Overfeeding, currently defined as providing excessive energy and nutrients beyond metabolic requirements, is a common yet often overlooked issue in the intensive care unit (ICU) setting. Understanding the factors contributing to overfeeding and implementing strategies to prevent it is essential for optimizing patient care in the ICU. Several factors contribute to overfeeding in the ICU, including inaccurate estimation of energy requirements, formulaic feeding protocols, and failure to adjust nutritional support based on individual patient needs. Prolonged overfeeding can lead to insulin resistance and hepatic dysfunction, exacerbating glycemic control, increasing the risk of infectious complications, and worsening clinical outcomes. Clinically, overfeeding has been linked to delayed weaning from mechanical ventilation, prolonged ICU stay, and increased mortality rates. Regular review and adjustment of feeding protocols, incorporating advances in enteral and parenteral nutrition strategies, are essential for improving patient outcomes. Clinicians must be proficient in interpreting metabolic data, understanding the principles of energy balance, and implementing appropriate feeding algorithms. Interdisciplinary collaboration among critical care teams, including dieticians, physicians, and nurses, is crucial for ensuring consistent and effective nutritional management. Overfeeding remains a significant concern in the ICU after discharge as well, implying further complications for patient safety and integrity. By understanding the causes, consequences, and strategies for the prevention of overfeeding, healthcare providers can optimize nutrition therapy and mitigate the risk of metabolic complications. Through ongoing education, interdisciplinary collaboration, and evidence-based practice, the ICU community can strive to deliver personalized and precise nutritional support to critically ill patients.

Viral infections disrupt glucose metabolism; however, their impact on disease prognosis in highly pathogenic viruses remains largely unknown. There is an additional need to investigate the antiviral mechanisms of glucose-lowering therapeutics. Here, our multicenter clinical study shows that hyperglycemia and pre-existing diabetes are independent risk factors for mortality in patients infected with severe fever with thrombocytopenia syndrome virus (SFTSV), an emerging and highly pathogenic bunyavirus. SFTSV infection triggers gluconeogenesis, which, in turn, inhibits AMPK activity and subsequent interferon I (IFN-I) responses, thereby facilitating viral replication. In vitro and animal studies further reveal that metformin inhibits SFTSV replication by suppressing autophagy through the AMPK-mTOR pathway, contributing to protection against lethal SFTSV infection in mice. Importantly, our large cohort study demonstrates that metformin reduces viremia and SFTSV-related mortality in patients with hyperglycemia or pre-existing diabetes, contrasting with the disadvantageous effect of insulin. These findings highlight the promising therapeutic potential of metformin in treating viral infections, particularly among individuals with hyperglycemia or diabetes.

Severe fever with thrombocytopenia syndrome virus (SFTSV), an emerging tick-borne bunyavirus, causes severe hemorrhagic fever with a high mortality rate. Previous studies have shown metabolic disturbances, particularly hyperglycemia, in SFTSV-infected individuals. However, the mechanism underlying this metabolic derangement remains unclear, and further investigation is needed to determine whether glucose-lowering therapeutics could be beneficial for SFTSV-infected patients. In this study, our multicenter clinical data show that hyperglycemia and pre-existing diabetes are independent risk factors for mortality in patients with SFTSV infection. Furthermore, we observed that SFTSV infection triggers gluconeogenesis, which promotes viral replication through the regulation of the AMPK-IFN-I signaling pathway. Notably, metformin significantly reduces viremia and SFTSV-related mortality in patients with hyperglycemia or pre-existing diabetes, attributed to its inhibitory effect on autophagy through the AMPK-mTOR pathway. Therefore, our study uncovers the interaction between SFTSV infection and glucose metabolic disorder and highlights the promising therapeutic potential of metformin for treating SFTSV infection.

SARS-CoV-2 significantly impacts the human metabolome. This study aims to evaluate the predictive capability of a comprehensive module clustering approach in plasma metabolomics for identifying the risk of critical complications in COVID-19 patients admitted to intensive care units (ICUs). We conducted a prospective monocenter study, gathering blood samples within 24 h of ICU admission, alongside clinical, biological, and demographic patient characteristics. Subsequently, we quantified patients' plasma metabolome using a comprehensive untargeted metabolomics approach. First, we stratified patients based on a composite outcome score indicating critical status. Analysis of potential predictors revealed that older patients with higher severity scores and pronounced alterations in key biological parameters are more likely to experience critical complications. Next, we identified 6,667 metabolic features clustered into 57 annotated metabolic modules across all patients by employing an integrative metabolomics approach. Furthermore, we identified the most differentially expressed metabolic modules related to patients' outcomes. Moreover, we defined the top five most predictive metabolites of critical status: homoserine, urobilinogen, methionine, xanthine and pipecolic acid. These five predictors alone demonstrated similar or superior performance compared to clinical and demographic variables in predicting patients' outcomes. This innovative metabolic module inference approach offers a valuable framework for identifying patients prone to complications upon ICU admission for COVID-19. Its potential applications extend to enhancing patient management across diverse clinical settings.

The role of diabetes mellitus (DM) in hospitalized COVID-19 patients and of HbA1c in hospitalized COVID-19 patients with DM were not studied adequately in the past.

It was a retrospective cohort study. In this study, data from 305 hospitalized COVID-19 patients was analyzed. The study objective was to determine the association of DM with in-hospital mortality in COVID-19 patients. Another study objective was to determine the association of HbA1c with mortality in COVID-19 patients with DM.

In this retrospective study, DM was present in 41.3% (126/305) of the study population. The multivariate Cox regression analysis showed a significant association between DM and mortality (adjusted hazard ratio (aHR): 2.116, 95% CI: 1.088-4.116, p = 0.027). The median HbA1c in diabetic patients was 8.9% (7.5-11.0). HbA1c was found to be associated with mortality in diabetic patients in the multivariate cox-regression analysis (aHR:1.272, 95% CI: 1.028-1.574, p = 0.027). The multivariate Cox regression analysis also showed the association of HbA1c (10.5%≤HbA1c > 10.5%) as a dichotomous variable with in-hospital mortality (aHR: 2.53, 95% CI: 2.606-194.81, p = 0.005) in diabetic patients.

