The clinical features and outcomes of diabetes patients infected with COVID-19: a systematic review and meta-analysis comprising 192,693 patients

Abstract

Objectives
We sought to explore the relevance of analyses that include critical laboratory parameters and drug treatment, clinical characteristics of diabetic patients who are infected with COVID-19, to the development of individualized treatment strategies for diabetic patients infected with COVID-19.

Methods
We searched Cochrane, Embase, FMRS, Pubmed, Springer, Web of Science databases for systematic reviews and meta-analyses to estimate the clinical characteristics and prognosis of confirmed covid-19 infections in patients with and without diabetes.

Results
Our meta-analysis included a total of 32 studies with 192,693 COVID-19 patients. Common comorbidities in the diabetic group were hypertension, cerebrovascular disease, chronic kidney disease and cardiovascular disease. We discovered that white blood cell count, neutrophil count, inflammatory marker levels, D-dimer, urea, precursor of the brain natriuretic peptide (Pro-BNP) increased and lymphocyte count, estimated glomerular filtration rate (eGFR), albumin decreased significantly in the diabetic group in laboratory test results. Compared with the non-diabetic group, the diabetic group had a higher incidence of complications in acute respiratory distress syndrome (ARDS), shock, acute heart injury, acute kidney injury and more regularly used oxygen therapy, invasive ventilation, non-invasive ventilation, continuous renal replacement therapy (CRRT), extracorporeal membrane oxygenation (ECMO) treatment. Mortality and intensive care unit (ICU) hospitalization rates were highest in the diabetic group than in the non-diabetic group (p < 0.05).

Conclusion
Diabetic patients hospitalized with COVID-19 have an increased risk of death, lower discharge rates, and higher ICU admission rates. Their presence of hypertension, cerebrovascular disease, chronic kidney disease (CKD), higher levels of inflammatory markers. Multiple complications are all predictors of poor outcomes in people with diabetes. Our findings will help identify elevated risk factors in diabetics, which will benefit early prediction.

Full text

Frontiers in Medicine 01 frontiersin.org
The clinical features and
outcomes of diabetes patients
infected with COVID-19: a
systematic review and
meta-analysis comprising
192,693 patients
KaiLiu
1, ShuLiu
2, Ting-tingXu
1 and HongQiao
1*
1 Department of Endocrinology and Metabolism, The Second Afiliated Hospital of Harbin Medical
University, Harbin, China, 2 Physical Examination Center, The Fourth Afiliated Hospital of Harbin
Medical University, Harbin, China, 3 Health Management Centre, Fourth Aliated Hospital of Harbin
Medical University, Harbin, Heilongjiang, China
Objectives: We sought to explore the relevance of analyses that include critical
laboratory parameters and drug treatment, clinical characteristics of diabetic
patients who are infected with COVID-19, to the development of individualized
treatment strategies for diabetic patients infected with COVID-19.
Methods: We searched Cochrane, Embase, FMRS, Pubmed, Springer, Web
of Science databases for systematic reviews and meta-analyses to estimate
the clinical characteristics and prognosis of confirmed covid-19 infections in
patients with and without diabetes.
Results: Our meta-analysis included a total of 32 studies with 192,693 COVID-19
patients. Common comorbidities in the diabetic group were hypertension,
cerebrovascular disease, chronic kidney disease and cardiovascular disease.
We discovered that white blood cell count, neutrophil count, inflammatory
marker levels, D-dimer, urea, precursor of the brain natriuretic peptide (Pro-
BNP) increased and lymphocyte count, estimated glomerular filtration rate
(eGFR), albumin decreased significantly in the diabetic group in laboratory test
results. Compared with the non-diabetic group, the diabetic group had a higher
incidence of complications in acute respiratory distress syndrome (ARDS), shock,
acute heart injury, acute kidney injury and more regularly used oxygen therapy,
invasive ventilation, non-invasive ventilation, continuous renal replacement
therapy (CRRT), extracorporeal membrane oxygenation (ECMO) treatment.
Mortality and intensive care unit (ICU) hospitalization rates were highest in the
diabetic group than in the non-diabetic group (p < 0.05).
Conclusion: Diabetic patients hospitalized with COVID-19 have an increased risk
of death, lower discharge rates, and higher ICU admission rates. Their presence
of hypertension, cerebrovascular disease, chronic kidney disease (CKD), higher
levels of inflammatory markers. Multiple complications are all predictors of poor
outcomes in people with diabetes. Our findings will help identify elevated risk
factors in diabetics, which will benefit early prediction.
KEYWORDS
COVID-19, SARS-CoV-2, diabetes, mortality, clinical features, meta-analysis
OPEN ACCESS
EDITED BY
Pranav Kumar Prabhakar,
Parul University, India
REVIEWED BY
Bharath Kanakapura Sundararaj,
Boston University, UnitedStates
Harpreet Kaur,
Panjab University, India
*CORRESPONDENCE
Hong Qiao
brilliantan123@126.com;
qiaohong@hrbmu.edu.cn
RECEIVED 12 November 2024
ACCEPTED 09 January 2025
PUBLISHED 29 January 2025
CITATION
Liu K, Liu S, Xu T-t and Qiao H (2025) The
clinical features and outcomes of diabetes
patients infected with COVID-19: a systematic
review and meta-analysis comprising 192,693
patients.
Front. Med. 12:1523139.
doi: 10.3389/fmed.2025.1523139
COPYRIGHT
© 2025 Liu, Liu, Xu and Qiao. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Systematic Review
PUBLISHED 29 January 2025
DOI 10.3389/fmed.2025.1523139

