The anti-diabetic drug, metformin, exerts its action through AMP-activated protein kinase (AMPK), and Sirtuin (Sirt1) signaling. Insulin-like growth factor (IGF)-binding protein 2 (IGFBP-2) prevents IGF-1 binding to its receptors, thereby contributing to modulate insulin sensitivity. In this study, we demonstrate that metformin upregulates Igfbp-2 expression through the AMPK-Sirt1-PPARα cascade pathway. In the liver of high fat diet, ob/ob, and db/db mice, Igfbp-2 expression was significantly decreased compared to the expression levels in the wild-type mice (p < 0.05). Upregulation of Igfbp-2 expression by metformin administration was disrupted by gene silencing of Ampk and Sirt1, and this phenomenon was not observed in Pparα-null mice. Notably, activation of IGF-1 receptor (IGF-1R)-dependent signaling by IGF-1 was inhibited by metformin. Finally, when compared to untreated type 2 diabetes patients, the metformin-treated diabetic patients showed increased IGFBP-2 levels with diminished serum IGF-1 levels. Taken together, these findings indicate that IGFBP-2 might be a new target of metformin action in diabetes and the metformin-AMPK-Sirt1-PPARα-IGFBP-2 network may provide a novel pathway that could be applied to ameliorate metabolic syndromes by controlling IGF-1 bioavailability.

Abdominal or visceral obesity is a well-known risk factor for metabolic diseases. However, whether abdominal obesity significantly affects plasma lipid profile during the development of type 2 diabetes has not been fully elucidated. We investigated the differences in plasma lipid concentrations in 63 participants categorized into six groups (middle-aged Korean men); Normal, Pre-diabetes (pre-DM), and Diabetes mellitus (DM) with or without abdominal obesity (AO or lean). The lipidomic profiles were determined by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Sphingomyelin (SM) levels in plasma were significantly higher in the pre-DM with AO than in pre-DM with lean (p = 0.021). SM concentrations correlated positively with waist-to-hip ratio (WHR) (r = 0.256, p = 0.044), cholesteryl ester (CE) (r = 0.483, p < 0.0001), ceramide (r = 0.489, p < 0.0001) and plasmanyl phosphatidylcholine (PC) (r = 0.446, p < 0.0001). The present study found that pre-diabetic patients with AO were characterized by increased plasma concentrations of SM. Plasma SM levels in individuals with AO may be an early prognostic biomarker to better predict the progression toward type 2 diabetes and metabolic syndrome.

Adpsin is an adipokine that stimulates insulin secretion from β-cells and improves glucose tolerance. Its expression has been found to be markedly reduced in obese animals. However, it remains unclear what factors lead to downregulation of adipsin in the context of obesity. Endoplasmic reticulum (ER) stress response is activated in various tissues under obesity-related conditions and can induce transcriptional reprogramming. Therefore, we aimed to investigate the relationship between adipsin expression and ER stress in adipose tissues during obesity. We observed that obese mice exhibited decreased levels of adipsin in adipose tissues and serum and increased ER stress markers in adipose tissues compared to lean mice. We also found that ER stress suppressed adipsin expression via adipocytes-intrinsic mechanisms. Moreover, the ER stress-mediated downregulation of adipsin was at least partially attributed to decreased expression of peroxisome proliferator-activated receptor γ (PPARγ), a key transcription factor in the regulation of adipocyte function. Finally, treatment with chemical chaperones recovered the ER stress-mediated downregulation of adipsin and PPARγ in vivo and in vitro. Our findings suggest that activated ER stress in adipose tissues is an important cause of the suppression of adipsin expression in the context of obesity.

Background: Type 2 diabetes mellitus (T2DM) is a progressive metabolic disease. Early detection of prediabetes is important to reduce the risk of T2DM. Some cytokines are known to be associated with T2DM. Therefore, we aimed to identify cytokines as novel biomarkers of glucose dysmetabolism. Methods: The fast stage of the study included 43 subjects (13 subjects with newly diagnosed T2DM, 13 with prediabetes. and 16 with normoglycemia) for cytokine microarray analysis. Blood samples of the subjects were assessed for 310 cytokines to identify potential indicators of prediabetes. The second stage included 142 subjects (36 subjects with T2DM, 35 with prediabetes, and 71 with normoglycemia) to validate the potential cytokines associated with prediabetes. Results: We identified 41 cytokines that differed by 1.5-fold or more in at least one out of the three comparisons (normoglycemia vs. prediabetes, normoglycemia vs. T2DM, and prediabetes vs. T2DM) among 310 cytokines. Finally, we selected protein Z (PROZ) and validated this finding to determine its association with prediabetes. Plasma PROZ levels were found to be decreased in patients with prediabetes (1,490.32 +/- 367.19 pg/mL) and T2DM (1,583.34 +/- 465.43 pg/mL) compared to those in subjects with normoglycemia (1,864.07 +/- 450.83 pg/mL) (P<0.001). There were significantly negative correlations between PROZ and fasting plasma glucose (P =0.001) and hemoglobin Alc (P=0.010). Conclusion: PROZ levels were associated with prediabetes and T2DM. We suggest that PROZ may be a promising biomarker for the early detection of prediabetes. Further large-scale studies are needed to evaluate the relationship and mechanism between PROZ and prediabetes and T2DM.

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability.