Community-acquired pneumonia in diabetic patients is characterised by a distinct pathogen spectrum and enhanced inflammation: results from CAPNETZ, a prospective observational cohort study

Community-acquired pneumonia in diabetic patients is characterised by a distinct pathogen spectrum and enhanced inflammation: results from CAPNETZ, a prospective observational cohort study | 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 Community-acquired pneumonia in diabetic patients is characterised by a distinct pathogen spectrum and enhanced inflammation: results from CAPNETZ, a prospective observational cohort study Belén Millet Pascual-Leone, Facundo Fiocca Vernengo, David Hillus, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7394046/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Oct, 2025 Read the published version in Infection → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose Diabetes mellitus (DM) is a relevant risk factor for enhanced susceptibility to and adverse outcomes in infections, including community-acquired pneumonia (CAP). We aimed to characterise clinical outcomes, inflammatory and organ failure markers and microbial etiologies in diabetic (DM+) versus non-diabetic (DM-) patients in a European CAP cohort. Methods Comparative analyses using data from the CAPNETZ multicenter, prospective, observational study including 13,611 patients with CAP enrolled between 2002–2022, with and without a history of DM, were conducted. Results Seventeen percent (2,310/13,611) had a history of DM (DM+). Compared to DM- patients, DM + patients had a higher 180 days mortality rate following CAP (13% (292/2,310) vs. 7% (766/11,301), p < 0.0001) and higher C-reactive protein and leucocyte counts (median CRP 97 mg/L (IQR: 31–202) vs. 86 mg/L (IQR: 24–190), p < 0.0001; median leucocyte count 12/nl (IQR: 9–16)vs. 11/nl (IQR: 8–15), p < 0.0001). Pathogens were identified in 23.4% (540/2,310) of the DM + and 21.7% (2,414/11,301) of the DM- patients (p = 0.03), respectively. Overall, pathogen distribution differed between the two groups, with higher frequencies of Enterobacteriaceae in the DM + group (13.0% (70/539) vs. 8.0% (194/2,414), p adj < 0.01). Conclusions CAP in DM + is characterised by a distinct microbial spectrum and enhanced inflammation. While further studies are needed to elucidate the clinical impact of our findings, we recommend early and comprehensive CAP pathogen testing in DM + patients. Diabetes mellitus community-acquired pneumonia pathogen spectrum Enterobacteriaceae Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lower respiratory tract infections (LRIs), including community-acquired pneumonia (CAP), are the world’s most deadly transmittable diseases, ranked as the fourth leading cause of death by the WHO in 2019 [ 1 ] and associated with a global mortality rate of 27.7 per 100,000 across all ages in 2021 [ 2 ]. Effective antibiotic therapy is crucial to mitigate adverse outcomes of CAP. The pathogen distribution in CAP forms the rational basis for local empirical antibiotic therapy guidelines. As microbiological testing is typically not performed in the outpatient setting and pathogen detection rates are only between 20 and 40% in those tested, most CAP patients are treated empirically [ 3 , 4 ]. Therefore, continuous investigation into likely pathogens across various patient populations remains essential to optimise treatment recommendations. It is widely accepted that the causative pathogen spectra in CAP vary in accordance to the geographical region, observation period and pathogen identification methods used [ 5 ]. CAP aetiology also depends on host characteristics. Cardiac, cerebrovascular, chronic respiratory and kidney disease, nursing home residence, prior antimicrobial therapy and immunosuppression have been associated with a higher risk of pneumonia with multidrug-resistant pathogens and/or Enterobacteriaceae and non-fermenting gram-negative bacilli such as Pseudomonas aeruginosa [ 6 , 7 ]. Diabetes mellitus (DM) is among the most frequent comorbidities in CAP populations [ 8 , 9 ]. Previous analyses identified a prevalence of known DM (irrespective of diabetes type) of 15% in the full CAPNETZ cohort [ 10 ]. Evidence suggests that diabetic (further called DM+) patients are more susceptible to certain types of infections (e.g., LRIs, urinary tract, and skin infections) compared to non-diabetic (DM-) individuals [ 11 , 12 ]. A systematic review found DM to be associated with increased post-discharge and hyperglycaemia with increased in-hospital mortality following CAP [ 13 ]. While it is plausible that the presence of DM may differentially influence susceptibility to specific microbes, including opportunistic pathogens [ 11 , 12 ], there is, to the best of our knowledge, no large study examining CAP aetiologies in DM + versus DM- patients in Europe [ 14 , 15 ]. The aim of this work was to compare the pathogen spectrum in DM + versus DM- patients with CAP along with clinical characteristics, outcomes, parameters of inflammation and organ dysfunction in DM + vs. DM- patients in the German Community-Acquired Pneumonia Competence Network (CAPNETZ) cohort. Methods Capnetz cohort Data was obtained from patients enrolled into the CAPNETZ study [ 16 ], a multicenter prospective cohort study on CAP conducted in hospitals and private practices in Germany, Switzerland, Austria, the Netherlands, Denmark and Italy [ 17 ]. The study was conducted in accordance with the Declaration of Helsinki, as well as guidelines of Good Clinical Practice. It was approved by the institutional ethics board of the Hannover Medical School, Germany (Ethics approval No. 301–2008). Inclusion criteria were: age ≥ 18 years, informed consent, evidence of lung infiltrate by imaging, and at least one of the following: active coughing, purulent sputum, positive auscultation findings, or fever. Exclusion criteria were: hospitalisation for more than 48 hours before CAP diagnosis and newly diagnosed, active pulmonary tuberculosis. Demographic and clinical data, including previous medication, therapies, and comorbidities, laboratory data, results from microbiological and virological testing, and data on predefined outcomes (ICU admission within 28 days, death from any cause within 180 days from enrolment into the study) were collected in a case report file. Patients enrolled between October 1st, 2002 and June 30th, 2022 were included into this retrospective analysis (see Fig. 1 ). Patients with immunosuppression (any of: HIV infection, cytostatic therapy within the last 28 days, neutropenia, steroid therapy (>/= 20 mg prednisolone-equivalent/day), or immunosuppressive therapy after organ or bone-marrow transplantation within the preceding three months) were excluded. The CRB-65 score was used to assess CAP severity [ 18 ]. Standard laboratory parameters Laboratory parameters were obtained as part of routine clinical diagnostics within the first 48 hours following patient enrolment. We selected available parameters that best reflect 1) the host immune response and 2) organ dysfunction and sepsis severity, inspired by components of the Sequential Organ Failure Assessment (SOFA) score including PaO₂/FiO₂ ratio (Horovitz index), bilirubin, creatinine, thrombocyte count, lactate, and confusion at admission as a proxy for central nervous system dysfunction. Pathogen identification Methods used for microbiological diagnosis and laboratory processing procedures have been described previously [ 6 , 19 , 20 ]. In brief, all respiratory specimens and blood cultures collected at the time of inclusion were immediately processed in the local microbiological laboratories of the participating clinical centers. From 2017 onwards, multiplex PCR analysis was performed additionally to detect the following pathogens from sputum/nasopharyngeal swabs: adenovirus, Human bocavirus, Human coronavirus 229E/HKU1/NL63/OC4, enterovirus, influenza virus A/A H1N1 pdm09, influenza virus B, Human metapneumovirus, parainfluenza virus 1/2/3/4, parechovirus, rhinovirus, Mycoplasma pneumoniae , and respiratory syncytial virus A/B. In addition, specific PCRs for Chlamydophila pneumoniae , Legionella pneumophila , and Bordetella pertussis were performed in all patients. Detection of SARS-CoV-2 infection was carried out using rapid antigen testing and/or an updated multiplex PCR method (Siemens Healthineers, Eschborn, Germany) introduced in 2020. The microbiological diagnosis in the CAPNETZ cohort was established for patients who tested positive by PCR and/or culture diagnostics from respiratory samples and/or positive blood cultures in patients with moderate to severe disease (both performed in local laboratories associated with the respective hospitals), and/or urinary antigen testing for L. pneumophila and S. pneumoniae . Pathogens were considered the causative organisms for CAP according to the criteria published by Krüger et al [ 20 ]. The identified pathogens were categorised into the following seven biologically relevant groups: 1) S. pneumoniae , 2) Haemophilus influenzae , 3) Enterobacteriaceae (including Citrobacter spp ., E. coli , Enterobacter spp ., Hafnia alvei , Klebsiella oxytoca , K. pneumoniae , Klebsiella spp., Morganella morganii , Proteus mirabilis , P. vulgaris , Serratia marcescens, Serratia spp.), 4) non-fermenting gram-negative bacteria (including Acinetobacter spp., Pseudomonas aeruginosa, Pseudomonas spp., Stenotrophomonas maltophilia ), 5) atypical bacteria (including L. pneumophila , M. pneumoniae and C. pneumoniae ), 6) Staphylococcus aureus and 7) viruses (including influenza A and B, SARS-CoV-2, Human coronavirus HKU1, Human coronavirus OC43, parainfluenza virus 2, 3 and 4, adenovirus, enterovirus, respiratory syncytial virus A and B, rhinovirus, Human metapneumovirus A and B, Human bocavirus ). Due to low sample sizes, we summarised all other microbes identified under 8) “other pathogens”. Diabetes mellitus and subgroups definitions History of DM and, for patients enrolled from 2017 onwards, type of DM, was documented according to self-report. See Fig. 1 for definition of subgroups. A subgroup analysis was conducted among patients with documented DM type, comparing individuals with type 2 diabetes (DM2) to DM-, while excluding all other types of DM due to low sample sizes. This group is referred to as the “diabetes type 2 subcohort” (DM2 subcohort). Because parameters related to organ dysfunction and sepsis were only consistently documented in critically ill patients, the analysis of these variables was restricted to individuals from the DM2 subcohort admitted to the intensive care unit (ICU) - the "ICU type 2 diabetes subcohort” (ICU DM2). Additionally, glycated haemoglobin (HbA1c) values were available for a subgroup of 1,961 patients from a previous CAPNETZ project and were included as a separate subcohort - the “HbA1c subcohort”. HbA1c was measured as previously described [ 10 ]. DM was defined according to the 2025 American Diabetes Association diagnostic criteria [ 21 ] as HBA1c ≥ 6.5% (≥ 48 mmol/mol) as measured upon inclusion and/or self-reported diagnosis of DM. Prediabetes was defined as HBA1c 5.7–6.4% (39–47 mmol/mol) and no DM was defined as HBA1c < 5.7% (< 39 mmol/mol) with no self-reported DM diagnosis [ 21 ]. To account for the inaccuracy of self-reporting, we performed a sensitivity analysis of pathogen distribution between DM-, prediabetic and DM + patients in the HbA1c subcohort. Statistical analysis Data are shown as percentages, relative frequencies, medians and interquartile ranges (IQRs) or averages with standard deviation (SD), depending on the