Impact of COVID-19 on Metabolic Parameters in Patients with Type 2 Diabetes Mellitus

Impact of COVID-19 on Metabolic Parameters in Patients with Type 2 Diabetes Mellitus | 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 Impact of COVID-19 on Metabolic Parameters in Patients with Type 2 Diabetes Mellitus Motahare Shabestari, Forouzan Salari, Reyhaneh Azizi, Akram Ghadiri-Anari, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5319823/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background and aim Since the onset of the Coronavirus disease 2019 (COVID-19) pandemic, individuals with Type 2 diabetes mellitus (T2DM) have been one of the most susceptible populations to contracting the virus. Also, COVID-19 and its related lockdown restrictions have affected metabolic regulatory mechanisms within the T2DM population. The present study aims to determine the impact of the viral infection and its lockdown on the physiological parameters in individuals with T2DM. Methods: In this retrospective cohort study, the medical records of 118 individuals with prior diagnoses of T2DM were collected. All the subjects had undergone at least one laboratory test within a maximum of three months before the onset of the COVID-19 pandemic in Iran. Fifty-nine patients with a confirmed COVID-19 infection within the first three months of the pandemic underwent a follow-up lab test six months after their initial COVID-19 diagnosis. The individuals without a history of the infection underwent a follow-up lab test six months after the onset of the pandemic. Noninfected populations were matched for age and gender with infected patients. Comparative analysis of clinical and laboratory data within each group was conducted. Results: The ‘COVID-19 positive’ group exhibited a significant decrease in levels of triglycerides (TG) (P = 0.001) and total cholesterol (TC) (P = 0.028), body mass index (BMI) (P = 0.034), atherogenic index of plasma (AIP) (P = 0.027), triglyceride glucose (TyG) index (P = 0.001), and triglyceride glucose -BMI (TyG-BMI) index (P < 0.001) during the first six months post-infection compared to pre-pandemic. Other variables did not show notable changes. The ‘COVID-19 negative’ group had a significant reduction in TC (P = 0.001) and low-density lipoprotein cholesterol (LDL-C) (P = 0.01). Conclusion: Individuals with T2DM and mild to moderate COVID-19 infection experienced an improvement in the levels of TC, TG, BMI, and insulin-related indices. Restrictions in the first lockdown were associated with a decrease in the levels of TC and LDL-C in the T2DM population who did not have a history of the viral infection. Type 2 diabetes COVID-19 metabolic profile cohort study 1. Introduction Type 2 diabetes mellitus (T2DM) is a chronic and prevalent metabolic disorder affecting 10.5% of adults globally. Insulin resistance and hyperglycemia are the most important characteristics of the disease (1) that predispose the T2DM population to a wide range of pathogens and infections, even the common type (2). A cohort study showed that this population had a 30% higher risk of lower respiratory tract infection compared to the control group after one year of follow-up (3). The hyperglycemic condition impairs the glycolytic metabolism of immune cells and their ATP production, which decreases their functionality against infection (4). In addition, hyperglycemia has been shown to enhance the replication of several pathogens and fulfill their glycemic requirements, particularly in the case of SARS-CoV-2 infection (5). The SARS-CoV-2 infection caused the outbreak of Coronavirus disease 2019 (COVID-19), which was declared a pandemic in March 2020 (6). A bidirectional relationship between COVID-19 and T2DM has been observed, with diabetes exacerbating the severity of COVID-19 infection and the virus potentially worsening dysglycemia in patients with pre-existing diabetes(7–10). The development of SARS-CoV-2 infection might be caused by its role in delaying the production of type-I interferons in host cells (11). Type-I interferons are produced in all nucleated cells in response to infection and act as an alarm signal to immune cells to gather at the site of infection. In addition to the virus, high levels of lactate, which are seen in the blood of individuals with T2DM, have been shown to impair the production of type-I interferons (12). Altogether, these events make T2DM patients susceptible to COVID-19 infection. The precise pathophysiological mechanisms underlying COVID-19-induced dysglycemia remain unclear. It has been reported that Angiotensin I-Converting Enzyme type 2 (ACE2) is abundantly expressed in pancreatic beta cells and serves as a receptor for COVID-19 (13–15). This interaction potentially disrupts insulin secretion during acute infection. Additionally, the proinflammatory cytokine cascade, including TNF-α, IL-1, and IL-6, triggered by the immune response to the virus, may contribute to increased insulin resistance.(16). Alongside the COVID-19 infection, its related lockdowns affected the lifestyle and healthcare access of individuals with T2DM. During the lockdown in Iran, this population faced a shortage of routine outpatient visits and medical facilities. However, the use of telemedicine and online visits increased, which advised patients to have self-management behaviors, drink enough water, eat healthy food, exercise, and monitor their blood sugar (17). Most of the previous literature has focused on the impacts of the pandemic, with limited information available on the long-term effects of COVID-19 infection on metabolic parameters post-recovery from the initial phase of the infection (18–21). This investigation aimed to provide insights into the metabolic changes caused by COVID-19 in patients with preexisting T2DM. 2 Methods 2.1 Study design This retrospective cohort study was conducted at the Yazd Diabetes Research Center in Yazd, Iran. It utilized data collected retrospectively from the medical records of all eligible patients who attended the center between February 20, 2020 (the date when the first case of COVID-19 was officially announced in Iran) and October 21, 2020. The study was approved by the Research Ethics Council of Shahid Sadoughi University of Medical Sciences in Yazd, Iran (IR.SSU.REC.1401.097). 2.2 Participants The number of T2DM patients who had at least one visit to our medical center between February 20, 2020, and October 21, 2020, was 372. However, only 118 of them met the study’s criteria and were eligible to be enrolled in the study. They were asked to express their consent to use their medical records without publishing their personal information in this study through the informed consent form. Clinical data and laboratory measurements were extracted from the medical records of the 118 participants. The duration of diabetes in the subjects ranged from 3 to 7 years, and they had minimal diabetic complications. Prior to the pandemic, the participants were receiving insulin and/or oral diabetic medications under the supervision of healthcare professionals at our medical center. Furthermore, individuals over 40 years old with type 2 diabetes mellitus (T2DM) were prescribed the optimal dose of statins. None of the subjects received new treatments during the study period, except for adjusting the previous medication dosage based on laboratory tests. All the participants were between 30 and 60 years old, as individuals within this age range typically had regular follow-up visits