Liu et al. 10.3389/fmed.2025.1523139
Frontiers in Medicine 02 frontiersin.org
Introduction
e COVID-19 caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) infection, it is a novel and serious global
health threat that is spreading rapidly across the globe, with conrmed
infections and death. e number of infections is growing, with
around 500 million conrmed cases worldwide and more than 6
million deaths. Most patients have mild symptoms, but some may
develop severe complications, including ARDS, multiple organ failure,
septic shock and hypercoagulability, which may eventually result in
death (1–3). While large-scale vaccine production has provided a
glimmer of hope for humanity for now, the absence of global
vaccination and the continued mutation of the virus make eradication
of SARS-CoV-2 challenging. Among them, the largest COVID-19
study in the United States found that among 5,700 hospitalized
patients with COVID-19, diabetes was one of the most common
comorbidities (33.8%), and chronic disease comorbidities had a
signicant impact on the clinical outcomes of patients with COVID-19
(4). Studies have shown that people with underlying comorbidities of
diabetes are more likely to experience adverse outcomes from
COVID-19. e COVID-19 pandemic has placed a huge burden on
healthcare facilities, especially for the patients who are with them.
Most studies report that diabetes is associated with a higher risk of
serious events and mortality (5, 6), while others have no clear
association (7, 8), so whether diabetes is associated with adverse
outcomes in COVID-19 patients controversy remains. is
inconsistency may be related to dierent sample sizes, dierent
populations, and varying levels of confounding adjustment. Numerous
articles show the clinical features of COVID-19 patients in various
countries (9, 10), but few studies specically compare the clinical
features of COVID-19in diabetic and non-diabetic patients. is
study can provide information on risk factors by correlative analysis
of data on essential laboratory parameters and drug treatment for
COVID-19 patients with and without diabetes, while helping inform
the development of tailored treatment strategies for diabetic
COVID-19 patients.
Methods
Literature search: identification and
selection of studies
e protocol for this systematic review and meta-analysis is
available online at PROSPERO; registration number CRD42022312394.
All procedures utilized in systematic review and meta-analysis
were in accordance with the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive
search was performed on Cochrane, Embase, FMRS, Pubmed,
Springer, Web of Science databases between December 1, 2019 and
April 1, 2022. Titles and abstracts of potentially eligible articles were
manually reviewed and potentially relevant articles were assessed for
eligibility. Two investigators (KL and SL) independently searched for
studies. In the event of disagreement over study eligibility, a third
investigator (HQ) was required to participate in order to reach
consensus. Related unpublished clinical trial results were similarly
manually searched for additional potential studies. Wesearched using
a combination of the following keywords: “COVID-19,”
“SARS-CoV-2,” “coronavirus,” “2019-nCoV,” “diabet*,” “T1DM” and
“T2DM.” e PRISMA owchart was used to present the search
strategy and studies included in the meta-analysis (Figure1). PRISMA
2020 (Supplementary Table4), Meta-analysis of Observational Studies
in Epidemiology (MOOSE) (Supplementary Table 5) were also
adhered to for reporting.
Inclusion and exclusion criteria
Among the patients included were a signicant number of people
with type 2 diabetes, most of whom had previously been diagnosed
with diabetes, and the remainder who were newly diagnosed with
diabetes on admission. For studies to be included, the following
inclusion criteria were applied: (a) age ≥ 18 years; (b) cohort studies
reporting clinical characteristics of patients with conrmed SARS-
CoV2 infection in both diabetic mellitus (DM) and non-diabetic
mellitus (non-DM) groups or case–control studies; (c) analysis of one
or more clinical characteristics, including demographic characteristics,
clinical symptoms, laboratory ndings, comorbidities, treatments,
outcomes of complications; (d) conrmed patients in a hospital
setting, and (f) studies with excellent methodological design
(appropriate sample size is considered to beat least more than 20
patients per group). In addition, the following criteria were used to
exclude studies: (a) non-human/animal studies; (b) duplicate
publications; (c) no full text articles; (d) case reports, guidelines,
clinical meetings, letters, systematic reviews and meta-analysis; (e)
studies that did not provide diabetes and non-diabetic related data or
related clinical outcomes.
Data extraction
Two researchers (KL and SL) independently extracted data from
eligible studies to minimize bias. Any disagreements will bediscussed
with a third investigator (HQ) to reach consensus. Weextracted and
analyzed items from eligible studies, including country, year,
publication date, number of reported cases, sex, age, clinical signs and
symptoms, comorbidities, laboratory ndings, complications and
outcomes of infected patients.
Quality assessment of including studies
All articles were independently evaluated and compared by two
raters. Any inconsistencies should beconsidered or further consulted
by an independent expert. We used the Newcastle-Ottawa Scale
(NOS) to assess the risk of bias of included studies
(Supplementary Table 1), and a NOS score greater than 7 was
considered to beof decent quality (11).
Statistical analysis
Aer STATA 17.0 soware analyses, the Odds ratio (OR) and the
corresponding 95% condence interval (CI) of the relevant factors in
each study are calculated. Heterogeneity between studies was assessed
using Cochrane Q and I
2
statistics. I
2
reects the fraction of