underlying distribution. No imputation of missing data was performed. Group comparisons were assessed using the Wilcoxon or the Mann-Whitney U test for continuous, or the Chi-square test for categorical data and a P-value of less than 0.05 was considered significant. The Pearson correlation coefficient was used to analyze the relation between continuous variables. P-values were adjusted for multiple testing when comparing every pathogen group against all other pathogen groups with the Bonferroni-Sidak method. To account for the interdependence of clinical variables, we performed a logistic regression on the outcomes ICU admission versus not within 28 days and death from any cause versus not within 180 days within the whole cohort using the following predictors: age, sex, BMI, diabetes mellitus, malignant disease, chronic cardiac disease, chronic cerebrovascular disease, chronic kidney disease and chronic respiratory disease. Clinical data were analysed using Jmp Pro, version 18.2 (SAS Institute Inc, USA) and GraphPad Version 10 (GraphPad Prism, USA). Results Clinical characteristics A total of 13,611 patients were included in this analysis. The study flow chart is shown in Fig. 1 . Of all patients, 17% (2,310/13,611) had self-reported DM, irrespective of type. Clinical characteristics of all patients, and comparisons between DM+ (all types) and DM- are shown in Table 1 . DM2 was the most prevalent of all DM types in those with available information (94%, 317/337), DM type 1 accounted for 3.8% (13/337). We observed similar differences regarding clinical characteristics between DM2 + and DM- patients as between DM + and DM- in the main cohort. (Supplementary Table 1). Table 1 Clinical characteristics of all, DM + and DM- patients in the CAPNETZ-cohort between 2002–2022 All patients (n = 13,611) Diabetes (n = 2,310) No diabetes (n = 11,301) p-value Age in years (Mean (SD)) 61 (18) 71 (12) 59 (18) < 0.0001 Male Sex at birth, (% (n/ N)) 57 (7772/13611) 63 (1444/2310) 56 (6328/11301) < 0.0001 BMI (kg/m2), Median (IQR), available n 25 (22–29), 13221 28 (24–32), 2226 25 (22–28), 10995 < 0.0001 Nursing home residency (%, n/available N) 6 (805/13599) 11 (244/2307) 5 (561/11292) < 0.0001 Cigarette smoking during the last 12 months (%, n/available N) 29 (3821/13316) 20 (454/2222) 30 (3367/11094) < 0.0001 Chronic heart failure (%, n/available N) 17 (2244/13572) 34 (791/2308) 13 (1453/11264) < 0.0001 Chronic respiratory/pulmonary disease (%, n/available N) 36 (4912/13611) 43 (986/2310) 35 (3926/11301) < 0.0001 Vascular/Cerebrovascular disease (%, n/available N) 16 (2159/13572) 29 (678/2301) 13 (1481/11271) < 0.0001 Chronic kidney disease (%, n/available N) 9 (1272/13566) 22 (517/2303) 7 (755/11263) < 0.0001 Previous antibiotic therapy within the last 4 weeks (%, n/available N) 24 (3257/13544) 16 (373/2287) 26 (2884/11257) < 0.0001 Malignant disease (%, n/available N) 10 (1366/13611) 12 (268/2310) 10 (1098/11301) < 0.05 Oxygen Therapy (%, n/available N) 5 (642/13611) 7 (169/2310) 4 (473/11301) < 0.0001 Available n is indicated where variables were not available for all patients. P-values comparing diabetic and non-diabetic patients (based on self-reporting) were calculated using the Wilcoxon test for medians, and the unadjusted Chi-square test for frequencies of comorbidities. All clinical variables shown were significantly different between diabetic and non-diabetic patients. Abbreviation s: DM+: patients with a history of diabetes mellitus, DM-: patients without a history of diabetes mellitus IQR : interquartile range, SD: standard deviation, BMI : body mass index. Outcome analysis The distribution of CRB-65 scores differed significantly between the two groups (DM+: median: 1, IQR: 1–2 vs. DM-: 1, IQR: 0–1) (Fig. 2a). DM + patients were hospitalised for longer than DM- patients (median: 10 days, IQR 8–14 days, vs. median: 9 days, IQR: 6–13 days respectively) (Fig. 2b). DM + patients has increased rates of ICU admission within 28 days (DM+: 12% (163/1,389), DM-: 5% (429/7,972)) and death from any cause within 180 days post enrolment as compared to DM- (DM+: 13% (292/2,310), DM-: 7% (766/11,301)) (Fig. 2c, d). In a logistic regression with baseline characteristics and comorbidities as predictors, DM had an adjusted Odd’s ratio of 1.56 (1.26–1.93) for ICU admission and 1.31 (1.11–1.54) for death (Supplementary Table 2). Clinical outcome parameters in the DM2 subcohort were similar to the findings in the main cohort (Supplementary Fig. 1a-d). Inflammatory response and organ dysfunction While CRP and leukocyte values were elevated in both groups at enrolment, they were significantly higher in DM + compared to DM- patients, although the differences are minimal (Fig. 3a, b). In the ICU DM2 subcohort (see methods), lactate levels and creatinine levels were significantly higher in DM2 + patients compared to DM- patients while other parameters of organ dysfunction did not differ (Supplementary Fig. 2a-f). Pathogen spectrum in DM- versus DM + patients Information on a causative pathogen for CAP was accessible in 21.7% (2,954/13,611) of all patients, and in 21.4% (2,414/11,301) of DM- and 23.4% (540/2,310) of DM + patients (p = 0.03). We observed an overall difference in pathogen distribution in DM- versus DM + patients (p < 0.001, Fig. 4a). S. pneumoniae was the most frequently identified pathogen in both groups with 38.5% (1,140/2,954) overall, 39.1% (944/2,414) in DM- patients, and 36.3% (196/540) in DM + patients. H. influenzae accounted for 10.4% (307/2,954) overall, 11.0% (266/2,414) in DM- patients, and 7.6% (41/540) in DM + patients. Atypical bacteria were detected in 11.5% (340/2,954) of all cases, in 12.0% (290/2,414) of DM- patients, and in 9.3% (50/540) of DM + patients. Enterobacteriaceae accounted for 8.9% (264/2,954) of all isolated pathogens in all, 8.0% (194/2,414) in DM- and 13.0% (70/540) in DM + patients. This difference was statistically significant when comparing the relative frequency of Enterobacteriaceae to that of all other pathogens between the groups (p adj <0.005, Fig. 4b). A detailed pathogen distribution from the Enterobacteriaceae group showed that Escherichia coli was the most frequently isolated pathogen in both DM- and DM + patients, but no further analysis was feasible (Fig. 4c). S. aureus was isolated in 4.3% (126/2,954) of all patients, 4.4% (105/2,414) of DM- and 3.9% (21/540) in DM+. The non-fermenting gram-negative bacterial group including Pseudomonas spp. and Acinetobacter baumannii accounted for 2.6% (78/2,954) of all identified pathogens, 2.5% (61/2,414) of DM- patients and 3.2% (17/540) of DM + patients. Viruses were detected in 19.3% (569/2,954) of all CAP patients, in 18.6% (449/2,414) of DM- and in 22.2% (120/540) of DM + patients. The “others” pathogen group (see methods) accounted for 4.4% (130/2,954) of all isolated pathogens, with 4.4% (105/2,414) in DM-, and 4.6% (25/540) in DM + patients. Except for the difference in Enterobacteriaceae, no significant differences were observed between DM- and DM + patients when analysing the distribution of every pathogen group against all other pathogens when adjusting for multiple comparisons (Supplementary Table 3). Similar to the observations in the main cohort, we observed a trend towards a lower relative frequency of S. pneumoniae and a higher frequency of Enterobacteriaceae in DM + as compared with DM- patients in the HbA1c subcohort. For the aforementioned pathogen groups, prediabetic patients showed frequencies in between DM- and DM + patients (Supplementary Fig. 3). Discussion In this descriptive analysis of a prospective, observational European cohort of patients with CAP, we identified relevant differences between patients with and without DM regarding microbial aetiology, inflammatory parameters, and clinical outcomes. DM + patients presented a distinct clinical profile at baseline, including older age and a greater burden of comorbidities, such as cardiovascular disease, chronic kidney disease, and obesity—all of which have independently been associated with worse outcomes in CAP [ 14 ]. Patients with DM had higher rates of adverse clinical outcomes, as indicated by higher ICU admission rates and increased 180-day mortality following CAP. DM + patients were less frequently treated with antibiotics before enrolment into the study potentially reflecting earlier hospital presentations or a lower likelihood of outpatient treatment. The clinical profile of patients with DM in our cohort is consistent with previous studies [ 22 , 23 ]. Our first key finding is that the pathogen spectrum in DM + patients differed significantly from that in DM- patients. Notably, DM + individuals showed higher relative frequencies of bacteria belonging to the Enterobacteriaceae family. This pathogen group ranked as the second most frequently identified in DM + patients—after S. pneumoniae and ahead of H. influenzae . The pathogen detection rate of 21.7% across all patients reflects typical clinical settings [ 3 , 4 ] and is consistent with earlier CAPNETZ reports [ 19 ]. An increased prevalence of Enterobacteriaceae has previously been described for other comorbidities including cardiac, cerebrovascular, respiratory and kidney diseases, but, to the best of our knowledge, our study is the first showing this association with DM [ 6 , 7 ]. A generally increasing prevalence of this uncommon pathogen group has previously been described for the whole CAPNETZ cohort over time [ 19 ]. It also aligns with global estimates showing an increase in pathogens belonging to the Enterobacteriaceae family in patients across all ages with LRIs, along with a decrease in, i.e., S. pneumoniae [ 2 ], suggesting a broader epidemiological shift in CAP aetiology. Our second key finding is that DM + patients exhibited an enhanced inflammatory response at enrolment, as evidenced by higher median CRP levels and leucocyte counts compared to DM- patients. In addition, we observed higher median levels of creatinine and lactate in an ICU subcohort, indicating kidney and cardiovascular impairment in severe CAP patients. However, these findings should be interpreted with caution due to low sample size and lacking information on the baseline in both groups. Earlier mechanistic studies described altered innate immunity in DM, which highlights the need to further investigate immune-pathophysiological pathways in CAP [ 24 , 25 ]. A strength of our study lies in the well-characterised cohort, which is one of the largest prospective CAP cohorts world-wide [ 26 ]. However, our data should be interpreted in light of changes in pathogen detection techniques over the 20-year observation period, shifts in bacterial aetiology, and the cohort’s characteristics, consisting of mostly hospitalised patients with a predominantly mild to moderate course of CAP, limiting the generalisability of our findings. Another limitation of our study is that DM diagnosis was based on self-report, which may have led to misclassification and a potential underestimation of DM prevalence. Reassuringly, we observed the same trend regarding pathogen distribution in a subcohort with available HBA1c values. Prevalence of diabetes may additionally have changed over time. Information on DM type and glycemic control was limited. However, differences in outcome were observed not only in the overall cohort but also in a DM2 subcohort. We were not able to conduct diabetes subtype-specific analyses and did not have reliable data on antidiabetic medication which may modify outcome [ 27 ]. We believe that our data, showing a higher frequency of Enterobacteriaceae in DM + patients, are robust and