Composition Vendor Newgen KnowledgeWorks (P) Ltd. PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0319801 PONE-D-24-22312 Research Article Medicine and health sciencesEndocrinologyEndocrine disordersDiabetes mellitus Medicine and health sciencesMedical conditionsMetabolic disordersDiabetes mellitus Biology and life sciencesPopulation biologyPopulation metricsDeath rates Medicine and health sciencesMedical conditionsInfectious diseasesViral diseasesCOVID 19 Engineering and technologyTransportationAmbulances Medicine and health sciencesHealth careHealth care facilitiesHospitalsIntensive care units Medicine and health sciencesNephrologyMedical dialysis Medicine and health sciencesMedical conditionsInfectious diseasesRespiratory infections Medicine and health sciencesMedical conditionsRespiratory disordersRespiratory infections Medicine and health sciencesPulmonologyRespiratory disordersRespiratory infections Medicine and health sciencesEpidemiologyMedical risk factors Diabetes with COVID-19 was a significant risk factor for mortality, mechanical ventilation, and renal replacement therapies: A multicenter retrospective study in Japan Diabetes with COVID-19 as a risk factor for mortality, mechanical ventilation, and renal replacement therapies https://orcid.org/0000-0001-9102-630X Suwanai Hirotsugu Conceptualization Writing – original draft 1 * Kanda Masato Data curation 2 3 Harada Kazuharu Data curation 4 Ishii Keitaro Data curation Formal analysis 1 Iwasaki Hajime Data curation Formal analysis 1 Hara Natsuko Data curation Formal analysis Writing – review & editing 1 Kobayashi Yoshio Writing – review & editing 2 https://orcid.org/0000-0001-8788-7684 Matsumura Hajime Supervision Writing – review & editing 5 Inoue Takahiro Data curation Supervision Writing – review & editing 3 https://orcid.org/0000-0002-2965-6906 Suzuki Ryo Supervision Writing – review & editing 1 Department of Diabetes, Metabolism, and Endocrinology, Tokyo Medical University, Tokyo, Japan Department of Cardiovascular Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan Healthcare Management Research Center, Chiba University Hospital, Chiba, Japan Department of Health Data Science, Tokyo Medical University, Tokyo, Japan Department of Plastic and Reconstructive Surgery, Tokyo Medical University, Tokyo, Japan Yamaguchi Fumihiro Editor Showa University Fujigaoka Hospital, JAPAN

The authors have declared that no competing interests exist.

 * E-mail: suwanai-h@umin.ac.jp 1932025 2025 20 3 e0319801 462024 722025 2025 Suwanai et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

We conducted a multicenter retrospective cohort study across 38 hospitals in Chiba, Japan, between February 1, 2020 and November 31, 2021 to investigate the effect of coronavirus disease 2019 (COVID-19) on patients with diabetes mellitus receiving inpatient care. We collected inpatient medical data through Diagnosis procedure combination (DPC), the diagnoses and payment system of medical insurance, from each hospital. We excluded patients younger than 18 years, those who were pregnant, and those who had diabetes but were not treated with diabetic medication. A total of 10,776 patients were included: 7,679 in the non-diabetic (control) group and 3,097 in the diabetic group. Patients in the diabetic group were older and had a higher body mass index (BMI) than those in the control group. In the diabetes group, 88.4% of the patients were treated with insulin therapy and 44.2% were treated with oral hypoglycemic agents. The length of hospital days was significantly longer in the diabetes group. The in-hospital mortality rate was significantly higher especially between 50 and 59 years old. The rates of in-hospital mortality, mechanical ventilation, intensive care unit (ICU) admission, renal replacement therapies such as hemodialysis (HD), and continuous hemodiafiltration (CHDF) were all higher, even after adjusting for age, sex, BMI, and ambulance use. In conclusion, diabetes was a significant risk factor of the severe clinical outcomes especially for in-hospital mortality, mechanical ventilation usage, ICU admission, HD, and CHDF in Japan.

 The author(s) received no specific funding for this work. Data Availability Data cannot be shared publicly because of the research protocol approved by the ethics committee of Chiba University Hospital (approval number 3309 since 27th June 2022). Data are available from the Chiba University Hospital for researchers who meet the criteria for access to confidential data. Introduction

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is highly contagious and has caused the coronavirus disease 2019 (COVID-19) pandemic. As the COVID-19 continued to spread, researchers have investigated the factors contributing to the severity of the illness and its treatment, driven by its high mortality rate and the prevalence of cases. Hyperglycemia and diabetes were independent risk factors during the SARS-COV-1 outbreak in 2002–2003 [1]. Additionally, diabetes was reported to be a risk for severe disease in 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) [2]. Moreover, not only diabetes but also older age, male, hypertension, obesity, cancer, and kidney failure were identified as the multiple risk factors of COVID-19 [3–5].

COVID-19 is associated with multi-organ damage, making the clinical manifestations critical for understanding the pathophysiology of the disease. It often leads to acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) requiring hemodialysis, and other forms of organ failures [6]. These complications contribute to increased admissions to intensive care unit (ICU), where the use of mechanical ventilation and dialysis is common [7]. Treatment of COVID-19 in Japan followed Japanese guidelines, including the introduction of mechanical ventilation [8]. Although the mortality rate from SARS-CoV-2 infections has been relatively low in Japan, an elevated risk of mortality has been reported in patients with diabetes [9]. However, comprehensive studies examining the treatment and outcomes of COVID-19 in patients with diabetes remain limited.

Here, we conducted a study using real-world data on inpatient medical information and Diagnosis Procedure Combination (DPC) data in Japan to analyze the background of hospitalized patients. The aims of this research were to assess the impact of COVID-19 on patients with diabetes over time and to investigate their association with mechanical ventilation, ICU admissions, hemodialysis, and continuous hemodiafiltration (CHDF), as well as the implications for medical resource utilization. Finally, we evaluated the various effects of COVID-19 on mortality among patients with diabetes. The findings of this study may offer valuable insights for managing future infectious disease outbreaks.

 Materials and methods Data source

The DPC system is a classification system for hospital reimbursements from insurers with regular inspections by government in Japan. The DPC database contains inpatient clinical information, including disease diagnoses, comorbidities, medications, and medical treatments such as mechanical ventilation, intensive care units (ICU), and hemodialysis. Diseases were indicated according to the International Classification of Diseases, Tenth Revision (ICD-19) codes [10].

