Secondary Logo

Journal Logo

Original Article

Diabetes Is Not Associated with Increased 10-week Mortality Risk in Patients with Cryptococcal Meningitis

Xu, Lijun1,2,∗; Chen, Ying3; Zhou, Minghan3; Tao, Ran1,2; Guo, Yongzheng1,2; Lou, Fangyuan1; Yang, Zongxing4

Editor(s): Wang, Haijuan

Author Information
Infectious Diseases & Immunity: April 2022 - Volume 2 - Issue 2 - p 93-99
doi: 10.1097/ID9.0000000000000043
  • Open

Abstract

Introduction

China accounts for nearly 25% of the global diabetes population.[1,2] Individuals with diabetes have dysfunction of B cells and T cells[3–5] and are therefore at high risk of contracting viral, bacterial, and fungal infections[6–8] and, especially, of requiring hospitalization for serious illness.[9,10]

Invasive fungal infection is a cause of mortality in diabetics, with poor control of blood glucose often hindering effective treatment.[11,12] Diabetes is an independent risk factor for cryptococcal disease.[13] The clinical features of cryptococcosis in diabetics have been described earlier,[13,14] but the characteristics of cryptococcal meningitis (CM) in this population remain unclear. Most of the present understanding of CM in diabetics is from sporadic case reports.[15,16] Diabetic individuals often have comorbid conditions, and the effect of these comorbidities on the clinical picture and prognosis of CM has not been fully studied. The aim of this retrospective study was to determine the clinical characteristics of CM in individuals with diabetes and to examine the effect of diabetes on survival.

Materials and methods

Ethical approval

The study protocol was in accordance with the 1975 Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital, School of Medicine, Zhejiang University (No. 2017-688).

Study cohort

From January 2008 to December 2018, a total of 535 CM patients were admitted to the First Affiliated Hospital, College of Medicine of Zhejiang University. These 535 patients have been partly described in our previous studies.[17,18] Among the 535 patients there were a total of 56 diabetic patients with CM. Forty-nine diabetic CM patients and 98 non-diabetic CM patients without human immunodeficiency virus (HIV) infection were selected as observed population after propensity score-matching method on sex, age, and body mass index (BMI). Demographic characteristics, comorbidities, symptoms, clinical findings, and laboratory test results were compared between CM patients with and without diabetes. The flowchart of patient selection was indicated in Figure 1.

F1
Figure 1:
Study flowchart for patient selection. CM: cryptococcal meningitis; HBV: Hepatitis B virus; HIV: Human immunodeficiency virus.

Diagnosis of CM

The diagnostic criteria for CM have been described in our previous studies.[17,18] Briefly, CM was diagnosed if at least one of the following was present: (1) cerebrospinal fluid (CSF) culture positive for Cryptococcus neoformans; (2) India ink staining of centrifuged CSF sediment positive for C. neoformans; (3) 5 to 10 μm encapsulated yeast cells observed in brain tissue biopsy stained with Gömöri methenamine-silver and/or periodic acid-Schiff; and (4) clinical symptoms of meningitis, with positive cryptococcal antigen in the CSF.

Cryptococcemia was diagnosed if blood culture was positive for C. neoformans.

The procedure of Cryptococcus count was briefed as follows: 1 mL of CSF was collected and centrifuged at the speed of 3000 rpm × 10 to 15 min, then the sediment (≈100 μL) was mixed with a small drop of India ink. A large coverslip was applied over 100 μL of mixture on the glass slide and pressed gently to obtain a thin mount. The slide was scanned under low power field in reduced light with a microscope, and then switched to high power field (HPF) for counting if cryptococcal capsules were found. The Cryptococcus count (cells/HPF) = (total number of Cryptococcus capsules in 10 HPFs)/10.

The Charlson comorbidity index was calculated as previously described.[19,20]

Data collection and follow-up

Demographic and clinical data were extracted from the electronic medical records system of hospitals. Patients were followed up from the first day of antifungal therapy for up to 10 weeks.

