Type 2 diabetes mellitus (T2DM) and cancer share common risk factors including obesity, inflammation, hyperglycemia, and hyperinsulinemia. High insulin levels activate the PI3K/Akt/mTOR signaling pathway promoting cancer cell growth, survival, proliferation, metastasis, and anti-apoptosis. The inhibition of the PI3K/Akt/mTOR signaling pathway for cancer remains a promising therapy; however, drug resistance poses a major problem in clinical settings resulting in limited efficacy of agents; thus, combination treatments with therapeutic inhibitors may solve the resistance to such agents. Understanding the metabolic link between diabetes and cancer can assist in improving the therapeutic strategies used for the management of cancer patients with diabetes and vice versa. This review provides an overview of shared molecular mechanisms between diabetes and cancer as well as discusses established and emerging therapeutic anti-cancer agents targeting the PI3K/Akt/mTOR pathway in cancer management.

Background: Accurate assessment of drug cytotoxicity in vitro is essential for preclinical evaluation of anticancer agents. Methodological parameters such as cell density and solvent concentrations can significantly influence the reproducibility and reliability of cell-based assay results. Objective: This study aims to optimize cell seeding density and evaluate the cytotoxic effects of common solvents (DMSO and ethanol) on different cancer cell lines, complemented by in silico analysis to elucidate underlying mechanisms. Materials and Methods: Six cancer cell lines (HepG2, Huh7, HT29, SW480, MCF-7, and MDA-MB-231) were seeded at different densities to determine the optimal cell seeding number ideal for cell viability assay at 24, 48, and 72 h. The cytotoxicity of DMSO and ethanol was assessed in these cell lines using an MTT assay at multiple time points. In silico docking studies were conducted to investigate the interactions between solvents and key proteins involved in apoptosis, membrane function, and metabolism. Results: A cell density of 2000 cells per well yielded consistent linear viability across cell lines and time points. DMSO at 0.3125% showed minimal cytotoxicity across all cell lines (except MCF-7) and time points; the cytotoxic effect at higher concentrations is variable depending on cell type and exposure duration. Ethanol exhibited rapid and concentration-dependent cytotoxicity, reducing viability by more than 30% at as low as 0.3125% concentration after 24 h. Docking analyses revealed that DMSO binds specifically to apoptotic and membrane proteins, suggesting a role in inducing apoptosis. In contrast, ethanol primarily interacts with metabolic proteins, consistent with its effect on membrane disruption and rapid cell death. Conclusion: DMSO at 0.3125% is a good choice as a solvent since it has low toxicity in most tested cell lines; however, the safe concentration limit is dependent on cell type and exposure duration. Ethanol exhibited higher cytotoxicity, necessitating careful concentration management. The in silico analysis supports these findings, indicating that DMSO interacts with apoptosis-related proteins, whereas ethanol primarily affects metabolic processes. These results highlight the importance of precise cell density optimization and solvents for reliable cytotoxicity assessment in cell-based assays.

Type 2 Diabetes mellitus (T2DM) and cancer share common risk factors including obesity, in-flammation, hyperglycemia, and hyperinsulinemia. High insulin levels activate PI3K/Akt/mTOR signaling pathway promoting cancer cell growth, survival, proliferation, metastasis, and an-ti-apoptosis. The inhibition of PI3K/Akt/mTOR signaling pathway for cancer remains a promising therapy, however, the combination of therapeutic inhibitors may solve the resistance to such agents. Understanding the metabolic link between diabetes and cancer can assist in improving the therapeutic strategies used for the management of cancer patients with diabetes and vice versa. This review provides an overview of shared molecular mechanisms between diabetes and cancer as well as discusses established and emerging therapeutic anti-cancer agents targeting the PI3K/Akt/mTOR pathway in cancer management.

Plasma lipoproteins exist as several subpopulations with distinct particle number and size that are not fully reflected in the conventional lipid panel. In this study, we sought to quantify lipoprotein subpopulations in patients with type 2 diabetes mellitus (T2DM) to determine whether specific lipoprotein subpopulations are associated with insulin resistance and inflammation markers. The study included 57 patients with T2DM (age, 61.14 ± 9.99 years; HbA1c, 8.66 ± 1.60%; mean body mass index, 35.15 ± 6.65 kg/m²). Plasma lipoprotein particles number and size were determined by nuclear magnetic resonance spectroscopy. Associations of different lipoprotein subpopulations with lipoprotein insulin resistance (LPIR) score and glycoprotein acetylation (GlycA) were assessed using multi-regression analysis. In stepwise regression analysis, VLDL and HDL large particle number and size showed the strongest associations with LPIR (R² = 0.960; p = 0.0001), whereas the concentrations of the small VLDL and HDL particles were associated with GlycA (R² = 0.190; p = 0.008 and p = 0.049, respectively). In adjusted multi-regression analysis, small and large VLDL particles and all sizes of lipoproteins independently predicted LPIR, whereas only the number of small LDL particles predicted GlycA. Conventional markers HbA1c and Hs-CRP did not exhibit any significant association with lipoprotein subpopulations. Our data suggest that monitoring insulin resistance-induced changes in lipoprotein subpopulations in T2DM might help to identify novel biomarkers that can be useful for effective clinical intervention.