DM was independently associated with mortality in hospitalized COVID-19 patients in the multivariate analysis. In COVID-19 patients with DM, HbA1c was associated with mortality as a continuous and dichotomous variable in the multivariate analysis.

Coronavirus disease 2019 (COVID-19) survivors are concerned about the likelihood of developing further diseases. This study examines the global trends in scientific research on diabetes associated with COVID-19 from several perspectives. Bibliometric analyses are used to undertake a scientific review of the literature. The Web of Science Core Collection (WoSCC) database was used to acquire bibliographic information on diabetes related to COVID-19 from Jan 2020 to Dec. 2023. The visual map was built via advanced CiteSpace 6.2.R6. 7,348 papers were found. Khunti Kamlesh and Rizzo-Manfredi are the most well-known high-yield authors in this area, and the top ten authors collaborate extensively. Most of these papers came from universities. Harvard Medical School has the most publications, followed by Wuhan University and Huazhong University of Science and Technology. China and the United States are the countries with the most publications. Angiotensin-converting enzymes, chronic disease, intensive care unit, viral infection, and gestational diabetes mellitus were scored 0-11, 2, 3, and 4, respectively. Zhou et al.'s work on this topic, which appeared in the prominent medical journal The Lancet, was cited 1,366 times, highlighting its importance. "clinical characteristics," "diabetes mellitus," "metabolic syndrome," and "angiotensin-converting enzyme" were used as keywords for reference co-citation and clustering data identify. Over the last four years, related investigations have focused primarily on observing clinical aspects. This report is important for developing treatment strategies, directing future research, and guiding clinical practice.

To investigate the real-world effectiveness of COVID-19 vaccines in a large cohort of patients with diabetes mellitus (DM), we analyzed all >18-year-old patients with COVID-19 registered in a Brazilian nationwide surveillance database between February 2020 and February 2023. The primary outcome of interest was vaccine effectiveness against death, evaluated using multivariate logistic regression models. Among the 2,131,089 patients registered in the SIVEP-Gripe, 482,677 (22.6%) had DM. After adjusting for covariates, patients with DM had a higher risk of death than those without comorbidities (adjusted odds ratio [aOR] = 1.43, 95% CI, 1.39-1.47). For patients without comorbidities (72.7%, 95% CI, 70.5-74.7) and those with DM (73.4%, 95% CI, 68.2-76.7), vaccine effectiveness was similar after the booster dose. However, it was reduced in patients with DM associated with other comorbidities (60.5%; 95% CI, 57.5-63.2). The strongest factor associated with booster failure was the omicron variant (aOR = 27.8, 95% CI, 19.9-40.1). Our study revealed that COVID-19 vaccines provided robust protection against death in individuals with DM. However, our findings underscore the need to update vaccines and develop tailored strategies for individuals with diabetes, especially those with additional underlying conditions.

COVID-19 ranges from asymptomatic cases to severe disease with high mortality. Corticosteroids are crucial in treatment, reducing mortality and morbidity. However, the use of corticosteroids poses additional challenges in maintaining glycemic control in COVID-19 patients This study aims to eva-luate the impact of insulin requirement on mortality and morbidity in non-diabetic ICU patients and investigate its correlation with disease severity.

This retrospective cohort study included non-diabetic COVID-19 patients aged ≥ 18 years admitted to the ICU of Prof. Dr. Cemil Taşcıoğlu City Hospital (Turkey) between September 1, 2020, and May 31, 2021. Patients requiring ≥ 24 h of insulin therapy were compared with those who did not need insulin. Data on demographics, severity scores (SOFA, APACHE II, SAPS II), insulin initiation and duration, corticosteroid therapy, mechanical ventilation, antiviral and immunomodulatory treatments, laboratory markers, and infection parameters were analyzed. Mortality and incidence of new-onset diabetes mellitus within the first six months post-discharge were assessed. Statistical analyses were performed using SPSS v22.0, with p < 0.05 considered statistically significant.

Patients with insulin requirements had higher SOFA (p = 0.001), APACHE II (p < 0.001), and SAPS II (p = 0.041) scores, along with increased mechanical ventilation duration (p < 0.001). While corticosteroid type had no effect, > 1 mg/kg/day methylprednisolone or equivalent dexamethasone significantly increased insulin demand (p = 0.002). Among laboratory markers, only peak CRP levels were significantly higher in insulin-requiring patients (p = 0.001). ICU and total hospital stays were significantly longer in the insulin group (p < 0.001). Although in-hospital mortality was similar, 6-month mortality was significantly higher in insulin-requiring patients (p = 0.022). New-onset DM rates were 4.2% in the non-insulin group vs. 31.1% in the insulin group (p = 0.001).

Insulin requirement in non-diabetic COVID-19 ICU patients is a predictor of 6-month mortality. High-dose corticosteroids exacerbate glycemic dysregulation, increasing insulin needs. SARS-CoV-2-induced beta-cell damage and hyperinflammation-related stress hyperglycemia elevate the risk of post-discharge DM. Close monitoring and diabetes screening are essential in this population.

To evaluate previous diabetes and hyperglycemia in post-COVID-19 mortality.

We evaluated patients with diabetes (previous diabetes and use of medication and random glucose ≥ 200 mg/dl and HbA1C ≥ 6.5 % or 48 mmol/mol) and patients without diabetes with hyperglycemia 1 year before COVID-19. Hazard ratios (HR) and 95 % confidence intervals, 95 %CI were calculated for all-cause mortality (1-week to 1-year) among those with diabetes (only), diabetes with comorbidities (hypertension, chronic kidney disease-CKD), and patients without diabetes but with hyperglycemia (≥ 100 ≥ 110 and ≥ 126 mg/dl).