and were using prescription medications. It is recommended that each individual with T2DM undergo clinical and laboratory assessments by a primary care physician every three to six months, based on the national guidelines (22). Individuals who had irregular follow-up visits, lack of medical records within three months before the pandemic onset, and a history of immunodeficiency, neoplasia, co-infection, and smoking were excluded. The "COVID-19 positive" group consisted of 59 patients who tested positive for COVID-19 using the polymerase chain reaction (PCR) technique from February 20 to May 19, 2020, but were not hospitalized and recovered on an outpatient basis. Corticosteroid therapy was not administered during the infection phase, and the dosage of diabetic-related medications was adjusted based on individual laboratory tests. Also, those who were not receiving statins prior to contracting COVID-19 did not receive this medication during the infection. Patients without medical records six months after their first positive PCR test, as well as those with a documented reinfection during this period, were excluded. The "COVID-19 negative" group consisted of 59 individuals with no documented history of COVID-19 infection from February 20 to October 21, 2020. Those without medical records six months after the pandemic initiation in Iran were excluded. Gender and age matching was performed to ensure population homogeneity across the two groups. 2.3 Clinical variables and laboratory measures Clinical data included age, gender, body mass index (BMI), and systolic and diastolic blood pressure. Blood samples obtained after 12 hours of fasting were analyzed for hemoglobin A1C (HbA1C), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (Cr). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI formula (23). Some of the lipid-related indices, including triglyceride glucose (TyG) index, triglyceride glucose-body mass index (TyG-BMI), and atherogenic index of plasma (AIP), were calculated using the following equations: (24, 25). TyG Index = Ln (fasting glucose (mg/dL) × triglycerides (mg/dL)/2) TyG-BMI = BMI × TyG index API = \(\:log\frac{Triglycerides}{HDL}\) 2.4 Statistical analysis Statistical analyses were performed using SPSS version 27. The normality of data was assessed using the Shapiro–Wilk test. Variables following a normal distribution were presented as means ± standard deviation (SD), while non-normally distributed variables were reported as medians and interquartile ranges. Differences within each group were analyzed using the paired t-test if variables were normal-distributed; otherwise, the Wilcoxon test was used. A P-value below 0.05 was considered statistically significant. 3. Results Each group included 59 patients with T2DM for analysis. Of these patients, 42 were female (71.2%), and 17 were male (28.8%). The median age of the ‘COVID-19 positive’ group was 55 (interquartile range 8), and the median age of the ‘COVID-19 negative group was 56 (interquartile range 8) (P = 0.84). None of the infected patients were hospitalized but were treated on an outpatient basis. Six months after recovering from COVID-19, the ‘COVID-19 positive’ group exhibited a significant decrease in weight (P = 0.036), BMI (P = 0.034), AIP (P = 0.027), TyG index (P = 0.001), and TyG-BMI index (P < 0.001) compared to pre-pandemic tests. Additionally, TG (P = 0.001) and TC (P = 0.028) decreased during this period (Table 1 ). No significant changes were observed in other lipid profiles, including HDL-C and LDL-C, as well as Systolic and diastolic blood pressure, glycemic indices, and renal function. In the ‘COVID-19 negative’ group, only TC (P = 0.001) and LDL-C (P = 0.01) levels showed significant reductions during the first six months of the lockdown (Table 2 ). Other clinical and laboratory variables did not exhibit notable changes Table 1 Clinical and laboratory data of patients in the ‘COVID-19 positive’ group Variables Before contracting COVID-19 After contracting COVID-19 P-Value Weight a (kg) 81.8 ± 15 80.9 ± 14.3 0.036 BMI a (kg/m2) 28.9 ± 5.3 28.6 ± 5 0.034 Systolic BP (mmHg) 130 ± 20 125 ± 30 0.202 Diastolic BP (mmHg) 75 ± 10 75 ± 10 0.371 MAP a (mmHg) 93.4 ± 9.4 92.1 ± 10.3 0.34 FPG (mg/dL) 145 ± 61 135 ± 49 0.1 HbA1C a (%) 7.3 ± 1.4 7.4 ± 1.5 0.684 Urea (mg/dL) 28.6 ± 9.5 27.2 ± 7.7 0.196 Cr (mg/dL) 0.94 ± 0.32 0.9 ± 0.2 0.814 eGFR a (mL/min/1.7m2) 73.3 ± 16.1 73.7 ± 21.1 0.88 TG (mg/dL) 202 ± 85 148 ± 73 0.001 TC (mg/dL) 170.5 ± 40 159 ± 34.2 a 0.028 LDL-C (mg/dL) a 86.19 ± 33.12 81.91 ± 27.99 0.34 HDL-C (mg/dL) 47 ± 11.79 47.07 ± 11.26 a 0.16 AIP a 0.57 ± 0.18 0.52 ± 0.21 0.027 TyG index 4.14 ± 0.22 a 4.01 ± 0.27 0.001 TyG-BMI index a 119.73 ± 21.12 115.51 ± 21.44 < 0.001 Note : Values were presented as median ± Interquartile Range or number. a Values with normal distribution were presented as mean ± Standard deviation. Abbreviations : COVID-19, coronavirus disease 2019; BMI, body mass index; BP, blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; Cr, creatinine; eGFR, estimated glomerular filtration rate; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIP, atherogenic index of plasma; TyG index, triglyceride–glucose index; TyG-BMI, triglyceride-glucose body mass index Table 2 Clinical and laboratory data of patients in ‘COVID-19 negative’ group Values Before the pandemic Six months after the pandemic P-Value Weight a (kg) 76.3 ± 10.2 75.8 ± 10.1 0.168 BMI a (kg/m2) 27 ± 3.6 26.8 ± 3.5 0.168 Systolic BP (mmHg) 130 ± 25 130 ± 20 0.805 Diastolic BP (mmHg) 70 ± 15 70 ± 15 0.469 MAP a (mmHg) 93.2 ± 9.2 92.5 ± 10.2 0.896 FPG a (mg/dL) 145.4 ± 40 141.7 ± 41.8 0.614 HbA1C a (%) 7.2 ± 1.1 7.3 ± 1.3 0.589 Urea (mg/dL) 30.9 ± 15.2 27.5 ± 12 0.714 Cr (mg/dL) 0.91 ± 0.23 0.9 ± 0.2 0.472 eGFR (mL/min/1.7/m2) 74.4 ± 18.6 a 73.6 ± 23.7 0.461 TG (mg/dL) 139 ± 72 147.5 ± 62.8 a 0.865 TC (mg/dL) 150 ± 65 145.6 ± 32.9 0.001 LDL-C (mg/dL) 74 ± 38 71.3 ± 24.4 a 0.01 HDL-C (mg/dL) 48 ± 14 49 ± 12 a 0.689 AIP 0.45 ± 0.21 0.44 ± 0.24 a 0.43 TyG index 3.98 ± 0.29 3.95 ± 0.24 a 0.87 TyG-BMI index 107.44 ± 19.12 a 105.82 ± 29.68 0.89 Note : Values were presented as median ± Interquartile Range or number. a Values with normal distribution were presented as mean ± Standard deviation. Abbreviations : COVID-19, coronavirus disease 2019; BMI, body mass index; BP, blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; Cr, creatinine; eGFR, estimated glomerular filtration rate; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIP, atherogenic index of plasma; TyG index, triglyceride–glucose index; TyG-BMI, triglyceride-glucose body mass index 4. Discussion We conducted a retrospective cohort study to investigate the effect of COVID-19 infection and pandemic-related restrictions on metabolic parameters in individuals with type 2 diabetes mellitus (T2DM). A total of 118 participants were involved and categorized into two groups: ‘COVID-19 positive’ and ‘COVID-19 negative’. The subjects were selected based on age and gender matching between the two groups. The present study showed that there was a significant decrease in TC and TG levels over six months following infection in the ‘COVID-19 positive’ group. A Mendelian randomization study suggested that dyslipidemia during the acute phase of COVID-19 may be primarily due to the exacerbation of pre-existing dyslipidemia rather than a direct effect of the virus (26), which might indicate a potential improvement in acute-phase dyslipidemia in T2DM patients as the COVID-19-related