Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis

Abstract

Background
Many studies on COVID-19 have reported diabetes to be associated with severe disease and mortality, however, the data is conflicting. The objectives of this meta-analysis were to explore the relationship between diabetes and COVID-19 mortality and severity, and to determine the prevalence of diabetes in patients with COVID-19.

Methods
We searched the PubMed for case-control studies in English, published between Jan 1 and Apr 22, 2020, that had data on diabetes in patients with COVID-19. The frequency of diabetes was compared between patients with and without the composite endpoint of mortality or severity. Random effects model was used with odds ratio as the effect size. We also determined the pooled prevalence of diabetes in patients with COVID-19. Heterogeneity and publication bias were taken care by meta-regression, sub-group analyses, and trim and fill methods.

Results
We included 33 studies (16,003 patients) and found diabetes to be significantly associated with mortality of COVID-19 with a pooled odds ratio of 1.90 (95% CI: 1.37–2.64; p < 0.01). Diabetes was also associated with severe COVID-19 with a pooled odds ratio of 2.75 (95% CI: 2.09–3.62; p < 0.01). The combined corrected pooled odds ratio of mortality or severity was 2.16 (95% CI: 1.74–2.68; p < 0.01). The pooled prevalence of diabetes in patients with COVID-19 was 9.8% (95% CI: 8.7%–10.9%) (after adjusting for heterogeneity).

Conclusions
Diabetes in patients with COVID-19 is associated with a two-fold increase in mortality as well as severity of COVID-19, as compared to non-diabetics. Further studies on the pathogenic mechanisms and therapeutic implications need to be done.

Full text

Is diabetes mellitus associated with mortality and severity of COVID-
19? A meta-analysis
Ashish Kumar
a
,
*
, Anil Arora
a
, Praveen Sharma
a
, Shrihari Anil Anikhindi
a
,
Naresh Bansal
a
, Vikas Singla
a
, Shivam Khare
a
, Abhishyant Srivastava
b
a
Institute of Liver, Gastroenterology, &Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, New Delhi, India
b
Dr. Baba Saheb Ambedkar Medical College and Hospital, Rohini, New Delhi, India
article info
Article history:
Received 24 April 2020
Received in revised form
27 April 2020
Accepted 28 April 2020
Keywords:
Coronavirus
2019-nCoV
nCoV-2019
Novel coronavirus
SARS-CoV-2
COVID-19
Diabetes mellitus
abstract
Background: Many studies on COVID-19 have reported diabetes to be associated with severe disease and
mortality, however, the data is conflicting. The objectives of this meta-analysis were to explore the
relationship between diabetes and COVID-19 mortality and severity, and to determine the prevalence of
diabetes in patients with COVID-19.
Methods: We searched the PubMed for case-control studies in English, published between Jan 1 and Apr
22, 2020, that had data on diabetes in patients with COVID-19. The frequency of diabetes was compared
between patients with and without the composite endpoint of mortality or severity. Random effects
model was used with odds ratio as the effect size. We also determined the pooled prevalence of diabetes
in patients with COVID-19. Heterogeneity and publication bias were taken care by meta-regression, sub-
group analyses, and trim and fill methods.
Results: We included 33 studies (16,003 patients) and found diabetes to be significantly associated with
mortality of COVID-19 with a pooled odds ratio of 1.90 (95% CI: 1.37e2.64; p <0.01). Diabetes was also
associated with severe COVID-19 with a pooled odds ratio of 2.75 (95% CI: 2.09e3.62; p <0.01). The
combined corrected pooled odds ratio of mortality or severity was 2.16 (95% CI: 1.74e2.68; p <0.01). The
pooled prevalence of diabetes in patients with COVID-19 was 9.8% (95% CI: 8.7%e10.9%) (after adjusting
for heterogeneity).
Conclusions: Diabetes in patients with COVID-19 is associated with a two-fold increase in mortality as
well as severity of COVID-19, as compared to non-diabetics. Further studies on the pathogenic mecha-
nisms and therapeutic implications need to be done.
©2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction
Coronavirus Disease 2019 (COVID-19) is a new disease, which
within four months of its origin in Wuhan, China, has now spread to
more than two hundred countries around the world, affecting more
than 2,818,000 people and has caused more than 196,000 deaths,
as of April 25, 2020 [1]. On March 11, 2020, the World Health Or-
ganization (WHO) had declared COVID-19 a pandemic because of
alarming levels of its spread, severity and inaction [2]. COVID-19 is
caused by Severe Acute Respiratory Syndrome Coronavirus 2
(SARS-CoV-2), which is sufficiently genetically divergent from the
closely related Severe Acute Respiratory Syndrome Coronavirus
(SARS-CoV), to be considered a new human-infecting betacor-
onavirus [3]. It mainly affects the respiratory tract and the illness
ranges in severity from asymptomatic or mild to severe or critical
disease. Although the current estimate of the case fatality rate of
COVID-19 is <5%, up to 15e18% of patients may become severe or
critically ill, some of them requiring ICU care and mechanical
ventilation [4].
Since COVID-19 is a new disease, knowledge about this disease
is still incomplete and evolving. Many case-control studies have
shown that patients of COVID-19, who have underlying diabetes
mellitus, develop a severe clinical course, and also have increased
mortality. However, most of these studies have small sample size,
*Corresponding author. Institute of Liver, Gastroenterology, &Pancreatico-Biliary
Sciences Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India.
E-mail addresses: ashishk10@yahoo.com (A. Kumar), dranilarora50@gmail.com
(A. Arora), drpraveen_sharma@yahoo.com (P. Sharma), dr.anikhindi@gmail.com
(S.A. Anikhindi), drnbansal@ymail.com (N. Bansal), singlavikas1979@gmail.com
(V. Singla), khare.shivam.shivam.1987@gmail.com (S. Khare), abhishyant.s@gmail.
com (A. Srivastava).
Contents lists available at ScienceDirect
Diabetes &Metabolic Syndrome: Clinical Research &Reviews
journal homepage: www.elsevier.com/locate/dsx
https://doi.org/10.1016/j.dsx.2020.04.044
1871-4021/©2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.
Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545

