Ma YH, Wei XH, Liu Y, Gu LP, Shen TT, Fan TT, Qin Q, Zhang AF, Wang RP, Wang YF. Visit-to-visit variability of creatinine levels associated with proteinuria progression in Chinese patients with type 2 diabetes mellitus. World J Diabetes 2026; 17(6): 119913 [DOI: 10.4239/wjd.119913]
Corresponding Author of This Article
Yu-Fan Wang, MD, Doctor, Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 Haining Road, Shanghai 200080, China. yyffwang@sina.com
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Endocrinology & Metabolism
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Ma YH, Wei XH, Liu Y, Gu LP, Shen TT, Fan TT, Qin Q, Zhang AF, Wang RP, Wang YF. Visit-to-visit variability of creatinine levels associated with proteinuria progression in Chinese patients with type 2 diabetes mellitus. World J Diabetes 2026; 17(6): 119913 [DOI: 10.4239/wjd.119913]
Yu-Hang Ma, Xiao-Hui Wei, Yue Liu, Li-Ping Gu, Ting-Ting Shen, Ting-Ting Fan, Qin Qin, Ai-Fang Zhang, Yu-Fan Wang, Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
Rui-Ping Wang, Clinical Research Center, Shanghai Skin Disease Hospital, Skin Disease Hospital of Tongji University, Shanghai 200072, China
Co-corresponding authors: Rui-Ping Wang and Yu-Fan Wang.
Author contributions: Wang YF, Wang RP, Ma YH, and Wei XH designed the study; Wang RP, Ma YH, and Wei XH analyzed the data; Zhang AF, Fan TT, Qin Q, and Shen TT collected blood samples and collected anthropometric measurements; Liu Y and Gu LP contributed to clinical data collection and information verification; Ma YH, Wei XH and Liu Y drafted the manuscript; Wang YF and Wang RP have primary responsibility for final content and are the guarantors of this work; Ma YH and Wei XH contributed equally to this manuscript and are co-first authors; Wang YF and Wang RP contributed equally to this manuscript and are co-corresponding authors; all authors contributed to data acquisition, revised the manuscript for important intellectual content and approved the manuscript, read and approved the final version to be published.
Supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project, No. 2023ZD0508104; Shanghai Science and Technology Commission Foundation, No. 23ZR1451500; National Health Commission Medical Health Science and Technology Development Research Center “Innovative Medicine Post-Marketing Clinical Research Project”, No. WKZX2023CX150002; and the Fourth Round of “Committee and Hospital Cooperation” (Shanghai Songjiang District Health Commission and Shanghai General People’s Hospital); Joint Research Project and the Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center, No. GWJJMB202510024038.
Institutional review board statement: This study was approved by the Ethics Committee of the Shanghai General Hospital affiliated with Shanghai Jiao Tong University School of Medicine (No. 2017KY209).
Clinical trial registration statement: The study cohort was acquired from the database of the Shanghai General Hospital; a branch of the National Standardized Management Center for Metabolic Diseases in China (ClinicalTrials.gov ID: NCT03811470).
Informed consent statement: Written informed consent was obtained from all participants prior to their involvement in the study.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Corresponding author: Yu-Fan Wang, MD, Doctor, Department of Endocrinology and Metabolism, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 Haining Road, Shanghai 200080, China. yyffwang@sina.com
Received: February 10, 2026 Revised: March 12, 2026 Accepted: April 10, 2026 Published online: June 15, 2026 Processing time: 121 Days and 21.3 Hours
Abstract
BACKGROUND
Proteinuria is a critical therapeutic target in the early management of diabetic kidney disease in individuals with type 2 diabetes mellitus (T2DM). The relationship between metabolic parameter variability and proteinuria in T2DM remains unclear. Few studies have focused on the stability of serum creatinine in diabetic patients and its relationship with the development of proteinuria.
AIM
To explore the relationship between metabolic parameter variability and the development of proteinuria in patients with T2DM.
METHODS
A total of 3297 patients were recruited into this prospective study at Shanghai General Hospital. Ultimately, 1453 patients with T2DM were analyzed (median follow-up time 26 months). Variability was assessed using standard deviation (SD) of serial measurements. The means and SDs of metabolic indicators were categorized into four percentiles to analyze their impact on proteinuria progression. Logistic regression analysis was used to identify the independent risk factors for proteinuria progression.
