Introduction
Overweight and obesity remain major global public
health concerns, with prevalence and incidence rates continuing to
rise worldwide (1,2). According to the World Health
Organization (WHO), over 2.5 billion adults aged 18 years or older
were overweight in 2022, of which over 890 million were classified
as obese (www.who.int). Recently, the WHO updated
BMI criteria for overweight and obesity class-I and class-II in
adults in the Asia-Pacific region to 23.0-24.9, 25.0-29.9, and ≥30
kg/m², respectively (3,4). These revised cutoffs were proposed
because Asians tend to be at greater risk of type 2 diabetes
mellitus and cardiovascular diseases (CVDs) at lower BMIs than
Caucasians (5).
Evidently, individuals who are overweight and obese
are most at risk of dyslipidemia (6,7).
Dyslipidemia and/or overweight/obesity are characterized not only
by metabolic dysregulation but also by changes in innate and
adaptive immune responses and, ultimately, higher chronic low-grade
inflammation (8,9). NK cells are a key component of the
innate immune system and may be altered under these aberrant
conditions (10). As is well known,
NK cells play vital roles in immunosurveillance and the elimination
of virus-infected and malignant cells through signaling pathways
that activate perforin-granzyme exocytosis, antibody-dependent
cellular cytotoxicity and death receptor signaling mechanisms
(11-13).
Moreover, NK cells regulate immune homeostasis by secreting
numerous cytokines and interacting with other immune cells to
modulate inflammation (14).
Typically, NK cells are divided into two subpopulations, classified
by the intensity of anti-CD56 (NCAM1) staining: Weak
(CD56Dim) and strong (CD56Bright).
CD56Dim NK cells mainly support cytotoxic activity by
secreting perforin and granzymes, whereas CD56Bright NK
cells function mainly as specialized immunoregulators and cytokine
producers rather than primary killers (11,15,16).
Accumulating evidence suggests that metabolic
abnormalities associated with excessive adiposity and/or
dyslipidemia may drive quantitative, phenotypic and functional
changes in NK cells. These changes may contribute gradually to
impaired immune surveillance, increased susceptibility to viral
infections and the development of non-communicable diseases (NCDs)
(10,17-19).
However, the underlying mechanisms and consequences of
dyslipidemia- and/or excessive adiposity-induced alterations in NK
cells remain poorly understood, especially in Asian individuals.
Moreover, the reported data have often been conflicting, and
definitive conclusions remain unclear.
Therefore, the present study examined the frequency
and functional changes linking metabolic dysregulation and NK
cells. It examines the immunological profiles of NK cells, both
overall and by CD56-based subpopulations, in 125 Thai individuals
stratified by lipid profiles and BMI. The findings are expected to
be beneficial for preventive medicine, particularly in the context
of NCDs.
Materials and methods
Study setting and participants
The present study was approved by the Ethical
Committee of Khon Kaen University (approval number HE 671043) and
was conducted in accordance with the 1964 Declaration of Helsinki.
It examined leftover peripheral blood samples collected in
ethylenediaminetetraacetic acid (EDTA) collection tubes from 125
Thai individuals aged 18-65 years (female/male, 68/57), obtained
from the Medical Check-up Unit at Srinagarind Hospital (affiliated
with Khon Kaen University) in Khon Kaen, Thailand (between February
2024 and June 2026). Individuals with underlying diseases,
including hypertension, diabetes mellitus, immunological diseases,
infectious diseases, and hematological diseases, were excluded, as
were those taking anti-inflammatory drugs.
All participants were categorized into five
subgroups based on their lipid profiles and BMI (classified
according to the WHO criteria for Asians): Normal lipid and normal
weight (NL/NW; BMI=18.5-22.9 kg/m2), normal lipid and
overweight (NL/OW; BMI=23.0-24.9 kg/m2), dyslipidemia
and normal weight (DL/NW; BMI=18.5-22.9 kg/m2),
dyslipidemia and overweight (DL/OW; BMI=23.0-24.9 kg/m2)
and dyslipidemia and obesity class-I (DL/OB-I; BMI=25.0-29.9
kg/m2). DL was defined as at least one abnormal lipid
profiles value according to the Japan Atherosclerosis Society's
guidelines (20): Total cholesterol
(TC) ≥200 mg/dl, triglyceride (TG) ≥150 mg/dl, low-density
lipoprotein-cholesterol (LDL-C) ≥120 mg/dl and/or high-density
lipoprotein-cholesterol (HDL-C) <40 mg/dl. Moreover, other
laboratory parameters including fasting blood sugar (FBS), glycated
hemoglobin A1c (HbA1c), creatinine (Cr), alkaline phosphatase (ALT)
was also examined. The summarized data were represented in Table I.
