Roles of plasma leptin and resistin in novel subgroups of type 2 diabetes driven by cluster analysis

Exploring the interplay between adipokine-mediated celastrol target genes and T cells in diabetic nephropathy: a mendelian randomization-based causal inference

BACKGROUND

Diabetic nephropathy (DN) is influenced by dysregulated adipokines, which play a key role in inflammation, immune responses, and lipid metabolism. However, the precise molecular mechanisms linking adipokine dysregulation, immune cell infiltration, and metabolic reprogramming in DN remain poorly understood. Celastrol, a bioactive lipid regulator, has been shown to mitigate renal immune-inflammatory damage by inhibiting the PI3K/Akt/NF-κB signaling pathway. Yet, its specific impact on adipokine-mediated immune responses and lipid metabolism in DN is unclear. This study aims to elucidate the interplay between adipokine-mediated target genes in DN and investigate how celastrol modulates these interactions.

METHODS

Gene expression profiles of DN patients were obtained from GEO datasets (GSE30122 and GSE30528) and analyzed for differentially expressed genes (DEGs) using the limma package. Gene set variation analysis (GSVA) was conducted to assess lipid metabolism pathways, while Mendelian randomization (MR) and Pearson correlation evaluated the association between DEGs and adipokines. Immune cell infiltration was analyzed using the IOBR R package (MCP-counter and xCell methods), followed by MR analysis of DN-related immune responses. Celastrol target genes were identified using the SEA database.

RESULTS

A total of 70 intersecting DEGs were identified. GSVA revealed that brown and beige adipocyte differentiation pathways were downregulated, while adipocyte-related pathways were upregulated in DN (p < 0.05). MR analysis demonstrated that adiponectin was negatively associated with DN (OR = 0.77, P = 0.005), whereas leptin (OR = 1.92, P = 0.016) and resistin (OR = 1.43, P < 0.001) were positively associated. Three key genes, MAGI2, FGF9, and THBS2 were linked to DN risk and T cell infiltration. THBS2 was positively correlated with T cell infiltration (OR = 0.51, P = 6.7e-06), while FGF9 (OR = -0.8, P = 2.2e-16) and MAGI2 (OR = 0.75, P = 1.3e-13) were negatively correlated. 22 celastrol target genes, including MAGI2, FGF9, and THBS2, were identified.

CONCLUSION

Our findings reveal that celastrol modulates DN progression through adipokine-immune crosstalk, with FGF9, MAGI2, and THBS2 emerging as key regulatory genes. These insights provide new avenues for biomarker discovery and therapeutic implications in the development of DN.

Biomarkers in Systemic Lupus Erythematosus along with Metabolic Syndrome

Metabolic syndrome (MetS) is a group of physiological abnormalities characterized by obesity, insulin resistance (IR), and hypertriglyceridemia, which carry the risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2D). Immune and metabolic alterations have been observed in MetS and are associated with autoimmune development. Systemic lupus erythematosus (SLE) is an autoimmune disease caused by a complex interaction of environmental, hormonal, and genetic factors and hyperactivation of immune cells. Patients with SLE have a high prevalence of MetS, in which elevated CVD is observed. Among the efforts of multidisciplinary healthcare teams to make an early diagnosis, a wide variety of factors have been considered and associated with the generation of biomarkers. This review aimed to elucidate some primary biomarkers and propose a set of assessments to improve the projection of the diagnosis and evolution of patients. These biomarkers include metabolic profiles, cytokines, cardiovascular tests, and microRNAs (miRs), which have been observed to be dysregulated in these patients and associated with outcomes.

Diabetes Mellitus and Thyroid Cancers: Risky Correlation, Underlying Mechanisms and Clinical Prevention

The incidences of thyroid cancer and diabetes are rapidly increasing worldwide. The relationship between thyroid cancer and diabetes is a popular topic in medicine. Increasing evidence has shown that diabetes increases the risk of thyroid cancer to a certain extent. This mechanism may be related to genetic factors, abnormal thyroid-stimulating hormone secretion, oxidative stress injury, hyperinsulinemia, elevated insulin-like growth factor-1 levels, abnormal secretion of adipocytokines, and increased secretion of inflammatory factors and chemokines. This article reviews the latest research progress on the relationship between thyroid cancer and diabetes, including the association between diabetes and the risk of developing thyroid cancer, its underlying mechanisms, and potential anti-thyroid cancer effects of hypoglycemic drugs. It providing novel strategies for the prevention, treatment, and improving the prognosis of thyroid cancer.

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial

AIMS/HYPOTHESIS

Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673).

METHODS

Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection.

RESULTS

A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10^-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID.

CONCLUSIONS/INTERPRETATION

We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping.

TRIAL REGISTRATION

ORIGIN trial, ClinicalTrials.gov NCT00069784.