PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0314287 PONE-D-23-29894 Research Article Medicine and health sciencesDiagnostic medicineDiabetes diagnosis and managementHbA1c Biology and life sciencesBiochemistryProteinsHemoglobinHbA1c Medicine and health sciencesPharmacologyDrugsAntimicrobialsAntibiotics Biology and life sciencesMicrobiologyMicrobial controlAntimicrobialsAntibiotics Medicine and health sciencesEndocrinologyEndocrine disordersDiabetes mellitusType 2 diabetesType 2 diabetes risk Medicine and health sciencesMedical conditionsMetabolic disordersDiabetes mellitusType 2 diabetesType 2 diabetes risk Medicine and health sciencesEndocrinologyEndocrine disordersDiabetes mellitusType 2 diabetes Medicine and health sciencesMedical conditionsMetabolic disordersDiabetes mellitusType 2 diabetes Medicine and health sciencesHealth carePrimary care Medicine and health sciencesEndocrinologyEndocrine disordersDiabetes mellitus Medicine and health sciencesMedical conditionsMetabolic disordersDiabetes mellitus Medicine and health sciencesEpidemiologyMedical risk factorsCancer risk factors Medicine and health sciencesOncologyCancer risk factors Medicine and health sciencesOncologyCancer treatment Glycemic control and bacterial infectious risk in type 2 diabetes: A retrospective cohort from a primary care database Glycemic control and bacterial infectious risk in type 2 diabetes https://orcid.org/0009-0006-1120-5492 Lemoine Edouard Conceptualization Investigation Methodology Writing – original draft Writing – review & editing 1 * Dusenne Mikaël Data curation Formal analysis Validation 2 Schuers Matthieu Conceptualization Supervision Validation 1 2 3 Department of General Practice, UNIROUEN, Normandie Université, Rouen, France Department of Medical Information and Informatics, CHU Rouen, Rouen, France Medical Informatics and e-Health Knowledge Engineering Laboratory, INSERM, U1142, LIMICS, Sorbonne University, Paris, France Gao Peng Editor Army Medical University, CHINA

The authors have declared that no competing interests exist

 * E-mail: edouard.lemoine1@univ-rouen.fr 13 12 2024 2024 19 12 e0314287 6 10 2023 7 11 2024 2024 Lemoine et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Objective

The prevalence of diabetes was estimated at 5.3% of the French population in 2020. People with type 2 diabetes have an increased risk of infection. Currently, there is no consensus on the impact of glycemic control on infectious risk. The objective was to evaluate whether glycemic control and diabetes severity were associated with infectious risk in type 2 diabetes.

 Materials and methods

We designed a multicenter retrospective cohort study using data from a French primary care database. Data were collected from January 2012 to January 2022. Glycemic control was estimated by the threshold of glycated hemoglobin and diabetes severity by the number, and the type, of antidiabetic treatments. Infectious risk was evaluated by the mean of antibiotic prescriptions per year.

 Results

Among 59,020 patients, 1959 patients were included in the final analysis. The threshold of glycated hemoglobin was not associated with the mean of antibiotic prescriptions per year (ANOVA p = 0.228). Secondary analyses did not show an association between the number, or the type, of antidiabetic treatments and the mean of antibiotic prescriptions per year (p = 0.53 and p = 0.018, respectively).

No association was observed between glycemic control, diabetes severity and infectious risk in patients with type 2 diabetes. This is the first European study using data from primary care to examine bacterial infectious risk in patients with type 2 diabetes, demonstrating the possibilities offered by the use of databases in primary care research.

 Conclusion

Long-term glycemic control was not associated with bacterial infectious risk in patients with type 2 diabetes.

 The author(s) received no specific funding for this work. Data Availability Access to aggregated raw data is possible through a request to PRIMEGE Normandie's ethical and scientific committee as described in the database's internal regulations, either by contacting the data manager at primege.normandie@univ-rouen.fr, or by contacting the doctor in charge of IT at mikaeldusenne@gmail.com. Other researchers have the option of redoing the analyses, reproducing the studies carried out following a request to the ethical and scientific committee. The application form is available in the rules of procedure of the Ethics and Scientific Committee. Introduction

Diabetes affected 537 million people worldwide in 2021 [1] and 5.3% of the French population in 2020 [2]. The management of diabetes is based on the control of glycemia through the implementation of hygienic and dietary rules, oral antidiabetic treatments, insulin treatments and the screening and prevention of microvascular and macrovascular complications [3]. The total amount of reimbursements to people with pharmacologically treated diabetes was estimated at 12.5 billion euros in 2007 in France [4].

In recent meta-analyses [5–8], strict glycemic control by intensification of drug therapies did not lower the risk of macrovascular and microvascular complications with respect to clinically relevant variables: mortality by myocardial infarction, stroke, amputation, renal failure requiring dialysis, blindness, neuropathic pain. The intensification of glucose-lowering therapy is frequently associated with severe adverse events such as hypoglycemia [5,8]. These data give rise to a reflection on a "non-gluco-centric" management of people with type 2 diabetes.

The risk of infection was estimated to be higher in people with type 2 diabetes than in those without diabetes. Notably, with an Odds Ratio (OR) of 1.32 for lower respiratory infections, an OR of 1.24 for urinary tract infections, an OR of 1.33 for bacterial infections of the skin and mucous membranes and an OR of 1.44 for fungal infections of the skin and mucous membranes [9]. This infectious risk is due to immunological changes whose pathophysiological mechanism remains unclear [10]. There is no consensus on the impact of long-term glycemic control on this infectious risk and the results of studies on this subject seem heterogeneous depending on patient inclusion criteria such as hospital or primary care recruitment, duration of diabetes, type of diabetes and definition of glycemic control [11].