RESULTS
Proteinuria progression was observed in 26.84% of patients. The SDs of body mass index, systolic blood pressure, diastolic blood pressure, uric acid, fasting blood glucose, and creatinine were greater in patients with proteinuria progression during follow-up. Multivariable logistic regression analysis revealed that the SD of creatinine during follow-up was an independent risk factor for proteinuria progression in T2DM patients [creatinine SD: Above percentiles 75 (P75th) vs below P75th, odds ratio (OR) = 1.47, 95% confidence interval (CI): 1.07-2.03; above P67th vs below P67th, OR = 1.39, 95%CI: 1.03-1.87] after adjusting for confounding factors. The results were consistent in subgroup analysis according to different albumin-to-creatinine ratio stages at baseline.
CONCLUSION
Creatinine level fluctuations independently predicted proteinuria development and progression in T2DM. Dynamic creatinine variability may serve as a valuable adjunct to static indicators in refining proteinuria risk assessment for T2DM.
Core Tip: This was a prospective cohort study to comprehensively analyze the relationship between variability of metabolic parameters and proteinuria in type 2 diabetes mellitus (T2DM). Variability in creatinine levels during follow-up was an independent risk factor for proteinuria progression, and this finding was consistent across patients stratified by different baseline urinary albumin-to-creatinine ratio. These results offer a reference for incorporating dynamic creatinine variability into proteinuria risk monitoring strategies in T2DM patients.
Citation: Ma YH, Wei XH, Liu Y, Gu LP, Shen TT, Fan TT, Qin Q, Zhang AF, Wang RP, Wang YF. Visit-to-visit variability of creatinine levels associated with proteinuria progression in Chinese patients with type 2 diabetes mellitus. World J Diabetes 2026; 17(6): 119913
According to latest estimates by the International Diabetes Federation, approximately 589 million individuals worldwide suffer from type 2 diabetes mellitus (T2DM). The number of people with T2DM is expected to grow by 44.82% to 853 million by 2050[1]. The prevalence of diabetes-related chronic kidney disease (CKD) ranges from 20% to 40%. Diabetic kidney disease (DKD) is one of the most common complications of diabetes and a primary cause of end-stage renal disease (ESRD)[2]. Clinically, DKD is often diagnosed based on the persistent elevation of urinary albumin-to-creatinine ratio (UACR) and/or a decline in estimated glomerular filtration rate (eGFR), while excluding other forms of CKD. The early stages of DKD are typically asymptomatic, with proteinuria being one of the earliest indicators[3,4]. Proteinuria is commonly assessed using UACR[5]. An increase in UACR is closely linked to a decrease in eGFR, as well as heightened risks of ESRD, cardiovascular events, and mortality among diabetic patients[6-8]. Hence, the early identification and intervention of UACR in clinical practice is crucial for preventing and managing DKD onset and progression.
Previous studies have indicated that various factors contribute to proteinuria onset and progression in diabetic patients. These factors encompass irreversible factors such as age, gender, ethnicity, duration of diabetes, genetic predispositions, and family history, as well as reversible risk factors, including lifestyle choices (such as smoking and alcohol consumption), obesity, blood glucose levels, blood pressure (BP), lipid metabolism, and uric acid[9-12]. Therefore, the American Diabetes Association guidelines underscore the necessity for a comprehensive management approach for diabetic patients. This approach should entail the modification of unhealthy lifestyles, control of risk factors (such as hyperglycemia, hypertension, and dyslipidemia)[3].
Recent evidence suggests that beyond the absolute values of metabolic indicators such as blood glucose, BP, and lipid levels, the visit-to-visit variability of those metabolic indicators also affect the risk of developing complications and comorbidities in diabetes[13-16]. Numerous studies have focused on the fluctuations in fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) over long-term follow-up, confirming that significant variability in these markers serves as a risk factor for the onset of proteinuria and DKD in diabetic patients[17-21]. Similarly, the variability of other metabolic indicators, such as BP and uric acid, has also been associated with DKD occurrence and progression[22-24]. However, there is a scarcity of research into the fluctuations of multiple metabolic indicators and their effect on proteinuria progression in diabetic patients during long-term follow-up. It is widely accepted that the presence and severity of proteinuria are closely associated with progressive decline in renal function. Notably, however, non-albuminuric DKD has emerged as the most common and rapidly growing phenotype of diabetic nephropathy. A clinical phenomenon of increasing interest is that creatinine fluctuations are not always synchronized with proteinuria changes[25].
