Asprosin Levels in Adults with Type 2 Diabetes Mellitus and Diabetic Kidney Disease: A Systematic Review and Meta-Analysis

Abstract

Purpose: Diabetic kidney disease (DKD) significantly affects health and healthcare costs due to chronic kidney disease complications. Given asprosin’s potential as a biomarker for disease progression, we conducted the first systematic review and meta-analysis on its relationship with DKD in adults with type 2 diabetes mellitus (T2DM).

Methods: PubMed, Embase, Cochrane, and Web of Science were systematically searched. Standard mean differences (SMD) with 95% confidence intervals (CI) and Fisher’s Z transformation were used to examine the relationship between asprosin and DKD. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) and its version for cross-sectional studies. Heterogeneity (I² > 50%) was analyzed with a random-effects model.

Results: Six studies (n = 1340) were included. Meta-analysis results indicated that T2DM patients with DKD (micro/macroalbuminuria) had significantly higher circulating asprosin levels than normoalbuminuric T2DM patients (SMD: 1.5, 95% CI: 0.69– 2.32, p = 0.0003). Meta-analysis of correlation revealed a positive association of asprosin with urinary albumin excretion ratio (UACR) (Fisher’s Z = 0.4; 95% CI: 0.240– 0.554, p < 0.001) and body mass index (BMI) (Fisher’s Z = 0.17; 95% CI: 0.036– 0.301, p = 0.013), and a negative association with estimated glomerular filtration rate (eGFR) (Fisher’s Z = − 0.35; 95% CI: − 0.471 to − 0.239, p < 0.001).

Conclusion: Asprosin is elevated in T2DM patients with pre-DKD (early stage DKD) and DKD and correlates with key markers of disease severity. Additional research is required to better understand the pathophysiological mechanisms of asprosin and its role in DKD.

Introduction

Diabetes mellitus, particularly type 2 (T2DM), is the leading global cause of chronic kidney disease (CKD) and end-stage renal disease.¹ Over 25% of individuals with diabetes are estimated to have CKD, with approximately 40% developing it during their lifetime.² This prevalence has risen alongside increasing diabetes cases. Diabetic kidney disease (DKD) refers to CKD caused specifically by diabetes, though its true incidence is challenging to determine due to limited biopsy data. Globally, DKD has shown a steady increase in incidence, mortality, and disability-adjusted life years over recent decades, imposing a substantial economic burden.^3–6

In patients aged 45–64 with both CKD and T2DM, average annual healthcare costs have been reported at $35,649, rising significantly with disease progression. Advanced CKD requires costly interventions such as dialysis and kidney transplantation, further escalating expenditures.⁷ Additionally, managing CKD-related complications, including myocardial infarction and heart failure, contributes significantly to healthcare costs.⁸

Albuminuria and estimated glomerular filtration rate (eGFR) are the most frequently used markers of DKD.^9,10 However, both have notable limitations, particularly in individuals with non-albuminuric DKD or reduced muscle mass.^11–13 To enhance early detection and improve prediction of DKD, extensive research over the past decade has focused on identifying novel biomarkers. These emerging biomarkers hold promise not only for refining risk stratification in DKD patients but also for deepening our understanding of its complex pathophysiology and guiding the development of new therapeutic strategies.^10,14,15

Adipokines, hormones primarily secreted by white adipose tissue, have recently been identified as key regulators of organ function and contributors to the pathophysiology of DKD through inflammatory mechanisms.¹⁶ Among these, asprosin, a C-terminal cleavage product of profibrillin (FBN1) secreted by white adipocytes, has gained attention for its roles in glucose metabolism and inflammation (Figure 1).¹⁷ Research on asprosin has focused on its involvement in T2DM and diabetic nephropathy, a common manifestation of DKD.¹⁸ Meta-analyses have demonstrated elevated circulating asprosin levels in patients with T2DM,^19–21 metabolic syndrome,²¹ and polycystic ovary syndrome (PCOS).²² Furthermore, asprosin levels rise progressively with DKD severity, being higher in pre-DKD (early stage DKD) and DKD groups than in diabetes alone.^18,23–27 These findings highlight asprosin’s potential as a therapeutic target for preventing and managing diabetes-related complications.

Figure 1 Graphical conceptual model of the relationship between asprosin and DKD. Abbreviations: FBN, Fibrillin-1; TLR-4, Toll-like receptor 4; JNK, c-Jun N-terminal kinase; NFκB, Nuclear factor kappa-light-chain-enhancer of activated B cells; Il-6, Interleukin-6; IκB, Inhibitor of kappa B. The figure was edited using GIMP image-editing software.

Asprosin’s role in DKD is further supported by its correlation with metabolic and inflammatory markers, including body mass index (BMI),^{18,23–26,28} urinary albumin excretion ratio (UACR),^23–27 low-density lipoprotein cholesterol (LDL-C),^25,26,28 and creatinine (Cr)^{18,23–26,28} while inversely correlating with eGFR.^18,23–27 These findings suggest that asprosin contributes to DKD pathogenesis by modulating glucose and lipid metabolism and driving inflammation, exacerbating renal damage. Elevated asprosin levels have also been linked to microvascular and macrovascular complications, underscoring its potential as a biomarker for monitoring DKD progression and complications, offering new opportunities for early detection and intervention.²⁹

In consideration of the impact of DKD on the health of individuals and health system costs, especially due to the complications of CKD, and the clinical potential of asprosin as a biomarker of disease progression, we decided to perform a first systematic review and meta-analysis of studies that have analyzed the relationship of asprosin and DKD in adults with T2DM.

Methods

Registration and Reporting

This systematic review and meta-analyses were performed and reported in line with the recommendations from the Cochrane Collaboration³⁰ and with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.³¹ The systematic review protocol was previously registered in PROSPERO [CRD42024589910].

Eligibility Criteria

Inclusion in the meta-analyses was restricted to studies that met the following eligibility criteria: (1) Observational studies (cross-sectional, cohort, and case-control); in (2) adult patients (aged ≥18 years or older) with T2DM that (3) measured asprosin levels. In addition, studies were included only if they reported any of the outcomes of interest, as reported afterward. We excluded: (1) duplicated articles; (2) conference abstracts; (3) scoping reviews; (4) systematic reviews; (5) narrative reviews; (6) case reports; (7) studies with participants younger than 18 years old; (8) patients with T1DM; (9) in vitro or laboratory animal studies.