Statistical analysis

Normally distributed continuous variables were presented as means ± standard deviation and non-normally distributed continuous variables as medians (interquartile ranges; IQR). Categorical variables were summarized as numbers (percentages). Continuous variables were compared by the Student's t test or the Mann-Whitney U test, and categorical variables by the chi-square test or Fisher exact test. Pearson's correlation was used to evaluate the relationship between CSF white blood cell (WBC) count and CSF fungal burden. Age, sex, and BMI were used to perform propensity score matching at a match tolerance of 0.15 and a ratio of 1:2 among HIV-uninfected CM patients. The Kaplan-Meier method was used for survival analysis. CM-related death was defined as an “event.” Data of patients were censored at the date of the final visit (for those alive at the end of the follow-up period), at the date they were last known to be alive (for those with unknown vital signs), or the date of participation for those wherein the cause of death was not known to be CM-related. Patients with missing data were excluded from multivariate analysis. P 0.05 (two-tailed) was considered statistically significant. Data analysis was performed using SPSS version 24.0 (IBM, Armonk, NY, USA) and GraphPad Prism version 6.0 (GraphPad Software, La Jolla, California, USA).

Results

Demographic characteristics of patients

A total of 147 CM patients were included in this study: 49 with diabetes and 98 without diabetes. The two groups were comparable with respect to baseline demographic characteristics and prevalence of comorbidities. Concomitant predisposing conditions in CM patients with diabetes are shown in Table 1. The prevalence was 18.4% (9/49) for long-term cytotoxic medicine, 16.3% (8/49) for autoimmune diseases, 10.2% (5/49) for liver cirrhosis, 16.3% (8/49) for chronic kidney diseases, 8.2% (4/49) for solid organ transplantation, 4.1% (2/49) for chronic hematological diseases, and 4.1% (2/49) for malignancies. The prevalence of steroid usage (40.8%, 20/49) in diabetic CM patients was higher than that of (22.4%, 22/98) in non-diabetic CM patients (P = 0.020). In addition, the Charlson comorbidity index was significantly higher in the diabetic CM patients than in non-diabetic CM patients (1.9 ± 1.3 vs. 0.7 ± 1.1; P < 0.001).

Table 1 - Demographic characteristics of CM patients with and without diabetes
Parameter Diabetic (n = 49) Non-diabetic (n = 98) P
Age (years) 58.2 ± 13.8 58.2 ± 13.7 0.977
BMI (kg/m2) 21.9 ± 3.5 21.8 ± 3.0 0.954
Sex [male, n (%)] 23 (46.9) 57 (58.2) 0.198
Comorbidities [n (%)]
 Corticosteroid use 20 (40.8) 22 (22.4) 0.020
 Cytotoxic medicine use 9 (18.4) 8 (8.2) 0.068
 Liver cirrhosis 5 (10.2) 10 (10.2) 1.000
 Chronic kidney disease 8 (16.3) 8 (8.2) 0.134
 Solid organ transplantation 4 (8.2) 3 (3.1) 0.171
 Malignancies 2 (4.1) 6 (6.1) 0.719
 Chronic hematological disease 2 (4.1) 4 (4.1) 1.000
 Autoimmune disease 8 (16.3) 12 (12.2) 0.496
Antifungal treatment [n (%)]
 Amphotericin B included regimen 22 (44.9) 50 (51.0) 0.484
BMI: Body mass index; no.: Number.

Signs and symptoms

Symptoms were not significantly different between the two groups [Table 2]. In both groups, the most common symptoms were headache, fever, nausea, and dizziness. Furthermore, 81.6% (40/49) in patients with diabetes and 90.8% (89/98) in patients without diabetes had at least one of these four symptoms (P = 0.109). The time from symptoms onset to diagnosis was 20.0 days (IQR, 10.0– 55.0 days) in patients with diabetes versus 30.0 days (IQR, 11.9– 52.5 days) in patients without diabetes (P = 0.749).