 Study population

The fully anonymized DPC data of 11,601 patients diagnosed and hospitalized with COVID-19 between February 1, 2020 and November 31, 2021, were extracted from 38 hospitals. The Data were accessed for research purposes and obtained on 6th July 2022. These hospitals had a research collaboration with the Chiba University Hospital Director’s Planning Office and maintained the DPC data. This retrospective study was conducted using these data. The data were fully anonymized, and authors did not have access to information that could identify individual participants during or after data collection.

 Outcomes and statistical analysis

The primary outcome was in-hospital mortality. Parameters of mechanical ventilation, ICU, hemodialysis, and continuous hemodiafiltration were investigated. All statistical analyses were performed using SPSS Statistics 28 (IBM, Armonk, NY, USA). Continuous variables were assessed using Student’s t-test, and categorical variables were analyzed using Fisher’s exact test. Logistic regression analyses were performed to determine the association between diabetes and outcomes after adjusting for confounders, including age, sex, body mass index (BMI), and ambulance. A P < 0.05 was considered statistically significant.

 Ethics statements

This study adhered to the ethical standards of the Declaration of Helsinki. The study was approved by the ethics committee of Chiba University Hospital (approval number 3309 since 27th June 2022). This research is not registered on the clinical trials registry system because the design of this study is retrospective and public by opt-out method presenting on hospital websites. The requirement for direct informed consent was waived owing to the anonymity of the data. We believe that the clinical trials registry system would not affect the results and conclusions of the study strongly.

 Result

Of the 11,601 cases, 825 were excluded:402 were under the age of 18, 114 were pregnant, and 309 were cases with diabetes mellitus but not on diabetic medication. Although diagnosis of diabetes using DPC data has been reported to be accurate [11], we excluded cases of diabetes without medication, which were less than 3% among all cases, to analyze diabetes group more accurately. Of the remaining 10,776 cases, 7,679 and 3,097 were classified in the non-diabetic (control) and diabetic group, respectively (Fig 1). Among 3,097 cases of diabetes, those are mostly type 2 diabetes except 15 cases were type 1 diabetes (0.48%).

10.1371/journal.pone.0319801.g001Flow chart of the population selection for this study.

The data was extracted between February2020 and November 2021. Among 11,601 cases, 825 were excluded because 402 were under the age of 18, 114 were pregnant, and 309 were cases with diabetes mellitus but not on diabetic medication. The group of control had 7,679 cases while the group of diabetes had 3,097 cases.

The background results showed a correlation of age, male sex, and body mass index (BMI). The mean age of patients in the diabetic group was 67.4 years (55.7 years in the control group, p-value < 0.001). The proportion of males was higher (68.2%) in the diabetes group than in the control group (54.9%) (odds ratio [OR] 1.75, 95% confidence interval [CI] 1.60–1.91, p-value < 0.001). For patients with diabetes, the BMI was 25.6 kg/m2, whereas that of the control was 23.6 kg/m2 (p < 0.001). The existence of diabetes augmented the probability of hospitalization via ambulance (22.2% in the control group vs 40.7% in the diabetes group, OR 2.41, 95% CI 2.21–2.63, p < 0.001). Regarding diabetes treatment, oral hypoglycemic agents were administered in 44.2% of patients with diabetes. The rate of insulin use was 88.4%, indicating a high rate of insulin therapy in the diabetes group (Table 1). Among hypoglycemic agents, DPP-4 inhibitors were the most common medication (42.4%). All background of hypoglycemic agents is presented in data (S1 Table).

10.1371/journal.pone.0319801.t001Clinical characteristics of controls and patients with diabetes. Abbreviations: BMI=body mass index, OHA=Oral hypoglycemic agents.
Total (n = 10776) Controls (n = 7679) Patients with diabetes (n = 3097) p-value Odds ratio 95% confidence interval
Age (years), mean(SD) 59.0(20.1) 55.7(21.1) 67.1(14.5) <0.001
Male, n(%) 6325(58.7) 4214(54.9) 2111(68.2) <0.001 1.75 1.60-1.91
BMI (kg/m2), mean(SD) 24.2(5.1) 23.6(4.9) 25.6(5.4) <0.001
Ambulance, n(%) 2963(27.5) 1702(22.2) 1261(40.7) <0.001 2.41 2.21-2.63
OHA, n(%) 1370(12.7) 0(0.0) 1370(44.2)
Insulin therapy, n(%) 2739(25.4) 0(0.0) 2739(88.4)

The therapeutic interventions for patients with diabetes were more severe than those for controls. The rate of steroid use was significantly higher in the diabetes (77.8%) than in control (28.35) group because patients with diabetes often have pneumonia and are treated with steroid (OR 8.86, 95% CI 8.03–9.77, p < 0.001). The continuous hemodiafiltration (CHDF) and hemodialysis (HD) were introduced at a high rate in the diabetes group, indicating that they had potentially severe circulation dynamics, renal, and heart failures (CHDF: OR 14.05, 95% CI 8.49–23.26, p < 0.001; HD: OR 3.91, 95% CI 2.85–5.36, p < 0.001). Because patients with severe symptoms of COVID-19 were treated with mechanical ventilation in ICU, the usage of mechanical ventilation and ICU were more common for patients with diabetes (mechanical ventilation: OR 13.35, 95% CI 10.93–16.30, p < 0.001; ICU: OR 7.07, 95% CI 6.00–8.33, p < 0.001) (Table 2).