Of 455 patients, 30.1 % had diabetes. Diabetes only had a high mortality risk in 7 days (HR: 6.24; 95 %CI: 1.03-37.77). Diabetes with hypertension and CKD were associated with increased mortality risks from 1-week (HR: 7.98; 95 %CI, 1.03-61.70) to 1-year (HR: 2.27; 95 %CI: 1.03-4.99) after COVID-19. Among those without diabetes, FBG ≥ 110 and ≥ 126 mg/dl were associated with more than double the risk of dying in 1-year after COVID-19 infection [1-year HR: 2.80 (95 %CI: 1.10 - 7.22) and 1-year HR: 2.54 (95 %CI:1.10-5.88), respectively].

Diabetes associated with other comorbidities had the highest risks of all-cause mortality in the short and long-term, hyperglycemia was a long-term marker of poor prognostic after COVID-19 infection.

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.

Systematic review and meta-analysis using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).

EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials and LILACS were searched between 1 December 2019 and 14 January 2025.

Eligible studies included case-control and cohort studies involving PLWD categorised into two groups: those with confirmed SARS-CoV-2 infection and those without.

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.

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, I2=99%), acute kidney injury (3.69, 95% CI 2.75 to 4.94, I2=0%), random plasma glucose in subjects with type 1 diabetes (MD 20.38 mg/dL, 95% CI 7.39 to 33.36, I2=0%), haemoglobin A1C in subjects with type 2 diabetes (0.21%, 95% CI 0.05 to 0.38, I2=13%), creatinine (0.12 mg/dL, 95% CI 0.04 to 0.19, I2=0%), C reactive protein (38.30 mg/L, 95% CI 4.79 to 71.82, I2=82%) and D-dimer (1.52 µg/mL, 95% CI 0.73 to 2.31, I2=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.

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.

CRD42023418039.

The global prevalence of diabetes, a chronic condition that disrupts glucose homeostasis, is rapidly increasing. Patients with diabetes face heightened challenges due to the COVID-19 pandemic, which exacerbates symptoms associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this study, we developed a mathematical model utilizing the Mittag-Leffler kernel in conjunction with a generalized fractal fractional operator to explore the complex dynamics of diabetes progression and control. This model effectively captures the disease's inherent memory effects and delayed responses, demonstrating improved accuracy over traditional integer-order models. We identified a single equilibrium point that represents the stable glucose level in healthy individuals. To establish the existence and uniqueness of the model, we employed fixed point theory alongside the Lipschitz condition. The Ulam-Hyers stability of the proposed model was also examined. Subsequently, we analyzed the chaotic behavior of the diabetic model using feedback control approaches, focusing on controllability and PID techniques. The application of chaos theory revealed that glucose-insulin dynamics are highly sensitive to initial conditions, leading to complex oscillatory behavior that can result in unstable glucose levels. By implementing fractional-order PID controllers, we effectively stabilized chaotic glucose dynamics, achieving more reliable blood sugar regulation compared to conventional methods, with a notable reduction in oscillation amplitude. We conducted numerical simulations to validate our findings, employing the Newton polynomial method across various fractal and fractional order values to assess the robustness of the results. A discussion of the graphical outcomes from the numerical simulations, conducted using MATLAB version 18, is provided, illustrating the dynamics of glucose regulation under different fractal-fractional orders. This comprehensive approach enhances our understanding of the underlying mechanics driving chaotic behavior in glucose-insulin dynamics.

In the context of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), metabolic research has become crucial for in-depth exploration of viral infection mechanisms and in searching for therapeutic strategies. This paper summarizes the interrelationships between carbohydrate, lipid, and amino acid metabolism and COVID-19 infection, discussing their roles in infection progression. SARS-CoV-2 infection leads to insulin resistance and increased glycolysis, reducing glucose utilization and shifting metabolism to use fat as an energy source. Fat is crucial for viral replication, and imbalances in amino acid metabolism may interfere with immune regulation. Consequently, metabolic changes such as hyperglycemia, hypolipidemia, and deficiency of certain amino acids following SARS-CoV-2 infection can contribute to progression toward severe conditions. These metabolic pathways not only have potential value in prediction and diagnosis but also provide new perspectives for the development of therapeutic strategies. By monitoring metabolic changes, infection severity can be predicted early, and modulating these metabolic pathways may help reduce inflammatory responses, improve immune responses, and reduce the risk of thrombosis. Research on the relationship between metabolism and SARS-CoV-2 infection provides an important scientific basis for addressing the global challenge posed by COVID-19, however, further studies are needed to validate these findings and provide more effective strategies for disease control.

Several studies have reported a reduced risk of COVID-19-related mortality in patients taking antidiabetic medications. This is an umbrella review, meta-analysis, and Bayesian sensitivity assessment of SGLT2 inhibitors (SGLT2is) in COVID-19 patients with type 2 diabetes mellitus (T2DM).

A search was conducted on the MEDLINE (PubMed), EMBASE, Cochrane, and ClinicalTrials.gov databases on 5/12/2023. We performed an umbrella review of systematic reviews and meta-analyses on the effects of SGLT2is in T2DM patients with COVID-19 and critically appraised them using AMSTAR 2.0. Trials investigating SGLT2i use in COVID-19 patients post-hospitalisation and observational studies on prior SGLT2i use among COVID-19 patients were included in the meta-analysis, adhering to the PRISMA guidelines.

SGLT2is exhibited significantly lower odds of mortality (OR 0.67, 95% CI 0.53-0.84) and hospitalisation (OR 0.84, 0.75-0.94) in COVID-19 patients with T2DM. Bayesian sensitivity analyses corroborated most of the findings, with differences observed in hospitalisation and mortality outcomes. SGLT-2 inhibitors showed an OR of 1.20 (95% CI 0.64-2.27) for diabetic ketoacidosis. Publication bias was observed for hospitalisation, but not for mortality. The GRADE assessment indicated a low to very low quality of evidence because of the observational studies included.

The prophylactic use of SGLT2is reduces mortality and hospitalisation among COVID-19 patients, particularly in patients with diabetes. The utility of SGLT2is after hospitalisation is uncertain and warrants further investigation. A limited efficacy has been observed under critical conditions. Individualised assessment is crucial before integration into COVID-19 management.