Table 2 - Comparison of clinical features of CM patients with and without diabetes
Factors Diabetic (n = 49) Non-diabetic (n = 98) P
Symptom [n (%)]
 Fever 28 (57.1) 57 (58.2) 0.906
 Headache 31 (63.3) 76 (77.6) 0.067
 Nausea/vomiting 12 (24.5) 33 (33.7) 0.255
 Dizziness 7 (14.3) 16 (16.3) 0.748
 Seizure 1 (2.0) 1 (1.0)
 Hearing loss 2 (4.1) 5 (5.1) 1.000
 Visual damage 1 (2.0) 3 (3.1)
 Mental change 5 (10.2) 9 (9.2) 1.000
 Limb weakness 1 (2.0) 2 (2.0)
Routine blood test
 WBC [×109/L, M (IQR)] 7.7 (5.4–10.0) 8.1 (6.0–10.2) 0.413
 C-reactive protein [mg/L, M (IQR)] 5.2 (2.6–15.8) 8.2 (2.4–19.7) 0.609
 Hemoglobin [g/L, mean±SD] 120.0 ± 24.6 121.2 ± 22.9 0.765
 Platelet [×109/L, mean±SD] 181.7 × 66.8 203.4 ± 86.5 0.133
Liver function
 Alb [g/L, mean±SD] 36.3 ± 7.0 38.4 ± 7.4 0.107
 ALT [U/L, M (IQR)] 20.0 (12.0–28.0) 19.0 (10.0–32.0) 0.981
 AST [U/L, M (IQR)] 17.0 (12.0–26.0) 17.0 (12.0–23.8) 0.998
Serum glucose [mmol/L, mean±SD] 7.9 ± 4.1 5.7 ± 1.3 0.001
Renal function [M (IQR)]
 Creatinine (μmol/L) 61.0 (49.0–78.0) 60.5 (46.0–70.0) 0.290
 Urea (mmol/L) 4.8 (3.7–7.1) 4.9 (3.4–6.5) 0.500
 GFR (mL/min) 96.9 (64.0–126.6) 95.9 (72.1–115.3) 0.930
CSF profiles
 ICP [mm H2O, M (IQR)] 220.0 (113.5–316.0) 250.0 (147.5–350.6) 0.220
 WBC count [×106/L, M (IQR)] 111.0 (18.0–242.5) 50.0 (10.0–140.0) 0.034
 Total protein [g/L, M (IQR)] 1.1 (0.6–1.6) 0.9 (0.6–1.2) 0.263
 Glucose (mmol/L, mean±SD) 3.3 ± 2.2 2.1 ± 1.4 <0.001
 Chlorine (mmol/L, mean±SD) 116.6 ± 7.3 115.4 ± 8.2 0.518
 India ink stain positive [n (%)] 20 (40.8) 59 (60.2) 0.039
 Fungal count [cells/HPF, M (IQR)] 0 (0–3) 1.5 (0–9.2) 0.140
 Culture positive [n (%)] 21 (42.9) 59 (60.2) 0.047
 Cryptococcemia [n (%)] 3 (6.1) 14 (14.3) 0.145
WBC: white blood cell; M (IQR): median (interquartile range); SD: standard deviation; Alb: serum albumin; ALT: Alanine aminotransferase; AST: aspartate aminotransferase; GFR: glomerular filtration rate; CSF: cerebrospinal fluid; HPF: high power field; ICP: intracranial pressure.

Laboratory test results

Total WBC count, platelet count, hemoglobin, serum albumin, serum aspartate transaminase (AST), serum alanine aminotransferase (ALT), creatinine, and blood urea nitrogen (BUN) were comparable between the two groups [Table 2]. However, no significant differences were found on WBC (P = 0.413), hemoglobin (P = 0.765), platelet (P = 0.133), serum albumin (P = 0.107), AST (P = 0.998), ALT (P = 0.981), creatinine (P = 0.290), and BUN (P = 0.500) expect blood glucose (diabetic vs. non-diabetic: 7.9 ± 4.1mmol/L vs. 5.7 ± 1.3mmol/L; P = 0.001).