10.1371/journal.pone.0319801.t002Clinical information of inpatient care for controls and patients with diabetes. Abbreviations: CHDF=continuous hemodiafiltration, ICU=intensive care unit.
Total (n = 10776) Controls (n = 7679) Patients with diabetes (n = 3097) p-value Odds ratio 95% confidence interval
Steroid, n(%) 4585(42.6) 2176(28.3) 2409(77.8) <0.001 8.86 8.03-9.77
CHDF, n(%) 117(1.1) 18(0.2) 99(3.2) <0.001 14.05 8.49-23.26
Hemodyalysis, n(%) 165(1.5) 65(0.8) 100(3.2) <0.001 3.91 2.85-5.36
Mechanical ventilation, n(%) 676(6.3) 123(7.8) 553(17.9) <0.001 13.35 10.93-16.30
ICU, n(%) 742(6.9) 216(2.8) 526(17.0) <0.001 7.07 6.00-8.33
Days in hospitals, mean(SD) 12.4(11.4) 10.2(8.5) 17.8(15.3) <0.001
In-hospital mortality, n(%) 671(6.3) 271(3.5) 400(12.9) <0.001 4.05 3.45-4.78
Days in hospitals until mortality, mean(SD) 17.0(14.6) 13.4(11.5) 19.4(15.9) <0.001

Patients with diabetes were hospitalized for longer periods of time. The mean length of hospital stay was 17.8 days (10.2 days in the control group), and the in-hospital mortality rate was high at 12.9% (3.5% in the control group (OR, 4.05; p < 0.001). Additionally, the terms in hospitals until mortality were longer. The mean number of days of hospitalization until mortality in the diabetes group was 19.4 days (13.4 days in the control group) (Table 2).

The number of patients varied over time. The first case of COVID-19 was confirmed in Japan on 15th January, 2020. Thereafter, the number of affected patients has increased. From 7th April to 25th May, 2020, the government issued its first emergency declaration, and the activities were restrained. The number of patients increased in the latter half of 2020. The number of patients decreased due to the issuance of a second emergency declaration from 8th January to 21st March in2021 (Fig 2A).

10.1371/journal.pone.0319801.g002Overview of control group and patients with diabetes in each month.

A) Monthly movement of number of patients of each group B) Monthly rate of patient age. The graph shows each group aged between 18 and 39 years; 40 and 69 years; and over 70 years C) Monthly mortality in the control group and patients with diabetes.

Vaccination began from 17th February, 2021 starting with healthcare workers and older adults. Subsequently, the number of infected patients aged >  70 years has decreased. A third state of emergency was declared from 25th April to 20th June, 2021. The fourth emergency declaration was from 12th July to 30th September, 2021 (Fig 2B).

Mortality remained higher in the diabetes group than in the control group. Mortality was relatively high among patients with diabetes from March to June 2020, during the early stages of the COVID-19 pandemic. With an increase in the number of hospitalized patients, there was an increasing trend in mortality from October to December 2020. Thereafter, mortality declined as the number of hospitalizations decreased and vaccines became more widely available in 2021. After December 2021, omicron strains, which were reported to have a lower mortality rate, became predominant, and the data were excluded from the analysis (Fig 2C).

We conducted a binary logistic regression analysis to evaluate the contribution of various factors to mortality of COVID-19. The analysis included age, BMI, sex (male), diabetes, and ambulance use as response variables. Among these, diabetes emerged as the most significant predictor of mortality (OR 2.67, p value < 0.01) (S2 Table).

Subgroup analyses of the age were performed to determine the correlation between diabetes and mortality. We observed an increase in diabetes prevalence with age, peaking between 70 and 79 years old. The prevalence of diabetes was higher in male patients than in female (Fig 3A). The OR was high in patients aged >  40 years. In particular, the peak OR was 12.8 (95%CI 3.71–44.1, p < 0.01) in ages between 50 and 59 years, indicating that diabetes was a strong risk factor for COVID-19 mortality in the middle-aged generation (Fig 3B and 3C).

10.1371/journal.pone.0319801.g003Subgroup analyses of the age in diabetes prevalence and mortality.

A) Diabetes prevalence classified according to age and sex. B) Mortality rate of control and patients with diabetes in each generation. C) Odds ratio of in-hospital mortality for patients with diabetes compared to controls at each generation.

Regression analysis was performed to test the effect of diabetes on the outcomes. The ORs with diabetes for in-hospital mortality, mechanical ventilation, ICU, HD, and CHDF were significantly higher compared to the control group in unadjusted analysis (in-hospital mortality: OR 4.05, 95%CI 3.45–4.76; mechanical ventilation: OR 13.35, 95%CI 10.93–16.32; ICU:7.07, 95%CI 6.00–8.33; HD: OR 3.91 95%CI 2.85-5.36; and CHDF: OR 14.06, 95%CI 8.49–23.27). Because there were significant differences in age, sex, BMI, and ambulance use between the control and diabetes groups, adjustment model of multiple regression analysis was conducted. As we conducted multiple regression analysis using adjustment with age, sex, BMI, ambulance, the ORs for all outcomes remained significant, proving that diabetes contributed as an independent factor of outcomes (in-hospital mortality: OR 2.67, 95%CI 2.17–3.28; mechanical ventilation: OR 8.31, 95%CI 6.61–10.45; ICU:4.19, 95%CI 3.46–5.08, HD: OR 3.95 95%CI 2.69–5.79; and CHDF: OR 10.40, 95%CI 5.70–18.99) (Fig 4). All those findings indicated that diabetes was a significant risk factor of the severe clinical outcomes especially for in-hospital mortality, mechanical ventilation use, ICU admission, renal replacement therapies such as HD, and CHDF.

10.1371/journal.pone.0319801.g004Forest plot of multiple regression analysis for in-hospital mortality, mechanical ventilation, ICU, HD, and CHDF.

Regression analysis was performed to test the effect of diabetes on the outcomes. The ORs with diabetes for in-hospital mortality, mechanical ventilation, ICU, HD, and CHDF were significantly higher compared to the control group in unadjusted analysis. Multiple regression model with adjustment of age, sex, BMI, and ambulance use was used for each outcome.

 Discussion

In this study, we analyzed the effects of COVID-19 on patients with and without diabetes in Japan using real-world data. Diabetes was a significant factor in in-hospital mortality, mechanical ventilation, ICU stay, HD, and CHDF, even after adjusting for age, sex, BMI, and ambulance use, indicating that diabetes is associated with a worse prognosis for COVID-19. In addition, the mortality rate was higher in the diabetes group, especially among those aged 18–79 years, than in the control group, indicating that the presence of diabetes increases risk in the younger generation.

Previous studies have heavily implicated diabetes and COVID-19 mortality rates [12,13]. Our data show a similar trend. Additionally, we analyzed treatment data and found that diabetes itself was an independent risk factor for mortality, mechanical ventilation, ICU stay, HD, and CHDF, even after adjusting for age, sex, BMI, and ambulance use.

