Clinical characteristics and Outcomes of Diabetic Ketoacidosis in Patients with Type 2 Diabetes During Acute Systemic Stress

Clinical characteristics and Outcomes of Diabetic Ketoacidosis in Patients with Type 2 Diabetes During Acute Systemic Stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical characteristics and Outcomes of Diabetic Ketoacidosis in Patients with Type 2 Diabetes During Acute Systemic Stress Rania Al Sayed Abd Albaky Mohamed, Laila Mahmoud Ali Hendawy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8988395/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Many reports indicate that diabetes is one of the main risk factors for COVID-19 complications. Nevertheless, few studies have examined how DKA develops in T2DM patients who have SARS-CoV-2 infection. Objectives This study aimed to assess β-cell function, identify risk factors for DKA, and evaluate clinical outcomes in hospitalized patients with T2DM and COVID-19. Methods A retrospective, single-center, case–control study was conducted at Ain Shams University Isolation Hospital from August 2021 to August 2022. The study included 70 adults with T2DM and confirmed COVID-19, categorized into two groups: 35 patients with DKA (cases) and 35 patients without DKA (controls). Clinical, laboratory, and outcome data were extracted from medical records. Results Fasting C-peptide levels did not differ significantly between the DKA and non-DKA groups (median difference: −0.06 ng/mL, 95% CI − 0.28 to 0.16; p = 0.363), suggesting that absolute insulin deficiency was not the primary driver of DKA in this cohort. The DKA group was significantly older (mean difference: 9.8 years, 95% CI 4.2 to 16.0; p = 0.003) and had a longer median diabetes duration (median difference: 3.7 years, 95% CI 1.3 to 6.1; p = 0.009). These patients also presented increased levels of inflammatory and stress markers, including D-dimer (mean difference: 0.21 ng/mL, 95% CI 0.05 to 0.37; p = 0.020) and HOMA-IR (median difference: 1.93, 95% CI 0.45 to 3.60; p = 0.012). Mortality was significantly greater in the DKA group (22.9% vs. 0%, risk difference 22.9%, 95% CI 8.4% to 37.4%; p = 0.003). Conclusion In patients with T2DM and COVID-19, DKA was not characterized by absolute insulinopenia but was associated with older age, longer diabetes duration, severe insulin resistance, and systemic inflammation. These factors contribute to significantly increased morbidity and mortality. Our findings highlight the multifactorial nature of DKA in this setting and underscore the importance of aggressive monitoring and management in high-risk patients. Diabetic ketoacidosis COVID-19 Type 2 diabetes Mortality Insulin resistance Risk factors INTRODUCTION Diabetes mellitus has been associated with poor outcomes in patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 ) ( 1 ). COVID-19 may cause new-onset diabetes or uncover diabetes that was previously undiagnosed ( 2 ). Patients with diabetes have a greater risk of infection and acute respiratory distress syndrome than the general population does ( 3 ). While DKA is traditionally linked to absolute insulin deficiency in type 1 diabetes(T1D), its occurrence in T2DM patients during acute illness suggests a distinct pathophysiology driven by severe insulin resistance, inflammatory stress, and relative insulin deficiency ( 4 , 5 ). Longitudinal data indicate a significant increase in the incidence of newly diagnosed T2DM during the pandemic, which is attributed to direct viral effects, systemic inflammation, glucocorticoid therapy, and pandemic-related lifestyle disruptions ( 6 , 7 ). SARS-CoV-2 may infect pancreatic β-cells via ACE2 receptors, potentially impairing insulin secretion ( 8 , 27 ). Concurrently, the cytokine storm and metabolic stress associated with severe infection can exacerbate insulin resistance and precipitate hyperglycemic crises in susceptible individuals. ( 9 ) Despite these associations, the specific risk factors, β-cell function, and outcomes of DKA in T2DM patients with COVID-19 remain incompletely characterized, particularly in relation to preserved β-cell function and the interplay between metabolic reserve and inflammatory stress. This study aimed to evaluate these aspects by comparing T2DM patients with COVID-19 who developed DKA to those who did not. Objectives : This study aimed to assess β-cell function, identify risk factors for DKA, and evaluate clinical outcomes in hospitalized patients with T2DM and COVID-19. PATIENTS AND METHODS Study Design and Setting: The study protocol received retrospective approval from the Institutional Review Board of Ain Shams University Faculty of Medicine (Approval No. FMASU MSO 27/2024( for analysis of anonymized clinical data collected between August 2021 and August 2022. The need for informed consent was waived per institutional policy for retrospective audit-based studies. Patients: The study included 70 adult patients (≥ 18 years) with a confirmed diagnosis of T2DM and laboratory-confirmed SARS-CoV-2 infection by RT–PCR. Patients were divided into two groups: · Group 1 (Cases) Thirty-five patients who developed DKA during hospitalization. · Group 2 (Controls) Thirty-five patients with T2DM and COVID-19 who did not develop DKA. DKA was diagnosed according to standard criteria: blood glucose > 200 mg/dL, arterial pH < 7.3, serum bicarbonate < 15 mEq/L, and the presence of ketonuria ( 10 ). Ketones were measured in urine using the nitroprusside reaction test (Acetest® reagent strips, Siemens Healthineers). Blood β-hydroxybutyrate measurement was not routinely available. Euglycemic DKA (blood glucose ≤ 200 mg/dL) was excluded,, which may limit generalizability to contemporary presentations but ensures specificity for hyperglycemic DKA in this analysis. T2DM was diagnosed on the basis of the American Diabetes Association criteria ( 11 ). The exclusion criteria included age < 18 years, type 1 diabetes, end-stage organ failure. Sodium-glucose cotransporter-2 inhibitor use was verified via medication reconciliation from admission records; no patients were using these medications at admission. Controls (non-DKA patients) were consecutively selected from the same hospital during the study period, fulfilling the inclusion/exclusion criteria but without a diagnosis of DKA. Sample size The sample size was calculated via G*Power software (version 3.1) on the basis of a previous study reporting a 15% prevalence of DKA among hospitalized diabetic patients with COVID-19 ( 12 ). With an α error of 0.05 and a power of 80%, a minimum of 35 patients per group was needed. This provides adequate power for primary clinical outcomes (e.g., mortality) but may be underpowered to detect subtle differences in β-cell function markers. Methods: All participants underwent the following procedures: History taking (either from the patients or patients’ sheets) : Personal history: age; sex; residency; special habits such as smoking; medical history: diabetes (duration, medications, diabetic complications); hypertension; cardiac, hepatic, and renal diseases; family history of diabetes; diabetic complications; and COVID-19 infection. Clinical examination body weight, height, body mass index, and arterial oxygen saturation were measured via pulse oximetry. Chest radiology (at baseline and when requested during follow-up): Chest X-ray, noncontrast CT. Chest CT severity was assessed using a semi-quantitative scoring system ranging from 0 to 5. The scoring scales are based on the areal size of involvement; and the percentages and scores are designated as follows: 0 = 0% involvement; 1 = 1%–25% involvement; 2 = 26%–50% involvement; 3 = 51%–75% involvement; and 4 = 75%–100% involvement Laboratory investigations Blood samples for key analyses (fasting glucose, C-peptide, insulin, inflammatory markers, arterial blood gases, and electrolytes) were drawn within 24 hours of admission prior to the initiation of intravenous insulin therapy for DKA patients. This timing was chosen to assess baseline metabolic status at the point of peak acute stress C-peptide and insulin were measured by radioimmunoassay using commercial kits (C-Peptide IRMA, DiaSorin; Insulin IRMA, Beckman Coulter). Reference ranges: C-peptide 1.1–4.4 ng/mL, insulin 2.6–24.9 µU/mL. The homeostatic model assessment for insulin resistance (HOMA-IR) score was calculated as [fasting insulin (µU/mL) × fasting glucose (mg/dL)] / 405. The homeostatic model assessment for beta-cell function (HOMA-B) score was calculated as [20 × fasting insulin (µU/mL)] / [fasting glucose (mg/dL) − 63]. The HOMA-IR and HOMA-B indices were originally developed for use in metabolically stable outpatient populations. Their application in acutely ill, hospitalized patients—particularly those with systemic inflammation, hypoxia, and stress hormone excess—has important limitations. Therefore, the HOMA indices in this study should be interpreted as surrogate markers rather than precise measures of insulin resistance or β-cell function. Management of COVID- 19 All patients were managed according to the national guidelines adopted by the Egyptian Ministry of Health. A national protocol for COVID-19 treatment was published first in May 2020 by the Egyptian Ministry of Health & Population, which underwent several modifications according to recent worldwide guidelines. Antibiotics were prescribed for clinical suspicion of bacterial infections as per our center's antibiotic policy. All patients received anticoagulation (either enoxaparin or direct oral anticoagulants) unless contraindicated. Mild cases did not receive antiviral therapy, mild to moderate cases received favipiravir, and remdesivir was prescribed for those who did not respond well to favipiravir after 3 days. Patients with moderate to severe cases received remdesivir from the beginning of the study. The typical dose of favipiravir was 1600 mg/12 hours on the 1st day and 600 mg/12 hours for 4 days. The typical dose of remdesivir was 200 mg on the 1st day, followed by 100 mg/day for 4 days. Ivermectin (12 mg twice daily for 5 days) was added as a second antiviral in a few cases. Patients with moderate-to-severe disease received steroids in the form of 80 mg/day methylprednisolone for 3–5 days. Oxygen therapy via an oxygen mask or reservoir oxygen mask, a high-flow nasal cannula, continuous positive airway pressure (CPAP) and mechanical ventilation was used according to the clinical situation and case progression ( 13 , 14 ). Quantitative data on specific pharmacologic treatments (e.g., corticosteroid dosing schedules, precise oxygen flow rates) and detailed respiratory support modalities were not systematically recorded in the clinical database available for this retrospective analysis. Consequently, these variables could not be included in the comparative analysis. This limitation is acknowledged as a critical potential source of confounding, as treatment intensity may reflect and influence both COVID-19 severity and metabolic decompensation. The study therefore focuses on characterizing the phenotype and outcomes of DKA in this setting, while explicitly acknowledging this fundamental constraint on causal inference. Management of diabetic ketoacidosis (DKA) DKA patients were managed with IV fluid, electrolyte abnormality correction, insulin therapy (IV continuous infusion) and IV sodium bicarbonate if the pH was less than or equal to 7. Statistical analysis Data were analyzed using SPSS version 27. Continuous variables are presented as mean ± standard deviation or median (interquartile range), as appropriate, and compared using Student's t-test or Mann–Whitney U test. Categorical variables are presented as frequencies (%) and compared using chi-square or Fisher's exact test. Effect sizes with 95% confidence intervals (CI) are reported for all key comparisons. To account for potential renal confounding of C-peptide, a linear regression model was constructed with C-peptide as the dependent variable and DKA status and serum creatinine as covariates. Multivariable adjustment was not performed due to the limited number of events and risk of overfitting.Missing data were minimal (< 5%) and handled by complete-case analysis. RESULTS The study included 70 patients with a mean age of 56.3 ± 13.9 years; 57.1% were female. The overall median diabetes duration was 8 years (IQR: 5–13). Hypertension was prevalent (68.6%). As shown in Table 1 , the DKA group was significantly older (61.1 vs. 51.4 years, mean difference 9.8 years, 95% CI 4.2 to 16.0; p = 0.003) and had a longer median diabetes duration (10 vs. 7 years, median difference 3.7 years, 95% CI 1.3 to 6.1; p = 0.009) than the non-DKA group. No significant differences were found in sex, BMI, or baseline blood pressure. The DKA group presented with higher heart rates, respiratory rates, and body temperatures (all p < 0.01), indicating greater clinical severity. ( Table 2 ) Metabolic and Laboratory Parameters: Fasting C-peptide levels were comparable between the DKA and non-DKA groups (median 1.99 vs 2.05 ng/mL), with a median difference of − 0.06 ng/mL (95% CI − 0.28 to 0.16). In contrast, insulin resistance was significantly higher in the DKA group, as reflected by higher HOMA-IR values (median difference 1.93; 95% CI 0.45–3.60). Glycemic control was poorer in the DKA group, with a mean HbA1c difference of 0.78% (95% CI 0.17–1.39). ( Table 3 ) After adjustment for serum creatinine, DKA status was not significantly associated with C-peptide levels (β 0.224 ng/mL; 95% CI − 0.140 to 0.587; p = 0.223).( Table 4 ) The DKA group presented markers of greater inflammatory and thrombotic burdens, including significantly higher D-dimer levels (mean difference 0.21 ng/mL, 95% CI 0.05 to 0.37; p = 0.020) and more profound lymphocytopenia (median difference − 940 cells/µL, 95% CI − 1358 to − 457; p < 0.01). Outcomes: The clinical outcomes are presented in Table 5 . In-hospital mortality was higher in the DKA group (22.9%) compared with the non-DKA group (0%), corresponding to an absolute risk difference of 22.9% (95% CI 8.4%–37.4%). Length of hospital stay was slightly shorter in the DKA group (mean difference − 1.4 days; 95% CI − 2.7 to − 0.1), likely reflecting early mortality. Risk Factor Analysis: Univariate logistic regression identified multiple factors associated with DKA, including age > 62 years, diabetes duration > 10 years, HbA1c > 7%, elevated HOMA-IR, and elevated D-dimer. The univariate associations should be interpreted as identifying potential risk factors rather than independent predictors. ( Table 6 ). Table 1 Baseline Characteristics of All Studied Patients (n = 70) No. = 70 Demographics Age (years) Mean ± SD 56.26 ± 13.89 Sex Female No. (%) 40 (57.1%) Male No. (%) 30 (42.9%) Diabetic duration (years) Median (IQR) 8 ( 5 – 13 ) Range 0.5–23 Weight (kg) Mean ± SD 89.41 ± 10.63 BMI (kg/m2) Mean ± SD 31.11 ± 3.39 Associated medical disorders Hypertension No. (%) 48 (68.6%) Cardiac diseases No. (%) 9 (12.9%) Acute kidney injury No. (%) 4 (5.7%) Chronic kidney disease No. (%) 3 (4.3%) Liver disease (Cirrhosis) 2 (2.8%) Chronic obstructive pulmonary disease No. (%) 5 (7.1%) Clinical data Systolic BP (mmHg) Mean ± SD 123.07 ± 17.60 Diastolic BP (mmHg) Mean ± SD 78.21 ± 9.33 SaO2 (%) Mean ± SD 92.29 ± 4.10 Notes: Data are presented as mean ± standard deviation (SD), median (interquartile range, IQR), or number (percentage, %) as appropriate. Table 2 Comparison of Clinical Characteristics Between DKA and Non-DKA Groups Variable Group 1 (n = 35) Group 2 (n = 35) Effect Size (95% CI) p-value Age (years) 61.1 ± 13.5 51.4 ± 12.7 9.8 (4.2 to 16.0) 0.003 Female, n (%) 20 (57.1%) 20 (57.1%) — 1.000 Diabetes duration (years) 10 ( 5 – 16 ) 7 ( 4 – 10 ) 3.7 (1.3 to 6.1) 0.009 Weight (kg) 91.3 ± 10.0 87.5 ± 11.1 3.7 (− 1.1 to 8.7) 0.142 BMI (kg/m²) 31.1 ± 3.3 31.2 ± 3.5 −0.06 (− 1.6 to 1.6) 0.937 Respiratory rate 22.8 ± 2.8 18.6 ± 5.3 4.2 (2.2 to 6.2) < 0.001 Temperature (°C) 38.2 ± 0.5 37.6 ± 0.7 0.6 (0.3 to 0.9) < 0.001 Heart rate (bpm) 93.3 ± 10.6 82.9 ± 9.5 10.3 (5.5 to 15.1) < 0.001 SpO₂ (%) 92.3 ± 5.1 92.3 ± 2.8 0.1 (− 1.5 to 1.6) 0.954 Notes: Continuous variables are expressed as mean ± SD or median (IQR). Between-group comparisons were performed using Student’s t-test for normally distributed variables and Mann–Whitney U test for non-normally distributed variables. Categorical variables were compared using chi-square or Fisher’s exact test. *Hodges–Lehmann median difference and 95% confidence interval are reported for non-normally distributed variables.CI = confidence interval. Table 3 Comparison of Laboratory Parameters Between DKA and Non-DKA Groups Parameter Group 1 (n = 35) Group 2 (n = 35) Effect Size (95% CI) p-value Hemoglobin (g/dL) 12.24 ± 1.74 13.00 ± 1.09 −0.76 (− 1.43 to − 0.11) 0.031 Leucocyte (×10⁹/L) 10.0 (7.1–15.7) 13.1 (9.3–15.6) −3.1 (− 5.9 to − 0.1) 0.042 Lymphocytes (×10⁹/L) 550 (258–900) 1490 (900–2150) −940 (− 1358 to − 457) < 0.001 FPG(mg/dL) 170.5 ± 27.6 138.7 ± 23.3 31.8 (20.2 to 44.9) < 0.001 HbA1c (%) 7.22 ± 1.74 6.44 ± 0.50 0.78 (0.17 to 1.39) 0.013 Fasting C-peptide (ng/mL) 1.99 (1.56–2.45) 2.05 (1.70–2.53) −0.06 (− 0.28 to 0.16) 0.363 HOMA-IR 5.33 (3.66–8.75) 3.40 (2.10–6.90) 1.93 (0.45 to 3.60) 0.012 HOMA-B 30.49 (21.69–43.35) 57.24 (33.16–89.06) -25.2(-42.22 to – 8.5) 0.004 D-dimer (ng/mL) 0.87 ± 0.44 0.66 ± 0.26 0.21 (0.05 to 0.37) 0.020 pH 7.31 ± 0.07 7.39 ± 0.03 −0.08 (− 0.11 to − 0.06) < 0.001 HCO₃ (mEq/L) 17.40 ± 3.31 23.68 ± 1.29 −6.28 (− 7.47 to − 5.10) < 0.001 Potassium (mmol/L) 3.61 ± 0.95 4.32 ± 0.66 −0.71 (− 1.11 to − 0.35) < 0.001 Notes: Data are presented as mean ± SD or median (IQR). Comparisons were made using Student’s t-test or Mann–Whitney U test as appropriate. Effect sizes are expressed as mean differences or Hodges–Lehmann median differences* with 95% CIs. FPG = fasting plasma glucose; HOMA-IR = homeostatic model assessment of insulin resistance. Table 4 Creatinine-Adjusted Analysis of Fasting C-peptide Predictor B (unstandardized) 95% Confidence Interval p-value DKA (yes vs no) 0.224 −0.140 to 0.587 0.223 Serum creatinine (mg/dL) 0.290 −0.067 to 0.647 0.109 Notes: Linear regression model with fasting C-peptide as the dependent variable. Model statistics: R² = 0.055, Adjusted R² = 0.023, ANOVA p = 0.185. Table 5 Clinical Outcomes Between DKA and Non-DKA Groups Outcome Group 1 (n = 35) Group 2 (n = 35) Effect Size (95% CI) p-value In-hospital mortality, n (%) 8 (22.9%) 0 (0%) 22.9% (8.4% to 37.4%) 0.003 Length of stay (days) 8.1 ± 3.1 9.5 ± 2.4 −1.4 (− 2.7 to − 0.1) 0.037 Notes: Mortality compared using Fisher’s exact test; length of stay compared using Student’s t-test. Effect sizes are risk difference (%) and mean difference with 95% CI. Table 6 Univariate Logistic Regression for Factors Associated with DKA Factor OR (95% CI) p-value Age > 62 years 5.74 (1.91–17.28) 0.002 Diabetes duration > 10 years 20.09 (2.45–164.64) 0.005 HbA1c > 7% 15.58 (3.23–75.18) 0.001 HOMA-IR > 3.5 5.74 (1.91–17.28) 0.002 D-dimer > 0.77 ng/mL 3.43 (1.25–9.40) 0.017 Notes: OR = odds ratio; CI = confidence interval. Univariate logistic regression was performed for each variable. DISCUSSION This retrospective case–control study provides critical insights into the pathogenesis, risk factors, and clinical outcomes of diabetic ketoacidosis (DKA) in patients with type 2 diabetes mellitus (T2DM) during acute SARS-CoV-2 infection. Our findings challenge the conventional paradigm of absolute insulin deficiency as the primary driver of DKA in this population and instead highlight a multifactorial pathophysiology involving severe insulin resistance, preexisting metabolic vulnerability, and systemic inflammatory stress. Preserved β-cell Function and Pathophysiological Implications The most significant finding of our study was the comparable fasting C-peptide levels between the DKA and non-DKA groups (median 1.99 vs. 2.05 ng/mL, p = 0.363), suggesting that absolute insulin deficiency—characteristic of type 1 diabetes-related DKA—was not the predominant mechanism in T2DM patients with COVID-19. This observation remained non-significant after adjustment for serum creatinine (β 0.224, p = 0.223), suggesting that group differences in renal function did not account for the lack of difference in C-peptide levels between groups. This aligns with the emerging concept of "ketosis-prone type 2 diabetes" (KPD), where patients with phenotypic T2DM develop DKA under conditions of extreme metabolic stress despite having preserved endogenous insulin secretory capacity ( 15 ). The pathophysiology appears to involve a dual insult: severe insulin resistance induced by the cytokine storm and counterregulatory hormone excess of COVID-19, coupled with relative insulin deficiency in the context of long-standing β-cell exhaustion ( 16 ). Our finding of significantly higher HOMA-IR in the DKA group (5.33 vs. 3.40, p = 0.012) supports this hypothesis, although we emphasize the limitations of HOMA-IR as a surrogate marker in this acutely ill cohort( 17 ). The preserved C-peptide levels must be interpreted cautiously. While they suggest adequate β-cell mass, they do not exclude functional β-cell impairment under acute stress. SARS-CoV-2 has been shown to infect pancreatic β-cells through ACE2 receptors, potentially causing transient dysfunction ( 18 ). Additionally, the timing of measurement is critical; our samples were drawn at admission before significant insulin therapy, but the dynamic nature of insulin secretion during evolving DKA could not be captured. Nonetheless, our data contribute to the growing evidence that DKA in T2DM patients during the COVID-19 pandemic represents a distinct entity from classic T1D DKA, with important therapeutic implications. Risk Factor Profile: Metabolic Vulnerability Meets Inflammatory Storm Univariate analysis showed that longer diabetes duration (> 10 years) was strongly associated with the development of DKA (OR: 20.09, 95% CI: 2.45–164.64, p = 0.005). This finding underscores the concept of “metabolic reserve” exhaustion, where patients with long-standing T2DM have diminished β-cell functional capacity to compensate for acute insulin resistance ( 19 ). The DKA group also exhibited poorer chronic glycemic control (HbA1c 7.22% vs. 6.44%, p = 0.013), which may reflect both disease severity and therapeutic inertia prior to hospitalization. The DKA group demonstrated a pronounced proinflammatory and prothrombotic state, with significantly elevated D-dimer (0.87 vs. 0.66 ng/mL, p = 0.020) and profound lymphocytopenia (median 550 vs. 1490 cells/µL, p < 0.01). These markers likely reflect greater overall COVID-19 severity than direct causative factors for DKA. The bidirectional relationship between hyperglycemia and inflammation is well established: hyperglycemia promotes a proinflammatory state through mitochondrial reactive oxygen species generation and advanced glycation end-product formation, whereas cytokines such as TNF-α and IL-6 directly induce insulin resistance ( 20 ). This creates a vicious cycle in which COVID-19-induced inflammation worsens hyperglycemia, which in turn amplifies the inflammatory response. Notably, emerging evidence suggests that anti-inflammatory interventions may mitigate metabolic dysregulation in patients with COVID-19. Observational studies indicate that aspirin use is associated with a reduced incidence of new-onset