and the data in them are heterogenous and conflicting. In addition,
the data on prevalence of diabetes in patients with COVID-19 is also
not clear.
Hence, this meta-analysis was conducted with the primary
objective of exploring the relationship between underlying dia-
betes and severity and mortality of COVID-19 disease; and with the
secondary objective of determining the prevalence of diabetes in
patients with COVID-19.
2. Materials and methods
Since, this is a meta-analysis, therefore an institutional board or
an ethics committee approval was not required. The PRISMA
(Preferred Reporting Items for Systematic Reviews and Meta-
Analyses) and MOOSE (Meta-analysis Of Observational Studies in
Epidemiology) guidelines were consulted during the stages of
design, analysis, and reporting of this meta-analysis [5e7]. The
protocol of this meta-analysis is registered with the International
Prospective Register of Systematic Reviews (PROSPERO) vide
registration number CRD42020181756 and is available in full on the
NIHR (National Institute for Health Research) website (https://
www.crd.york.ac.uk/prospero/display_record.php?
RecordID¼181756).
2.1. Search strategy and study selection
Three authors (AK, SAA and SK) independently searched,
screened and selected the studies according to the search, inclusion
and exclusion criteria. PubMed database was searched for papers in
English language using the following keywords: “2019-nCoV”,
“nCoV-2019”,“novel Coronavirus 2019
00
,“SARS-CoV-2”,“COVID-
19”,“coronavirus”,“coronavirus covid-19”, and “corona virus”.
Since the first report of COVID-19 disease was published on
December 31, 2019 [8], we limited our search to articles published
since January 01, 2020, and the last search was performed on April
22, 2020. Since there is a high likelihood of duplicate publications
on COVID-19 [9], especially, same set of patients being reported in
English as well as Chinese or other languages, hence we restricted
our search to papers published in English language only. For the
same reason we restricted our search to PubMed database only and
did not search other databases. In addition, each included study
was carefully evaluated for study setting and author list to exclude
any duplicate publication.
The inclusion and exclusion criteria of studies were as follows:
(1) The studies should be in English language in the PubMed
database.
(2) The study design should be case-control and should have
categorized the patients into two or more groups depending
on the severity, clinical course, or mortality of the patients
with COVID-19 (i.e. composite endpoint). Studies without
this categorization were not included. The study should have
data of diabetes mellitus in each group.
(3) The study should be observational (retrospective or pro-
spective). Interventional studies such as controlled or un-
controlled drug trials were excluded.
(4) The study should have included at least 100 patients of
COVID-19.
(5) The participants should be adult patients with COVID-19.
Studies describing exclusively pediatric population were
excluded, however, studies which had both adult and pedi-
atric patients were included. Studies describing exclusively
pregnant women were also excluded.
2.2. Data extraction
The following data were extracted from each study: date of
online publication, PMID number, study setting, total number of
patients, their demographic data, number of patients with com-
posite endpoint, and number of patients with diabetes mellitus
among patients with or without the composite endpoint. For
studies with missing data, the corresponding authors of those
studies were contacted with a request to provide the missing data.
2.3. Study outcome
The primary outcome of interest was the occurrence of com-
posite endpoint which for the purpose of our study was labelled as
‘severe clinical course’and defined as occurrence of one of the two
endpoints depending on each study’s individual endpoint:
1. For studies comparing survival and mortality emortality of
COVID-19 patients was taken as the composite endpoint;
2. For studies not having mortality as the endpoint, one of the
following were chosen as the composite endpoint of ‘severe
disease’, depending on study’s individual endpoint:
a. Patients requiring invasive ventilation; or
b. Patients requiring ICU care; or
c. Patients having progressive disease; or
d. Patients having refractory disease; or
e. Patients categorized as severe/critical according to one of the
standard predefined criteria:
i. WHO criteria [10]; or
ii. National Health Commission of the People’s Republic of
China (version 3e5) criteria [11,12]; or
iii. American Thoracic Society guidelines [13].
Patients not having any of the above features of ‘severe clinical
course’were categorized into ‘good clinical course’.
The secondary outcome of interest was to study the prevalence
of diabetes mellitus in patients with COVID-19.
2.4. Assessment of quality of studies
For the assessment of quality of studies, including the risk of
bias, the National Institute of Health (NIH) tool for case-control
studies was used. This tool has been developed jointly by the Na-
tional Heart, Lung and Blood Institute (NHLBI) and the Research
Triangle Institute International [14]. It uses a composite score of
twelve domains, with each domain scored as ‘1’or ‘0’depending on
the response ‘yes’or ‘no’, respectively. The studies were categorized
as good quality if they scored 8 points, fair quality if they scored
6e7 points, and poor quality if they scored <6 points.
2.5. Statistical analysis
The categorical data was displayed as n and % and continuous
data as mean and SD. If the study had reported the data as median
with IQR or range, the method described by Wan et al. was used to
calculate the mean and SD [15].
To study the prevalence of diabetes mellitus in patients with
COVID-19, pooled proportion and 95% confidence interval (CI) was
taken as the effect size. First the raw proportion from each study
was extracted and transformed with the Freeman-Tukey double
arcine method to stabilize the variance [16], then the pooled pro-
portion was obtained using the DerSimonian-Laird random effects
model [17].
To study the association of diabetes mellitus with the composite
endpoint (severe clinical course), pooled odds ratio (with 95% CI)
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545536