Therefore, we conducted a prospective cohort study to investigate whether the mean values and variability of various metabolic indicators during follow-up are significantly and independently associated with proteinuria onset and progression in patients with T2DM. This study also focused on whether creatinine variability affects proteinuria progression.
MATERIALS AND METHODS
Study population
The study cohort was acquired from the database of the Shanghai General Hospital; a branch of the National Standardized Management Center for Metabolic Diseases (MMC) in China (NCT No. 03811470). The MMC aims to establish and promote a standardized management system for metabolic diseases across the country, with all patients undergoing regular follow-up and assessment of their metabolic indicators as required. A detailed description of the MMC has been previously published[26]. This study included 3297 patients with T2DM from June 2017 to March 2022. Inclusion criteria were as follows: (1) Underwent at least three repeated UACR measurements; and (2) Completed metabolic indicators. Individuals with incomplete baseline information, follow-up duration < 1 year; those followed for > 1 year but with fewer than three assessments of metabolic indicators; and those with UACR ≥ 300 mg/g (macroalbuminuria) at baseline were excluded. Ultimately, 1453 patients were included in the analysis, with a median follow-up of 26 months (Supplementary Figure 1). This study received approval from the Ethics Committee of the Shanghai General Hospital affiliated to Shanghai Jiao Tong University School of Medicine (Ethical No. 2017KY209).
Data collection and variables
Medical history was collected for all patients with T2DM at their first visit, and the relevant information was updated during follow-up. The main characteristics included gender, age, history of smoking and drinking, family history of diabetes, previous disease history, and medication use. Height, weight, waist circumference, hip circumference, and BP were uniformly measured by trained endocrinology nurses. All patients fasted for 10-12 hours 1 day before the laboratory examination. Venous blood samples were collected on an empty stomach in the early morning of the examination day. Metabolic indicators such as FBG, fasting insulin, fasting C-peptide, HbA1c, glycated albumin (GA), creatinine, uric acid, total cholesterol (TCH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and UACR were collected and measured as previously described[27]. Serum creatinine concentration was measured by an enzymatic method and the results were expressed as μmol/L. All measurements were performed in accordance with the manufacturer’s instructions and standard laboratory quality control procedures. Recommended follow-up time was every 3-6 months. Variability was assessed using the SD of serial measurements.
Outcome assessment
The determination of proteinuria was based on UACR values of: (1) UACR < 30 mg/g was considered normal; (2) UACR 30-299 mg/g indicated microalbuminuria; and (3) UACR ≥ 300 mg/g was classified as macroalbuminuria[3]. T2DM with proteinuria progression was defined as a change in UACR from normal to micro/macroalbuminuria, or from microalbuminuria to macroalbuminuria during follow-up. At baseline, 1397 patients were within the normal range and 56 were diagnosed with microalbuminuria. During follow-up, 1063 patients maintained normal status, 331 were diagnosed with microalbuminuria, and 59 progressed to macroalbuminuria. Therefore, 390 patients experienced proteinuria progression during follow-up; 334 were within normal levels at baseline, 331 developed microalbuminuria, and three patients directly progressed to macroalbuminuria. Fifty-six patients with baseline microalbuminuria advanced to macroalbuminuria (Supplementary Figure 2).