The objective of this study was to evaluate whether glycemic control and severity of diabetes were associated with bacterial infectious risk in type 2 diabetes.

 Materials and methods Design of the study

We designed a retrospective observational multicenter cohort study using data from a primary care database in Normandy, France.

 Data source

Data were extracted from the electronic medical records of 103,649 patients who had primary care visits with 39 family practitioners in four primary care centers in Normandy. The extracted data concerned primary care visits between January 2012 and January 2022. Data were derived from primary care visits, observations, biometric measurements, history, prescribed treatments, and results of laboratory tests.

 Inclusion of patients

The patients included had to be aged 18 years and older, had type 2 diabetes, and at least three visits in a primary care center. Patients were identified using data from their electronic medical records. We searched International Classification of Primary Care (ICPC) T90 in coded diagnoses or reasons for consultations, labels: “Diabetes II”, Type 2 diabetes”, “Diabetes mellitus type 2” in history and observations, and Anatomical Therapeutic Chemical (ATC) category A10B which corresponds to antidiabetic treatments in coded prescriptions. Patients who did not have at least three glycated hemoglobin (HbA1c) determinations were excluded from the final analysis.

 Assessment of glycemic control

Glycemic control was estimated by calculating the threshold of HbA1c values of patients with type 2 diabetes. The HbA1c threshold is an average of HbA1c values considering the time between different HbA1c determinations. This method was described by Maple-Brown LJ et al [12] as having a better predictive value than mean HbA1c on the occurrence of microvascular complications. HbA1c thresholds range from < 5.5% to > 9.5%.

 Primary outcome

The primary outcome was an association between the threshold of HbA1c and the mean of antibiotic prescriptions per year. The latter was calculated as the total number of antibiotic prescriptions divided by the number of years of follow-up. Antibiotic prescriptions were identified by searching for the ATC category J01.

 Secondary outcomes

Secondary outcomes were an association between the number, and the type, of antidiabetic treatments and the mean of antibiotic prescriptions per year. We identified all antidiabetic treatments prescribed during the follow-up period, and then the antidiabetic treatments prescribed in the last prescription of each patient using the A10B category of the ATC terminology. These antidiabetic treatments were classified by type of antidiabetic treatment (No antidiabetics, oral antidiabetics, injectable non-insulin antidiabetics, insulin) and by therapeutic class (Biguanides, Sulfonamides, Dipeptidyl Peptidase 4 inhibitors, Glucagon-Like-Peptide-1 receptor agonists, Sodium-Glucose Cotransporter Type 2 inhibitors, Glinides, intestinal-acting antidiabetics, slow-acting insulins, intermediate-acting insulins, rapid-acting insulins and rapid-acting insulin analogues).

Available confounding factors and Hba1c were controlled by a case-control method comparing type 2 diabetic patients who had received at least one antibiotic prescription during follow-up with those who had not.

 Statistical analysis

The number of subjects required was based on the incidence rates of bacterial infections in type 2 diabetic patients with an HbA1c between 5.5 and 6.5% and those with an HbA1c between 8.5 and 9.5% in the Mor et al study (15), for an alpha risk of 5% and a beta risk of 20%, giving a required number of subjects of 276. A first statistical analysis looked for a difference in the mean of antibiotic prescriptions per year by the threshold of HbA1c, and secondly an analysis of the mean of antibiotic prescriptions per year according to the number and type of antidiabetic treatment by ANOVA test and then with a graphical representation by boxplots. The mean of antibiotic prescriptions per year has undergone a logarithmic transformation for reasons of visual clarity of the graphical representation, requiring that the value “zero” be artificially changed to the value 0.0001. Secondly, we performed a multivariate analysis using a negative binomial distribution model, adjusted with HbA1c threshold, age, BMI, history of COPD and asthma, history of cardiac event including stent placement, cancer pathology identified by ICPC codes and text searches in the electronic medical records, number of chronic treatments and number of antidiabetic treatments. The case-control study was carried out using a Student’s t test, a Mann Whitney test and a Cohen’s d test for quantitative variables, and a chi 2 test for qualitative variables.

 Regulatory

The use of these data was submitted to a scientific and ethical committee: “Plateforme Régionale d’Information en Medecine Générale” (PRIMEGE) ethics committee DA-CSE2023-003 and declared to the Commission Nationale Informatique et Liberté (CNIL) as complying with the reference methodology 004 (MR-004) and conforms to the declaration of Helsinki. Patients were informed by posters in the health centers. Data have been fully anonymized, and we had no access to identifying data before, during or after the study. Access to data was requested on 03/14/2023 and was available from the approval of the ethical and scientific committee on 04/14/2023.

 Results Description of the population

The database contained the data of 103,649 patients, 84,012 of whom were at least 18 years old at the time of data collection and 72,386 had at least three primary care visits. A total of 59,020 patients met the age and follow-up criteria. We have summarized the process of inclusion and exclusion in a flow chart (Fig 1). The mean year of birth for the overall population was 1973. The male-to-female sex ratio was 0.76 (24,985/32,727). The mean BMI was 27.44 kg/m2.

10.1371/journal.pone.0314287.g001 Flow chart of the inclusion and exclusion process of type 2 diabetic patients.

Hba1c: Glycated hemoglobin.

 Population with type 2 diabetes

Analysis of electronic medical records identified 9247 patients with diabetes: 994 patients by informed diagnosis, 2178 patients by history, 765 patients by reason of visit, 2273 patients by visit data, and 3037 patients by antidiabetic treatments. Combining these methods of detection, 3411 patients (3.29%) were identified as having type 2 diabetes, of whom 1452 patients were excluded because they did not have at least three HbA1c determinations during follow-up. A total of 1959 patients (1.89%) with type 2 diabetes were included in the final analysis. The characteristics of patients with diabetes are shown in (Table 1).