diabetes following SARS-CoV-2 infection, potentially through the modulation of NLRP3 inflammasome activity and platelet-mediated inflammation ( 21 , 22 , 38 ). While our study design did not allow assessment of the effects of aspirin, this represents a promising avenue for future intervention studies in high-risk populations. Clinical Outcomes and Mortality Determinants The notable difference in mortality between the groups (22.9% vs. 0%, p = 0.003) highlights the lethal synergy between metabolic decompensation and severe viral pneumonia. All fatalities in the DKA group occurred in patients requiring advanced respiratory support, suggesting that mortality resulted from the combined burden of DKA complications, hypoxemic respiratory failure, and thromboinflammatory sequelae rather than DKA alone. This mortality rate is consistent with previous reports; Stevens et al. ( 23 ) reported 36.9% mortality in COVID-19 patients with DKA versus 28.8% without DKA, whereas Beliard et al. ( 24 ) reported mortality rates approaching 50% in systematic reviews. The paradoxically shorter hospital stay in the DKA group (8.1 vs. 9.5 days, p = 0.037) likely reflects higher early mortality rather than more rapid recovery, a phenomenon observed in other studies of critical illness ( 25 ). This finding underscores the need for aggressive early intervention in DKA patients with COVID-19, as the window for effective treatment may be narrow. Our study contributes to the growing body of evidence that SARS-CoV-2 infection serves as a potent metabolic stress test, unmasking and exacerbating underlying dysglycemia. Longitudinal cohort studies have documented a significant increase in the incidence of new-onset type 2 diabetes during the pandemic ( 26 , 39 ). Multiple mechanisms likely contribute directly to viral damage to β-cells ( 18 ), inflammation-induced insulin resistance ( 20 ), glucocorticoid therapy ( 28 ), and pandemic-related lifestyle disruptions ( 29 ). Our findings, specifically in patients with preexisting T2DM, suggest that those with longer disease duration and poorer control are particularly vulnerable to this decompensation. COVID-19 as a Catalyst for Metabolic Decompensation The intersection of COVID-19, diabetes, and DKA exists within the broader context of CKM syndrome, which recognizes the interconnected pathophysiology of cardiovascular, kidney, and metabolic diseases ( 30 ). Emerging evidence suggests that SARS-CoV-2 infection may accelerate CKM progression through multiple pathways: endothelial dysfunction, persistent low-grade inflammation, and immune dysregulation ( 31 ). Furthermore, molecular analyses of long COVID suggest persistent viral reservoirs, autoimmunity, and chronic inflammation may contribute to new-onset diabetes, highlighting long-term pathobiological relationships ( 37 ). Patients who survive DKA during COVID-19 may represent a high-risk subgroup requiring intensified long-term CKM monitoring and management. Future studies should investigate whether acute metabolic decompensation during COVID-19 predicts accelerated progression of microvascular and macrovascular complications. This concern is magnified by data indicating the COVID-19 pandemic has significantly increased the global burden of type 2 diabetes ( 40 ). Comparison with Existing Literature Our demographic findings align with those of several previous studies. The older age and longer diabetes duration in our DKA group mirror observations by Goldman et al. ( 12 ) and Dell'Aquila et al. ( 32 ), who reported similar risk profiles. The absence of sex predominance in our study contrasts with some Western cohorts showing male predominance ( 23 , 32 ), possibly reflecting regional differences in diabetes epidemiology and healthcare access. Our laboratory findings corroborate previous reports of elevated inflammatory markers in DKA patients with COVID-19. Similarly, Mondal et al. ( 33 ) reported increased D-dimer levels in DKA patients, whereas Meza et al. ( 34 ) reported profound lymphocytopenia in a case series. The electrolyte abnormalities we observed, particularly hypokalemia, are consistent with Reddy et al.'s early case reports ( 35 ) and Kempegowda et al.'s comparative study ( 36 ). Strengths and Limitations The strengths of our study include its case–control design with carefully defined criteria, comprehensive laboratory assessment including β-cell function markers, and real-world management reflecting contemporary COVID-19 care. To our knowledge, this is the first study to systematically evaluate fasting insulin and C-peptide levels in patients with T2DM, COVID-19, and DKA, providing direct insights into the pathophysiological mechanisms underlying metabolic decompensation in this population. The inclusion of a control group of T2DM patients with COVID-19 but without DKA provides valuable comparative data lacking in many case series. Our findings challenge the traditional paradigm of absolute insulin deficiency in DKA and contribute to the evolving understanding of ketosis-prone diabetes phenotypes. However, several limitations must be acknowledged. First, theretrospective, single-center design introduces potential selection bias and limits generalizability. Second, the relatively small sample size, while adequate for primary outcomes, reduces statistical power for secondary and multivariable analyses and inflates the uncertainty around some effect estimates. Third, significant baseline differences existed between groups (age, diabetes duration); however, only descriptive and univariate analyses were performed, and residual confounding remains likely. Fourth, and most critically, the absence of detailed data on COVID-19-specific treatments (corticosteroid dosing, antiviral agents, intensity of respiratory support) represents a major unmeasured confounder. The DKA group presented with greater clinical severity, making it highly probable they received more intensive immunomodulatory and respiratory support. Since corticosteroids are potent inducters of insulin resistance and hyperglycemia, this confounding severely limits our ability to disentangle the independent contributions of DKA, severe COVID-19, and its treatment to the observed outcomes. This limitation fundamentally constrains causal inference; our results should be interpreted as characterizing a high-risk clinical phenotype rather than establishing independent causal pathways. Fifth, the cross-sectional assessment of β-cell function provides only a snapshot;longitudinal measurements would better characterize dynamic changes. Sixth, the diagnosis of DKA relied on urinary ketone testing without systematic serum β-hydroxybutyrate measurement, which may affect the precision of diagnosis and severity assessment. Seventh, the exclusion of euglycemic DKA, while ensuring specificity, limits the generalizability of our findings to the full spectrum of DKA presentations, particularly in the context of SGLT2 inhibitor use. Finally, the lack of longitudinal follow-up precludes assessment of post-discharge metabolic outcomes and long-term cardiovascular or renal sequelae in this high-risk population. Clinical Implications and Future Directions Our findings have several practical implications. First, they underscore the need for vigilant glucose monitoring in hospitalized T2DM patients with COVID-19, particularly those with longer disease durations and suboptimal control. Second, they suggest that management strategies should address both hyperglycemia and the underlying inflammatory state. Third, they highlight the importance of multidisciplinary care that integrates endocrinology, infectious disease, and critical care expertise. Future research should focus on several areas: prospective validation of our risk factors in larger multicenter cohorts; investigation of optimal insulin regimens for COVID-19-related DKA; assessment of anti-inflammatory agents (including aspirin) for metabolic protection; and longitudinal studies of CKM outcomes in survivors. Additionally, mechanistic studies exploring the interplay among SARS-CoV-2, insulin signaling pathways, and ketogenesis are needed to elucidate the pathophysiology fully. CONCLUSION In conclusion, our study demonstrated that DKA in patients with T2DM during COVID-19 represents a distinct clinical entity characterized by preserved insulin secretion but severe insulin resistance in the context of systemic inflammation. Longer diabetes duration emerged as the most consistently associated clinical characteristic among patients who developed DKA, highlighting the importance of chronic disease management in pandemic preparedness. The high mortality observed underscore the critical need for early recognition and aggressive management of this dual metabolic–infectious emergency. As we continue to navigate the long-term consequences of the pandemic, understanding these complex interactions will be essential for optimizing care for patients with diabetes facing acute systemic stressors. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE: The study protocol was approved by the Institutional Review Board of Ain Shams University Faculty of Medicine (Approval No. FMASU MSO 27/2024). The need for informed consent was waived by the committee due to the retrospective nature of the study, which involved the analysis of anonymized clinical data. Clinical Trial Number Not applicable. HUMAN AND ANIMAL RIGHTS: No animals were used in this study. All the procedures performed in this study involving human participants were in accordance with the ethical standards of the Institutional Research Committee and the 1975 Declaration of Helsinki as revised in 2013. Consent to Publish: Not applicable. This study involved retrospective analysis of anonymized data, and no identifiable patient information is included. FUNDING: None. Author Contribution Rania Al Sayed Abd Albaky Mohamed: Conceptualization, Investigation, Data Curation, Writing – Original Draft. Laila Mahmoud Ali Hendawy: Methodology, Validation, Formal analysis, Writing – Review & Editing. Eman Elsayed Mahmoud Farghaly: Methodology, Resources, Visualization, Writing – Review & Editing. Amr Mahmoud Mohamed Abd El Hady Saleh: Conceptualization, Supervision, Project Administration, Formal analysis, Writing – Review & Editing, Final approval Acknowledgements: The authors would like to thank the medical and nursing staff involved in the care of the studied patients. No external funding or third-party assistance was received. CONFLICT OF INTEREST: The authors declare that they have no conflicts of interest, financial or otherwise. Data Availability Data are available on request. References Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. Kuchay MS, Reddy PK, Gagneja S, Mishra SK, Krishnan S. Short-term follow-up of patients presenting with acute onset diabetes and diabetic ketoacidosis during an episode of COVID-19. Diabetes Metab Syndr. 2020;14(6):2039–41. Gregory JM, Slaughter JC, Duffus SH, Smith TJ, LeStourgeon LM, Jaser SS, et al. COVID-19 severity is tripled in the diabetes community: a prospective analysis of the pandemic's impact in type 1 and type 2 diabetes. Diabetes Care. 2020;43(8):1608–16. Eskandarani RM, Sawan S. Diabetic ketoacidosis on hospitalization with COVID-19 in a previously nondiabetic patient: a review of pathophysiology. Clin Med Insights Endocrinol Diabetes. 2020;13:1179551420984125. Muneer M, Akbar I. Acute metabolic emergencies in diabetes: DKA, HHS and EDKA. Adv Exp Med Biol. 2021;1307:85–114. Sattar N, McInnes IB, McMurray JJ. Obesity, diabetes, and COVID-19: a great combination. Lancet Diabetes Endocrinol. 