Statistical analysis
This study evaluated metabolic indicators including body mass index (BMI), systolic BP (SBP), diastolic BP (DBP), TCH, TG, LDL, HDL, FBG, HbA1c, GA, uric acid, and creatinine. The visit-to-visit variability of these indicators during follow-up was assessed using SD. To assess the robustness of the exposure-effect relationship between creatinine variability and proteinuria progression, a percentile-based grouping strategy was adopted. Primary analyses used the 67th (upper tertile) and 75th (upper quartile) percentiles as high-risk thresholds. This was then extended to the 80th and 85th percentiles to evaluate plateau or threshold effects in the dose-response relationship. Sensitivity analyses across multiple percentiles tested whether the association depended on any single threshold. χ2 and t tests were used to compare differences between the progression and non-progression groups of proteinuria, with continuous variables expressed as mean ± SD and noncontinuous variables represented as median (interquartile range). Backward logistic regression analysis was used to identify independent risk factors for proteinuria progression. Variable selection was performed using backward elimination with a P threshold of 0.10 for removal. The final model included variables with P < 0.05. Logistic regression models calculated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup analysis was performed according to baseline ACR categories and proteinuria progression defined as an increase from 30-299 mg/g to > 300 mg/g or from < 30 mg/g to 30-299 mg/g. All significance tests were two-tailed, with P < 0.05 considered statistically significant. All data analyses were conducted using SPSS version 22.0. Forest plots were generated using GraphPad Prism 9.
RESULTS
Baseline characteristics in T2DM patients with and without proteinuria progression
We analyzed 1453 patients, of which 64.21% were male. The median follow-up was 26 months, and 390 (26.84%) patients developed proteinuria progression. Compared with T2DM without proteinuria progression, those with proteinuria progression were older, had a longer duration of diabetes (P < 0.01), and had higher BMIs (P = 0.02), waist circumference (P = 0.02), SBP, prevalence of hypertension, incidence of stroke, UACR (all P < 0.01), and high sensitivity C-reactive protein (P < 0.05). Additionally, a greater proportion of patients with proteinuria progression used glucagon-like peptide 1 receptor agonists (GLP-1RA)(P < 0.01). In contrast, the rates of smoking (P = 0.03) and alcohol consumption (P < 0.01), as well as family history of diabetes (P = 0.02), serum creatinine (P < 0.01), serum TCH (P < 0.01), and serum LDL-C levels were lower in this group (P = 0.03) (Table 1).
Table 1 Demographic and baseline clinical features in type 2 diabetes mellitus patients with or without proteinuria progression, mean ± SD/n (%).
Comparison of mean values and visit-to-visit variability of metabolic indicators in T2DM patients with and without proteinuria progression during follow-up
We analyzed visit-to-visit variability of 12 metabolic indicators using mean values and SD during follow-up. T2DM patients with proteinuria progression exhibited significantly higher mean BMI (P = 0.04), SBP (P < 0.01), FBG (P < 0.01), HbA1c (P < 0.01), GA (P = 0.02), and TG (P = 0.04) during follow-up compared with those without proteinuria progression. We did not observe any significant differences in the mean values of DBP, TCH, HDL, LDL, creatinine, and uric acid levels throughout follow-up. Additionally, T2DM patients with proteinuria progression demonstrated higher SD in BMI (P = 0.01), SBP (P < 0.01), DBP (P = 0.02), FBG (P < 0.01), creatinine (P < 0.01), and uric acid (P = 0.02) compared with those without proteinuria progression. However, the SD of HbA1c, GA, TCH, TG, HDL and LDL were not significantly different between the two groups (Table 2). We categorized means and SDs of the continuous variables of metabolic indicators into four percentiles (85th, 80th, 75th, and 67th). The results indicated that during follow-up, the variability of BMI, SBP, FBG, HbA1c, and serum creatinine levels were associated with an increased rate of proteinuria onset and progression among T2DM patients (Supplementary Table 1).
Table 2 Mean and standard deviation of the 12 metabolic indexes among type 2 diabetes mellitus patients with and without the proteinuria progression during follow-up, mean ± SD.
Impact of mean levels and visit-to-visit variability of metabolic indicators on proteinuria progression during follow-up in T2DM patients
To assess whether variability in metabolic indicators influences proteinuria progression during follow-up in T2DM patients, we categorized the variability of each metabolic indicator into groups based on the 75th and 67th percentiles and performed logistic regression full model analysis. At the 75th and 67th percentiles, high variability in creatinine levels became an independent risk factor for proteinuria progression in T2DM patients during follow-up, with ORs of 1.58 (95%CI: 1.14-2.18) and 1.41 (95%CI: 1.05-1.90) (Figure 1). Male sex and a family history of diabetes served as protective factors, while prolonged disease duration, hypertension, and GLP-1RA use were identified as independent risk factors (Figure 1). These findings remained consistent across various adjustment models while adjusting for demographic characteristics, clinical features, and metabolic index, with OR of 1.65 (95%CI: 1.14-2.18) and 1.51 (95%CI: 1.13-2.01) for creatinine SD at the 75th and 67th percentiles, respectively. We classified the variability of each metabolic indicator based on the 85th and 80th percentiles and obtained the same result (Supplementary Figure 3). Figure 1 and Supplementary Figure 3 share identical X-axis ranges and Y-axis scales, enabling direct cross-comparison.
