Search strategy and selection criteria

This is the first edition of a living systematic review and meta-analysis, which was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [19]. We plan to update the living review frequently, as long as relevant evidence on this topic becomes available. We searched PubMed (https://pubmed.ncbi.nlm.nih.gov), Web of Science (www.webofknowledge.com), Epistemonikos (www.epistemonikos.org) and the COVID-19 Research Database (WHO) (https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov). A protocol was prospectively registered on PROSPERO (CRD42020193692). The literature search was conducted from inception up to 14 August 2020 by using predefined search terms (see electronic supplementary material (ESM) Table 1). To identify studies that were published after the last update, we continuously searched PubMed using the e-mail alert service, which was based on our search terms described above. The last update was on 10 October 2020. We did not apply any restrictions or filters. The screening of the studies was performed by two independent researchers (AL, MN) and any discrepancies were resolved by discussion with two other researchers (CH, SS). Titles and abstracts were scanned according to the predefined inclusion and exclusion criteria (see below) and potentially relevant full texts were assessed for eligibility. Reference lists of included studies and relevant systematic reviews on this topic were screened for further relevant studies.

We included studies of any design that reported risk estimates (HR, RR or OR with 95% CI) for associations between phenotypes (general characteristics of individuals, diabetes-specific characteristics, presence of diabetes-related complications or underlying comorbidities, and laboratory parameters) and death and severity of COVID-19 in individuals with diabetes and WHO-defined confirmed SARS-CoV-2 infection [20]. Severity of COVID-19 was defined as a composite endpoint, including death, endotracheal intubation for mechanical ventilation, acute respiratory distress syndrome, septic shock, ICU admission, multiple organ dysfunction or failure, or hospital admission. Studies without primary clinical data (including modelling studies), editorials, letters, commentaries, reviews, articles not in English and guidelines were excluded. If studies on the same cohort/data were identified, we selected the study with the largest number of cases. Studies with mixed populations (including individuals without diabetes or without COVID-19) were excluded [21–23]. We successfully contacted study authors and received missing data or corrections for implausible data [16, 17, 24–27] and, thus, no study had to be excluded due to missing data. As the authors of one report did not reply to our request [28], we assumed that in statistical analyses of continuous measures, the variable was investigated as per 1 unit increase.

Data extraction and risk of bias assessment

One investigator extracted relevant data using a pre-piloted form and another investigator double-checked it for accuracy (AL, MN). Any discrepancies were discussed and resolved by discussion with a third researcher (SS). The extracted data of interest are listed in ESM Table 2.

Three researchers (AL, MN, SS) independently assessed the risk of bias of included studies in pairs of two by applying the validated Cochrane tool, Quality In Prognosis Studies (QUIPS) [29]. Any discrepancies were resolved by discussion. The tool includes the following six domains: study participation, study attrition, prognostic factor measurements, outcome measurements, study confounding and statistical analysis/reporting (see ESM Methods and ESM Table 3 for more details).

Data analysis

For similar exposures (with similar reference groups [e.g., men vs women; obese vs normal weight; use of insulin: yes vs no; pre-existing comorbidities: yes vs no]), meta-analyses were conducted separately for the two outcomes: death and severity. We calculated summary RRs (SRR) and 95% CIs using DerSimonian and Laird random effects models and I² statistic to assess statistical heterogeneity. Publication bias was assessed by visual inspection of the funnel plots and applying Egger’s test if more than ten studies were identified for one association [30]. To explore the influence of potential confounding, we conducted stratified analysis by adjustment for important confounders (low/moderate risk vs high risk of bias in the confounding domain of the QUIPS tool). We defined low risk of bias as inclusion of a minimal adjustment set in the statistical analysis (including age, sex, BMI, at least one comorbid condition), moderate if one of the aforementioned confounders was missing, and high if more than one of the aforementioned confounders was missing and/or univariate analyses were conducted. Since it has been shown that the 95% CIs derived from the DerSimonian and Laird method can provide false-positive findings when summarising few studies with small sample sizes, we conducted a sensitivity analysis by calculating the 95% CIs derived from the Hartung-Knapp-Sidik-Jonkman method, which has been shown to result in more adequate error rates than the DerSimonian and Laird method [31]. All statistical analyses were conducted with Stata software version 15.1 (Stata Corporation, College Station, TX, USA).