Search Strategy and Data Extraction

We systematically searched PubMed, Embase, Cochrane, and Web of Science up to August 13, 2024. We used the following search terms: asprosin AND (T2DM OR “type 2 diabetes” OR “Type 2 Diabetes Mellitus” OR “insulin resistance” OR nephropathy OR “renal function” OR “diabetic kidney disease”). We did not limit our search by date or language. The references from all included studies were also searched manually for any additional studies. Following the databases search, the articles were uploaded into Rayyan,³² a program for data management. Screening by title and abstract was independently performed by two authors (J. R. and S. K.) using the established selection criteria. After this first screening, the full text of each article was carefully examined by three authors (J. R, S. K. and S. M.), and all potential discrepancies were solved by consensus. Three authors (J. R, S. K and S. M.) independently extracted the data following the predefined search criteria and quality assessment.

Endpoints

The main outcome was asprosin levels. We compared asprosin levels between three groups: patients with T2DM and DKD, patients with T2DM and pre-DKD, and T2DM patients without DKD. Meta-analysis of correlation was performed to further investigate the relationship between asprosin and UACR, BMI, eGFR, and LDL-C.

Quality Assessment

We evaluated the risk of bias using the Newcastle-Ottawa Scale (NOS)³³ and the Newcastle–Ottawa Scale for Cross-Sectional Studies (NOS-C)³⁴ recommended by the Agency for Healthcare Research and Quality and Methodological Index (AHRQ) for observational and non-randomized studies. The risk of bias was assessed separately by three reviewers (J. R, G. P-J. and S. M). A rating of < 7 stars indicated a high risk of bias, whereas a rating of ≥ 7 stars indicated a low risk of bias in both scales.

To explore potential sources of heterogeneity, we conducted meta-regression analyses using a random-effects model with restricted maximum likelihood (REML) estimation. Moderator variables were selected a priori based on clinical relevance and availability across studies, including DKD definition criteria, sample type, geographic region, and year of publication. Each moderator was initially assessed in univariable models due to the limited number of included studies.

A minimum of 10 studies is generally recommended for the reliable assessment of publication bias using funnel plots; however, our analysis included only 6 studies, which did not meet this threshold. As a result, funnel plots and Egger tests were not utilized due to the risk of unreliable interpretations arising from insufficient statistical power. Instead, we performed a “leave-one-out” sensitivity analysis to evaluate the robustness of our findings and to identify any potential influence of individual studies on the overall results. This approach ensured a thorough examination of the data despite the limited number of included studies.

Statistical Analysis

The data extracted from the articles were reported as mean ± standard deviation (SD). However, for studies that reported data as median and interquartile range, we estimated the mean and the standard deviation using the approaches outlined by Luo et al³⁵ and Wan et al.³⁶ To estimate differences in circulating asprosin levels between groups, the standard mean difference (SMD) and corresponding 95% confidence intervals (CI) were calculated in the meta-analyses. For each study, Spearman or Pearson correlation coefficients were recorded, and Fisher’s Z transformation was applied to calculate a mean transformed correlation weighted by sample size based on the data provided. Means and standard deviations were combined where appropriate following the guidelines outlined in the Cochrane Handbook.³⁷ This approach was applied when merging two reported subgroups into a single group was desirable to ensure accurate and comprehensive data synthesis.

The significance of the overall effects was assessed using the Z test, and forest plots were generated to illustrate effect sizes and their corresponding 95% CI. Heterogeneity was evaluated using Cochran’s Q test, the I² statistic, and the tau-squared statistic for between-study variance. Substantial heterogeneity was assumed when I² > 50%, in which case a random-effects model was applied (DerSimonian and Laird). Additionally, sensitivity analyses were conducted through a leave-one-out approach, where each study was omitted sequentially to evaluate its impact on the pooled results. This analysis tested the robustness of the findings by comparing the sensitivity results to the main analysis outcomes. Statistical analyses were conducted via Review Manager 5.4 (Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark) and MedCalc 23.0.9 (MedCalc Software, Ostend, Belgium). Meta-regression analyses were performed using RStudio (version 2024.12.0+467, Boston, USA). The significance p-value was set as 0.05.

Results

Study Selection

The search strategy initially identified 384 results from all databases. Of these, 204 duplicates were eliminated, and 155 articles were excluded by reviewing the title and abstract. Finally, 25 articles underwent full text screening and 6 were included in the qualitative and quantitative synthesis.^18,23–27 The selection process is illustrated in Figure 2.

Figure 2 PRISMA flow diagram of study screening and selection.

Characteristics of the Included Studies

Six studies were included, of which 5 were cross-sectional and 1 was case-control. Altogether, 1340 participants were included, with 765 males and 575 females. Five studies were conducted in China^23–27 and one study was in Iran.¹⁸ Two studies^23,24 defined T2DM using the World Health Organisation diagnostic criteria from 1999, three studies^18,25,27 used the standards of the American Diabetes Association, and one study²⁶ did not specify its criteria. All articles defined stages of DKD based on UACR. Patients with normoalbuminuria (UACR < 30 mg/g) are considered T2DM patients without DKD, those with microalbuminuria (30 ≤ UACR <300 mg/g) have early or pre-DKD, and patients with macroalbuminuria (UACR ≥300 mg/g) are referred to as having DKD. In all articles, asprosin levels were determined by enzyme-linked immunosorbent assay using serum, except for one manuscript²³ that used plasma. The characteristics of the studies are presented in Table 1.

Table 1 Characteristics of the Included Studies

Risk of Bias and Sensitivity Analysis

Based on the NOS and NOS-C, all studies had a low risk of bias (Supplementary Table 1).

The sensitivity analysis showed that excluding any single study from the analysis did not significantly affect the overall asprosin result. (Supplementary Tables 2–4). However, when comparing T2DM patients with DKD to those with pre-DKD we found that excluding the study by Wang et al²⁶ revealed a significant difference between these two groups (SMD: 0.45, 95% CI: 0.03–0.87, I²: 67%, p = 0.04, (Supplementary Table 5).

