Identification of Novel Circulating Biomarkers Predicting Rapid Decline in Renal Function in Type 2 Diabetes: The Fremantle Diabetes Study Phase II

CD5L up-regulates the TGF-β signaling pathway and promotes renal fibrosis

Renal fibrosis is the common final pathway of progressive renal diseases, in which the macrophages play an important role. ELISA was used to detect CD5 antigen-like (CD5L) in serum samples from end-stage renal disease (ESRD), as well as in mice serum with unilateral ureteral occlusion (UUO). Recombinant CD5L was injected into UUO mice to assess renal injury, fibrosis, and macrophage infiltration. The expression of CD5L was significantly upregulated in the serum of patients with ESRD and UUO mice. Histological analysis showed that rCD5L-treated UUO mice had more severe renal injury and fibrosis. Furthermore, rCD5L promoted the phenotypic transfer of monocytes from Ly6Chigh to LyC6low. RCD5L promoted TGF-β signaling pathway activation by promoting Smad2/3 phosphorylation. We used Co-IP to identify HSPA5 interact with CD5L on cell membrane could inhibit the formation of the Cripto/HSPA5 complex, and promote the activation of the TGF-β signaling pathway. The CD5L antibody could reduce the degree of renal fibrosis in UUO mice.

Kidney function trajectories, associated factors, and outcomes in multiethnic Asian patients with type 2 diabetes

BACKGROUND

We examined the trajectory of estimated glomerular filtrate rate (eGFR), associated risk factors, and its relationship with end-stage kidney disease (ESKD) among a multiethnic patient population with type 2 diabetes in Singapore.

METHODS

A follow-up study included 62 080 individuals with type 2 diabetes aged ≥18 years in a multi-institutional SingHealth Diabetes Registry between 2013 and 2019. eGFR trajectories were analyzed using latent class linear mixed models. Factors associated with eGFR trajectories were evaluated using multinomial logistic regression. The association of eGFR trajectories with ESKD was assessed via competing risk models.

RESULTS

Trajectory of kidney function, determined by eGFR, was nonlinear. The trajectory pattern was classified as stable initially then gradual decline (75%), progressive decline (21.9%), and rapid decline (3.1%). Younger age, female sex, Malay ethnicity, lower-income housing type, current smoking, higher glycated hemoglobin, lower low-density lipoprotein, higher triglyceride, uncontrolled blood pressure, albuminuria, cardiovascular disease, hypertension, and higher eGFR levels each were associated with progressive or rapid decline. Compared with the trajectory of stable initially then gradual eGFR decline, progressive decline increased the hazard of ESKD by 6.14-fold (95% confidence interval [CI]: 4.96-7.61)) and rapid decline by 82.55 folds (95% CI: 55.90-121.89).

CONCLUSIONS

Three nonlinear trajectory classes of kidney function were identified among multiethnic individuals with type 2 diabetes in Singapore. About one in four individuals had a progressive or rapid decline in eGFR. Our results suggest that eGFR trajectories are correlated with multiple social and modifiable risk factors and inform the risk of ESKD.

Protective role for kidney TREM2high macrophages in obesity- and diabetes-induced kidney injury

Diabetic kidney disease (DKD), the most common cause of kidney failure, is a frequent complication of diabetes and obesity, and yet to date, treatments to halt its progression are lacking. We analyze kidney single-cell transcriptomic profiles from DKD patients and two DKD mouse models at multiple time points along disease progression-high-fat diet (HFD)-fed mice aged to 90-100 weeks and BTBR ob/ob mice (a genetic model)-and report an expanding population of macrophages with high expression of triggering receptor expressed on myeloid cells 2 (TREM2) in HFD-fed mice. TREM2high macrophages are enriched in obese and diabetic patients, in contrast to hypertensive patients or healthy controls in an independent validation cohort. Trem2 knockout mice on an HFD have worsening kidney filter damage and increased tubular epithelial cell injury, all signs of worsening DKD. Together, our studies suggest that strategies to enhance kidney TREM2high macrophages may provide therapeutic benefits for DKD.

CD5L is upregulated upon infection with Mycobacterium tuberculosis with no effect on disease progression

Tuberculosis (TB) alone caused over a billion deaths in the last 200 years, making it one of the deadliest diseases to humankind. Understanding the immune mechanisms underlying protection or pathology in TB is key to uncover the much needed innovative approaches to tackle TB. The scavenger receptor cysteine-rich molecule CD5 antigen-like (CD5L) has been associated with TB, but whether and how CD5L shapes the immune response during the course of disease remains poorly understood. Here, we show an upregulation of CD5L in circulation and at the site of infection in C57BL/6 Mycobacterium tuberculosis-infected mice. To investigate the role of CD5L in TB, we studied the progression of M. tuberculosis aerosol infection in a recently described genetically engineered mouse model lacking CD5L. Despite the increase of CD5L during infection of wild-type mice, absence of CD5L did not impact bacterial burden, histopathology or survival of infected mice. Absence of CD5L associated with a modest increase in the numbers of CD4+ T cells and the expression of IFN-γ in the lungs of infected mice, with no major effect in overall immune cell dynamics. Collectively, this study confirms CD5L as a potential diagnostic biomarker to TB, showing no discernible impact on the outcome of the infection.

Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

BACKGROUND

Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.

METHODS

We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).

RESULTS

Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.

CONCLUSIONS

Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

IMPORTANCE AND AIMS

Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

METHODS

A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

RESULTS

Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

CONCLUSIONS AND RELEVANCE

There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

Background

Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk.

Methods

Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA.

Results

All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated.

Conclusions

Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

The effects of apoptosis inhibitor of macrophage in kidney diseases

Kidney disease is a progressive and irreversible condition in which immunity is a contributing factor that endangers human health. It is widely acknowledged that macrophages play a significant role in developing and causing numerous kidney diseases. The increasing focus on the mechanism by which macrophages express apoptosis inhibitor of macrophages (AIM) in renal diseases has been observed. AIM is an apoptosis inhibitor that stops different things that cause apoptosis from working. This keeps AIM-bound cell types alive. Notably, the maintenance of immune cell viability regulates immunity. As our investigation progressed, we concluded that AIM has two sides when it comes to renal diseases. AIM can modulate renal phagocytosis, expedite the elimination of renal tubular cell fragments, and mitigate tissue injury. AIM can additionally exacerbate the development of renal fibrosis and kidney disease by prolonging inflammation. IgA nephropathy (IgAN) may also worsen faster if more protein is in the urine. This is because IgA and immunoglobulin M are found together and expressed. In the review, we provide a comprehensive overview of prior research and concentrate on the impacts of AIM on diverse subcategories of nephropathies. We discovered that AIM is closely associated with renal diseases by playing a positive or negative role in the onset, progression, or cure of kidney disease. AIM is thus a potentially effective therapeutic target for kidney diseases.