Mean CSF glucose was significantly higher in patients with diabetes than in patients without diabetes (3.3 ± 2.2mmol/L vs. 2.1 ± 1.4mmol/L; P < 0.001). CSF total WBC count was also significantly higher in the former group: [111.0 (18.0–242.5) × 106/Lvs. 50.0 (10.0–140.0) × 106/L; P = 0.034]. The positivity of India ink staining of CSF in patients with diabetes was lower than in patients without diabetes (40.8% (20/49) vs. 60.2% (59/98), P = 0.039). The proportion of positive CSF culture was 42.9% (21/49), which was lower than 60.2% (59/98) in CM patients without diabetes (P = 0.047). The proportion of patients with blood culture positive for C. neoformans was lower in the diabetic group than in the non-diabetic group, but the difference was not statistically significant [6.1% (3/49) vs. 14.3% (14/90); P = 0.145]. No statistically significant differences were found between the groups in intracranial pressure (ICP), CSF protein, CSF chloride, and CSF fungal count [Table 2].

Induction antifungal therapy and 10-week survival rate

For induction therapy, 12.2% (6/49) diabetic patients and 9.2% (9/98) non-diabetic patients received intravenous voriconazole+ flucytosine. Thirty four point seven percent (17/49) diabetic patients and 42.9% (42/98) non-diabetic patients used amphotericin B+flucytosine±fluconazole. Thirty eight point eight percent (19/49) patients with diabetes and 28.6% (28/98) patients without diabetes received fluconazole±flucytosine. Ten point two percent (5/49) patients with diabetes and 8/98 (8.2%) without diabetes used liposome amphotericin B+flucytosine; and 4.1% (2/49) patients with diabetes and 11/98 (11.2%) without diabetes received other combinations.

Over the 10weeks of follow-up, 16.3% (8/49) patients had died in the diabetic group versus 13.3% (13/98) in the non-diabetic group (P = 0.617). The 10-week cumulative survival rate was not significantly different between the two groups (79.7% in diabetic patients vs. 83.2% in non-diabetic patients; log-rank P = 0.794) [Figure 2A].

F2
Figure 2:
Cumulative survival rate of patients with diabetes mellitus (DM). (A) Comparison of 10-week cumulative survival rate between cryptococc meningitis patients with DM (DM+) and without DM (DM) (79.7% vs. 83.2%, log-rank P = 0.794); (B) Ten-week cumulative survival rate was 83.7% for patients with diabetes mellitus but no other predisposing disease (DM+PD), 76.7% for CM patients with diabetes mellitus and other predisposing disea (DM+PD+), 83.8% for patients without diabetes mellitus but with other predisposing disease (DMPD+), and 82.2% for CM patients without diabe mellitus or other predisposing diseases (DMPD) (log-rank P = 0.990).

To examine the influence of comorbidities on prognosis we separated the patients into four groups: patients with diabetes mellitus but no other predisposing disease (DM+PD; n = 21); patients with diabetes plus other predisposing disease (DM+PD+; n = 28); patients without diabetes but with other predisposing disease (DMPD+; n = 45); and patients without diabetes or other predisposing disease (DMPD; n = 53). The 10-week cumulative survival rate was not significantly different between the groups (83.7% for DM+PDvs. 76.7% for DM+PD+vs. 83.8% for DMPD+vs. 82.2% for DMPD; log-rank P = 0.990) [Figure 2B].

Factors that could affect patient outcome in those with and without diabetes were assessed, which included age, BMI, diabetes, hemoglobin, serum albumin level, C-reactive protein (CRP), CSF glucose level, CSF fungal load, CSF protein level, ICP, and antifungal therapies [Table 3].