Meta-Regression Analysis

Meta-regression analyses were performed to examine the impact of study-level moderators on asprosin levels across different patient comparisons. Results comparing asprosin levels in T2DM patients with pre-DKD/DKD to those without DKD identified sample type (p = 0.0044, R² = 61%) and publication year (p = 0.0316, R² = 55.7%) as significant moderators. Despite this, residual heterogeneity remained high (I² = 96.08% and 95.03%, respectively). Similarly, results comparing T2DM patients with pre-DKD to those without DKD showed sample type (p = 0.0001, R² = 83%) and publication year (p = 0.0008, R² = 83%) as significant moderators, but residual heterogeneity stayed elevated (I² = 96.40% and 93.82%). The results are showed in Supplementary Tables 6–9. The persistently high residual heterogeneity suggests that other unmeasured factors may contribute to variability between studies.

Asprosin Levels in T2DM Patients

The pooled analysis showed that T2DM patients with pre-DKD or with DKD were associated with higher levels of circulating asprosin compared to T2DM patients without DKD (SMD: 1.5, 95% CI: 0.69–2.32, I²: 97%, p = 0.0003; Figure 3).

Figure 3 Forest plot showing asprosin levels in T2DM patients with pre-DKD/DKD compared to T2DM patients without DKD. Abbreviations: T2DM, Type 2 Diabetes Mellitus; DKD, Diabetic Kidney Disease; SD, Standard Deviation; CI, Confidence Interval; IV, Inverse Variance.

Moreover, analyses comparing pre-DKD patients vs T2DM patients without DKD (SMD: 2.08, 95% CI: 0.99–3.17, I²: 97%, p = 0.0002; Figure 4) and DKD patients vs T2DM patients without DKD (SMD: 3.51, 95% CI: 0.93–6.08, I²: 99%, p = 0.008; Figure 5) both showed an association with higher circulating asprosin levels.

Figure 4 Forest plot showing asprosin levels in T2DM patients with pre-DKD compared to T2DM patients without DKD. Abbreviations: T2DM, Type 2 Diabetes Mellitus; DKD, Diabetic Kidney Disease; SD, Standard Deviation; CI, Confidence Interval; IV, Inverse Variance.

Figure 5 Forest plot showing asprosin levels in T2DM patients with DKD compared to T2DM patients without DKD. Abbreviations: T2DM, Type 2 Diabetes Mellitus; DKD, Diabetic Kidney Disease; SD, Standard Deviation; CI, Confidence Interval; IV, Inverse Variance.

We found no difference in circulating asprosin levels when we compared T2DM patients with DKD to those with pre-DKD (SMD: 1.47, 95% CI: −0.04–2.98, I²: 98%, p = 0.06, Figure 6). However, excluding the study by Wang et al²⁶ revealed a significant difference between these two groups (SMD: 0.45, 95% CI: 0.03–0.87, I²: 67%, p = 0.04, Supplementary Table 5).

Figure 6 Forest plot showing asprosin levels in T2DM patients with pre-DKD compared to T2DM patients with DKD. Abbreviations: T2DM, Type 2 Diabetes Mellitus; DKD, Diabetic Kidney Disease; SD, Standard Deviation; CI, Confidence Interval; IV, Inverse Variance.

Association of Asprosin with eGFR, UACR, BMI, LDL-C

To explore the association between asprosin and eGFR, a meta-analysis of correlations was performed on all 6 studies^18,23–27 using Fisher’ Z transformation to compare the given Spearman or Pearson correlation coefficients. Random effect models were used as significant heterogeneity was observed (eGFR: I² = 75.88%, p = 0.0009). The results revealed a negative association of asprosin with eGFR, with a Fisher’s Z of −0.35 (eGFR: 95% CI: −0.471 to −0.239, p < 0.001, Figure 7 and Supplementary Table 10).

Figure 7 Forest plot of correlation between asprosin and eGFR.

To explore the association between asprosin and UACR, a meta-analysis of correlations was performed on 5 studies.^23–27 Random effect models were used as significant heterogeneity was observed (UACR: I² = 87.98%, p < 0.0001). The results revealed a positive association of asprosin with UACR, with a Fisher’s Z of 0.4 (UACR: 95% CI: 0.240 to 0.554, p < 0.001, Figure 8 and Supplementary Table 11).

Figure 8 Forest plot of correlation between asprosin and UACR.

The results from 5 analysed studies^18,23–26 indicated a positive association of asprosin with BMI. Fisher’s Z was 0.17 (95% CI: 0.036–0.301, p = 0.013, Figure 9 and Supplementary Table 12).

Figure 9 Forest plot of correlation between asprosin and BMI.

As for the association between asprosin and LDL-C, data from 5 studies was analysed.^18,23–26 The results showed no significant association of asprosin with LDL-C, neither negative nor positive correlation. Fisher’s Z was 0.01 (95% CI: −0.109 to 0.131, p = 0.858) (Figure 10 and Supplementary Table 13).

Figure 10 Forest plot of correlation between asprosin and LDL-C.

Discussion

Our study synthesized existing evidence on the relationship between asprosin and DKD. This is the first systematic review and meta-analysis to explore the connection between asprosin and DKD. The findings revealed that asprosin levels are higher in T2DM patients with pre-DKD and DKD compared to T2DM patients without DKD. Further analyses identified significant associations between circulating asprosin levels and eGFR, BMI, and UACR. However, no association was observed between asprosin and LDL-C. Given that DKD significantly affects individual health and healthcare costs, and asprosin shows promise as a biomarker for disease progression, our findings provide evidence supporting further investigation into asprosin as a potential biomarker.