Coronavirus 2019 (COVID-19) has spread throughout the world and the current COVID-19 vaccines have shown to be the most effective means of combating the COVID-19. This study focused to examine the status of serum biomarkers in individuals infected and non-infected with SARS-CoV-2, both before and after COVID-19 pandemic and vaccination.

This study comprised 133 adults aged 35 and older including both academic and non-academic personnel associated with Shahjalal University of Science and Technology in Sylhet, Bangladesh. Participants were evaluated before and after COVID-19 pandemic, as well as following two doses of vaccination. Blood samples were collected to measure different serum biomarkers, including fasting blood sugar (FBS), serum creatinine, serum alanine transaminase (ALT), total cholesterol (TC), triglyceride (TG), Low density lipoprotein-cholesterol (LDL-C), and High density lipoprotein-cholesterol (HDL-C). Statistical analysis was performed using SPSS software.

In all participants, serum creatinine, FBS and TC levels significantly increased after two doses of vaccination (p = 0.022, 0.006, 0.05) compared to pre-vaccination levels. Notably, all serum biomarkers showed a significant elevation (p ≤ 0.05) in the self-reported SARS-CoV-2 infected group (n = 44). Additionally, 31% of participants were newly diagnosed with hyperglycemia after receiving the COVID-19 vaccine.

The findings indicate that both self-reported SARS-CoV-2 infection and COVID-19 vaccination could influence different serum biomarker levels. However, further comprehensive research is necessary to discern the precise factors contributing to the alterations observed in the serum biomarker levels for future health management strategy.

During the Omicron infection wave, diabetic patients are susceptible to COVID-19, which is linked to a poor prognosis. However, research on the real-world effectiveness and safety of Azvudine, a common medication for COVID-19, is insufficient in those with pre-existing diabetes.

In this retrospective study, we included 32,864 hospitalized COVID-19 patients from 9 hospitals in Henan Province. Diabetic patients were screened and divided into the Azvudine group and the control group, via 1:1 propensity score matching. The primary outcome was all-cause mortality, and the secondary outcome was composite disease progression. Laboratory abnormal results were used for safety evaluation.

A total of 1,417 patients receiving Azvudine and 1,417 patients receiving standard treatment were ultimately included. Kaplan-Meier curves suggested that all-cause mortality (P = 0.0026) was significantly lower in the Azvudine group than in the control group, but composite disease progression did not significantly differ (P = 0.1). Cox regression models revealed Azvudine treatment could reduce 26% risk of all-cause mortality (95% CI: 0.583-0.942, P = 0.015) versus controls, and not reduce the risk of composite disease progression (HR: 0.91, 95% CI: 0.750-1.109, P = 0.355). The results of subgroup analysis and three sensitivity analyses were consistent with the previous findings. Safety analysis revealed that the incidence rates of most adverse events were similar between the two groups.

In this study, Azvudine demonstrated good efficacy in COVID-19 patients with diabetes, with a lower all-cause mortality rate. Additionally, the safety was favorable. This study may provide a new strategy for the antiviral management of COVID-19 patients with diabetes.

Achieving an in-range glycated haemoglobin (HbA1c) is essential for managing diabetes mellitus (DM). However, this parameter provides an estimate of long-term blood glucose control rather than daily glycaemic variations. Glycaemic variability can be more predictive than HbA1c in terms of identifying those at risk for diabetes complications, including risk of severe respiratory virus infections and is usually measured via a continuous glucose monitor (CGM). For individuals for whom a CGM is not available, serum 1,5 anhydroglucitol (1,5-AG) level has shown potential as an alternative method for monitoring glycaemic variability. Despite this, at present 1,5-AG is not routinely used in the clinical assessment of DM. Here, we aim to determine whether assessing 1,5-AG, in addition to HbA1c, is of any potential clinical utility to the management of DM for patients.

Using machine learning and data derived from 78 patients with type I DM (for whom CGM data is available) we show that the combination of 1,5-AG and HbA1c improves the prediction of a patient's glycemia risk index (GRI) compared to HbA1c alone.

The GRI is an essential tool in the management of DM as it reflects both clinical priorities and patient centred outcomes. The inclusion of 1,5-AG in this prediction was particularly important for individuals who had very high or very low GRI. Furthermore, in the context of glycaemic variability and susceptibility to severe respiratory virus infections, we show that reduced 1,5-AG in the plasma is associated with reduced ex vivo CD4 + T cell cytokine responses to influenza virus in individuals with a matched HbA1c.

Taken together, these data argue for an increased monitoring of 1,5-AG in the clinic for individuals without a CGM to provide additional insights for diabetes management.

Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear.

Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D).

We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control.

Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes.

Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.

There is limited information on the association between smoking/smoking cessation and sarcopenia in Chinese populations. We conducted a retrospective cohort study to investigate the association between smoking/smoking cessation and its duration with the risk of sarcopenia.

This cohort included 6,719 adults over 45 from the CHARLS between 2011 and 2015. Smoking status was categorized into smokers (current smokers/quitters) and non-smokers. Duration of smoking was defined as < 20, 20-29, 30-39, and ≥ 40 years. Duration of smoking cessation was defined as ≤ 1, 2-4, and > 4 years. Sarcopenia was defined according to AWGS 2019. Cox proportional hazards regression models were used to estimate the hazard ratio for the risk of developing sarcopenia.

The median age of the cohort was 57.0 years, and 47.0% were male. Over a 3.7-year follow-up period, 9.7% of participants developed sarcopenia. Compared to non-smokers, smokers had a higher risk of developing sarcopenia (HR: 1.27, 95% CI 1.02-1.59). Among individuals with a smoking duration exceeding 40 years, the likelihood of developing sarcopenia was 39.0% higher (HR: 1.39, 95% CI 1.08-1.79). The elevated risk persists regardless of alcohol consumption. Quitters had a lower risk of sarcopenia compared to current smokers (HR: 0.67, 95% CI 0.47-0.97). Individuals who had quit smoking for > 4 years had a lower risk of sarcopenia compared to current smokers (HR: 0.43, 95% CI 0.24-0.78).

