Keyword: Albuminuria
3 results found.
Congress Abstract
Central Asian Journal of Nephrology, 2(2, Suppl. 1), 2026, cajn_A18, https://doi.org/10.63946/cajn/19512
ABSTRACT:
Introduction: Chronic kidney disease (CKD) is frequently diagnosed after clinically meaningful loss of kidney function. Creatinine-based estimated glomerular filtration rate (eGFR) may fail to identify early renal dysfunction, while cystatin C, albuminuria and artificial intelligence (AI)-based prediction models may improve early detection and risk stratification. This umbrella review synthesizes current evidence regarding the diagnostic and prognostic value of these approaches and identifies limitations to their clinical implementation.
Methods: We synthesized published systematic reviews and meta-analyses evaluating adults with CKD or individuals at risk of CKD. Evidence addressing cystatin C, albuminuria, or AI/machine-learning models for CKD detection or progression was considered. Outcomes included diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and prognostic associations. Findings were summarized descriptively according to reported pooled estimates and study limitations.
Results: A meta-analysis of 19 studies evaluating serum cystatin C for CKD detection reported pooled sensitivity of 0.85 (95% CI, 0.81–0.89), specificity of 0.87 (95% CI, 0.84–0.90), and AUC of 0.92 (95% CI, 0.90–0.94). Another meta-analysis including 35 studies and 23,667 participants found that the combined creatinine/cystatin C CKD-EPI equation achieved 7.50% higher accuracy than creatinine-based eGFR alone. A 2025 systematic review of risk-factor-based CKD screening included 24 studies from 11 countries; eGFR was used in 22 studies and albumin-creatinine ratio in 14, while confirmed CKD prevalence ranged from 4.4% to 17.1%. For AI, a systematic review identified 68 eligible studies from 648 records, but only 6/68 were conducted in clinical settings. A subsequent meta-analysis of 33 AI studies reported pooled sensitivity of 0.43, specificity of 0.92 and AUC of 0.89, with substantial heterogeneity.
Conclusion: Cystatin C and combined creatinine/cystatin C assessment demonstrate improved diagnostic performance compared with creatinine-based assessment alone. Albuminuria remains an important component of targeted CKD screening, while AI models show promising predictive performance but limited clinical validation. Prospective multicenter studies integrating biomarkers and interpretable AI models are warranted to establish whether these approaches improve clinically meaningful early CKD detection.
Methods: We synthesized published systematic reviews and meta-analyses evaluating adults with CKD or individuals at risk of CKD. Evidence addressing cystatin C, albuminuria, or AI/machine-learning models for CKD detection or progression was considered. Outcomes included diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and prognostic associations. Findings were summarized descriptively according to reported pooled estimates and study limitations.
Results: A meta-analysis of 19 studies evaluating serum cystatin C for CKD detection reported pooled sensitivity of 0.85 (95% CI, 0.81–0.89), specificity of 0.87 (95% CI, 0.84–0.90), and AUC of 0.92 (95% CI, 0.90–0.94). Another meta-analysis including 35 studies and 23,667 participants found that the combined creatinine/cystatin C CKD-EPI equation achieved 7.50% higher accuracy than creatinine-based eGFR alone. A 2025 systematic review of risk-factor-based CKD screening included 24 studies from 11 countries; eGFR was used in 22 studies and albumin-creatinine ratio in 14, while confirmed CKD prevalence ranged from 4.4% to 17.1%. For AI, a systematic review identified 68 eligible studies from 648 records, but only 6/68 were conducted in clinical settings. A subsequent meta-analysis of 33 AI studies reported pooled sensitivity of 0.43, specificity of 0.92 and AUC of 0.89, with substantial heterogeneity.
Conclusion: Cystatin C and combined creatinine/cystatin C assessment demonstrate improved diagnostic performance compared with creatinine-based assessment alone. Albuminuria remains an important component of targeted CKD screening, while AI models show promising predictive performance but limited clinical validation. Prospective multicenter studies integrating biomarkers and interpretable AI models are warranted to establish whether these approaches improve clinically meaningful early CKD detection.
Congress Abstract
Central Asian Journal of Nephrology, 2(2, Suppl. 1), 2026, cajn_A13, https://doi.org/10.63946/cajn/19510
ABSTRACT:
Background: Evidence on chronic kidney disease (CKD) in Central Asia remains limited, particularly for community-based detection using both kidney function and albuminuria. This study assessed the frequency of CKD markers among adults in three cities of Kazakhstan and examined whether point-of-care (POC) creatinine testing could support community screening.