inflammation subsides. In a study of diabetic patients during the post-COVID-19 period, no evidence was found to indicate that the patients were involved in chronic inflammation six months after contracting the virus (27). Limiting inflammation to the acute phase of the disease and its cessation during the recovery period can allow the patients to return to a normal lipid profile. Our results on the improvement in TC and TG levels, along with the lack of decrease in HDL-C, are consistent with these studies. We identified that non-hospitalized patients with mild to moderate COVID-19 had a decrease in weight and BMI. This could be related to a prolonged period of diminished appetite in post-COVID-19, which may persist up to 180 days after recovery (28). This phenomenon is likely underpinned by the psychological stress of potential re-infection and inadequate information about the virus and its treatments, which precipitate reduction in body weight (29). Loss of appetite and taste disorders were commonly observed after the acute phase of infection and left patients vulnerable to malnutrition, which is associated with poor dietary quality and low protein intake (30). Malnutrition was reported to be more prevalent among non-hospitalized patients compared to those who were hospitalized and led to weight loss (31). Ongoing energy demands for tissue repair, on the one hand, and decreased fat and protein intake, on the other hand, led to weight loss and contributed to a further decrease in TC and TG. Lipid-related indices, including AIP, TyG, and TyG-BMI, demonstrated decreased levels in our study. These indices exhibited a positive correlation with insulin resistance and visceral obesity in the diabetic population (32, 33). A decrease in TG levels causes a significant reduction in calculating the indices without a concomitant decrease in insulin resistance, particularly in malnourished individuals with deficient protein and several minerals intake (34). In our study, post-COVID-19-related appetite loss and weight loss could result in decreased TG levels in affected individuals. However, this decrease did not lead to an improvement in insulin sensitivity, as evidenced by the absence of enhancements in glycemic control and HDL-C level. Our findings are in line with a previous study suggesting that insulin resistance persists for up to six months after a COVID-19 infection (27). No significant increase in HbA1C was observed in the ‘COVID-19 positive’ group. The transient dysfunction of pancreatic beta cells induced by COVID-19, leading to hyperglycemia during the acute phase of infection, did not demonstrate evidence of progressing into a chronic disorder. Therefore, Insulin production reverted to the pre-infection level (27). The alteration in HbA1C during the recovery period was attributed to the interplay between insulin secretion and increased insulin resistance following the viral contraction. The ‘COVID-19 negative’ group had a notable reduction in TC and LDL-C levels over the first six months of the lockdown period. This finding aligns with previous studies (19, 26, 35, 36), although some other research has shown inconsistencies (20, 35, 37). However, there were no statistically significant changes in TG and HDL-C levels during the same period (38). The first public health emergency declaration prompted diabetic patients in numerous countries to adopt a healthier dietary regimen (18–20). Adherence to dietary advice promoting reduced intake of saturated fats and increased consumption of soluble fibers purportedly influenced lipid profiles by lowering serum TC and LDL-C levels while showing no marked impact on TG and HDL-C levels (39, 40). Nonetheless, it is important to highlight that there was a lack of data regarding alterations in the dosage of statins for individuals with T2DM who had been administered them according to the physician’s opinion before the pandemic. This made it difficult to assess how these changes, combined with dietary modifications, affected their lipid profiles. There were no significant changes in weight and BMI within the noninfected population during the COVID-19 pandemic. The impact of the pandemic on BMI remains inconclusive, with some studies reporting an increase in BMI among uninfected individuals (20, 37, 41) while others had inconsistent results (35, 42, 43). The lack of notable weight changes may be attributed to a complex interplay of factors, such as changes in dietary patterns, physical activity, and emotional stress. Restrictions of the pandemic forced people to stay at home and resulted in the closure of gyms and clubs, leading to an overall decrease in physical activity (44). The population with T2DM was reported to have 21.9 more minutes of inactive time per day than their pre-pandemic physical activity (45). On the other hand, this population tended to drink more water, as well as eat increased amounts of fruits, vegetables, and home-cooked meals (46, 47). Our findings showed that there were no significant changes in other clinical and laboratory measurements, such as FPG, HbA1C, Urea, Cr, eGFR, and blood pressure, in the ‘COVID-19 negative’ group. Prior studies on these parameters have yielded conflicting results (48–54). Differences in the duration of patient follow-up and pre-lockdown glycemic control may explain the variation in the studies’ findings. To the best of our knowledge, this is the first study that simultaneously investigated the impact of the pandemic restrictions and mild COVID-19 infection on metabolic indices in individuals with T2DM who received the same level of healthcare. The limitations of the present study include the lack of data concerning changes in the dosage of lipid and glucose control medications in both participant groups. Additionally, we did not have information regarding the level of physical activity and dietary patterns adopted by our patients during the first months of the pandemic. 5. Conclusion Our study highlights the impact of COVID-19 and associated lockdown measures on the metabolic parameters of individuals with T2DM. Our findings suggest that individuals with well-managed type 2 diabetes prior to the pandemic, who subsequently experienced mild to moderate COVID-19 infection, demonstrated a decrease in specific metabolic parameters, potentially attributing to the sustained impact of the acute phase of the infection. Notably, the study reveals that pandemic-related restrictions did not exacerbate diabetes control in uninfected individuals. This insight suggests that well-controlled diabetes may not pose an important concern in the context of mild to moderate COVID-19 infection. These findings hold significant implications for clinical management strategies in individuals with type 2 diabetes during global health crises. Declarations Ethics approval and consent to participate: The study was approved by the Research Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (IR.SSU.REC.1401.097). Written informed consent to participate was obtained from all enrolled subjects. The study was performed in accordance with the principles of the Helsinki Declaration. Consent for publication: Not applicable Availability of data and materials: The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests: This study was conducted without any financial support. None of the authors have any potential conflict of interest associated with this research. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions: Motahare Shabestari: Methodology, Software, Formal analysis, Data Curation, Writing - Original Draft. Forouzan Salari: Methodology, Writing - Original Draft Reyhaneh Azizi: Writing - Review & Editing, Investigation. Akram Ghadiri-Anari : Conceptualization , Validation , Investigation , Supervision , Project administration Nasim Namiranian : Conceptualization , Validation , Investigation , Supervision. Acknowledgements: Non. References Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. Muller LM, Gorter KJ, Hak E, Goudzwaard WL, Schellevis FG, Hoepelman AI, et al. Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis. 2005;41(3):281-8. Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. 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The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli. J Diabetes Metab Disord. 2021;20(2):1461-7. D'Onofrio L, Pieralice S, Maddaloni E, Mignogna C, Sterpetti S, Coraggio L, et al. Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study. Diabetes Obes Metab. 2021;23(7):1624-30. Hartley L, May MD, Loveman E, Colquitt JL, Rees K. Dietary fibre for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2016;2016(1):Cd011472. Rees K, Dyakova M, Ward K, Thorogood M, Brunner E. Dietary advice for reducing cardiovascular risk. Cochrane Database Syst Rev. 2013(3):Cd002128. Ojo O, Wang XH, Ojo OO, Orjih E, Pavithran N, Adegboye ARA, et al. The Effects of COVID-19 Lockdown on Glycaemic Control and Lipid Profile in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2022;19(3). Önmez A, Gamsızkan Z, Özdemir Ş, Kesikbaş E, Gökosmanoğlu F, Torun S, et al. The effect of COVID-19 lockdown on glycemic control in patients with type 2 diabetes mellitus in Turkey. Diabetes Metab Syndr. 2020;14(6):1963-6. Rastogi A, Hiteshi P, Bhansali A. Improved glycemic control amongst people with long-standing diabetes during COVID-19 lockdown: a prospective, observational, nested cohort study. Int J Diabetes Dev Ctries. 2020;40(4):476-81. Lashkarbolouk N, Mazandarani M, Pourghazi F, Eslami M, Khonsari NM, Ghonbalani ZN, et al. How did lockdown and social distancing policies change the eating habits of diabetic patients during the COVID-19 pandemic? A systematic review. Front Psychol. 2022;13:1002665. Rowlands AV, Henson JJ, Coull NA, Edwardson CL, Brady E, Hall A, et al. The impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in individuals with type 2 diabetes. Diabet Med. 2021;38(10):e14549. Takahara M, Watanabe H, Shiraiwa T, Maeno Y, Yamamoto K, Shiraiwa Y, et al. Lifestyle changes and their impact on glycemic control and weight control in patients with diabetes during the coronavirus disease 2019 pandemic in Japan. J Diabetes Investig. 2022;13(2):375-85. Tanaka N, Hamamoto Y, Kurotobi Y, Yamasaki Y, Nakatani S, Matsubara M, et al. Lifestyle changes as a result of COVID-19 containment measures: Bodyweight and glycemic control in patients with diabetes in the Japanese declaration of a state of emergency. J Diabetes Investig. 2021;12(9):1718-22. Abdulan IM, Feller V, Oancea A, Maștaleru A, Alexa AI, Negru R, et al. Evolution of Cardiovascular Risk Factors in Post-COVID Patients. J Clin Med. 2023;12(20). Akpek M. Does COVID-19 Cause Hypertension? Angiology. 2022;73(7):682-7. Bento GAO, Leite VLT, Campos RP, Vaz FB, Daher EF, Duarte DB. Reduction of estimated glomerular filtration rate after COVID-19-associated acute kidney injury. J Bras Nefrol. 2023;45(4):488-94. Bielecka E, Sielatycki P, Pietraszko P, Zapora-Kurel A, Zbroch E. Elevated Arterial Blood Pressure as a Delayed Complication Following COVID-19-A Narrative Review. Int J Mol Sci. 2024;25(3). Li Q, Lin M, Deng Y, Huang H. The causal relationship between COVID-19 and estimated glomerular filtration rate: a bidirectional Mendelian randomization study. BMC Nephrol. 2024;25(1):21. Liu Y, Xia P, Cao W, Liu Z, Ma J, Zheng K, et al. Divergence between serum creatine and cystatin C in estimating glomerular filtration rate of critically ill COVID-19 patients. Ren Fail. 2021;43(1):1104-14. Profili F, Seghieri G, Francesconi P. Effect of diabetes on short-term mortality and incidence of first hospitalizations for cardiovascular events after recovery from SARS-CoV-2 infection. Diabetes Res Clin Pract. 2022;187:109872. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 30 Oct, 2024 Editor assigned by journal 30 Oct, 2024 Submission checks completed at journal 30 Oct, 2024 First submitted to journal 23 Oct, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5319823","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372290086,"identity":"6fee9412-714a-49c1-98d5-82e434474027","order_by":0,"name":"Motahare Shabestari","email":"","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Motahare","middleName":"","lastName":"Shabestari","suffix":""},{"id":372290089,"identity":"d3e19871-a88c-4e92-ba3e-7117bad96291","order_by":1,"name":"Forouzan Salari","email":"","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Forouzan","middleName":"","lastName":"Salari","suffix":""},{"id":372290092,"identity":"29c8a8b0-ea23-46ce-86cc-ee6fa798e13e","order_by":2,"name":"Reyhaneh Azizi","email":"","orcid":"","institution":"Diabetes Research Center, Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Reyhaneh","middleName":"","lastName":"Azizi","suffix":""},{"id":372290093,"identity":"4b765ce1-e0b2-41ed-bed8-18f55717bde9","order_by":3,"name":"Akram Ghadiri-Anari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCSBKAJHsDQzMJGrhOUCKFggjgUgt8tHND2883GMhrzvzjeHnggobBv727gS8WgzvHDO2SHgmYbjtdo6x9IwzaQwSZ85uwK9lRoKZRMIBCUagFgNp3rbDDAYSuYS0pH8DabHfdvOM8W+itMhL5IBtSdx2g8eMOFsMJHKKLYBakredSSuz5jmTxkPQL/Iz0jfe/HGgznbb8cObb/NU2Mjxt/cSsOUAnMlhACJ58CoH29IAZ7I/IKh6FIyCUTAKRiYAAPVaSCsCMZoYAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Akram","middleName":"","lastName":"Ghadiri-Anari","suffix":""},{"id":372290096,"identity":"062ad5e5-dba2-4d6d-87c4-04e9cb08662f","order_by":4,"name":"Nasim Namiranian","email":"","orcid":"","institution":"Diabetes Research Center, Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nasim","middleName":"","lastName":"Namiranian","suffix":""}],"badges":[],"createdAt":"2024-10-23 14:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5319823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5319823/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-025-03529-9","type":"published","date":"2025-02-03T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930443,"identity":"9aedda4f-c5e1-437b-8deb-e7dfc8756d80","added_by":"auto","created_at":"2025-02-10 16:11:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5319823/v1/e8b21300-35ae-4872-b905-966bd8eec7fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of COVID-19 on Metabolic Parameters in Patients with Type 2 Diabetes Mellitus","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a chronic and prevalent metabolic disorder affecting 10.5% of adults globally. Insulin resistance and hyperglycemia are the most important characteristics of the disease (1) that predispose the T2DM population to a wide range of pathogens and infections, even the common type (2). A cohort study showed that this population had a 30% higher risk of lower respiratory tract infection compared to the control group after one year of follow-up (3). The hyperglycemic condition impairs the glycolytic metabolism of immune cells and their ATP production, which decreases their functionality against infection (4). In addition, hyperglycemia has been shown to enhance the replication of several pathogens and fulfill their glycemic requirements, particularly in the case of SARS-CoV-2 infection (5).