10.1371/journal.pone.0314287.t001 Characteristics of the overall population and of the population with type 2 diabetes.
Overall population(n = 59 020) Non diabetic population(n = 57 061) Population with type 2 diabetes(n = 1959)
Mean year of birth 1973 ± 20.5 1981 ± 22.5 1950 ±12.5
Sex ratio (male/female) 0.76 0.75 1.24
Mean BMI (kg/m2) 27.44 27.60 30.39
Influenza vaccination between 2013 and 2021, n (%) 843 (1.72%) 706 (1.24%) 137 (7.01%)
Pneumococcal vaccination between 2019 and 2022, n (%) 127 (0.26%) 117 (0.21%) 10 (0.51%)
Covid vaccination between 2020 and 2022, n (%) 6241 (12.75%) 5564 (9.75%) 677 (34.65%)
Tobacco use, n (%) 1959 (4.0%) 1687 (2.96%) 272 (13.92%)
Antidiabetic treatmentsNo antidiabetic treatment, n (%) 57 243 (96.99%) NA 182 (9.29%)
Oral antidiabetics, n (%) 1041 (1.76%) NA 1041 (53.14%)
Injectable non-insulin antidiabetics, n (%) 233 (0.39%) NA 233 (11.89%)
Insulin, n (%) 503 (0.85%) NA 503 (25.68%)
Antibiotic treatmentsNo antibiotic treatment, n (%) NA NA 720 (36.77%)
One antibiotic prescription, n (%) NA NA 934 (47.68%)
Two antibiotic prescriptions, n (%) NA NA 208 (10.62%)
At least three antibiotic prescriptions, n (%) NA NA 111 (5.67%)

NA: Not Applicable / Not Available, SD: Standard deviation.

Antidiabetic treatments were prescribed in 90.71% of patients with type 2 diabetes (n = 1777). Non-insulin treatment was prescribed in 65.03% of patients (n = 1274) and insulin treatment in 25.68% (n = 503). The mean threshold of HbA1c of patients with type 2 diabetes was 6.92%. The mean follow-up between the first HbA1c result, and the last primary care visit was 5.32 ± 2.79 years.

 Antidiabetic treatments

Antidiabetic treatments were classified by the number, and the type, of antidiabetic treatments: no antidiabetic treatment, oral antidiabetics, injectable non-insulin antidiabetics, insulin, or a combination. We identified 3750 prescriptions of antidiabetic treatments on the last prescription of patients with type 2 diabetes. The three most prescribed treatments were metformin (37.84%), gliclazide (11.47%) and insulin glargine (10.35%).

 Antibiotic treatments

Antibiotic treatments were prescribed in 1253 (64%) patients (Table 1). We identified 4694 antibiotic prescriptions. Most of them (51.57%) concerned the penicillin class with amoxicillin (31.26%) and amoxicillin/clavulanic acid (15.70%). Macrolides represented 12.46%, streptogramins with pristinamycin 9.16%, quinolones 8.73%, cephalosporins 5.00%, and then phosphonic acids, nitrofurans, sulfonamides associated with diaminopyrimidines and penicillin relatives.

 Primary analysis

The primary analysis did not show an association between the threshold of HbA1c and the mean of antibiotic prescriptions per year on ANOVA test (p = 0.228) (Table 2) or between the threshold of HbA1c and the log-transformed mean of antibiotic prescriptions per year on graphical representation (Fig 2).

10.1371/journal.pone.0314287.g002 Boxplot of log-transformed mean of antibiotic prescriptions per year by the categories of threshold glycated hemoglobin.

Glycated hemoglobin (HbA1c) is expressed as a percentage (%).

 10.1371/journal.pone.0314287.t002 Antibiotic prescriptions per year according to threshold of HbA1c, and number, and type, of antidiabetic treatments.
Patients with type 2 diabetesn (%) Antibiotic prescriptions per yearmean ± SD ANOVA
Threshold of HbA1c < 5.5% 27 (1.38%) 0.88 ± 1.1 } p = 0.228
5.5–6.5% 473 (24.14%) 0.62 ± 1.14
6.5–7.5% 817 (41.70%) 0.54 ± 1.03
7.5–8.5% 422 (21.54%) 0.50 ± 0.78
8.5–9.5% 158 (8.07%) 0.57 ± 0.83
> 9.5% 62 (3.16%) 0.63 ± 0.83
Number of antidiabetic treatments 01 182 (9.29%)897 (45.79%) 0.63 ± 1.130.53 ± 0.93 } p = 0.530
2 468 (23.89%) 0.57 ± 1.11
> 3 412 (21.03%) 0.59 ± 0.91
Type of antidiabetic treatments No antidiabetic treatment 182 (9.29%) 0.63 ± 1.13 } p = 0.018
Oral antidiabetics 1041 (53.14%) 0.49 ± 0.90
Injectable non-insulin antidiabetics 233 (11.89%) 0.59 ± 1.04
Insulin 503 (25.68%) 0.65 ± 1.08

SD: Standard deviation.

BMI was not detected in the electronic medical records of 764 patients, who were therefore not included in the multivariate analysis. In multivariate analysis, COPD/asthma was statistically associated with antibiotic prescription with strong coefficients (coefficient 0.60 95% CI [0.383, 0.813] p < 0.001). Number of chronic treatments, and age were statistically associated with antibiotic prescription with a lower coefficient. HbA1c threshold, cardiac history, number of antidiabetics, or malignancy history or BMI were not statistically associated with antibiotic prescriptions. (HbA1c threshold coefficient -0.097 95% CI [-0.207, 0.007] p = 0.070) (Table 3).