Certainty of evidence

Certainty of evidence of pooled associations was evaluated by two authors independently (MN, SS) using the GRADE tool [32]. Discrepancies were resolved by discussion. The tool covers the following aspects: study design, risk of bias, imprecision, inconsistency, indirectness, publication bias, the magnitude of effects, dose–response relations and the impact of residual confounding. The certainty of evidence could be rated as high, moderate, low or very low. The certainty of evidence is described as the ‘extent of the confidence that a risk estimate of an association is correct or is adequate with the aim to support a particular decision or recommendation’ [33]. A high certainty of evidence means that it is very unlikely that the inclusion of future studies will change the effect estimate, whereas a very low certainty of evidence means that it is very likely that the inclusion of future studies will change the results.

Literature search and characteristics of included studies

In total, 4150 records were identified from the databases. After exclusion of duplicates, the titles and abstracts from 2546 articles were screened and, out of these, 213 articles were read in full length. Five relevant articles were identified from the PubMed e-mail alert service. In total, 22 articles [16–18, 24–28, 34–47] were included (Fig. 1). The number of people (with diabetes and COVID-19) ranged from 29 (smallest study) to 9460 (largest study). In total, our systematic review included 17,687 individuals. The reasons for exclusion of studies are provided in ESM Table 4. Most of the studies (n = 14) were conducted in Asia (China, n = 8; South Korea, n = 3; Singapore, n = 1; Iran, n = 1; Israel n = 1), whilst five studies were conducted in North America (USA, n = 4; Mexico, n = 1) and three studies in Europe (France, n = 1; Italy, n = 1; Spain, n = 1). The majority of the studies were conducted in the hospital setting and used data from hospital-based records, with a few exceptions: one study included data from a national registry [17] and two used data from health insurance records [40, 41]. Type of diabetes was not specified in n = 13 studies, n = 5 studies only included individuals with type 2 diabetes and n = 4 studies focused on both type 1 and type 2 diabetes. The characteristics of the studies are shown in Table 1 and more detailed information about the setting, the identified exposures and considered confounders in each study is shown in ESM Table 5. Risk of bias was low in n = 6 studies, moderate in n = 8 studies and high in n = 8 studies (ESM Fig. 1). The main reason for high risk of bias was insufficient adjustment for confounding factors and/or inappropriate statistical analysis and reporting of the findings (ESM Fig. 2).