Comprehensive Factors for Predicting the Complications of DiabetesMellitus: A Systematic Review

BACKGROUND

This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases are considered in the study.

METHOD

The features used to predict the complications are identified and categorised by scrutinising the standards of electronic health records.

RESULT

Overall, 102 research articles have been reviewed, resulting in 59 frequent features being identified. Nineteen attributes are recognised as a standard in all four considered complications, which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP, HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics models to predict the complications of diabetes mellitus is a vital and contemporary requirement. A widely accepted feature set is beneficial for benchmarking the risk factors of complications of diabetes.

CONCLUSION

This study is a thorough literature review to provide a clear state of the art for academicians, clinicians, and other stakeholders regarding the risk factors and their importance.

Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy

Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN. Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN. Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples. Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue.

Canagliflozin Attenuates PromarkerD Diabetic Kidney Disease Risk Prediction Scores

PromarkerD is a biomarker-based blood test that predicts kidney function decline in people with type 2 diabetes (T2D) who may otherwise be missed by current standard of care tests. This study examined the association between canagliflozin and change in PromarkerD score (Δ score) over a three-year period in T2D participants in the CANagliflozin cardioVascular Assessment Study (CANVAS). PromarkerD scores were measured at baseline and Year 3 in 2008 participants with preserved kidney function (baseline eGFR ≥60 mL/min/1.73 m2). Generalized estimating equations were used to assess the effect of canagliflozin versus placebo on PromarkerD scores. At baseline, the participants (mean age 62 years, 32% females) had a median PromarkerD score of 3.9%, with 67% of participants categorized as low risk, 14% as moderate risk, and 19% as high risk for kidney function decline. After accounting for the known acute drop in eGFR following canagliflozin initiation, there was a significant treatment-by-time interaction (p < 0.001), whereby participants on canagliflozin had decreased mean PromarkerD scores from baseline to Year 3 (Δ score: -1.0% [95% CI: -1.9%, -0.1%]; p = 0.039), while the scores of those on placebo increased over the three-year period (Δ score: 6.4% [4.9%, 7.8%]; p < 0.001). When stratified into PromarkerD risk categories, participants with high risk scores at baseline who were randomized to canagliflozin had significantly lower scores at Year 3 (Δ score: -5.6% [-8.6%, -2.5%]; p < 0.001), while those on placebo retained high scores (Δ score: 4.5% [0.3%, 8.8%]; p = 0.035). This post hoc analysis of data from CANVAS showed that canagliflozin significantly lowered PromarkerD risk scores, with the effect greatest in those T2D participants who were classified at study entry as at high risk of a subsequent decline in kidney function.

Circulating thrombospondin-2 level for identifying individuals with rapidly declining kidney function trajectory in type 2 diabetes: a prospective study of the Hong Kong West Diabetes Registry

BACKGROUND

Thrombospondin-2 (TSP2) is a matricellular protein with tissue expression induced by hyperglycaemia. TSP2 has been implicated in non-diabetic renal injury in preclinical studies and high circulating levels were associated with worse kidney function in cross-sectional clinical studies. Therefore, we investigated the prospective associations of circulating TSP2 level with kidney function decline and the trajectories of estimated glomerular filtration rate (eGFR) in type 2 diabetes.

METHODS

Baseline serum TSP2 level was measured in 5471 patients with type 2 diabetes to evaluate its association with incident eGFR decline, defined as ≥ 40% sustained eGFR decline, using multivariable Cox regression analysis. Among participants with relatively preserved kidney function (Baseline eGFR ≥ 60 ml/min/1.73m2), joint latent class modelling was employed to identify three different eGFR trajectories. Their associations with baseline serum TSP2 was evaluated using multinomial logistic regression analysis. The predictive performance of serum TSP2 level was examined using time-dependent c-statistics and calibration statistics.

RESULTS

Over a median follow-up of 8.8 years, 1083 patients (19.8%) developed eGFR decline. Baseline serum TSP2 level was independently associated with incident eGFR decline (HR 1.21, 95%CI 1.07-1.37, P = 0.002). With internal validation, incorporating serum TSP2 to a model of clinical risk factors including albuminuria led to significant improvement in c-statistics from 83.9 to 84.4 (P < 0.001). Among patients with eGFR ≥ 60 ml/min/1.73m2, baseline serum TSP2 level was independently associated with a rapidly declining eGFR trajectory (HR 1.63, 95%CI 1.26-2.10, P < 0.001).

CONCLUSION

Serum TSP2 level was independently associated with incident eGFR decline, particularly a rapidly declining trajectory, in type 2 diabetes.

Global-feature of autoimmune glomerulonephritis using proteomic analysis of laser capture microdissected glomeruli

Background

IgA nephropathy (IgAN), (LN), membranous nephropathy (MN), and minimal change nephropathy (MCN) are all belonged to autoimmune glomerulonephritis. This study aimed to identify the specific proteomic characteristics of the four GNs diseases in order to provide frameworks for developing the appropriate drug for patients diagnosed with GNs disease.

Methods

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was utilized to investigate proteomic features of glomerular tissues obtained by laser capture microdissection (LCM). 8 normal control cases, 11 IgAN cases, 19 LN cases, 5 MN cases, and 3 MCN cases in this study were selected for bioinformatics analyses.

Results

The shared overlapping proteins among the top 100 DEPs of each GNs type were mostly downregulated, in which only FLII was significantly downregulated in the four GNs diseases. A2M was significantly upregulated in MN, IgAN, and LN subgroups. The pathway of complement and coagulation cascades was notably activated with NES value ranging 2.77 to 3.39 among MCN, MN, IgAN, and LN diseases, but the pattern of protein expression level were significantly different. In LN patients, the increased activity of complement and coagulation cascades was contributed by the high expression of multiple complements (C1QB, C3, C4A, C4B, C6, C8B, C8G, C9). Meanwhile, both C1QC and C4B were remarkably upregulated in MN patients. On the contrary, complement-regulating proteins (CD59) was substantially decreased in MCN and IgAN subgroup.

Conclusions

The integrative proteomics analysis of the four GNs diseases provide insights into unique characteristics of GNs diseases and further serve as frameworks for precision medicine diagnosis and provide novel targets for drug development.