Liu et al. 10.3389/fmed.2025.1523139
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heterogeneity in the total variation of the eect sizes. Values <25%
indicate low heterogeneity, values between 25 and 50% indicate
moderate, > 50% strong heterogeneity. If I
2
is greater than 50%,
indicating greater heterogeneity, the pooled SMD values and the
corresponding 95% CI are calculated using the DerSimonian-Laird
method using a random eects model. If I
2
is less than 50 percent, the
xed eect model is calculated. e Egger’s test is used to assess
publication bias, which is suspected if the Egger’s test have a p-value
<0.05. e sensitivity analysis was performed aer a stepwise exclusion
of studies, followed by a comparison of the raw results with those from
the re-analysis to conrm the stability of our primary meta-analysis.
If the combined eect point falls within the condence interval of the
total eect size, the analysis results are robust and reliable, weneed to
becareful in interpreting the results and drawing conclusions if the
combined eect point falls outside the condence interval for the total
eect size, or if the combined eect point diers signicantly from the
total eect size.
Results
Literature search and characteristics of
including studies
In order to identify these possible risk factors and severity
predictors that could beuseful for clinical treatment in the future
treatment of patients with diabetes aected by COVID-19, weused a
meta-analysis that combined demographic and clinical characteristics
from each study. Some outcomes including gender, age, symptoms,
complications, comorbidities, treatment, laboratory measures and
clinical outcomes were observed to dier between DM and non-DM
cases. A total of 17,197 records were identied from the database.
Aer excluding duplicates, the titles and abstracts of 2,438 articles
were screened, wefrom the articles screened from literature and
online sources, a total of 32 articles were included aer exclusion (31
retrospective studies and 1 prospective study) eligible for inclusion
prespecied criteria for analysis (Figure1). Numbers ranged from 29
(the smallest study) to 33,478 (the largest study). Overall, our
systematic review included 192,693 individuals. Most studies were
conducted in Asia (China, n = 14; South Korea, n = 3; Iran, n = 3;
Kuwait, n = 1; SaudiArabia, n = 1; UnitedArabEmirates, n = 1), while
in North America (UnitedStates, n = 2) and 6 studies in Europe
(France, n = 2; Italy, n = 1; Denmark, n = 1; turkey, n = 2) (Table1).
All articles included in the meta-analysis were of high quality
according to the NOS tool, as described in Supplementary Table3.
Demographic and clinical characteristics
Aer the analysis, as can beseen in Supplementary Table2, it can
beobserved that the age and BMI of SARS-CoV-2 infected people in
the DM group are older, and the length of hospitalization in this group
FIGURE1
Flow diagrams for literature selection.