The strength of our study is that we used real-world data and based on actual treatments administered during hospitalization. We believe that it is particularly important to comprehensively analyze mortality rates and interventions that require diabetes treatment, classified into age group. The results of this study revealed that the risk of mortality was significantly higher in the group that required diabetes treatment.

It is believed that COVID-19 infects not only the lungs but also kidneys and pancreatic beta cells, causing inflammation [14,15]. The viral infection depends on the expression of angiotensin-converting enzyme 2 (ACE2) and TMPRSS-2 transmembrane protease, which are the main cellular factors involved in viral entry. ACE2 is expressed in the lungs, and the virus infects the lungs and causes ARDS [16]. COVID-19 stimulates strong immune response, resulting in a cytokine storm. It is possible that patients with diabetes are unable to suppress inflammation and that the virus enters the organs via those proteins, further exacerbating the inflammation and hyperglycemia [15,17].

In this study, diabetes was a significant factor in in-hospital mortality, mechanical ventilation, ICU stay, HD, and CHDF. AKI associated with COVID-19 is mainly caused by proximal tubular dysfunction. ACE2 expression is abundant in the proximal tubules of the kidney, and it is believed that SARS-CoV-2 infects these cells, causing damage [18,19]. AKI complications were reported in 36.6% of patients admitted with COVID-19 [20]. The combination of renal and respiratory failure is thought to have led to the use of mechanical ventilation, continuous hemodialysis (CHDF), and hemodialysis (HD) to support critically ill patients in the ICU.

SARS-CoV-2 infects pancreatic beta cells and reduces their function. In this study, the diabetes group was defined as patients receiving diabetes care during hospitalization. It is assumed that diabetes treatment was performed because of elevated blood glucose levels. This indicates that the virus may have infected pancreatic beta cells, potentially leading to a significant impact on blood glucose levels [21–23].

Japanese individuals are characterized by lower insulin secretion than Westerners. Asians, including the Japanese, often develop type 2 diabetes, even if their obesity is relatively modest compared to Westerners [24,25]. Due to the government’s emergency declaration, activities were restricted. The following factors were noted among those who gained weight after the spread of COVID-19: shortened sleeping hours during long periods of time indoors, increased snacking after dinner, lack of exercise, overeating due to stress, and loss of motivation to restrict eating, suggesting the importance of daily lifestyle habits [26]. As obese individuals with high insulin resistance may easily become hyperglycemic due to infection, it is necessary to raise awareness of the importance of proper weight control in preventing the spread of infection.

This study had some limitations. Our data provide information on diagnoses and treatments during hospitalization but not prior to hospitalization. Additionally, our data did not include information on blood examinations, vital signs, laboratory parameters, radiographic data including PaO2/FiO2 ratio and severity classification of COVID-19. We also could not obtain information on vaccination histories. However, we believe that our study sheds light on the clinical characteristics of COVID-19 and diabetes for improved medication in the future.

In conclusion, diabetes was an independent risk factor for in-hospital mortality, mechanical ventilation, ICU stay, HD, and CHDF, even after adjusting for age, sex, BMI, and ambulance use. The mortality rate was higher in the diabetes group, especially among those aged 18–79 years, than in the control group. Diabetes has been confirmed as a risk factor for clinical severity and in-hospital mortality with COVID-19.

 Supporting information Content of hypoglycemic agents for patients with diabetes.

Abbreviations: DPP-4 = dipeptidyl peptidase 4, SGLT2 = sodium glucose co-transporter 2, AGI=Alpha-glucosidase inhibitor.

(DOCX)

 Multiple regression analysis of mortality adjusted with each factor, diabetes, age, sex (male), BMI, and ambulance.

Abbreviation: BMI=Body mass index.

(DOCX)

 Spreadsheet of hypoglycemic agents with generic names for patients with diabetes.

(CSV)

The authors are grateful to all participants in this study. We would like to thank Editage (www.editage.jp) for English language editing.