Table 3 - Risk factors for 10-week mortality in CM patients with univariate/multivariate Cox proportional hazards models
Univariate Multivariate


Factor Number (n = 147) CM deaths (n = 21) HR (95% CI) P HR (95% CI) P
Sex
 Male 80 12 (15.0) 1.1 (0.6–1.8) 0.787
 Female 67 9 (13.4) 1
Age (years)
 <50.0 42 8 (19.0) 1 0.297
 ≥50.0 105 14 (13.3) 0.6 (0.2–1.6)
BMI
 <18.0 22 3 (13.6) 1 0.925
 ≥18.0 125 18 (14.4) 1.1 (0.3–4.0)
WBC (×109/L)
 Missing data 5 3 (60.0)
 <10.0 104 13 (12.5) 1 0.917 -
 ≥10.0 38 5 (13.2) 1.1 (0.4–3.2)
Hemoglobin (g/L)
 Missing data 4 3
 <110.0 41 7 (17.1) 1.7 (0.6–4.8) 0.305
 ≥110.0 102 11 (10.8) 1
Albumin (g/L)
 Missing data 5 2
 <35.0 45 8 (17.8) 1.2 (0.8–1.8) 0.294 -
 ≥35.0 97 11 (11.3) 1
Diabetes
 With 49 8 (16.3) 1.3 (0.5–3.3) 0.617
 Without 98 13 (13.3) 1
CRP (mg/L)
 <10.0 89 10 (11.2) 1.0 0.191
 ≥10.0 58 11 (19.0) 1.8 (0.7–4.7)
CSF fungal cells (cells/HPF)
 Missing data 3 0
 <10 112 13 (11.6) 1
 ≥10 32 8 (25.0) 2.5 (0.9–6.8) 0.085
CSF glucose (mmol/L)
 Missing data 1 1
 <2.0 61 13 (21.3) 3.3 (1.1–8.1) 0.023
 ≥2.0 85 7 (8.2) 1
CSF protein (g/L)
 Missing data 8 2 (25.0)
 <0.9 68 10 (14.7) 1 0.728
 ≥0.9 71 9 (12.6) 0.8 (0.3–2.2)
CSF WBC (×106/L)
 Missing data 4 1
 <20 44 11 (25.0) 3.3 (1.3–8.8) 0.011 3.2 (1.3–8.0) 0.012
 ≥20 99 9 (9.1) 1 1
ICP (mmH2O)
 Missing data 18 2 (11.1)
 <250.0 70 8 (11.4) 1 0.249
 ≥250.0 59 11 (18) 1.8 (0.7–4.8)
Mental change
 No 133 17 (12.8) 1 0.118
 Yes 14 4 (28.6) 2.7 (0.8–9.7)
Treatments
 AmB based 72 7 (9.7) 1 0.121
 Non-AmB based 75 14 (18.7) 2.1 (0.8–5.6)
CM: Cryptococcal Meningitis; HR: hazard ratio; BMI: body mass index; WBC: white blood cell; CRP: C-reactive protein; CSF: Cerebrospinal fluid; HPF: high power field; ICP: intracranial pressure; AmB: Amphotericin B; CI: Confidence interval. Blank means no data.

In the unadjusted model, our data suggested that CSF glucose and CSF WBC are related with 10-week mortality in CM patients without PD, while CRP, CSF fungal burden, mental change, and non-amphotericin B regiment were marginally associated with 10-week mortality. In the multivariate Cox proportional hazards model, our data indicated that lower CSF WBC (<20.0 × 106/L) were independent risk factors associated with higher 10-week mortality in patients. The hazard ratio (HR) were 3.2 (95% confidence interval: 1.3 − 8.0) for patients with CSF WBC < 20.0 × 106/L compared to patients with CSF WBC ≥ 20.0 × 106/L mmol/L (P = 0.012). Diabetes was not related to 10-week mortality in patients [Table 3].

Discussion

Diabetes is recognized as a predisposing factor for CM,[13] but its effect on mortality of CM patients is unclear. In this study, we found that symptoms and 10-week mortality were similar in CM patients with and without diabetes. Patients with diabetes had higher CSF leukocyte count and lower positivity of CSF India ink, CSF culture, and blood culture. In addition, patients with diabetes had higher Charlson comorbidity score than those without diabetes.