Previous meta-analyses have demonstrated elevated asprosin levels in patients with T2DM,^19–21 metabolic syndrome,²¹ women with PCOS,²² and children with obesity.³⁸ Several other studies have also showed correlation of asprosin with various metabolic and anthropometric characteristics in patients with impaired glucose tolerance.^39–45 A study by Liang et al showed that asprosin was correlated with obesity in community-based T2DM patients and hypothesized that serum asprosin could be used as a risk predictor of T2DM.⁴⁶ A recent meta-analysis by Zeng et al revealed a significant difference in asprosin levels between diabetic patients without complications and those with complications. Individuals affected with complications such as diabetic nephropathy, diabetic peripheral neuropathy and diabetic retinopathy exhibited elevated asprosin levels in comparison to those not afflicted with complications.²⁰ Our meta-analysis of correlation showed a significant negative association between asprosin and eGFR as well as a positive association with BMI and UACR. The association between blood-circulating asprosin and UACR could permit early identification of renal disturbances in patients without symptoms or in the early onset stage of kidney dysfunction. Moreover, the correlation between high asprosin levels and BMI could point to the importance of the lipidic tissue as a regulator for metabolism and its impact on organic balance as a whole. These results are compatible with previous studies that compared asprosin values in saliva and blood related to BMI.^47,48

While the exact pathophysiological mechanisms of this adipokine remain under investigation, current studies highlight its role in glucose metabolism and inflammation.^49,50 Asprosin is a protein secreted from white adipose tissue, consisting of 140 amino acid residues derived from the C-terminal fragment of profibrillin.¹⁷ It is released into the bloodstream and targets the liver, where it promotes glucose production by activating the OR4M1 receptor, a type of olfactory G-protein-coupled receptor.⁵¹ Additionally, asprosin crosses the blood-brain barrier to influence the hypothalamus, stimulating appetite by activating AgRP neurons and suppressing POMC neurons. However, the receptor involved in this neural mechanism remains unknown.⁵¹ Beyond its effects on glucose metabolism and appetite, asprosin also plays a critical role in the health of pancreatic β-cells. Fatty acids such as palmitate, elevated during obesity, enhance asprosin production in β-cells, leading to inflammation marked by NFκB activation and the release of pro-inflammatory cytokines like TNF and MCP-1, impairing insulin secretion and reducing cell viability. Furthermore, asprosin activates the TLR4/JNK pathway, intensifying inflammation and inducing β-cell apoptosis.⁵² Such inflammation is also observed in T2DM patients.^53,54 Asprosin also disrupts autophagy in β-cells by downregulating the AMPK pathway and upregulating mTOR activity, resulting in decreased levels of autophagy-related proteins (LC3-II/LC3-I, beclin 1) and increased apoptosis markers. Activation of the AMPK pathway, however, can partially restore autophagy and mitigate the harmful effects caused by asprosin, further highlighting its significant role in β-cell dysfunction.⁵⁵ The AMPK and mTOR signaling pathways are also key regulators in the progression of DKD, as they help preserve the function of podocytes and tubular epithelial cells. Excessive mTOR activation combined with reduced AMPK activity disrupts lipid metabolism, promotes cellular damage, and heightens the risk of kidney injury.^56–58 A study by Mishra et al researching asprosin-neutralizing antibodies yielded promising results. These monoclonal antibodies have demonstrated the potential to lower blood sugar, reduce appetite, and promote weight loss, highlighting asprosin’s therapeutic importance.⁵⁹

Traditionally, DKD has been considered a non-inflammatory condition. However, emerging research and growing evidence suggest that DKD is an inflammatory disease characterized by complex interactions among multiple factors.^{10,15,60–68} A recent study showed that one of the key elements linking vascular complications in DKD to hyperglycemia is oxidative stress.⁶⁴ Hyperglycemia increases the levels of crucial proinflammatory cytokines such as IL-6 and TNFα,^63,64 and elevates the production of reactive oxygen species (ROS).^64,66,67 This disrupts the balance between oxidants and antioxidants in the body, triggering the activation of immune cells, the complement system, and various signaling pathways, all of which contribute to the deterioration of kidney function.^64,66–69 Macrophages are central players in the inflammation associated with DKD, linking impaired lipid metabolism to inflammation and disease progression.^58,70 They rely on fatty acid synthesis and aerobic glycolysis,⁷⁰ activate the Toll-like receptor 4 (TLR4) signaling pathway,^71,72 and promote the production of pro-inflammatory cytokines.^58,70–72 Other immune cells, including monocytes, neutrophils and T cells, as well as Nlrp-3 inflammasomes, also infiltrate and accumulate in DKD^10,73,74 Recent studies have investigated the role of neutrophil extracellular traps (NETs) in the progression of DKD and found that NETs contribute to glomerular endothelial cell dysfunction.^75,76 The study by Ye et al identified elevated IL-33 levels in two NET-related subtypes, suggesting its contribution to intensified inflammation.⁷⁵ Renal intrinsic cells further contribute to the immune response, with different types of renal cells showing varying sensitivity to metabolic stress.^58,77 Podocytes, which are essential for maintaining the integrity of the glomerular filtration barrier, are especially vulnerable. Elevated activity of fatty acid metabolism enzymes in podocytes results in lipid accumulation, leading to structural damage and disruption of the glomerular filtration barrier.^58,78,79 Additionally, podocytes showed the highest expression of insulin receptor (IR) and insulin receptor substrate-1 (IRS1) compared to mesangial and endothelial cells, suggesting that they may benefit the most from improved insulin sensitivity in vascular and glomerular tissues, thereby reducing the risk of diabetic nephropathy.⁸⁰ A study by Lay et al found that glycoprotein synthesis in podocytes may play a role in DKD progression, as glycoproteins are essential for forming the glomerular basement membrane, endothelial glycocalyx, and slit diaphragm. The cell-type-specific pathway disruptions observed in DKD, particularly in podocytes, highlight the potential for targeted therapies to address mitochondrial dysfunction and glycoprotein abnormalities.⁸¹

Preventing and managing diabetic nephropathy necessitates early recognition in order to minimize the risk of additional complications and mortality. In current clinical practice, the detection and monitoring of DKD primarily rely on albuminuria and eGFR.^9,10 However, around 10–30% of DKD patients do not exhibit albuminuria.^82–84 Additionally, disease progression is not always linear - some patients may not transition from microalbuminuria to macroalbuminuria, and others may even revert to normoalbuminuria.⁸⁵ Some patients may present with early tubulointerstitial damage with absence of proteinuria.^15,86 Albumin levels can also be affected by unrelated conditions such as liver disease, nephrotic syndrome, or dehydration, limiting its specificity.⁸⁷ eGFR, while widely used, also has limitations. One major issue is the variability introduced by different equations used to calculate it.⁸⁸ For instance, a study by Moazzeni et al demonstrated higher incidence rates of CKD when using the Modification of Diet in Renal Disease (MDRD) equation compared to the CKD Epidemiology Collaboration (CKD-EPI) equation.⁸⁹ Furthermore, creatinine-based eGFR may not accurately reflect kidney function in individuals with low muscle mass.¹³ Although cystatin C was introduced to address this limitation, it is also affected by factors such as fat mass and systemic inflammation.⁹⁰ Due to the complex pathophysiology of DKD, future advancements in detection and monitoring of disease progression will potentially rely on identifying novel biomarkers, such as asprosin, and integrating them with existing markers to enhance diagnostic accuracy and guide clinical decision-making.