Single-cell RNA sequencing highlights the roles of C1QB and NKG7 in the pancreatic islet immune microenvironment in type 1 diabetes mellitus

Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Using this technology, this study focuses on the mechanism of C1QB and NKG7 in pancreatic islet immune microenvironment in type 1 diabetes mellitus (T1DM). T1DM-related scRNA-seq data were downloaded from GEO database, followed by batch effect removal, cluster analysis, cell annotation and enrichment analysis. Thereafter, T1DM-related Bulk RNA-seq data were downloaded from GEO database. The infiltrating immune cell abundance was estimated and its correlation with the expression of immune cell marker genes was determined. Functional assays were performed in a constructed rat model of T1DM and cultured monocytes and lymphocytes for further validation. A large number of highly variable genes were found in pancreatic islet samples in T1DM. T1DM islet-derived cells may consist of 14 cell types. Macrophages and T lymphocytes were the major cells in pancreatic islet immune microenvironment. C1QB and NKG7 may be the key genes affecting macrophages and T lymphocytes, respectively. Silencing C1QB inhibited the differentiation of monocytes into macrophages and reduced the number of macrophages. Silencing NKG7 prevented T lymphocyte activation and proliferation. In vivo data confirmed that silencing C1QB and NKG7 reduced the number of macrophages and T lymphocytes in the pancreatic islet of T1DM rats, respectively, and alleviated pancreatic islet β-cell damage. Overall, C1QB and NKG7 can increase the number of macrophages and T lymphocytes, respectively, causing pancreatic islet β-cell damage and promoting T1DM in rats.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Apolipoprotein A-IV reduced metabolic inflammation in white adipose tissue by inhibiting IKK and JNK signaling in adipocytes

Apolipoprotein A-IV (ApoA-IV) plays a role in satiation and serum lipid transport. In diet-induced obesity (DIO) C57BL/6J mice, ApoA-IV deficiency induced in ApoA-IV-/-knock-out (KO mice) resulted in increased bodyweight, insulin resistance (IR) and plasma free fatty acid (FFA), which was partially reversed by stable ApoA-IV-green fluorescent protein (KO-A4-GFP) transfection in KO mice. DIO KO mice exhibited increased M1 macrophages in epididymal white adipose tissue (eWAT) as well as in the blood. Based on RNA-sequencing analyses, cytokine-cytokine receptor interactions, T cell and B cell receptors, and especially IL-17 and TNF-α, were up-regulated in eWAT of DIO ApoA-IV KO compared with WT mice. Supplemented ApoA-IV suppressed lipopolysaccharide (LPS)-induced IKK and JNK phosphorylation in Raw264.7 macrophage cell culture assays. When the culture medium was supplemented to 3T3-L1 adipocytes they exhibited an increased sensitivity to insulin. ApoA-IV protects against obesity-associated metabolic inflammation mainly through suppression in M1 macrophages of eWAT, IL17-IKK and IL17-JNK activity.

Apolipoprotein A-IV Has Bi-Functional Actions in Alcoholic Hepatitis by Regulating Hepatocyte Injury and Immune Cell Infiltration

Alcohol abuse can lead to alcoholic hepatitis (AH), a worldwide public health issue with high morbidity and mortality. Here, we identified apolipoprotein A-IV (APOA4) as a biomarker and potential therapeutic target for AH. APOA4 expression was detected by Gene Expression Omnibus (GEO) databases, Immunohistochemistry, and qRT-PCR in AH. Bioinformatics Methods (protein-protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Set Enrichment Analysis (GSEA) were used to show down-stream gene and pathways of APOA4 in AH. AML-12 cells were used to evaluate the biological function of APOA4 using an ELISA kit (AST, ALT, and IL-1β) and flow cytometry (ROS activity). Both in vivo and in vitro, APOA4 expression was significantly elevated in the AH model induced by alcohol (ETOH). AML-12 cell damage was specifically repaired by APOA4 deficiency, while AST, ALT, and IL-1β activity that was increased by ETOH (200 µmol, 12 h) were suppressed. APOA4 inhibition increased intracellular ROS induced by ETOH, which was detected by flow cytometry. Functional and PPI network analyses showed Fcgamma receptor (FCGR) and platelet activation signaling were potential downstream pathways. We identified CIDEC as a downstream gene of APOA4. The CIDEC AUC values for the ROC curves were 0.861. At the same time, APOA4 silencing downregulated the expression of CIDEC, whereas the knockdown of CIDEC did not influence the expression of APOA4 in AML-12 cells. Collectively, APOA4 regulates CIDEC expression and immune cell infiltration and may hold great potential as a biomarker and therapeutic target for AH.

Novel biomarkers for diabetic kidney disease

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor-1 and -2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule-1, beta-2-microglobulin and neutrophil gelatinase-associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Evaluation of the clinical utility of the PromarkerD in-vitro test in predicting diabetic kidney disease and rapid renal decline through a conjoint analysis

BACKGROUND

Early identification of patients at risk of developing diabetic kidney disease or rapid renal decline is imperative for appropriate patient management, but traditional methods of predicting renal decline are limited.

OBJECTIVE

This study evaluated the impact of PromarkerD, a biomarker-based blood test predicting the risk of diabetic kidney disease (DKD) and rapid renal decline.

METHODS

Conjoint analysis clarified the importance of PromarkerD and other patient attributes to physician decisions for type 2 diabetes patients. Forty-two patient profiles were generated, with varying levels of albuminuria, estimated glomerular filtration rate (eGFR), blood pressure, hemoglobin A1c (HbA1c), age, and PromarkerD result. A web-based survey asked each physician to make monitoring/treatment decisions about eight randomly selected profiles. Data were analyzed using multivariable logit models.

RESULTS

Two hundred three primary care physicians and 197 endocrinologists completed the survey. PromarkerD result was most important for increasing the frequency of risk factor monitoring. PromarkerD was second to HbA1c in importance for deciding to prescribe sodium/glucose cotransporter-2 inhibitors (SGLT2s) with a DKD indication, second to blood pressure for increasing the dose of lisinopril, and second to eGFR for replacing ibuprofen with a non-nephrotoxic medication. Compared with no PromarkerD results, a high-risk PromarkerD result was associated with significantly higher odds of increasing monitoring frequency (odds ratio [OR]: 2.56, 95% confidence interval: 1.90-3.45), prescribing SGLT2s (OR: 1.98 [1.56-2.52]), increasing lisinopril dose (OR: 1.48 [1.17-1.87]), and replacing ibuprofen (OR: 1.78 [1.32-2.40]). A low-risk PromarkerD result was associated with significantly lower odds of increasing monitoring frequency (OR: 0.48 [0.37-0.64]), prescribing SGLT2s (OR: 0.70 [0.56-0.88]), and replacing ibuprofen (OR: 0.75 [0.57-0.99]).

CONCLUSION

PromarkerD could increase adoption of renoprotective interventions in patients at high risk for renal decline and lower the likelihood of aggressive treatment in those at low risk. Further studies are needed to assess patient outcomes with PromarkerD in real-world practice.