10.1371/journal.pone.0314287.t003 Multivariate analysis of antibiotic prescription per year using the negative binomial distribution model.
Coefficient 95% CI P value
HbA1c threshold -0.092 [-0.192, 0.007] p = 0.0688
Age -0.016 [-0.023, -0.009] p < 0.001
BIM 0.003 [-0.013, 0.018] p = 0.711
Number of chronic treatments 0.077 [0.058, 0.097] p < 0.001
Number of antidiabetic treatments 0.003 [-0.046, 0.053] p = 0.892
Cardiovascular history 0.203 [-0.032, 0.439] p = 0.090
COPD/Asthma history 0.580 [0.379, 0.781] p < 0.001
Malignancy history 0.184 [-0.006, 0.374] p = 0.057

HbA1c: Glycated hemoglobin, BMI: Body Mass Index, COPD: Chronic Obstructive Pulmonary Disease.

 Secondary analysis

Secondary analysis did not show an association between the number, or the type, of antidiabetic treatments and the mean of antibiotic prescriptions per year on ANOVA test (p = 0.53 and p = 0.018, respectively) (Table 2) or boxplot (Fig 3A and 3B, respectively).

10.1371/journal.pone.0314287.g003 <p>(A) Boxplot of the mean of antibiotic prescriptions per year by the number of antidiabetic treatments, (B) Boxplot of the mean of antibiotic prescriptions per year by the type of antidiabetic treatments. OAD: Oral Anti Diabetic treatments.</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0314287.g003" xlink:type="simple"/> </fig> <p>The case control showed a significant difference between patients who had at least one antibiotic prescription and those who had not, in terms of sex ratio (1.13 ± 0.13 vs 1.46 ± 0.22 p = 0.007), BMI (31.66 ± 8.65 vs 30.82 ± 7.68, p = 0.048 and Cohen’s d-test = 0.12), age, pneumococcal, influenza and Covid 19 vaccination, COPD/Asthma history, malignancy history, number of chronic treatments and number of antidiabetic treatments. There was no significant difference in HbA1c threshold (7.23 ± 1.03 vs 7.17 ± 1.00, p = 0.229), history of tabacco use or cardiovascular history (<xref ref-type="table" rid="pone.0314287.t004">Table 4</xref>).</p> <table-wrap id="pone.0314287.t004" position="float"> <object-id pub-id-type="doi">10.1371/journal.pone.0314287.t004</object-id> <label>Table 4</label> <caption><title>Cofounding factors and HbA1c according to diabetic patients with at least one antibiotic prescription and diabetic patients with no antibiotic prescription.
Diabetic patients with no prescription (n = 707) Diabetics patients with at least one prescription (n = 1252) Statistical analysis
HbA1c Threshold (%) 7.17 ± 1.00 7.23 ± 1.03 p = 0.2291
Sex ratio M/F 1.46 ± 0.22 1.13 ± 0.13 p = 0.0072
Age (year) 69.11 ± 12.68 70.71 ± 12.16 p = 0.0061
BMI (kg/m2) 30.82 ± 7.68 31.66 ± 8.65 p = 0.0481
History of tabacco use (%) 19.0 ± 2.90 17.0 ± 2.10 p = 0.4302
Pneumococcal vaccination (%) 3.50 ± 1.40 8.80 ± 1.60 p < 0.0012
Influenza vaccination (%) 14.0 ± 2.60 21.0 ± 2.20 p < 0.0012
Covid-19 vaccination (%) 33.0 ± 3.50 37.0 ± 2.70 p < 0.0012
COPD/Asthma history (%) 3.70 ± 1.40 13.0 ± 1.90 p < 0.0012
Cardiovascular history (%) 7.20 ± 1.90 9.10 ± 1.60 p = 0.1482
Malignancy history (%) 13.0 ± 2.50 21.0 ± 2.30 p < 0.0012
Number of antidiabetic treatments 2.1 ± 1.61 2.58 ± 2.0 p < 0.0011
Number of chronic treatments 7.27 ± 3.83 8.55 ± 4.07 p < 0.0011

HbA1c: Glycated hemoglobin, BMI: Body Mass Index.

1 Student t test.

2 Chi 2 test.

 Discussion

The main finding of this study was an absence of association between glycated hemoglobin, a surrogate for glycemic control, and the number of antibiotic prescriptions, a surrogate for infectious risk, in patients with type 2 diabetes followed in primary care centers. Multivariate analysis revealed a significant relation between COPD or asthma history and antibiotic prescription. Numerous chronic treatments, which may be an indicator of polymorbidities, and age were also two significant factors, but with a low coefficient, meaning a minor impact on infectious risk. Glycemic control estimated by HbA1c threshold was not associated with a higher number of antibiotic prescriptions during follow-up. These results suggest that the bacterial infectious risk in patients with diabetes is not correlated with chronic glycemic control, however, this risk could be higher in patients with multiple comorbidities, notably pulmonary. Moderate glycemic control with glycated hemoglobin below 9.5% seems to be tolerable in terms of bacterial infectious risk. This risk could be increased during episodes of acute hyperglycemia either by direct glucose toxicity or by its action on the immune cells [13].

Secondary analyses revealed an absence of association between the number, and the type, of antidiabetic treatments, a surrogate for diabetes severity, and the number of antibiotic prescriptions. The case-control study identified confounding factors that could potentially influence infectious risk, namely gender, BMI, COPD/Asthma history, malignancy history, and the number of antidiabetic treatments. Nevertheless, even if there is statistical significance for BMI, Cohen’s low d test shows that this factor has a low impact on the risk of infection in our population of type 2 diabetics. Vaccination rates were higher in type 2 diabetic patients with at least one antibiotic prescription. This difference between the two groups is probably not due to the vaccination statue but may be associated with GPs clinical uncertainty, who vaccinate more the patients they consider at risk of bacterial infection. The case control did not reveal any difference in terms of glycemic control, which confirms the results of the primary analysis.

Comparison with the literature

In an analysis of the UK Clinical Practice Research Datalink (CPRD) database, authors demonstrated a strong association between poor glycemic control and infectious risk, mainly for glycated hemoglobin levels above 11%, with a relative risk of 8.71 for osteoarticular infections, 5.56 for endocarditis, 2.68 for pneumonia, 2.29 for skin infections compared to glycated hemoglobin levels between 6% and 6.5% [14]. This difference in results with our study may be explained by a higher power due to a larger number of patients, especially for extreme values of glycated hemoglobin. Secondly, the authors had access to hospital data to study severe infectious episodes.

A Danish study reported a more moderate association between glycemic control and risk of infection in patients with a mean glycated hemoglobin level of more than 10.5%, with a 1.2-fold increase in the risk of community-acquired infections and a 1.6-fold increase in the risk of hospital-acquired infections. These authors suggested that this risk of infection could be correlated with acute glycemic imbalances, and therefore possibly reversible when glycemic control was restored [15]. These differences in results with our study may be related to several limitations of this study, namely, the lack of identification of patients with diabetes treated by diet alone, and the estimation of glycemic control by a measurement of glycated hemoglobin in the period of acute infection managed in a hospital setting which could overestimate the possible association between chronic glycemic control and infectious risk. Finally, the large number of patients identified with type 2 diabetes increased the power and provided a better estimate of infectious risk in subgroups with extreme values of glycated hemoglobin.

In an analysis of data from the Royal College of General Practitioners Research and Surveillance Center (RCGP RSC), authors did not observe an association between glycemic control and the risk of ocular infections or a difference in infectious events between patients with and without diabetic retinopathy [16]. We report similar results using the same method of HbA1c threshold. The use of this method was of greater interest in this study since it also focused on a microvascular complication, namely diabetic retinopathy. However, this study focused only on ocular infections.

A study on the RCGP RSC database showed a moderately higher risk of infection in patients with type 2 diabetes with moderate glycemic control (HbA1c between 7–8.5%) or poor glycemic control (HbA1c greater than 8.5%) compared to those considered well controlled (HbA1c less than 7%) except for viral and gastrointestinal infections [17]. These results differ from ours because the infectious events were determined by the diagnosis of primary care visits coded in the electronic medical records of patients which allowed them to reach a significant difference for certain infections, in particular for acute bronchitis which does not require antibiotic therapy and for genital and perineal infections represented mainly by genital candidiasis and thus treated by antifungals.

 Strengths of the study

The strengths of this study lie firstly in the analysis of a previously unexplored primary care database, which has enabled the evaluation of a large cohort of patients with type 2 diabetes, representing a real-life research approach. To the best of our knowledge, this is the first European study to examine the global risk of bacterial infection in patients with type 2 diabetes based on data from primary care.

Secondly, this study considers chronic glycemic exposure over several years, estimated by threshold of glycated hemoglobin, and the estimation of glycemic control by categorization into multiple groups. The sample of patients with type 2 diabetes identified in the database can be considered representative.

 Limitations of the study

The main limitation of this study is the lack of power due to the limited number of patients with type 2 diabetes and the number of subgroups of these patients.

Another limitation is the absence in primary care data of information on antibiotic prescriptions in hospitals, where most of severe infectious events are managed [13,14]. Also, the data extraction methods used did not allow us to obtain perfectly precise data. In addition, data were approximate due to their retrospective nature and the fact that they were collected during primary care visits.

Finally, primary care data do not include prescriptions, or information such as history and diagnoses that have not been coded or digitized. In a study in Normandy and Provence Alpes Côte d’Azur French regions, 51.7% of family practitioners declared that they never coded and 19.9% did not identify the treatments prescribed using the software database [18]. Finally, we considered that one antibiotic prescription corresponded to one infectious episode, even though in practice there may be several prescriptions for the same infectious episode. The number of infectious bacterial episodes may therefore have been overestimated, especially as a Dutch study suggested that 25% of infectious episodes led to an appropriate or inappropriate antibiotic prescription, and 7.9 to 9% of antibiotic prescriptions where prescribed additional a first prescription within one infectious disease episode [19]. Furthermore, in an analysis of French antibiotic prescriptions, 50% were unnecessary [20]. Nevertheless, Mor.A et al had demonstrated that type 2 diabetic patients were at greater risk of infectious episodes by observing antibiotic prescriptions in Denmark [21].

 Primary care databases

Beyond the results of this study, this research work highlights the possibilities offered by the analysis of primary care databases. The data collected during primary care visits constitute a vast source of information for clinical or epidemiological research purposes and for improving practices to ensure optimal and increasingly evidence-based care for patients. The use of databases requires continuous improvement of the quality of the data collected during visits and their extraction, by developing automatic coding tools for the medical software of family practitioners [22]. Larger databases such as the RCGP RSC or the CPRD in the United Kingdom demonstrate these research possibilities. For example, the CPRD database contains the data of 16 million patients and has produced 3000 publications since its creation 30 years ago [23], proving its ability to improve the practice and quality of scientific publications in primary care [24].

 Research perspectives

Until now, infectious complications of type 2 diabetes have not been explored either in prospective cohort studies or in randomized clinical trials including patients with type 2 diabetes. Pearson et al. underlined a lack of data in the literature notably on the duration of infection or the severity of diabetes, the impact of the type of diabetes on infectious risk, as well as the antibiotic sensitivities of infectious events [11].

To avoid the biases associated with retrospective analysis, future studies could evaluate the antibiotic consumption of each patient according to their glycemic variations over time. It would be relevant to look for an association between the severity of diabetes and in particular the presence of microvascular and/or macrovascular complications and the risk of infection.