 References Yang JK, Feng Y, Yuan MY, Yuan SY, Fu HJ, Wu BY, et al. Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med. 2006;23(6):623–8. doi: 10.1111/j.1464-5491.2006.01861.x 16759303 Alraddadi BM, Watson JT, Almarashi A, Abedi GR, Turkistani A, Sadran M, et al. Risk Factors for Primary Middle East Respiratory Syndrome Coronavirus Illness in Humans, Saudi Arabia, 2014. Emerg Infect Dis. 2016;22(1):49–55. doi: 10.3201/eid2201.151340 26692185 Dennis JM, Mateen BA, Sonabend R, Thomas NJ, Patel KA, Hattersley AT, et al. Type 2 Diabetes and COVID-19-Related Mortality in the Critical Care Setting: A National Cohort Study in England, March-July 2020. Diabetes Care. 2021;44(1):50–7. doi: 10.2337/dc20-1444 33097559 Clift AK, Coupland CAC, Keogh RH, Diaz-Ordaz K, Williamson E, Harrison EM, et al. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ. 2020;371:m3731. doi: 10.1136/bmj.m3731 33082154 Dessie ZG, Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infect Dis. 2021;21(1):855. doi: 10.1186/s12879-021-06536-3 34418980 Moledina DG, Simonov M, Yamamoto Y, Alausa J, Arora T, Biswas A, et al. The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study. Am J Kidney Dis. 2021;77(4):490-499.e1. doi: 10.1053/j.ajkd.2020.12.007 33422598 Chang R, Elhusseiny KM, Yeh Y-C, Sun W-Z. COVID-19 ICU and mechanical ventilation patient characteristics and outcomes-A systematic review and meta-analysis. PLoS One. 2021;16(2):e0246318. doi: 10.1371/journal.pone.0246318 33571301 Kato Y. Case management of COVID-19 (secondary version). Jma j. 2021;4(3):191–7. Nojiri S, Irie Y, Kanamori R, Naito T, Nishizaki Y. Mortality Prediction of COVID-19 in Hospitalized Patients Using the 2020 Diagnosis Procedure Combination Administrative Database of Japan. Intern Med. 2023;62(2):201–13. doi: 10.2169/internalmedicine.0086-22 36328573 Hayashida K, Murakami G, Matsuda S, Fushimi K. History and Profile of Diagnosis Procedure Combination (DPC): Development of a Real Data Collection System for Acute Inpatient Care in Japan. J Epidemiol. 2021;31(1):1-11. Kanehara R, Goto A, Goto M, Takahashi T, Iwasaki M, Noda M, et al. Validation study of diabetes definitions using Japanese diagnosis procedure combination data among hospitalized patients. J Epidemiol. 2023;33(4):165–9. Holman N, Knighton P, Kar P, O’Keefe J, Curley M, Weaver A, et al. Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study. Lancet Diabetes Endocrinol. 2020;8(10):823–33. doi: 10.1016/S2213-8587(20)30271-0 32798471 Singh AK, Khunti K. COVID-19 and Diabetes. Annu Rev Med. 2022;73:129–47. doi: 10.1146/annurev-med-042220-011857 34379444 Khan S, Chen L, Yang C, Raghuram V, Khundmiri S, Knepper M. Does SARS-CoV-2 infect the kidney? J Am Soc Nephrol. 2020;31(12):2746–8. Poloni T, Moretti M, Medici V, Turturici E, Belli G, Cavriani E, et al. COVID-19 pathology in the lung, kidney, heart and brain: The different roles of T-cells, macrophages, and microthrombosis. Cells. n.d.;11(19):. Zipeto D, Palmeira J, Argañaraz G, Argañaraz E. ACE2/ADAM17/TMPRSS2 interplay may be the main risk factor for COVID-19. Frontiers in Immunology. 2020;11:576745. Feldman EL, Savelieff MG, Hayek SS, Pennathur S, Kretzler M, Pop-Busui R. COVID-19 and Diabetes: A Collision and Collusion of Two Diseases. Diabetes. 2020;69(12):2549–65. doi: 10.2337/dbi20-0032 32938731 Santoriello D, Khairallah P, Bomback AS, Xu K, Kudose S, Batal I, et al. Postmortem Kidney Pathology Findings in Patients with COVID-19. J Am Soc Nephrol. 2020;31(9):2158-67. Werion A, Belkhir L, Perrot M, Schmit G, Aydin S, Chen Z, et al. SARS-CoV-2 causes a specific dysfunction of the kidney proximal tubule. Kidney Int. 2020;98(5):1296–307. doi: 10.1016/j.kint.2020.07.019 32791255 Silver SA, Beaubien-Souligny W, Shah PS, Harel S, Blum D, Kishibe T, et al. The Prevalence of Acute Kidney Injury in Patients Hospitalized With COVID-19 Infection: A Systematic Review and Meta-analysis. Kidney Med. 2021;3(1):83-98.e1. doi: 10.1016/j.xkme.2020.11.008 33319190 Wu C-T, 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. 2021;33(8):1565-1576.e5. doi: 10.1016/j.cmet.2021.05.013 34081912 Montefusco L, Ben Nasr M, D’Addio F, Loretelli C, Rossi A, Pastore I, et al. Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection. Nat Metab. 2021;3(6):774–85. doi: 10.1038/s42255-021-00407-6 34035524 Steenblock C, Schwarz PEH, Ludwig B, Linkermann A, Zimmet P, Kulebyakin K, et al. COVID-19 and metabolic disease: mechanisms and clinical management. Lancet Diabetes Endocrinol. 2021;9(11):786–98. doi: 10.1016/S2213-8587(21)00244-8 34619105 Chiu M, Austin PC, Manuel DG, Shah BR, Tu JV. Deriving ethnic-specific BMI cutoff points for assessing diabetes risk. Diabetes Care. 2011;34(8):1741–8. doi: 10.2337/dc10-2300 21680722 Sone H, Ito H, Ohashi Y, Akanuma Y, Yamada N, Japan Diabetes Complication Study Group. Obesity and type 2 diabetes in Japanese patients. Lancet. 2003;361(9351):85. doi: 10.1016/S0140-6736(03)12151-4 12517507 Zachary Z, Brianna F, Brianna L, Garrett P, Jade W, Alyssa D, et al. Self-quarantine and weight gain related risk factors during the COVID-19 pandemic. Obes Res Clin Pract. 2020;14(3):210–6. doi: 10.1016/j.orcp.2020.05.004 32460966 10.1371/journal.pone.0319801.r001 Author response to Decision Letter 0 Submission Version 0

4 Jun 2024

 10.1371/journal.pone.0319801.r002 Decision Letter 0 Yamaguchi Fumihiro Academic Editor 2025 Fumihiro Yamaguchi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

20 Oct 2024

PONE-D-24-22312Diabetes with COVID-19 was a significant risk factor for mortality, respirator use, and renal replacement therapies: A multicenter retrospective study in JapanPLOS ONE

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Partly

**********

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Dear Authors

The current manuscript is outstanding. Even so, there are some questions.

The current manuscript needs to improve some points. The results are descriptive and straightforward comparisons between the two groups (diabetes mellitus and control).

Introduction

The authors should briefly comment on the relationship between hypertension and COVID-19 and briefly describe the association between COVID-19 and outcomes such as AKI, the need for mechanical ventilation, and mortality.

Materials and Methods

What does it mean? The

-Diseases were indicated according to the International Classification of 83 Diseases, Tenth Revision (ICD-19) codes (6). The (????)-

Study Population

How did you ensure that you evaluated patients diagnosed with diabetes mellitus? And about COVID-19? Why didn't you run some blood tests? Was there blood glucose in the patients or glycated hemoglobin? What about creatinine, arterial blood gas analysis, and hemoglobin concentration?

Why didn't you perform some matching between the diabetes and control groups, such as age, sex, or comorbidities? If you had, it would have made comparisons more robust.

Results

Would the authors be able to inform the frequency of patients with type 1 diabetes in the sample?

It would be interesting for you to build a binary logistic regression table using mortality as the response variable.

You need to correct this sentence:

The data was extracted between February220 and November 2021

The proportion of males was higher (54.9%) in the control group than in the diabetes 132 group (68.2%) (odds ratio [OR] 1.75, 95% confidence interval [CI] 1.60–1.91). Report me about that, is 54.9% higher than 68,2%?