External validation of prognostic models for chronic kidney disease among type 2 diabetes

BACKGROUND

Various prognostic models have been derived to predict chronic kidney disease (CKD) development in type 2 diabetes (T2D). However, their generalisability and predictive performance in different populations remain largely unvalidated. This study aimed to externally validate several prognostic models of CKD in a T2D Thai cohort.

METHODS

A nationwide survey was linked with hospital databases to create a prospective cohort of patients with diabetes (n = 3416). We undertook a systematic review to identify prognostic models and traditional metrics (i.e., discrimination and calibration) to compare model performance for CKD prediction. We updated prognostic models by including additional clinical parameters to optimise model performance in the Thai setting.

RESULTS

Six relevant previously published models were identified. At baseline, C-statistics ranged from 0.585 (0.565-0.605) to 0.786 (0.765-0.806) for CKD and 0.657 (0.610-0.703) to 0.760 (0.705-0.816) for end-stage renal disease (ESRD). All original CKD models showed fair calibration with Observed/Expected (O/E) ratios ranging from 0.999 (0.975-1.024) to 1.009 (0.929-1.090). Hosmer-Lemeshow tests indicated a good fit for all models. The addition of routine clinical factors (i.e., glucose level and oral diabetes medications) enhanced model prediction by improved C-statistics of Low's of 0.114 for CKD and Elley's of 0.025 for ESRD.

CONCLUSIONS

All models showed moderate discrimination and fair calibration. Updating models to include routine clinical factors substantially enhanced their accuracy. Low's (developed in Singapore) and Elley's model (developed in New Zealand), outperformed the other models evaluated. These models can assist clinicians to improve the risk-stratification of diabetic patients for CKD and/or ESRD in the regions settings are similar to Thailand.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Insulin-Like Growth Factor Binding Proteins in Kidney Disease

The seven members of the insulin-like growth factor (IGF) binding protein family (IGFBPs) were initially considered to be the regulatory proteins of IGFs in the blood circulation, mainly as the subsequent reserve for bidirectional regulation of IGF function during environmental changes. However, in recent years, IGFBPs has been found to have many functions independent of IGFs. The role of IGFBPs in regulating transcription, inducing cell migration and apoptosis is closely related to the occurrence and development of kidney disease. IGFBP-1, IGFBP-3, IGFBP-4 are closely associated with diabetes and diabetic nephropathy. IGFBP-3, IGFBP-4, IGFBP-5, IGFBP-6 are involved in different kidney disease such as diabetes, FSGS and CKD physiological process as apoptosis proteins, IGFBP-7 has been used in clinical practice as a biomarker for early diagnosis and prognosis of AKI. This review focuses on the differential expression and pathogenesis of IGFBPs in kidney disease.

Trajectories of kidney function in diabetes: a clinicopathological update

Diabetic nephropathy has been traditionally diagnosed based on persistently high albuminuria and a subsequent decline in glomerular filtration rate (GFR), which is widely recognized as the classical phenotype of diabetic kidney disease (DKD). Several studies have emphasized that trajectories of kidney function in patients with diabetes (specifically, changes in GFR and albuminuria over time) can differ from this classical DKD phenotype. Three alternative DKD phenotypes have been reported to date and are characterized by albuminuria regression, a rapid decline in GFR, or non-proteinuric or non-albuminuric DKD. Although kidney biopsies are not typically required for the diagnosis of DKD, a few studies of biopsy samples from patients with DKD have demonstrated that changes in kidney function associate with specific histopathological findings in diabetes. In addition, various clinical and biochemical parameters are related to trajectories of GFR and albuminuria. Collectively, pathological and clinical characteristics can be used to predict trajectories of GFR and albuminuria in diabetes. Furthermore, cohort studies have suggested that the risks of kidney and cardiovascular outcomes might vary among different phenotypes of DKD. A broader understanding of the clinical course of DKD is therefore crucial to improve risk stratification and enable early interventions that prevent adverse outcomes.

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

BACKGROUND

Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models.

METHODS

Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D).

RESULTS

In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively.

CONCLUSIONS

Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018105287.

Diabetic kidney disease: new clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on "The natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function"

Aims

This joint document of the Italian Diabetes Society and the Italian Society of Nephrology reviews the natural history of diabetic kidney disease (DKD) in the light of the recent epidemiological literature and provides updated recommendations on anti-hyperglycemic treatment with non-insulin agents.

Data Synthesis

Recent epidemiological studies have disclosed a wide heterogeneity of DKD. In addition to the classical albuminuric phenotype, two new albuminuria-independent phenotypes have emerged, i.e., “nonalbuminuric renal impairment” and “progressive renal decline”, suggesting that DKD progression toward end-stage kidney disease (ESKD) may occur through two distinct pathways, albuminuric and nonalbuminuric. Several biomarkers have been associated with decline of estimated glomerular filtration rate (eGFR) independent of albuminuria and other clinical variables, thus possibly improving ESKD prediction. However, the pathogenesis and anatomical correlates of these phenotypes are still unclear. Also the management of hyperglycemia in patients with type 2 diabetes and impaired renal function has profoundly changed during the last two decades. New anti-hyperglycemic drugs, which do not cause hypoglycemia and weight gain and, in some cases, seem to provide cardiorenal protection, have become available for treatment of these individuals. In addition, the lowest eGFR safety thresholds for some of the old agents, particularly metformin and insulin secretagogues, have been reconsidered.

Conclusions

The heterogeneity in the clinical presentation and course of DKD has important implications for the diagnosis, prognosis, and possibly treatment of this complication. The therapeutic options for patients with type 2 diabetes and impaired renal function have substantially increased, thus allowing a better management of these individuals.

Assessment of biomarkers associated with rapid renal decline in the detection of retinopathy and its progression in type 2 diabetes: The Fremantle Diabetes Study Phase II

AIMS

To determine whether biomarkers for diabetic kidney disease (DKD) can be used to determine the prevalence, progression and/or incidence of diabetic retinopathy (DR) complicating type 2 diabetes.

METHODS

Proteomic biomarkers were measured in baseline fasting plasma from 958 Fremantle Diabetes Study Phase II participants whose baseline and, in those returning for follow-up (n = 764), Year 4 fundus photographs were graded for DR presence/severity. The performance of PromarkerD (three biomarkers and readily available clinical variables which identify prevalent DKD and predict incident DKD and estimated glomerular filtration rate decline ≥30% over four years) for detecting DR prevalence, progression and incidence was assessed using the area under the receiver operating curve (AUC). Logistic regression determined whether individual proteins were associated with DR outcomes after adjusting for the most parsimonious model.

RESULTS

Plasma apolipoprotein A-IV (APOA4) was independently associated with moderate non-proliferative DR at baseline (OR (95% CI): 1.64 (1.01, 2.67), P = 0.047). Model discrimination was poor for all PromarkerD predicted probabilities against all DR outcomes (AUC ≤0.681).

