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Diabetes mellitus (DM) and osteoporosis are two common diseases that may develop as a cause-and-effect relationship since the incidence of osteoporotic fractures is significantly increased in DM patients. However, the pathophysiology of diabetic osteoporosis is yet to be clearly understood. Iron overload has been reported to lead to bone loss and closely related to osteoporosis. In this study, we hypothesized that high glucose and high fat (HGHF) may induce osteoblastic ferroptosis for the pathogenesis of diabetic osteoporosis and explored the possible molecular mechanisms behind. Using the diabetic rat model established by HGHF feeding with a subsequent intraperitoneal injection of a single low dose of streptozocin, we found that the serum ferritin level (a biomarker for body iron store) was significantly elevated in HGHF-fed rats and the expression of SLC7A11 and GPX4 (inhibitory marker proteins for ferroptosis) was markedly attenuated in the bone tissue of the rats with diabetic bone loss as compared to the normal rats. In an osteoblast cell model, treatment of pre-osteoblastic MC3T3-E1 cells with high glucose and palmitic acid (HGPA) not only suppressed osteoblast differentiation and mineralization but also triggered ferroptosis-related osteoblastic cell death. m 6 A-seq revealed that m 6 A methylation on ASK1 was 80.9-fold higher in HGPA-treated cells. The expression of p-ASK1 and p-p38 was also significantly elevated in the HGPA-treated cells. Knockout of METTL3 (methyltransferaselike 3), one of the major m 6 A methyltransferases, in MC3T3-E1 cells not only abrogated HGPA-induced activation of ASK1-p38 signaling pathway but also attenuated the level of ferroptosis. Therefore, HGHF-induced ferroptosis in osteoblasts may be the main cause of osteoporosis in DM via activation of METTL3/ How to cite this article: Lin Y, Shen X, Ke Y, et al. Activation of osteoblast ferroptosis via the METTL3/ASK1-p38 signaling pathway in high glucose and high fat (HGHF)-induced diabetic bone loss.

Background The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20–70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n = 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 67.3% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.

Ferritin light chain (FTL) reduces the free iron concentration by forming ferritin complexes with ferritin heavy chain (FTH). Thus, FTL competes with the Fenton reaction by acting as an antioxidant. In the present study, we determined that FTL influences the lipopolysaccharide (LPS)-induced inflammatory response. FTL protein expression was regulated by LPS stimulation in RAW264.7 cells. To investigate the role of FTL in LPS-activated murine macrophages, we established stable FTL-expressing cells and used shRNA to silence FTL expression in RAW264.7 cells. Overexpression of FTL significantly decreased the LPS-induced production of tumor necrosis factor alpha (TNF-α), interleukin 1β (IL-1β), nitric oxide (NO) and prostaglandin E2 (PGE2). Additionally, overexpression of FTL decreased the LPS-induced increase of the intracellular labile iron pool (LIP) and reactive oxygen species (ROS). Moreover, FTL overexpression suppressed the LPS-induced activation of MAPKs and nuclear factor-κB (NF-κB). In contrast, knockdown of FTL by shRNA showed the reverse effects. Therefore, our results indicate that FTL plays an anti-inflammatory role in response to LPS in murine macrophages and may have therapeutic potential for treating inflammatory diseases.

Aims/Introduction To investigate the relationship between different body mass index (BMI) levels and vascular complications in type 2 diabetes mellitus patients. Materials and Methods Data were collected from 3,224 individuals with type 2 diabetes mellitus (male/female: 1,635/1,589; age 61.31 ± 11.45 years), using a retrospective case study design. The association of BMI quintiles and diabetes mellitus vascular complications was assessed using multiple logistic regression models adjusting for age, sex, diabetes duration, smoking status, drinking and other confounders, using those with the lowest quintile of BMI as the reference group. Results With increasing BMI, the detection rate of diabetic peripheral neuropathy and peripheral arterial disease initially decreased and then it increased, whereas the detection rate of diabetic kidney disease and carotid atherosclerotic plaques showed an upward trend; however, diabetic retinopathy was irregular. The odds ratios of diabetic peripheral neuropathy decreased as BMI increased from the 21st percentile to the 80th percentile initially, and increased when BMI was in >80th percentile. The same result was shown in peripheral arterial disease. BMI >80th percentile showed a 1.426‐fold risk of diabetic kidney disease and a 1.336 ‐fold risk of carotid atherosclerotic plaque. Conclusions In patients with type 2 diabetes mellitus, the relationship between different BMIs and vascular complications varies. A U‐shaped relationship was observed between BMI and diabetic peripheral neuropathy, as well as BMI and peripheral arterial disease. BMI is positively correlated with diabetic kidney disease and carotid atherosclerotic plaque; however, it is not correlated with diabetic retinopathy.