For patients with 133 diabetes, the BMI was 25.6%, whereas that of the control was 23.6 (p <0.001). What does it mean? BMI, 25.6 kg/m²? And 23.6 kg/m²?

What kind of hypoglycemic agents were used for patients with diabetes mellitus?

Could the authors report the PaO2/FiO2 ratio at baseline in patients undergoing mechanical ventilation?

Discussion

The authors need to discuss the hyperglycemia observed in patients with infection or even the fact that infections, including SARS-CoV-2, decompensate diabetes mellitus.

The authors need to discuss the relationship between the need for mechanical ventilation, AKI, dialysis requirement, and mortality in patients with diabetes mellitus and COVID-19.

Reviewer #2: I have some comments and questions for the authors

1. What is the reason for the existence of diabetes to augmented the probability of hospitalization via ambulance?

2. 77.8% of patients with diabetes used steroids for the treatment of pneumonia. I think it is important to know what percentage of pneumonias. What percentage were severe pneumonias?

3. 7.8% of the control group and 17.9% of the group with diabetes required mechanical ventilation. What was the main reason why the patients required this support? Do you have respiratory data such as paO2/fiO2

ratio, or others?

4. An important conclusion is that diabetes was an independent risk factor for in-hospital mortality. However, other conclusions such as risk factor for respirator use require further discussion.

5. The manuscript does not describe severity scores for the control group and the diabetes group. I think it is important to establish the severity of each group

6. We cannot see in the manuscript hemodynamic and inflammatory variables and other comorbidities of each group. As in point 5, it is important to know these data

Reviewer #3: The submitted manuscript presents an interesting topic, aiming to examine the effect of COVID-19 on patients with diabetes mellitus receiving inpatient care.

To enhance the reliability and comprehensibility of this work for researchers, the following recommendations may be considered:

1. In the introduction, line 70, you mentioned that this study was conducted using real-world data on inpatient medical information and Diagnosis Procedure Combination (DPC) data in Japan to analyze the background of hospitalized patients. However, the authors did not mention the aims of the study. I think you should clearly explain the main aims of this study.

2. The numbers and percentages in Tables 1 and 2 are inconsistent. For example, in Table 1, for the control group, the mean age in (years) is in the first line, the SD is in the second line. However, for the patient group, both are in the same line. Could you please make the numbers and percentages consistent for both Tables 1 and 2?

3. The authors concluded that diabetes was a significant risk factor for severe clinical outcomes, especially for in-hospital mortality, respirator use, ICU admission, renal replacement therapies such as HD and CHDF. The manuscript would benefit from deeper biological interpretations and a more thorough exploration of the underlying mechanisms. For example, what are the broader implications of COVID-19 on these parameters (in-hospital mortality, respirator use, ICU admission, renal replacement therapies such as HD and CHDF)? How might these findings tie into the pathophysiology of COVID-19 cases?

4. Another important point is whether the COVID-19 cases included in this study were mild, moderate, or severe. If possible, could you identify the effect of COVID-19 on mortality, respirator use, ICU admission, and renal replacement therapies in terms of COVID-19 severity.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes:  OMEED DARWEESH

**********

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 10.1371/journal.pone.0319801.r003 Author response to Decision Letter 1 Submission Version 1

3 Dec 2024

We appreciate all reviewers for thoughtful suggestions and opinions. We improved manuscripts and responded to all the questions written below.

� Introduction

� The authors should briefly comment on the relationship between hypertension and COVID-19 and briefly describe the association between COVID-19 and outcomes such as AKI, the need for mechanical ventilation, and mortality.

Thank you for your insightful suggestion. We improved the introduction.

Line62

Moreover, not only diabetes but also older age, male, hypertension, obesity, cancer, and kidney failure were identified as the multiple risk factors of COVID-19 (3-5).

COVID-19 is associated with multi-organ damage, making the clinical manifestations critical for understanding the pathophysiology of the disease. It often leads to acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) requiring hemodialysis, and other forms of organ failures(6). These complications contribute to increased admissions to intensive care unit (ICU), where the use of mechanical ventilation and dialysis is common(7).

� Materials and Methods

� What does it mean? The

� -Diseases were indicated according to the International Classification of 83 Diseases, Tenth Revision (ICD-19) codes (6). The (????)-

Thank you for your comment. It was a mistake and corrected.

Line 91

Diseases, Tenth Revision (ICD-19) codes (6).

� Study Population

� How did you ensure that you evaluated patients diagnosed with diabetes mellitus? And

about COVID-19? Why didn't you run some blood tests? Was there blood glucose in the patients or glycated hemoglobin? What about creatinine, arterial blood gas analysis, and hemoglobin concentration?

This data is based on the combination data of diagnoses and payment system of medical insurance from each hospital. The diagnoses were based on the criteria of diabetes based on international standard and the medical doctors and hospitals claimed the insurance based on the diagnostic criteria of real clinical data with regular government inspections. The diagnosis of diabetes using DPC data has been reported to be accurate and we cited the article for the study of rationale. The data include the accurate diagnosis of diseases and payment information, but the blood data is not included unfortunately. We improved the explanation of the data in to make the article easy to understand.

Line 38

We collected inpatient medical data through Diagnosis procedure combination (DPC), the diagnoses and payment system of medical insurance, from each hospital.

Line 87

The DPC system is a classification system for hospital reimbursements from insurers with regular inspections by government in Japan.

Line 123

diagnosis of diabetes using DPC data has been reported to be accurate(11)

11. Kanehara R, Goto A, Goto M, Takahashi T, Iwasaki M, Noda M, et al. Validation Study of Diabetes Definitions Using Japanese Diagnosis Procedure Combination Data Among Hospitalized Patients. J Epidemiol. 2023;33(4):165-9.

Line 314

This study had some limitations. Our data provide information on diagnoses and treatments during hospitalization but not prior to hospitalization. Additionally, our data did not include information on blood examinations, vital signs, laboratory parameters, or radiographic data.

� Why didn't you perform some matching between the diabetes and control groups, such as age, sex, or comorbidities? If you had, it would have made comparisons more robust.

Thank you for your insightful comments. As you pointed out, matching is a robust and powerful method for strengthening the causal interpretation of results when using sufficient confounders. We honestly analyzed the data using propensity score matching. However, for the following reasons, we discussed the data and decided to employ and choose multiple logistic regression to evaluate the statistical association between diabetes and COVID-related outcomes under adjustment through our internal discussions.

