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Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics.

ABSTRACT: Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hinting at their biomarker potential. Combining Mfuzz clustering with weighted gene co-expression network analysis (WGCNA) highlighted five candidates (HMGB1, CD44, FBLN1, PTPRG, and ADAMTSL4). HMGB1 emerged as a promising biomarker, closely correlated with renal function changes. Experimental validation supported HMGB1's upregulation under high glucose conditions, reinforcing its potential as an early detection biomarker for DN. This research advances DN understanding and identifies five potential biomarkers, notably HMGB1, as a promising early monitoring target. These findings set the stage for future clinical diagnostic applications in DN.

SUBMITTER: Peng R

PROVIDER: S-EPMC10830865 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics.

Peng Rui R Zuo Siyang S Li Xia X Huang Yun Y Chen Siyu S Zou Xue X Long Hehua H Chen Min M Yang Yuan Y Yuan Huixiong H Zhao Qingqing Q Guo Bing B Liu Lirong L

iScience 20240108 2

Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hi ...[more]

PMID: 38303703

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Project description:Diabetic nephropathy (DN), as the one of most common complications of diabetes, is generally diagnosed based on a longstanding duration, albuminuria, and decreased kidney function. Some patients with the comorbidities of diabetes and other primary renal diseases have similar clinical features to DN, which is defined as non-diabetic renal disease (NDRD). It is necessary to distinguish between DN and NDRD, considering they differ in their pathological characteristics, treatment regimes, and prognosis. Renal biopsy provides a gold standard; however, it is difficult for this to be conducted in all patients. Therefore, it is necessary to discover non-invasive biomarkers that can distinguish between DN and NDRD. In this research, the urinary exosomes were isolated from the midstream morning urine based on ultracentrifugation combined with 0.22 μm membrane filtration. Data-independent acquisition-based quantitative proteomics were used to define the proteome profile of urinary exosomes from DN (n = 12) and NDRD (n = 15) patients diagnosed with renal biopsy and Type 2 diabetes mellitus (T2DM) patients without renal damage (n = 9), as well as healthy people (n = 12). In each sample, 3372 ± 722.1 proteins were identified on average. We isolated 371 urinary exosome proteins that were significantly and differentially expressed between DN and NDRD patients, and bioinformatic analysis revealed them to be mainly enriched in the immune and metabolic pathways. The use of least absolute shrinkage and selection operator (LASSO) logistic regression further identified phytanoyl-CoA dioxygenase domain containing 1 (PHYHD1) as the differential diagnostic biomarker, the efficacy of which was verified with another cohort including eight DN patients, five NDRD patients, seven T2DM patients, and nine healthy people. Additionally, a concentration above 1.203 μg/L was established for DN based on the ELISA method. Furthermore, of the 19 significantly different expressed urinary exosome proteins selected by using the protein-protein interaction network and LASSO logistic regression, 13 of them were significantly related to clinical indicators that could reflect the level of renal function and hyperglycemic management.

Project description:Aims/hypothesisNo established blood-based biomarker exists to monitor diabetic sensorimotor polyneuropathy (DSPN) and evaluate treatment response. The neurofilament light chain (NFL), a blood biomarker of neuroaxonal damage in several neurodegenerative diseases, represents a potential biomarker for DSPN. We hypothesised that higher serum NFL levels are associated with prevalent DSPN and nerve dysfunction in individuals recently diagnosed with diabetes.MethodsThis cross-sectional study included 423 adults with type 1 and type 2 diabetes and known diabetes duration of less than 1 year from the prospective observational German Diabetes Study cohort. NFL was measured in serum samples of fasting participants in a multiplex approach using proximity extension assay technology. DSPN was assessed by neurological examination, nerve conduction studies and quantitative sensory testing. Associations of serum NFL with DSPN (defined according to the Toronto Consensus criteria) were estimated using Poisson regression, while multivariable linear and quantile regression models were used to assess associations with nerve function measures. In exploratory analyses, other biomarkers in the multiplex panel were also analysed similarly to NFL.ResultsDSPN was found in 16% of the study sample. Serum NFL levels increased with age. After adjustment for age, sex, waist circumference, height, HbA1c, known diabetes duration, diabetes type, cholesterol, eGFR, hypertension, CVD, use of lipid-lowering drugs and use of non-steroidal anti-inflammatory drugs, higher serum NFL levels were associated with DSPN (RR [95% CI] per 1-normalised protein expression increase, 1.92 [1.50, 2.45], p<0.0001), slower motor (all p<0.0001) and sensory (all p≤0.03) nerve conduction velocities, lower sural sensory nerve action potential (p=0.0004) and higher thermal detection threshold to warm stimuli (p=0.023 and p=0.004 for hand and foot, respectively). There was no evidence for associations between other neurological biomarkers and DSPN or nerve function measures.Conclusions/interpretationOur findings in individuals recently diagnosed with diabetes provide new evidence associating higher serum NFL levels with DSPN and peripheral nerve dysfunction. The present study advocates NFL as a potential biomarker for DSPN.

Dataset Information

Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics.

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Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics.

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