ObjectiveTo evaluate the screening performance of commonly used renal function indicators for chronic kidney disease (CKD) in subjects of different ages, so as to explore the appropriate screening regimen for Chinese population.MethodsA total of 2 131 adult subjects in Sichuan Orthopaedic Hospital or Mianyang Central Hospital from May 2016 to October 2017 were selected. They were divided into three groups according to age: group A (18–39 years, n=278), group B (40–64 years, n=1 167), and group C (≥65 years, n=686). Serum levels of creatinine (SCr), urea, and cystatin C [CysC; to calculate estimated glomerular filtration rate (eGFR) based on this index], as well as urine levels of albumin, total protein and creatinine were measured, and urine albumin to creatinine ratio (ACR) and urine protein to creatinine ratio (PCR) were calculated respectively. CKD was diagnosed according to the Kidney Disease: Improving Global Outcomes (KDIGO) Guideline (2012 Edition). The receiver-operating characteristic (ROC) curve analysis was finally performed to investigate the diagnostic performance of each indicator for CKD.ResultsThe prevalences of CKD in group A, B, and C were 10.8% (30/278), 16.4% (191/1 167), and 45.8% (314/686), respectively, and the difference among these groups was statistically significant (χ2=233.525, P<0.001). In addition, the levels of the six renal function indicators between CKD and non-CKD subjects also had statistically significant differences in different age groups (P<0 05="" otherwise="" roc="" curve="" analysis="" revealed="" that="" the="" diagnostic="" values="" of="" these="" indicators="" were:="" acr="" or="" pcr=""> eGFR or CysC > serum urea or SCr (AllP<0 05="" except="" that="" egfr="" cysc="" serum="" urea="" and="" scr="" in="" group="" a="" assessed="" the="" similar="" diagnostic="" performance="" among="" the="" three="" indicators="" recommended="" by="" kdigo="" guideline="" the="" diagnostic="" performances="" of="" acr="" and="" pcr="" in="" different="" age="" groups="" were:="" group="" b="" 0="" 915="" 0="" 914=""> group A (0.885, 0.890) > group C (0.841, 0.846), while the diagnostic performance of eGFR was group C (0.807) > group B (0.728) > group A (0.696). The best boundary values of ACR and PCR were lower while the value of eGFR was higher than the corresponding KDIGO recommended medical decision levels.ConclusionsACR is the first choice for screening CKD when the renal function index creatinine is normal. Moreover, eGFR can further improve the diagnostic value, especially in subjects over 65 years old.
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.