Finally, the pathophysiology of infectious risk in patients with type 2 diabetes remains unclear in the literature. To bridge this gap, clinical and fundamental research is needed to better understand these mechanisms.

 Conclusion

Although we were unable to show an association between long-term glycemic control and bacterial infectious risk, the fact that we used data from a primary care database demonstrates the feasibility of using databases in primary care research.

The authors are grateful to Nikki Sabourin-Gibbs, CHU Rouen, for her help in editing the manuscript.

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M.; et al. (2005). Increased Risk of Common Infections in Patients with Type 1 and Type 2 Diabetes Mellitus. Clinical Infectious Diseases, 41(3), 281–288. doi: 10.1086/431587 16007521 Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 Diabetes and its Impact on the Immune System. Curr Diabetes Rev. mai 2020;16(5):442–9. doi: 10.2174/1573399815666191024085838 31657690 Pearson-Stuttard J, Blundell S, Harris T, Cook DG, Critchley J. Diabetes and infection: assessing the association with glycaemic control in population-based studies. The Lancet Diabetes & Endocrinology. 1 févr 2016;4(2):148–58. doi: 10.1016/S2213-8587(15)00379-4 26656292 Maple-Brown LJ, Ye C, Retnakaran R. Area-under-the-A1c-Curve above the Normal Range and the Prediction of Microvascular Outcomes: An analysis of data from the Diabetes Control and Complications Trial. Diabet Med. janv 2013;30(1): doi: 10.1111/dme.12004 22937915 Jafar N, Edriss H, Nugent K. The Effect of Short-Term Hyperglycemia on the Innate Immune System. Am J Med Sci. févr 2016;351(2):201–11. doi: 10.1016/j.amjms.2015.11.011 26897277 Critchley JA, Carey IM, Harris T, DeWilde S, Hosking FJ, Cook DG. Glycemic control and risk of infections among people with type 1 or type 2 diabetes in a large primary care cohort study. Diabetes Care. 2018;41(10):2127–35. doi: 10.2337/dc18-0287 30104296 Mor A, Dekkers OM, Nielsen JS, Beck-Nielsen H, Sørensen HT, Thomsen RW. Impact of Glycemic Control on Risk of Infections in Patients with Type 2 Diabetes: A Population-Based Cohort Study. American Journal of Epidemiology. 2017;186(2):227–36. doi: 10.1093/aje/kwx049 28459981 Ansari AS, de Lusignan S, Hinton W, Munro N, McGovern A. The association between diabetes, level of glycaemic control and eye infection: Cohort database study. Prim Care Diabetes. oct 2017;11(5):421–9. doi: 10.1016/j.pcd.2017.05.009 28648963 Hine JL, de Lusignan S, Burleigh D, Pathirannehelage S, McGovern A, Gatenby P, et al. Association between glycaemic control and common infections in people with Type 2 diabetes: a cohort study. Diabetic Medicine. 2017;34(4):551–7. doi: 10.1111/dme.13205 27548909 Godard M. Organization and coding of data in medical records. 20 September 2022. Van den Broek d’Obrenan J, Verheij TJM, Numans ME, van der Velden AW. Antibiotic use in Dutch primary care: relation between diagnosis, consultation and treatment. Journal of Antimicrobial Chemotherapy. 1 June 2014;69(6):1701–7. doi: 10.1093/jac/dku005 24508898 Etienne C, Pulcini C. Prospective evaluation of antibiotic prescriptions in a sample of French general practitioners. La Presse Médicale. 1 March 2015;44(3):e59–66. Mor A, Berencsi K, Nielsen JS, Rungby J, Friborg S, Brandslund I, et al. Rates of Community-based Antibiotic Prescriptions and Hospital-treated Infections in Individuals With and Without Type 2 Diabetes: A Danish Nationwide Cohort Study, 2004–2012. Clin Infect Dis. 15 août 2016;63(4):501–11. doi: 10.1093/cid/ciw345 27353662 Siefridt C, Grosjean J, Lefebvre T, Rollin L, Darmoni S, Schuers M. Evaluation of automatic annotation by a multi-terminological concepts extractor within a corpus of data from family medicine consultations. International Journal of Medical Informatics. 1 janv 2020;133:104009. doi: 10.1016/j.ijmedinf.2019.104009 31715451 Clinical Practice Research Datalink | CPRD Disponible sur: https://www.cprd.com/node/120. Kousoulis AA, Rafi I, de Lusignan S. The CPRD and the RCGP: building on research success by enhancing benefits for patients and practices. Br J Gen Pract. févr 2015;65(631):54–5. doi: 10.3399/bjgp15X683353 25624277 10.1371/journal.pone.0314287.r001 Decision Letter 0 Gao Peng Academic Editor 2024 Peng Gao This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

16 Apr 2024

PONE-D-23-29894Glycemic control and bacterial infectious risk in type 2 diabetes: a retrospective cohort from a primary care database.PLOS ONE

Dear Dr. Lemoine,

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PLOS ONE

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: This study conducted a multicenter retrospective cohort study using data from a French primary care database to evaluate whether glycemic control and severity of diabetes were associated with bacterial infectious risk in type 2 diabetes. Primary analyses showed the threshold of glycated hemoglobin was not associated with the mean of antibiotic prescriptions per year. Secondary analyses did not show an association between the number, or the type, of antidiabetic treatments and the mean of antibiotic prescriptions per year. In conclusion, long-term glycemic control was not associated with bacterial infectious risk in patients with type 2 diabetes.

Question 1：Were sample size calculation carried out? How was it done? This needs to be detailed in the manuscript.

Question 2：There is no consensus on the impact of glycemic control on this infectious risk and the results of studies on this subject seem heterogeneous, lack of information regarding the heterogeneous results.

Question 3：What’s the follow-up criteria of overall population, as “The patients included had to be aged 18 years and older, had type 2 diabetes, and at least three visits in a primary care center” is for population with type 2 diabetes.