\u003c/p\u003e \u003cp\u003eThe SARS-CoV-2 infection caused the outbreak of Coronavirus disease 2019 (COVID-19), which was declared a pandemic in March 2020 (6). A bidirectional relationship between COVID-19 and T2DM has been observed, with diabetes exacerbating the severity of COVID-19 infection and the virus potentially worsening dysglycemia in patients with pre-existing diabetes(7\u0026ndash;10).\u003c/p\u003e \u003cp\u003eThe development of SARS-CoV-2 infection might be caused by its role in delaying the production of type-I interferons in host cells (11). Type-I interferons are produced in all nucleated cells in response to infection and act as an alarm signal to immune cells to gather at the site of infection. In addition to the virus, high levels of lactate, which are seen in the blood of individuals with T2DM, have been shown to impair the production of type-I interferons (12). Altogether, these events make T2DM patients susceptible to COVID-19 infection.\u003c/p\u003e \u003cp\u003eThe precise pathophysiological mechanisms underlying COVID-19-induced dysglycemia remain unclear. It has been reported that Angiotensin I-Converting Enzyme type 2 (ACE2) is abundantly expressed in pancreatic beta cells and serves as a receptor for COVID-19 (13\u0026ndash;15). This interaction potentially disrupts insulin secretion during acute infection. Additionally, the proinflammatory cytokine cascade, including TNF-α, IL-1, and IL-6, triggered by the immune response to the virus, may contribute to increased insulin resistance.(16).\u003c/p\u003e \u003cp\u003eAlongside the COVID-19 infection, its related lockdowns affected the lifestyle and healthcare access of individuals with T2DM. During the lockdown in Iran, this population faced a shortage of routine outpatient visits and medical facilities. However, the use of telemedicine and online visits increased, which advised patients to have self-management behaviors, drink enough water, eat healthy food, exercise, and monitor their blood sugar (17).\u003c/p\u003e \u003cp\u003eMost of the previous literature has focused on the impacts of the pandemic, with limited information available on the long-term effects of COVID-19 infection on metabolic parameters post-recovery from the initial phase of the infection (18\u0026ndash;21). This investigation aimed to provide insights into the metabolic changes caused by COVID-19 in patients with preexisting T2DM.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted at the Yazd Diabetes Research Center in Yazd, Iran. It utilized data collected retrospectively from the medical records of all eligible patients who attended the center between February 20, 2020 (the date when the first case of COVID-19 was officially announced in Iran) and October 21, 2020. The study was approved by the Research Ethics Council of Shahid Sadoughi University of Medical Sciences in Yazd, Iran (IR.SSU.REC.1401.097).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eThe number of T2DM patients who had at least one visit to our medical center between February 20, 2020, and October 21, 2020, was 372. However, only 118 of them met the study\u0026rsquo;s criteria and were eligible to be enrolled in the study. They were asked to express their consent to use their medical records without publishing their personal information in this study through the informed consent form.\u003c/p\u003e \u003cp\u003eClinical data and laboratory measurements were extracted from the medical records of the 118 participants. The duration of diabetes in the subjects ranged from 3 to 7 years, and they had minimal diabetic complications. Prior to the pandemic, the participants were receiving insulin and/or oral diabetic medications under the supervision of healthcare professionals at our medical center. Furthermore, individuals over 40 years old with type 2 diabetes mellitus (T2DM) were prescribed the optimal dose of statins. None of the subjects received new treatments during the study period, except for adjusting the previous medication dosage based on laboratory tests.\u003c/p\u003e \u003cp\u003eAll the participants were between 30 and 60 years old, as individuals within this age range typically had regular follow-up visits and were using prescription medications. It is recommended that each individual with T2DM undergo clinical and laboratory assessments by a primary care physician every three to six months, based on the national guidelines (22). Individuals who had irregular follow-up visits, lack of medical records within three months before the pandemic onset, and a history of immunodeficiency, neoplasia, co-infection, and smoking were excluded.\u003c/p\u003e \u003cp\u003eThe \"COVID-19 positive\" group consisted of 59 patients who tested positive for COVID-19 using the polymerase chain reaction (PCR) technique from February 20 to May 19, 2020, but were not hospitalized and recovered on an outpatient basis. Corticosteroid therapy was not administered during the infection phase, and the dosage of diabetic-related medications was adjusted based on individual laboratory tests. Also, those who were not receiving statins prior to contracting COVID-19 did not receive this medication during the infection. Patients without medical records six months after their first positive PCR test, as well as those with a documented reinfection during this period, were excluded.\u003c/p\u003e \u003cp\u003eThe \"COVID-19 negative\" group consisted of 59 individuals with no documented history of COVID-19 infection from February 20 to October 21, 2020. Those without medical records six months after the pandemic initiation in Iran were excluded. Gender and age matching was performed to ensure population homogeneity across the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinical variables and laboratory measures\u003c/h2\u003e \u003cp\u003eClinical data included age, gender, body mass index (BMI), and systolic and diastolic blood pressure. Blood samples obtained after 12 hours of fasting were analyzed for hemoglobin A1C (HbA1C), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (Cr). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI formula (23). Some of the lipid-related indices, including triglyceride glucose (TyG) index, triglyceride glucose-body mass index (TyG-BMI), and atherogenic index of plasma (AIP), were calculated using the following equations: (24, 25).\u003c/p\u003e \u003cp\u003eTyG Index\u0026thinsp;=\u0026thinsp;Ln (fasting glucose (mg/dL) \u0026times; triglycerides (mg/dL)/2)\u003c/p\u003e \u003cp\u003eTyG-BMI\u0026thinsp;=\u0026thinsp;BMI \u0026times; TyG index\u003c/p\u003e \u003cp\u003eAPI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:log\\frac{Triglycerides}{HDL}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 27. The normality of data was assessed using the Shapiro\u0026ndash;Wilk test. Variables following a normal distribution were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed variables were reported as medians and interquartile ranges. Differences within each group were analyzed using the paired t-test if variables were normal-distributed; otherwise, the Wilcoxon test was used. A P-value below 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eEach group included 59 patients with T2DM for analysis. Of these patients, 42 were female (71.2%), and 17 were male (28.8%). The median age of the \u0026lsquo;COVID-19 positive\u0026rsquo; group was 55 (interquartile range 8), and the median age of the \u0026lsquo;COVID-19 negative group was 56 (interquartile range 8) (P\u0026thinsp;=\u0026thinsp;0.84). None of the infected patients were hospitalized but were treated on an outpatient basis.