plausible in light of previous publications with similar observations in comorbidities associated with DM. It might be reasonable to assume that the higher frequency of CAP caused by the Enterobacteriaceae family could be related to compromised early antibacterial immune responses in DM patients [ 12 , 28 – 32 ]. The lower prevalence of S. pneumoniae in DM patients may be associated with higher vaccination rates in patients with comorbidities following national recommendations [ 33 ], thereby relatively increasing the proportion of less common pathogens in the microbiological spectrum. Further studies are needed to decipher the clinical meaning and impact of the distinct microbial spectrum and of the enhanced inflammatory response we observed in diabetic CAP patients. Based on our findings, we propose extending current recommendations to include early, comprehensive, and ideally rapid microbiological testing in at-risk patients with chronic underlying conditions— even in the absence of traditional indicators for hospital admission or microbial diagnostics. When weighing the pros and cons of microbial testing in mild CAP, it is important to consider that a shift in CAP aetiology has been observed in our and other studies, particularly in patients with interrelated comorbidities such as DM. In these patients, Enterobacteriaceae and viral pathogens are increasingly replacing traditionally expected CAP pathogens. Early pathogen identification is essential for enabling timely adjustment or discontinuation of antibiotic therapy, which has been shown to improve CAP outcomes and is critical for avoiding both under- and overtreatment [ 5 , 34 ]. Taken together, our findings underscore that patients with DM represent a highly prevalent, specific, and clinically vulnerable population in the context of CAP—highlighting the need for increased awareness among healthcare professionals, appropriate consideration in CAP guidelines, and further research aiming at reducing adverse outcomes. Declarations Conflict of interest: G.R. received personal fees from Astra Zeneca, Atriva, Boehringer Ingelheim, GSK, Insmed, MSD, Sanofi, Novartis and Pfizer for consultancy during advisory board meetings and personal fees from Astra Zeneca, Berlin Chemie, BMS, Boehringer Ingelheim, Chiesi, Essex Pharma, Grifols, GSK, Insmed, MSD, Roche, Sanofi, Solvay, Takeda, Novartis, Pfizer and Vertex for lectures. M.P. received consulting fees and/or payment for honoraria for lectures and presentations from Pfizer, MSA, Sanofi, Janssen, GSK, Astrazeneca, Shionogi and Infectiopharm, Biomerieux and Sanofi, support for attending meetings and/or travel from Pfizer, MSD, has a patent planned with bioactive glass element, participated in data safety monitoring board or advisory board of Biomerieux and Sanofi and is president of the Paul Ehrlich Society for Antiinfective Chemotherapy and Board of Director of CAPNETZ and German Sepsis Society. M.W. received funding from the German Research Foundation – SFB 1449 (project ID 431232613), sub-project B02, from the German Federal Ministry of Research, Technology and Space in the framework of e:Med SYMPATH (01ZX2206A, 01ZX1906A), NUM-NAPKON (01KX2121, 01KX2021), CAP-TSD (031L0286B), PROGRESS (82DZLJ19C1, 82DZLJ19B1), NAPCODE (01EQ2406B), from the Federal Joint Committee (G-BA) – T-CABS (01NVF23109), from the Federal Ministry of Health (BMG) – PAIS Care (ZMII2-2524FSB105), from the Ministry of Defence – NoVAP (E/U2ED/PD014/OF550), and from Aptarion, Pantherna and Biotest for research outside the current study, and for lectures and advisory from Astra Zeneca, Chiesi, Insmed, Gilead, Pfizer, Boehringer, Biotest, Pantherna and Aptarion. Funding: CAPNETZ was funded by a grant from the German Federal Ministry of Education and Research (FKZ 01KI07145) 2001-2011 and has been an associated member of the German Center for Lung Research (FKZ 82DZL002B4) since 2013. M.P. was funded by a grant from the German Federal Ministry for Education and Research. C.T. is participant in the BIH Charité Clinician Scientist Program funded by the Charité – Universitätsmedizin Berlin and the Berlin Institute of Health at Charité (BIH). Acknowledgements We thank Claire Hartmann and the CAPNETZ study network. Members of the CAPNETZ study group except the authors: A. Fuchs, G. Paul, M. Ayoub (Augsburg); A. Prasse (Basel); W. Bauer, E. C. Diehl-Wiesenecker, N. Galtung, C. Kodde, Y.-M. Stoppe (Berlin); C. Boesecke, S. Breitschwerdt, D. Benke (Bonn); S. Schmager (Cottbus); A. Grünewaldt, J. Wheeler (Darmstadt); B. Schaaf, J. Kremling (Dortmund); M. Kolditz, B. Schulte-Hubbert, J. Ronczka (Dresden); A. Seeger, J. Kohlhäufl (Frankfurt), D. Stolz, S. Fähndrich, M. Panning (Freiburg); M. Unnewehr, R. Lim (Hamm); M. Hoeper, I. Pink, N. Drick, T. Fühner, T. Steinberg, G. Barten-Neiner, W. Kröner, O. Unruh, N. Adaskina, F. Eberhardt, T. Illig, N. Klopp (Hannover); B. T. Schleenvoigt, A. Moeser (Jena); D. Drömann, P. Parschke, K. Franzen, F. Waldeck, B. Gebel, N. Käding, S. Boutin (Lübeck); J. Schneider, J. Erber, F. Voit, (Munich); D. Heigener, I. Hering (Rotenburg/Wümme); W. Albrich, F. Rassouli, B. Wirth (St. Gallen); C. Neurohr (Stuttgart); A. Essig, S. Stenger, M. Wallner (Ulm); H. Burgmann, L. Traby, L. Schubert (Vienna); and all study nurses. Authors contributions Conception and design: B.M.P., G.K., J.R., G.R., M.W.P., M.W., N.S., L.E.S., A.V.J., B.O., C.T. Data acquisition: CAPNETZ study group Analysis and interpretation of data: B.M.P., F.F.V, D.H., C.W., A.V.J., C.T. Drafting the article: B.M.P., F.F.V., C.T. Critical article revision: G.K., J.R., G.R., M.W.P., M.W., N.S., L.E.S., A.V.J., B.O. Final approval of the version to be submitted: all authors References n.d. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death) (accessed June 12, 2025). 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Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis 2005;41:281–8. Chávez-Reyes J, Escárcega-González CE, Chavira-Suárez E, León-Buitimea A, Vázquez-León P, Morones-Ramírez JR, et al. Susceptibility for some infectious diseases in patients with diabetes: The key role of glycemia. Front Public Health 2021;9:559595. Barmanray RD, Cheuk N, Fourlanos S, Greenberg PB, Colman PG, Worth LJ. In-hospital hyperglycemia but not diabetes mellitus alone is associated with increased in-hospital mortality in community-acquired pneumonia (CAP): a systematic review and meta-analysis of observational studies prior to COVID-19. BMJ Open Diabetes Res Care 2022;10:e002880. Falguera M, Pifarre R, Martin A, Sheikh A, Moreno A. Etiology and outcome of community-acquired pneumonia in patients with diabetes mellitus. Chest 2005;128:3233–9. Klekotka RB, Mizgała E, Król W. The etiology of lower respiratory tract infections in people with diabetes. Pneumonol Alergol Pol 2015;83:401–8. Capnetz – Ziel und Zweck der CAPNETZ STIFTUNG ist die Förderung wissenschaftlicher Aktivitäten rund um das Thema „Ambulant erworbene Pneumonien (community-acquired pneumonia, CAP) und andere Infektionen des unteren Respirationstraktes“ n.d. http://www.capnetz.de (accessed June 19, 2025). Welte T, Suttorp N, Marre R. CAPNETZ-community-acquired pneumonia competence network. Infection 2004;32:234–8. Bauer TT, Ewig S, Marre R, Suttorp N, Welte T, CAPNETZ Study Group. CRB-65 predicts death from community-acquired pneumonia. J Intern Med 2006;260:93–101. Braeken DCW, Essig A, Panning M, Hoerster R, Nawrocki M, Dalhoff K, et al. Shift in bacterial etiology from the CAPNETZ cohort in patients with community-acquired pneumonia: data over more than a decade. Infection 2021;49:533–7. Krüger S, Ewig S, Papassotiriou J, Kunde J, Marre R, von Baum H, et al. Inflammatory parameters predict etiologic patterns but do not allow for individual prediction of etiology in patients with CAP: results from the German competence network CAPNETZ. Respir Res 2009;10:65. American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: Standards of care in diabetes-2025. Diabetes Care 2025;48:S27–49. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506. López-de-Andrés A, de Miguel-Díez J, Jiménez-Trujillo I, Hernández-Barrera V, de Miguel-Yanes JM, Méndez-Bailón M, et al. Hospitalisation with community-acquired pneumonia among patients with type 2 diabetes: an observational population-based study in Spain from 2004 to 2013. BMJ Open 2017;7:e013097. Lachmandas E, Vrieling F, Wilson LG, Joosten SA, Netea MG, Ottenhoff TH, et al. The effect of hyperglycaemia on in vitro cytokine production and macrophage infection with Mycobacterium tuberculosis. PLoS One 2015;10:e0117941. Dungu AM, Ryrsø CK, Hegelund MH, Jensen AV, Kristensen PL, Krogh-Madsen R, et al. Diabetes status, c-reactive protein, and insulin resistance in community-acquired pneumonia-A prospective cohort study. J Clin Med 2023;13. https://doi.org/10.3390/jcm13010245. Suttorp N, Welte T, Marre R, Stenger S, Pletz M, Rupp J, et al. CAPNETZ. The competence network for community-acquired pneumonia (CAP): Das Kompetenzzentrum für ambulant erworbene Pneumonie. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2016;59:475–81. Kantreva K, Katsaounou P, Saltiki K, Trakada G, Ntali G, Stratigou T, et al. The possible effect of anti-diabetic agents GLP-1RA and SGLT-2i on the respiratory system function. Endocrine 2025;87:378–88. Restrepo BI, Twahirwa M, Rahbar MH, Schlesinger LS. Phagocytosis via complement or Fc-gamma receptors is compromised in monocytes from type 2 diabetes patients with chronic hyperglycemia. PLoS One 2014;9:e92977. Fiocca Vernengo F, Röwekamp I, Boillot L, Caesar S, Dörner PJ, Tarnowski B, et al. Diabetes impairs IFNγ-dependent antibacterial defense in the lungs. Mucosal Immunol 2025;18:431–40. Lachmandas E, Thiem K, van den Heuvel C, Hijmans A, de Galan BE, Tack CJ, et al. Patients with type 1 diabetes mellitus have impaired IL-1β production in response to Mycobacterium tuberculosis. Eur J Clin Microbiol Infect Dis 2018;37:371–80. Tripathi D, Radhakrishnan RK, Sivangala Thandi R, Paidipally P, Devalraju KP, Neela VSK, et al. IL-22 produced by type 3 innate lymphoid cells (ILC3s) reduces the mortality of type 2 diabetes mellitus (T2DM) mice infected with Mycobacterium tuberculosis. PLoS Pathog 2019;15:e1008140. Lecube A, Pachón G, Petriz J, Hernández C, Simó R. Phagocytic activity is impaired in type 2 diabetes mellitus and increases after metabolic improvement. PLoS One 2011;6:e23366. n.d. https://www.rki.de/DE/Aktuelles/Publikationen/Epidemiologisches-Bulletin/2025/04_25.pdf?