2020;8(6):428–30. Liu Y, Wei Z, He M. Stress-induced hyperglycemia and its role in diabetes during the COVID-19 pandemic. J Endocrinol Metab. 2021;106(6):1604–12. Chong SM, Tan WY. Postacute sequelae of SARS-CoV-2 infection: the diabetes link. Diabetes Res Clin Pract. 2021;173:108653. Beliard K, Ebekozien O, Demeterco-Berggren C, Alonso GT, Gallagher MP, Clements MA. Increased DKA at presentation among newly diagnosed type 1 diabetes patients with or without COVID-19: Data from a multisite surveillance registry. J Diabetes. 2020;12(12):869–72. Kitabchi AE, Umpierrez GE, Miles JM, Fisher JN. Hyperglycemic crises in adult patients with diabetes. Diabetes Care. 2009;32(7):1335–43. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. Classification and diagnosis of diabetes: standards of care in diabetes—2023. Diabetes Care. 2023;46(Suppl 1):S19–40. Goldman N, Fink D, Cai J, Lee YN, Davies Z. High prevalence of COVID-19-associated diabetic ketoacidosis in UK secondary care. Diabetes Res Clin Pract. 2020;166:108291. Masoud H, Elassal G, Zaky S, Baki A, Ibrahem H, Amin W et al. Management protocol for COVID-19 patients (version 1.4, 30 May 2020). Cairo: Ministry of Health and Population (MOHP), Egypt; 2020. Available from: http://www.mohp.gov.eg/JobsDetails.aspx?job_id=3061 Masoud H, Elassal G, Hakim M, Shawky A, Zaky S, Baki A, Abdelbary A, Hassany M, Mohsen A, Taema K, Asem N, Kamal E, Ibrahem H, Abdalmohsen A, Eid A, Attia E, Din K, Mahdy A, Amin W. Management protocol for COVID-19 patients. COVID-19 Ministry of Health and Population, Egypt. Version 1.5; September 2021. Umpierrez GE, Smiley D, Kitabchi AE. Ketosis-prone type 2 diabetes: time to revise the classification of diabetes. Diabetes Care. 2006;29(12):2755–7. Ceriello A. Oxidative stress and glycemic regulation. Metabolism. 2000;49(2 Suppl 1):27–9. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9. Wu CT, Lidsky PV, Xiao Y, Lee IT, Cheng R, Nakayama T, et al. SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment. Cell Metab. 2022;34(8):1285–300. DeFronzo RA. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):773–95. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615–25. Yuan S, Chen P, Li H, Chen C, Wang F, Wang DW, et al. Associations of anti-inflammatory and antithrombotic drug use with risk of ischemic stroke, intracerebral hemorrhage, and vascular death in patients with COVID-19: a Swedish population-based cohort study. Lancet Reg Health Eur. 2022;18:100390. Bornstein SR, Rubino F, Khunti K, Mingrone G, Hopkins D, Birkenfeld AL, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol. 2020;8(6):546–50. Stevens JS, Bogun MM, McMahon DJ, Zucker J, Kurlansky P, Mohan S, et al. Diabetic ketoacidosis and mortality in COVID-19 infection. Diabetes Metab. 2021;47(6):101267. Pal R, Banerjee M, Yadav U, Bhattacharjee S. Clinical profile and outcomes in COVID-19 patients with diabetic ketoacidosis: a systematic review of literature. Diabetes Metab Syndr. 2020;14(6):1563–9. Esper AM, Martin GS. The impact of comorbid conditions on critical illness. Crit Care Med. 2009;37(10):2738–43. Khunti K, Del Prato S, Mathieu C, Kahn SE, Gabbay RA, Buse JB. COVID-19, hyperglycemia, and new-onset diabetes. Diabetes Care. 2021;44(12):2645–55. Yang JK, Lin SS, Ji XJ, Guo LM. Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. Acta Diabetol. 2010;47(3):193–9. Clore JN, Thurby-Hay L. Glucocorticoid-induced hyperglycemia. Endocr Pract. 2009;15(5):469–74. Chopra S, Malhotra A, Ranjan P, Vikram NK, Kumari A. Lifestyle-related behaviors and quality of life in patients with type 2 diabetes during COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(6):1767–72. Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-kidney-metabolic health: a presidential advisory from the American Heart Association. Circulation. 2023;148(20):1606–35. O'Sullivan JW, Banerjee A, Haimovich J. Long-term outcomes of COVID-19: cardiovascular and kidney complications. Nat Rev Nephrol. 2023;19(4):241–56. Dell'Aquila K, Lee J, Wang SH, Alamuri TT, Jennings R, Tang H, et al. Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: comparison with influenza and prepandemic data. Diabetes Obes Metab. 2023;25(3):732–41. Mondal S, DasGupta R, Lodh M, Ganguly A. D-dimer as a prognostic marker in COVID-19 patients: a meta-analysis. J Assoc Physicians India. 2021;69(10):11–2. Meza M, Dhamija S, Ramirez M, Ghanim H, Dandona P. Diabetic ketoacidosis in COVID-19: unique concerns and considerations. J Endocr Soc. 2020;4(11):bvaa117. Reddy PK, Kuchay MS, Mishra SK, Farooqui KJ, Singh AK, Wasir JS, et al. Diabetic ketoacidosis precipitated by COVID-19: a report of two cases and review of literature. Diabetes Metab Syndr. 2020;14(5):1459–62. Kempegowda P, Melson E, Johnson A, Wallett L, Thomas E, Chandan JS, et al. Effect of COVID-19 on the clinical course of diabetic ketoacidosis (DKA) in people with type 1 and type 2 diabetes. Endocr Connect. 2021;10(4):371–7. Ayoubkhani D, Khunti K, Nafilyan V, Maddox T, Humberstone B, Diamond I, et al. Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study. BMJ. 2021;372:n693. Yuan S, Larsson SC. Adiposity, diabetes, lifestyle factors and COVID-19 risk: a Mendelian randomization study. Metabolism. 2022;133:155217. Xie Y, Al-Aly Z. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10(5):311–21. GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 27 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8988395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600603592,"identity":"fab2ba88-fddc-413c-bce7-5c08b6ccfa9f","order_by":0,"name":"Rania Al Sayed Abd Albaky Mohamed","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Rania","middleName":"Al Sayed Abd Albaky","lastName":"Mohamed","suffix":""},{"id":600603593,"identity":"056fbe6c-956d-4536-b3dd-afe5bec59bcd","order_by":1,"name":"Laila Mahmoud Ali Hendawy","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Laila","middleName":"Mahmoud Ali","lastName":"Hendawy","suffix":""},{"id":600603594,"identity":"eedcd2ac-0a79-41a6-815b-92e288959435","order_by":2,"name":"Eman Elsayed Mahmoud Farghaly","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"Elsayed Mahmoud","lastName":"Farghaly","suffix":""},{"id":600603595,"identity":"9a3a097f-b7bb-4846-b91c-a773aea6df88","order_by":3,"name":"Amr Mahmoud Mohamed Abd El Hady Saleh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYNCCAoYEfiAlAeUaEKHFgCFBsoFkLQYHiNXCP+3s4w8/DGzyjK8dPniDoaYusYG9eZsEQ0UtTi0St9PNJHsM0orNbqclWzAcO5zYwHOsTILhzHHc1txOY2PgMTicuO12jpkEA9uBxAYJIIOx7RhOHfK305g//jH4n7h5dv43CYZ/QIfJvwFq+Ydbi8HtNAZpHoMDiRukc9iAhjMDbeEBammowanFEOgwaRmD5MQZt9OMLRL7Dhu38aQVWyQcO4BTixzIYW8q7BL7Zyc/vPHhW51sP/vhjTc+1NTh9j4KSABiNgjjMJFakACxtoyCUTAKRsEIAABCYVPtZwlfSgAAAABJRU5ErkJggg==","orcid":"","institution":"Ain Shams University","correspondingAuthor":true,"prefix":"","firstName":"Amr","middleName":"Mahmoud Mohamed Abd El Hady","lastName":"Saleh","suffix":""}],"badges":[],"createdAt":"2026-02-27 13:09:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8988395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8988395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical characteristics and Outcomes of Diabetic Ketoacidosis in Patients with Type 2 Diabetes During Acute Systemic Stress ","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus has been associated with poor outcomes in patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2\u003cb\u003e)\u003c/b\u003e (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). COVID-19 may cause new-onset diabetes or uncover diabetes that was previously undiagnosed (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e). Patients with diabetes have a greater risk of infection and acute respiratory distress syndrome than the general population does (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile DKA is traditionally linked to absolute insulin deficiency in type 1 diabetes(T1D), its occurrence in T2DM patients during acute illness suggests a distinct pathophysiology driven by severe insulin resistance, inflammatory stress, and relative insulin deficiency (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLongitudinal data indicate a significant increase in the incidence of newly diagnosed T2DM during the pandemic, which is attributed to direct viral effects, systemic inflammation, glucocorticoid therapy, and pandemic-related lifestyle disruptions (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e). SARS-CoV-2 may infect pancreatic β-cells via ACE2 receptors, potentially impairing insulin secretion (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Concurrently, the cytokine storm and metabolic stress associated with severe infection can exacerbate insulin resistance and precipitate hyperglycemic crises in susceptible individuals. (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite these associations, the specific risk factors, β-cell function, and outcomes of DKA in T2DM patients with COVID-19 remain incompletely characterized, particularly in relation to preserved β-cell function and the interplay between metabolic reserve and inflammatory stress. This study aimed to evaluate these aspects by comparing T2DM patients with COVID-19 who developed DKA to those who did not.\u003c/p\u003e\n\u003ch3\u003eObjectives :\u003c/h3\u003e\n\u003cp\u003eThis study aimed to assess β-cell function, identify risk factors for DKA, and evaluate clinical outcomes in hospitalized patients with T2DM and COVID-19.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003ch2\u003eStudy Design and Setting:\u003c/h2\u003e\u003cp\u003eThe study protocol received retrospective approval from the Institutional Review Board of Ain Shams University Faculty of Medicine (Approval No. FMASU MSO 27/2024( for analysis of anonymized clinical data collected between August 2021 and August 2022. The need for informed consent was waived per institutional policy for retrospective audit-based studies.\u003c/p\u003e\u003ch3\u003ePatients:\u003c/h3\u003e\u003cp\u003eThe study included 70 adult patients (≥ 18 years) with a confirmed diagnosis of T2DM and laboratory-confirmed SARS-CoV-2 infection by RT–PCR. Patients were divided into two groups:\u003c/p\u003e\u003cp\u003e \u003cstrong\u003e· Group 1 (Cases)\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThirty-five patients who developed DKA during hospitalization.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003e· Group 2 (Controls)\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThirty-five patients with T2DM and COVID-19 who did not develop DKA.\u003c/p\u003e\u003cp\u003eDKA was diagnosed according to standard criteria: blood glucose \u0026gt; 200 mg/dL, arterial pH \u0026lt; 7.3, serum bicarbonate \u0026lt; 15 mEq/L, and the presence of ketonuria (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). Ketones were measured in urine using the nitroprusside reaction test (Acetest® reagent strips, Siemens Healthineers). Blood β-hydroxybutyrate measurement was not routinely available. Euglycemic DKA (blood glucose ≤ 200 mg/dL) was excluded,, which may limit generalizability to contemporary presentations but ensures specificity for hyperglycemic DKA in this analysis. T2DM was diagnosed on the basis of the American Diabetes Association criteria (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). The exclusion criteria included age \u0026lt; 18 years, type 1 diabetes, end-stage organ failure. Sodium-glucose cotransporter-2 inhibitor use was verified via medication reconciliation from admission records; no patients were using these medications at admission.\u003c/p\u003e\u003cp\u003eControls (non-DKA patients) were consecutively selected from the same hospital during the study period, fulfilling the inclusion/exclusion criteria but without a diagnosis of DKA.