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TABLE1 Basic information of included studies.
Study Year Country Study design Total patients
(non-DM/DM)
Overall age non-DM Age
(mean ± SD)
DM age
(mean ± SD)
Sex (Male/
Female)
Literature
quality
Shi etal. (1) 2020 China Retrospective 306 (153/153) 64 ± 11.9 64 ± 11.9 64 ± 11.9 156/150 9
Akbariqomi etal. (2) 2020 Iran Retrospective 595 (447/148) 56.3 ± 16 57.4 ± 16.3 53.2 ± 14.9 401/194 9
Khalili etal. (3) 2020 Iran Retrospective 254 (127/127) 65.7 ± 12.5 65 ± 12.5 66.4 ± 12.5 142/112 8
Demirci etal. (4) 2021 Tur k e y Retrospective 148,586
(115,108/33478)
41.6 ± 32.2 38 ± 15.5 54 ± 60 77,912/70674 8
Alshukry etal. (6) 2021 Kuwa it Retrospective 417 (273/144) 45.3 ± 17 39.55 ± 16.59 56.44 ± 11.64 262/155 7
Calvisi etal. (27) 2021 Ital y Prospective 169 (118/51) 63.2 ± 19.1 63 ± 20.6 70 ± 13.0 113/56 7
Cheng etal. (28) 2020 China Retrospective 236 (133/103) 54.5 ± 19.2 48 ± 20.9 63 ± 12.7 128/108 7
Yan etal. (29) 2020 China Retrospective 193 (145/48) 61.9 ± 18.5 57 ± 20.9 69 ± 11.4 114/79 8
Zhang etal. (30) 2020 China Retrospective 145 (84/61) 62 ± 14.5 59.4 ± 16.0 65.6 ± 11.4 74/71 9
Kim SW etal. (31) 2021 Korea Retrospective 1,019 (802/217) 59 ± 17.5 56.4 ± 18.0 68.7 ± 11.2 352/667 7
Cai etal. (32) 2020 China Retrospective 941 (818/123) 57.4 ± 56.7 56.3 ± 57.1 64.7 ± 54 454/487 8
Chen etal. (33) 2020 China Retrospective 563 (476/87) 51.5 ± 20.5 49.2 ± 20.8 64.2 ± 12.8 NA 9
Vasbinder etal. (34) 2022 UnitedStates Retrospective 2044 (1,358/686) 60 ± 16.3 58 ± 17 64 ± 14 1191/853 7
Elemam etal. (35) 2021 UnitedArabEmirates Retrospective 350 (239/111) 47.4 ± 14.4 44.6 ± 14.3 53.7 ± 12.7 274/76 8
Cheng etal. (36) 2021 China Retrospective 407 (357/50) 47.3 ± 16.3 46.2 ± 16.3 55.2 ± 14.0 195/212 8
Zhang etal. (37) 2020 China Retrospective 250 (166/84) 52.8 ± 20.5 48.1 ± 22.4 62.3 ± 11.3 106/144 8
Ling etal. (38) 2020 China Retrospective 702 (651/51) 42.4 ± 15.5 41.2 ± 15.1 58.4 ± 11.2 384/318 7
Kim MK etal. (39) 2020 Korea Retrospective 1,082 (847/235) 59 ± 17.5 56.5 ± 18.0 68.3 ± 11.9 384/698 8
Han etal. (40) 2020 China Retrospective 306 (177/129) 59.2 ± 16.4 55.0 ± 17.9 65 ± 11.9 174/132 7
Li etal. (41) 2020 China Retrospective 199 (123/76) 62 ± 15.5 57.9 ± 15.7 68.7 ± 12.8 110/89 8
You etal. (42) 2020 Korea Retrospective 5,473 (4,978/495) NA NA NA 2439/3034 9
Sun etal. (43) 2020 China Retrospective 1,618 (1,392/226) 55.2 ± 15.7 54.2 ± 16.3 61.5 ± 9.6 733/885 7
Yang etal. (44) 2021 China Retrospective 1,247 (572/675) 61.2 ± 14.3 64.2 ± 12.6 58.7 ± 15.2 598/649 7
Chen etal. (45) 2020 China Retrospective 208 (112/96) 62.8 ± 11 61.3 ± 12 64.6 ± 9.7 101/107 9
Sutter etal. (46) 2021 France Retrospective 1,206 (603/603) 71.1 ± 14.5 71.3 ± 15.9 71 ± 13 745/461 8
Alguwaihes etal.
(26)
2020 SaudiArabia Prospective 439 (139/300) NA NA NA 2439/3034 7
Bode etal. (47) 2020 UnitedStates Retrospective 1,122 (671/451) 60.3 ± 58.2 59.9 ± 61.6 61.1 ± 52.8 624/498 7
(Continued)