Figure 1 Factors associated with proteinuria progression based on multivariable logistic regression by different variation of the 12 metabolic indexes identified by percentiles (percentiles 75 and percentiles 67) of their standard deviation among type 2 diabetes mellitus patients.
A: Full model based on logistic regression; B: Model adjusted with demographic and clinical feature based on logistic regression; C: Model adjusted with demographic, clinical feature and metabolic index based on logistic regression. OR: Odds ratios; CI: Confidence intervals; BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; HbA1c: Glycated hemoglobin; T2DM: Type 2 diabetes mellitus; TC: Total cholesterol; TG: Triglyceride; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; Cr: Creatinine; UA: Uric acid; GA: Glycated albumin; GLP-1: Glucagon-like peptide 1; Y: Yes; N: No; P: Percentiles.
We adjusted the model for the means of BMI, SBP, DBP, TG, uric acid, and HbA1c. At the 75th and 67th percentiles, high variability in creatinine remained an independent risk factor for proteinuria progression in T2DM patients during follow-up, with ORs of 1.47 (95%CI: 1.07-2.03) and 1.39 (95%CI: 1.03-1.87). Male sex and a family history of diabetes served as protective factors, whereas prolonged disease duration and presence of hypertension were identified as independent risk factors. However, the use of GLP-1RAs was not significantly different after adjustment (Figure 2). We classified the variability of each metabolic indicator based on the 85th and 80th percentiles and obtained similar results (Supplementary Figure 4). Figure 2 and Supplementary Figure 4 share identical X-axis ranges and Y-axis scales, enabling direct cross-comparison.
Figure 2 Factors associated with proteinuria progression among type 2 diabetes mellitus patients based on variations of metabolic indexes identified by different percentiles (percentiles 75 and percentiles 67) and adjusted for potential confounding factors including the mean values of body mass index, systolic blood pressure, diastolic blood pressure, triglyceride, uric acid and glycated hemoglobin.
A: Variations of metabolic indexes identified by percentiles 75; B: Variations of metabolic indexes identified by percentiles 67. OR: Odds ratios; CI: Confidence intervals; BMI: Body mass index; SBP: Systolic blood pressure; T2DM: Type 2 diabetes mellitus; LDL: Low-density lipoprotein; Cr: Creatinine; GLP-1: Glucagon-like peptide 1; Y: Yes; N: No; P: Percentiles.
Multivariate logistic regression by different variation of metabolic indexes
We further classified T2DM patients with proteinuria progress into two groups: Those progressing from normo-albuminuria to microalbuminuria (UACR changed from < 30 to 30-299); and those progressing from microalbuminuria to macroalbuminuria (UACR changed from 30-299 to ≥ 300). We explored the factors influencing proteinuria progression in these different groups. In patients progressing from normo-albuminuria to microalbuminuria, high variability in creatinine remained an independent risk factor for proteinuria progression during follow-up, with ORs of 1.47 (95%CI: 1.07-2.03) and 1.39 (95%CI: 1.03-1.87). The results demonstrated that high variability in creatinine levels remained an independent risk factor for both groups of patients at the 75th and 67th percentiles: Progressing from normo-albuminuria to microalbuminuria with OR of 1.26 (95%CI: 1.01-1.87) and 1.38 (95%CI: 1.00-1.91), and progressing from microalbuminuria to macroalbuminuria with OR of 2.05 (95%CI: 1.37-3.08) and 1.73 (95%CI: 1.16-2.84) (Figure 3).
Figure 3 Subgroup analysis among type 2 diabetes mellitus patients with proteinuria progression identified by urinary albumin-to-creatinine ratio (changed from 30 to 30-299) or (changed from 30-299 to > 300) based on multivariable logistic regression by different variation of metabolic indexes, which was calculated by the percentiles (percentiles 75 and percentiles 67) of their standard deviation among type 2 diabetes mellitus patients.