Fig. 1

Flow chart of the literature search

Table 1

Characteristics of included studies

Authors, year [reference]	Country; study setting	Study design; follow-up	Sex; mean age; diabetes type	Total number of participants/number with outcome	Outcome	Outcome assessment	Relevant exposure	Exposure assessment
Acharya et al, 2020 [28]	South Korea; hospital based	Retrospective study; ND	m/w; 69.8 years; T2D	N = 55/death: n = 11	Death	Medical records	General characteristics, diabetes-specific factors, comorbidities, laboratory markers at admission	Medical records
Agarwal et al, 2020 [25]	USA; hospital based	Retrospective study; ND	m/w; 67.9 years; T1D and T2D	N = 1279/death: n = 394	In-hospital death	Electronic health records	Diabetes-specific factors, comorbidities	Electronic health records
Bello-Chavolla et al, 2020 [17]	Mexico; national register data	Retrospective study; 30 days	m/w; 57.2 years; ND	N = 9460/death: n = 2062	Death	Open source dataset	General characteristics, comorbidities	Open source dataset
Cariou et al, 2020 [16]	France; hospital based	Prospective study; 7 days	m/w; 69.8 years; T1D and T2D	N = 1317/MV and/or death: n = 382; death: n = 140	MV and/or death, death	Medical files	General characteristics, diabetes-specific factors, comorbidities, medication use, laboratory markers at admission	Medical files; if needed, general or specialist practitioner, regular pharmacist or biomedical laboratory
Chen et al, 2020 [34]	China; hospital based	Retrospective study; ND	m/w; 66.0 years; ND	N = 136/death: n = 26; severity: n = 93	In-hospital death, poor prognosis	Electronic medical records, CT, evaluation by experienced clinicians	General characteristics, diabetes-specific factors, comorbidities, medication use, laboratory markers at admission	Electronic medical records
Chung et al, 2020 [35]	Korea; hospital based	Retrospective study; 28 days	m/w; 66.3 years; ND	N = 29/severity: n = 13	Severe and critical outcome	Electronic medical records	General characteristics, diabetes-specific factors, medication use	Electronic medical records
Crouse et al, 2021 [36]	USA; hospital based	Retrospective study; ND	m/w; ND; T1D and T2D	N = 239/death: n = 45	Death	Electronic medical records	General characteristics, diabetes-specific factors	Electronic medical records
Dalan et al, 2020 [37]	Singapore; hospital based	Retrospective study; ND	m/w; ND; T2D	N = 76/cases: ND	Oxygen, ICU admission, MV	Medical records	Diabetes-specific factors	Medical records
de Abajo et al, 2020 [38]	Spain; hospital based	Case-population study; ND	m/w, 69.1 years; ND	N = 1440/cases: n = 182	Admission to hospital	Electronic primary healthcare records	Medication use	Hospital medical records
Li et al, 2020 [26]	China; hospital based	Retrospective study; ND	m/w; 65.0 years; T1D and T2D	N = 132/death: n = 15; severity: n = 31	In-hospital death, severity	Electronic medical records	General characteristics, diabetes-specific factors, comorbidities, laboratory markers at admission	Electronic medical records
Liu et al, 2020 [18]	China; hospital based	Retrospective study; ND	m/w; 66.0 years; ND	N = 64/cases: n = 12	MV and/or death	Electronic medical records	Diabetes-specific factors, medication use, laboratory markers at admission	Electronic medical records
Luo et al, 2020 [39]	China; hospital based	Retrospective study; ND	m/w; 64.3 year; ND	N = 283/death: n = 25	In-hospital death	Electronic medical records	Diabetes-specific factors, medication use	Electronic medical records
Merzon et al, 2020 [40]	Israel; health insurance data	Retrospective study; ND	m/w; 61.8 years; ND	N = 183/cases: n = 46	Hospitalisation	LHS electronic medical records	General characteristics, diabetes-specific factors, comorbidities	LHS electronic medical records
Rastad et al, 2020 [24]	Iran; hospital based	Retrospective study; ND	m/w; 54.8 years; ND	N = 267/death: ND	In-hospital death	Electronic medical records	General characteristics, diabetes-specific factors, comorbidities, laboratory markers at admission	Electronic medical records
Rhee et al, 2021 [41]	Korea; health insurance data	Retrospective study; ND	m/w; 61.8 years; ND	N = 832/cases: n = 34	Intensive care or death	HIRA database	Diabetes-specific factors, medication use	HIRA database
Seiglie et al, 2020 [42]	USA; hospital based	Prospective study; 14 days	m/w; 66.7 years; ND	N = 168/death: n = 28; ICU: n = 75; MV: n = 66	ICU admission, MV, death	Manual chart review	General characteristics, diabetes-specific factors,	Manual chart review and EDW
Shah et al, 2020 [43]	USA; hospital based	Retrospective study; ND	m/w; 60.1 years; ND	N = 228/cases: ND	In-hospital death, severity	Electronic medical records	Medication use	Electronic medical records
Shang et al, 2020 [44]	China; hospital based	Retrospective study; ND	m/w; 59.0 years; ND	N = 84/cases: n = 17	Death	Electronic medical records	Diabetes-specific factors	Electronic medical records
Shi et al, 2020 [27]	China; hospital based	Retrospective study; ND	m/w; 64.0 years; ND	N = 153/death: n = 31	In-hospital death	Electronic medical records	General characteristics, comorbidities, laboratory markers at admission	Electronic medical records
Solerte et al, 2020 [45]	Italy; hospital based	Retrospective case–control study; 30 days	m/w; 69.0 years; T2D	N = 338/death: n = 94; ICU: n = 40; MV: n = 23	In-hospital death, intensive care, MV	Electronic medical records	General characteristics, comorbidities,	Electronic medical records
Xu et al, 2020 [46]	China; hospital based	Case series study; ND	m/w; 66.0 years; T2D	N = 114/death: n = 27	Death	Electronic medical records	Diabetes-specific factors	Electronic medical records
Zhu et al, 2020 [47]	China; hospital based	Retrospective study; 28 days	m/w; 62.7 years; T2D	N = 810/death: n = 61; severity: n = 133	Death, severity	Electronic medical records	Diabetes-specific factors	Electronic medical records