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is longer compared to the non-DM group. On admission, there were
no signicant dierences in body temperature, heart rate, diastolic
blood pressure between the DM group and the non-DM group (all
p > 0.05), but there were obvious dierences in respiratory rate and
systolic blood pressure (all p = 0.00). A higher incidence in men than
in women was seen in diabetic patients infected with SARS-CoV-2
[0.46, 95% CI (0.2–0.71%), I
2
-97.81%], with hypertension being the
most common comorbidity [1.34, 95% CI (1.13–1.56%), I2-96.26%],
followed by cerebrovascular disease [1.11, 95%CI (0.73–1.48%), I
2
-
81.29%] and CKD [1.26, 95% CI (0.95 ~ 1.57%), I
2
-94.28%].
Interestingly, the incidence of DM combination with COPD was
minimal (Figure2), with dyslipidaemia [2.09, 95% CI (1.87–2.31%),
I
2
-95.22%] having the highest probability, but the included studies
were few and could be validated by continuing to observe other
studies. e most common symptoms were dyspnea [0.39, 95% CI
(0.10–0.67%), I
2
-82.98%], cough [0.13, 95% CI (0.01–0.24%), I
2
-
49.29%]. An increased incidence of headache [−0.37, 95% CI
(−0.57 ~ −0.17%), I2-96.26%] was seen in the non-DM group.
Complications and treatment
Common complications in patients with SARS-CoV-2 infection
include ARDS, shock, acute kidney injury, acute heart injury and
secondary infection (Figure2). Patients with DM were more likely to
develop ARDS, acute kidney injury and acute cardiac injury, while shock
and secondary infection were increased markedly compared with
non-DM patients (all, p = 0.00).
In terms of treatment, patients in the DM group were more likely to
receive antibiotics, antiviral therapy, systemic corticosteroids, high-ow
oxygen therapy, mechanical ventilation including invasive and
non-invasive ventilation, ECMO, CRRT (Figure 2). Aer statistical
analysis, in terms of hypoglycemic therapy, insulin, metformin and DPP4
inhibitors are the most used in patients. When patients have
hypertension, ACEIs/ARBs are the rst choice, followed by Beta-
blockers, CCB, and Diuretics (Figure2).
Radiology and laboratory test results
As wecan see in Supplementary Table 1, the most common
imaging nding was bilateral pulmonary inltrates [0.50, 95%CI
(0.07–0.92%), I
2
-78.65%]. Regarding the laboratory test results,
wecould nd that the DM group patients had increased white blood
cell count, neutrophil count, neutrophil%, brinogen, ferritin,
D-dimer, ESR and higher levels of Pro-BNP in routine blood tests,
however, lymphocyte count, platelets, hemoglobin decreased
(Figure3), lactate dehydrogenase [0.33, 95%CI (0.14 ~ 0.51%), I
2
-
92.16%] was signicantly increased, albumin [−0.50, 95%CI
(−0.57 ~ −0.44%), I
2
-49.26%] was decreased. Compared with
non-DM group, creatinine [−0.23, 95%CI (0.15 ~ 0.32%), I
2
-62.09%]
was strikingly higher, eGFR [−0.39, 95% CI (−0.49 ~ −0.29%), I
2
-
95.80%] showed a decline. e results of blood lipid analysis showed
that triglyceride [0.23, 95%CI (0.04 ~ 0.42%), I
2
-68.48%] maintained
a peak level. Inammatory markers such as tumor necrosis factor
alpha (TNF-α), procalcitonin, C-reactive protein (CRP), interleukin
6 (IL-6) and IL-8 were dramatically improved (Figure3), but CD4+
and CD8+ were denitely reduced. Wesubgroup analysis of D-dimer
TABLE1 (Continued)
Study Year Country Study design Total patients
(non-DM/DM)
Overall age non-DM Age
(mean ± SD)
DM age
(mean ± SD)
Sex (Male/
Female)
Literature
quality
Al-Salameh etal.
(48)
2021 France Retrospective 432 (317/115) 72.1 ± 17.6 71.9 ± 18.6 72.8 ± 14.6 238/194 8
Mansour etal. (49) 2020 Iran Retrospective 353 (242/111) 61.6 ± 16.3 60.7 ± 17.5 63.6 ± 13.3 203/150 8
Chung etal. (50) 2020 Korea Retrospective 110 (81/29) 56.8 ± 16.9 53.5 ± 17.9 66.3 ± 8.9 48/62 9
Alhakak etal. (51) 2022 Denmark Retrospective 3,295 (2,117/1178) 72.3 ± 15.2 71.9 ± 16.8 73.2 ± 11.8 1853/1442 8
Sonmez etal. (52) 2021 Tur k e y Retrospective 18,426 (9,213/9213) NA NA NA 7980/10446 9