Question 4：A flow chart of study identification, inclusion and exclusion criteria will be helpful.

Reviewer #2: This multicenter retrospective cohort study aim to analysis the relationship between glycemic control and diabetes severity and infectious risk. It is the first study to explore the global risk of bacterial infection in T2DM. However, there are several areas that require further clarification.

Major comments:

1. Although the author provided detailed explanations on the inclusion criteria, the exclusion criteria were not clear enough. For example, diabetes patients with other diseases may also increase the risk of infection.

2. The statistical analysis in this article is not very appropriate. In table 1, the overall population should include the population with type2 diabetes, so the description of the antidiabetic treatments is incorrect. It is better to compare the diabetics who had an infection with diabetic case-controls who did not have an infection to ensure consistency between the baselines of the two groups or whether the conclusions are influenced by other factors.

3. Similarly, in Table 2, we still do not know whether the two groups are matched. And whether the mean of antibiotic prescription per year has undergone a logarithmic transformation? Moreover, the “SD value” is greater than the “mean value”, making the data confusing.

4. Some references are in French and English should be used uniformly.

**********

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Reviewer #2: No

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 10.1371/journal.pone.0314287.r002 Author response to Decision Letter 0 Submission Version 1

18 Jun 2024

When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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The title page has been updated to meet PLOS ONE’s style requirements.

Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information

Data were fully anonymized, and we had no access to identifying data before, during and after the study. Under French regulations, retrospective database studies do not require patient consent. They do, however, require information, which has been provided by posters in physicians' offices as recommended by the CNIL Commission nationale de l'informatique et des libertés (French Data Protection Authority). If patients did not wish their data to be used, they could contact the PRIMEGE Normandie data manager to have their data withheld.

We uploaded the Rules of Procedure of the scientific and ethical committee of the PRIMEGE health database.

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We note that you have indicated that there are restrictions to data sharing for this study. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

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Reviewer 1 :

Q1 : Were sample size calculation carried out? How was it done? This needs to be detailed in the manuscript

The number of subjects required was based on the incidence rates of bacterial infections in T2DM patients with an HbA1c of between 5.5 and 6.5% and those with an HbA1c of between 8.5 and 9.5% in the Mor et al study (1), for an alpha risk of 5% and a beta risk of 20%, giving a required number of subjects of 276.

1. Mor A, Dekkers OM, Nielsen JS, Beck-Nielsen H, Sørensen HT, Thomsen RW. Impact of Glycemic Control on Risk of Infections in Patients with Type 2 Diabetes: A Population-Based Cohort Study. American Journal of Epidemiology. 2017;186(2):227 36

Q2 : There is no consensus on the impact of glycemic control on this infectious risk and the results of studies on this subject seem heterogeneous, lack of information regarding the heterogeneous results

Concerning the heterogeneity of results from other studies, results from studies investigating the link between glycemic control and incidence of infection are heterogeneous, with a significant difference depending on whether the study population is from primary care or hospital recruitment, and on the methods or the definition used to determine glycemic control or infectious risk. The study by Pearson et al. (1) identified a need for research into the impact of long-term glycemic control, particularly HbA1c, and infectious susceptibility in diabetic patients.

We may need to clarify in the sentence you quote "impact of long-term glycemic control" which is justified by the literature review by Pearson et al and detail the criteria by which we judge these studies to be heterogeneous.

1. Pearson-Stuttard J, Blundell S, Harris T, Cook DG, Critchley J. Diabetes and infection: assessing the association with glycaemic control in population-based studies. The Lancet Diabetes & Endocrinology. 1 févr 2016;4(2):148 58

Q3 : What’s the follow-up criteria of overall population, as “The patients included had to be aged 18 years and older, had type 2 diabetes, and at least three visits in a primary care center” is for population with type 2 diabetes.

- The follow-up criteria of the overall population are the same as for type 2 diabetes patients, i.e. be aged 18 years and older and at least 3 consultations at the primary care center.

Q4 : A flow chart of study identification, inclusion and exclusion criteria will be helpful.

- The flow chart is now done. (Fig 1)

Reviewer 2 :

Q1 : Although the author provided detailed explanations on the inclusion criteria, the exclusion criteria were not clear enough. For example, diabetes patients with other diseases may also increase the risk of infection.

You are perfectly right to point out that other chronic diseases are likely to increase the risk of infection (COPD, HIV, heart failure, etc.), as shown by the example of invasive pneumococcal infections (1). We did not adjust for other chronic diseases increasing the risk of infection, and their prevalences were probably significant in the different groups. This is a limitation that we must indeed stipulate in the article and should be taken in consideration in further studies.

The retrospective cohort study design makes it possible to exclude as few patients as possible, with the aim of increasing the statistical power of the study and carrying out a real-life study.

1. Moe H. Kyaw, Charles E. Rose, Alicia M. Fry, James A. Singleton, Zack Moore, Elizabeth R. Zell, Cynthia G. Whitney, for the Active Bacterial Core Surveillance Program of the Emerging Infections Program Network, The Influence of Chronic Illnesses on the Incidence of Invasive Pneumococcal Disease in Adults, The Journal of Infectious Diseases, Volume 192, Issue 3, 1 August 2005, Pages 377–386, https://doi.org/10.1086/431521

Q2 : The statistical analysis in this article is not very appropriate. In table 1, the overall population should include the population with type2 diabetes, so the description of the antidiabetic treatments is incorrect. It is better to compare the diabetics who had an infection with diabetic case-controls who did not have an infection to ensure consistency between the baselines of the two groups or whether the conclusions are influenced by other factors.

