Kai-Pi Cheng

Hepassocin, also known as fibrinogen-like protein 1 (FGL-1), is a liver-derived secretory protein initially identified as a mitogenic factor involved in hepatocyte proliferation and liver regeneration. Increasing evidence has subsequently suggested that FGL-1 functions as a hepatokine linking hepatic metabolic stress to systemic metabolic regulation. Experimental and clinical studies have demonstrated that circulating FGL-1 levels are associated with obesity, insulin resistance, metabolic dysfunction-associated steatotic liver disease (MASLD), and type 2 diabetes mellitus (T2DM). Mechanistically, FGL-1 appears to contribute to metabolic dysfunction by impairing insulin signaling and promoting hepatic lipid accumulation, although its precise molecular targets remain incompletely defined. In addition to its metabolic roles, FGL-1 has been identified as a major ligand of lymphocyte activation gene-3 (LAG-3), implicating it in immune modulation and tumor progression, particularly in hepatocellular carcinoma (HCC). However, most available human data are observational, and conflicting findings from experimental models suggest that FGL-1 may function as a context-dependent mediator rather than a purely pathogenic factor. Given the expanding but sometimes conflicting evidence, a comprehensive understanding of FGL-1 biology may provide important insights into the complex interactions among hepatic stress responses, metabolic dysfunction, and immune regulation. This review therefore examines the current evidence regarding the physiological and pathological roles of FGL-1 and highlights key unresolved questions that may influence future translational research and therapeutic development.

Glucagon-like peptide 1 receptor agonist (GLP-1 RA) is a potent antidiabetic agent with cardiorenal and weight-losing benefits in patients with type 2 diabetes (T2D). The combination of GLP-1 RA with basal insulin has been suggested in several clinical studies as a useful treatment for intensifying insulin therapy in T2D. However, there has been no real-world evidence study comparing the glycemic effects of GLP-1 RAs add-on to background treatment with and without insulin. A retrospective study was performed in 358 patients with T2D who initiated liraglutide or dulaglutide. Among them, 147 patients were prior and concurrent insulin users, and 211 patients were non-insulin users. After 12 months of GLP-1 RA treatment, the changes in hemoglobin A1c (HbA1C) and body weight were evaluated. The effectiveness of GLP-1 RAs on HbA1C reduction was greater in insulin users than non-insulin users at 12 months (−1.17% vs. −0.76%; p = 0.018). There was no significant difference in body weight change between insulin users and non-insulin users at 12 months (−1.42 kg vs. −1.87 kg; p = 0.287). The proportion of responders (decrease of HbA1C > 1%) in insulin users was much higher than that in non-insulin users (48% vs. 37 %; p = 0.04). In insulin users, those who had increased insulin dosage at 12 months had significantly less HbA1C reduction than that of non-increased patients (−0.62% vs. −1.57%; p = 0.001). GLP-1 RAs provide superior glucose-lowering effects in insulin-treated patients compared with non-insulin-treated patients with T2D without significant differences in body weight decrease.

Background: People with type 2 diabetes mellitus (T2DM) tend to be vulnerable to geriatric syndromes such as sarcopenia and frailty. Reduced physical activity also accompanies sarcopenia and frailty, which is generally typical of patients with T2DM. However, a comprehensive assessment of physical fitness in patients with T2DM has seldom been carried out and verified. This study is thus an attempt to determine the associations among sarcopenia, frailty, and the SFT in diabetic patients and non-diabetic controls to provide a more comprehensive understanding of such associations in future evaluations of T2DM in older individuals. Methods: Sarcopenia, frailty, and the senior fitness test (SFT) were compared between 78 older men with T2DM (66.5 ± 9.0 years) and 48 age-matched normoglycemic controls (65.8 ± 5.3 years) in this case–control study. The skeletal muscle index (SMI), grip strength, and 4-m walk test were employed to assess for sarcopenia. Frailty was evaluated using the Study of Osteoporotic Fractures index (SOF). The SFT comprises five components, including body composition, muscle strength, flexibility, balance, and aerobic endurance. Results: The risk level of sarcopenia was significantly higher (p < 0.05) in the T2DM group as compared to the control group. No significant difference between-group differences were found in SMI and grip strength in the T2DM and control groups. However, the T2DM group showed a significant decrease in gait speed (p < 0.01) in comparison with the control group, as well as significant increases in frailty (p < 0.01) and depression (p < 0.05). With respect to the SFT, obvious elevation in BMI, significant declines in extremity muscle strength (elbow extensor, knee flexor, hip abductor, hip flexor, sit to stand), static/dynamic balance (single leg stand: p < 0.05; up-and-go: p < 0.01) and aerobic endurance (2-min step: p < 0.01; 6-min walk: p < 0.01) were found in the T2DM group. Furthermore, the SOF (OR = 2.638, 95% CI = 1.333–5.221), BMI (OR = 1.193, 95% CI = 1.041–1.368) and up-and-go (OR = 2.089, 95% CI = 1.400–3.117) were found to be positively and significantly associated with T2DM. Conclusions: The findings of this study indicated the importance of countering frailty and maintaining physical fitness, especially dynamic balance, during the early physical deterioration taking place in diabetic patients.