CONCLUSIONS

PromarkerD and its constituent biomarkers were not consistently associated with DR prevalence or temporal change. APOA4 was associated with prevalent DR, but not DR incidence or progression. Distinct pathophysiological mechanisms may underlie DKD and DR.

Novel urinary glycan profiling by lectin array serves as the biomarkers for predicting renal prognosis in patients with IgA nephropathy

In IgA nephropathy (IgAN), IgA1 molecules are characterized by galactose deficiency in O-glycans. Here, we investigated the association between urinary glycosylation profile measured by 45 lectins at baseline and renal prognosis in 142 patients with IgAN. The primary outcome was estimated glomerular filtration rate (eGFR) decline (> 4 mL/min/1.73 m²/year), or eGFR ≥ 30% decline from baseline, or initiation of renal replacement therapies within 3 years. During follow-up (3.4 years, median), 26 patients reached the renal outcome (Group P), while 116 patients were with good renal outcome (Group G). Multivariate logistic regression analyses revealed that lectin binding signals of Erythrina cristagalli lectin (ECA) (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.11–7.28) and Narcissus pseudonarcissus lectin (NPA) (OR 2.32, 95% CI 1.11–4.85) adjusted by age, sex, eGFR, and urinary protein were significantly associated with the outcome, and they recognize Gal(β1-4)GlcNAc and high-mannose including Man(α1-6)Man, respectively. The addition of two lectin-binding glycan signals to the interstitial fibrosis/tubular atrophy score further improved the model fitness (Akaike’s information criterion) and incremental predictive abilities (c-index, net reclassification improvement, and integrated discrimination improvement). Urinary N-glycan profiling by lectin array is useful in the prediction of IgAN prognosis, since ECA and NPA recognize the intermediate glycans during N-glycosylation of various glycoproteins.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Biomarkers of Inflammation and Glomerular Filtration Rate in Individuals with Recent-Onset Type 1 and Type 2 Diabetes

CONTEXT

While inflammation has been associated with kidney function in long-standing diabetes, its possible association in newly diagnosed diabetes is unknown.

OBJECTIVE

To investigate cross-sectional and prospective associations between biomarkers of inflammation and kidney function in recent-onset diabetes.

METHODS

The study included individuals with type 1 and type 2 diabetes with known diabetes duration of <1 year from the German Diabetes Study. Baseline serum concentrations of 74 biomarkers were measured using proximity extension assay technology and their associations with estimated glomerular filtration rate (eGFR) and kidney function decline over 5 years were tested using multiple linear and logistic regression analysis.

RESULTS

The cross-sectional analysis included 165 individuals with type 1 diabetes and 291 with type 2 diabetes. Baseline eGFR was higher in type 1 compared with type 2 diabetes (102 ± 15 vs 90 ± 16 mL/min/1.73 m2; P < 0.0001). After full adjustment for covariates and multiple testing, 7 biomarkers were associated with lower baseline eGFR in type 1 diabetes and 24 were associated with lower baseline eGFR in type 2 diabetes. Among these biomarkers, 6 biomarkers (CD5, CCL23, CST5, IL-10RB, PD-L1, TNFRSF9) were inversely associated with eGFR in both diabetes types. The prospective analysis did not detect associations between inflammatory biomarkers and kidney function decline. No evidence of an interaction between diabetes type and inflammatory biomarkers was found.

CONCLUSION

Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.

Identification of Proteins Associated with the Early Restoration of Insulin Sensitivity After Biliopancreatic Diversion

CONTEXT

Insulin resistance (IR) is a risk factor for type 2 diabetes, diabetic kidney disease, cardiovascular disease and nonalcoholic steatohepatitis. Biliopancreatic diversion (BPD) is the most effective form of bariatric surgery for improving insulin sensitivity.

OBJECTIVE

To identify plasma proteins correlating with the early restoration of insulin sensitivity after BPD.

DESIGN

Prospective single-center study including 20 insulin-resistant men with morbid obesity scheduled for BPD. Patient characteristics and blood samples were repeatedly collected from baseline up to 4 weeks postsurgery. IR was assessed by homeostatic model assessment for insulin resistance (HOMA-IR), Matsuda Index, and by studying metabolic profiles during meal tolerance tests. Unbiased proteomic analysis was performed to identify plasma proteins altered by BPD. Detailed plasma profiles were made on a selected set of proteins by targeted multiple reaction monitoring mass spectrometry (MRM/MS). Changes in plasma proteome were evaluated in relation to metabolic and inflammatory changes.

RESULTS

BPD resulted in improved insulin sensitivity and reduced body weight. Proteomic analysis identified 29 proteins that changed following BPD. Changes in plasma levels of afamin, apolipoprotein A-IV (ApoA4), and apolipoprotein A-II (ApoA2) correlated significantly with changes in IR.

CONCLUSION

Circulating levels of afamin, ApoA4, and ApoA2 were associated with and may contribute to the rapid improvement in insulin sensitivity after BPD.

The New and the Old: Platform Cross-Validation of Immunoaffinity MASS Spectrometry versus ELISA for PromarkerD, a Predictive Test for Diabetic Kidney Disease

PromarkerD is a proteomics derived test for predicting diabetic kidney disease that measures the concentrations of three plasma protein biomarkers, APOA4, CD5L and IBP3. Antibodies against these proteins were developed and applied to a multiplexed immunoaffinity capture mass spectrometry assay. In parallel, and facilitating current clinical laboratory workflows, a standard ELISA was also developed to measure each protein. The performance characteristics of the two technology platforms were compared using a cohort of 100 samples, with PromarkerD test scores demonstrating a high correlation (R = 0.97). These technologies illustrate the potential for large scale, high throughput clinical applications of proteomics now and into the future.

A robust multiplex immunoaffinity mass spectrometry assay (PromarkerD) for clinical prediction of diabetic kidney disease

Background

PromarkerD is a novel proteomics derived blood test for predicting diabetic kidney disease (DKD). The test is based on an algorithm that combines the measurement of three plasma protein biomarkers (CD5L, APOA4, and IBP3) with three clinical variables (age, HDL-cholesterol, and eGFR). The initial format of the assay used immunodepletion of plasma samples followed by targeted mass spectrometry (MRM-LCMS). The aim of this study was to convert the existing assay into an immunoaffinity approach compatible with higher throughput and robust clinical application.

Methods

A newly optimised immunoaffinity-based assay was developed in a 96 well format with MRM measurements made using a low-flow LCMS method. The stability, reproducibility and precision of the assay was evaluated. A direct comparison between the immunoaffinity method and the original immunodepletion method was conducted on a 100-person cohort. Subsequently, an inter-lab study was performed of the optimised immunoaffinity method in two independent laboratories.