This study has several key strengths. A thorough search of multiple databases was conducted to ensure a comprehensive literature review. All included studies demonstrated a low risk of bias based on the NOS and NOS-C. To the best of our knowledge, this is the first study to aggregate the existing evidence on asprosin levels in individuals with T2DM and DKD. This was accomplished through an in-depth and meticulous systematic review and meta-analysis of the available data. Our study also synthesizes the diverse pathophysiological mechanisms underlying DKD, providing a clear, evidence-based foundation to support and guide future research in this critically important field.

However, this study must be interpreted in light of its limitations. First, the findings are based on a small number of observational studies, as this is a relatively new and emerging research area that requires further exploration. The lack of randomization may introduce bias and limits the ability to establish causality. Additionally, multiple outcomes (eGFR, UACR, BMI, LDL-C) were analyzed, which increases the risk of Type I error due to multiple comparisons. Although no formal corrections for multiplicity were applied, the results should be interpreted with caution, emphasizing the overall consistency rather than isolated significant findings. Furthermore, the limited number of studies prevented formal assessment of publication bias through funnel plots or Egger’s tests, yet the possibility of such bias remains and should be considered when interpreting the results. Second, all the studies included in this analysis were conducted in Asian countries, with the majority (5 out of 6) taking place in China. This restricts the generalizability of the results to other populations. Third, DKD was defined solely by albuminuria levels, excluding non-albuminuric DKD patients, which may affect the study’s comprehensiveness. Finally, there was considerable heterogeneity across the studies included in the analysis. To reduce heterogeneity and improve the reliability of future results, researchers should aim to develop standardized study protocols, use consistent sample types, and employ the same measurement kits. This is particularly important in biomarker research, where variability often arises from the use of different analytical platforms, making cross-study comparisons challenging.

Conclusion

Asprosin is elevated in T2DM patients with pre-DKD and DKD, and correlates with key markers of disease severity. Incorporating asprosin into routine testing could be a revolutionary step toward identifying patients at risk of renal impairment before overt dysfunction appears, while also enabling better monitoring of disease progression. Furthermore, asprosin’s involvement in metabolic and inflammatory pathways suggests it may also represent a promising therapeutic target. Further research, particularly through prospective cohort studies and interventional trials, is essential to validate its predictive value and to explore its potential role in therapeutic strategies for T2DM and DKD.

Data Sharing Statement

All of the data is provided in the published paper and the supplementary files. Further inquiries can be directed to the corresponding authors.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by University of East Sarajevo, Faculty of Medicine Foca, RS, BiH (No. 01-3-36).

Disclosure

The authors report no conflicts of interest in this work.

References

1. Ying M, Shao X, Qin H, et al. Disease burden and epidemiological trends of chronic kidney disease at the global, regional, national levels from 1990 to 2019. Nephron. 2024;148(2):113–123. doi:10.1159/000534071

2. de Boer IH, Khunti K, Sadusky T, et al. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: improving Global Outcomes (KDIGO). Kidney Int. 2022;102(5):974–989. doi:10.1016/j.kint.2022.08.012

3. Liu B, Li L, Cui H, et al. Analysis of the global burden of CKD-T2DM in young and middle-aged adults in 204 countries and territories from 2000 to 2019: a systematic study of the global burden of disease in 2019. Diabet Res Clin Pract. 2024;217:111884. doi:10.1016/j.diabres.2024.111884

4. Deng Y, Li N, Wu Y, et al. Global, regional, and national burden of diabetes-related chronic kidney disease from 1990 to 2019. Front Endocrinol. 2021;12:672350. doi:10.3389/fendo.2021.672350

5. Xie D, Ma T, Cui H, et al. Global burden and influencing factors of chronic kidney disease due to type 2 diabetes in adults aged 20–59 years, 1990–2019. Sci Rep. 2023;13(1):20234. doi:10.1038/s41598-023-47091-y

6. Ding X, Li X, Ye Y, et al. Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990–2019. Front Endocrinol. 2024;15:1383777. doi:10.3389/fendo.2024.1383777

7. Chung H, Crowe CL, Kong SX, et al. Descriptive study of the economic burden among patients with type 2 diabetes mellitus, chronic kidney disease, and chronic kidney disease and type 2 diabetes mellitus in a large US commercially insured population. J Manag Care Spec Pharm. 2023;29(1):80–89. doi:10.18553/jmcp.2023.29.1.80

8. Betts KA, Song J, Faust E, et al. Medical costs for managing chronic kidney disease and related complications in patients with chronic kidney disease and type 2 diabetes. Am J Manag Care. 2021;27(20 Suppl):S369–S374.

9. ElSayed NA, Aleppo G, Bannuru RR, American Diabetes Association Professional Practice Committee. 11. chronic kidney disease and risk management: standards of care in diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S219–S230. doi:10.2337/dc24-S011

10. Jung CY, Yoo TH. Pathophysiologic mechanisms and potential biomarkers in diabetic kidney disease. Diabetes Metab J. 2022;46(2):181–197. doi:10.4093/dmj.2021.0329

11. Scilletta S, Di Marco M, Miano N, et al. Update on Diabetic Kidney Disease (DKD): focus on non-albuminuric DKD and cardiovascular risk. Biomolecules. 2023;13(5):752. doi:10.3390/biom13050752

12. Shi S, Ni L, Gao L, et al. Comparison of nonalbuminuric and albuminuric diabetic kidney disease among patients with type 2 diabetes: a systematic review and meta-analysis. Front Endocrinol. 2022;13:871272. doi:10.3389/fendo.2022.871272