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(Supplementary Figure 1), < 1ug/ml was [0.35, 95%CI
(−0.18 ~ 0.89%), I2-91.90%], and ≥ 1ug/ml was [0.57, 95% CI
(0.38 ~ 0.67%), I2-88.45%]. Wethen performed a subgroup analysis
of ESR (Supplementary Figure 2), < 40 mm/h was [0.44, 95%CI
(0.23 ~ 0.65%), I2-66.89%], ≥ 40 mm/h was [0.58, 95%CI
(0.37 ~ 0.80%), I2-74.25%], and nally subgroup analysis of
hemoglobin A1c (HbA1c) (Supplementary Figure3), < 7.5% was
[1.19, 95%CI (0.42–1.96%), I
2
-96.92%], ≥ 7.5% was [2.05, 95% CI
(1.51 ~ 2.58%), I2-98.33%].
Clinical outcome
Outcomes of COVID-19 patients included ICU admission [1.00,
95%CI (0.71–1.28%), I
2
-95.79%], hospital discharge [−0.74, 95% CI
(−0.94–0.54%), I2-40.75%] and death [1.05, 95%CI (0.74–1.35%), I2-
95.19%] (Supplementary Table1). e DM group had lower discharge
rates and higher death rates than the non-DM group. At the same
time, a large number of diabetic patients were transferred to the ICU
for additional treatment.
FIGURE2
Forest plots comparing comorbidities, symptoms, radiological findings, complications, clinical outcomes and treatment in the DM and non-DM groups
of SARS-CoV-2 infected patients. COPD, chronic obstructive pulmonary disease; ARDS, acute respiratory distress syndrome; CRRT, continuous renal
replacement therapy; ECMO, extracorporeal membrane oxygenation; DPP4, dipeptidyl peptidase-4; ACEs, angiotensin-converting enzyme; ARBs,
angiotensin II receptor blockers; CCB, calcium channel blocker.

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Discussion
e rapid global spread of COVID-19 suggests that SARS-CoV-2
has a strong transmission potential in humans. In this systematic review
and meta-analysis of 32 studies and 192,693 patients, wesystematically
reviewed and analyzed numerous clinical and laboratory signatures of
predisposition leading to COVID-19 related mortality. Multiple lines of
evidence suggest that diabetes is one of the major risks of death in
COVID-19 patients, and is considered to bethe underlying mechanism
of microvascular disease, endothelial dysfunction, severe pneumonia,
inammatory storm, which underlie the adverse outcomes of COVID-19
(12, 13). To the best of our knowledge, this meta-analysis leverages the
FIGURE3
Forest plots comparing admission signs and laboratory tests in the DM and non-DM groups of SARS-CoV-2 infected patients. BMI, body mass index;
BP, blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; APTT, activated partial thromboplastin time; PT, prothrombin time;
ESR, erythrocyte sedimentation rate; INR, international normalized ratio; HbA1c, hemoglobin A1c; BUN, blood urea nitrogen; eGFR, estimated
glomerular filtration rate; LDL, low-density lipoprotein; BNP, brain natriuretic peptide; TNF, tumor necrosis factor; CRP, C-reactive protein; IL,
interleukin.