Results

Processing of plasma samples was greatly simplified by switching to an immunoaffinity bead capture method, coupled to a faster and more robust microflow LCMS system. Processing time was reduced from seven to two days and the chromatography reduced from 90 to 8 min. Biomarker stability by temperature and time difference treatments passed acceptance criteria. Intra/Inter-day test reproducibility and precision were within 11% CV for all biomarkers. PromarkerD test results from the new immunoaffinity method demonstrated excellent correlation (R = 0.96) to the original immunodepletion method. The immunoaffinity assay was successfully transferred to a second laboratory (R = 0.98) demonstrating the robustness of the methodology and ease of method transfer.

Conclusions

An immunoaffinity capture targeted mass spectrometry assay was developed and optimised. It showed statistically comparable results to those obtained from the original immunodepletion method and was also able to provide comparable results when deployed to an independent laboratory. Taking a research grade assay and optimising to a clinical grade workflow provides insights into the future of multiplex biomarker measurement with an immunoaffinity mass spectrometry foundation. In the current format the PromarkerD immunoaffinity assay has the potential to make a significant impact on prediction of diabetic kidney disease with consequent benefit to patients.

PromarkerD Predicts Renal Function Decline in Type 2 Diabetes in the Canagliflozin Cardiovascular Assessment Study (CANVAS)

The ability of current tests to predict chronic kidney disease (CKD) complicating diabetes is limited. This study investigated the prognostic utility of a novel blood test, PromarkerD, for predicting future renal function decline in individuals with type 2 diabetes from the CANagliflozin CardioVascular Assessment Study (CANVAS). PromarkerD scores were measured at baseline in 3568 CANVAS participants (n = 1195 placebo arm, n = 2373 canagliflozin arm) and used to predict incident CKD (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m² during follow-up in those above this threshold at baseline) and eGFR decline ≥30% during the 4 years from randomization. Biomarker concentrations (apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L/AIM) and insulin-like growth factor-binding protein 3 (IGFBP3) measured by mass spectrometry were combined with clinical data (age, serum high-density lipoprotein (HDL)-cholesterol, eGFR) using a previously defined algorithm to provide PromarkerD scores categorized as low-, moderate- or high-risk. The participants (mean age 63 years, 33% females) had a median PromarkerD score of 2.9%, with 70.5% categorized as low-risk, 13.6% as moderate-risk and 15.9% as high-risk for developing incident CKD. After adjusting for treatment, baseline PromarkerD moderate-risk and high-risk scores were increasingly prognostic for incident CKD (odds ratio 5.29 and 13.52 versus low-risk, respectively; both p < 0.001). Analysis of the PromarkerD test system in CANVAS shows the test can predict clinically significant incident CKD in this multi-center clinical study but had limited utility for predicting eGFR decline ≥30%.

Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes

Background

Individuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk (APOL1-HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification.

Methods

We selected two cohorts from the Mount Sinai BioMe Biobank: T2D (n=871) and African ancestry with APOL1-HR (n=498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories.

Results

Overall, 23% of those with T2D and 18% of those with APOL1-HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1-HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P<0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%).

Conclusions

In patients with T2D or APOL1-HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models.

Design and validation of a scoring model for differential diagnosis of diabetic nephropathy and nondiabetic renal diseases in type 2 diabetic patients

BACKGROUND

We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabetic patients through a combination of clinical variables.

METHODS

A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRD patients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively.

RESULTS

On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800-0.925; Hosmer-Lemeshow [H-L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815-0.985) in the validation cohort proved the stability of the model.

CONCLUSIONS

We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabetic patients with renal impairment.

Proteomic Analysis of Human Serum from Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important public health problem in the world. The aim of our research was to identify novel potential serum biomarkers of renal injury. ELISA assay showed that cytokines and chemokines IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p70), IL-13, IL-15, IL-17, Eotaxin, FGFb, G-CSF, GM-CSF, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-1bb, RANTES, TNF-α and VEGF were significantly higher (R > 0.6, p value < 0.05) in the serum of patients with CKD compared to healthy subjects, and they were positively correlated with well-established markers (urea and creatinine). The multiple reaction monitoring (MRM) quantification method revealed that levels of HSP90B2, AAT, IGSF22, CUL5, PKCE, APOA4, APOE, APOA1, CCDC171, CCDC43, VIL1, Antigen KI-67, NKRF, APPBP2, CAPRI and most complement system proteins were increased in serum of CKD patients compared to the healthy group. Among complement system proteins, the C8G subunit was significantly decreased three-fold in patients with CKD. However, only AAT and HSP90B2 were positively correlated with well-established markers and, therefore, could be proposed as potential biomarkers for CKD.

Validation of a protein biomarker test for predicting renal decline in type 2 diabetes: The Fremantle Diabetes Study Phase II

AIMS

To validate the prognostic utility of a novel plasma biomarker panel, PromarkerD, for predicting renal decline in an independent cohort of people with type 2 diabetes.

METHODS

Models for predicting rapid estimated glomerular filtration rate (eGFR) decline defined as i) incident diabetic kidney disease (DKD), ii) eGFR decline ≥30% over four years, and iii) annual eGFR decline ≥5 mL/min/1.73 m² were applied to 447 participants from the longitudinal observational Fremantle Diabetes Study Phase II. Model performance was assessed using discrimination and calibration.

RESULTS

During 4.2 ± 0.3 years of follow-up, 5-10% of participants experienced a rapid decline in eGFR. A consensus model comprising apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L), insulin-like growth factor-binding protein 3 (IGFBP3), age, serum HDL-cholesterol and eGFR showed the best performance for predicting incident DKD (AUC = 0.88 (95% CI 0.84-0.93)); calibration Chi-squared = 5.6, P = 0.78). At the optimal score cut-off, this model provided 86% sensitivity, 78% specificity, 30% positive predictive value and 98% negative predictive value for four-year risk of developing DKD.

CONCLUSIONS

The combination of readily available clinical and laboratory features and the PromarkerD biomarkers (apoA4, CD5L, IGFBP3) proved an accurate prognostic test for future renal decline in an independent validation cohort of people with type 2 diabetes.

Diabetic kidney disease: New clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on "The natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function"

AIMS

This joint document of the Italian Diabetes Society and the Italian Society of Nephrology reviews the natural history of diabetic kidney disease (DKD) in the light of the recent epidemiological literature and provides updated recommendations on anti-hyperglycemic treatment with non-insulin agents.