\u003c/p\u003e \u003cp\u003eSix months after recovering from COVID-19, the \u0026lsquo;COVID-19 positive\u0026rsquo; group exhibited a significant decrease in weight (P\u0026thinsp;=\u0026thinsp;0.036), BMI (P\u0026thinsp;=\u0026thinsp;0.034), AIP (P\u0026thinsp;=\u0026thinsp;0.027), TyG index (P\u0026thinsp;=\u0026thinsp;0.001), and TyG-BMI index (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to pre-pandemic tests. Additionally, TG (P\u0026thinsp;=\u0026thinsp;0.001) and TC (P\u0026thinsp;=\u0026thinsp;0.028) decreased during this period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant changes were observed in other lipid profiles, including HDL-C and LDL-C, as well as Systolic and diastolic blood pressure, glycemic indices, and renal function.\u003c/p\u003e \u003cp\u003eIn the \u0026lsquo;COVID-19 negative\u0026rsquo; group, only TC (P\u0026thinsp;=\u0026thinsp;0.001) and LDL-C (P\u0026thinsp;=\u0026thinsp;0.01) levels showed significant reductions during the first six months of the lockdown (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Other clinical and laboratory variables did not exhibit notable changes\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\u003eClinical and laboratory data of patients in the \u0026lsquo;COVID-19 positive\u0026rsquo; group\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore contracting COVID-19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter contracting COVID-19\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\u003eWeight \u003csup\u003ea\u003c/sup\u003e (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI \u003csup\u003ea\u003c/sup\u003e (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\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\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\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\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP \u003csup\u003ea\u003c/sup\u003e (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\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\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR \u003csup\u003ea\u003c/sup\u003e (mL/min/1.7m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202\u0026thinsp;\u0026plusmn;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u0026thinsp;\u0026plusmn;\u0026thinsp;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.5\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159\u0026thinsp;\u0026plusmn;\u0026thinsp;34.2 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.19\u0026thinsp;\u0026plusmn;\u0026thinsp;33.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.91\u0026thinsp;\u0026plusmn;\u0026thinsp;27.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.07\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI index \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.73\u0026thinsp;\u0026plusmn;\u0026thinsp;21.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.51\u0026thinsp;\u0026plusmn;\u0026thinsp;21.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;\u003cb\u003e0.001\u003c/b\u003e\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\u003e \u003cb\u003eNote\u003c/b\u003e: Values were presented as median\u0026thinsp;\u0026plusmn;\u0026thinsp;Interquartile Range or number. \u003csup\u003ea\u003c/sup\u003e Values with normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard deviation. \u003cb\u003eAbbreviations\u003c/b\u003e: COVID-19, coronavirus disease 2019; BMI, body mass index; BP, blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; Cr, creatinine; eGFR, estimated glomerular filtration rate; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIP, atherogenic index of plasma; TyG index, triglyceride\u0026ndash;glucose index; TyG-BMI, triglyceride-glucose body mass index\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\u003eClinical and laboratory data of patients in \u0026lsquo;COVID-19 negative\u0026rsquo; group\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore the pandemic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSix months after the pandemic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight \u003csup\u003ea\u003c/sup\u003e (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI \u003csup\u003ea\u003c/sup\u003e (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\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\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\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\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP \u003csup\u003ea\u003c/sup\u003e (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG \u003csup\u003ea\u003c/sup\u003e (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.4\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.7\u0026thinsp;\u0026plusmn;\u0026thinsp;41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.7/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.8 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u0026thinsp;\u0026plusmn;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.6\u0026thinsp;\u0026plusmn;\u0026thinsp;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u0026thinsp;\u0026plusmn;\u0026thinsp;12 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.44\u0026thinsp;\u0026plusmn;\u0026thinsp;19.12 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.82\u0026thinsp;\u0026plusmn;\u0026thinsp;29.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: Values were presented as median\u0026thinsp;\u0026plusmn;\u0026thinsp;Interquartile Range or number. \u003csup\u003ea\u003c/sup\u003e Values with normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard deviation. \u003cb\u003eAbbreviations\u003c/b\u003e: COVID-19, coronavirus disease 2019; BMI, body mass index; BP, blood pressure; MAP, mean arterial pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; Cr, creatinine; eGFR, estimated glomerular filtration rate; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIP, atherogenic index of plasma; TyG index, triglyceride\u0026ndash;glucose index; TyG-BMI, triglyceride-glucose body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe conducted a retrospective cohort study to investigate the effect of COVID-19 infection and pandemic-related restrictions on metabolic parameters in individuals with type 2 diabetes mellitus (T2DM). A total of 118 participants were involved and categorized into two groups: \u0026lsquo;COVID-19 positive\u0026rsquo; and \u0026lsquo;COVID-19 negative\u0026rsquo;. The subjects were selected based on age and gender matching between the two groups.\u003c/p\u003e \u003cp\u003eThe present study showed that there was a significant decrease in TC and TG levels over six months following infection in the \u0026lsquo;COVID-19 positive\u0026rsquo; group. A Mendelian randomization study suggested that dyslipidemia during the acute phase of COVID-19 may be primarily due to the exacerbation of pre-existing dyslipidemia rather than a direct effect of the virus (26), which might indicate a potential improvement in acute-phase dyslipidemia in T2DM patients as the COVID-19-related inflammation subsides. In a study of diabetic patients during the post-COVID-19 period, no evidence was found to indicate that the patients were involved in chronic inflammation six months after contracting the virus (27). Limiting inflammation to the acute phase of the disease and its cessation during the recovery period can allow the patients to return to a normal lipid profile. Our results on the improvement in TC and TG levels, along with the lack of decrease in HDL-C, are consistent with these studies.