An update on chronic complications of diabetes mellitus: from molecular mechanisms to therapeutic strategies with a focus on metabolic memory.

Diabetes mellitus, a chronic metabolic disease, often leads to numerous chronic complications, significantly contributing to global morbidity and mortality rates. High glucose levels trigger epigenetic modifications linked to pathophysiological processes like inflammation, immunity, oxidative stress, mitochondrial dysfunction, senescence and various kinds of cell death. Despite glycemic control, transient hyperglycemia can persistently harm organs, tissues, and cells, a latent effect termed "metabolic memory" that contributes to chronic diabetic complications. Understanding metabolic memory's mechanisms could offer a new approach to mitigating these complications. However, key molecules and networks underlying metabolic memory remain incompletely understood. This review traces the history of metabolic memory research, highlights its key features, discusses recent molecules involved in its mechanisms, and summarizes confirmed and potential therapeutic compounds. Additionally, we outline in vitro and in vivo models of metabolic memory. We hope this work will inform future research on metabolic memory's regulatory mechanisms and facilitate the development of effective therapeutic compounds to prevent diabetic complications.

The role of metabolic memory in diabetic kidney disease: identification of key genes and therapeutic targets.

Diabetic kidney disease (DKD) is characterized by complex pathogenesis and poor prognosis; therefore, an exploration of novel etiological factors may be beneficial. Despite glycemic control, the persistence of transient hyperglycemia still induces vascular complications due to metabolic memory. However, its contribution to DKD remains unclear. Using single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database, we clustered 12 cell types and employed enrichment analysis and a cell‒cell communication network. Fibrosis, a characteristic of DKD, was found to be associated with metabolic memory. To further identify genes related to metabolic memory and fibrosis in DKD, we combined the above datasets from humans with a rat renal fibrosis model and mouse models of metabolic memory. After overlapping, NDRG1, NR4A1, KCNC4 and ZFP36 were selected. Pharmacology analysis and molecular docking revealed that pioglitazone and resveratrol were possible agents affecting these hub genes. Based on the ex vivo results, NDRG1 was selected for further study. Knockdown of NDRG1 reduced TGF-ß expression in human kidney-2 cells (HK-2 cells). Compared to that in patients who had diabetes for more than 10 years but not DKD, NDRG1 expression in blood samples was upregulated in DKD patients. In summary, NDRG1 is a key gene involved in regulating fibrosis in DKD from a metabolic memory perspective. Bioinformatics analysis combined with experimental validation provided reliable evidence for identifying metabolic memory in DKD patients.

Associations between life's essential 8 and metabolic health among us adults: insights of NHANES from 2005 to 2018.

BACKGROUND: Metabolic unhealth (MUH) is closely associated with cardiovascular disease (CVD). Life's Essential 8 (LE8), a recently updated cardiovascular health (CVH) assessment, has some overlapping indicators with MUH but is more comprehensive and complicated than MUH. Given the close relationship between them, it is important to compare these two measurements. METHODS: This population-based cross-sectional survey included 20- to 80-year-old individuals from 7 National Health and Nutrition Examination Survey (NHANES) cycles between 2005 and 2018. Based on the parameters provided by the American Heart Association, the LE8 score (which ranges from 0 to 100) was used to classify CVH into three categories: low (0-49), moderate (50-79), and high (80-100). The MUH status was evaluated by blood glucose, blood pressure, and blood lipids. The associations were assessed by multivariable regression analysis, subgroup analysis, restricted cubic spline models, and sensitivity analysis. RESULTS: A total of 22,582 participants were enrolled (median of age was 45 years old), among them, 11,127 were female (weighted percentage, 49%) and 16,595 were classified as MUH (weighted percentage, 73.5%). The weighted median LE8 scores of metabolic health (MH) and MUH individuals are 73.75 and 59.38, respectively. Higher LE8 scores were linked to lower risks of MUH (odds ratio [OR] for every 10 scores increase, 0.53; 95% CI 0.51-0.55), and a nonlinear dose-response relationship was seen after the adjustment of potential confounders. This negative correlation between LE8 scores, and MUH was strengthened among elderly population. CONCLUSIONS: Higher LE8 and its subscales scores were inversely and nonlinearly linked with the lower presence of MUH. MUH is consistent with LE8 scores, which can be considered as an alternative indicator when it is difficult to collect the information of health behaviors.

Development and validation of a nomogram to estimate future risk of type 2 diabetes mellitus in adults with metabolic syndrome: prospective cohort study.

OBJECTIVES: To develop and validate the 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome. DESIGN: Retrospective cohort study of a large multicenter cohort with broad validation. SETTINGS: The derivation cohort was from 32 sites in China and the geographic validation cohort was from Henan population-based cohort study. RESULTS: 568 (17.63) and 53 (18.67%) participants diagnosed diabetes during 4-year follow-up in the developing and validation cohort, separately. Age, gender, body mass index, diastolic blood pressure, fasting plasma glucose and alanine aminotransferase were included in the final model. The area under curve for the training and external validation cohort was 0.824 (95% CI, 0.759-0.889) and 0.732 (95% CI, 0.594-0.871), respectively. Both the internal and external validation have good calibration plot. A nomogram was constructed to predict the probability of diabetes during 4-year follow-up, and on online calculator is also available for a more convenient usage ( https://lucky0708.shinyapps.io/dynnomapp/ ). CONCLUSION: We developed a simple diagnostic model to predict 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome, which is also available as web-based tools ( https://lucky0708.shinyapps.io/dynnomapp/ ).