13. Nankivell BJ, Nankivell LFJ, Elder GJ, et al. How unmeasured muscle mass affects estimated GFR and diagnostic inaccuracy. EClinicalMedicine. 2020;29-30:100662. doi:10.1016/j.eclinm.2020.100662

14. Shetty S, Suvarna R, Awasthi A, et al. Emerging biomarkers and innovative therapeutic strategies in diabetic kidney disease: a pathway to precision medicine. Diagnostics. 2025;15(8):973. doi:10.3390/diagnostics15080973

15. Joumaa JP, Raffoul A, Sarkis C, et al. Mechanisms, biomarkers, and treatment approaches for diabetic kidney disease: current insights and future perspectives. J Clin Med. 2025;14(3):727. doi:10.3390/jcm14030727

16. Cai YY, Zhang HB, Fan CX, et al. Renoprotective effects of brown adipose tissue activation in diabetic mice. J Diabetes. 2019;11(12):958–970. doi:10.1111/1753-0407.12938

17. Romere C, Duerrschmid C, Bournat J, et al. Asprosin, a fasting-induced glucogenic protein hormone. Cell. 2016;165(3):566–579. doi:10.1016/j.cell.2016.02.063

18. Goodarzi G, Setayesh L, Fadaei R, et al. Circulating levels of asprosin and its association with insulin resistance and renal function in patients with type 2 diabetes mellitus and diabetic nephropathy. Mol Biol Rep. 2021;48(7):5443–5450. doi:10.1007/s11033-021-06551-2

19. Mahat RK, Jantikar AM, Rathore V, et al. Circulating asprosin levels in type 2 diabetes mellitus: a systematic review and meta-analysis. Clin Epidemiol Glob Health. 2024;25:101502. doi:10.1016/j.cegh.2023.101502

20. Zeng X, Sun X, He W, et al. Relationship of asprosin and diabetes: a meta-analysis. BMC Endocr Disord. 2025;25(1):15. doi:10.1186/s12902-025-01843-1

21. Ulloque-Badaracco JR, Al-Kassab-Córdova A, Hernandez-Bustamante EA, et al. Asprosin levels in patients with type 2 diabetes mellitus, metabolic syndrome and obesity: a systematic review and meta-analysis. Diabetes Metab Syndr. 2024;18(7):103095. doi:10.1016/j.dsx.2024.103095

22. Pérez-López FR, López-Baena MT, Pérez-Roncero GR, et al. Asprosin levels in women with and without the polycystic ovary syndrome: a systematic review and meta-analysis. Gynecol Endocrinol. 2023;39(1):2152790. doi:10.1080/09513590.2022.2152790

23. Xu M, Zhang C, Zhang L, et al. Plasma asprosin concentrations are associated with progression of diabetic kidney disease. Diabetes Metab Syndr Obes. 2024;17:2235–2242. doi:10.2147/DMSO.S447465

24. Xu L, Cui J, Li M, et al. Association between serum asprosin and diabetic nephropathy in patients with type 2 diabetes mellitus in the community: a cross-sectional study. Diabetes Metab Syndr Obes. 2022;15:1877–1884. doi:10.2147/DMSO.S361808

25. Deng X, Zhao L, Guo C, et al. Higher serum asprosin level is associated with urinary albumin excretion and renal function in type 2 diabetes. Diabetes Metab Syndr Obes. 2020;13:4341–4351. doi:10.2147/DMSO.S283413

26. Wang R, Lin P, Sun H, et al. Increased serum asprosin is correlated with diabetic nephropathy. Diabetol Metab Syndr. 2021;13(1):51. doi:10.1186/s13098-021-00668-x

27. Zhang H, Hu W, Zhang G. Circulating asprosin levels are increased in patients with type 2 diabetes and associated with early-stage diabetic kidney disease. Int Urol Nephrol. 2020;52(8):1517–1522. doi:10.1007/s11255-020-02509-8

28. Ma L, Wang Z, Sun L, et al. Association analysis between serum asprosin and metabolic characteristics, complications in type 2 diabetic patients with different durations. J Diabetes Investig. 2024;15(12):1781–1787. doi:10.1111/jdi.14313

29. Senyigit A, Durmus S, Tabak O, et al. The associations between asprosine, clusterin, zinc alpha-2-glycoprotein, nuclear factor kappa B, and peroxisome proliferator-activated receptor gamma in the development of complications in type 2 diabetes mellitus. J Clin Med. 2024;13(20):6126. doi:10.3390/jcm13206126

30. Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.5 (updated August 2024). Cochrane; 2024. Available from: www.training.cochrane.org/handbook.AccessedJuly19, 2025 .

31. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;372:1.

32. Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. doi:10.1186/s13643-016-0384-4

33. Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2021. Available from: https://www.ohri.ca//programs/clinical_epidemiology/oxford.asp.AccessedJuly19, 2025 .

34. Alarcón-Braga EA, Hernandez-Bustamante EA, Salazar-Valdivia FE, et al. Acceptance towards COVID-19 vaccination in Latin America and the Caribbean: a systematic review and meta-analysis. Travel Med Infect Dis. 2022;49:102369. doi:10.1016/j.tmaid.2022.102369

35. Luo D, Wan X, Liu J, Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res. 2018;27(6):1785–1805. doi:10.1177/0962280216669183

36. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14:135. doi:10.1186/1471-2288-14-135

37. Higgins JPT, Li T, Deeks JJ, et al. Chapter 6: choosing effect measures and computing estimates of effect [last updated August 2023]. In: Higgins JPT, Thomas J, Chandler J, et al. editors. Cochrane Handbook for Systematic Reviews of Interventions Version 6.5 (Updated August 2024). Cochrane:2024. Available from: www.training.cochrane.org/handbook.