A: Type 2 diabetes mellitus (T2DM) patients with proteinuria progression based on albumin-to-creatinine ratio (ACR) (from < 30 to 30-299) by 75 percentiles of SD; B: T2DM patients with proteinuria progression based on ACR (from < 30 to 30-299) by 67 percentile of SD; C: T2DM patients with proteinuria progression based on ACR (from 30-299 to > 300) by 75 percentile of SD; D: T2DM patients with proteinuria progression based on ACR (from 30-299 to > 300) by 67 percentile of SD. OR: Odds ratios; CI: Confidence intervals; BMI: Body mass index; SBP: Systolic blood pressure; T2DM: Type 2 diabetes mellitus; LDL: Low-density lipoprotein; Cr: Creatinine; GLP-1: Glucagon-like peptide 1; Y: Yes; N: No; P: Percentiles.
DISCUSSION
Proteinuria is a critical target for the early management of CKD associated with T2DM, and multidisciplinary guidelines recommend the use of UACR to assess kidney damage[3,28,29]. The presence of proteinuria indicates renal impairment, and its severity tends to increase with duration of diabetes. Without intervention, most patients will progress to kidney disease. Even among T2DM patients with preserved renal function, changes in glomerular structure are closely associated with elevated levels of urinary microalbumin[30].
In this study, we focused on the dynamic changes of multiple clinically relevant metabolic and renal function assessment indicators, including BMI, SBP, DBP, TCH, TG, LDL, HDL, FBG, HbA1c, GA, uric acid, and creatinine levels throughout the follow-up period. Patients with proteinuria progression exhibited higher mean values of FBG, HbA1c, and GA compared with those without proteinuria progression, consistent with previous findings. Previous studies have confirmed that long-term, stringent glycemic control can delay proteinuria onset and progression and diabetic nephropathy[31-33]. This underscores the protective role of long-term glycemic control in renal complications among patients with T2DM.
In recent years, researchers have focused on the absolute values of blood glucose levels and the impact of glycemic variability on chronic complications in diabetic patients. In our study, we found that the variability in FBG among T2DM patients with proteinuria progression was significantly higher than that of those without proteinuria progression. Post hoc analyses from the Control Cardiovascular Risk in Diabetes (ACCORD) and the Veteran Affairs Diabetes Trial (VADT) cohorts revealed that variability in FBG serves as an independent risk factor for microvascular complications in T2DM[34]. Similarly, analyses of the United Kingdom Prospective Diabetes Study, ACCORD, and VADT have indicated that despite efforts to lower overall glycemic control, glycemic variability is associated with an increased risk of moderate to severe kidney disease (eGFR < 45 mL/minute/1.73 m2)[35]. Beyond FBG, the variability of HbA1c and GA has also been robustly linked to significant microvascular complications[21,36-38].
The effect of blood glucose variability on proteinuria was inconsistent, and we did not identify any differences in the variability of HbA1c and GA between the two groups. This may be related to the median follow-up duration of 26 months. Previous research has suggested that long-term glycemic variability, rather than short-term fluctuations, exerts a more substantial influence on proteinuria onset in T2DM[39]. It is possible that with extended follow-up, the impacts of variability in HbA1c, GA, and FBG on the occurrence of proteinuria will become more pronounced.
The control of BP, lipids, and uric acid also plays a crucial role in proteinuria onset and progression in diabetic patients[3]. Our study found that T2DM patients with proteinuria progression exhibited significantly higher baseline SBP, average SBP during follow-up, and SBP variability compared with those without proteinuria progression. Previous studies have demonstrated that intensive BP management can reduce the incidence and progression of cardiovascular events and CKD in diabetic patients[40]. In addition to BP control, variability is a significant factor in the development of CKD. Research from Japan has identified that a combination of higher BP variability and elevated average SBP serves as an independent risk factor for CKD in the general population[41]. Similarly, the Hanzhong Adolescent Hypertension Study from China found that long-term BP variability from childhood to middle age was associated with a higher risk of subclinical kidney damage and albuminuria in adulthood, independent of cumulative BP exposure during follow-up[42]. In diabetic populations, variability in SBP has also been identified as an independent risk factor for the development of microalbuminuria and is associated with increased urinary albumin variability[43]. Our results were consistent with previous research, and the SD of SBP and DBP were also greater during follow-up. This underscores the importance of BP management, particularly the long-term maintenance of stable BP, in proteinuria onset and progression in diabetic patients.