__blob=publicationFile&v=10 (accessed June 19, 2025). Uematsu H, Hashimoto H, Iwamoto T, Horiguchi H, Yasunaga H. Impact of guideline-concordant microbiological testing on outcomes of pneumonia. Int J Qual Health Care 2014;26:100–7. Additional Declarations Competing interest reported. G.R. received personal fees from Astra Zeneca, Atriva, Boehringer Ingelheim, GSK, Insmed, MSD, Sanofi, Novartis and Pfizer for consultancy during advisory board meetings and personal fees from Astra Zeneca, Berlin Chemie, BMS, Boehringer Ingelheim, Chiesi, Essex Pharma, Grifols, GSK, Insmed, MSD, Roche, Sanofi, Solvay, Takeda, Novartis, Pfizer and Vertex for lectures. M.P. received consulting fees and/or payment for honoraria for lectures and presentations from Pfizer, MSA, Sanofi, Janssen, GSK, Astrazeneca, Shionogi and Infectiopharm, Biomerieux and Sanofi, support for attending meetings and/or travel from Pfizer, MSD, has a patent planned with bioactive glass element, participated in data safety monitoring board or advisory board of Biomerieux and Sanofi and is president of the Paul Ehrlich Society for Antiinfective Chemotherapy and Board of Director of CAPNETZ and German Sepsis Society. M.W. received funding from the German Research Foundation – SFB 1449 (project ID 431232613), sub-project B02, from the German Federal Ministry of Research, Technology and Space in the framework of e:Med SYMPATH (01ZX2206A, 01ZX1906A), NUM-NAPKON (01KX2121, 01KX2021), CAP-TSD (031L0286B), PROGRESS (82DZLJ19C1, 82DZLJ19B1), NAPCODE (01EQ2406B), from the Federal Joint Committee (G-BA) – T-CABS (01NVF23109), from the Federal Ministry of Health (BMG) – PAIS Care (ZMII2-2524FSB105), from the Ministry of Defence – NoVAP (E/U2ED/PD014/OF550), and from Aptarion, Pantherna and Biotest for research outside the current study, and for lectures and advisory from Astra Zeneca, Chiesi, Insmed, Gilead, Pfizer, Boehringer, Biotest, Pantherna and Aptarion. Supplementary Files SupplementDMCAPInfection.docx Cite Share Download PDF Status: Published Journal Publication published 12 Oct, 2025 Read the published version in Infection → Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 18 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 17 Aug, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7394046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504609040,"identity":"83e9121d-cb0b-4f1f-b0a1-074893ea6a4d","order_by":0,"name":"Belén Millet Pascual-Leone","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Belén","middleName":"Millet","lastName":"Pascual-Leone","suffix":""},{"id":504609041,"identity":"46360797-d809-41fd-8e9e-62f096c47b21","order_by":1,"name":"Facundo Fiocca Vernengo","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Facundo","middleName":"Fiocca","lastName":"Vernengo","suffix":""},{"id":504609042,"identity":"d70a5bff-cf78-4977-8f19-fb9b46e59c8e","order_by":2,"name":"David Hillus","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hillus","suffix":""},{"id":504609043,"identity":"3b470869-1f5b-4823-a8cb-a04279e8d6c9","order_by":3,"name":"Charlotte Wernicke","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Wernicke","suffix":""},{"id":504609044,"identity":"e422e4de-a721-4f1b-9ebd-094e2e316d56","order_by":4,"name":"Gopinath Krishnamoorthy","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Gopinath","middleName":"","lastName":"Krishnamoorthy","suffix":""},{"id":504609045,"identity":"05bf1ac6-7a25-4f05-adce-41b60007ca26","order_by":5,"name":"Jan Rupp","email":"","orcid":"","institution":"CAPNETZ Stiftung","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Rupp","suffix":""},{"id":504609046,"identity":"721dca59-c94c-4cfe-a3f4-1d63857636d5","order_by":6,"name":"Gernot Rohde","email":"","orcid":"","institution":"CAPNETZ Stiftung","correspondingAuthor":false,"prefix":"","firstName":"Gernot","middleName":"","lastName":"Rohde","suffix":""},{"id":504609047,"identity":"25df4f6f-6fae-4e32-9442-e9b9cf3e6ab1","order_by":7,"name":"Mathias W. Pletz","email":"","orcid":"","institution":"CAPNETZ Stiftung","correspondingAuthor":false,"prefix":"","firstName":"Mathias","middleName":"W.","lastName":"Pletz","suffix":""},{"id":504609049,"identity":"bef38cd8-21e3-43d4-8739-33240d681d05","order_by":8,"name":"Martin Witzenrath","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Witzenrath","suffix":""},{"id":504609050,"identity":"894a5d49-bda5-4fa4-a34a-e64a1519bf27","order_by":9,"name":"Norbert Suttorp","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Norbert","middleName":"","lastName":"Suttorp","suffix":""},{"id":504609051,"identity":"deee567d-04f5-4b83-8368-83fa2da87466","order_by":10,"name":"Leif Erik Sander","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Leif","middleName":"Erik","lastName":"Sander","suffix":""},{"id":504609052,"identity":"b0b68713-07c9-4520-b8e1-db1cc24b6211","order_by":11,"name":"Andreas Vestergaard Jensen","email":"","orcid":"","institution":"Nordsjaellands Hospital","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"Vestergaard","lastName":"Jensen","suffix":""},{"id":504609054,"identity":"1c960fd6-9634-448a-a76b-e51790794fd7","order_by":12,"name":"Bastian Opitz","email":"","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":false,"prefix":"","firstName":"Bastian","middleName":"","lastName":"Opitz","suffix":""},{"id":504609055,"identity":"9443577e-fc13-4733-81d5-7a70ae35acef","order_by":13,"name":"Charlotte Thibeault","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABL0lEQVRIie3QsUrDQBjA8Ts+uC6tt14W8wonAUFQ+yo5Au1ScOgSUDQlcE51jtiHEFw6Rg7SJQ+g6JAsnToEOhVEvSTFJYk4Otwfwl0CP77LIWQy/ceYfrJ6S5AI9NJDOMNB+arrdxAXcb3AngAArwj5C8E1IazadBD6EK4L1/+4pogmWb48tbmiib9bnl8c2GGMNn5zyHviRG7KmRVAj4t0dPSogLzOU+9EEuLiRdognLkOEpIzHuvzCKlwSd6wBE5In8NAtpDxtiLDmnwNSzLF8kYTWsDgs4VM9lNQRWJREsBSlVMQDILmv7xMpshNHStScMxE6nn3ChxrLleajPjzImneWDR+QoV/SOntbG3pizq7W83yYicvuR2qPNtcNW/5J2j7GP8CTCaTydTdN1EFYb9ahftyAAAAAElFTkSuQmCC","orcid":"","institution":"Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin","correspondingAuthor":true,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Thibeault","suffix":""}],"badges":[],"createdAt":"2025-08-17 18:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7394046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7394046/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s15010-025-02659-w","type":"published","date":"2025-10-12T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89982732,"identity":"9c86dd42-421c-43b1-bbf9-b503f60cdd71","added_by":"auto","created_at":"2025-08-27 06:30:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart \u003c/strong\u003eAll patients included into the CAPNETZ study between 2002-2002 were considered for this analysis. After exclusion of immunosuppressed patients, 13,611 patients with (DM+) and without (DM-) self-reported diabetes were included retrospectively into the main cohort for comparative analyses of clinical profile and outcome, inflammatory response and pathogen spectrum. HbA1c values were available for n=1,961 patients. In this cohort, pathogen spectrum was analysed comparatively in DM-, DM+ and prediabetic patients. The type of diabetes was documented as part of the protocol from 2017 on. In n=156 patients with type 2 diabetes or DM- admitted to the intensive care unit (ICU subcohort), markers of organ dysfunction were comparatively assessed. Yellow colour indicates analyses performed: univariate analyses were performed and adjusted for multiple testing, where applicable. * regarding outcome, logistic regression analyses were performed.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/10b52cbba1956b93b2548865.jpeg"},{"id":89984531,"identity":"04205bac-5d62-4126-81ed-1101318a1a89","added_by":"auto","created_at":"2025-08-27 06:38:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOutcome DM- versus DM+ patients \u003c/strong\u003e(a, b) Box plots showing the distribution (median and IQR) of CRB-65 scores (a) and hospitalization days (b) in patients without (DM-) and with a history of diabetes mellitus (DM+) and all patients (available 12,425/13,611 for a) and 10,259/10,312 for b)). Whiskers represent the 2.5 - 97.5 percentiles. (c, d) Percentages of ICU admissions within 28 days (b) and death within 180 days (c), both from any cause following CAP in DM- vs DM+ patients, as well as all patients in the CAPNETZ cohort (available n =9,361/13,611 for c) and 13,611/13,611 for d)). Mann-Whitney statistical analysis was used for a) and b), and the Chi-square test was used to analyze the shown groups in c) and d). ****: p\u0026lt;0,0001. CRB-65: pneumonia severity index, ICU: intensive care unit (see methods).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/5860000c7ca31dacb20dca55.jpeg"},{"id":89982725,"identity":"eef544b5-11ff-48d8-91f7-e7da5207945f","added_by":"auto","created_at":"2025-08-27 06:30:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInflammatory response in DM- and DM+ patients \u003c/strong\u003eBox plots representing CRP (a) and leucocyte (b) values for the baseline blood sample taken within the first 48 hours after enrolment in patients without (DM-) and with a history of diabetes mellitus (DM+). Whiskers represent the 2.5 - 97.5 percentiles. Mann-Whitney test was performed. Data were available for 12,878/13,611 patients for a) and for 12,940/13,611 patients for b). ****: p\u0026lt;0.0001. CRP: C-reactive protein.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/0c0326adf09242592d986b26.jpeg"},{"id":89982722,"identity":"0c1f113a-1f08-42be-98ba-f4350aa6dc7f","added_by":"auto","created_at":"2025-08-27 06:30:16","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative frequencies of identified pathogens in DM- vs DM+ patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Relative frequencies of identified pathogens in patients without (DM-) and with a history of diabetes mellitus (DM+). Detected pathogens were grouped into seven categories and “other”. Chi-square analysis was performed comparing the overall pathogen distribution between DM- vs DM+ patients. (b) Relative frequency of patients with or without detected Enterobacteriaceae versus all other pathogens grouped\u003cem\u003e \u003c/em\u003ein DM- vs DM+ patients. (c) Species distribution within the Enterobacteriaceae group in DM- and DM+ patients. For a) and b), a Chi-square analysis was performed between both study groups and the p value in b) was adjusted for multiple comparisons (see methods). **: p\u0026lt;0.01; ***: p\u0026lt;0.001; n.d.: not detected.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/69ed13b0068fae843e38cf5b.jpeg"},{"id":93419699,"identity":"786709de-bab7-4f7d-abe6-2df41d3f5593","added_by":"auto","created_at":"2025-10-13 16:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1256054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/afcdc4f6-6abb-4256-9747-b2153fad8c34.pdf"},{"id":89982735,"identity":"cc3ece69-48a4-4fe0-88dd-354c1ddb8184","added_by":"auto","created_at":"2025-08-27 06:30:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":607914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementDMCAPInfection.docx","url":"https://assets-eu.researchsquare.com/files/rs-7394046/v1/8507b233e1962449f898b21c.docx"}],"financialInterests":"Competing interest reported. G.R. received personal fees from Astra Zeneca, Atriva, Boehringer Ingelheim, GSK, Insmed, MSD, Sanofi, Novartis and Pfizer for consultancy during advisory board meetings and personal fees from Astra Zeneca, Berlin Chemie, BMS, Boehringer Ingelheim, Chiesi, Essex Pharma, Grifols, GSK, Insmed, MSD, Roche, Sanofi, Solvay, Takeda, Novartis, Pfizer and Vertex for lectures. \nM.P. received consulting fees and/or payment for honoraria for lectures and presentations from Pfizer, MSA, Sanofi, Janssen, GSK, Astrazeneca, Shionogi and Infectiopharm, Biomerieux and Sanofi, support for attending meetings and/or travel from Pfizer, MSD, has a patent planned with bioactive glass element, participated in data safety monitoring board or advisory board of Biomerieux and Sanofi and is president of the Paul Ehrlich Society for Antiinfective Chemotherapy and Board of Director of CAPNETZ and German Sepsis Society. \nM.W. received funding from the German Research Foundation – SFB 1449 (project ID 431232613), sub-project B02, from the German Federal Ministry of Research, Technology and Space in the framework of e:Med SYMPATH (01ZX2206A, 01ZX1906A), NUM-NAPKON (01KX2121, 01KX2021), CAP-TSD (031L0286B), PROGRESS (82DZLJ19C1, 82DZLJ19B1), NAPCODE (01EQ2406B), from the Federal Joint Committee (G-BA) – T-CABS (01NVF23109), from the Federal Ministry of Health (BMG) – PAIS Care (ZMII2-2524FSB105), from the Ministry of Defence – NoVAP (E/U2ED/PD014/OF550), and from Aptarion, Pantherna and Biotest for research outside the current study, and for lectures and advisory