38. Zhang Y, Zhang Y, Yang B, et al. Circulating levels of asprosin in children with obesity: a systematic review and meta-analysis. BMC Endocr Disord. 2024;24(1):36. doi:10.1186/s12902-024-01565-w

39. Ertuna GN, Sahiner ES, Yilmaz FM, et al. The role of irisin and asprosin level in the pathophysiology of prediabetes. Diabet Res Clin Pract. 2023;199:110642. doi:10.1016/j.diabres.2023.110642

40. Hong T, Li JY, Wang YD, et al. High serum asprosin levels are associated with presence of metabolic syndrome. Int J Endocrinol. 2021;2021:6622129. doi:10.1155/2021/6622129

41. Timurkaan M, Timurkaan ES. Two important players for type 2 diabetes mellitus: metrnl and asprosin. Clin Lab. 2022;68(9). doi:10.7754/Clin.Lab.2021.211015

42. Zhang X, Jiang H, Ma X, Wu H. Increased serum level and impaired response to glucose fluctuation of asprosin is associated with type 2 diabetes mellitus. J Diabetes Investig. 2020;11(2):349–355. doi:10.1111/jdi.13148

43. Zhang L, Chen C, Zhou N, et al. Circulating asprosin concentrations are increased in type 2 diabetes mellitus and independently associated with fasting glucose and triglyceride. Clin Chim Acta. 2019;489:183–188. doi:10.1016/j.cca.2017.10.034

44. Farrag M, Ait Eldjoudi D, González-Rodríguez M, et al. Asprosin in health and disease, a new glucose sensor with central and peripheral metabolic effects. Front Endocrinol. 2023;13:1101091. doi:10.3389/fendo.2022.1101091

45. Yigitdol I, Gulumsek E, Demirtas D, et al. The role of serum asprosin levels in predicting the severity of coronary artery disease in patients with diabetes mellitus. Ir J Med Sci. 2024;193(3):1239–1247. doi:10.1007/s11845-024-03616-6

46. Liang D, Li X, Xiang C, et al. The correlation between serum asprosin and type 2 diabetic patients with obesity in the community. Front Endocrinol. 2025;16:1535668. doi:10.3389/fendo.2025.1535668

47. Ugur K, Aydin S. Saliva and blood asprosin hormone concentration associated with obesity. Int J Endocrinol. 2019;2019:2521096. doi:10.1155/2019/2521096

48. Gül SNS, Eminoğlu DÖ, Laloğlu E, et al. Salivary and serum asprosin hormone levels in the 2018 EFP/AAP classification of periodontitis stages and body mass index status: a case-control study. Clin Oral Investig. 2024;28(1):91. doi:10.1007/s00784-024-05494-9

49. Zhang Y, Yang P, Zhang X, et al. Asprosin: its function as a novel endocrine factor in metabolic-related diseases. J Endocrinol Invest. 2024;47(8):1839–1850. doi:10.1007/s40618-024-02360-z

50. Mazur-Bialy AI. Asprosin enhances cytokine production by a co-culture of fully differentiated mature adipocytes and macrophages leading to the exacerbation of the condition typical of obesity-related inflammation. Int J Mol Sci. 2023;24(6):5745. doi:10.3390/ijms24065745

51. Hoffmann JG, Xie W, Chopra AR. Energy regulation mechanism and therapeutic potential of asprosin. Diabetes. 2020;69(4):559–566. doi:10.2337/dbi19-0009

52. Lee T, Yun S, Jeong JH, Jung TW. Asprosin impairs insulin secretion in response to glucose and viability through TLR4/JNK-mediated inflammation. Mol Cell Endocrinol. 2019;486:96–104. doi:10.1016/j.mce.2019.03.001

53. Eguchi K, Nagai R. Islet inflammation in type 2 diabetes and physiology. J Clin Invest. 2017;127(1):14–23. doi:10.1172/JCI88877

54. Tsalamandris S, Antonopoulos AS, Oikonomou E, et al. The role of inflammation in diabetes: current concepts and future perspectives. Eur Cardiol. 2019;14(1):50–59. doi:10.15420/ecr.2018.33.1

55. Wang R, Hu W. Asprosin promotes β-cell apoptosis by inhibiting the autophagy of β-cell via AMPK-mTOR pathway. J Cell Physiol. 2021;236(1):215–221. doi:10.1002/jcp.29835

56. Declèves AE, Zolkipli Z, Satriano J, et al. Regulation of lipid accumulation by AMP-activated kinase [corrected] in high fat diet- induced kidney injury. Kidney Int. 2014;85(3):611–623. doi:10.1038/ki.2013.462

57. Huynh C, Ryu J, Lee J, et al. Nutrient-sensing mTORC1 and AMPK pathways in chronic kidney diseases. Nat Rev Nephrol. 2023;19(2):102–122. doi:10.1038/s41581-022-00648-y

58. Yu W, Haoyu Y, Ling Z, et al. Targeting lipid metabolic reprogramming to alleviate diabetic kidney disease: molecular insights and therapeutic strategies. Front Immunol. 2025;16:1549484. doi:10.3389/fimmu.2025.1549484

59. Mishra I, Duerrschmid C, Ku Z, et al. Asprosin-neutralizing antibodies as a treatment for metabolic syndrome. Elife. 2021;10:e63784.

60. Mima A. Mitochondria-targeted drugs for diabetic kidney disease. Heliyon. 2022;8(2):e08878. doi:10.1016/j.heliyon.2022.e08878

61. Mima A, Yasuzawa T, King GL, et al. Obesity-associated glomerular inflammation increases albuminuria without renal histological changes. FEBS Open Bio. 2018;8(4):664–670. doi:10.1002/2211-5463.12400

62. Fathy SA, Mohamed MR, Ali MAM, et al. Influence of IL-6, IL-10, IFN-γ and TNF-α genetic variants on susceptibility to diabetic kidney disease in type 2 diabetes mellitus patients. Biomarkers. 2019;24(1):43–55. doi:10.1080/1354750X.2018.1501761

63. Cao L, Boston A, Jegede O, et al. Inflammation and kidney injury in diabetic African American men. J Diabetes Res. 2019;2019:5359635. doi:10.1155/2019/5359635

64. Wang N, Zhang C. Oxidative stress: a culprit in the progression of diabetic kidney disease. Antioxidants. 2024;13(4):455. doi:10.3390/antiox13040455

65. Leucuța DC, Fumeaux PA, Almășan O, et al. Inflammatory markers as predictors of diabetic nephropathy in type 2 diabetes mellitus: a systematic review and meta-analysis. Medicina. 2025;61(2):216. doi:10.3390/medicina61020216

66. Takasu M, Kishi S, Nagasu H, et al. The role of mitochondria in diabetic kidney disease and potential therapeutic targets. Kidney Int Rep. 2024;10(2):328–342. doi:10.1016/j.ekir.2024.10.035