Current smokers face a higher risk of sarcopenia, especially those with a prolonged smoking history. Promoting smoking cessation is an essential strategy for lowering the risk of sarcopenia and mitigating its burden among smokers.

The world is currently grappling with the potentially life-threatening coronavirus disease 2019 (COVID-19), marking it as the most severe health crisis in the modern era. COVID-19 has led to a pandemic, with the World Health Organization (WHO) predicting that individuals with diabetes are at a higher risk of contracting the virus compared to the general population. This review aims to provide a practical summary of the long-term impacts of COVID-19 on patients with diabetes. Specifically, it focuses on the effects of SARS-CoV-2 on different types of diabetic patients, the associated mortality rate, the underlying mechanisms, related complications, and the role of vitamin D and zinc in therapeutic and preventive approaches.

Relevant literature was identified through searches on PubMed, Web of Science, and Science Direct in English, up to April 2023.

COVID-19 can lead to distressing symptoms and pose a significant challenge for individuals living with diabetes. Older individuals and those with pre-existing conditions such as diabetes, coronary illness, and asthma are more susceptible to COVID-19 infection. Managing COVID-19 in individuals with diabetes presents challenges, as it not only complicates the fight against the infection but also potentially prolongs the recovery time. Moreover, the virus may thrive in individuals with high blood glucose levels. Various therapeutic approaches, including antidiabetic drugs, are available to help prevent COVID-19 in diabetic patients.

Diabetes increases the morbidity and mortality risk for patients with COVID-19. Efforts are globally underway to explore therapeutic interventions aimed at reducing the impact of diabetes on COVID-19.

Several studies were performed to evaluate the relationship between CBC and patients with diabetes mellitus (DM). In this review, we discussed the prognostic value of CBC parameters in DM patients.

English literature was searched and retrieved from the Google Scholar search engine and PubMed database (1980-2024). "Diabetes mellitus," "Blood cell count," "Mean platelet volume," "Leukocytes," and "Inflammation" were used as keywords.

DM increases vascular inflammation and oxidative stress, while vascular inflammation affects erythropoiesis and red blood cell deformation, thus increasing red cell distribution width (RDW). Mean platelet volume (MPV) is another useful prognostic biomarker for DM patients. Additionally, elevated neutrophil-lymphocyte ratio (NLR) levels are associated with poor glycemic control in T2DM patients, so it can be used as a screening tool in diabetic follow-up.

RDW can be used as a valuable independent biomarker to assess the prognosis of patients with DM. MPV can also be used as a noninvasive, widely available, and low-cost marker as a key factor as well as a Prognostic/diagnostic biomarker that could be used for DM patients. Total white blood cell count, NLR, Mean platelet volume lymphocyte ratio (MPVLR), and monocyte to high-density lipoprotein ratio (MHR) are valuable biomarkers in predicting DM.

The COVID-19 pandemic has posed unprecedented challenges to global public health, especially for pregnant women and their offspring. However, little is known about the impact of maternal SARS-CoV-2 infection on neonatal outcomes, particularly in the context of coexisting gestational diabetes mellitus (GDM).

Hospitalized pregnant women with SARS-CoV-2 infection were retrospectively enrolled between November 2022 and January 2023, and matched with pregnant subjects free of SARS-CoV-2 infection based on their propensity scores. All women were tested for SARS-CoV-2 upon admission as part of routine procedures, then divided into groups of pregnant women with SARS-CoV-2 infection and GDM (SARS2+GDM), pregnant women with SARS-CoV-2 infection but without GDM (SARS2+noGDM), and pregnant women without SARS-CoV-2 infection or GDM (Normal group). A logistic regression model was used to study the risk of GDM, perinatal SARS-CoV-2 infection, and their interaction on neonatal SARS-CoV-2 infection.

Of 378 pregnant women with SARS-CoV-2 infection, the neonatal infection rate was higher in the GDM group as compared to the SARS-CoV-2 infection only group, but both SARS-CoV-2 infection rates were lower than that of the normal control group. Logistic regression analysis identified an interaction between maternal SARS-CoV-2 infection and GDM on neonatal infection, where maternal SARS-CoV-2 infection (odds ratio [OR] = 0.31, 95%CI: 0.22-0.44) and vaccination for anti-SARS-CoV-2 (OR = 0.70, 95%CI: 0.50-0.98) were associated with lower odds of neonatal infection, while higher pre-pregnancy body mass index (BMI) (OR = 1.06, 95% CI: 1.02-1.10) and GDM (OR = 1.97, 95%CI: 1.21-3.21) were associated with higher odds of neonatal infection.

We demonstrate that the coexistence of GDM and perinatal SARS-CoV-2 infection was associated with an increased probability of neonatal SARS-CoV-2 infection.

Diabetes mellitus (DM) is recognized and classified as a group of conditions marked by persistent high blood glucose levels. It is also an inflammatory condition that may influence concurrent disease states, including Coronavirus Disease 2019 (COVID-19). Currently, no effective drug has been found to treat COVID-19, especially in DM patients. Many herbal medicines, such as the well-known Andrographis paniculata, have been explored as drugs and complementary therapies due to their antidiabetic, antibacterial, antiviral, anti-inflammatory, and immunomodulatory effects. This study aimed to examine the potential of herbal medicines as complementary therapy in DM patients with COVID-19 complications, drawing from in-vitro and in-vivo investigations. This study analyzed articles published within the last 15 years using keywords including "herbal medicines", "COVID-19", "Diabetes Mellitus", "antidiabetics", "antiviral", and "anti-inflammatory". The results showed that several herbal medicines could serve as complementary therapy for DM patients with COVID-19 complications. These include Andrographis paniculata, Ageratum conyzoides, Artocarpus altilis, Centella asiatica, Momordica charantia, Persea gratissima, Phyllanthus urinaria, Physalis angulata, Tinospora cordifolia, and Zingiber zerumbet. Herbal medicines may serve as a complementary therapy for DM patients with COVID-19, but these claims need experimental validation in infection models and among affected patients.