Hyperuricemia and dyslipidemia are also recognized as risk factors for proteinuria in diabetic patients, and effective control of uric acid and lipid levels can help mitigate the occurrence of CKD in this population[3]. In our study, we did not observe any association between the levels and variability of uric acid and lipids and the occurrence of proteinuria. Fluctuations in uric acid and lipid levels have been previously associated with cardiovascular diseases and all-cause mortality[44-46]. However, the relationship between variability in uric acid and lipids and the occurrence of proteinuria in diabetic patients remains to be clarified through large-scale prospective studies.
Regardless of the presence of diabetes, obesity and overweight are widely recognized as risk factors for proteinuria. Our study revealed that patients with an average BMI > 25 kg/m2 had a higher mean BMI during follow-up; particularly among those who developed proteinuria compared with those who did not. Previous studies have demonstrated that the relationship between BMI and proteinuria follows a J-shaped curve[47]. A study conducted in Japan indicated that individuals with BMI > 27.0 kg/m2 or < 18.9 kg/m2 had a significantly higher incidence of proteinuria compared with those with BMI 21.0-22.9 kg/m2[48]. Our results are consistent with previous reports, which have suggested that when assessing the relationship between renal function and BMI, it is also important to consider fluctuations in BMI[49]. The benefits of weight loss for renal function in obese and overweight populations remain a topic of debate[50]. We observed that while the variability in BMI among patients who developed proteinuria was greater than that in patients who did not (P = 0.01), this phenomenon could not be consistently observed across different percentiles of BMI variability. A prospective study indicated that even within the normal BMI range, men who experienced a BMI increase > 10% were at a significantly higher risk for CKD compared with those who maintained their BMI within 5% of baseline[51]. Conversely, a study from Korea demonstrated that weight changes were associated with CKD incidence following a J-shaped curve, with the lowest risk observed in patients whose weight fluctuated between -0.25 kg and 0.25 kg annually[52]. Therefore, the relationship between BMI fluctuations and the occurrence of proteinuria in diabetic patients warrants further investigation.
Few studies have focused on the stability of serum creatinine and its relationship with the occurrence of proteinuria in diabetic patients. We found that fluctuations in serum creatinine levels were an independent risk factor for T2DM patients with proteinuria progression. Moreover, after correcting for related variables, serum creatinine variability emerged as the only stable risk factor affecting the occurrence of proteinuria in T2DM, underscoring the importance of this metric. Low et al[53] demonstrated that higher variability in serum creatinine was an independent predictor of the onset and progression of albuminuria in diabetic individuals, and this metric was particularly significant in patients whose baseline urinary protein levels were normal before transitioning to abnormal. While the precise mechanisms by which serum creatinine variability contributes to albuminuria onset remain unclear, such fluctuations may reflect changes in glomerular hemodynamics. The instability in renal hemodynamics could potentially lead to proteinuria development[54]. Our findings suggest a clinical implication: Efforts should be made to minimize factors that cause fluctuations in serum creatinine levels in diabetic patients, such as exposure to contrast agents, prolonged use of nonsteroidal anti-inflammatory drugs, and abnormalities in metabolic indicators[55-57].
Several mechanisms may link creatinine fluctuations to proteinuria progression: Renin-angiotensin-aldosterone system activation induces both glomerular hypertension and tubular dysfunction, and renal hemodynamic instability can cause fluctuations in glomerular filtration rate. Impaired tubule-glomerular feedback causes tubular injury, affecting glomerular filtration. Changes in muscle mass and hydration status can lead to nonrenal creatinine fluctuations. Recurrent subclinical acute kidney injury may accelerate renal function decline through cumulative tubular damage. These mechanisms suggest that creatinine variability may reflect underlying renal and systemic processes rather than directly driving proteinuria. Further validation is needed.