DATA SYNTHESIS

Recent epidemiological studies have disclosed a wide heterogeneity of DKD. In addition to the classical albuminuric phenotype, two new albuminuria-independent phenotypes have emerged, i.e., "nonalbuminuric renal impairment" and "progressive renal decline", suggesting that DKD progression toward end-stage kidney disease (ESKD) may occur through two distinct pathways, albuminuric and nonalbuminuric. Several biomarkers have been associated with decline of estimated glomerular filtration rate (eGFR) independent of albuminuria and other clinical variables, thus possibly improving ESKD prediction. However, the pathogenesis and anatomical correlates of these phenotypes are still unclear. Also the management of hyperglycemia in patients with type 2 diabetes and impaired renal function has profoundly changed during the last two decades. New anti-hyperglycemic drugs, which do not cause hypoglycemia and weight gain and, in some cases, seem to provide cardiorenal protection, have become available for treatment of these individuals. In addition, the lowest eGFR safety thresholds for some of the old agents, particularly metformin and insulin secretagogues, have been reconsidered.

CONCLUSIONS

The heterogeneity in the clinical presentation and course of DKD has important implications for the diagnosis, prognosis, and possibly treatment of this complication. The therapeutic options for patients with type 2 diabetes and impaired renal function have substantially increased, thus allowing a better management of these individuals.

Kidney as modulator and target of "good/bad" HDL

The strong inverse relationship between low levels of high-density lipoproteins (HDLs) and atherosclerotic cardiovascular disease (CVD) led to the designation of HDL as the "good" cholesterol. The atheroprotection is thought to reflect HDL's capacity to efflux cholesterol from macrophages, followed by interaction with other lipoproteins in the plasma, processing by the liver and excretion into bile. However, pharmacologic increases in HDL-C levels have not led to expected clinical benefits, giving rise to the concept of dysfunctional HDL, in which increases in serum HDL-C are not beneficial due to lost or altered HDL functions and transition to "bad" HDL. It is now understood that the cholesterol in HDL, measured by HDL-C, is neither a marker nor the mediator of HDL function, including cholesterol efflux capacity. It is also understood that besides cholesterol efflux, HDL functionality encompasses many other potentially beneficial functions, including antioxidant, anti-inflammatory, antithrombotic, anti-apoptotic, and vascular protective effects that may be critical protective pathways for various cells, including those in the kidney parenchyma. This review highlights advances in our understanding of the role kidneys play in HDL metabolism, including the effects on levels, composition, and functionality of HDL particles, particularly the main HDL protein, apolipoprotein AI (apoAI). We suggest that normal apoAI/HDL in the glomerular filtrate provides beneficial effects, including lymphangiogenesis, that promote resorption of renal interstitial fluid and biological particles. In contrast, dysfunctional apoAI/HDL activates detrimental pathways in tubular epithelial cells and lymphatics that lead to interstitial accumulation of fluid and harmful particles that promote progressive kidney damage.

Dysfunctional HDL and inflammation: a noxious liaison in adolescents with type 1 diabetes

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Serum Lipids and Risk of Rapid Renal Function Decline in Treated Hypertensive Adults With Normal Renal Function

BACKGROUND

We aim to evaluate the effect of different lipids parameters, including triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), the TG to HDL-C (TG:HDL-C) ratio, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C), on the risk of rapid renal function decline and examine any possible effect modifiers in general hypertensive patients with normal renal function.

METHODS

A total of 12,549 hypertensive patients with estimated glomerular filtration rate (eGFR) ≥60 ml/min/1.73 m2 in the renal sub-study of the China Stroke Primary Prevention Trial were included in the analyses. The primary outcome was rapid renal function decline, defined as an average decline in eGFR ≥ 5 ml/min/1.73 m2 per year.

RESULTS

The median treatment duration was 4.4 years. After the full adjustment for TC, TG, HDL-C, and other major covariates, a significantly higher risk of rapid renal function decline was found in participants with higher TG [≥150 vs. <150 mg/dl, 7.7% vs. 5.5%; odds ratios (OR): 1.27; 95% confidence interval (CI): 1.06-1.51], higher TG:HDL-C ratio [≥2.7 (median) vs. <2.7, 7.7% vs. 5.0%; OR: 1.39; 95% CI: 1.14-1.71), lower TC (≥200 vs. <200 mg/dl, 6.0% vs. 7.0%; OR: 0.79; 95% CI: 0.67-0.93), or lower LDL-C levels (≥130 vs. <130 mg/dl, 6.1% vs. 7.0%; OR: 0.79; 95% CI: 0.67-0.94). Moreover, the increased risk of the primary outcome associated with elevated TG was particularly evident among individuals with lower total homocysteine levels [<12.4 (median) vs. ≥ 12.4 μmol/l, P interaction = 0.036].

CONCLUSIONS

Higher TG and TG:HDL-C ratio were independent risk factors for rapid renal function decline in hypertensive adults with normal renal function.

Methylglyoxal down-regulates the expression of cell cycle associated genes and activates the p53 pathway in human umbilical vein endothelial cells

Although methylglyoxal (MGO) has emerged as key mediator of diabetic microvascular complications, the influence of MGO on the vascular transcriptome has not thoroughly been assessed. Since diabetes is associated with low grade inflammation causing sustained nuclear factor-kappa B (NF-κB) activation, the current study addressed 1) to what extent MGO changes the transcriptome of human umbilical vein endothelial cells (HUVECs) exposed to an inflammatory milieu, 2) what are the dominant pathways by which these changes occur and 3) to what extent is this affected by carnosine, a putative scavenger of MGO. Microarray analysis revealed that exposure of HUVECs to high MGO concentrations significantly changes gene expression, characterized by prominent down-regulation of cell cycle associated genes and up-regulation of heme oxygenase-1 (HO-1). KEGG-based pathway analysis identified six significantly enriched pathways of which the p53 pathway was the most affected. No significant enrichment of inflammatory pathways was found, yet, MGO did inhibit VCAM-1 expression in Western blot analysis. Carnosine significantly counteracted MGO-mediated changes in a subset of differentially expressed genes. Collectively, our results suggest that MGO initiates distinct transcriptional changes in cell cycle/apoptosis genes, which may explain MGO toxicity at high concentrations. MGO did not augment TNF-α induced inflammation.

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography

Background

Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).

Hypothesis

Clinical and biomarker data can predict AKI more accurately.

Methods

Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO).

Results

Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001).

Conclusions

We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

The role of mass spectrometry in studies of glycation processes and diabetes management

In the last decade, mass spectrometry has been widely employed in the study of diabetes. This was mainly due to the development of new, highly sensitive, and specific methods representing powerful tools to go deep into the biochemical and pathogenetic processes typical of the disease. The aim of this review is to give a panorama of the scientifically valid results obtained in this contest. The recent studies on glycation processes, in particular those devoted to the mechanism of production and to the reactivity of advanced glycation end products (AGEs, AGE peptides, glyoxal, methylglyoxal, dicarbonyl compounds) allowed to obtain a different view on short and long term complications of diabetes. These results have been employed in the research of effective markers and mass spectrometry represented a precious tool allowing the monitoring of diabetic nephropathy, cardiovascular complications, and gestational diabetes. The same approaches have been employed to monitor the non-insulinic diabetes pharmacological treatments, as well as in the discovery and characterization of antidiabetic agents from natural products. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 38:112-146, 2019.

Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases

OBJECTIVE

Use of the frailty index to measure an accumulation of deficits has been proven a valuable method for identifying elderly people at risk for increased vulnerability, disease, injury, and mortality. However, complementary molecular frailty biomarkers or ideally biomarker panels have not yet been identified. We conducted a systematic search to identify biomarker candidates for a frailty biomarker panel.

METHODS

Gene expression databases were searched (http://genomics.senescence.info/genes including GenAge, AnAge, LongevityMap, CellAge, DrugAge, Digital Aging Atlas) to identify genes regulated in aging, longevity, and age-related diseases with a focus on secreted factors or molecules detectable in body fluids as potential frailty biomarkers. Factors broadly expressed, related to several "hallmark of aging" pathways as well as used or predicted as biomarkers in other disease settings, particularly age-related pathologies, were identified. This set of biomarkers was further expanded according to the expertise and experience of the authors. In the next step, biomarkers were assigned to six "hallmark of aging" pathways, namely (1) inflammation, (2) mitochondria and apoptosis, (3) calcium homeostasis, (4) fibrosis, (5) NMJ (neuromuscular junction) and neurons, (6) cytoskeleton and hormones, or (7) other principles and an extensive literature search was performed for each candidate to explore their potential and priority as frailty biomarkers.

RESULTS

A total of 44 markers were evaluated in the seven categories listed above, and 19 were awarded a high priority score, 22 identified as medium priority and three were low priority. In each category high and medium priority markers were identified.

CONCLUSION

Biomarker panels for frailty would be of high value and better than single markers. Based on our search we would propose a core panel of frailty biomarkers consisting of (1) CXCL10 (C-X-C motif chemokine ligand 10), IL-6 (interleukin 6), CX3CL1 (C-X3-C motif chemokine ligand 1), (2) GDF15 (growth differentiation factor 15), FNDC5 (fibronectin type III domain containing 5), vimentin (VIM), (3) regucalcin (RGN/SMP30), calreticulin, (4) PLAU (plasminogen activator, urokinase), AGT (angiotensinogen), (5) BDNF (brain derived neurotrophic factor), progranulin (PGRN), (6) α-klotho (KL), FGF23 (fibroblast growth factor 23), FGF21, leptin (LEP), (7) miRNA (micro Ribonucleic acid) panel (to be further defined), AHCY (adenosylhomocysteinase) and KRT18 (keratin 18). An expanded panel would also include (1) pentraxin (PTX3), sVCAM/ICAM (soluble vascular cell adhesion molecule 1/Intercellular adhesion molecule 1), defensin α, (2) APP (amyloid beta precursor protein), LDH (lactate dehydrogenase), (3) S100B (S100 calcium binding protein B), (4) TGFβ (transforming growth factor beta), PAI-1 (plasminogen activator inhibitor 1), TGM2 (transglutaminase 2), (5) sRAGE (soluble receptor for advanced glycosylation end products), HMGB1 (high mobility group box 1), C3/C1Q (complement factor 3/1Q), ST2 (Interleukin 1 receptor like 1), agrin (AGRN), (6) IGF-1 (insulin-like growth factor 1), resistin (RETN), adiponectin (ADIPOQ), ghrelin (GHRL), growth hormone (GH), (7) microparticle panel (to be further defined), GpnmB (glycoprotein nonmetastatic melanoma protein B) and lactoferrin (LTF). We believe that these predicted panels need to be experimentally explored in animal models and frail cohorts in order to ascertain their diagnostic, prognostic and therapeutic potential.

10-Year Renal Function Trajectories in Community-Dwelling Older Adults: Exploring the Risk Factors for Different Patterns

Longitudinal changes of renal function help inform patients’ clinical courses and improve risk stratification. Rare studies address risk factors predicting changes in estimated glomerular filtration rate (eGFR) over time in older adults, particularly of Chinese ethnicity. We identified prospectively enrolled community-dwelling older adults (≥65 years) receiving annual health examinations between 2005 and 2015 with serum creatinine available continuously in a single institute, and used linear regression to derive individual’s annual eGFR changes, followed by multivariate logistic regression analyses to identify features associated with different eGFR change patterns. Among 500 elderly (71.3 ± 4.2 years), their mean annual eGFR changes were 0.84 ± 1.67 mL/min/1.73 m²/year, with 136 (27.2%) and 238 (47.6%) classified as having downward (annual eGFR change <0 mL/min/1.73 m²/year) and upward eGFR (≥1 mL/min/1.73 m²/year) trajectories, respectively. Multivariate logistic regression showed that higher age (odds ratio (OR) 1.08), worse renal function (OR 13.2), and more severe proteinuria (OR 9.86) or hematuria (OR 3.39) were predictive of a declining eGFR while greater waist circumference (OR 1.06) and higher leukocyte counts (OR 1.21) were predictive of an uprising 10-year eGFR. These findings elucidate important features associated with geriatric renal function variations, which are expected to improve their renal care.

Lower serum calcium levels are a risk factor for a decrease in eGFR in a general non-chronic kidney disease population

Association between serum calcium (Ca) levels and kidney dysfunction progression in a non-chronic kidney disease (CKD) population has not been well elucidated, especially in consideration for classical metabolic risk conditions such as hypertension, dyslipidemia, and diabetes, and those related to Ca metabolism. Among participants of the population-based Iwaki study of Japanese people, those with an estimated glomerular filtration rate (eGFR) ≧60 ml/min/1.73 m² and age ≧40 years, and who attended the study consecutively in 2014 and 2015 were enrolled (gender (M/F): 218/380; age: 58.9 ± 10.2). Regression analysis showed a significant correlation between serum Ca levels and a change in eGFR in the 1-year period (∆eGFR) after adjustment with multiple factors including those related to Ca metabolism (β = 0.184, p < 0.001). When subjects were stratified into tertiles based on their serum Ca levels (higher >9.6 mg/dL, middle 9.4–9.6 mg/dL, lower <9.4 mg/dL), lower serum Ca levels were a significant risk for a rapid decliner of eGFR designated as the lower one third of ∆eGFR (<−4.40 ml/min/1.73 m²) (odds ratio 2.41, 95% confidence interval 1.47–3.94). Lower serum Ca levels are a significant risk for rapid decrease in eGFR, independent of previously reported metabolic risk factors in this general population with non-CKD, or eGFR ≧60 ml/min/1.73 m².