Methods: Adults were recruited through community screening events in Astana, Ust-Kamenogorsk, and Turkestan. Laboratory serum creatinine was used to calculate eGFR with the CKD-EPI 2021 equation, and urine albumin-to-creatinine ratio (ACR) was measured to identify albuminuria. Participants were classified as having screening-detected CKD markers if eGFR was <60 mL/min/1.73 m² and/or ACR was ≥30 mg/g on a single assessment. Multivariable logistic regression was used to examine associated factors. Capillary POC creatinine was compared with laboratory creatinine, and its diagnostic performance for identifying eGFR <60 mL/min/1.73 m² was evaluated.
Results: Of 1,022 participants with complete laboratory kidney measurements, 100 (9.8%; 95% CI 8.1–11.8) had screening-detected CKD markers. Albuminuria was identified in 7.7%, while reduced eGFR was present in 3.4%. Among participants with CKD markers, 52.0% reported no previous awareness of abnormal kidney findings. Hypertension was associated with approximately twice the adjusted odds of CKD markers (aOR 2.05, 95% CI 1.24–3.41). POC and laboratory creatinine were available for 987 participants. POC creatinine exceeded laboratory values by an average of 18.5 µmol/L, with wide limits of agreement. For identifying laboratory-defined reduced eGFR, POC-derived eGFR had 84.4% sensitivity, 83.4% specificity, 14.5% positive predictive value, 99.4% negative predictive value, and an AUC of 0.919.
Conclusions: CKD markers were detected in roughly one in ten screened adults, and albuminuria accounted for a substantial proportion of identified abnormalities. The high proportion of previously unrecognized findings supports greater use of combined eGFR and ACR assessment in CKD case-finding. POC creatinine may be useful for triage because of its strong rule-out performance, but positive findings should be confirmed with standardized laboratory testing.
Methods: Adults were recruited through community screening events in Astana, Ust-Kamenogorsk, and Turkestan. Laboratory serum creatinine was used to calculate eGFR with the CKD-EPI 2021 equation, and urine albumin-to-creatinine ratio (ACR) was measured to identify albuminuria. Participants were classified as having screening-detected CKD markers if eGFR was <60 mL/min/1.73 m² and/or ACR was ≥30 mg/g on a single assessment. Multivariable logistic regression was used to examine associated factors. Capillary POC creatinine was compared with laboratory creatinine, and its diagnostic performance for identifying eGFR <60 mL/min/1.73 m² was evaluated.
Results: Of 1,022 participants with complete laboratory kidney measurements, 100 (9.8%; 95% CI 8.1–11.8) had screening-detected CKD markers. Albuminuria was identified in 7.7%, while reduced eGFR was present in 3.4%. Among participants with CKD markers, 52.0% reported no previous awareness of abnormal kidney findings. Hypertension was associated with approximately twice the adjusted odds of CKD markers (aOR 2.05, 95% CI 1.24–3.41). POC and laboratory creatinine were available for 987 participants. POC creatinine exceeded laboratory values by an average of 18.5 µmol/L, with wide limits of agreement. For identifying laboratory-defined reduced eGFR, POC-derived eGFR had 84.4% sensitivity, 83.4% specificity, 14.5% positive predictive value, 99.4% negative predictive value, and an AUC of 0.919.
Conclusions: CKD markers were detected in roughly one in ten screened adults, and albuminuria accounted for a substantial proportion of identified abnormalities. The high proportion of previously unrecognized findings supports greater use of combined eGFR and ACR assessment in CKD case-finding. POC creatinine may be useful for triage because of its strong rule-out performance, but positive findings should be confirmed with standardized laboratory testing.
Congress Abstract
Central Asian Journal of Nephrology, 2(2, Suppl. 1), 2026, cajn_A1, https://doi.org/10.63946/cajn/19504
ABSTRACT:
Background: Diabetic chronic kidney disease (DKD) is a leading cause of kidney failure and cardiovascular morbidity worldwide. Although albuminuria and estimated glomerular filtration rate (eGFR) remain central markers of risk stratification, early progression is frequently driven by a broader cluster of metabolic, hemodynamic, inflammatory, and cardiometabolic factors. Identification of simple clinical predictors may support earlier intensification of nephroprotective therapy. This study aimed to evaluate clinical, biochemical, and renal predictors associated with early DKD progression.
Methods: This prospective observational study included 112 patients with type 2 diabetes mellitus and established chronic kidney disease. Baseline assessment included age, sex, diabetes duration, body mass index, systolic and diastolic blood pressure, glycated hemoglobin (HbA1c), fasting plasma glucose, lipid profile, serum creatinine, eGFR, urinary albumin-to-creatinine ratio (UACR), hemoglobin, uric acid, C-reactive protein, smoking status, hypertension, obesity, dyslipidemia, and cardiovascular disease history. Early CKD progression was defined as clinically significant eGFR decline during follow-up. Patients were divided into early progression and stable/slow progression groups. Between-group comparisons and multivariable logistic regression were performed.