was taken as the effect size. We performed the meta-analysis using
the generic inverse variance approach and DerSimonian-Laird
random effects model [17]. A p value of <0.05 was used to show
statistically significant association. The meta-analysis was sub-
grouped according to the composite endpoint of severe disease
and mortality.
To assess the heterogeneity among studies I
2
statistic was
calculated. An I
2
value >50% indicated substantial heterogeneity. To
take care of heterogeneity among the studies, and to calculate a
more conservative result, the odds ratios were pooled using only
the random effects model. To explore the source of heterogeneity
meta-regression analysis was done using age, type of composite
endpoint (severity versus mortality), country of study (China versus
others), number of patients, quality score, and quality type (good
versus fair) as co-variates. In addition, if the heterogeneity among
the studies was 50%, a sensitivity analysis was also performed
after identifying and removing the outlier studies.
We evaluated the publication bias through visual inspection of
funnel plot and Begg [18] and Egger [19] tests. When the funnel plot
was symmetrical and the p value of Begg and Egger tests were
>0.05, no significant publication bias was considered to exist in the
meta-analysis. However, if publication bias was found, a trim and
fill analysis of Duval and Tweedie [20] was used to evaluate the
number of missing studies, and recalculation of the pooled odds
ratio was done after addition of those missing hypothetical studies.
Review Manager software (version 5.3.5, The Nordic Cochrane
Centre, Copenhagen, Denmark), OpenMetaAnalyst software
(version 10.12) [21], JASP software (version 0.12.1, University of
Amsterdam, The Netherlands), and Microsoft Excel (version 16.35)
were employed for the meta-analysis and statistical analyses.
3. Results
3.1. Study selection and data collection
Using the keywords “2019-nCoV”,“nCoV-2019”,“novel Coro-
navirus 2019
00
,“SARS-CoV-2”,“COVID-19”,“coronavirus”,“corona-
virus covid-19”, and “corona virus”and limiting the Entrez date
from 01-Jan-2020 through 22-Apr-2020, initially 5834 publications
in English language were retrieved from the PubMed database,
which were screened for relevance (Fig. 1). After carefully going
through the abstracts and full texts (if needed) of these publica-
tions, only 207 potentially relevant studies were selected and
evaluated in detail for potential inclusion. Of these 174 studies were
excluded because of the following reasons: (1) 144 studies did not
have comparative data on COVID-19 patients with and without
composite endpoint; (2) 22 studies were small with less than 100
participants; (3) 7 studies did not have diabetes as one of the
comparative factors; and (4) 1 study was a duplicate publication.
Hence, remaining 33 studies were included in the qualitative as
well as quantitative synthesis meta-analysis (Fig. 1).
3.2. Characteristics and quality of the included studies
The study characteristics of the 33 included studies are given in
Table 1. The online publication date of the studies in the PubMed
database was from February 7, 2020 through April 17, 2020. Twenty
out of 33 (61%) studies were from single centres, while remaining
13 (39%) were multi-centre studies. Most studies (30/33, 91%) were
from mainland China, and of the remaining 3 studies, two (6%)
were from USA, and one (3%) from France. The total included pa-
tients were 16,003, and of them 8849 (55%) were reported from
Mainland China, 7030 (44%) from USA, and 124 (1%) from France.
The median number of patients included in the studies was 214
(IQR: 139e368).
The quality of study was assessed using the NIH tool for case-
control studies [14] and the results are shown in Table 1. The
scores were as follows: 9/12 score (27 studies [82%]); 8/12 score (5
studies [15%]); and 7/12 score (1 study [3%]). Out of the twelve
domains assessed by this tool, the three domains in which all the
studies were given ‘0’score were: sample size justification, blinding
of assessors, and adjusting for confounding variables. Thus 32
studies (97%) were judged as good quality (scores of 8) and
remaining 1 study (3%) was judged as fair quality (scores 6e7).
None of the included study was judged poor. The single study with
fair quality was the paper published by the CDC, USA on the COVID-
19 cases reported to it from all over the US [22]. Thus, it was a
registry data, rather than a hospital-based study.
3.3. Characteristics of the included patients
Table 2 shows the characteristics of the included patients. The
total number of patients was 16,003, with proportion of males
being 54% (5068/9366). Thus the male: female ratio was approxi-
mately 1.2 : 1. The pooled mean age was 52.6 ±17.4 years.
Of the 16,003 patients, 2827 (18%) patients had the composite
endpoint (labelled ‘severe clinical course’). The reasons for com-
posite endpoint were mortality in 9 studies (613/2827 [22%] pa-
tients) and severity in 24 studies (2214/2827 [78%] patients). Of the
24 studies having severity as the composite endpoint, the reasons
were as follows: Pre-defined criteria (16 studies); ICU requirement
versus no requirement (2 studies); invasive ventilation require-
ment versus no requirement (2 studies); progressive disease versus
stable disease (2 studies); refractory disease versus responsive
disease (1 study); and ARDS versus no ARDS (1 study).
3.4. Prevalence of diabetes mellitus in patients with COVID-19
(secondary outcome)
Diabetes was present in 1724 patients out of total 16,003 pa-
tients of COVID-19. The pooled prevalence of diabetes was calcu-
lated to be 11.2% (95% CI: 9.5%e13.0%) by using the Freeman-Tukey
double arcine transformation and DerSimonian-Laird random ef-
fects model (Fig. 2). However, the heterogeneity among the studies
was substantial with an I
2
value of 92%. To explore the source of
heterogeneity meta-regression analyses were done using age, type
of composite endpoint (severity versus mortality), country of study
(China versus others), number of patients, quality score, and quality
type (good versus fair) as co-variates (Supplementary Table 1 and
Supplementary Fig. 1). The results of meta-regression showed that
proportion of diabetes in patients with COVID-19 was influenced by
age (with studies with higher patient age having higher proportion
of diabetes, p <0.001), type of composite endpoint (with studies
reporting mortality endpoint having higher proportion of diabetes,
p¼0.004), and country of study (with studies outside of China
having higher proportion of diabetes, p ¼0.006). There was no
influence of number of patients in studies or quality score of
studies. A sub-group analysis revealed that proportion of diabetes
mellitus in China was 10.5% (95% CI: 8.7%e12.3%) while in countries
other than China (mainly USA) it was 19.3% (95% CI: 8.4%e30.3%),
but with high heterogeneity (data not shown). A sensitivity analysis
was also done by excluding 13 outlier studies, which revealed a
pooled prevalence of diabetes to be 9.8% (95% CI: 8.7%e10.9%) in
patients with COVID-19 with an acceptable I
2
value of 46%
(Supplementary Fig. 2).
3.5. Association of diabetes mellitus with mortality or severity of
COVID-19 (primary outcome)
Of the 33 included studies in this meta-analysis, 24 had used
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545 537