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSample size\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe sample size was calculated via G*Power software (version 3.1) on the basis of a previous study reporting a 15% prevalence of DKA among hospitalized diabetic patients with COVID-19 (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). With an α error of 0.05 and a power of 80%, a minimum of 35 patients per group was needed. This provides adequate power for primary clinical outcomes (e.g., mortality) but may be underpowered to detect subtle differences in β-cell function markers.\u003c/p\u003e\u003ch3\u003eMethods:\u003c/h3\u003e\u003ch2\u003eAll participants underwent the following procedures:\u003c/h2\u003e\u003cp\u003e \u003cb\u003eHistory taking (either from the patients or patients’ sheets)\u003c/b\u003e: Personal history: age; sex; residency; special habits such as smoking; medical history: diabetes (duration, medications, diabetic complications); hypertension; cardiac, hepatic, and renal diseases; family history of diabetes; diabetic complications; and COVID-19 infection.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eClinical examination\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ebody weight, height, body mass index, and arterial oxygen saturation were measured via pulse oximetry.\u003c/p\u003e\u003cp\u003e \u003cb\u003eChest radiology\u003c/b\u003e (at baseline and when requested during follow-up): Chest X-ray, noncontrast CT.\u003c/p\u003e\u003cp\u003eChest CT severity was assessed using a semi-quantitative scoring system ranging from 0 to 5. The scoring scales are based on the areal size of involvement; and the percentages and scores are designated as follows: 0 = 0% involvement; 1 = 1%–25% involvement; 2 = 26%–50% involvement; 3 = 51%–75% involvement; and 4 = 75%–100% involvement\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eLaboratory investigations\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eBlood samples for key analyses (fasting glucose, C-peptide, insulin, inflammatory markers, arterial blood gases, and electrolytes) were drawn within 24 hours of admission prior to the initiation of intravenous insulin therapy for DKA patients. This timing was chosen to assess baseline metabolic status at the point of peak acute stress\u003c/p\u003e\u003cp\u003eC-peptide and insulin were measured by radioimmunoassay using commercial kits (C-Peptide IRMA, DiaSorin; Insulin IRMA, Beckman Coulter). Reference ranges: C-peptide 1.1–4.4 ng/mL, insulin 2.6–24.9 µU/mL.\u003c/p\u003e\u003cp\u003eThe homeostatic model assessment for insulin resistance (HOMA-IR) score was calculated as [fasting insulin (µU/mL) × fasting glucose (mg/dL)] / 405.\u003c/p\u003e\u003cp\u003eThe homeostatic model assessment for beta-cell function (HOMA-B) score was calculated as [20 × fasting insulin (µU/mL)] / [fasting glucose (mg/dL) − 63].\u003c/p\u003e\u003cp\u003eThe HOMA-IR and HOMA-B indices were originally developed for use in metabolically stable outpatient populations. Their application in acutely ill, hospitalized patients—particularly those with systemic inflammation, hypoxia, and stress hormone excess—has important limitations. Therefore, the HOMA indices in this study should be interpreted as surrogate markers rather than precise measures of insulin resistance or β-cell function.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eManagement of COVID- 19\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAll patients were managed according to the national guidelines adopted by the Egyptian Ministry of Health. A national protocol for COVID-19 treatment was published first in May 2020 by the Egyptian Ministry of Health \u0026amp; Population, which underwent several modifications according to recent worldwide guidelines. Antibiotics were prescribed for clinical suspicion of bacterial infections as per our center's antibiotic policy. All patients received anticoagulation (either enoxaparin or direct oral anticoagulants) unless contraindicated. Mild cases did not receive antiviral therapy, mild to moderate cases received favipiravir, and remdesivir was prescribed for those who did not respond well to favipiravir after 3 days. Patients with moderate to severe cases received remdesivir from the beginning of the study. The typical dose of favipiravir was 1600 mg/12 hours on the 1st day and 600 mg/12 hours for 4 days. The typical dose of remdesivir was 200 mg on the 1st day, followed by 100 mg/day for 4 days. Ivermectin (12 mg twice daily for 5 days) was added as a second antiviral in a few cases.\u003c/p\u003e\u003cp\u003ePatients with moderate-to-severe disease received steroids in the form of 80 mg/day methylprednisolone for 3–5 days. Oxygen therapy via an oxygen mask or reservoir oxygen mask, a high-flow nasal cannula, continuous positive airway pressure (CPAP) and mechanical ventilation was used according to the clinical situation and case progression (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eQuantitative data on specific pharmacologic treatments (e.g., corticosteroid dosing schedules, precise oxygen flow rates) and detailed respiratory support modalities were not systematically recorded in the clinical database available for this retrospective analysis. Consequently, these variables could not be included in the comparative analysis. This limitation is acknowledged as a critical potential source of confounding, as treatment intensity may reflect and influence both COVID-19 severity and metabolic decompensation. The study therefore focuses on characterizing the phenotype and outcomes of DKA in this setting, while explicitly acknowledging this fundamental constraint on causal inference.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eManagement of diabetic ketoacidosis (DKA)\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eDKA patients were managed with IV fluid, electrolyte abnormality correction, insulin therapy (IV continuous infusion) and IV sodium bicarbonate if the pH was less than or equal to 7.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eData were analyzed using SPSS version 27. Continuous variables are presented as mean ± standard deviation or median (interquartile range), as appropriate, and compared using Student's t-test or Mann–Whitney U test. Categorical variables are presented as frequencies (%) and compared using chi-square or Fisher's exact test.\u003c/p\u003e\u003cp\u003eEffect sizes with 95% confidence intervals (CI) are reported for all key comparisons. To account for potential renal confounding of C-peptide, a linear regression model was constructed with C-peptide as the dependent variable and DKA status and serum creatinine as covariates. Multivariable adjustment was not performed due to the limited number of events and risk of overfitting.Missing data were minimal (\u0026lt; 5%) and handled by complete-case analysis.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study included 70 patients with a mean age of 56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 years; 57.1% were female. The overall median diabetes duration was 8 years (IQR: 5\u0026ndash;13). Hypertension was prevalent (68.6%). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the DKA group was significantly older (61.1 vs. 51.4 years, mean difference 9.8 years, 95% CI 4.2 to 16.0; p\u0026thinsp;=\u0026thinsp;0.003) and had a longer median diabetes duration (10 vs. 7 years, median difference 3.7 years, 95% CI 1.3 to 6.1; p\u0026thinsp;=\u0026thinsp;0.009) than the non-DKA group. No significant differences were found in sex, BMI, or baseline blood pressure. The DKA group presented with higher heart rates, respiratory rates, and body temperatures (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating greater clinical severity. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eMetabolic and Laboratory Parameters:\u003c/h3\u003e\n\u003cp\u003eFasting C-peptide levels were comparable between the DKA and non-DKA groups (median 1.99 vs 2.05 ng/mL), with a median difference of \u0026minus;\u0026thinsp;0.06 ng/mL (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.28 to 0.16). In contrast, insulin resistance was significantly higher in the DKA group, as reflected by higher HOMA-IR values (median difference 1.93; 95% CI 0.45\u0026ndash;3.60). Glycemic control was poorer in the DKA group, with a mean HbA1c difference of 0.78% (95% CI 0.17\u0026ndash;1.39).\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAfter adjustment for serum creatinine, DKA status was not significantly associated with C-peptide levels (β 0.224 ng/mL; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.140 to 0.587; p\u0026thinsp;=\u0026thinsp;0.223).( Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe DKA group presented markers of greater inflammatory and thrombotic burdens, including significantly higher D-dimer levels (mean difference 0.21 ng/mL, 95% CI 0.05 to 0.37; p\u0026thinsp;=\u0026thinsp;0.020) and more profound lymphocytopenia (median difference\u0026thinsp;\u0026minus;\u0026thinsp;940 cells/\u0026micro;L, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1358 to \u0026minus;\u0026thinsp;457; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003ch3\u003eOutcomes:\u003c/h3\u003e\n\u003cp\u003eThe clinical outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In-hospital mortality was higher in the DKA group (22.9%) compared with the non-DKA group (0%), corresponding to an absolute risk difference of 22.9% (95% CI 8.4%\u0026ndash;37.4%). Length of hospital stay was slightly shorter in the DKA group (mean difference\u0026thinsp;\u0026minus;\u0026thinsp;1.4 days; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;2.7 to \u0026minus;\u0026thinsp;0.1), likely reflecting early mortality.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factor Analysis:\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression identified multiple factors associated with DKA, including age\u0026thinsp;\u0026gt;\u0026thinsp;62 years, diabetes duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years, HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7%, elevated HOMA-IR, and elevated D-dimer. The univariate associations should be interpreted as identifying potential risk factors rather than independent predictors. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of All Studied Patients (n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. = 70\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.26\u0026thinsp;\u0026plusmn;\u0026thinsp;13.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetic duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.41\u0026thinsp;\u0026plusmn;\u0026thinsp;10.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAssociated medical disorders\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease (Cirrhosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.07\u0026thinsp;\u0026plusmn;\u0026thinsp;17.