Although creatinine fluctuation is considered an analytical variable in this study, its clinical significance needs to be re-examined beyond merely a “risk factor”. When interpreting the association of creatinine fluctuations with proteinuria in observational studies, two possible forms of reverse causality must be considered. First, clinical reverse causality: Patients with early renal impairment (e.g., mild fluctuations in creatinine or mild declines in eGFR) may be more likely to seek health care proactively, which could lead to spurious associations between exposure and outcome. Second, two-way interaction at the pathophysiological level: Creatinine fluctuation and proteinuria may originate from the same upstream pathway, and both may result from early damage markers and mutual amplification, rather than a simple causal chain. Proteinuria itself can further aggravate dysfunctional renal tubular excretion of creatinine by inducing renal tubular epithelial cell damage and activating inflammatory and fibrotic pathways, thereby amplifying creatinine fluctuations. This bidirectional relationship suggests that creatinine fluctuation and proteinuria may form a vicious cycle: Early renal tubular injury leads to creatinine fluctuation, and the decline in renal tubular function reflected by creatinine fluctuation may in turn exacerbate the susceptibility to proteinuria[25,58]. This new perspective has important implications for clinical practice: In diabetic patients, even if urinary protein is still in the normal range, the appearance of creatinine fluctuations should alert us to early renal injury and prompt earlier intervention. Future studies using longitudinal designs or causal inference methods are needed to clarify the temporal order and direction of this complex relationship.
The strengths of our research focus on the average levels and fluctuations of various metabolic indicators and comprehensively evaluated their impact on albuminuria. Our findings revealed that higher mean levels and fluctuations in BMI, SBP, FBG, HbA1c, and serum creatinine are associated with proteinuria progression in T2DM patients. These results suggest that maintaining a healthy weight, controlling SBP and blood glucose levels help delay proteinuria progression in T2DM patients. Fluctuations in serum creatinine levels remained significantly impactful even after adjusting for other influencing factors.
This study has limitations. Firstly, it was a single-center observational study, which may have introduced bias in the sample selection. Some potential confounders were not included in the analysis, such as diet, exercise habits, family history, and medication adherence. We had no serial data on dietary protein, hydration, muscle mass, or nephrotoxic exposures: Such unmeasured factors could affect creatinine and may have biased the observed creatinine-variability outcome association and effect estimates. Moreover, in the absence of incremental predictive metrics and external validation, the present study does not support direct clinical application. Accordingly, the results should be interpreted as exploratory and hypothesis-generating, furnishing preliminary clues for future confirmatory investigations. Future multicenter studies with larger sample sizes and longer follow-up are needed to validate our conclusions. Inherent flaws in the observational design and the complexity of confounding factors prevent causal inferences about protective effects of specific drugs. The possibility of residual confounding resulting from changes in medication use and treatment cannot be entirely eliminated. Future studies should further explore the independent effects of different antidiabetic drugs on proteinuria progression in patients with diabetic nephropathy using more elaborate research designs and larger multicenter datasets. Secondly, our follow-up period may not have been sufficiently long, and the impact of fluctuations in glycemic and glycated indicators on proteinuria onset may not have become apparent. The relatively short median follow-up may not have been sufficient to capture outcome events of interest, which is probably one of the main reasons why some common measures that we expected to show statistical significance did not. Owing to the limited follow-up time and the limited number of events, the study may have been underpowered to detect small effect sizes.
CONCLUSION
In this study, we examined the relationship between metabolic indicators and proteinuria occurrence and progression in T2DM patients during follow-up. These data indicate that reducing creatinine level fluctuations, rather than focusing solely on average levels, may be a potential strategy for preventing proteinuria onset in T2DM patients.
ACKNOWLEDGEMENTS
We thank all patients who participated in this study.
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Scientific quality: Grade B, Grade B, Grade C, Grade C
Novelty: Grade A, Grade C, Grade C
Creativity or innovation: Grade A, Grade C, Grade C
Scientific significance: Grade B, Grade B, Grade C
P-Reviewer: Hwu CM, MD, Professor, Taiwan; Shrivastav D, PhD, Assistant Professor, India; Zaman S, PhD, Researcher, Pakistan S-Editor: Fan M L-Editor: Filipodia P-Editor: Xu ZH