from Astra Zeneca, Chiesi, Insmed, Gilead, Pfizer, Boehringer, Biotest, Pantherna and Aptarion.","formattedTitle":"Community-acquired pneumonia in diabetic patients is characterised by a distinct pathogen spectrum and enhanced inflammation: results from CAPNETZ, a prospective observational cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLower respiratory tract infections (LRIs), including community-acquired pneumonia (CAP), are the world\u0026rsquo;s most deadly transmittable diseases, ranked as the fourth leading cause of death by the WHO in 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and associated with a global mortality rate of 27.7 per 100,000 across all ages in 2021 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Effective antibiotic therapy is crucial to mitigate adverse outcomes of CAP. The pathogen distribution in CAP forms the rational basis for local empirical antibiotic therapy guidelines. As microbiological testing is typically not performed in the outpatient setting and pathogen detection rates are only between 20 and 40% in those tested, most CAP patients are treated empirically [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, continuous investigation into likely pathogens across various patient populations remains essential to optimise treatment recommendations. It is widely accepted that the causative pathogen spectra in CAP vary in accordance to the geographical region, observation period and pathogen identification methods used [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CAP aetiology also depends on host characteristics. Cardiac, cerebrovascular, chronic respiratory and kidney disease, nursing home residence, prior antimicrobial therapy and immunosuppression have been associated with a higher risk of pneumonia with multidrug-resistant pathogens and/or Enterobacteriaceae and non-fermenting gram-negative bacilli such as \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDiabetes mellitus (DM) is among the most frequent comorbidities in CAP populations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous analyses identified a prevalence of known DM (irrespective of diabetes type) of 15% in the full CAPNETZ cohort [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Evidence suggests that diabetic (further called DM+) patients are more susceptible to certain types of infections (e.g., LRIs, urinary tract, and skin infections) compared to non-diabetic (DM-) individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A systematic review found DM to be associated with increased post-discharge and hyperglycaemia with increased in-hospital mortality following CAP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While it is plausible that the presence of DM may differentially influence susceptibility to specific microbes, including opportunistic pathogens [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], there is, to the best of our knowledge, no large study examining CAP aetiologies in DM\u0026thinsp;+\u0026thinsp;versus DM- patients in Europe [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of this work was to compare the pathogen spectrum in DM\u0026thinsp;+\u0026thinsp;versus DM- patients with CAP along with clinical characteristics, outcomes, parameters of inflammation and organ dysfunction in DM\u0026thinsp;+\u0026thinsp;vs. DM- patients in the German Community-Acquired Pneumonia Competence Network (CAPNETZ) cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCapnetz cohort\u003c/h2\u003e\u003cp\u003eData was obtained from patients enrolled into the CAPNETZ study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a multicenter prospective cohort study on CAP conducted in hospitals and private practices in Germany, Switzerland, Austria, the Netherlands, Denmark and Italy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The study was conducted in accordance with the Declaration of Helsinki, as well as guidelines of Good Clinical Practice. It was approved by the institutional ethics board of the Hannover Medical School, Germany (Ethics approval No. 301\u0026ndash;2008). Inclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, informed consent, evidence of lung infiltrate by imaging, and at least one of the following: active coughing, purulent sputum, positive auscultation findings, or fever. Exclusion criteria were: hospitalisation for more than 48 hours before CAP diagnosis and newly diagnosed, active pulmonary tuberculosis. Demographic and clinical data, including previous medication, therapies, and comorbidities, laboratory data, results from microbiological and virological testing, and data on predefined outcomes (ICU admission within 28 days, death from any cause within 180 days from enrolment into the study) were collected in a case report file.\u003c/p\u003e\u003cp\u003ePatients enrolled between October 1st, 2002 and June 30th, 2022 were included into this retrospective analysis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients with immunosuppression (any of: HIV infection, cytostatic therapy within the last 28 days, neutropenia, steroid therapy (\u0026gt;/= 20 mg prednisolone-equivalent/day), or immunosuppressive therapy after organ or bone-marrow transplantation within the preceding three months) were excluded. The CRB-65 score was used to assess CAP severity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStandard laboratory parameters\u003c/h3\u003e\n\u003cp\u003eLaboratory parameters were obtained as part of routine clinical diagnostics within the first 48 hours following patient enrolment. We selected available parameters that best reflect 1) the host immune response and 2) organ dysfunction and sepsis severity, inspired by components of the Sequential Organ Failure Assessment (SOFA) score including PaO₂/FiO₂ ratio (Horovitz index), bilirubin, creatinine, thrombocyte count, lactate, and confusion at admission as a proxy for central nervous system dysfunction.\u003c/p\u003e\n\u003ch3\u003ePathogen identification\u003c/h3\u003e\n\u003cp\u003eMethods used for microbiological diagnosis and laboratory processing procedures have been described previously [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In brief, all respiratory specimens and blood cultures collected at the time of inclusion were immediately processed in the local microbiological laboratories of the participating clinical centers. From 2017 onwards, multiplex PCR analysis was performed additionally to detect the following pathogens from sputum/nasopharyngeal swabs: adenovirus, Human bocavirus, Human coronavirus 229E/HKU1/NL63/OC4, enterovirus, influenza virus A/A H1N1 pdm09, influenza virus B, Human metapneumovirus, parainfluenza virus 1/2/3/4, parechovirus, rhinovirus, \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, and respiratory syncytial virus A/B. In addition, specific PCRs for \u003cem\u003eChlamydophila pneumoniae\u003c/em\u003e, \u003cem\u003eLegionella pneumophila\u003c/em\u003e, and \u003cem\u003eBordetella pertussis\u003c/em\u003e were performed in all patients. Detection of SARS-CoV-2 infection was carried out using rapid antigen testing and/or an updated multiplex PCR method (Siemens Healthineers, Eschborn, Germany) introduced in 2020. The microbiological diagnosis in the CAPNETZ cohort was established for patients who tested positive by PCR and/or culture diagnostics from respiratory samples and/or positive blood cultures in patients with moderate to severe disease (both performed in local laboratories associated with the respective hospitals), and/or urinary antigen testing for \u003cem\u003eL. pneumophila\u003c/em\u003e and \u003cem\u003eS. pneumoniae\u003c/em\u003e. Pathogens were considered the causative organisms for CAP according to the criteria published by Kr\u0026uuml;ger et al [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe identified pathogens were categorised into the following seven biologically relevant groups: 1) \u003cem\u003eS. pneumoniae\u003c/em\u003e, 2) \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, 3) Enterobacteriaceae (including \u003cem\u003eCitrobacter spp\u003c/em\u003e., \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eEnterobacter spp\u003c/em\u003e., \u003cem\u003eHafnia alvei\u003c/em\u003e, \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., \u003cem\u003eMorganella morganii\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003eP. vulgaris\u003c/em\u003e, \u003cem\u003eSerratia marcescens, Serratia\u003c/em\u003e spp.), 4) non-fermenting gram-negative bacteria (including \u003cem\u003eAcinetobacter spp., Pseudomonas aeruginosa, Pseudomonas spp., Stenotrophomonas maltophilia\u003c/em\u003e), 5) atypical bacteria (including \u003cem\u003eL. pneumophila\u003c/em\u003e, \u003cem\u003eM. pneumoniae\u003c/em\u003e and \u003cem\u003eC. pneumoniae\u003c/em\u003e), 6) \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and 7) viruses (including \u003cem\u003einfluenza A and B, SARS-CoV-2, Human coronavirus HKU1, Human coronavirus OC43, parainfluenza virus 2, 3 and 4, adenovirus, enterovirus, respiratory syncytial virus A and B, rhinovirus, Human metapneumovirus A and B, Human bocavirus\u003c/em\u003e). Due to low sample sizes, we summarised all other microbes identified under 8) \u0026ldquo;other pathogens\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eDiabetes mellitus and subgroups definitions\u003c/h3\u003e\n\u003cp\u003eHistory of DM and, for patients enrolled from 2017 onwards, type of DM, was documented according to self-report. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for definition of subgroups. A subgroup analysis was conducted among patients with documented DM type, comparing individuals with type 2 diabetes (DM2) to DM-, while excluding all other types of DM due to low sample sizes. This group is referred to as the \u0026ldquo;diabetes type 2 subcohort\u0026rdquo; (DM2 subcohort). Because parameters related to organ dysfunction and sepsis were only consistently documented in critically ill patients, the analysis of these variables was restricted to individuals from the DM2 subcohort admitted to the intensive care unit (ICU) - the \"ICU type 2 diabetes subcohort\u0026rdquo; (ICU DM2).\u003c/p\u003e\u003cp\u003eAdditionally, glycated haemoglobin (HbA1c) values were available for a subgroup of 1,961 patients from a previous CAPNETZ project and were included as a separate subcohort - the \u0026ldquo;HbA1c subcohort\u0026rdquo;. HbA1c was measured as previously described [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. DM was defined according to the 2025 American Diabetes Association diagnostic criteria [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] as HBA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% (\u0026ge;\u0026thinsp;48 mmol/mol) as measured upon inclusion and/or self-reported diagnosis of DM. Prediabetes was defined as HBA1c 5.7\u0026ndash;6.4% (39\u0026ndash;47 mmol/mol) and no DM was defined as HBA1c\u0026thinsp;\u0026lt;\u0026thinsp;5.7% (\u0026lt;\u0026thinsp;39 mmol/mol) with no self-reported DM diagnosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To account for the inaccuracy of self-reporting, we performed a sensitivity analysis of pathogen distribution between DM-, prediabetic and DM\u0026thinsp;+\u0026thinsp;patients in the HbA1c subcohort.