severity as the composite endpoint and 9 had used mortality as the
composite endpoint. Presence of diabetes was found to be signifi-
cantly associated with severe COVID-19 (pooled odds ratio 2.75
[95% CI: 2.09e3.62; p <0.01]) as well as mortality due to COVID-19
(pooled odds ratio 1.90 [95% CI: 1.37e2.64; p <0.01]). The combine
pooled odds ratio for both the composite endpoints (labelled as
severe clinical course) was 2.49 (95% CI: 1.98e3.14; p <0.01)
(Fig. 3).
For the mortality endpoint, the heterogeneity among the studies
was low (I
2
¼32%), while for the severity endpoint the heterogeneity
among the studies was substantial (I
2
¼63%). Thus the combined
heterogeneity was also substantial (I
2
¼63%). To explore the source
of heterogeneity meta-regression analyses were done using age,
type of composite endpoint (severity versus mortality), country of
study (China versus others), number of patients, quality score, and
quality type (good versus fair) as co-variates (Supplementary Table2
and Supplementary Fig. 3). The results of meta-regression showed
that odds ratio was influenced by age (with studies with higher
patients’age having lower odds ratio, p <0.001). In addition it was
found that the CDC study from USA [22], which was of not good
quality (being a registry data), significantly influenced the outcome
of this meta-analysis and was mainly responsible for the significant
Fig. 1. PRISMA flow chart showing the flow of study selection.
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545538

heterogeneity. Hence, a sensitivity analysis was performed after
excluding this study, which again revealed a significant combined
pooled odds ratio of 2.33 (95% CI: 1.90e2.85;p <0.01) and an I
2
value
of 41% (acceptable heterogeneity) (Supplementary Fig. 4).
3.6. Influence of publication bias
For the main outcome of this meta-analysis, i.e. association of
diabetes mellitus with severe clinical course of COVID-19, publi-
cation bias was evaluated through the visual inspection of funnel
plot and Begg and Egger tests [18,19]. The funnel plot (Fig. 4)was
found to be mildly asymmetric and the Begg’s rank correlation test
for funnel plot asymmetry (Kendall’s
t
¼0.439) as well as Egger’s
regression test for funnel plot asymmetry (z ¼2.561) were statis-
tically significant (p <0.05). Hence, a trim and fill analysis of Duval
and Tweedie [20] was used to evaluate the number of missing
studies and we recalculated the pooled odds ratio with the addition
of those missing hypothetical studies. The recalculated pooled odds
ratio of association of diabetes mellitus with severe clinical course
of COVID-19 was 2.26 (95% CI: 1.78e2.87; p <0.01) (Supplementary
Fig. 5). The redrawn funnel plot after addition of four missing hy-
pothetical studies was now symmetrical (Supplementary Fig. 6).
After adjusting for both, heterogeneity as well as publication
bias, the corrected pooled odds ratio for diabetes mellitus being
associated with severe clinical course of COVID-19 (i.e.both mor-
tality and severity) was still significant (2.16 [95% CI: 1.74e2.68];
p<0.01) (Supplementary Fig. 7).
4. Discussion
To summarise the results of this meta-analysis of 33 studies
(16,003 patients), we found diabetes mellitus to be significantly
associated with mortality risk of COVID-19 with a pooled odds ratio
of 1.90 (95% CI: 1.37e2.64; p <0.01) with low heterogeneity
(I
2
¼32%). In addition, diabetes mellitus was associated with severe
COVID-19, including risk of ARDS, ICU requirement, and invasive
ventilatory requirement, with a pooled odds ratio of 2.75 (95% CI:
2.09e3.62; p <0.01). The combined pooled odds ratio of diabetics
developing severe COVID-19 or dying due to it (i.e. composite
endpoint) was 2.49 (95% CI: 1.98e3.14; p <0.01). After adjusting for
both, heterogeneity among the studies as well as publication bias,
the corrected pooled odds ratio for diabetes being associated with
severe clinical course of COVID-19 was still significantly high (2.16
[95% CI: 1.74e2.68]; p <0.01). As a secondary outcome, we also
calculated the pooled prevalence of diabetes mellitus in patients
with COVID-19, which was 11.2% (95% CI: 9.5%e13.0%)(uncorrected)
and 9.8% (95% CI: 8.7%e10.9%) (after adjusting for heterogeneity).
There are many strengths of this meta-analysis. First, to the best
of our knowledge this is the first large meta-analysis on specific
influence of diabetes on severity of COVID-19, as well as on its
mortality. In addition, we also studied the prevalence of diabetes
among COVID-19 patients. Second, we have included a large
number of studies, with patient population above sixteen thou-
sand, spanning three continents. Third, we have included only large
studies, with more that 100 patients, thus each study contributed a
robust data on diabeteseCOVID19 association without increasing
Table 1
Characteristics and quality of studies included in the meta-analysis.
Author Date of
publication
PMID Setting Remarks Quality
score
Wang D [55] 07-Feb-20 32031570 Single centre in Wuhan, Hubei Province, China 9
Zhang JJ [56] 19-Feb-20 32077115 Single centre in Wuhan, Hubei Province, China 9
Guan WJ [57] 28-Feb-20 32109013 552 hospitals in 30 provinces, autonomous regions, and municipalities in
mainland China
9
Ruan Q [58] 03-Mar-20 32125452 Two centres in Wuhan, Hubei Province, China 9
Zhou F [59] 11-Mar-20 32171076 Two hospitals in Wuhan, Hubei Province, China 9
Wu C [60] 13-Mar-20 32167524 Single centre in Wuhan, Hubei Province, China 9
Mo P [61] 16-Mar-20 32173725 Single centre in Wuhan, Hubei Province, China 9
Shi Y [62] 18-Mar-20 32188484 Multi-centre in Zhejiang Province, China 8
Zhang X [63] 20-Mar-20 32205284 Multi-centre in Zhejiang Province, China 9
Deng Y [64] 20-Mar-20 32209890 Two tertiary hospitals in Wuhan, Hubei Province, China 8
Wan S [65] 21-Mar-20 32198776 Multi-centre in Chongqing, China 9
Chen T [66] 26-Mar-20 32217556 Single centre in Wuhan, Hubei Province, China 9
Wang L [67] 30-Mar-20 32240670 Single centre in Wuhan, Hubei Province, China Only elderly >60 years
patients
9
Wang L [68] 31-Mar-20 32229732 Single centre in Wuhan, Hubei Province, China 9
Cai Q [69] 02-Apr-20 32239761 Single centre in Shenzhen, Guangdong Province, China 9
Cao J [70] 02-Apr-20 32239127 Single centre in Wuhan, Hubei Province, China 9
CDC COVID-19
[22]
03-Apr-20 32240123 Cases reported from all over US to CDC, USA Registry data 7
Wang X [71] 03-Apr-20 32251842 Single centre in Wuhan, Hubei Province, China Only non-critical patients 9
Wang Y [72] 08-Apr-20 32267160 Single centre in Wuhan, Hubei Province, China Only ICU patients 9
Du RH [73] 08-Apr-20 32269088 Single centre in Wuhan, Hubei Province, China 9
Zhang G [74] 09-Apr-20 32311650 Single centre in Wuhan, Hubei Province, China 9
Zheng F [75] 09-Apr-20 32271459 Single centre in Changsha, Hunan Province, China 8
Simonnet A [76] 09-Apr-20 32271993 Single centre in Lille, France Only ICU patients 9
Feng Y [77] 10-Apr-20 32275452 Three hospitals in China 9
Yang Z [78] 10-Apr-20 32275643 Single centre in Shanghai, China 9
Liu Y [79] 10-Apr-20 32283162 Single centre in Wuhan, Hubei Province, China 9
Mao L [80] 10-Apr-20 32275288 Multi-centre in Wuhan, Hubei Province, China 9
Shen L [81] 10-Apr-20 32283164 Multi-centre in Xiangyang, Hubei Province, China 9
Zhang R [82] 11-Apr-20 32279115 Single centre in Wuhan, Hubei Province, China 9
Li X [83] 12-Apr-20 32294485 Single centre in Wuhan, Hubei Province, China 9
Wei YY [84] 16-Apr-20 32305487 Multi-centre in Anhui Province, China 8
Wan S [85] 16-Apr-20 32297671 Single centre in Chongqing, China 9
Goyal P [86] 17-Apr-20 32302078 Two hospitals in New York City, USA 8
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545 539