 | Table IDemographic and clinical
characteristics of participants. |
Table I
Demographic and clinical
characteristics of participants.
| Parameter | Reference
range | NL/NW (Mean ±
SD) | NL/OW (Mean ±
SD) | DL/NW (Mean ±
SD) | DL/OW (Mean ±
SD) | DL/OB-I (Mean ±
SD) | P-values
(Jonckheere-Terptra) |
|---|
| Number of subjects
(n=125) | | 25 | 25 | 25 | 25 | 25 | |
| Age (years) | | 46.4±7.7 | 47.0±10.2 | 48.3±9.8 | 49.1±10.3 | 47.5±9.6 | NS |
| Sex
(female/male) | | 16/9 | 10/15 | 14/11 | 14/11 | 14/11 | NS |
| BMI
(kg/m2) | | 21.1±1.3 |
24.0±0.6a |
20.8±1.4b |
24.0±0.7a,c |
26.5±1.3a,b,c,d | <0.001 |
| Blood pressure | | | | | | | |
|
Systolic
(mmHg) | 100-139 | 111.6±9.2 | 119.4±9.5
a |
119.8±8.6a |
119.0±10.2a |
124.3±10.6a | <0.001 |
|
Diastolic
(mmHg) | 60-89 | 66.7±11.4 | 71.2±8.4 | 69.6±8.4 | 71.8±8.7 |
76.1±7.7a,c | <0.01 |
| PR (times/min) | 60-100 | 72.5±9.8 | 72.6±9.9 | 71.5±9.0 | 74.8±10.5 | 72.0±10.1 | NS |
| Blood sugar | | | | | | | |
|
FBS
(mg/dl) | 70-110 | 88.8±9.3 | 88.8±9.2 | 88.9±10.7 | 89.1±6.4 | 90.7±6.7 | NS |
|
HbA1c
(%) | 4.6-7.0 | 5.3±0.4 | 5.4±0.6 |
5.6±0.3a,b |
5.6±0.3a,b | 5.6±0.4
a | <0.01 |
| Lipid profiles | | | | | | | |
|
TC
(mg/dl) | <200 | 171.6±18.6 | 173.9±16.5 |
219.3±29.8a,b |
239.6±41.7a,b |
231.2±36.8a,b | <0.001 |
|
HDL-C
(mg/dl) | ≥40 | 66.0±13.2 | 59.8±13.3 | 62.4±18.6 | 60.2±19.3 |
55.2±14.7a | <0.05 |
|
LDL-C
(mg/dl) | <120 | 95.6±17.7 | 102.6±13.3 |
146.8±25.7a,b |
162.2±40.1a,b |
154.1±38.5a,b | <0.001 |
|
TG
(mg/dl) | <150 | 73.3±32.6 | 86.6±34.0 |
103.0±36.0a |
132.8±85.9a |
161.4±119.2a,b | <0.001 |
|
Cr
(mg/dl) | 0.5-1.5 | 0.83±0.14 | 0.87±0.20 | 0.88±0.13 | 0.88±0.19 | 0.91±0.19 | NS |
|
ALT
(U/l) | 4-36 | 16.6±5.5 | 16.9±8.7 |
26.2±17.1a,b | 22.4±11.1 |
25.0±15.0a,b | <0.05 |
| Behavior
factors | | | | | | | |
|
Smoking | | 2 | 2 | 2 | 0 | 1 | NS |
|
Alcohol
drinking | | 3 | 1 | 5 | 3 | 3 | NS |
Surface staining of NK cells
First, 100 µl of fresh peripheral whole blood (n=25
per group) was stained with the following monoclonal antibodies
(mAbs) at dilution 1:50 in the dark for 10 min: Fluorescein
isothiocyanate (FITC)-conjugated anti-CD3 (cat. no. 555916, BD
Pharmingen; BD Biosciences) and peridinin-chlorophyll-protein
complex-cyanine 5.5 (PerCP-Cy5.5)-conjugated anti-CD56 (cat. no.
