Impact of COVID-19 infection on mortality, diabetic complications and haematological parameters in patients with diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives SARS-CoV-2 poses significant challenges to people living with diabetes (PLWD). This systematic review aimed to explore the impact of COVID-19 on mortality, complications associated with diabetes and haematological parameters among PLWD. Design Systematic review and meta-analysis using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Data sources EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials and LILACS were searched between 1 December 2019 and 14 January 2025. Eligibility criteria for selecting studies Eligible studies included case-control and cohort studies involving PLWD categorised into two groups: those with confirmed SARS-CoV-2 infection and those without. Data extraction and synthesis Meta-analyses estimated the odds ratios (ORs) and mean differences (MDs) of outcomes including mortality, intensive care unit (ICU) admission, diabetic ketoacidosis (DKA), acute kidney injury, hospitalisation length and haematological parameters. We pooled results using random-effects models and assessed study quality with the Newcastle-Ottawa Scale. A funnel plot was used to detect potential publication bias. The overall certainty of evidence was assessed using GRADE. Results 25 of 7266 unique studies were eligible, including 1 154674 PLWD (561 558 with COVID-19 and 593 116 without COVID-19). SARS-CoV-2 infection in PLWD was associated with significantly increased mortality (OR 2.52, 95% CI 1.45 to 4.36, I 2 =99%), acute kidney injury (3.69, 95% CI 2.75 to 4.94, I 2 =0%), random plasma glucose in subjects with type 1 diabetes (MD 20.38 mg/dL, 95% CI 7.39 to 33.36, I 2 =0%), haemoglobin A1C in subjects with type 2 diabetes (0.21%, 95% CI 0.05 to 0.38, I 2 =13%), creatinine (0.12 mg/dL, 95% CI 0.04 to 0.19, I 2 =0%), C reactive protein (38.30 mg/L, 95% CI 4.79 to 71.82, I 2 =82%) and D-dimer (1.52 µg/mL, 95% CI 0.73 to 2.31, I 2 =0%). No significant differences were observed in the incidence of ICU admission and DKA, hospitalisation length, haemoglobin, leucocyte, lymphocyte, neutrophil to lymphocyte ratio, platelet, blood urea nitrogen, estimated glomerular filtration rate, procalcitonin, albumin, ferritin and bilirubin among PLWD with and without SARS-CoV-2 infection. Conclusions SARS-CoV-2 infection is associated with elevated risks of mortality and acute kidney injury and poor glycaemic control in PLWD, alongside increased levels of inflammatory and coagulation biomarkers. These findings underscore the urgent need for tailored clinical management strategies for PLWD with COVID-19. PROSPERO registration number CRD42023418039.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".