‑CoV‑2 in adipocytes and activation of the interferon‑alpha response. J Endocrinol
Invest 2022;45(5):1021‑1029. https://doi.org/10.1007/s40618‑022‑01742‑5
31. Martínez‑Colón GJ, Ratnasiri K, Chen H, etal. SARS‑CoV‑2 infection drives an
inammatory response in human adipose tissue through infection of adipocytes
and macrophages. Sci Transl Med 2022;14(674):eabm9151. https://doi.org/10.1126/
scitranslmed.abm9151
32. Stein SR, Ramelli SC, Grazioli A, etal. SARS‑CoV‑2 infection and persistence in
the human body and brain at autopsy. Nature 2022;612(7941):758‑763. https://doi.
org/10.1038/s41586‑022‑05542‑y
33. Shang W, Kang L, Cao G, etal. Percentage of asymptomatic infections among SARS‑
CoV‑2 Omicron variant‑positive individuals: A systematic review and meta‑analysis.
Vaccines (Basel) 2022;10(7):1049. https://doi.org/10.3390/vaccines10071049
34. El‑Ghitany EM, Hashish MH, Farghaly AG, Omran EA, Osman NA, Fekry MM.
Asymptomatic versus symptomatic SARS‑CoV‑2 infection: A cross‑sectional
seroprevalence study. Trop Med Health 2022;50(1):98. https://doi.org/10.1186/
s41182‑022‑00490‑9
Received 18 October 2023. Accepted 14 June 2024. Published 11 October 2024.