heterogeneity. Fourth, we have avoided including any duplicate
studies by limiting our search to single database, limiting search to
English articles only, and carefully going through each included
article’s study setting and author list. Fifth, while synthesizing re-
sults we have taken care of both heterogeneity as well as publica-
tion bias by appropriate statistical tools.
First discussing about the secondary outcome of our meta-
analysis, we determined the corrected pooled prevalence of dia-
betes mellitus in COVID-19 patients to be close to 10%, with a higher
prevalence in USA than China. Our results on prevalence are similar
to a large Chinese nationwide study of 1590 patients which had
shown the prevalence of diabetes in COVID-19 patients to be 8.2%
[23]. Another small meta-analysis of 12 Chinese studies (2108 pa-
tients) by Fadini et al. [24] also reported the prevalence of diabetes
in COVID-19 patients as 10.3%. Our study as well as these other
previous studies indicate that the prevalence of diabetes in patients
with COVID-19 is in the range of 10%, which is similar to the pop-
ulation prevalence of diabetes in the general population of China
and the USA (10.9% and 11.1%, respectively) [25,26]. Thus our meta-
analysis supports the previously held notion that the susceptibility
of diabetic population to COVID-19 infection might not be increased
but be similar to the non-diabetic population [27].
The primary and the more important outcome of our meta-
analysis was to study the association of diabetes with mortality
and severity of COVID-19 disease. We found that diabetic patients
with COVID-19 are twice more likely to develop severe COVID-19
disease and twice more likely to die due to it (odds ratio close to
2 for severity as well as mortality). Thus patients with COVID-19
and diabetes are more likely to develop ARDS, need ICU care,
need invasive ventilation, and are morevulnerable to succumb to it.
Our results are similar to two small meta-analyses, by Fadini et al.
(6 studies, 1687 patients) and Wang et al. (6 studies, 1558 patients),
which gave odds ratio of 2.26 and 2.47, respectively, for diabetic
patients developing more adverse disease due to SARS-CoV-2
infection [24,28]. Another systematic review of 7 studies by Singh
et al. also suggested that diabetes is a determinant of severity and
mortality of COVID-19 patients [29]. However, our meta-analysis is
the largest with 33 studies, and we have now conclusively shown
the association of diabetes with COVID-19 mortality as well as
severity.
Whether diabetes is an independent determinant of severity
was studied by Guo et al. in their case-control study from China
[30], in which they compared diabetic and non-diabetic COVID-19
patients, and found that even in absence of other comorbidities,
diabetics were at higher risk of severe pneumonia, uncontrolled
inflammatory response, higher levels of tissue injury-related en-
zymes, and higher hypercoagulable state. Further they found,
serum levels of inflammatory biomarkers such as C-reactive pro-
tein, D-dimer, IL-6, serum ferritin and coagulation index, were
significantly higher in diabetic patients compared to those without,
suggesting that patients with diabetes are more susceptible to an
inflammatory storm that leads to worsening of COVID-19 [30].
The pathogenesis of increased mortality and severity of COVID-
19 in patients with diabetes is still unclear. Severe acute respiratory
syndrome (SARS) outbreak in 202e2004 and Middle East Respira-
tory Syndrome (MERS) outbreaks in 2012 and 2015, had also
Table 2
Characteristics of the included patients.
Author Number of patients Age (years) Males Patients with composite endpoint Patients with
diabetes
Mean SD n % n % Reason n %
Wang D [55] 138 55.3 19.50 75 54% 36 26% ICU 14 10%
Zhang JJ [56] 140 56.5 11.80 71 51% 58 41% Criteria 17 12%
Guan WJ [57] 1099 46.7 17.10 640 58% 173 16% Criteria 81 7%
Ruan Q [58] 150 57.7 12.50 102 68% 68 45% Died 25 17%
Zhou F [59] 191 56.3 15.70 119 62% 54 28% Died 36 19%
Wu C [60] 201 51.3 12.70 128 64% 84 42% ARDS 22 11%
Mo P [61] 155 54.0 18.00 86 55% 85 55% Refractory 15 10%
Shi Y [62] 487 46.0 19.00 259 53% 49 10% Criteria 29 6%
Zhang X [63] 597 45.3 14.34 328 55% 64 11% Criteria 48 8%
Deng Y [64] 225 55.4 19.04 124 55% 109 48% Died 26 12%
Wan S [65] 135 46.0 14.24 72 53% 40 30% Criteria 12 9%
Chen T [66] 274 58.7 19.38 171 62% 113 41% Died 47 17%
Wang L [67] 339 70.0 8.19 166 49% 65 19% Died 54 16%
Wang L [68] 116 53.7 23.27 67 58% 57 49% Criteria 18 16%
Cai Q [69] 298 47.2 20.86 145 49% 58 19% Criteria 18 6%
Cao J [70] 102 52.7 22.56 53 52% 17 17% Died 11 11%
CDC COVID-19 [22] 6637 No data No data No data No data 457 7% ICU 730 11%
Wang X [71] 1012 51.3 11.30 524 52% 100 10% Progression 27 3%
Wang Y [72] 344 62.7 14.89 179 52% 133 39% Died 64 19%
Du RH [73] 179 57.6 13.70 97 54% 21 12% Died 33 18%
Zhang G [74] 221 53.5 20.52 108 49% 55 25% Criteria 22 10%
Zheng F [75] 161 45.2 17.58 80 50% 30 19% Criteria 7 4%
Simonnet A [76] 124 60.3 14.25 91 73% 85 69% Ventilation 28 23%
Feng Y [77] 476 52.3 17.85 271 57% 124 26% Criteria 49 10%
Yang Z [78] 273 49.1 13.75 134 49% 71 26% Progression 18 7%
Liu Y [79] 245 54.0 16.90 114 47% 33 13% Died 23 9%
Mao L [80] 214 52.7 15.50 87 41% 88 41% Criteria 30 14%
Shen L [81] 119 49.3 17.26 56 47% 20 17% Criteria 12 10%
Zhang R [82] 120 45.4 15.60 43 36% 30 25% Criteria 7 6%
Li X [83] 548 59.0 15.61 279 51% 269 49% Criteria 83 15%
Wei YY [84] 167 42.3 15.29 95 57% 30 18% Criteria 11 7%
Wan S [85] 123 46.2 15.15 66 54% 21 17% Criteria 8 7%
Goyal P [86] 393 61.5 18.68 238 61% 130 33% Ventilation 99 25%
Total 16003 52.6 17.37 5068 54% 2827 18% 1724 11%
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545540