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largest number of studies and largest sample size to date to assess the
association between disease severity and mortality risk in COVID-19.
Our ndings suggest that diabetes in COVID-19 patients is associated
with an increased risk of serious infection and mortality compared to
non-diabetic patients. Our study provides evidence of how diabetes
mediates outcomes in hospitalized adults with COVID-19.
Upon analysis, it was found that the infected patients in the DM
group were older, had a higher BMI and were mostly male compared to
those without DM, suggesting that they were at higher risk of SARS-
CoV-2 infection, with more males than females. is may bedue to the
fact that females produce extra strong immune response, as estrogen and
progesterone can help increase innate and adaptive immune responses,
estrogen also promotes B-cell activation, maturation (14, 15).
Hypertension is commonly reported as the most common disease
associated with COVID-19 patients. It is also an independent risk factor
for higher mortality and morbidity in patients with coronavirus infection
(16), persistent hyperglycemia and metabolic changes in patients with
diabetes and coexisting risk factors. Hypertension causes microvascular
changes as well as macrovascular changes, creating a vicious cycle that
also leads to cardiovascular events. erefore, additional attention should
be paid to diabetics with underlying comorbidities, especially
hypertension. e analysis found that the prevalence of high blood
pressure, cerebrovascular disease, chronic kidney disease was signicantly
higher in the large number of diabetic patients infected with SARS-
CoV-2, who were also older than the non-diabetic patients. Diabetes-
related comorbidities and uncontrolled hyperglycemia increase the risk
of composite endpoints and mortality in COVID-19 patients, especially
the increased cardiovascular risk associated with diabetes and
hypertension, which additionally contribute to poor outcomes in
COVID-19. Common early symptoms of COVID-19 patients include
fever, cough, sputum production and additional symptoms of lower
respiratory tract infection. As the most common symptom, more than
80% of patients had a fever, but about 40% had a fever on admission,
indicating that many patients hadintermittent fevers. However, in this
meta-analysis there can beno dierences in fever between the two groups
with cough and dyspnea (Supplementary Table2). Radiographic ndings
hinted that bilateral pneumonia obtained on chest CT was more prevalent
in diabetic patients, suggesting that these patients had more
severe pneumonia.
e COVID-19 virus spreads through the respiratory mucosa and
induces a cytokine storm in the body, producing a series of immune
responses that alter peripheral white blood cells and lymphocytes, thus
increasing inammation levels. Cell counts increased but lymphocyte
counts were signicantly lower. e ndings may suggest that people with
diabetes are more susceptible to viral infections and more susceptible to
bacterial infections. Hyperglycemia inhibits neutrophil chemotaxis,
reduces phagocytosis of neutrophils, macrophages, monocytes, and
impairs cell-mediated immunity (17). e reduction in lymphocyte
counts indicates that SARS-CoV-2 depletes immune cells and suppresses
the body’s immune function. In addition, severe patients had signicantly
fewer lymphocytes than non-severe patients, suggesting that the degree
of lymphocyte decline can beused to assess the severity of the disease. e
continued decline of lymphocytes in the cells is also an indicator of disease
progression. e levels of inammatory markers including CRP,
erythrocyte sedimentation rate (ESR), TNF-α, Procalcitonin, IL-6, and
IL-8 in diabetic patients were signicantly higher than those in
non-diabetic patients, while CD4+ and CD8+ were lower than in the
control group (Supplementary Table3). CRP is simply an inammatory
biochemical marker, elevated levels of CRP hint the introduction of a
cytokine storm by 2019-nCoV, which is critical for the progression of
2019-nCoV. A higher PCT indicates an increased risk of systemic
infection and sepsis among diabetic patients infected with COVID-19.
Elevated glucose levels directly induce viral replication and
pro-inammatory cytokine expression, which primarily aect
lymphocytes, especially T cells, with an increased proportion of
pro-inammatory 17 CD4+ T cells and cytokine levels. CD4+ and
CD8+ peripheral counts of T cells decreased. Meanwhile, viral infection
promotes T cell programmed cell death protein 1 (PD-1) expression.
us, hyperglycemic patients may exhibit impaired antiviral interferon
responses and delayed 1/17 activation, which lead to
hyperinammatory responses (18, 19), it may explain why blood glucose
levels are elevated during SARS-CoV-2 infection cause T cell dysfunction
and lymphopenia. Studies have shown that hyperglycemia plays a
deleterious role in the overproduction of IL-6, which is associated with
increased lung inltration and severity of COVID-19, for elevated IL-6,
anti-IL-6 therapeutic strategies (Tocilizumab or Janus kinase inhibitors)
may beparticularly eective in DM patients with severe COVID-19 (12,
20). One study pointed out that inammatory markers such as CRP levels,
serum ferritin and ESR in COVID-19 cases were positively correlated
with glycated hemoglobin, while SaO2 was negatively correlated with
glycated hemoglobin (21), therefore, low and elevated HbA1c levels may
have a positive correlation. Identication of risk of death and adverse
outcomes in hospitalized COVID-19 patients. A recent study showed that
even patients with diabetes who had properly-controlled HbA1c (6–7%)
had a risk of serious infections compared with patients without diabetes,
and that this risk increased with increased HbA1c (22). At the same time,
hypoglycemia or hyperglycemia is associated with poor prognosis and
poor clinical outcomes. Some studies on the management of hospitalized
patients with hyperglycemia (especially in the ICU setting) suggest that
blood glucose levels should bemaintained between 7.8-10 mmol/L to
avoid excessive hyperglycemia or moderate/severe hypoglycemia,
preventing multiple organ failure and fatal consequences. Second, DM
may induce clotting in COVID-19 patients, especially D-dimer produced
from brin degradation, reecting the severity of the clotting condition.
In addition to deep vein thrombosis, elevated D-dimer can be the
expression of capillary microthrombi, which leads to an increased risk of
death due to pulmonary capillary endothelial damage (23), some
preventive regimens should be taken in clinical work. In addition,
indicators of kidney injury, including serum creatinine and blood urea
nitrogen are associated with higher mortality in patients with COVID-19,
plenty of patients with diabetes have signicantly lower eGFR on
admission compared with non-diabetic patients, which is due to the
incidence of acute kidney injury in patients with diabetes higher than
non-diabetic patients. Diabetics commonly develop a chronic
inammatory condition. It makes these patients more vulnerable to the
devastating eects of the so-called COVID-19 cytokine storm, causing
multiple organ damage and secondary pathophysiological changes in
tissues (24), leading to severe complications such as ARDS, shock, acute
heart and kidney damage in novel coronavirus pneumonia. Respiratory
support for patients with RSV is critical to reducing mortality because the
disease is so severe. It is essential to note that most patients require
hyperbaric oxygen therapy. Some patients require mechanical ventilation,
both invasive and noninvasive. As can beseen from the data, infected
patients in the diabetes group required more mechanical ventilation.
Wefound that patients with diabetes were more likely to betransferred to
the ICU and were treated most frequently with antibiotics, antivirals,
corticosteroids, and especially advanced life support including ECMO,
mechanical ventilation, and continuous renal replacement therapy.