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaO2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), median (interquartile range, IQR), or number (percentage, %) as appropriate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Clinical Characteristics Between DKA and Non-DKA Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect Size (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.8 (4.2 to 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7 (1.3 to 6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7 (\u0026minus;\u0026thinsp;1.1 to 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;\u0026thinsp;1.6 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 (2.2 to 6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.3 to 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3 (5.5 to 15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO₂ (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 (\u0026minus;\u0026thinsp;1.5 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR). Between-group comparisons were performed using Student\u0026rsquo;s t-test for normally distributed variables and Mann\u0026ndash;Whitney U test for non-normally distributed variables. Categorical variables were compared using chi-square or Fisher\u0026rsquo;s exact test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Hodges\u0026ndash;Lehmann median difference and 95% confidence interval are reported for non-normally distributed variables.CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Laboratory Parameters Between DKA and Non-DKA Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect Size (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.76 (\u0026minus;\u0026thinsp;1.43 to \u0026minus;\u0026thinsp;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucocyte (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (7.1\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1 (9.3\u0026ndash;15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.1 (\u0026minus;\u0026thinsp;5.9 to \u0026minus;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (258\u0026ndash;900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1490 (900\u0026ndash;2150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;940 (\u0026minus;\u0026thinsp;1358 to \u0026minus;\u0026thinsp;457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.5\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.8 (20.2 to 44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.17 to 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting C-peptide (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99 (1.56\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05 (1.70\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;\u0026thinsp;0.28 to 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33 (3.66\u0026ndash;8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40 (2.10\u0026ndash;6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93 (0.45 to 3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.49 (21.69\u0026ndash;43.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.24 (33.16\u0026ndash;89.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25.2(-42.22 to \u0026ndash; 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21 (0.05 to 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.08 (\u0026minus;\u0026thinsp;0.11 to \u0026minus;\u0026thinsp;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO₃ (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;6.28 (\u0026minus;\u0026thinsp;7.47 to \u0026minus;\u0026thinsp;5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.71 (\u0026minus;\u0026thinsp;1.11 to \u0026minus;\u0026thinsp;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR). Comparisons were made using Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test as appropriate. Effect sizes are expressed as mean differences or Hodges\u0026ndash;Lehmann median differences* with 95% CIs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFPG\u0026thinsp;=\u0026thinsp;fasting plasma glucose; HOMA-IR\u0026thinsp;=\u0026thinsp;homeostatic model assessment of insulin resistance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCreatinine-Adjusted Analysis of Fasting C-peptide\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (unstandardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDKA (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.140 to 0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.067 to 0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Linear regression model with fasting C-peptide as the dependent variable. Model statistics: R\u0026sup2; = 0.055, Adjusted R\u0026sup2; = 0.023, ANOVA p\u0026thinsp;=\u0026thinsp;0.185.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Outcomes Between DKA and Non-DKA Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect Size (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.9% (8.4% to 37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.4 (\u0026minus;\u0026thinsp;2.7 to \u0026minus;\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Mortality compared using Fisher\u0026rsquo;s exact test; length of stay compared using Student\u0026rsquo;s t-test. Effect sizes are risk difference (%) and mean difference with 95% CI.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Logistic Regression for Factors Associated with DKA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;62 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.74 (1.91\u0026ndash;17.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.09 (2.45\u0026ndash;164.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.58 (3.23\u0026ndash;75.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u0026thinsp;\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.74 (1.91\u0026ndash;17.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer\u0026thinsp;\u0026gt;\u0026thinsp;0.77 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.43 (1.25\u0026ndash;9.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval. Univariate logistic regression was performed for each variable.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis retrospective case\u0026ndash;control study provides critical insights into the pathogenesis, risk factors, and clinical outcomes of diabetic ketoacidosis (DKA) in patients with type 2 diabetes mellitus (T2DM) during acute SARS-CoV-2 infection. Our findings challenge the conventional paradigm of absolute insulin deficiency as the primary driver of DKA in this population and instead highlight a multifactorial pathophysiology involving severe insulin resistance, preexisting metabolic vulnerability, and systemic inflammatory stress.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePreserved β-cell Function and Pathophysiological Implications\u003c/h2\u003e \u003cp\u003eThe most significant finding of our study was the comparable fasting C-peptide levels between the DKA and non-DKA groups (median 1.99 vs. 2.05 ng/mL, p\u0026thinsp;=\u0026thinsp;0.363), suggesting that absolute insulin deficiency\u0026mdash;characteristic of type 1 diabetes-related DKA\u0026mdash;was not the predominant mechanism in T2DM patients with COVID-19. This observation remained non-significant after adjustment for serum creatinine (β 0.224, p\u0026thinsp;=\u0026thinsp;0.223), suggesting that group differences in renal function did not account for the lack of difference in C-peptide levels between groups. This aligns with the emerging concept of \"ketosis-prone type 2 diabetes\" (KPD), where patients with phenotypic T2DM develop DKA under conditions of extreme metabolic stress despite having preserved endogenous insulin secretory capacity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The pathophysiology appears to involve a dual insult: severe insulin resistance induced by the cytokine storm and counterregulatory hormone excess of COVID-19, coupled with relative insulin deficiency in the context of long-standing β-cell exhaustion (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our finding of significantly higher HOMA-IR in the DKA group (5.33 vs. 3.40, p\u0026thinsp;=\u0026thinsp;0.012) supports this hypothesis, although we emphasize the limitations of HOMA-IR as a surrogate marker in this acutely ill cohort(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe preserved C-peptide levels must be interpreted cautiously. While they suggest adequate β-cell mass, they do not exclude functional β-cell impairment under acute stress. SARS-CoV-2 has been shown to infect pancreatic β-cells through ACE2 receptors, potentially causing transient dysfunction (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, the timing of measurement is critical; our samples were drawn at admission before significant insulin therapy, but the dynamic nature of insulin secretion during evolving DKA could not be captured. Nonetheless, our data contribute to the growing evidence that DKA in T2DM patients during the COVID-19 pandemic represents a distinct entity from classic T1D DKA, with important therapeutic implications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factor Profile: Metabolic Vulnerability Meets Inflammatory Storm\u003c/h2\u003e \u003cp\u003eUnivariate analysis showed that longer diabetes duration (\u0026gt;\u0026thinsp;10 years) was strongly associated with the development of DKA (OR: 20.09, 95% CI: 2.45\u0026ndash;164.64, p\u0026thinsp;=\u0026thinsp;0.005). This finding underscores the concept of \u0026ldquo;metabolic reserve\u0026rdquo; exhaustion, where patients with long-standing T2DM have diminished β-cell functional capacity to compensate for acute insulin resistance (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The DKA group also exhibited poorer chronic glycemic control (HbA1c 7.22% vs. 6.44%, p\u0026thinsp;=\u0026thinsp;0.013), which may reflect both disease severity and therapeutic inertia prior to hospitalization.\u003c/p\u003e \u003cp\u003eThe DKA group demonstrated a pronounced proinflammatory and prothrombotic state, with significantly elevated D-dimer (0.87 vs. 0.66 ng/mL, p\u0026thinsp;=\u0026thinsp;0.020) and profound lymphocytopenia (median 550 vs. 1490 cells/\u0026micro;L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These markers likely reflect greater overall COVID-19 severity than direct causative factors for DKA. The bidirectional relationship between hyperglycemia and inflammation is well established: hyperglycemia promotes a proinflammatory state through mitochondrial reactive oxygen species generation and advanced glycation end-product formation, whereas cytokines such as TNF-α and IL-6 directly induce insulin resistance (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This creates a vicious cycle in which COVID-19-induced inflammation worsens hyperglycemia, which in turn amplifies the inflammatory response.\u003c/p\u003e \u003cp\u003eNotably, emerging evidence suggests that anti-inflammatory interventions may mitigate metabolic dysregulation in patients with COVID-19. Observational studies indicate that aspirin use is associated with a reduced incidence of new-onset diabetes following SARS-CoV-2 infection, potentially through the modulation of NLRP3 inflammasome activity and platelet-mediated inflammation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). While our study design did not allow assessment of the effects of aspirin, this represents a promising avenue for future intervention studies in high-risk populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical Outcomes and Mortality Determinants\u003c/h2\u003e \u003cp\u003eThe notable difference in mortality between the groups (22.9% vs. 0%, p\u0026thinsp;=\u0026thinsp;0.003) highlights the lethal synergy between metabolic decompensation and severe viral pneumonia. All fatalities in the DKA group occurred in patients requiring advanced respiratory support, suggesting that mortality resulted from the combined burden of DKA complications, hypoxemic respiratory failure, and thromboinflammatory sequelae rather than DKA alone. This mortality rate is consistent with previous reports; Stevens et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) reported 36.9% mortality in COVID-19 patients with DKA versus 28.8% without DKA, whereas Beliard et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) reported mortality rates approaching 50% in systematic reviews.