resulted in increased severity and fatality in patients with diabetes
mellitus [31e35]. All these previous outbreaks were also caused by
other coronaviruses, namely SARS-CoV and MERS-CoV, respec-
tively. To elucidate the mechanism of enhanced disease severity in
diabetics following MERS-CoV infection, Kulcsar et al. [36] used an
animal model in which mice were made susceptible to MERS-CoV
infection by expressing human dipeptidyl peptidase 4 (DPP4),
and type 2 diabetes was induced by administering a high-fat diet.
Upon infection with MERS-CoV, diabetic mice had a prolonged
phase of severe disease and delayed recovery that was independent
of viral titres. Histological examination revealed that diabetic mice
had delayed but prolonged systemic inflammation, fewer inflam-
matory monocyte/macrophages and CD4
þ
T cells, lower levels of
chemokine ligand 2 and C-X-C motif chemokine 10 expression,
lower levels of tumor necrosis factor alpha (TNF
a
), interleukin (IL)
6, IL 12b, and arginase 1 expression and higher levels of IL 17a
expression. The data suggested that the increased disease severity
observed in diabetes was likely due to a dysregulated immune
response, which resulted in more severe and prolonged lung pa-
thology [36]. Since patients with diabetes have multiple immune
dysregulations such as phagocytic cell dysfunction, inhibition of
neutrophil chemotaxis, impaired T-cell mediated immune
response, altered cytokine production, and ineffective microbial
clearance [37], these dysregulated immune responses may result
into a cytokine profile resembling secondary haemophagocytic
lymphohistiocytosis in patients with severe SARS-CoV-2 infection,
characterised by increased IL 2, IL 7, granulocyte-colony stimulating
factor, interferon-
g
inducible protein 10, monocyte chemo-
attractant protein 1, macrophage inflammatory protein 1-
a
, and
TNF
a
[38,39].
In addition, type 2 diabetes mellitus and coronavirus infection
also have shared pathogenic pathways, which has therapeutic im-
plications [40]. Two of the coronavirus receptors, angiotensin
converting enzyme 2 (ACE2) and DPP4 are also transducers of
metabolic pathways regulating glucose homeostasis, renal and
cardiovascular physiology, and inflammation. DPP4 inhibitors are
widely used in subjects with type 2 diabetes because of their effect
of lowering blood glucose levels. However, the effects of DPP4 in-
hibition on the immune response in patients with diabetes is still
controversial and not completely understood [41]. Two recent
meta-analyses had shown that DPP4 inhibitors increased the risk of
various infections [42,43] while a third meta-analysis showed that
there is no increased risk of infections with DPP4 inhibitors [44].
Whether DPP4 inhibitors increase the susceptibility or severity of
SARS-CoV-2 infection needs to be studied in future trials.
The results our meta-analysis has three major implications
during the current COVID-19 pandemic. First, since diabetes can
lead to severe COVID-19, its prevention in diabetics is imperative. It
should be the responsibility of the treating physicians to advice
their diabetic patients to take extra-precautions of social distancing
and hand hygiene to protect themselves from coronavirus infection
[45]. Second, there should be an increased vigilance in the out-
patient clinics of diabetes for COVID-19, and the threshold for
testing for this infection in diabetic patients should be lowered [46].
Third, any patient with COVID-19, who has co-morbid diabetes,
should be taken as potentially serious, even though he or she may
show only mild or no symptoms at presentation. These patients
will need extra monitoring, and their threshold for hospital and ICU
Fig. 2. Pooled proportion of diabetes mellitus in COVID-19 patients.
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545 541