Background: Cardiovascular disease (CVD) is the leading cause of mortality in obese patients with type 2 diabetes mellitus (T2DM). Conventional biomarkers often fail to detect early endothelial dysfunction and oxidative stress. Haptoglobin (Hp), an acute-phase protein with antioxidant and hemoglobin-binding properties, may indicate vascular injury. While plasma Hp (pl-Hp) reflects systemic inflammation, urinary Hp (u-Hp) could signal renal and microvascular damage. We hypothesize that elevated u-Hp and altered pl-Hp levels are associated with increased oxidized LDL and may serve as sensitive indicators of early vascular injury, thereby identifying obese patients with T2DM at higher cardiovascular risk. This study aims to investigate the associations between u-Hp, pl-Hp, and oxidized LDL (ox-LDL) in obese patients with T2DM, and to evaluate the potential role of Hp as an early biomarker of cardiovascular risk in this high-risk population. Methods and Results: The study included 57 patients with T2DM (mean age 61 ± 10 years, HbA1c 8.66 ± 1.60%, and BMI 35.15 ± 6.65 kg/m²). Notably, 95% of the patients had hypertension, 82% had dyslipidemia, and 59% had an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Pl-Hp and u-Hp concentrations, as well as ox-LDL levels, were assessed using an enzyme-linked immunosorbent assay (ELISA). Correlations and multivariate regression analyses were employed to investigate the associations between Hp, ox-LDL, and clinical cardiovascular risk factors. Pl-Hp was positively correlated with ox-LDL (r = 0.358, p < 0.006) and negatively correlated with C-reactive protein (CRP) (r = −0.364, p < 0.013), while u-Hp correlated positively with HbA1C and apoB levels (r = 0.298, p < 0.030 and r = 0.310, p < 0.021, respectively). Multivariate analysis indicated that pl-Hp, but not u-Hp, was independently associated with ox-LDL (β = 0.536, p < 0.027) after adjusting for potential confounding factors, including age, gender, BMI, HbA1c, liver enzymes, hs-CRP and creatinine. The Stepwise analysis identified IL-6 as the most significant predictor of cardiovascular disease risk, suggesting its pivotal role in subclinical vascular inflammation among obese individuals with T2DM. Furthermore, the significant positive association between pl-Hp and ox-LDL was stronger in patients with declining renal function as expressed by the estimated glomerular filtration rate (eGFR) (eGFR < 30 mL/min/1.73 m²: β = 2.173, p < 0.031 and eGFR 30–59 mL/min/1.73 m²: β = 1.318, p < 0.002). This association also appeared in early and low-normal ranges of serum albumin: creatinine ratio (s-ACR) (s-ACR < 0.2714 mg/mmol: β = 2.304, p < 0.005 and s-ACR 0.2714–0.3649 mg/mmol: β = 1.000, p < 0.041), suggesting that pl-Hp and ox-LDL rise before overt kidney damage. Elevated IL-6 (≥32.93 pg/mL) further strengthened this link (β = 1.037, p < 0.005), highlighting the role of inflammation in amplifying oxidative stress and acute-phase responses. Conclusions: Taken together, these findings emphasize the interconnected contributions of renal impairment, inflammation, and oxidative stress to vascular injury. While these results need to be confirmed in larger prospective longitudinal studies, monitoring pl-Hp levels in conjunction with inflammatory and kidney function markers could be a sensitive and non-invasive way to identify early CVD risk in high-risk groups, such as obese patients with T2DM.

Atrial natriuretic peptide (ANP) and oxidized low-density lipoprotein (ox-LDL) play essential roles in the development and progression of vascular complications associated with type 2 diabetes mellitus (T2DM), and both are independently linked to cardiovascular diseases (CVD). However, the relationship between ANP and ox-LDL in patients with T2DM remains unclear as previous studies have primarily focused on circulating levels in various diseases. This study investigated the relationship between ANP and ox-LDL levels in obese individuals with T2DM. The cohort included 57 patients with T2DM (mean age 61.14 ± 9.99 years; HbA1c 8.66 ± 1.60%; BMI 35.15 ± 6.65 kg/m²). Notably, 95% of the patients had hypertension, 82% had dyslipidemia, 59% had an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m², 14% had coronary artery disease (CAD), and 5% had a history of stroke. Plasma concentrations of ANP and ox-LDL were measured using ELISA. Adipokines and cytokines levels were measured using the multiplex^® MAP Human Adipokine Magnetic Beads Spearman’s correlation analysis which revealed a negative correlation between ANP and ox-LDL (r = −0.446, p = 0.001) as well as with the ox-LDL/apoB ratio (r = −0.423, p = 0.001) and ox-LDL/LDLc ratio (r = −0.307, p = 0.038). Multivariable regression analysis indicated that ANP was independently associated with ox-LDL (β = −115.736, p = 0.005). Stepwise linear regression further identified TNFα, leptin, and adiponectin as the strongest predictors influencing the relationship between ANP and ox-LDL levels (β = −64.664, p = 0.0311, and r² = 0.546 for the model). However, these factors did not significantly mediate this association. This study emphasizes the need for further exploration of the complex interaction between ANP and ox-LDL in larger patient populations. This could provide valuable insights into potential therapeutic approaches for managing vascular complications in obese individuals with T2DM.