318322, BioLegend, Inc.) at room temperature (RT) for 10 min. Next,
red blood cells were lysed using FACS lysing solution (BD
Biosciences) in the dark for 10 min. Then, the immune cells were
washed with 1X phosphate-buffered saline (PBS) and then measured
using a FACSLyric flow cytometer (BD Biosciences). Cells were
categorized as NK cells if they were CD3-CD56+. NK cells were
further stratified by CD56 staining intensity (15,16):
weak (CD56Dim) and strong (CD56Bright). The
percentages of NK cells were calculated relative to the total
lymphocytes (FlowJo Software, v10; BD Biosciences). A
representative example of NK cell detection, stratification, and
analysis is shown in Fig. 1.
Intracellular staining to detect
IFN-γ
Peripheral blood mononuclear cells (PBMCs) were
isolated from six randomly selected samples per group using
Lymphosep Lymphocyte Separation Medium (Biowest). Next, the cells
were stimulated with phorbol-12-myristate-13-acetate (PMA;
MilliporeSigma) and ionomycin calcium salt (MilliporeSigma) at 37˚C
for 2 h at final concentrations of 100 ng/ml and 1,000 ng/ml,
respectively, along with brefeldin A (Golgi apparatus inhibitor;
eBioscience; Thermo Fisher Scientific, Inc.) at a final
concentration of 3 µg/ml. Then, the cells were stained with
FITC-conjugated anti-CD3 and PerCP-Cy5.5-conjugated anti-CD56 mAbs
(dilution 1:50) in the dark at RT for 10 min, fixed with 2%
formaldehyde at RT for 10 min, incubated with a
permeabilization/wash buffer (R&D Systems, Inc.) at RT for 10
min, and stained with a Brilliant Violet 421 (BV421)-conjugated
anti-IFN-γ mAb (cat. no. 502532, BioLegend, Inc.) at dilution 1:20,
RT for 30 min. Finally, the cells were measured by FACSLyric flow
cytometer (BD Biosciences), along with FACSuite software (v1.5, BD
Biosciences). An unstimulated cell (medium) was measured in
parallel. The percentages of all IFN-γ-producing NK cells and their
subpopulations were referenced to the percentages within its own
populations of total NK cells and each subset.
Statistical analysis
Data were analyzed by using FlowJo Software (BD
Biosciences), all graphs were created using GraphPad Prism
(Dotmatics) and all statistical analyses were performed using SPSS
(v28, IBM Corp.). Data were compared between multiple groups using
the Jonckheere-Terpstra test which was highly suitable multiple
group analysis for trend data, followed by pairwise Dunn's tests if
significant. Correlations between variables were assessed using
Spearman's rank correlation coefficient (R). The effects of
variable factors (sex, smoking and alcohol drinking as shown in
Table I) on multiple outcome
variables (the frequencies of total NK cells, their subpopulations,
and their IFN-γ-producing counterparts, as displayed in Figs. 2 and 3) were assessed using multivariate
analysis of covariance (MANCOVA) while controlling for covariates
(age, blood pressures, pulse rate, and laboratory parameters as
shown in Table I). All graphs were
represented as medians of percentages with the inter quartile range
(IQR). P<0.05 was considered to indicate a statistically
significant difference.
Results
Participants' demographic and clinical
characteristics
The present study enrolled leftover samples obtained
from a total of 125 Thai individuals aged 18-65 years. Since the
dataset had a trending pattern, the Jonckheere-Terpstra test was
used to compare multiple groups. As well as BMI and lipid profiles,
systolic and diastolic blood pressures, HbA1c and ALT were also
statistically significant. However, the present study did not find
any overall differences in age, sex, pulse rate (PR), FBS, Cr, or
behavioral factors (including smoking and alcohol drinking). The
pairwise comparisons for all variables are also shown in Table I.
Total and subpopulation percentages of
NK cells by lipid profiles and BMI category
The present study analyzed the frequencies of total
NK cells and NK subpopulations from five subgroups of participants.
For multiple group analysis using the Jonckheere-Terpstra test,
total NK cells had a statistical significance (P<0.01). For
pairwise Dunn's tests, although total NK cell percentages were
higher in all DL groups, they were only significantly higher in the
DL/OB-I group than in the NL/NW (P<0.01) and NL/OW (P<0.05)
groups (Fig. 2A). Regarding NK cell
subpopulations, multiple analyses exhibited statistically
significant for CD56Dim NK cells (P<0.01, Fig. 2B) but not for CD56Bright
NK cells (Fig. 2C). In pairwise
comparisons, CD56Dim NK cell percentages were highest in
the DL/OB-I group, differing significantly from those in the NL/NW
(P<0.01) and NL/OW (P<0.05) groups. A summary of the
statistical values of total NK cell and subpopulation frequencies
is shown in the Table SI.