Firstly, our study aims to assess whether diabetes is a risk factor for several COVID-related outcomes, or in other words, whether it serves as a useful factor for predicting poor prognosis at the time of hospitalization. We believe that the multiple logistic regression we conducted is a straightforward and appropriate method for evaluating the association between diabetes and outcomes while adjusting for the key factors that were available in this study.

Secondly, even if all confounding factors could be collected, the choice between matching and regression is not straightforward, as each method has its advantages. For example, Brazauskas and Logan (2016, *1) highlight situations where regression may be superior to matching.

Lastly, this study is an observational study using real-world data (RWD), which offers the strength of evaluating the clinical significance of diabetes as a risk factor in actual clinical practice. However, as noted in the Discussion, certain critical factors, such as blood test results and vaccination history, were difficult to collect. This limitation can be considered a trade-off inherent in analyzing real-world clinical practice using RWD. Based on our findings, exploring the causal relationship between diabetes and the prognosis of COVID-19 patients through a more experimental study design is considered to be interesting for further research.

*1 Brazauskas R, Logan BR. Observational Studies: Matching or Regression? Biol Blood Marrow Transplant. 2016 Mar;22(3):557-63. doi: 10.1016/j.bbmt.2015.12.005. Epub 2015 Dec 19. PMID: 26712591; PMCID: PMC4756459.

� Results

� Would the authors be able to inform the frequency of patients with type 1 diabetes in the sample?

I added the information. The frequency of Type 1 diabetes is quite low in Japan (E Kawasaki, N Matsuura, K Eguchi. Type 1 diabetes in Japan: Review. Diabetologia. 2006;49:8). Also the therapy for Type 1 diabetes is centered to professional treatment hospitals which have departments of pediatric endocrinology therefore the frequency might be lower among this data.

Line 127

Among 3,097 cases of diabetes, those are mostly type 2 diabetes except 15 cases were type 1 diabetes (0.48%).

� It would be interesting for you to build a binary logistic regression table using mortality as the response variable.

Thank you for your suggestion. We conducted a binary logistic regression analysis and included results in the article.

Line 210

We conducted a binary logistic regression analysis to evaluate the contribution of various factors to mortality of COVID-19. The analysis included age, BMI, sex (male), diabetes, and ambulance use as response variables. Among these, diabetes emerged as the most significant predictor of mortality (OR 2.67, p value <0.01) (Supplementary Table 2).

Supplementary Table 2. Multiple regression analysis of mortality adjusted with each factor, diabetes, age, sex (male), BMI, and ambulance.

� You need to correct this sentence:

� The data was extracted between February220 and November 2021

Thank you for your comment. It was a mistake and corrected.

Line 131

The data was extracted between February 2020 and November 2021.

� The proportion of males was higher (54.9%) in the control group than in the diabetes group (68.2%) (odds ratio [OR] 1.75, 95% confidence interval [CI] 1.60–1.91). Report me about that, is 54.9% higher than 68,2%?

Thank you for your comment. It was a mistake and corrected.

Line138

The proportion of males was higher (68.2%) in the diabetes group than in the control group (54.9%) (odds ratio [OR] 1.75, 95% confidence interval [CI] 1.60–1.91, p-value<0.001).

� For patients with 133 diabetes, the BMI was 25.6%, whereas that of the control was 23.6 (p <0.001). What does it mean? BMI, 25.6 kg/m²? And 23.6 kg/m²?

Thank you for your comment. It was a mistake and corrected.

Line140

For patients with diabetes, the BMI was 25.6 kg/m², whereas that of the control was 23.6 kg/m² (p <0.001)

� What kind of hypoglycemic agents were used for patients with diabetes mellitus?

We created the table to show hypoglycemic agents for patients. We attached CSV file of breakdown of oral medications as supporting information.

Line 145

Among hypoglycemic agents, DPP-4 inhibitors were the most common medication (42.4%). All background of hypoglycemic agents is presented in data (Supplementary Table 1, supporting information).

Supplementary Table 1. Hypoglycemic agents for patients with diabetes. Abbreviations: AGI=Alpha-glucosidase inhibitor

� Could the authors report the PaO2/FiO2 ratio at baseline in patients undergoing mechanical ventilation?

Blood data of PaO2/FiO2 is not included unfortunately. We added the information in limitations

Line 311

This study had some limitations. Our data provide information on diagnoses and treatments during hospitalization but not prior to hospitalization. Additionally, our data did not include information on blood examinations, vital signs, laboratory parameters, radiographic data including PaO2/FiO2 ratio and severity classification of COVID-19.

� Discussion

� The authors need to discuss the hyperglycemia observed in patients with infection or even the fact that infections, including SARS-CoV-2, decompensate diabetes mellitus.

Thank you for your suggestion. We added a paragraph with new citations in discussion.

Line 278

It is believed that COVID-19 infects not only the lungs but also kidneys and pancreatic beta cells, causing inflammation(14, 15). The viral infection depends on the expression of angiotensin-converting enzyme 2 (ACE2) and TMPRSS-2 transmembrane protease, which are the main cellular factors involved in viral entry. ACE2 is expressed in the lungs, and the virus infects the lungs and causes ARDS(16). COVID-19 stimulates strong immune response, resulting in a cytokine storm. It is possible that patients with diabetes are unable to suppress inflammation and that the virus enters the organs via those proteins, further exacerbating the inflammation and hyperglycemia(15, 17).

14. Khan S, Chen L, Yang CR, Raghuram V, Khundmiri SJ, Knepper MA. Does SARS-CoV-2 Infect the Kidney? J Am Soc Nephrol. 2020;31(12):2746-8.

15. Poloni TE, Moretti M, Medici V, Turturici E, Belli G, Cavriani E, et al. COVID-19 Pathology in the Lung, Kidney, Heart and Brain: The Different Roles of T-Cells, Macrophages, and Microthrombosis. Cells. 2022;11(19).

16. Zipeto D, Palmeira JDF, Argañaraz GA, Argañaraz ER. ACE2/ADAM17/TMPRSS2 Interplay May Be the Main Risk Factor for COVID-19. Front Immunol. 2020;11:576745.