Liu et al. 10.3389/fmed.2025.1523139
Frontiers in Medicine 09 frontiersin.org
Intravenous corticosteroids are indicated primarily for acute respiratory
distress syndrome in mechanically ventilated patients with novel
coronavirus pneumonia. ey are administered in the shortest amount of
time to reduce side eects. In terms of hypoglycemia treatment, current
recommendations for hypoglycemia medication for diabetics with
COVID-19 mainly contain the use of metformin and DPP-4 inhibitors
for mild cases and the addition of insulin for severe cases. In terms of
antihypertensive therapy, angiotensin converting enzyme 2 (ACE2) may
genuinely protect against severe respiratory infections by converting
angiotensin II to angiotensin with signicant anti-inammatory
properties, so an angiotensin converting enzyme inhibitors (ACEI) that
results in increased ACE2 expression may really bebenecial, using ACEI
or angiotensin II receptor blockers (ARBs) may benet COVID-19
outcomes and positively modulate its outcomes, the recent meta-analyses
further support the role of ACEIs and ARBs in disease progression
benecial eect (25). Studies have suggested that another signicant
factor contributing to poor outcomes is the use of beta-blockers in
hospitalized COVID-19 patients, although controversial, β-blockers may
bebenecial because they reduce pulmonary vascular ow, ultimately
reducing additional damage to the lungs of patients with suspected
ARDS (26).
In summary, this is the largest meta-analysis to date of the clinical
characteristics and outcomes of diabetic patients infected with SARS-
CoV-2. A large global multicenter study of data showed that patients
with diabetes who were hospitalized with COVID-19 had an increased
risk of death, lower hospital discharge rates and higher ICU admission
rates than patients without diabetes. Hypertension, cerebrovascular
disease, CKD, higher levels of inammatory markers, and multiple
complications in COVID-19 patients with diabetes are all predictors of
poor outcomes in people with diabetes. Our ndings will help identify
elevated risk factors in diabetes patients, which will aid in early
prediction, accurate diagnosis and treatment of COVID-19 patients.
Limitations and future directions
ere are several limitations to our study. First, wend signicant
heterogeneity between studies and signicant publication bias in
several variables. is may beexplained by dierences in study design,
patient population, and sample size. Second, a stratied analysis by
type of diabetes is not feasible. ird, although wemanually excluded
some studies to avoid including any duplicates, it is still possible that
some overlapping patients were included in our meta-analysis, which
may have slightly aected our results. Fourth, dierent follow-up
periods and missing follow-up information may have skewed some of
the results, particularly mortality. Finally, most of the studies included
in our meta-analysis were retrospective, but only one was prospective,
meaning that the criteria for inclusion in the diabetes group relied
primarily on prior clinical history, which would have led us to exclude
some cases of original diabetes.
Data availability statement
e original contributions presented in the study are included in
the article/Supplementary material, further inquiries can bedirected
to the corresponding author.
Author contributions
KL: Conceptualization, Data curation, Investigation,
Methodology, Writing – original draft, Writing – review &
editing. SL: Conceptualization, Data curation, Investigation,
Methodology, Writing – original draft, Writing – review &
editing. T-tX: Software, Supervision, Visualization, Writing –
original draft. HQ: Conceptualization, Data curation,
Investigation, Methodology, Writing– original draft, Writing–
review & editing.
Funding
e author(s) declare that nancial support was received for the
research, authorship, and/or publication of this article. is work was
supported by the National Natural Science Foundation of China
(grant numbers: 82073491).
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Generative AI statement
e authors declare that no Gen AI was used in the creation of
this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may beevaluated in this article, or
claim that may bemade by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
e Supplementary material for this article can befound online
at: https://www.frontiersin.org/articles/10.3389/fmed.2025.1523139/
full#supplementary-material
SUPPLEMENTARY FIGURE1
D-dimer was divided into two subgroups of < 1ug/ml and ≥ 1ug/ml, and the
figure show the forest plot of the two subgroups analyzed.
SUPPLEMENTARY FIGURE2
ESR was divided into two subgroups of < 40mm/h and ≥ 40mm/h, and the
figure shows the forest plot of the two subgroups analyzed.
SUPPLEMENTARY FIGURE3
Hemoglobin A1c was divided into two subgroups of < 7.5% and ≥ 7.5%, and the
figure shows the forest plot of the two subgroups analyzed.

Liu et al. 10.3389/fmed.2025.1523139
Frontiers in Medicine 10 frontiersin.org
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