To investigate determinants of new onset diabetes after COVID-19 (NODAC) and its recovery at 6 months.

This was an observational follow up study conducted from August, 2020 to July, 2023, recruiting patients with preexisting DM and COVID 19 patients with no history of DM. Multivariate regression analysis was used to determine the factors responsible for severity of COVID 19 infection in preexisting DM group. Clinical, laboratory and glycometabolic parameters were estimated at baseline and 6 months in NODAC and euglycemic group to determine the factors responsible for NODAC and its persistence at 6 months.

Of 1310 patients, 855 (65.3%) COVID 19 patients were further divided based on their glycemic status: preexisting DM (19%), NODAC (8.5%) and euglycemia (72.5%). Older age and male gender were independent risk factors for severe COVID 19 disease in patients with preexisting diabetes. Prevalence of NODAC in present study was 8.5%. Patients with NODAC had higher mean fasting blood glucose (FBG), random blood glucose (RBG) and HbA1c at baseline as compared to COVID with euglycemic group with no difference in serum C-peptide levels. Female gender, family history of DM, signs of insulin resistance, higher BMI, WHR, HbA1c, serum insulin levels, FBG and RBG predicted persistence of NODAC at 6 months.

Preexisting DM is a risk factor for severe COVID 19 disease. Patients with NODAC have evidence of persistence insulin resistance on follow up, underscoring the need for long term glycemic monitoring.

Traditional Chinese medicine (TCM) has developed a rich theoretical system and practical experience in fighting to infectious diseases over the past thousands of years, and has played an important role in controlling the spread owing to its unique advantages. In particular, its significant contribution to the prevention and control of Corona Virus Disease 2019 (COVID-19) is widely recognized. COVID-19 infection is mainly non-severe with a favorable overall outcome, but patients with comorbidities tend to have a poor prognosis. However, a comprehensive review of TCM for preventing and treating COVID-19 with comorbidities across various systems is still lacking. Hence, this scoping review aims to conduct a comprehensive investigation on treatment outcome of TCM for treating COVID-19 with comorbidities across various systems.

The scoping review was conducted by searching English databases including PubMed and Web of Science, and Chinese databases including China National Knowledge Infrastructure and Wanfang between January 2020 and January 2024. We followed the inclusion and exclusion criteria to identify relevant literature. Information for inclusion in the literature were subsequently extracted and consolidated.

We enrolled 13 literature that met the inclusion criteria in the review finally. Our analysis revealed that research on COVID-19 with comorbidities was mostly focused on circulatory diseases, including hypertension, heart failure, and cerebrovascular diseases, most common comorbidities were hypertension. Followed by endocrine and metabolic diseases such as diabetes, respiratory diseases including pulmonary tuberculosis and chronic obstructive pulmonary disease have been also addressed. However, there were few studies on co-infectious urogenital system disease, and no studies on the rheumatic, immune, hematological, nervous, reproductive, and skin systems diseases. Based on existing studies, TCM has significantly improved the clinical symptoms of COVID-19 with comorbidities such as fever, fatigue, dry cough, anorexia and asthma, the absorption of lung lesions, shortened the duration of viral shedding and the course of disease.

TCM has great application prospects in treating COVID-19 with comorbidities. These findings could provide important evidence for clinicians to treat COVID-19 with comorbidities. Multi-center studies are required to confirm our results in the future.

Background: Understanding the interactions between age and comorbidities is crucial for assessing COVID-19 mortality, particularly in patients with cardiac and pulmonary conditions. This study investigates the relationship between comorbidities and mortality outcomes in a cohort of hospitalized COVID-19 patients, emphasizing the interplay of age, cardiac, and pulmonary conditions. Methods: We analyzed a cohort of 3005 patients hospitalized with COVID-19 between 2020 and 2022. Key variables included age, comorbidities (diabetes, cardiac, pulmonary, and neoplasms), and clinical outcomes. Chi-square tests and logistic regression models were used to assess the association between comorbidities and mortality. Stratified analyses by age, diabetes, and pulmonary conditions were conducted to explore interaction effects. Additionally, interaction terms were included in multivariable logistic regression models to evaluate the combined impact of age, comorbidities, and mortality. Results: Cardiac conditions such as hypertension, ischemic cardiopathy, and myocardial infarction showed significant protective effects against mortality in younger patients and in those without pulmonary conditions (p < 0.001). However, these protective effects were diminished in older patients and those with pulmonary comorbidities. Age was found to be a significant modifier of the relationship between cardiac conditions and mortality, with a stronger protective effect observed in patients under the median age (p < 0.001). Pulmonary comorbidities significantly increased the risk of mortality, particularly when co-occurring with cardiac conditions (p < 0.001). Diabetes did not significantly modify the relationship between cardiac conditions and mortality. Conclusions: The findings highlight the complex interactions between age, cardiac conditions, and pulmonary conditions in predicting COVID-19 mortality. Younger patients with cardiac comorbidities show a protective effect against mortality, while pulmonary conditions increase mortality risk, especially in older patients. These insights suggest that individualized risk assessments incorporating age and comorbidities are essential for managing COVID-19 outcomes.

The impact of COVID-19 lockdown on glycemic control in patients with diabetes mellitus (DM) remains unclear. This study aimed to investigate the effects of lockdown measures on the metabolic parameters of patients with DM, with particular emphasis on geriatric populations. In this retrospective, longitudinal cohort study, 1224 patients were analyzed. Three periods were identified to examine the effects of the lockdown: pre-lockdown, lockdown, and post-lockdown. Each period spanned 1-year. Within each 1-year period, at least 2 measurements were taken at least 3 months apart, and their arithmetic mean was calculated. Only patients who presented to the hospital for DM management during all 3 periods were included in the study. While HbA1c levels significantly increased in patients over 65 years old during the lockdown period (P = .017), we observed a significant decrease in HbA1c levels in patients under 65 years old (P = .014). Upon further stratification of patients over 65 by age groups, HbA1c levels increased the most among those aged 75 to 85 years, with a significant rise also observed in those aged 65 to 75 years during the lockdown. However, there was no change in HbA1c levels for patients over 85 years old during the lockdown. These findings highlight the need for careful monitoring of elderly patients with DM during lockdown periods, facilitated via home care or telehealth services. Structured diet and exercise programs should also be provided for at home adherence.