- For statistical analysis, the ANOVA test can be used to determine whether at least one of the groups has a statistically different result from the others, which is not the case in our study. Statistical analysis of multiple groups is made difficult here by the heterogeneity of the size of the different groups.

- Concerning table 1, We have modified the table by adding a 3rd column including non-diabetic patients for greater clarity and corrected the Overall Population column. However, we did not search for anti-diabetic treatments in non-diabetic patients, so the proportions of the number of treatments are probably wrong in this population. Perhaps we should therefore leave the term NA for these results.

- On your recommendation, we carried out a case-control study of diabetic patients who had received at least one antibiotic prescription, compared with those who had not. The results enabled us to identify certain potentially confounding factors, such as gender and BMI, although further statistical analysis showed their impact to be low.

In addition, glycated hemoglobin was not different between the two groups, supporting the main findings of the study. We add a table with results. (Table 3)

Q3 : Similarly, in Table 2, we still do not know whether the two groups are matched. And whether the mean of antibiotic prescription per year has undergone a logarithmic transformation? Moreover, the “SD value” is greater than the “mean value”, making the data confusing.

- The population studied is composed solely of identified type 2 diabetics, who have been separated and analyzed into several groups according to their glycemic control, the number of antidiabetic treatments and the type of antidiabetic treatment, without any matching between the different groups.

- Concerning the logarithmic transformation, it only concerns the graphical representation in boxplot for reasons of visual clarity.

- Regarding standard deviations that are higher than the mean, this can be explained by the fact that most values are quite low with a long tail of high values (skew). This can be seen with values that can only be positive, with a concentration towards zero, and whose distribution follows a multiplicative mode.

Q4 : Some references are in French and English should be used uniformly.

References have been translated.

Thank you again for your interest in our study and for helping to improve it. You have our gratitude.

If you have any further questions or comments, please do not hesitate to contact us.

Dr Lemoine Edouard

Submitted filename: Responses to Reviewers.docx

 10.1371/journal.pone.0314287.r003 Decision Letter 1 Gao Peng Academic Editor 2024 Peng Gao This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

15 Jul 2024

PONE-D-23-29894R1Glycemic control and bacterial infectious risk in type 2 diabetes: a retrospective cohort from a primary care database.PLOS ONE

Dear Dr. Lemoine,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 29 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Peng Gao, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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

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

Reviewer #2: Partly

Reviewer #3: No

**********

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

Reviewer #2: Yes

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

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

Reviewer #2: 1.Please check the spelling of HbAlc in Table 3.

2.Please make sure that the ways of the representation of continuous variables （mean（SD）or mean ± SD） are consistent in Tables 1, 2, and 3.

Reviewer #3: Despite revising most of the review comments, the authors did not address two key issues. Firstly, the potential impact of other major diseases was not ruled out, especially considering the possibility that widespread neocoronary epidemics could elevate the risk of bacterial infections. Secondly, there was a lack of baseline comparison between diabetic patients without infections and those with infections. Furthermore, age and duration of diabetes are significant factors that may affect bacterial infection, yet no corrective analysis was conducted to account for these variables.

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Reviewer #2: No

Reviewer #3: No

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 10.1371/journal.pone.0314287.r004 Author response to Decision Letter 1 Submission Version 2

3 Oct 2024

Dear reviewers,

Thank you again for your interest in our article and for your comments and suggestions, which have greatly improved its quality.

Reviewer 1:

1 Please check the spelling of HbAlc in Table 3.

We have harmonized all HbA1c abbreviations.

2.Please make sure that the ways of the representation of continuous variables （mean（SD）or mean ± SD） are consistent in Tables 1, 2, and 3.

All standard deviations have been harmonized in the tables.

Reviewer 2:

Despite revising most of the review comments, the authors did not address two key issues. Firstly, the potential impact of other major diseases was not ruled out, especially considering the possibility that widespread neocoronary epidemics could elevate the risk of bacterial infections. Secondly, there was a lack of baseline comparison between diabetic patients without infections and those with infections. Furthermore, age and duration of diabetes are significant factors that may affect bacterial infection, yet no corrective analysis was conducted to account for these variables.

To improve identification of confounding factors, we supplemented the case-control section with other variables: Vaccination rate, smoking status, number of chronic treatments on the usual prescription, number of antidiabetic treatments, history of malignancy, COPD/Asthma or Cardiovascular event. The results have been presented in table 4.

We also performed a multivariate analysis using a negative binomial model, taking into consideration BMI, age, smoking status, HbA1c threshold, history of asthma and COPD, cardiovascular event and history of malignancy. This required us to design new pathology detection algorithms in the database.

This analysis enabled us to confirm our results and to identify confounding factors and the strength of their impact on the number of antibiotic prescriptions. The results are presented in Table 3.

Concerning the duration of diabetes, this is an important factor which is difficult to estimate in our database, as it is poorly structured. Nevertheless, this factor could be studied in the future, once the database has been linked with health insurance data.

Thank you for your comments, we hope we've answered them well.

Dr Lemoine Edouard, MD

Submitted filename: Rebutal Letter 2 (1).odt

 10.1371/journal.pone.0314287.r005 Decision Letter 2 Gao Peng Academic Editor 2024 Peng Gao This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 2

8 Nov 2024

Glycemic control and bacterial infectious risk in type 2 diabetes: a retrospective cohort from a primary care database.

PONE-D-23-29894R2

Dear Dr. Lemoine,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #3: Yes

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

Reviewer #2: The authors have adequately addressed the comments raised in a previous round of review and I feel that this manuscript is now acceptable for publication.

Reviewer #3: (No Response)

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Reviewer #2: No

Reviewer #3: No

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 10.1371/journal.pone.0314287.r006 Acceptance letter Gao Peng Academic Editor 2024 Peng Gao This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

15 Nov 2024

PONE-D-23-29894R2

PLOS ONE

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