General risk factors and COVID-19-related death and COVID-19 severity in individuals with diabetes and COVID-19

There is high certainty of evidence that male sex compared with female sex was associated with increased risk of COVID-19-related death (SRR 1.28 [95% CI 1.02, 1.61]; n = 10 studies) and COVID-19 severity (SRR 1.36 [95% CI 1.13, 1.64]; n = 11 studies) in individuals with diabetes and COVID-19. In addition, older age (>65 years) was associated with higher risk of COVID-19-related death (SRR 3.49 [95% CI 1.82, 6.69]; n = 6 studies; moderate certainty of evidence) and with COVID-19 severity (SRR 1.67 [95% CI 1.00; 2.76]; n = 6 studies; low certainty of evidence). With each 5 year increase in age, the relative risk for COVID-19-related death increased by 43% (95% CI 12%, 83%; n = 5 studies) and for severity by 25% (95% CI 11%, 40%; n = 7 studies), both with moderate certainty of evidence. There were no clear associations between smoking, being overweight and being obese with risk of COVID-19-related death or COVID-19 severity (certainty of evidence ranged from very low to moderate) (Figs 2, 3, ESM Table 6 and ESM Table 7).

Fig. 2

Prognostic factors and COVID-19-associated death in individuals with diabetes and COVID-19. ^aPoorly controlled blood glucose was defined as when the lowest fasting blood glucose was ≥3.9 mmol/l and the highest 2 h plasma glucose level exceeded 10.0 mmol/l during the observation window. ^bRenin inhibitors included ACE inhibitors, angiotensin II receptor blockers (ARBs) and non-specified renin–angiotensin system (RAS) inhibitors. DPP4, dipeptidyl peptidase-4; LDH, lactate dehydrogenase

Fig. 3

Prognostic factors and severity of COVID-19 in individuals with diabetes and COVID-19. Severity is defined as a composite endpoint including death, tracheal intubation for mechanical ventilation, acute respiratory distress syndrome, septic shock, intensive care unit admission, multiple organ dysfunction or failure, or hospital admission. ^aPoorly controlled blood glucose defined as when the lowest fasting blood glucose was ≥3.9 mmol/l and the highest 2 h plasma glucose level exceeded 10.0 mmol/l during the observation window. ^bRenin inhibitors included ACE inhibitors, angiotensin II receptor blockers (ARBs) and non-specified renin–angiotensin system (RAS) inhibitors. DPP4, dipeptidyl peptidase-4; LDH, lactate dehydrogenase

Diabetes-specific risk factors and COVID-19-related death and COVID-19 severity in individuals with diabetes and COVID-19

Only a few studies investigated diabetes-specific factors related to COVID-19; thus the estimates were mostly imprecisely estimated and certainty of evidence was mainly low or very low. No association was observed between HbA_1c and risk of COVID-19-related death or severity of COVID-19. Higher blood glucose at admission was associated with increased risk of COVID-19-related death and severity. The strongest associations were observed for blood glucose levels >11 mmol/l at admission and death (SRR 8.60 [95% CI 2.25, 32.83]; n = 2 studies; moderate certainty of evidence). With each 1 mmol/l increase in blood glucose at admission, the relative risk for COVID-19-related death and severity of COVID-19 increased by 10% (death: 10% [95% CI 5%, 16%], n = 2 studies; severity: 10% [95% CI 4%, 16%], n = 3 studies; low certainty of evidence for both). Participants with chronic insulin use compared with non-users of insulin had a higher relative risk of dying (SRR 1.75 [95% CI 1.01, 3.03]; n = 5 studies; high certainty of evidence), while participants using metformin compared with non-users of metformin were at lower relative risk of dying (SRR 0.50 [95% CI 0.28, 0.90]; n = 4 studies; moderate certainty of evidence) (Figs 2, 3, ESM Table 6 and ESM Table 7).