\u003c/p\u003e \u003cp\u003eWe identified that non-hospitalized patients with mild to moderate COVID-19 had a decrease in weight and BMI. This could be related to a prolonged period of diminished appetite in post-COVID-19, which may persist up to 180 days after recovery (28). This phenomenon is likely underpinned by the psychological stress of potential re-infection and inadequate information about the virus and its treatments, which precipitate reduction in body weight (29).\u003c/p\u003e \u003cp\u003eLoss of appetite and taste disorders were commonly observed after the acute phase of infection and left patients vulnerable to malnutrition, which is associated with poor dietary quality and low protein intake (30). Malnutrition was reported to be more prevalent among non-hospitalized patients compared to those who were hospitalized and led to weight loss (31). Ongoing energy demands for tissue repair, on the one hand, and decreased fat and protein intake, on the other hand, led to weight loss and contributed to a further decrease in TC and TG.\u003c/p\u003e \u003cp\u003eLipid-related indices, including AIP, TyG, and TyG-BMI, demonstrated decreased levels in our study. These indices exhibited a positive correlation with insulin resistance and visceral obesity in the diabetic population (32, 33).\u003c/p\u003e \u003cp\u003eA decrease in TG levels causes a significant reduction in calculating the indices without a concomitant decrease in insulin resistance, particularly in malnourished individuals with deficient protein and several minerals intake (34). In our study, post-COVID-19-related appetite loss and weight loss could result in decreased TG levels in affected individuals. However, this decrease did not lead to an improvement in insulin sensitivity, as evidenced by the absence of enhancements in glycemic control and HDL-C level. Our findings are in line with a previous study suggesting that insulin resistance persists for up to six months after a COVID-19 infection (27).\u003c/p\u003e \u003cp\u003eNo significant increase in HbA1C was observed in the \u0026lsquo;COVID-19 positive\u0026rsquo; group. The transient dysfunction of pancreatic beta cells induced by COVID-19, leading to hyperglycemia during the acute phase of infection, did not demonstrate evidence of progressing into a chronic disorder. Therefore, Insulin production reverted to the pre-infection level (27). The alteration in HbA1C during the recovery period was attributed to the interplay between insulin secretion and increased insulin resistance following the viral contraction.\u003c/p\u003e \u003cp\u003eThe \u0026lsquo;COVID-19 negative\u0026rsquo; group had a notable reduction in TC and LDL-C levels over the first six months of the lockdown period. This finding aligns with previous studies (19, 26, 35, 36), although some other research has shown inconsistencies (20, 35, 37). However, there were no statistically significant changes in TG and HDL-C levels during the same period (38). The first public health emergency declaration prompted diabetic patients in numerous countries to adopt a healthier dietary regimen (18\u0026ndash;20). Adherence to dietary advice promoting reduced intake of saturated fats and increased consumption of soluble fibers purportedly influenced lipid profiles by lowering serum TC and LDL-C levels while showing no marked impact on TG and HDL-C levels (39, 40). Nonetheless, it is important to highlight that there was a lack of data regarding alterations in the dosage of statins for individuals with T2DM who had been administered them according to the physician\u0026rsquo;s opinion before the pandemic. This made it difficult to assess how these changes, combined with dietary modifications, affected their lipid profiles.\u003c/p\u003e \u003cp\u003eThere were no significant changes in weight and BMI within the noninfected population during the COVID-19 pandemic. The impact of the pandemic on BMI remains inconclusive, with some studies reporting an increase in BMI among uninfected individuals (20, 37, 41) while others had inconsistent results (35, 42, 43). The lack of notable weight changes may be attributed to a complex interplay of factors, such as changes in dietary patterns, physical activity, and emotional stress. Restrictions of the pandemic forced people to stay at home and resulted in the closure of gyms and clubs, leading to an overall decrease in physical activity (44). The population with T2DM was reported to have 21.9 more minutes of inactive time per day than their pre-pandemic physical activity (45). On the other hand, this population tended to drink more water, as well as eat increased amounts of fruits, vegetables, and home-cooked meals (46, 47).\u003c/p\u003e \u003cp\u003eOur findings showed that there were no significant changes in other clinical and laboratory measurements, such as FPG, HbA1C, Urea, Cr, eGFR, and blood pressure, in the \u0026lsquo;COVID-19 negative\u0026rsquo; group. Prior studies on these parameters have yielded conflicting results (48\u0026ndash;54). Differences in the duration of patient follow-up and pre-lockdown glycemic control may explain the variation in the studies\u0026rsquo; findings.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study that simultaneously investigated the impact of the pandemic restrictions and mild COVID-19 infection on metabolic indices in individuals with T2DM who received the same level of healthcare.\u003c/p\u003e \u003cp\u003eThe limitations of the present study include the lack of data concerning changes in the dosage of lipid and glucose control medications in both participant groups. Additionally, we did not have information regarding the level of physical activity and dietary patterns adopted by our patients during the first months of the pandemic.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study highlights the impact of COVID-19 and associated lockdown measures on the metabolic parameters of individuals with T2DM. Our findings suggest that individuals with well-managed type 2 diabetes prior to the pandemic, who subsequently experienced mild to moderate COVID-19 infection, demonstrated a decrease in specific metabolic parameters, potentially attributing to the sustained impact of the acute phase of the infection. Notably, the study reveals that pandemic-related restrictions did not exacerbate diabetes control in uninfected individuals. This insight suggests that well-controlled diabetes may not pose an important concern in the context of mild to moderate COVID-19 infection. These findings hold significant implications for clinical management strategies in individuals with type 2 diabetes during global health crises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Research Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (IR.SSU.REC.1401.097). Written informed consent to participate was obtained from all enrolled subjects. The study was performed in accordance with the principles of the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials:\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eThis study was conducted without any financial support. None of the authors have any potential conflict of interest associated with this research.