To examine inpatient COVID-19-related outcomes among patients with and without diabetes alone or with a history of established heart failure (HF) or established atherosclerotic cardiovascular disease (ASCVD).

Observational study; longitudinal analysis of registry data.

Hospitals in the USA reporting to the American Heart Association (AHA) COVID-19 Registry from January 2020 to May 2021.

20 796 individuals with diabetes (11 244 men; mean age 64.2) and 30 798 without diabetes (15 980 men; mean age 59.0) hospitalised for COVID-19 in the USA.

Primary outcome measures were all-cause mortality, inpatient major adverse cardiovascular events (MACE) and/or inpatient mechanical ventilation. Secondary outcome measures included the association with diabetes and these outcomes among those with and without pre-existing ASCVD and HF and the association with insulin use and these outcomes in patients hospitalised for COVID-19.

After adjustment for relevant covariates diabetes increased the risk of mortality (HR 1.12, 95% CI: 1.03 to 1.21), MACE (HR 1.32, 95% CI: 1.17 to 1.48) and mechanical ventilation (HR 1.33, 95% CI: 1.26 to 1.42). Among patients with established ASCVD or HF, diabetes did not modify the risk of adverse outcomes. There was a significant difference in the risk of mortality between patients taking insulin compared with those who were not (HR 1.32, 95% CI: 1.01 to 1.26); however, there was no difference in the risk of MACE or mechanical ventilation.

Diabetes was associated with a higher risk of in-hospital all-cause mortality, MACE and need for mechanical ventilation in patients hospitalised for COVID-19. Diabetes was independently associated with adverse outcomes, particularly among those without pre-existing cardiovascular disease.

Patients with type 2 diabetes (T2D) are more susceptible to severe respiratory viral infections, but the underlying mechanisms remain elusive. Here, we show that patients with T2D and coronavirus disease 2019 (COVID-19) infections, and influenza-infected T2D mice, exhibit defective T helper 1 (Th1) responses, which are an essential component of anti-viral immunity. This defect stems from intrinsic metabolic perturbations in CD4+ T cells driven by hyperglycemia. Mechanistically, hyperglycemia triggers mitochondrial dysfunction and excessive fatty acid synthesis, leading to elevated oxidative stress and aberrant lipid accumulation within CD4+ T cells. These abnormalities promote lipid peroxidation (LPO), which drives carbonylation of signal transducer and activator of transcription 4 (STAT4), a crucial Th1-lineage-determining factor. Carbonylated STAT4 undergoes rapid degradation, causing reduced T-bet induction and diminished Th1 differentiation. LPO scavenger ameliorates Th1 defects in patients with T2D who have poor glycemic control and restores viral control in T2D mice. Thus, this hyperglycemia-LPO-STAT4 axis underpins reduced Th1 activity in T2D hosts, with important implications for managing T2D-related viral complications.

Checkpoint inhibitor-related pneumonitis (CIP) is a rare but serious complication of immune checkpoint inhibitors (ICIs). While it typically occurs within the first few months of treatment, its onset after ICI discontinuation is relatively uncommon. This report presents a case of CIP occurring 2.5 months after cessation of pembrolizumab and reviews the existing literature on CIP after discontinuation of ICIs.

A 77-year-old female with stage IV right lung adenocarcinoma (T4N2M1a) developed pneumonitis 2.5 months after discontinuation of pembrolizumab (following 26 months of initial treatment). Initially suspected as community-acquired pneumonia, the patient received antiviral and antibiotic therapy with progressive deterioration. Microbiological investigations yielded negative results, and consultation suggested lung cancer recurrence. PET-CT revealed heightened metabolic activity in the lungs. Percutaneous lung biopsy demonstrated organizing pneumonia, and NGS testing of biopsy tissue showed no pathogenic organisms. Combined with CT findings and the patient's history of pembrolizumab use, the diagnosis of checkpoint inhibitor-related pneumonitis (CIP) was established. Short-term steroid therapy resulted in significant improvement.

Integration of clinical presentation, imaging findings, and medication history is crucial for diagnosis. This case underscores the need for vigilance for CIP even after discontinuing ICI therapy. Although this report provides insights into CIP after discontinuation based on a single case and a literature review, further studies involving larger cohorts are warranted to better understand the post-treatment risk of CIP.

Diabetic nephropathy (DN) and obesity bring a huge burden to society. Obesity plays a crucial role in the progression of type 2 DN, but the pathophysiology remains unclear. Thus, we aimed the explore the association between type 2 DN and obesity using bioinformatics method. The gene expression profiles of type 2 DN (GSE96804) and obesity (GSE94752) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with the thresholds defined as |log2FC| ≥1 and P<0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Subsequently, a protein-protein interaction network was constructed based on the STRING database. Hub genes were identified, and the co-expression network was constructed. Finally, the hub genes were verified in clinical samples of 24 patients by immunohistochemistry. A total of 17 common DEGs were identified. Finally, two overlapping hub genes were identified (CCL18, C1QC). C1QC has been verified in clinical specimens. Using bioinformatics methods, the present study analyzed the common DEGs and the potential pathogenic mechanisms involved in type 2 DN and obesity. C1QC was the hub gene. Further studies are needed to clarify the specific relationships among C1QC, type 2 DN and obesity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of coronavirus disease 2019 (COVID-19). Severe and fatal COVID-19 cases often display cytokine storm i.e. significant elevation of pro-inflammatory cytokines and acute respiratory distress syndrome (ARDS) with systemic hypoxia. Understanding the mechanisms of these pathogenic manifestations would be essential for the prevention and especially treatment of COVID-19 patients. Here, using a dual luciferase reporter assay for hypoxia-response element (HRE), we initially identified SARS-CoV-2 nonstructural protein 5 (NSP5), NSP16, and open reading frame 3a (ORF3a) to upregulate hypoxia-inducible factor-1α (HIF-1α) signaling. Further experiments showed NSP16 to have the most prominent effect on HIF-1α, thus contributing to the induction of COVID-19 associated pro-inflammatory response. We demonstrate that NSP16 interrupts von Hippel-Lindau (VHL) protein interaction with HIF-1α, thereby inhibiting ubiquitin-dependent degradation of HIF-1α and allowing it to bind HRE region in the IL-6 promoter region. Taken together, the findings imply that SARS-CoV-2 NSP16 induces HIF-1α expression, which in turn exacerbates the production of IL-6.