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData are shown as percentages, relative frequencies, medians and interquartile ranges (IQRs) or averages with standard deviation (SD), depending on the underlying distribution. No imputation of missing data was performed. Group comparisons were assessed using the Wilcoxon or the Mann-Whitney U test for continuous, or the Chi-square test for categorical data and a P-value of less than 0.05 was considered significant. The Pearson correlation coefficient was used to analyze the relation between continuous variables. P-values were adjusted for multiple testing when comparing every pathogen group against all other pathogen groups with the Bonferroni-Sidak method. To account for the interdependence of clinical variables, we performed a logistic regression on the outcomes ICU admission versus not within 28 days and death from any cause versus not within 180 days within the whole cohort using the following predictors: age, sex, BMI, diabetes mellitus, malignant disease, chronic cardiac disease, chronic cerebrovascular disease, chronic kidney disease and chronic respiratory disease. Clinical data were analysed using Jmp Pro, version 18.2 (SAS Institute Inc, USA) and GraphPad Version 10 (GraphPad Prism, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eClinical characteristics\u003c/h2\u003e\u003cp\u003eA total of 13,611 patients were included in this analysis. The study flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of all patients, 17% (2,310/13,611) had self-reported DM, irrespective of type. Clinical characteristics of all patients, and comparisons between DM+ (all types) and DM- are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. DM2 was the most prevalent of all DM types in those with available information (94%, 317/337), DM type 1 accounted for 3.8% (13/337). We observed similar differences regarding clinical characteristics between DM2\u0026thinsp;+\u0026thinsp;and DM- patients as between DM\u0026thinsp;+\u0026thinsp;and DM- in the main cohort. (Supplementary Table\u0026nbsp;1).\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 characteristics of all, DM\u0026thinsp;+\u0026thinsp;and DM- patients in the CAPNETZ-cohort between 2002\u0026ndash;2022\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13,611)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,310)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo diabetes\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11,301)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years (Mean (SD))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale Sex at birth, (% (n/ N))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (7772/13611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (1444/2310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (6328/11301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2), Median (IQR), available n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (22\u0026ndash;29), 13221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (24\u0026ndash;32), 2226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (22\u0026ndash;28), 10995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing home residency (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (805/13599)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (244/2307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (561/11292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCigarette smoking during the last 12 months (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (3821/13316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (454/2222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (3367/11094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic heart failure (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (2244/13572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (791/2308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (1453/11264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic respiratory/pulmonary disease (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (4912/13611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (986/2310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (3926/11301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular/Cerebrovascular disease (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (2159/13572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (678/2301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (1481/11271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (1272/13566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (517/2303)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (755/11263)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious antibiotic therapy within the last 4 weeks (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (3257/13544)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (373/2287)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (2884/11257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant disease (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (1366/13611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (268/2310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (1098/11301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen Therapy (%, n/available N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (642/13611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (169/2310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (473/11301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eAvailable n is indicated where variables were not available for all patients. P-values comparing diabetic and non-diabetic patients (based on self-reporting) were calculated using the Wilcoxon test for medians, and the unadjusted Chi-square test for frequencies of comorbidities. All clinical variables shown were significantly different between diabetic and non-diabetic patients. \u003cb\u003eAbbreviation\u003c/b\u003es: DM+: patients with a history of diabetes mellitus, DM-: patients without a history of diabetes mellitus IQR : interquartile range, SD: standard deviation, BMI : body mass index.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome analysis\u003c/h3\u003e\n\u003cp\u003eThe distribution of CRB-65 scores differed significantly between the two groups (DM+: median: 1, IQR: 1\u0026ndash;2 vs. DM-: 1, IQR: 0\u0026ndash;1) (Fig. 2a). DM\u0026thinsp;+\u0026thinsp;patients were hospitalised for longer than DM- patients (median: 10 days, IQR 8\u0026ndash;14 days, vs. median: 9 days, IQR: 6\u0026ndash;13 days respectively) (Fig. 2b). DM\u0026thinsp;+\u0026thinsp;patients has increased rates of ICU admission within 28 days (DM+: 12% (163/1,389), DM-: 5% (429/7,972)) and death from any cause within 180 days post enrolment as compared to DM- (DM+: 13% (292/2,310), DM-: 7% (766/11,301)) (Fig. 2c, d). In a logistic regression with baseline characteristics and comorbidities as predictors, DM had an adjusted Odd\u0026rsquo;s ratio of 1.56 (1.26\u0026ndash;1.93) for ICU admission and 1.31 (1.11\u0026ndash;1.54) for death (Supplementary Table 2). Clinical outcome parameters in the DM2 subcohort were similar to the findings in the main cohort (Supplementary Fig. 1a-d).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eInflammatory response and organ dysfunction\u003c/h2\u003e\n \u003cp\u003eWhile CRP and leukocyte values were elevated in both groups at enrolment, they were significantly higher in DM\u0026thinsp;+\u0026thinsp;compared to DM- patients, although the differences are minimal (Fig. 3a, b). In the ICU DM2 subcohort (see methods), lactate levels and creatinine levels were significantly higher in DM2\u0026thinsp;+\u0026thinsp;patients compared to DM- patients while other parameters of organ dysfunction did not differ (Supplementary Fig. 2a-f).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePathogen spectrum in DM- versus DM\u0026thinsp;+\u0026thinsp;patients\u003c/h2\u003e\n \u003cp\u003eInformation on a causative pathogen for CAP was accessible in 21.7% (2,954/13,611) of all patients, and in 21.4% (2,414/11,301) of DM- and 23.4% (540/2,310) of DM\u0026thinsp;+\u0026thinsp;patients (p\u0026thinsp;=\u0026thinsp;0.03). We observed an overall difference in pathogen distribution in DM- versus DM\u0026thinsp;+\u0026thinsp;patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. 4a). \u003cem\u003eS. pneumoniae\u003c/em\u003e was the most frequently identified pathogen in both groups with 38.5% (1,140/2,954) overall, 39.1% (944/2,414) in DM- patients, and 36.3% (196/540) in DM\u0026thinsp;+\u0026thinsp;patients. \u003cem\u003eH. influenzae\u003c/em\u003e accounted for 10.4% (307/2,954) overall, 11.0% (266/2,414) in DM- patients, and 7.6% (41/540) in DM\u0026thinsp;+\u0026thinsp;patients. Atypical bacteria were detected in 11.5% (340/2,954) of all cases, in 12.0% (290/2,414) of DM- patients, and in 9.3% (50/540) of DM\u0026thinsp;+\u0026thinsp;patients. Enterobacteriaceae accounted for 8.9% (264/2,954) of all isolated pathogens in all, 8.0% (194/2,414) in DM- and 13.0% (70/540) in DM\u0026thinsp;+\u0026thinsp;patients. This difference was statistically significant when comparing the relative frequency of Enterobacteriaceae to that of all other pathogens between the groups (p\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.005, Fig. 4b). A detailed pathogen distribution from the Enterobacteriaceae group showed that \u003cem\u003eEscherichia coli\u003c/em\u003e was the most frequently isolated pathogen in both DM- and DM\u0026thinsp;+\u0026thinsp;patients, but no further analysis was feasible (Fig. 4c). \u003cem\u003eS. aureus\u003c/em\u003e was isolated in 4.3% (126/2,954) of all patients, 4.4% (105/2,414) of DM- and 3.9% (21/540) in DM+. The non-fermenting gram-negative bacterial group including \u003cem\u003ePseudomonas spp.\u003c/em\u003e and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e accounted for 2.6% (78/2,954) of all identified pathogens, 2.5% (61/2,414) of DM- patients and 3.2% (17/540) of DM\u0026thinsp;+\u0026thinsp;patients. Viruses were detected in 19.3% (569/2,954) of all CAP patients, in 18.6% (449/2,414) of DM- and in 22.2% (120/540) of DM\u0026thinsp;+\u0026thinsp;patients. The \u0026ldquo;others\u0026rdquo; pathogen group (see methods) accounted for 4.4% (130/2,954) of all isolated pathogens, with 4.4% (105/2,414) in DM-, and 4.6% (25/540) in DM\u0026thinsp;+\u0026thinsp;patients. Except for the difference in Enterobacteriaceae, no significant differences were observed between DM- and DM\u0026thinsp;+\u0026thinsp;patients when analysing the distribution of every pathogen group against all other pathogens when adjusting for multiple comparisons (Supplementary Table 3).\u003c/p\u003e\n \u003cp\u003eSimilar to the observations in the main cohort, we observed a trend towards a lower relative frequency of \u003cem\u003eS. pneumoniae\u003c/em\u003e and a higher frequency of Enterobacteriaceae in DM\u0026thinsp;+\u0026thinsp;as compared with DM- patients in the HbA1c subcohort. For the aforementioned pathogen groups, prediabetic patients showed frequencies in between DM- and DM\u0026thinsp;+\u0026thinsp;patients (Supplementary Fig. 3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this descriptive analysis of a prospective, observational European cohort of patients with CAP, we identified relevant differences between patients with and without DM regarding microbial aetiology, inflammatory parameters, and clinical outcomes.