Comorbidities, complications and medication use and COVID-19-related death and COVID-19 severity in individuals with diabetes and COVID-19

Chronic obstructive pulmonary disease (COPD) was associated with increased risk of COVID-19-related death (SRR 1.40 [1.21, 1.62]; n = 5 studies) and COVID-19 severity (SRR 1.36 [95% CI 1.11, 1.66]; n = 6 studies), both graded as high certainty of evidence. Moderate certainty of evidence was observed for associations between total CVD (SRR 1.56 [95% CI 1.09, 2.24]; n = 8 studies) and chronic kidney disease (CKD; SRR 1.93 [95% CI 1.28, 2.90]; n = 6 studies) with COVID-19-related death, and low certainty of evidence was observed for associations between cerebrovascular diseases and death (SRR 2.11 [95% CI 1.36, 3.26]; n = 2 studies). In general, the associations for these comorbidities and complications were weaker for COVID-19 severity and imprecisely estimated, with the exception of COPD.

No clear associations could be observed for hypertension, cancer (type not specified), any comorbidity, liver disease, dementia, statin use and renin inhibitor use (including ACE inhibitors, angiotensin II receptor blockers [ARBs] and non-specified renin–angiotensin system [RAS] inhibitors) before admission with COVID-19 severity and/or COVID-19-related death (certainty of evidence very low to moderate). (Figs 2, 3, ESM Table 6 and ESM Table 7).

Laboratory parameters on admission and COVID-19-related death and COVID-19 severity in individuals with diabetes and COVID-19

There was an association of white blood cell and neutrophil counts with elevated relative risk of COVID-19-related death and COVID-19 severity (low to very low certainty of evidence), and for creatinine with COVID-19 severity (moderate certainty of evidence). Lymphocyte count (per 1 × 10⁹/l) was inversely associated with both outcomes (death: SRR 0.28 [95% CI 0.09, 0.87], n = 4 studies, low certainty of evidence; severity: SRR 0.33 [95% CI 0.14, 0.79], n = 4 studies, moderate certainty of evidence). For C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH) and albumin, associations were imprecisely estimated and certainty of evidence was very low (Figs 2, 3, ESM Table 6 and ESM Table 7).

Subgroup analysis, heterogeneity, publication bias and sensitivity analysis

For each association, meta-analyses were stratified by low/moderate vs high risk of bias due to confounding (ESM Fig. 3–41). Apparently, but imprecisely estimated, stronger associations were observed for older age (>65 years; ESM Fig. 4) and CKD (ESM Fig. 25) with COVID-19-related death in studies with high risk vs low/moderate risk of bias due to confounding (SRR [95%] for between studies for age > 65 years: 3.63 [0.86, 15.29], p_{between studies} = 0.07; SRR [95%] for between studies for CKD: 2.53 [0.93, 6.88], p_{between studies} = 0.06). In general, heterogeneity was higher for severity than for COVID-19-related death (Figs 2, 3), which could be explained by the inclusion of different criteria for severity and for outcomes as a composite outcome. In addition, we identified high heterogeneity especially for the laboratory findings, which is likely due to different analytical methods and reference ranges.

Publication bias was only examined for male sex (≥10 studies), and publication bias was not observed for COVID-19-related death or COVID-19 severity (ESM Fig. 42).

In a sensitivity analysis, we calculated the 95% CIs by applying the Hartung-Knapp-Sidik-Jonkman method. In general, the findings were comparable. The discrepancies were mainly observed for meta-analyses based on few numbers of primary studies (n ≤ 5; ESM Table 8 and ESM Table 9).