67. Senyigit A, Durmus S, Gelisgen R, et al. Oxidative stress and asprosin levels in type 2 diabetic patients with good and poor glycemic control. Biomolecules. 2024;14(9):1123. doi:10.3390/biom14091123

68. Wu Z, Gao Y, Zuo CY, et al. The status of studies on the mechanism of microcirculatory dysfunction in the process of diabetic kidney injury. Diabetol Metab Syndr. 2025;17(1):154. doi:10.1186/s13098-025-01718-4

69. Wang X, Wu T, Ma H, et al. VX-765 ameliorates inflammation and extracellular matrix accumulation by inhibiting the NOX1/ROS/NF-κB pathway in diabetic nephropathy. J Pharm Pharmacol. 2022;74(3):377–386. doi:10.1093/jpp/rgab112

70. Qi H. Desmosterol-driven atypical macrophage polarization regulates podocyte dynamics in diabetic nephropathy. Mol Biol Rep. 2024;51(1):213. doi:10.1007/s11033-023-09198-3

71. Kuwabara T, Mori K, Mukoyama M, et al. Macrophage-mediated glucolipotoxicity via myeloid-related protein 8/toll-like receptor 4 signaling in diabetic nephropathy. Clin Exp Nephrol. 2014;18(4):584–592. doi:10.1007/s10157-013-0922-5

72. Kuwabara T, Mori K, Mukoyama M, et al. Exacerbation of diabetic nephropathy by hyperlipidaemia is mediated by toll-like receptor 4 in mice. Diabetologia. 2012;55(8):2256–2266. doi:10.1007/s00125-012-2578-1

73. Shahzad K, Fatima S, Khawaja H, et al. Podocyte-specific Nlrp3 inflammasome activation promotes diabetic kidney disease. Kidney Int. 2022;102(4):766–779. doi:10.1016/j.kint.2022.06.010

74. Shahzad K, Bock F, Dong W, et al. Nlrp3-inflammasome activation in non-myeloid-derived cells aggravates diabetic nephropathy. Kidney Int. 2015;87(1):74–84. doi:10.1038/ki.2014.271

75. Ye Y, Huang A, Huang X, et al. IL-33, a neutrophil extracellular trap-related gene involved in the progression of diabetic kidney disease. Inflamm Res. 2025;74(1):15. doi:10.1007/s00011-024-01981-7

76. Zheng F, Ma L, Li X, et al. Neutrophil extracellular traps induce glomerular endothelial cell dysfunction and pyroptosis in diabetic kidney disease. Diabetes. 2022;71(12):2739–2750. doi:10.2337/db22-0153

77. van der Rijt S, Leemans JC, Florquin S, et al. Immunometabolic rewiring of tubular epithelial cells in kidney disease. Nat Rev Nephrol. 2022;18(9):588–603. doi:10.1038/s41581-022-00592-x

78. Fu Y, Sun Y, Wang M, et al. Elevation of JAML promotes diabetic kidney disease by modulating podocyte lipid metabolism. Cell Metab. 2020;32(6):1052–1062.e8. doi:10.1016/j.cmet.2020.10.019

79. Liu X, Ducasa GM, Mallela SK, et al. Sterol-O-acyltransferase-1 has a role in kidney disease associated with diabetes and Alport syndrome. Kidney Int. 2020;98(5):1275–1285. doi:10.1016/j.kint.2020.06.040

80. Mima A, Ohshiro Y, Kitada M, et al. Glomerular-specific protein kinase C-β-induced insulin receptor substrate-1 dysfunction and insulin resistance in rat models of diabetes and obesity. Kidney Int. 2011;79(8):883–896. doi:10.1038/ki.2010.526

81. Lay AC, Coward RJM. The evolving importance of insulin signaling in podocyte health and disease. Front Endocrinol. 2018;9:693. doi:10.3389/fendo.2018.00693

82. Dai Q, Chen N, Zeng L, et al. Clinical features of and risk factors for normoalbuminuric diabetic kidney disease in hospitalized patients with type 2 diabetes mellitus: a retrospective cross-sectional study. BMC Endocr Disord. 2021;21(1):104. doi:10.1186/s12902-021-00769-8

83. An JH, Cho YM, Yu HG, et al. The clinical characteristics of normoalbuminuric renal insufficiency in Korean type 2 diabetic patients: a possible early stage renal complication. J Korean Med Sci. 2009;24(Suppl 1):S75. doi:10.3346/jkms.2009.24.S1.S75

84. Mottl AK, Kwon KS, Mauer M, et al. Normoalbuminuric diabetic kidney disease in the U.S. population. J Diabetes Complications. 2013;27(2):123–127. doi:10.1016/j.jdiacomp.2012.09.010

85. MacIsaac RJ, Ekinci EI, Jerums G. ‘Progressive diabetic nephropathy. How useful is microalbuminuria?: Contra’. Kidney Int. 2014;86(1):50–57. doi:10.1038/ki.2014.98

86. Fiseha T, Tamir Z. urinary markers of tubular injury in early diabetic nephropathy. Int J Nephrol. 2016;2016:4647685. doi:10.1155/2016/4647685

87. Gremese E, Bruno D, Varriano V, et al. Serum albumin levels: a biomarker to be repurposed in different disease settings in clinical practice. J Clin Med. 2023;12(18):6017. doi:10.3390/jcm12186017

88. Castel-Branco MM, Lavrador M, Cabral AC, et al. Discrepancies among equations to estimate the glomerular filtration rate for drug dosing decision making in aged patients: a cross sectional study. Int J Clin Pharm. 2024;46(2):411–420. doi:10.1007/s11096-023-01677-8

89. Moazzeni SS, Arani RH, Hasheminia M, et al. High incidence of chronic kidney disease among Iranian diabetic adults: using CKD-EPI and MDRD equations for estimated glomerular filtration rate. Diabetes Metab J. 2021;45(5):684–697. doi:10.4093/dmj.2020.0109

90. Dedual MA, Wueest S, Challa TD, et al. Obesity-induced increase in cystatin C alleviates tissue inflammation. Diabetes. 2020;69(9):1927–1935. doi:10.2337/db19-1206