Consistent with previous studies,[13,14] we found that comorbid conditions are common in patients with diabetes. Although we adopted propensity score matching to select the comparison group, diabetic patients were found to have higher Charlson comorbidity score, confirming that diabetics tend to have more morbidities than non-diabetics.

Some studies have indicated that diabetes may protect against severity of infection by blunting the inflammatory response.[21,22] In the present study, diabetic patients with CM were less likely to have positive CSF culture and positive India ink stain results than those without diabetes. Furthermore, cryptococcemia was less common in diabetics than in non-diabetics (6.1% vs. 14.3%). Patients with diabetes had higher CSF WBC counts, which might be associated with the lower proportion of positive CSF culture and India ink stain results. Indeed, we found a negative correlation between CSF fungal burden and CSF WBC count (r = –353, P = 0.022). Our previous study had suggested that high CSF glucose and WBC count were both protective against mortality.[17]

The 10-week cumulative survival rate was not significantly different between patients with and without diabetes (79.7% vs. 83.2%); thus, diabetes does not appear to adversely affect survival in CM patients. Donnelly et al. found that while diabetes increased the risk of hospitalization for patients with infections, it did not increase 28-day mortality.[9] Other researchers found that diabetes is not associated with increased 90-day mortality in critically ill patients with severe infection.[23] Our data suggested that diabetes did not increase 10-week mortality of CM patients. However, diabetes might have long-term impacts on patient outcomes and has been reported to increase the 1-year mortality of CM patients.[13]

This study has some limitations. First, this was a retrospective study of a small sample of patients, and the risk factors associated with mortality were not fully studied. Second, although we observed that CM patients with and without diabetes had comparable 10-week mortality, we did not research how diabetes affected immunity on fungal infection. Third, baseline diabetes status was ascertained in our study, but we did not have data on diabetes control during the course of the follow-up period.

In conclusion, CM patients with diabetes appear to have higher CSF WBC count, lower likelihood of cryptococcal bioproofs in CSF, and higher Charlson comorbidity score than CM patients without diabetes. Diabetes does not seem to alter the clinical symptoms or affect 10-week cumulative mortality of patients with CM. Comorbidities, which are common in diabetics, may have potential impact on prognosis of CM patients than diabetes per se.

Funding

This work was supported by the National Science and Technology Major Project of China during the 13th Five-year plan period (2018ZX10302104), Independent Research Foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University (2020ZZ19).

Author Contributions

Lijun Xu conceived and designed the study. Ying Chen and Minghan Zhou performed main research work. Ran Tao, Yongzheng Guo, and Fangyuan Lou collected the data. Zongxing Yang analyzed the data. Lijun Xu drafted the manuscript and the other authors revised. All authors approved the final manuscript.

Conflicts of Interest

None.

