Objective To investigate the characteristics of dyslipidemia in patients with immunoglobulin G4-related disease (IgG4-RD) and its correlation with clinical indicators and organ involvement. Methods Patients diagnosed with IgG4-RD at West China Hospital of Sichuan University between September 2020 and October 2024 were retrospectively selected. Based on lipid levels, patients were divided into a high-TG group and a normal-TG group, as well as a high-TC group and a normal-TC group. The demographic characteristics, serological indicators (lipids, IgG4, IgE), and organ involvement of the two groups were analyzed, and the correlation between the indicators was evaluated. Results A total of 125 IgG4 RD patients were included. Among them, there were 89 males (71.2%) and 36 females (28.8%); 21 cases of high TG and 29 cases of high TC. The high-density lipoprotein cholesterol level was lower in the high TG group [(1.16±0.33) vs. (1.41±0.50) mmol/L, P=0.032], and the proportion of retroperitoneal fibrosis involvement was higher (23.8% vs. 9.6%). The low-density lipoprotein cholesterol level in the high TC group was higher than that in the normal TC group [(3.09±0.95) vs. (2.39±0.93) mmol/L, P<0.001]. Correlation analysis showed that TG was positively correlated with TC (r=0.37, P<0.05), IgG4 levels were positively correlated with the number of affected organs (r=0.24, P<0.05), while lipid parameters were not correlated with IgG4, IgE, and the number of affected organs. Conclusions Dyslipidemia is common in IgG4-RD patients. High TG is associated with low high-density lipoprotein and a tendency for retroperitoneal fibrosis involvement, while high TC is associated with elevated low-density lipoprotein. Serum IgG4 levels can reflect the extent of organ involvement, but lipid indicators show no significant correlation with disease-specific immune markers. It is recommended to emphasize lipid monitoring in clinical management, particularly for patients with retroperitoneal fibrosis or those on long-term glucocorti coid therapy.
ObjectivesTo investigate the prevalence of mycoplasma pneumonia (MP) in the pediatric outpatient department of Shanghai Pudong New District Zhoupu Hospital and its relationship with the concentration of fine particulate matter (PM 2.5) in the air.MethodsPatients with respiratory tract infection, from one month to 15 years old, diagnosed in the pediatric department of Shanghai Pudong New District Zhoupu Hospital from May 2016 to April 2017 were enrolled. Mycoplasma pneumonia antibody was detected by colloidal gold assay, and mycoplasma antibody was detected after 1 week of negative test. PM 2.5 index of the same period was also collected. SAS 9.1.3 software was used for statistical analysis.ResultsA total of 3 862 children were enrolled, of which 947 were positive for MP, with a positive rate of 24.52%. The positive rates of each age group were 10.02% for the one month to one year old group, 23.51% for the 1 to 4 years old group, 31.70% for the 4 to 7 years old group, and 22.81% for the 7 to 15 years old group. The positive rate of each age group had statistically significant difference (χ2=86.120 0, P<0.000 1). The positive rates of spring, summer, autumn, and winter were 25.06%, 20.43%, 19.52 and 33.61%, respectively. The difference was statistically significant (χ2=67.1446, P<0.000 1). Combining the PM 2.5 index in Shanghai Pudong area from May 2016 to April 2017, the positive correlation between PM 2.5 and positive rate of MP was found (r=0.9007, P<0.001).ConclusionsThe positive rate of MP in Shanghai Pudong area is 24.52%, and reach its highest level in the preschool children and in winter. It is positively correlated with the PM 2.5 index as well.
Objective To identify potential clinical phenotypes in immunoglobulin G4-related disease (IgG4-RD) and to characterize differences in immune and metabolic profiles across distinct phenotypic subgroups. Methods A total of 125 patients diagnosed with IgG4-RD at West China Hospital, Sichuan University between January 2020 and December 2024 were retrospectively selected, and data regarding 12 organs (including the prostate) were collected. The prostate data were utilized solely for descriptive statistical purposes. Latent class analysis (LCA) was conducted using 11 organ variables, excluding the prostate, to perform a data-driven unsupervised classification of multiple organ involvement patterns. The optimal model was selected by comprehensively considering model fit indices, class size distribution, and clinical interpretability. Serum levels of immunoglobulin (Ig)G4, IgE, and uric acid were compared across different phenotypes. Results Among 125 patients with IgG4-RD, 89 were male (71.2%) and 36 were female (28.8%). Involvement of the pancreas was observed in 25 cases (20.0%), lymph nodes in 23 cases (18.4%), biliary system in 18 cases (14.4%), lungs in 16 cases (12.8%), lacrimal glands in 13 cases (10.4%), salivary glands and retroperitoneal fibrosis in 11 cases each (8.8%), eyes and pituitary glands in 7 cases each (5.6%), kidneys and aorta in 6 cases each (4.8%). LCA supported a three-class solution as optimal. Three distinct clinical phenotypes were identified: Class 1 (pulmonary-involvement type, n=26), characterized by frequent lung and lymph node involvement; Class 2 (mild systemic involvement type, n=89), showing limited multi-organ engagement; and Class 3 (glandular-lymphatic type, n=10), defined by predominant involvement of salivary glands, lacrimal glands, and lymph nodes, features resembling Mikulicz’s disease. Immunological and metabolic analysis revealed that the serum IgG4 level in Group 3 was higher than that in Group 1 and Group 2, but there was no statistically significant difference between the three groups (P>0.05). Spearman’s rank correlation analysis indicated a positive correlation between uric acid and IgG4 levels (r=0.2454, P=0.0067) as well as between IgE and IgG4 levels (r=0.4169, P<0.001). Conclusions Three distinct intrinsic clinical phenotypes of IgG4-RD were identified by LCA. Each phenotype exhibits characteristic patterns of organ involvement, along with specific immune and metabolic profiles. This suggests that interactions between metabolic and immune pathways may contribute to phenotypic differentiation and disease progression.