\u003c/p\u003e\u003cp\u003eDM\u0026thinsp;+\u0026thinsp;patients presented a distinct clinical profile at baseline, including older age and a greater burden of comorbidities, such as cardiovascular disease, chronic kidney disease, and obesity\u0026mdash;all of which have independently been associated with worse outcomes in CAP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Patients with DM had higher rates of adverse clinical outcomes, as indicated by higher ICU admission rates and increased 180-day mortality following CAP. DM\u0026thinsp;+\u0026thinsp;patients were less frequently treated with antibiotics before enrolment into the study potentially reflecting earlier hospital presentations or a lower likelihood of outpatient treatment. The clinical profile of patients with DM in our cohort is consistent with previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur first key finding is that the pathogen spectrum in DM\u0026thinsp;+\u0026thinsp;patients differed significantly from that in DM- patients. Notably, DM\u0026thinsp;+\u0026thinsp;individuals showed higher relative frequencies of bacteria belonging to the Enterobacteriaceae family. This pathogen group ranked as the second most frequently identified in DM\u0026thinsp;+\u0026thinsp;patients\u0026mdash;after \u003cem\u003eS. pneumoniae\u003c/em\u003e and ahead of \u003cem\u003eH. influenzae\u003c/em\u003e. The pathogen detection rate of 21.7% across all patients reflects typical clinical settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and is consistent with earlier CAPNETZ reports [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. An increased prevalence of Enterobacteriaceae has previously been described for other comorbidities including cardiac, cerebrovascular, respiratory and kidney diseases, but, to the best of our knowledge, our study is the first showing this association with DM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A generally increasing prevalence of this uncommon pathogen group has previously been described for the whole CAPNETZ cohort over time [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It also aligns with global estimates showing an increase in pathogens belonging to the Enterobacteriaceae family in patients across all ages with LRIs, along with a decrease in, i.e., \u003cem\u003eS. pneumoniae\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], suggesting a broader epidemiological shift in CAP aetiology.\u003c/p\u003e\u003cp\u003eOur second key finding is that DM\u0026thinsp;+\u0026thinsp;patients exhibited an enhanced inflammatory response at enrolment, as evidenced by higher median CRP levels and leucocyte counts compared to DM- patients. In addition, we observed higher median levels of creatinine and lactate in an ICU subcohort, indicating kidney and cardiovascular impairment in severe CAP patients. However, these findings should be interpreted with caution due to low sample size and lacking information on the baseline in both groups. Earlier mechanistic studies described altered innate immunity in DM, which highlights the need to further investigate immune-pathophysiological pathways in CAP [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA strength of our study lies in the well-characterised cohort, which is one of the largest prospective CAP cohorts world-wide [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, our data should be interpreted in light of changes in pathogen detection techniques over the 20-year observation period, shifts in bacterial aetiology, and the cohort\u0026rsquo;s characteristics, consisting of mostly hospitalised patients with a predominantly mild to moderate course of CAP, limiting the generalisability of our findings. Another limitation of our study is that DM diagnosis was based on self-report, which may have led to misclassification and a potential underestimation of DM prevalence. Reassuringly, we observed the same trend regarding pathogen distribution in a subcohort with available HBA1c values. Prevalence of diabetes may additionally have changed over time. Information on DM type and glycemic control was limited. However, differences in outcome were observed not only in the overall cohort but also in a DM2 subcohort. We were not able to conduct diabetes subtype-specific analyses and did not have reliable data on antidiabetic medication which may modify outcome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe believe that our data, showing a higher frequency of Enterobacteriaceae in DM\u0026thinsp;+\u0026thinsp;patients, are robust and plausible in light of previous publications with similar observations in comorbidities associated with DM. It might be reasonable to assume that the higher frequency of CAP caused by the Enterobacteriaceae family could be related to compromised early antibacterial immune responses in DM patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The lower prevalence of \u003cem\u003eS. pneumoniae\u003c/em\u003e in DM patients may be associated with higher vaccination rates in patients with comorbidities following national recommendations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], thereby relatively increasing the proportion of less common pathogens in the microbiological spectrum.\u003c/p\u003e\u003cp\u003eFurther studies are needed to decipher the clinical meaning and impact of the distinct microbial spectrum and of the enhanced inflammatory response we observed in diabetic CAP patients. Based on our findings, we propose extending current recommendations to include early, comprehensive, and ideally rapid microbiological testing in at-risk patients with chronic underlying conditions\u0026mdash; even in the absence of traditional indicators for hospital admission or microbial diagnostics. When weighing the pros and cons of microbial testing in mild CAP, it is important to consider that a shift in CAP aetiology has been observed in our and other studies, particularly in patients with interrelated comorbidities such as DM. In these patients, Enterobacteriaceae and viral pathogens are increasingly replacing traditionally expected CAP pathogens. Early pathogen identification is essential for enabling timely adjustment or discontinuation of antibiotic therapy, which has been shown to improve CAP outcomes and is critical for avoiding both under- and overtreatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Taken together, our findings underscore that patients with DM represent a highly prevalent, specific, and clinically vulnerable population in the context of CAP\u0026mdash;highlighting the need for increased awareness among healthcare professionals, appropriate consideration in CAP guidelines, and further research aiming at reducing adverse outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.R. received personal fees from Astra Zeneca, Atriva, Boehringer Ingelheim, GSK, Insmed, MSD, Sanofi, Novartis and Pfizer for consultancy during advisory board meetings and personal fees from Astra Zeneca, Berlin Chemie, BMS, Boehringer Ingelheim, Chiesi, Essex Pharma, Grifols, GSK, Insmed, MSD, Roche, Sanofi, Solvay, Takeda, Novartis, Pfizer and Vertex for lectures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.P.\u0026nbsp;received consulting fees and/or payment for honoraria for lectures and presentations from \u0026nbsp;Pfizer, MSA, Sanofi, Janssen, GSK, Astrazeneca, Shionogi and Infectiopharm, Biomerieux and Sanofi, support for attending meetings and/or travel from Pfizer, MSD, has a patent planned with bioactive glass element, participated in data safety monitoring board or advisory board of Biomerieux and Sanofi and is president of the Paul Ehrlich Society for Antiinfective Chemotherapy and Board of Director of CAPNETZ and German Sepsis Society.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.W. received funding from the German Research Foundation – SFB 1449 (project ID 431232613), sub-project B02, from the German Federal Ministry of Research, Technology and Space in the framework of e:Med SYMPATH (01ZX2206A, 01ZX1906A), NUM-NAPKON (01KX2121, 01KX2021), CAP-TSD (031L0286B), PROGRESS (82DZLJ19C1, 82DZLJ19B1), NAPCODE (01EQ2406B), from the Federal Joint Committee (G-BA) – T-CABS (01NVF23109), from the Federal Ministry of Health (BMG) – PAIS Care (ZMII2-2524FSB105), from the Ministry of Defence – NoVAP (E/U2ED/PD014/OF550), and from Aptarion, Pantherna and Biotest for research outside the current study, and for lectures and advisory from Astra Zeneca, Chiesi, Insmed, Gilead, Pfizer, Boehringer, Biotest, Pantherna and Aptarion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCAPNETZ was funded by a grant from the German Federal Ministry of Education and Research \u0026nbsp;(FKZ 01KI07145) 2001-2011 and has been an associated member of the German Center for Lung Research (FKZ 82DZL002B4) since 2013.\u003c/p\u003e\n\u003cp\u003eM.P. was funded by a grant from the German Federal Ministry for Education and Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.T. is participant in the BIH Charité Clinician Scientist Program funded by the Charité – Universitätsmedizin Berlin and the Berlin Institute of Health at Charité (BIH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Claire Hartmann and the CAPNETZ study network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMembers of the CAPNETZ study group except the authors: A. Fuchs, G. Paul, M. Ayoub (Augsburg); A. Prasse (Basel); W. Bauer, E. C. Diehl-Wiesenecker, N. Galtung, C. Kodde, Y.-M. Stoppe (Berlin); C. Boesecke, S. Breitschwerdt, D. Benke (Bonn); S. Schmager (Cottbus); A. Grünewaldt, J. Wheeler (Darmstadt); B. Schaaf, J. Kremling (Dortmund); M. Kolditz, B. Schulte-Hubbert, J. Ronczka (Dresden); A. Seeger, J. Kohlhäufl (Frankfurt), D. Stolz, S. Fähndrich, M. Panning (Freiburg); M. Unnewehr, R. Lim (Hamm); M. Hoeper, I. Pink, N. Drick, T. Fühner, T. Steinberg, G. Barten-Neiner, W. Kröner, O. Unruh, N. Adaskina, F. Eberhardt, T. Illig, N. Klopp (Hannover); B. T. Schleenvoigt, A. Moeser (Jena); D. Drömann, P. Parschke, K. Franzen, F. Waldeck, B. Gebel, N. Käding, S. Boutin (Lübeck); J. Schneider, J. Erber, F. Voit, (Munich); D. Heigener, I. Hering (Rotenburg/Wümme); W. Albrich, F. Rassouli, B. Wirth (St. Gallen); C. Neurohr (Stuttgart); A. Essig, S. Stenger, M. Wallner (Ulm); H. Burgmann, L. Traby, L. Schubert (Vienna); and all study nurses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: B.M.P., G.K., J.R., G.R., M.W.P., M.W., N.S., L.E.S., A.V.J., B.O., C.T.\u003c/p\u003e\n\u003cp\u003eData acquisition: CAPNETZ study group\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: B.M.P., F.F.V, D.H., C.W., A.V.J., C.T.\u003c/p\u003e\n\u003cp\u003eDrafting the article: B.M.P., F.F.V., C.T.\u003c/p\u003e\n\u003cp\u003eCritical article revision: G.K., J.R., G.R., M.W.P., M.W., N.S., L.E.S., A.V.J., B.O.\u003c/p\u003e\n\u003cp\u003eFinal approval of the version to be submitted: all authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003en.d. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death) (accessed June 12, 2025).\u003c/li\u003e\n\u003cli\u003eGBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis 2024;24:974\u0026ndash;1002.\u003c/li\u003e\n\u003cli\u003eGadsby NJ, Musher DM. The microbial etiology of community-acquired pneumonia in adults: From classical bacteriology to host transcriptional signatures. Clin Microbiol Rev 2022;35:e0001522.