References

[1]. Xu Y, Wang L, He J, et al. Prevalence and control of diabetes in Chinese adults. JAMA 2013;310(9):948–959. doi: 10.1001/jama.2013.168118.
[2]. Chan JC, Zhang Y, Ning G. Diabetes in China: a societal solution for a personal challenge. Lancet Diabetes Endocrinol 2014;2(12):969–979. doi: 10.1016/s2213-8587(14)70144-5.
[3]. Touch S, Clément K, André S. T cell populations and functions are altered in human obesity and type 2 diabetes. Curr Diab Rep 2017;17(9):81. doi: 10.1007/s11892-017-0900-5.
[4]. Da Rosa LC, Boldison J, De Leenheer E, et al. B cell depletion reduces T cell activation in pancreatic islets in a murine autoimmune diabetes model. Diabetologia 2018;61(6):1397–1410. doi: 10.1007/s00125-018-4597-z.
[5]. Nam HW, Cho YJ, Lim JA, et al. Functional status of immune cells in patients with long-lasting type 2 diabetes mellitus. Clin Exp Immunol 2018;194(1):125–136. doi: 10.1111/cei.13187.
[6]. Papagianni M, Metallidis S, Tziomalos K. Herpes zoster and diabetes mellitus: a review. Diabetes Ther 2018;9(2):545–550. doi: 10.1007/s13300-018-0394-4.
[7]. Kumar Nathella P, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. Immunology 2017;152(1):13–24. doi: 10.1111/imm.12762.
[8]. Rodrigues CF, Rodrigues ME, Henriques M. Candida sp. infections in patients with diabetes mellitus. J Clin Med 2019;8(1):76. doi:10. 3390/jcm8010076.
[9]. Donnelly JP, Nair S, Griffin R, et al. Association of diabetes and insulin therapy with risk of hospitalization for infection and 28-day mortality risk. Clin Infect Dis 2017;64(4):435–442. doi: 10.1093/cid/ciw738.
[10]. Carey IM, Critchley JA, DeWilde S, et al. Risk of infection in type 1 and type 2 diabetes compared with the general population: a matched cohort study. Diabetes Care 2018;41(3):513–521. doi: 10.2337/dc17-2131.
[11]. Rotjanapan P, Chen YC, Chakrabarti A, et al. Epidemiology and clinical characteristics of invasive mould infections: a multicenter, retrospective analysis in five Asian countries. Med Mycol 2018;56(2):186–196. doi: 10.1093/mmy/myx029.
[12]. Gleckman RA, Czachor JS. Managing diabetes-related infections in the elderly. Geriatrics 1989;44(8):37–39. 43-44, 46.
[13]. Lin KH, Chen CM, Chen TL, et al. Diabetes mellitus is associated with acquisition and increased mortality in HIV-uninfected patients with cryptococcosis: a population-based study. J Infect 2016;72(5):608–614. doi: 10.1016/j.jinf.2016.01.016.
[14]. Li Y, Fang W, Jiang W, et al. Cryptococcosis in patients with diabetes mellitus II in mainland China: 1993–2015. Mycoses 2017;60(11):706–713. doi: 10.1111/myc.12645.
[15]. Akcaglar S, Sevgican E, Akalin H, et al. Two cases of cryptococcal meningitis in immunocompromised patients not infected with HIV. Mycoses 2007;50(3):235–238. doi: 10.1111/j.1439-0507.2006.01347.x.
[16]. Owuor OH, Chege P. Cryptococcal meningitis in a HIV negative newly diagnosed diabetic patient: a CASE report. BMC Infect Dis 2019;19(1):5. doi: 10.1186/s12879-018-3625-4.
[17]. Xu L, Zhang X, Guo Y, et al. Unique clinical features of cryptococcal meningitis among Chinese patients without predisposing diseases against patients with predisposing diseases. Med Mycol 2019;57(8):944–953. doi: 10.1093/mmy/myy154.
[18]. Xu L, Hu C, Hu H, et al. Importance of fibrosis 4 index score and mode of anti-fungal treatment to the outcome of Cryptococcal meningitis in hepatitis B virus-infected patients. Infect Dis 2019;51(2):113–121. doi: 10.1080/23744235.2018.1523553.
[19]. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8.
[20]. Lu KJ, Kearney LG, Ord M, et al. Age adjusted Charlson co-morbidity index is an independent predictor of mortality over long-term follow-up in infective endocarditis. Int J Cardiol 2013;168(6):5243–5248. doi: 10.1016/j.ijcard.2013.08.023.
[21]. Moss M, Guidot DM, Steinberg KP, et al. Diabetic patients have a decreased incidence of acute respiratory distress syndrome. Crit Care Med 2000;28(7):2187–2192. doi: 10.1097/00003246-200007000-00001.
[22]. Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. J Clin Invest 2005;115(5):1111–1119. doi: 10.1172/jci25102.
[23]. van Vught LA, Holman R, de Jonge E, et al. Diabetes is not associated with increased 90-day mortality risk in critically ill patients with sepsis. Crit Care Med 2017;45(10):e1026–e1035. doi:10.1097/ccm.0000000000002590.
Keywords:

Cryptococcus; Clinical characteristics; Diabetes; Meningitis; Prognosis; Propensity score match study

Copyright © 2022 The Chinese Medical Association, published by Wolters Kluwer Health, Inc.