IFN-γ-producing total NK cells and
their subpopulations
Given that NK cells are the primary producers of
IFN-γ, which determines their effector functions, differences in
IFN-γ production capacity of NK cells by lipid profiles and/or BMI
were examined, both overall and by subpopulations. Multiple group
analysis represented that IFN-γ-producing total NK cells and
CD56Dim NK cells had no statistical significance
(Fig. 3A and B). However, IFN-γ-producing
CD56Bright NK cells exhibited statistically significance
at P<0.05. In addition to pairwise analysis, the percentages of
IFN-γ-producing CD56Bright NK cells was highest in the
DL/OB-I group, differing significantly from those in the NL/NW and
DL/NW groups (P<0.05, Fig. 3C).
The summary of the statistical values of IFN-γ-producing NK cell
and their subpopulation percentages across five subgroups is
additionally provided in Table
SII.
Correlations between BMI and NK cell
percentages, and IFN-γ production
Finally, correlations between BMI and NK cell
percentages were examined, both overall and by subpopulations, and
IFN-γ production status. BMI correlated positively with total NK
cell percentages (R=0.214, P<0.05; Fig. 4A), CD56Dim NK cell
percentages (R=0.217, P<0.05; Fig.
4B), and IFN-γ-producing CD56Bright NK cell
percentages (R=0.463, P<0.01; Fig.
4C).
Discussion
Overweight and obesity remain major health problems
in the era of globalization, and their incidences are likely to
increase in the future (1,2). Overweight and obesity are associated
with a number of modifiable and non-modifiable risk factors,
including lifestyle, low physical activity, diet, advanced age,
genetics and the environment (21).
Numerous studies on obesity/dyslipidemia have shown alterations in
immune cell populations, phenotypes, and functions, predominantly
T, NKT-like, and NK cells (10,17,22-24).
However, research on NK cell changes remains limited, and the
evidence remains inconsistent regarding NK cells' total and
subpopulation percentages and functional capacities (10). Moreover, representative data on
individuals with a BMI of 25.0-29.9 (obesity class-I) and their
lipid profiles remain limited.
Therefore, the present study was the first, to the
best of the authors' knowledge, to compare NK cell percentages,
both overall and by subpopulations, and IFN-γ production status
among 125 Thai (Southeast Asian) individuals without underlying
diseases or anti-inflammatory using stratified by two-dimensional
categories: Lipid profiles (normal lipid and dyslipidemia) and BMI
(normal weight, overweight and obesity class-I). The participants
were therefore distinguished into five subgroups (Table I). This classification differs from
that used in prior studies, which mainly compared normal lipid vs.
dyslipidemia or normal BMI vs. class-II obesity (BMI ≥30.0
kg/m2). Therefore, the present study provided a
finer-grained analysis of NK cell changes associated with metabolic
alterations and increasing adiposity, which may help reveal such
changes. However, individuals with normal lipid plus obesity
class-I were not included in the present study due to their rarity
in the Thai population.
NK cells were examined, both overall and by
subpopulations, by flow cytometry and compared across the five
subgroups. Total NK cell percentages were higher in the DL groups
and the group-wise comparisons exhibited a corresponding stepwise
escalation. However, they were only markedly higher in the DL/OB-I
group compared with the other groups. It was hypothesized that
dyslipidemia may associate with the expansion of the total NK cell
population, especially in obesity class-I. For instance, Wouters,
et al (18) found that
subjects with obesity had markedly higher total NK cell percentages
than those who were lean. Obese individuals may associate greater
low-grade inflammation. By contrast, Xu et al (22) found higher NK cell percentages in
Chinese adults with dyslipidemia than in healthy controls, although
the difference was not significant. Bähr et al (24) found no significant difference
between subjects with obesity (BMI >30.0 kg/m2) and
normal weight (BMI <25.0 kg/m2).
Regarding NK cell subpopulations, although
CD56Dim NK cell percentages were higher in the DL
groups, only those in the DL/OB-I group were markedly higher than
those in the other groups. This finding contrasted with a previous
study on obesity (24). However, it
was therefore hypothesized that under dyslipidemia, especially in
higher BMI, individuals may experience greater low-grade
inflammation. For CD56Bright NK subpopulation, multiple
group analysis showed no statistical significance.