17. Feldman EL, Savelieff MG, Hayek SS, Pennathur S, Kretzler M, Pop-Busui R. COVID-19 and Diabetes: A Collision and Collusion of Two Diseases. Diabetes. 2020;69(12):2549-65.

� The authors need to discuss the relationship between the need for mechanical ventilation, AKI, dialysis requirement, and mortality in patients with diabetes mellitus and COVID-19.

Thank you for your suggestion. We added a paragraph with new citations in discussion.

Line289

In this study, diabetes was a significant factor in in-hospital mortality, mechanical ventilation, ICU stay, HD, and CHDF. AKI associated with COVID-19 is mainly caused by proximal tubular dysfunction. ACE2 expression is abundant in the proximal tubules of the kidney, and it is believed that SARS-CoV-2 infects these cells, causing damage(18, 19). AKI complications were reported in 36.6% of patients admitted with COVID-19(20). The combination of renal and respiratory failure is thought to have led to the use of mechanical ventilation, continuous hemodialysis (CHDF), and hemodialysis (HD) to support critically ill patients in the ICU.

18. Santoriello D, Khairallah P, Bomback AS, Xu K, Kudose S, Batal I, et al. Postmortem Kidney Pathology Findings in Patients with COVID-19. J Am Soc Nephrol. 2020;31(9):2158-67.

19. Werion A, Belkhir L, Perrot M, Schmit G, Aydin S, Chen Z, et al. SARS-CoV-2 causes a specific dysfunction of the kidney proximal tubule. Kidney Int. 2020;98(5):1296-307.

20. Silver SA, Beaubien-Souligny W, Shah PS, Harel S, Blum D, Kishibe T, et al. The Prevalence of Acute Kidney Injury in Patients Hospitalized With COVID-19 Infection: A Systematic Review and Meta-analysis. Kidney Med. 2021;3(1):83-98.e1.

� Reviewer #2: I have some comments and questions for the authors

� 1. What is the reason for the existence of diabetes to augmented the probability of hospitalization via ambulance?

Thank you for your suggestion. We looked up the article but we couldn`t find the previous study in database such as pubmed. We assume that after infection of COVID-19, people stay at home while they observe the natural course of the disease for a while. Those with diabetes tend to have critically ill symptoms and they need to call ambulances finally. We`re running a retrospective study of the COVID-19 and diabetes in our hospital. We`re going to analyze the data from the point of view.

� 2. 77.8% of patients with diabetes used steroids for the treatment of pneumonia. I think it is important to know what percentage of pneumonias. What percentage were severe pneumonias?

Thank you for your question. I agree to know the percentage of pneumonias (or stages of COVID-19). But we do not have the information. We added it in limitation.

Line311

This study had some limitations. Our data provide information on diagnoses and treatments during hospitalization but not prior to hospitalization. Additionally, our data did not include information on blood examinations, vital signs, laboratory parameters, radiographic data including PaO2/FiO2 ratio and severity classification of COVID-19.

� 3. 7.8% of the control group and 17.9% of the group with diabetes required mechanical ventilation. What was the main reason why the patients required this support? Do you have respiratory data such as paO2/fiO2

We follow the medical guideline for COVID-19 in Japan. Basically, SpO2 is below 93% with mask with reservoir, or nasal high-flow, or positive pressure ventilation, we introduce mechanical ventilation for patients. We assume the patients could not maintain their oxygen level because of ARDS. We do not have the data of PaO2/FiO2. We updated the article.

Line71

Treatment of COVID-19 in Japan followed Japanese guidelines, including the introduction of mechanical ventilation(8).

8. Kato Y. Case Management of COVID-19 (Secondary Version). Jma j. 2021;4(3):191-7.

Line311

This study had some limitations. Our data provide information on diagnoses and treatments during hospitalization but not prior to hospitalization. Additionally, our data did not include information on blood examinations, vital signs, laboratory parameters, radiographic data including PaO2/FiO2 ratio and severity classification of COVID-19.

� 4. An important conclusion is that diabetes was an independent risk factor for in-hospital mortality. However, other conclusions such as risk factor for respirator use require further discussion.

Thank you for your suggestions. We improved discussion and added paragraphs with citations.

Line278

It is believed that COVID-19 infects not only the lungs but also kidneys and pancreatic beta cells, causing inflammation(14, 15). The viral infection depends on the expression of angiotensin-converting enzyme 2 (ACE2) and TMPRSS-2 transmembrane protease, which are the main cellular factors involved in viral entry. ACE2 is expressed in the lungs, and the virus infects the lungs and causes ARDS(16). COVID-19 stimulates strong immune response, resulting in a cytokine storm. It is possible that patients with diabetes are unable to suppress inflammation and that the virus enters the organs via those proteins, further exacerbating the inflammation and hyperglycemia(15, 17).

In this study, diabetes was a significant factor in in-hospital mortality, mechanical ventilation, ICU stay, HD, and CHDF. AKI associated with COVID-19 is mainly caused by proximal tubular dysfunction. ACE2 expression is abundant in the proximal tubules of the kidney, and it is believed that SARS-CoV-2 infects these cells, causing damage(18, 19). AKI complications were reported in 36.6% of patients admitted with COVID-19(20). The combination of renal and respiratory f

Submitted filename: 202411 plos revise v2.docx

 10.1371/journal.pone.0319801.r004 Decision Letter 1 Yamaguchi Fumihiro Academic Editor 2025 Fumihiro Yamaguchi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

9 Feb 2025

Diabetes with COVID-19 was a significant risk factor for mortality, mechanical ventilation, and renal replacement therapies: A multicenter retrospective study in Japan

PONE-D-24-22312R1

Dear Dr. Suwanai,

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Kind regards,

Fumihiro Yamaguchi

Academic Editor

PLOS ONE

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Authors,

You have managed to do a multicenter retrospective analysis in 38 hospitals on the effect of COVID-19 on patients with diabetes mellitus.

I have no further questions.

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #3: Yes:  Dr Omeed Omar Darweesh

**********

 10.1371/journal.pone.0319801.r005 Acceptance letter Yamaguchi Fumihiro Academic Editor 2025 Fumihiro Yamaguchi This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PONE-D-24-22312R1

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