admission also needs to be lowered.
The results of our meta-analysis has also implications for India,
which is often called the ‘Diabetes Capital’of the world. According
to the 2019 estimate, the age standardised diabetes prevalence in
South-East Asia, including India, among ages 20e79 years, was
estimated to be 11.3% (95% CI: 8.0%e15.9%), with the actual number
of people with diabetes in India being more than 77 million [25,47 ].
Drivers of type 2 diabetes in south Asia include genetic and
epigenetic factors, intrauterine and early life factors, high carbo-
hydrate dietary patterns, and increase in physical inactivity [48]. All
these factors, not only increase the prevalence of diabetes, but are
also major factors in the causation of obesity, hypertension, meta-
bolic syndrome, fatty liver, cardiovascular and cerebrovascular
diseases, with a resultant increase in morbidity and mortality. In
fact, diabetes, along with cardiovascular disease and chronic kidney
disease accounted for 4%, 27%, and 3% of deaths, respectively, in
South Asia [49]. During the current COVID-19 pandemic, our meta-
analysis, as well as multiple other studies have shown that COVID-
19 is particularly more severe in patients with these comorbidities
with increased hospitalization, ICU and ventilatory requirements
[50,51]. With the huge population burden of diabetes in India, if
urgent and strong measures are not taken to flatten the curve of
COVID-19 pandemic in India, it will lead to disastrous consequences
with overburdening of already stretched healthcare system of In-
dia. Especially, elderly population of India with comorbidities such
as diabetes, hypertension, and cardiac diseases will need special
protection as enumerated in the preceding paragraph. Their blood
sugars need to be better controlled and their health condition need
to be better monitored, even in the face of lockdown, through
measures such as tele-consultation and tele-medicine [52].
4.1. Limitations
Our meta-analysis has two limitations. We have shown that
diabetes is associated with COVID-19 severity and mortality;
however, it cannot be said whether diabetes is acting as an inde-
pendent factor responsible for this severity and mortality, or it is
just a confounding factor. Many conditions such as elderly age,
hypertension, cardiovascular disease, and obesity, often co-exist
with diabetes, and each of these comorbidities have been shown
to be associated with severe COVID-19 and its mortality. In spite of
this limitation, the implication our meta-analysis will remain
Fig. 3. Forest plot showing pooled odds ratio of diabetes mellitus associated with severe clinical course including mortality.
A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545542

unchanged that diabetic need to be protected from COVID-19, and
they will need extra care if infected. The second limitation of this
meta-analysis is that we have not been able to document the role of
glycemic control on the severity or mortality of COVID-19. It has
been shown previously that poor glycemic control, in terms of high
HbA1c, was significantly associated with increased risk of various
infections [53,54]. However, none of the included studies on
COVID-19 in our meta-analysis had evaluated glycemic control as
one of the factors associated with severity and/or mortality; and
this needs to be explored in further trials.
In conclusion, we have shown in this meta-analysis that pres-
ence of underlying diabetes in patients with COVID-19 is associated
with two-fold increased risk of mortality, as well as two-fold
increased risk of severity of COVID-19. This necessitates enhanced
prevention of COVID-19 in diabetics, increased vigilance in patients
of diabetes for COVID-19, and a lower threshold for monitoring,
hospitalization, and ICU care if diabetics develop this infection.
Results of our meta-analysis emphasizes the need for further
investigation on the pathogenic mechanism of relationship be-
tween diabetes and COVID-19, and to explore its therapeutic
implications.
Funding
This research did not receive any funding.
Author contributions
AK designed the study. AK, SAA and SK searched, screened and
selected the articles. AK, PS, NB and AS extracted the data from the
articles. AK and PS performed data analysis and interpretation. AK
drafted the manuscript. All authors contributed in writing and
editing of the manuscript. AA supervised the study.
Declaration of competing interest
The authors declare that they have no conflicts of interest.
Acknowledgement
None.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.dsx.2020.04.044.
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A. Kumar et al. / Diabetes &Metabolic Syndrome: Clinical Research &Reviews 14 (2020) 535e545 545

Supplementary Material for:
Is Diabetes Mellitus Associated with Mortality and Severity of COVID-19? A Meta-analysis

Supplementary Material for:
Is Diabetes Mellitus Associated with Mortality and Severity of COVID-19? A Meta-analysis
Supplementary Table 1: Results of meta-regression showing the influence of age, type of
composite endpoint (severity versus mortality), country of study (China versus others),
number of patients, quality score, and quality type (good versus fair) on secondary outcome
(proportion of diabetes mellitus in patients with COVID-19).
Covariate
Studies
Coefficients
Lower bound
Upper bound
Std. error
p-Value
Intercept
-0.245
-0.343
-0.146
0.05
<0.001
Age
0.007
0.005
0.009
<0.001
<0.001
Intercept
0.098
0.08
0.116
0.009
<0.001
Endpoint Severity
24
Endpoint Mortality
9
0.054
0.017
0.09
0.018
0.004
Intercept
0.186
0.131
0.24
0.028
<0.001
Country Others
3
Country China
30
-0.081
-0.138
-0.024
0.029
0.006
Intercept
0.114
0.095
0.133
0.01
<0.001
No. of Patients
0
0
0
<0.001
0.732
Intercept
0.129
0.1
0.157
0.015
<0.001
No. of Patients (minus CDC
study)
0
0
0
<0.001
0.149
Intercept
0.061
-0.251
0.373
0.159
0.703
Quality score
0.006
-0.03
0.041
0.018
0.744
Intercept
0.11
0.016
0.204
0.048
0.021
Quality type fair
1
Quality type good
32
0.003
-0.093
0.098
0.049
0.955