\u003c/li\u003e\n\u003cli\u003eJain S, Self WH, Wunderink RG, Fakhran S, Balk R, Bramley AM, et al. Community-acquired pneumonia requiring hospitalization among U.s. adults. N Engl J Med 2015;373:415\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eVaughn VM, Dickson RP, Horowitz JK, Flanders SA. Community-acquired pneumonia: A review. JAMA 2024;332:1282\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003evon Baum H, Welte T, Marre R, Suttorp N, Ewig S, CAPNETZ study group. Community-acquired pneumonia through Enterobacteriaceae and Pseudomonas aeruginosa: Diagnosis, incidence and predictors. Eur Respir J 2010;35:598\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003eAliberti S, Di Pasquale M, Zanaboni AM, Cosentini R, Brambilla AM, Seghezzi S, et al. Stratifying risk factors for multidrug-resistant pathogens in hospitalized patients coming from the community with pneumonia. Clin Infect Dis 2012;54:470\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBenfield T, Jensen JS, Nordestgaard BG. Influence of diabetes and hyperglycaemia on infectious disease hospitalisation and outcome. Diabetologia 2007;50:549\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eAlmirall J, Bol\u0026iacute;bar I, Serra-Prat M, Roig J, Hospital I, Carandell E, et al. New evidence of risk factors for community-acquired pneumonia: a population-based study. Eur Respir J 2008;31:1274\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eJensen AV, Faurholt-Jepsen D, Egelund GB, Andersen SB, Petersen PT, Benfield T, et al. Undiagnosed diabetes mellitus in community-acquired pneumonia: A prospective cohort study. Clin Infect Dis 2017;65:2091\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMuller LMAJ, Gorter KJ, Hak E, Goudzwaard WL, Schellevis FG, Hoepelman AIM, et al. Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis 2005;41:281\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCh\u0026aacute;vez-Reyes J, Esc\u0026aacute;rcega-Gonz\u0026aacute;lez CE, Chavira-Su\u0026aacute;rez E, Le\u0026oacute;n-Buitimea A, V\u0026aacute;zquez-Le\u0026oacute;n P, Morones-Ram\u0026iacute;rez JR, et al. Susceptibility for some infectious diseases in patients with diabetes: The key role of glycemia. Front Public Health 2021;9:559595.\u003c/li\u003e\n\u003cli\u003eBarmanray RD, Cheuk N, Fourlanos S, Greenberg PB, Colman PG, Worth LJ. In-hospital hyperglycemia but not diabetes mellitus alone is associated with increased in-hospital mortality in community-acquired pneumonia (CAP): a systematic review and meta-analysis of observational studies prior to COVID-19. BMJ Open Diabetes Res Care 2022;10:e002880.\u003c/li\u003e\n\u003cli\u003eFalguera M, Pifarre R, Martin A, Sheikh A, Moreno A. Etiology and outcome of community-acquired pneumonia in patients with diabetes mellitus. Chest 2005;128:3233\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eKlekotka RB, Mizgała E, Kr\u0026oacute;l W. The etiology of lower respiratory tract infections in people with diabetes. Pneumonol Alergol Pol 2015;83:401\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCapnetz \u0026ndash; Ziel und Zweck der CAPNETZ STIFTUNG ist die F\u0026ouml;rderung wissenschaftlicher Aktivit\u0026auml;ten rund um das Thema \u0026bdquo;Ambulant erworbene Pneumonien (community-acquired pneumonia, CAP) und andere Infektionen des unteren Respirationstraktes\u0026ldquo; n.d. http://www.capnetz.de (accessed June 19, 2025).\u003c/li\u003e\n\u003cli\u003eWelte T, Suttorp N, Marre R. CAPNETZ-community-acquired pneumonia competence network. Infection 2004;32:234\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBauer TT, Ewig S, Marre R, Suttorp N, Welte T, CAPNETZ Study Group. CRB-65 predicts death from community-acquired pneumonia. J Intern Med 2006;260:93\u0026ndash;101.\u003c/li\u003e\n\u003cli\u003eBraeken DCW, Essig A, Panning M, Hoerster R, Nawrocki M, Dalhoff K, et al. Shift in bacterial etiology from the CAPNETZ cohort in patients with community-acquired pneumonia: data over more than a decade. Infection 2021;49:533\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eKr\u0026uuml;ger S, Ewig S, Papassotiriou J, Kunde J, Marre R, von Baum H, et al. Inflammatory parameters predict etiologic patterns but do not allow for individual prediction of etiology in patients with CAP: results from the German competence network CAPNETZ. Respir Res 2009;10:65.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: Standards of care in diabetes-2025. Diabetes Care 2025;48:S27\u0026ndash;49.\u003c/li\u003e\n\u003cli\u003eHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497\u0026ndash;506.\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-de-Andr\u0026eacute;s A, de Miguel-D\u0026iacute;ez J, Jim\u0026eacute;nez-Trujillo I, Hern\u0026aacute;ndez-Barrera V, de Miguel-Yanes JM, M\u0026eacute;ndez-Bail\u0026oacute;n M, et al. Hospitalisation with community-acquired pneumonia among patients with type 2 diabetes: an observational population-based study in Spain from 2004 to 2013. BMJ Open 2017;7:e013097.\u003c/li\u003e\n\u003cli\u003eLachmandas E, Vrieling F, Wilson LG, Joosten SA, Netea MG, Ottenhoff TH, et al. The effect of hyperglycaemia on in vitro cytokine production and macrophage infection with Mycobacterium tuberculosis. PLoS One 2015;10:e0117941.\u003c/li\u003e\n\u003cli\u003eDungu AM, Ryrs\u0026oslash; CK, Hegelund MH, Jensen AV, Kristensen PL, Krogh-Madsen R, et al. Diabetes status, c-reactive protein, and insulin resistance in community-acquired pneumonia-A prospective cohort study. J Clin Med 2023;13. https://doi.org/10.3390/jcm13010245.\u003c/li\u003e\n\u003cli\u003eSuttorp N, Welte T, Marre R, Stenger S, Pletz M, Rupp J, et al. CAPNETZ. The competence network for community-acquired pneumonia (CAP): Das Kompetenzzentrum f\u0026uuml;r ambulant erworbene Pneumonie. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2016;59:475\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eKantreva K, Katsaounou P, Saltiki K, Trakada G, Ntali G, Stratigou T, et al. The possible effect of anti-diabetic agents GLP-1RA and SGLT-2i on the respiratory system function. Endocrine 2025;87:378\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eRestrepo BI, Twahirwa M, Rahbar MH, Schlesinger LS. Phagocytosis via complement or Fc-gamma receptors is compromised in monocytes from type 2 diabetes patients with chronic hyperglycemia. PLoS One 2014;9:e92977.\u003c/li\u003e\n\u003cli\u003eFiocca Vernengo F, R\u0026ouml;wekamp I, Boillot L, Caesar S, D\u0026ouml;rner PJ, Tarnowski B, et al. Diabetes impairs IFN\u0026gamma;-dependent antibacterial defense in the lungs. Mucosal Immunol 2025;18:431\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eLachmandas E, Thiem K, van den Heuvel C, Hijmans A, de Galan BE, Tack CJ, et al. Patients with type 1 diabetes mellitus have impaired IL-1\u0026beta; production in response to Mycobacterium tuberculosis. Eur J Clin Microbiol Infect Dis 2018;37:371\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eTripathi D, Radhakrishnan RK, Sivangala Thandi R, Paidipally P, Devalraju KP, Neela VSK, et al. IL-22 produced by type 3 innate lymphoid cells (ILC3s) reduces the mortality of type 2 diabetes mellitus (T2DM) mice infected with Mycobacterium tuberculosis. PLoS Pathog 2019;15:e1008140.\u003c/li\u003e\n\u003cli\u003eLecube A, Pach\u0026oacute;n G, Petriz J, Hern\u0026aacute;ndez C, Sim\u0026oacute; R. Phagocytic activity is impaired in type 2 diabetes mellitus and increases after metabolic improvement. PLoS One 2011;6:e23366.\u003c/li\u003e\n\u003cli\u003en.d. https://www.rki.de/DE/Aktuelles/Publikationen/Epidemiologisches-Bulletin/2025/04_25.pdf?__blob=publicationFile\u0026amp;v=10 (accessed June 19, 2025).\u003c/li\u003e\n\u003cli\u003eUematsu H, Hashimoto H, Iwamoto T, Horiguchi H, Yasunaga H. Impact of guideline-concordant microbiological testing on outcomes of pneumonia. Int J Qual Health Care 2014;26:100\u0026ndash;7.\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":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diabetes mellitus, community-acquired pneumonia, pathogen spectrum, Enterobacteriaceae","lastPublishedDoi":"10.21203/rs.3.rs-7394046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7394046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eDiabetes mellitus (DM) is a relevant risk factor for enhanced susceptibility to and adverse outcomes in infections, including community-acquired pneumonia (CAP). We aimed to characterise clinical outcomes, inflammatory and organ failure markers and microbial etiologies in diabetic (DM+) versus non-diabetic (DM-) patients in a European CAP cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eComparative analyses using data from the CAPNETZ multicenter, prospective, observational study including 13,611 patients with CAP enrolled between 2002\u0026ndash;2022, with and without a history of DM, were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSeventeen percent (2,310/13,611) had a history of DM (DM+). Compared to DM- patients, DM\u0026thinsp;+\u0026thinsp;patients had a higher 180 days mortality rate following CAP (13% (292/2,310) vs. 7% (766/11,301), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and higher C-reactive protein and leucocyte counts (median CRP 97 mg/L (IQR: 31\u0026ndash;202) vs. 86 mg/L (IQR: 24\u0026ndash;190), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; median leucocyte count 12/nl (IQR: 9\u0026ndash;16)vs. 11/nl (IQR: 8\u0026ndash;15), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Pathogens were identified in 23.4% (540/2,310) of the DM\u0026thinsp;+\u0026thinsp;and 21.7% (2,414/11,301) of the DM- patients (p\u0026thinsp;=\u0026thinsp;0.03), respectively. Overall, pathogen distribution differed between the two groups, with higher frequencies of Enterobacteriaceae in the DM\u0026thinsp;+\u0026thinsp;group (13.0% (70/539) vs. 8.0% (194/2,414), p\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.01).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCAP in DM\u0026thinsp;+\u0026thinsp;is characterised by a distinct microbial spectrum and enhanced inflammation. While further studies are needed to elucidate the clinical impact of our findings, we recommend early and comprehensive CAP pathogen testing in DM\u0026thinsp;+\u0026thinsp;patients.\u003c/p\u003e","manuscriptTitle":"Community-acquired pneumonia in diabetic patients is characterised by a distinct pathogen spectrum and enhanced inflammation: results from CAPNETZ, a prospective observational cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:30:10","doi":"10.21203/rs.3.rs-7394046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T04:14:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T05:04:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T17:42:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214811022473568763364958445702137167512","date":"2025-09-01T09:20:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T12:37:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283395589605831571316004228031358989420","date":"2025-08-30T04:34:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167694570723422842006552489030859503836","date":"2025-08-29T16:23:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313322581854415421580777954051300620477","date":"2025-08-23T14:39:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-18T06:48:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T06:47:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T06:07:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infection","date":"2025-08-17T18:49:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e7d5c5f7-68d7-47e7-bfcd-9c57d4d661e6","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:00:43+00:00","versionOfRecord":{"articleIdentity":"rs-7394046","link":"https://doi.org/10.1007/s15010-025-02659-w","journal":{"identity":"infection","isVorOnly":false,"title":"Infection"},"publishedOn":"2025-10-12 15:57:32","publishedOnDateReadable":"October 12th, 2025"},"versionCreatedAt":"2025-08-27 06:30:10","video":"","vorDoi":"10.1007/s15010-025-02659-w","vorDoiUrl":"https://doi.org/10.1007/s15010-025-02659-w","workflowStages":[]},"version":"v1","identity":"rs-7394046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7394046","identity":"rs-7394046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}