Phan et al (25) previously showed that NK cell
populations differ markedly by sex, both overall and by
subpopulation. Thus, the present study further investigated whether
sex affects NK cell populations using data from the NL/NW group, as
it should best reflect sex differences without the confounding
effects of dyslipidemia and high BMI. The proportions of total NK
cells and CD56Dim NK cells were significantly higher in
males than in females (P<0.05; Fig.
SI), consistent with Phan et al (25). However, the proportion of
CD56Bright NK cells did not differ markedly across
groups. Nevertheless, the group-to-group comparison is unlikely to
have been affected, as the sex ratio was adjusted to be comparable
across subgroups.
The present study then observed the percentages of
IFN-γ-producing total NK cells and their subpopulations.
IFN-γ-producing CD56Bright NK cell percentages were
markedly higher in the DL/OB-I group than in the NL/NW and DL/NW
groups. Accordingly, it is hypothesized that higher BMI may be
associated with stronger immunological response driven by greater
cytokine-induced low-grade inflammation. Consistent with a previous
study (24), IFN-γ-producing
CD56Bright NK cell percentages were markedly higher in
obese individuals who had BMI ≥30.0 than in normal weight groups
(BMI <25.0), whereas IFN-γ-producing CD56Dim NK cell
percentages were markedly lower. However, the present study was
limited to functional cytokine production capacity based on
cellular responses stimulated by PMA (protein kinase C activator)
and calcium ionophore (ionomycin), which activate the
phosphoinositide 3-kinase (PI3k)/Akt signaling pathway (26). The present study was primarily an
observational one that paves the way for further investigations
into other functional aspects. In feasible cases of dyslipidemia
combined with obesity condition, higher pro-inflammatory adipokines
such as leptin and visfatin, as well as excessive long-chain fatty
acid, were associated with signaling pathways involved in IFN-γ
production. Possibly, leptin may activate Janus kinase 2/signal
transducer and activator of transcription 3 (STAT3) prior to
stimulating STAT4, T-box expressed in T cells (T-bet) transcription
factor and ultimately IFN-γ production (27,28).
As well as visfatin, it probably induces IFN-γ production via
nuclear factor kappa-light-chain-enhancer of activated B cells and
STAT3 signaling pathways (29).
Additionally, long-chain fatty acids can be obtained from dietary
fats and triglyceride components and also promote this cytokine
production. However, the associated pathway remains inconclusive
(30).
In other functional considerations of interest,
previous studies have investigated the effects of obesity on NK
cytotoxicity-related assay and degranulation function, consistently
showing decreased cytotoxic activity against tumor cells (31-34)
and reduced degranulation, as indicated by decreased
lysosome-associated membrane protein 1/CD107a expression (31,33) or
granzyme B production (33).
However, Zhang et al (19)
demonstrated that NK cell cytotoxicity, measured at an
effector-to-target cell (human leukemic cell line K562) ratio of
12.5:1, was significantly higher in women with recurrent pregnancy
loss and dyslipidemia than in those with normal lipid levels
(P<0.05).
Since the clinical characteristics of various
participants were found to be statistically significant following
the Jonckheere-Terpstra test, MANCOVA was also examined the effects
of variable factors on multiple outcomes of all NK cell percentages
and cytokine production capacity while controlling for covariates.
However, significant associations were not found. In the present
study, age, PR, FBS, Cr, as well as variables including sex,
smoking, and alcohol drinking, were tested simultaneously.
BMI correlated positively with the percentages of
total NK cells, CD56Dim NK cells, and IFN-γ-producing
CD56Bright NK cells. Thus, BMI may be possible physical
indicator reflecting immunological status for NK cell and their
function. Nevertheless, the observed significant positive
correlations were also largely driven by dyslipidemia, especially
in the DL/OB-I group, which made the effect appear more pronounced.
Similarly, Viel et al (17)
found that NK cell counts in peripheral blood correlated positively
with BMI. While the findings of Bӓhr et al (24) are largely consistent, the authors
reported negative correlations between BMI and both
CD56Dim NK percentage and cytokine production.
Nevertheless, the existing evidence is based on only a few studies
and further studies are required to advance and validate this
concept.