Results: Early DKD progression was observed in 34 of 112 patients (30.4%), while 78 patients (69.6%) had stable or slowly progressive disease. Patients with early progression had longer diabetes duration (12.8±4.1 vs 8.9±3.6 years; p<0.05), higher systolic blood pressure (148±16 vs 134±14 mmHg; p<0.05), higher HbA1c (8.7±1.1 vs 7.6±0.9%; p<0.01), greater UACR (286 [164–420] vs 118 [62–210] mg/g; p<0.01), and lower baseline eGFR (52.4±13.8 vs 64.7±15.2 mL/min/1.73 m²; p<0.05). Progressors also had higher body mass index (31.2±4.6 vs 28.7±4.2 kg/m²; p=0.006), triglycerides (2.3±0.7 vs 1.8±0.6 mmol/L; p<0.01 ), uric acid (421±76 vs 368±69 µmol/L; p=0.05), and C-reactive protein (5.8 [3.4–8.6] vs 3.1 [1.8–5.2] mg/L; p=0.002), with lower hemoglobin (118±14 vs 126±13 g/L; p=0.004). In multivariable analysis, independent predictors of early progression were UACR above 300 mg/g (odds ratio [OR] 3.42, 95% confidence interval [CI] 1.48-7.91; p=0.004), HbA1c at least 8.0% (OR 2.76, 95% CI 1.27-6.01; p=0.011), uncontrolled hypertension (OR 2.58, 95% CI 1.17-5.68; p=0.018), and hyperuricemia (OR 2.21, 95% CI 1.02-4.79; p=0.044).
Conclusion: Early DKD progression affected nearly one-third of patients. Albuminuria, poor glycemic control, uncontrolled hypertension, hyperuricemia, reduced baseline eGFR, obesity, dyslipidemia, inflammation, and anemia were associated with accelerated renal decline. These clinically accessible variables may improve early risk stratification and guide individualized nephroprotective management.
Methods: This prospective observational study included 112 patients with type 2 diabetes mellitus and established chronic kidney disease. Baseline assessment included age, sex, diabetes duration, body mass index, systolic and diastolic blood pressure, glycated hemoglobin (HbA1c), fasting plasma glucose, lipid profile, serum creatinine, eGFR, urinary albumin-to-creatinine ratio (UACR), hemoglobin, uric acid, C-reactive protein, smoking status, hypertension, obesity, dyslipidemia, and cardiovascular disease history. Early CKD progression was defined as clinically significant eGFR decline during follow-up. Patients were divided into early progression and stable/slow progression groups. Between-group comparisons and multivariable logistic regression were performed.
Results: Early DKD progression was observed in 34 of 112 patients (30.4%), while 78 patients (69.6%) had stable or slowly progressive disease. Patients with early progression had longer diabetes duration (12.8±4.1 vs 8.9±3.6 years; p<0.05), higher systolic blood pressure (148±16 vs 134±14 mmHg; p<0.05), higher HbA1c (8.7±1.1 vs 7.6±0.9%; p<0.01), greater UACR (286 [164–420] vs 118 [62–210] mg/g; p<0.01), and lower baseline eGFR (52.4±13.8 vs 64.7±15.2 mL/min/1.73 m²; p<0.05). Progressors also had higher body mass index (31.2±4.6 vs 28.7±4.2 kg/m²; p=0.006), triglycerides (2.3±0.7 vs 1.8±0.6 mmol/L; p<0.01 ), uric acid (421±76 vs 368±69 µmol/L; p=0.05), and C-reactive protein (5.8 [3.4–8.6] vs 3.1 [1.8–5.2] mg/L; p=0.002), with lower hemoglobin (118±14 vs 126±13 g/L; p=0.004). In multivariable analysis, independent predictors of early progression were UACR above 300 mg/g (odds ratio [OR] 3.42, 95% confidence interval [CI] 1.48-7.91; p=0.004), HbA1c at least 8.0% (OR 2.76, 95% CI 1.27-6.01; p=0.011), uncontrolled hypertension (OR 2.58, 95% CI 1.17-5.68; p=0.018), and hyperuricemia (OR 2.21, 95% CI 1.02-4.79; p=0.044).
Conclusion: Early DKD progression affected nearly one-third of patients. Albuminuria, poor glycemic control, uncontrolled hypertension, hyperuricemia, reduced baseline eGFR, obesity, dyslipidemia, inflammation, and anemia were associated with accelerated renal decline. These clinically accessible variables may improve early risk stratification and guide individualized nephroprotective management.