To investigate possible associations of glucose patterns with outcomes of Corona Virus Disease 19 (COVID-19) using continuous glucose monitoring (CGM) in 43 patients hospitalized for COVID-19 mild-to-moderate pneumonia, regardless of diabetes.

Prospective observational study conducted during two pandemic waves in 2020-2021. Glucose sensor metrics of 7-day recording were obtained from blinded CGM. Respiratory function was evaluated as arterial partial pressure of oxygen (PaO2) to fraction of inspired oxygen (FiO2) ratio (PaO2:FiO2).

PaO2:FiO2 ratio was positively correlated with time in tight range (TITR) 70-140 (r = 0.49, p < 0.001) and time in range (TIR) 70-180 (r = 0.32, p < 0.05), and negatively correlated with average glucose (r =- 0.31, p < 0.05), coefficient of glucose variation (CV) (r =- 0.47, p < 0.01) and time above range (TAR) > 140 (r =- 0.49, p < 0.001). No relations were observed with HbA1c. Multivariate regression analysis showed that normal respiratory function at time of CGM removal correlated positively with TITR 70-140 mg/dL (p < 0.01), negatively with CV and TAR > 140 mg/dL (both p < 0.05) and not with TIR 70-180 and average glucose.

Lower glucose variability and optimal glucose control, expressed as CV and TITR, are CGM metrics predictive of a better prognosis in COVID-19 patients with pneumonia.

Diabetes mellitus significantly increases the risk of severe respiratory virus disease like influenza and COVID-19. Early evidence suggests that this susceptibility to respiratory viral disease is driven by glycemic variability, rather than average blood glucose levels. Here, we use blood samples and constant glucose monitoring (CGM) data obtained from people living with type 1 diabetes (T1D) to determine the effects of glycemic variability on the ex vivo T cell response to influenza virus. We show that high glycemic variability in participants living with T1D is associated with a reduced proportion of CD8+CD107a-IFNγ-MIP1β-TNF+ T cells in response to stimulation with influenza virus and an influenza virus peptide pool. Thus, this study provides evidence that glycemic variability affects the ex vivo T cell response to respiratory viruses. These data suggest that monitoring glycemic variability may have important implications in understanding the antiviral immune response in people with diabetes.

Objective This study aimed to evaluate the role of 2-Deoxy-D-Glucose (2-DG) in moderate to severe Coronavirus Disease 2019 (COVID-19) cases. Methodology This study retrospectively analyzed the effects of 2-DG alongside Standard of Care (SOC) for moderate to severe COVID-19 in 150 patients. Eligible patients were aged 18-65, with confirmed COVID-19, who met clinical criteria for moderate or severe illness. Data collected included demographics, clinical status, treatment details, and outcomes, evaluated using the WHO's 10-point scale. The primary outcome measured was time to clinical improvement, with secondary outcomes including duration of oxygen supplementation, length of hospital stay, and viral clearance. Data analysis employed the Cox proportional hazard model, with significance at p < 0.05. Results In the study, initial oxygen saturation levels upon admission were similar between groups, averaging 92.6% in the 2-DG with SOC group and 91.8% in the SOC-only group (p = 0.97). The WHO ordinal scores, pulse, and respiratory rates improved significantly in the 2-DG group across multiple intervals. Oxygen supplementation needs to be decreased notably, with 2-DG patients requiring an average of 5.1 L/min by Day 5, showing significant reductions compared to the SOC group. The time to clinical improvement and length of hospital stay were also shorter in the 2-DG group (5.2 days vs. 7.5 days; 8.5 days vs. 10.5 days, respectively; p < 0.001). Adverse events were less frequent in the 2-DG group (6.7% vs. 13.3%, p = 0.03). Conclusion In conclusion, 2-DG demonstrates significant efficacy as an adjunct therapy for moderate to severe COVID-19, reducing both time to clinical improvement (5.2 vs. 7.5 days, p < 0.001) and hospital stay duration. Additionally, fewer adverse events were reported, and viral clearance rates were higher in the 2-DG group. These findings highlight 2-DG's potential to improve clinical outcomes in COVID-19 care.

Introduction The COVID-19 pandemic emerged globally in 2019, exposing healthcare vulnerabilities. This study delves into the impact of COVID-19 on cancer patients, a high-risk group with increased susceptibility and mortality rates. Recent research underscores cancer patients' vulnerability to severe disease, often due to compromised immunity. Materials and methods This retrospective study analyzed data from 474 adult COVID-19 patients, admitted between March 2020 and July 2023. Patients were categorized into two groups: those with a medically recorded oncological disease (237) and those without any malignant history (237). Demographic and hematologic analysis aim to unveil COVID-19 impact on individuals with cancer history. Results Statistically significant differences in blood parameters highlighted distinctions, with cancer patients exhibiting higher creatinine, leukocyte, and D Dimers levels as well as lower hemoglobin, neutrophile, lymphocyte, and Serum Glutamate-Pyruvate Transaminase (SGPT) levels. Non-significant differences in certain parameters prompted a nuanced exploration of metabolic and coagulation variations. Conclusion This study unveils global COVID-19 effects on cancer patients, emphasizing clinical and laboratory differences. Findings underscore the imperative need for targeted interventions and enhanced support for cancer patients during the pandemic. Study limitations stress careful interpretation, urging further exploration of COVID-19 and cancer interplay.