\u003c/p\u003e \u003cp\u003eThe paradoxically shorter hospital stay in the DKA group (8.1 vs. 9.5 days, p\u0026thinsp;=\u0026thinsp;0.037) likely reflects higher early mortality rather than more rapid recovery, a phenomenon observed in other studies of critical illness (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This finding underscores the need for aggressive early intervention in DKA patients with COVID-19, as the window for effective treatment may be narrow.\u003c/p\u003e \u003cp\u003eOur study contributes to the growing body of evidence that SARS-CoV-2 infection serves as a potent metabolic stress test, unmasking and exacerbating underlying dysglycemia. Longitudinal cohort studies have documented a significant increase in the incidence of new-onset type 2 diabetes during the pandemic (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Multiple mechanisms likely contribute directly to viral damage to β-cells (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), inflammation-induced insulin resistance (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), glucocorticoid therapy (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and pandemic-related lifestyle disruptions (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our findings, specifically in patients with preexisting T2DM, suggest that those with longer disease duration and poorer control are particularly vulnerable to this decompensation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCOVID-19 as a Catalyst for Metabolic Decompensation\u003c/h2\u003e \u003cp\u003eThe intersection of COVID-19, diabetes, and DKA exists within the broader context of CKM syndrome, which recognizes the interconnected pathophysiology of cardiovascular, kidney, and metabolic diseases (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Emerging evidence suggests that SARS-CoV-2 infection may accelerate CKM progression through multiple pathways: endothelial dysfunction, persistent low-grade inflammation, and immune dysregulation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Furthermore, molecular analyses of long COVID suggest persistent viral reservoirs, autoimmunity, and chronic inflammation may contribute to new-onset diabetes, highlighting long-term pathobiological relationships (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Patients who survive DKA during COVID-19 may represent a high-risk subgroup requiring intensified long-term CKM monitoring and management. Future studies should investigate whether acute metabolic\u003c/p\u003e \u003cp\u003edecompensation during COVID-19 predicts accelerated progression of microvascular and macrovascular complications. This concern is magnified by data indicating the COVID-19 pandemic has significantly increased the global burden of type 2 diabetes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Existing Literature\u003c/h2\u003e \u003cp\u003eOur demographic findings align with those of several previous studies. The older age and longer diabetes duration in our DKA group mirror observations by Goldman et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and Dell'Aquila et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), who reported similar risk profiles. The absence of sex predominance in our study contrasts with some Western cohorts showing male predominance (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), possibly reflecting regional differences in diabetes epidemiology and healthcare access.\u003c/p\u003e \u003cp\u003eOur laboratory findings corroborate previous reports of elevated inflammatory markers in DKA patients with COVID-19. Similarly, Mondal et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) reported increased D-dimer levels in DKA patients, whereas Meza et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) reported profound lymphocytopenia in a case series. The electrolyte abnormalities we observed, particularly hypokalemia, are consistent with Reddy et al.'s early case reports (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and Kempegowda et al.'s comparative study (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe strengths of our study include its case\u0026ndash;control design with carefully defined criteria, comprehensive laboratory assessment including β-cell function markers, and real-world management reflecting contemporary COVID-19 care. To our knowledge, this is the first study to systematically evaluate fasting insulin and C-peptide levels in patients with T2DM, COVID-19, and DKA, providing direct insights into the pathophysiological mechanisms underlying metabolic decompensation in this population. The inclusion of a control group of T2DM patients with COVID-19 but without DKA provides valuable comparative data lacking in many case series. Our findings challenge the traditional paradigm of absolute insulin deficiency in DKA and contribute to the evolving understanding of ketosis-prone diabetes phenotypes.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be acknowledged. First, theretrospective, single-center design introduces potential selection bias and limits generalizability. Second, the relatively small sample size, while adequate for primary outcomes, reduces statistical power for secondary and multivariable analyses and inflates the uncertainty around some effect estimates. Third, significant baseline differences existed between groups (age, diabetes duration); however, only descriptive and univariate analyses were performed, and residual confounding remains likely. Fourth, and most critically, the absence of detailed data on COVID-19-specific treatments (corticosteroid dosing, antiviral agents, intensity of respiratory support) represents a major unmeasured confounder. The DKA group presented with greater clinical severity, making it highly probable they received more intensive immunomodulatory and respiratory support. Since corticosteroids are potent inducters of insulin resistance and hyperglycemia, this confounding severely limits our ability to disentangle the independent contributions of DKA, severe COVID-19, and its treatment to the observed outcomes. This limitation fundamentally constrains causal inference; our results should be interpreted as characterizing a high-risk clinical phenotype rather than establishing independent causal pathways. Fifth, the cross-sectional assessment of β-cell function provides only a snapshot;longitudinal measurements would better characterize dynamic changes. Sixth, the diagnosis of DKA relied on urinary ketone testing without systematic serum β-hydroxybutyrate measurement, which may affect the precision of diagnosis and severity assessment. Seventh, the exclusion of euglycemic DKA, while ensuring specificity, limits the generalizability of our findings to the full spectrum of DKA presentations, particularly in the context of SGLT2 inhibitor use. Finally, the lack of longitudinal follow-up precludes assessment of post-discharge metabolic outcomes and long-term cardiovascular or renal sequelae in this high-risk population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e \u003cp\u003eOur findings have several practical implications. First, they underscore the need for vigilant glucose monitoring in hospitalized T2DM patients with COVID-19, particularly those with longer disease durations and suboptimal control. Second, they suggest that management strategies should address both hyperglycemia and the underlying inflammatory state. Third, they highlight the importance of multidisciplinary care that integrates endocrinology, infectious disease, and critical care expertise.\u003c/p\u003e \u003cp\u003eFuture research should focus on several areas: prospective validation of our risk factors in larger multicenter cohorts; investigation of optimal insulin regimens for COVID-19-related DKA; assessment of anti-inflammatory agents (including aspirin) for metabolic protection; and longitudinal studies of CKM outcomes in survivors. Additionally, mechanistic studies exploring the interplay among SARS-CoV-2, insulin signaling pathways, and ketogenesis are needed to elucidate the pathophysiology fully.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, our study demonstrated that DKA in patients with T2DM during COVID-19 represents a distinct clinical entity characterized by preserved insulin secretion but severe insulin resistance in the context of systemic inflammation. Longer diabetes duration emerged as the most consistently associated clinical characteristic among patients who developed DKA, highlighting the importance of chronic disease management in pandemic preparedness. The high mortality observed underscore the critical need for early recognition and aggressive management of this dual metabolic\u0026ndash;infectious emergency. As we continue to navigate the long-term consequences of the pandemic, understanding these complex interactions will be essential for optimizing care for patients with diabetes facing acute systemic stressors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE:\u003c/h2\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Ain Shams University Faculty of Medicine (Approval No. FMASU MSO 27/2024). The need for informed consent was waived by the committee due to the retrospective nature of the study, which involved the analysis of anonymized clinical data.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eHUMAN AND ANIMAL RIGHTS:\u003c/h2\u003e\n\u003cp\u003eNo animals were used in this study. All the procedures performed in this study involving human participants were in accordance with the ethical standards of the Institutional Research Committee and the 1975 Declaration of Helsinki as revised in 2013.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish:\u003c/h2\u003e\n\u003cp\u003eNot applicable. This study involved retrospective analysis of anonymized data, and no identifiable patient information is included.\u003c/p\u003e\n\u003ch2\u003eFUNDING:\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eRania Al Sayed Abd Albaky Mohamed: Conceptualization, Investigation, Data Curation, Writing \u0026ndash; Original Draft. Laila Mahmoud Ali Hendawy: Methodology, Validation, Formal analysis, Writing \u0026ndash; Review \u0026amp; Editing. Eman Elsayed Mahmoud Farghaly: Methodology, Resources, Visualization, Writing \u0026ndash; Review \u0026amp; Editing. Amr Mahmoud Mohamed Abd El Hady Saleh: Conceptualization, Supervision, Project Administration, Formal analysis, Writing \u0026ndash; Review \u0026amp; Editing, Final approval\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank the medical and nursing staff involved in the care of the studied patients. No external funding or third-party assistance was received.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST: The authors declare that they have no conflicts of interest, financial or otherwise.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuchay MS, Reddy PK, Gagneja S, Mishra SK, Krishnan S. Short-term follow-up of patients presenting with acute onset diabetes and diabetic ketoacidosis during an episode of COVID-19. Diabetes Metab Syndr. 2020;14(6):2039\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGregory JM, Slaughter JC, Duffus SH, Smith TJ, LeStourgeon LM, Jaser SS, et al. COVID-19 severity is tripled in the diabetes community: a prospective analysis of the pandemic's impact in type 1 and type 2 diabetes. Diabetes Care. 2020;43(8):1608\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskandarani RM, Sawan S. Diabetic ketoacidosis on hospitalization with COVID-19 in a previously nondiabetic patient: a review of pathophysiology. Clin Med Insights Endocrinol Diabetes. 2020;13:1179551420984125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuneer M, Akbar I. Acute metabolic emergencies in diabetes: DKA, HHS and EDKA. Adv Exp Med Biol. 2021;1307:85\u0026ndash;114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSattar N, McInnes IB, McMurray JJ. Obesity, diabetes, and COVID-19: a great combination. Lancet Diabetes Endocrinol. 2020;8(6):428\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Wei Z, He M. Stress-induced hyperglycemia and its role in diabetes during the COVID-19 pandemic. J Endocrinol Metab. 2021;106(6):1604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong SM, Tan WY. Postacute sequelae of SARS-CoV-2 infection: the diabetes link. Diabetes Res Clin Pract. 2021;173:108653.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeliard K, Ebekozien O, Demeterco-Berggren C, Alonso GT, Gallagher MP, Clements MA. Increased DKA at presentation among newly diagnosed type 1 diabetes patients with or without COVID-19: Data from a multisite surveillance registry. J Diabetes. 2020;12(12):869\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitabchi AE, Umpierrez GE, Miles JM, Fisher JN. Hyperglycemic crises in adult patients with diabetes. Diabetes Care. 2009;32(7):1335\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. Classification and diagnosis of diabetes: standards of care in diabetes\u0026mdash;2023. Diabetes Care. 2023;46(Suppl 1):S19\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldman N, Fink D, Cai J, Lee YN, Davies Z. High prevalence of COVID-19-associated diabetic ketoacidosis in UK secondary care. Diabetes Res Clin Pract. 2020;166:108291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasoud H, Elassal G, Zaky S, Baki A, Ibrahem H, Amin W et al. Management protocol for COVID-19 patients (version 1.4, 30 May 2020). Cairo: Ministry of Health and Population (MOHP), Egypt; 2020. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mohp.gov.eg/JobsDetails.aspx?job_id=3061\u003c/span\u003e\u003cspan address=\"http://www.mohp.gov.eg/JobsDetails.aspx?job_id=3061\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasoud H, Elassal G, Hakim M, Shawky A, Zaky S, Baki A, Abdelbary A, Hassany M, Mohsen A, Taema K, Asem N, Kamal E, Ibrahem H, Abdalmohsen A, Eid A, Attia E, Din K, Mahdy A, Amin W. Management protocol for COVID-19 patients. COVID-19 Ministry of Health and Population, Egypt. Version 1.5; September 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmpierrez GE, Smiley D, Kitabchi AE. Ketosis-prone type 2 diabetes: time to revise the classification of diabetes. Diabetes Care. 2006;29(12):2755\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCeriello A. Oxidative stress and glycemic regulation. Metabolism. 2000;49(2 Suppl 1):27\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu CT, Lidsky PV, Xiao Y, Lee IT, Cheng R, Nakayama T, et al. SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment. Cell Metab. 2022;34(8):1285\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeFronzo RA. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):773\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Chen P, Li H, Chen C, Wang F, Wang DW, et al. Associations of anti-inflammatory and antithrombotic drug use with risk of ischemic stroke, intracerebral hemorrhage, and vascular death in patients with COVID-19: a Swedish population-based cohort study. Lancet Reg Health Eur. 2022;18:100390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBornstein SR, Rubino F, Khunti K, Mingrone G, Hopkins D, Birkenfeld AL, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol. 2020;8(6):546\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens JS, Bogun MM, McMahon DJ, Zucker J, Kurlansky P, Mohan S, et al. Diabetic ketoacidosis and mortality in COVID-19 infection. Diabetes Metab. 2021;47(6):101267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal R, Banerjee M, Yadav U, Bhattacharjee S. Clinical profile and outcomes in COVID-19 patients with diabetic ketoacidosis: a systematic review of literature. Diabetes Metab Syndr. 2020;14(6):1563\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsper AM, Martin GS. The impact of comorbid conditions on critical illness. Crit Care Med. 2009;37(10):2738\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhunti K, Del Prato S, Mathieu C, Kahn SE, Gabbay RA, Buse JB. COVID-19, hyperglycemia, and new-onset diabetes. Diabetes Care. 2021;44(12):2645\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang JK, Lin SS, Ji XJ, Guo LM. Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. Acta Diabetol. 2010;47(3):193\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClore JN, Thurby-Hay L. Glucocorticoid-induced hyperglycemia. Endocr Pract. 2009;15(5):469\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChopra S, Malhotra A, Ranjan P, Vikram NK, Kumari A. Lifestyle-related behaviors and quality of life in patients with type 2 diabetes during COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(6):1767\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, et al. Cardiovascular-kidney-metabolic health: a presidential advisory from the American Heart Association. Circulation. 2023;148(20):1606\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Sullivan JW, Banerjee A, Haimovich J. Long-term outcomes of COVID-19: cardiovascular and kidney complications. Nat Rev Nephrol. 2023;19(4):241\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDell'Aquila K, Lee J, Wang SH, Alamuri TT, Jennings R, Tang H, et al. Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: comparison with influenza and prepandemic data. Diabetes Obes Metab. 2023;25(3):732\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMondal S, DasGupta R, Lodh M, Ganguly A. D-dimer as a prognostic marker in COVID-19 patients: a meta-analysis. J Assoc Physicians India. 2021;69(10):11\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeza M, Dhamija S, Ramirez M, Ghanim H, Dandona P. Diabetic ketoacidosis in COVID-19: unique concerns and considerations. J Endocr Soc. 2020;4(11):bvaa117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy PK, Kuchay MS, Mishra SK, Farooqui KJ, Singh AK, Wasir JS, et al. Diabetic ketoacidosis precipitated by COVID-19: a report of two cases and review of literature. Diabetes Metab Syndr. 2020;14(5):1459\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKempegowda P, Melson E, Johnson A, Wallett L, Thomas E, Chandan JS, et al. Effect of COVID-19 on the clinical course of diabetic ketoacidosis (DKA) in people with type 1 and type 2 diabetes. Endocr Connect. 2021;10(4):371\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyoubkhani D, Khunti K, Nafilyan V, Maddox T, Humberstone B, Diamond I, et al. Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study. BMJ. 2021;372:n693.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Larsson SC. Adiposity, diabetes, lifestyle factors and COVID-19 risk: a Mendelian randomization study. Metabolism. 2022;133:155217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Al-Aly Z. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10(5):311\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Diabetic ketoacidosis, COVID-19, Type 2 diabetes, Mortality, Insulin resistance, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-8988395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8988395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMany reports indicate that diabetes is one of the main risk factors for COVID-19 complications. Nevertheless, few studies have examined how DKA develops in T2DM patients who have SARS-CoV-2 infection.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to assess β-cell function, identify risk factors for DKA, and evaluate clinical outcomes in hospitalized patients with T2DM and COVID-19.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective, single-center, case\u0026ndash;control study was conducted at Ain Shams University Isolation Hospital from August 2021 to August 2022. The study included 70 adults with T2DM and confirmed COVID-19, categorized into two groups: 35 patients with DKA (cases) and 35 patients without DKA (controls). Clinical, laboratory, and outcome data were extracted from medical records.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFasting C-peptide levels did not differ significantly between the DKA and non-DKA groups (median difference: \u0026minus;0.06 ng/mL, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.28 to 0.16; p\u0026thinsp;=\u0026thinsp;0.363), suggesting that absolute insulin deficiency was not the primary driver of DKA in this cohort. The DKA group was significantly older (mean difference: 9.8 years, 95% CI 4.2 to 16.0; p\u0026thinsp;=\u0026thinsp;0.003) and had a longer median diabetes duration (median difference: 3.7 years, 95% CI 1.3 to 6.1; p\u0026thinsp;=\u0026thinsp;0.009). These patients also presented increased levels of inflammatory and stress markers, including D-dimer (mean difference: 0.21 ng/mL, 95% CI 0.05 to 0.37; p\u0026thinsp;=\u0026thinsp;0.020) and HOMA-IR (median difference: 1.93, 95% CI 0.45 to 3.60; p\u0026thinsp;=\u0026thinsp;0.012). Mortality was significantly greater in the DKA group (22.9% vs. 0%, risk difference 22.9%, 95% CI 8.4% to 37.4%; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn patients with T2DM and COVID-19, DKA was not characterized by absolute insulinopenia but was associated with older age, longer diabetes duration, severe insulin resistance, and systemic inflammation. These factors contribute to significantly increased morbidity and mortality. Our findings highlight the multifactorial nature of DKA in this setting and underscore the importance of aggressive monitoring and management in high-risk patients.\u003c/p\u003e","manuscriptTitle":"Clinical characteristics and Outcomes of Diabetic Ketoacidosis in Patients with Type 2 Diabetes During Acute Systemic Stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 04:41:31","doi":"10.21203/rs.3.rs-8988395/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-13T12:28:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T11:43:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T17:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184060315224192700162127930705408698115","date":"2026-03-09T14:10:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161729975903258444323146245019194160852","date":"2026-03-09T08:35:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136557347643333671342420129233615713138","date":"2026-03-09T04:56:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69001800577221264921080632774103621082","date":"2026-03-04T11:52:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232032302923634772615260332551404280579","date":"2026-03-04T00:49:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T23:14:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T14:00:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T13:59:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology – Endocrinology Reports","date":"2026-02-27T13:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"cardiovascular-diabetology-endocrinology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Cardiovascular Diabetology – Endocrinology Reports","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83c1778a-51a6-45c5-ba80-a88afbdc8f5e","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T22:53:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 04:41:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8988395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8988395","identity":"rs-8988395","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}