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthor contributions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotahare Shabestari:\u0026nbsp;\u003c/strong\u003eMethodology, Software, Formal analysis, Data Curation, Writing - Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eForouzan Salari:\u0026nbsp;\u003c/strong\u003eMethodology, Writing - Original Draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReyhaneh Azizi:\u0026nbsp;\u003c/strong\u003eWriting - Review \u0026amp; Editing, Investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAkram Ghadiri-Anari\u003c/strong\u003e: \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eValidation\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eInvestigation\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eSupervision\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eProject administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNasim Namiranian\u003c/strong\u003e: Conceptualization\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eValidation\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eInvestigation\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eSupervision.\u003c/p\u003e\n\u003cp\u003eAcknowledgements:\u003c/p\u003e\n\u003cp\u003eNon.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/li\u003e\n \u003cli\u003eMuller LM, Gorter KJ, Hak E, Goudzwaard WL, Schellevis FG, Hoepelman AI, et al. Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis. 2005;41(3):281-8.\u003c/li\u003e\n \u003cli\u003eRao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364(9):829-41.\u003c/li\u003e\n \u003cli\u003eGreiner EF, Guppy M, Brand K. Glucose is essential for proliferation and the glycolytic enzyme induction that provokes a transition to glycolytic energy production. 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Diabetes Res Clin Pract. 2021;174:108750.\u003c/li\u003e\n \u003cli\u003eSelek A, Gezer E, Altun E, S\u0026ouml;zen M, Topaloğlu \u0026Ouml;, K\u0026ouml;ksalan D, et al. The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli. J Diabetes Metab Disord. 2021;20(2):1461-7.\u003c/li\u003e\n \u003cli\u003eD\u0026apos;Onofrio L, Pieralice S, Maddaloni E, Mignogna C, Sterpetti S, Coraggio L, et al. Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study. Diabetes Obes Metab. 2021;23(7):1624-30.\u003c/li\u003e\n \u003cli\u003eHartley L, May MD, Loveman E, Colquitt JL, Rees K. Dietary fibre for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2016;2016(1):Cd011472.\u003c/li\u003e\n \u003cli\u003eRees K, Dyakova M, Ward K, Thorogood M, Brunner E. Dietary advice for reducing cardiovascular risk. 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Int J Mol Sci. 2024;25(3).\u003c/li\u003e\n \u003cli\u003eLi Q, Lin M, Deng Y, Huang H. The causal relationship between COVID-19 and estimated glomerular filtration rate: a bidirectional Mendelian randomization study. BMC Nephrol. 2024;25(1):21.\u003c/li\u003e\n \u003cli\u003eLiu Y, Xia P, Cao W, Liu Z, Ma J, Zheng K, et al. Divergence between serum creatine and cystatin C in estimating glomerular filtration rate of critically ill COVID-19 patients. Ren Fail. 2021;43(1):1104-14.\u003c/li\u003e\n \u003cli\u003eProfili F, Seghieri G, Francesconi P. Effect of diabetes on short-term mortality and incidence of first hospitalizations for cardiovascular events after recovery from SARS-CoV-2 infection. Diabetes Res Clin Pract. 2022;187:109872.\u003c/li\u003e\n\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":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, COVID-19, metabolic profile, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-5319823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5319823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aim\u003c/h2\u003e \u003cp\u003eSince the onset of the Coronavirus disease 2019 (COVID-19) pandemic, individuals with Type 2 diabetes mellitus (T2DM) have been one of the most susceptible populations to contracting the virus. Also, COVID-19 and its related lockdown restrictions have affected metabolic regulatory mechanisms within the T2DM population. The present study aims to determine the impact of the viral infection and its lockdown on the physiological parameters in individuals with T2DM.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this retrospective cohort study, the medical records of 118 individuals with prior diagnoses of T2DM were collected. All the subjects had undergone at least one laboratory test within a maximum of three months before the onset of the COVID-19 pandemic in Iran. Fifty-nine patients with a confirmed COVID-19 infection within the first three months of the pandemic underwent a follow-up lab test six months after their initial COVID-19 diagnosis. The individuals without a history of the infection underwent a follow-up lab test six months after the onset of the pandemic. Noninfected populations were matched for age and gender with infected patients. Comparative analysis of clinical and laboratory data within each group was conducted.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;COVID-19 positive\u0026rsquo; group exhibited a significant decrease in levels of triglycerides (TG) (P\u0026thinsp;=\u0026thinsp;0.001) and total cholesterol (TC) (P\u0026thinsp;=\u0026thinsp;0.028), body mass index (BMI) (P\u0026thinsp;=\u0026thinsp;0.034), atherogenic index of plasma (AIP) (P\u0026thinsp;=\u0026thinsp;0.027), triglyceride glucose (TyG) index (P\u0026thinsp;=\u0026thinsp;0.001), and triglyceride glucose -BMI (TyG-BMI) index (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during the first six months post-infection compared to pre-pandemic. Other variables did not show notable changes. The \u0026lsquo;COVID-19 negative\u0026rsquo; group had a significant reduction in TC (P\u0026thinsp;=\u0026thinsp;0.001) and low-density lipoprotein cholesterol (LDL-C) (P\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eIndividuals with T2DM and mild to moderate COVID-19 infection experienced an improvement in the levels of TC, TG, BMI, and insulin-related indices. Restrictions in the first lockdown were associated with a decrease in the levels of TC and LDL-C in the T2DM population who did not have a history of the viral infection.\u003c/p\u003e","manuscriptTitle":"Impact of COVID-19 on Metabolic Parameters in Patients with Type 2 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 08:35:35","doi":"10.21203/rs.3.rs-5319823/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T15:06:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-30T10:11:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-30T10:09:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-10-23T14:28:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8c3d8f5-36fe-4e53-accc-807300e85c8f","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:03:01+00:00","versionOfRecord":{"articleIdentity":"rs-5319823","link":"https://doi.org/10.1186/s12890-025-03529-9","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2025-02-03 15:57:48","publishedOnDateReadable":"February 3rd, 2025"},"versionCreatedAt":"2024-11-11 08:35:35","video":"","vorDoi":"10.1186/s12890-025-03529-9","vorDoiUrl":"https://doi.org/10.1186/s12890-025-03529-9","workflowStages":[]},"version":"v1","identity":"rs-5319823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5319823","identity":"rs-5319823","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}