Supplementary Table 2: Results of meta-regression showing the influence of age, type of
composite endpoint (severity versus mortality), country of study (China versus others),
number of patients, quality score, and quality type (good versus fair) on primary outcome
(association of diabetes mellitus with severe clinical course of COVID-19).
Covariate
Studies
Coefficients
Lower bound
Upper bound
Std. error
p-Value
Intercept
3.823
2.553
5.093
0.648
<0.001
Age
-0.056
-0.079
-0.033
0.012
<0.001
Intercept
0.657
0.303
1.011
0.181
<0.001
Endpoint Mortality
9
Endpoint Severity
24
0.334
-0.088
0.756
0.215
0.121
Intercept
0.887
0.672
1.103
0.11
<0.001
Country China
30
Country Others
3
0.061
-0.504
0.625
0.288
0.833
Intercept
0.763
0.579
0.947
0.094
<0.001
No. of Patients
0
0
0
<0.001
0.006
Intercept
0.822
0.508
1.137
0.16
<0.001
No. of Patients (minus CDC
study)
0
-0.001
0.001
<0.001
0.981
Intercept
2.942
0.286
5.599
1.355
0.03
Quality score
-0.236
-0.541
0.069
0.155
0.129
Intercept
0.81
0.635
0.985
0.089
<0.001
Quality type good
32
Quality type fair
1
0.718
0.181
1.255
0.274
0.009

Supplementary Figure 1: Meta-regression plots showing the influence of age (A), number
of patients (B), number of patients excluding the CDC study (C), and quality score (D) on
secondary outcome (proportion of diabetes mellitus in patients with COVID-19).
40 50 60 70
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Age
Proportion
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
0 2000 4000 6000 8000
0.00 0.05 0.10 0.15 0.20 0.25 0.30
No. of Pts
Proportion
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0 200 400 600 800 1000 1200
0.00 0.05 0.10 0.15 0.20 0.25 0.30
No. of Pts
Proportion
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
6.5 7.0 7.5 8.0 8.5 9.0 9.5
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Quality score
Proportion
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(minus CDC study)
A B
C D

Supplementary Figure 2: Sensitivity analysis of pooled prevalence of diabetes mellitus in
COVID-19 patients, after exclusion of 13 outlier studies.
Studies
Zhang R
Wan S (16−Apr)
Wei YY
Yang Z
Zhang X
Wan S (21−Mar)
Liu Y
Mo P
Zhang G
Shen L
Wang D
Feng Y
Cao J
Wu C
CDC COVID−19
Deng Y
Zhang JJ
Mao L
Wang L (31−Mar)
Ruan Q
Overall (I^2=4626 % , P=0.013)
Estimate (95% C.I.)
0.058 (0.016, 0.100)
0.065 (0.021, 0.109)
0.066 (0.028, 0.103)
0.066 (0.036, 0.095)
0.080 (0.059, 0.102)
0.089 (0.041, 0.137)
0.094 (0.057, 0.130)
0.097 (0.050, 0.143)
0.100 (0.060, 0.139)
0.101 (0.047, 0.155)
0.101 (0.051, 0.152)
0.103 (0.076, 0.130)
0.108 (0.048, 0.168)
0.109 (0.066, 0.153)
0.110 (0.102, 0.118)
0.116 (0.074, 0.157)
0.121 (0.067, 0.176)
0.140 (0.094, 0.187)
0.155 (0.089, 0.221)
0.167 (0.107, 0.226)
0.098 (0.087, 0.109)
Diabetes/Total
7/120
8/123
11/167
18/273
48/597
12/135
23/245
15/155
22/221
12/119
14/138
49/476
11/102
22/201
730/6637
26/225
17/140
30/214
18/116
25/150
1118/10554
0.05 0.1 0.15 0.2
Proportion

Supplementary Figure 3: Meta-regression plots showing the influence of age (A), number
of patients (B), number of patients excluding the CDC study (C), and quality score (D) on
primary outcome (association of diabetes mellitus with severe clinical course of COVID-19).
40 50 60 70
012345
Age
log Odds Ratio
●●●
●
●●
●
●
●●
●
0 2000 4000 6000 8000
012345
No of Pts
log Odds Ratio
●
●●
●
●
●
●
●●
●
●
●●
●
●●
●
●●●
●●
●●
●
●
●
●
●
●
●
●
0 200 400 600 800 1000 1200
012345
No of Pts
log Odds Ratio
●
●
●
●
●
●
●
●
●
●
●
6.5 7.0 7.5 8.0 8.5 9.0 9.5
012345
Quality
log Odds Ratio
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
(minus CDC study)
A B
C D

Supplementary Figure 4: Forest plot showing sensitivity analysis of pooled odds ratio of
diabetes mellitus associated with severe clinical course including mortality.

Supplementary Figure 5: Trim and fill forest plot showing pooled odds ratio of diabetes
mellitus associated with severe clinical course including mortality after addition of four
missing hypothetical studies.

Supplementary Figure 6: Trim and fill funnel plot showing correction of publication bias
after addition of four missing hypothetical studies.

Supplementary Figure 7: Forest plot showing corrected pooled odds ratio of diabetes
mellitus associated with severe clinical course including mortality after adjusting for
heterogeneity as well as publication bias.