Although it was known that obesity is an independent risk factor for metabolic disorders including diabetes, the factors that link these diseases were obscure. The Hedgehog-interacting protein (Hhip) is a negative regulator in tissue remodeling, and inhibits the proliferation of adipocytes, and promotes their differentiation. In addition, Hhip was positively associated with diabetes. However, the relationship between Hhip and obesity in the human body remains unclear. An analysis of the relationship between Hhip and normal weight, overweight, and obesity levels. Participants receiving a physical checkup were recruited. Anthropometric and biochemical data were collected. Serum Hhip levels were determined by enzyme-linked immunosorbent assay (ELISA). Subjects were classified into normal-weight, overweight, and obese groups based on their body mass index (BMI). The association between Hhip and obesity was examined by multivariate linear regression analysis. In total, 294 subjects who were either of a normal weight (n = 166), overweight (n = 90), or obese (n = 38) were enrolled. Hhip concentrations were 6.51 ± 4.86 ng/mL, 5.79 ± 4.33 ng/mL, and 3.97 ± 3.4 ng/mL in normal-weight, overweight, and obese groups, respectively (p for trend = 0.032). Moreover, the regression analysis showed that BMI (β = −0.144, 95% confidence interval (CI) = −0.397−0.046, p = 0.013) was negatively associated with Hhip concentrations after adjusting for sex and age. Being overweight (β = −0.181, 95% CI = −3.311−0.400, p = 0.013) and obese (β = −0.311, 95% CI = −6.393−2.384, p < 0.001) were independently associated with Hhip concentrations after adjusting for sex, age, fasting plasma glucose, the insulin level, and other cardiometabolic risk factors. Our results showed that overweight and obese subjects had lower Hhip concentrations than those of normal weight. Being overweight and obese were negatively associated with Hhip concentrations. Hhip might be a link between obesity and diabetes.

Background: The prevalence of diabetes is rapidly increasing worldwide and is highly associated with the incidence of cancers. In order to prevent diabetes, early diagnosis of prediabetes is important. However, biomarkers for prediabetes diagnosis are still scarce. The hedgehog-interacting protein (Hhip) is important in embryogenesis and is known to be a biomarker of several cancers. However, Hhip levels in subjects with diabetes are still unknown. Methods: In total, 314 participants were enrolled and divided into normal glucose tolerance (NGT; n = 75), impaired fasting glucose (IFG; n = 66), impaired glucose tolerance (IGT; n = 86), and newly diagnosed diabetes (NDD; n = 87) groups. Plasma Hhip levels were determined by an ELISA. The association between the Hhip and the presence of diabetes was examined by a multivariate linear regression analysis. Results: There were significant differences in the body mass index, systolic and diastolic blood pressure, fasting plasma glucose (FPG), post-load 2-h glucose, hemoglobin A1c (A1C), C-reactive protein, total cholesterol, triglyceride, and high- and low-density lipoprotein cholesterol levels among the groups. Concentrations of the Hhip were 2.45 ± 2.12, 4.40 ± 3.22, 4.44 ± 3.64, and 6.31 ± 5.35 ng/mL in subjects in the NGT, IFG, IGT, and NDD groups, respectively. In addition, we found that A1C and FPG were independently associated with Hhip concentrations. Using NGT as a reference group, IFG, IGT, and NDD were all independently associated with Hhip concentrations. Conclusions: Hhip was positively associated with prediabetes and type 2 diabetes mellitus.

Secretogranin III (SCG3) plays a crucial role in the biogenesis of secretory granules in endocrine cells, and thus affects glucose homeostasis by regulating insulin secretion by pancreatic beta cells. Insulin resistance and compensatory hyperinsulinemia are hallmarks of metabolic syndrome (MetS). However, the role of SCG3 in MetS remains unclear. Therefore, we investigated the relationship between serum SCG3 levels and metabolic parameters in subjects with and without MetS. This was a case control study, and 295 subjects were recruited. Serum SCG3 concentrations were compared between groups. Associations between SCG3 levels and clinico-metabolic parameters were also examined. We found serum SCG3 levels were higher in the MetS group than non-MetS group (122.6 ± 79.2 vs. 90.6 ± 58.5 nmol/L, p = 0.009). Specifically, elevated SCG3 levels were found in subjects with high fasting plasma glucose (FPG) levels, central obesity, or hypertriglyceridemia. Additionally, MetS was an independent factor of serum SCG3 levels in multivariate linear regression analyses. Moreover, FPG, free fatty acids, and waist circumference were positively associated with serum SCG3 concentrations after adjusting for insulin levels, high-sensitivity C-reactive protein, and cardiovascular risk factors. In conclusion, serum SCG3 concentrations were higher in subjects with MetS and were independently associated with FPG levels.