Overall, it appeared that the findings of the
present study leaned toward a higher low-grade inflammatory
outcome. Larger NK cell populations and changes in their function
might help drive occurring of the development of NCDs such as CVDs,
kidney diseases and type 2 diabetes mellitus. In CVDs, NK cells
were predominantly increased in human atherosclerotic lesions
(35,36). Consistently, symptomatic patients
showed increased percentages of IFN-γ-producing NK cells and
CD56Bright NK cells in atherosclerotic plaques (37), as well as higher levels of
circulating NK cells (38).
Moreover, elevated numbers of IFN-γ-producing NK cells increase
cholesterol accumulation in macrophages, inducing foam cell
formation and potentially activating macrophages, which, in turn,
release matrix metalloproteinases to degrade endothelial cells,
vascular smooth muscle cells and the extracellular matrix,
ultimately leading to destabilization and rupture of
atherosclerotic plaques (39,40).
In kidney diseases, greater infiltration of activated NK cells
might contribute to inflammation, stressed tubular and endothelial
cells, interstitial fibrosis, and chronic damage to glomerular
tissue through IFN-γ-mediated functions and cytolytic mediators
(41-43).
In non-diabetic individuals, a previous study showed that fasting
glucose levels correlated positively with the number of total NK
cells (44). An increase in NK
cells may serve as a marker of immune activation and may also be
associated with higher low-grade inflammation. However, the present
study found no significant differences in FBS among the groups (all
average values were within the normal range) and no correlation
between FBS and NK cell total or subpopulation percentages, or
between FBS and IFN-γ-producing NK cell total or subpopulation
percentages. In type 2 diabetes mellitus, NK cells could contribute
to insulin resistance primarily through obesity-induced low-grade
inflammation by enhancing the release of proinflammatory mediators,
such as the tumor necrosis factor-α and IFN-γ, which could disrupt
the normal insulin signaling pathway and downregulate key insulin
signaling components, including the insulin receptor, insulin
receptor substrate 1 and the insulin-regulated glucose transporter
type 4 (45,46). Therefore, greater numbers of NK
cells and IFN-γ-producing NK cells might contribute to, and
enhance, the microenvironment associated with the pathogenesis of
these NCDs.
In conclusion, the present study was the first, to
the best of the authors' knowledge, to report the immunological
profiles of NK cells, both overall and by subpopulations, in Thai
individuals stratified by lipid profiles and BMI, thereby likely
revealing clearer patterns than would be possible by assessing each
variable alone. The immunological status of individuals with
dyslipidemia was shifted toward a more proinflammatory state.
Notably, those with dyslipidemia and obesity class-I exhibited
predominant changes in both NK cell percentage and function.
Therefore, both BMI and lipid profiles must be controlled to
prevent immunological changes and potentially reduce low-grade
inflammation. However, even although the sample size achieved a
test power (1-β) >80%, the small number of participants remains
a limitation of the present study.
Supplementary Material
Percentages of total NK cells and
CD56Dim NK cells in males and females in the NL/NW
group. The percentages of (A) total NK cells and (B)
CD56Dim NK cells in males and females in the NL/NW
group. The horizontal line indicates the median, and the vertical
lines denote the IQR. *P<0.05. NK, natural killer;
NL/NW, normal lipid/normal weight IQR, interquartile range; NS, no
significance.
Summary of the percentages of total NK
cells, CD56Dim and CD56Bright NK cells by
group.
Summary of the percentage of
IFN-γ–producing total NK cells, CD56Dim and
CD56Bright NK cells by group.
Acknowledgements
Not applicable.
Funding
Funding: The present study was funded by the Young Research
Development Project of Khon Kaen University Year 2024, Khon Kaen
University, Thailand.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
ASu was responsible for conceptualization, review,
sample collection, data analysis, and writing of the first draft of
the manuscript. PN was responsible for sample administration and
data curation. ASu and PN confirm the authenticity of all the raw
data. KF, SP, PB, ASi, AJ, AN, KJ and KS were responsible for
conceptualization and manuscript review and editing. WP was
responsible for funding acquisition, conceptualization, data
analysis, manuscript editing, final manuscript preparation and
correspondence. All authors have read and approved the final
manuscript.
Ethics approval and consent to
participate
The present study was approved by the Ethics
Committee for Human Research of Khon Kaen University (approval no.
HE 671043) and conducted in accordance with the 1964 Declaration of
Helsinki.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
Authors' information
Wisitsak Phoksawat ORCID ID: 0000-0002-8866-9459
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