The Relationship Between Blood Hypoxia-Inducible Factor-1α, Fetuin-A, Fibrinogen, Homocysteine, and Amputation Level

Reduced life expectancy has resulted from an increased incidence of chronic complications in patients with diabetes. The diabetic foot is one of these complications and generally presents together with diabetic neuropathy and vascular insufficiency. Hypoxia-inducible factor-1α (HIF-1α) is important in developing the adaptation response to hypoxia and facilitates healing through regulation of keratinocyte migration and epithelium restoration in wounds. Fetuin-A is a transporter protein that is synthesized in the liver and inhibits vascular and ectopic calcifications. It has been observed that altered fetuin-A is associated with peripheral artery disease through vascular calcification and is associated with inflammation and metabolic syndrome occurrence in diabetic patients. Fibrinogen is an acute-phase reactant and has a major role in homeostasis, tissue repair, and wound healing. Increased fibrinogen blood level is one of the factors that facilitates the hypercoagulability in diabetics. Homocysteine has atherogenic features and causes vascular toxicity by enhancing low-density lipoprotein oxidation. We evaluated the association of serum HIF-1α, fetuin-A, fibrinogen, and homocysteine levels with amputation in 31 patients diagnosed with diabetes mellitus. According to our evaluation, a negative correlation was determined between fetuin-A and amputation level ( P = .012, r = −0.450), which was statistically significant. Unfortunately, there was no significant correlation between HIF-1α, fibrinogen, homocysteine, and amputation level ( P > .05). As a result, it was suggested that vascular calcification due to fetuin-A deficiency may be important in the diabetic foot pathogenesis and that fetuin-A levels may be a predictor for amputation level.

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Therapeutic effects of Hypoxia-Inducible Factor-1α (HIF-1α) on bone formation around implants in diabetic mice

10.1101/392670 ◽

2018 ◽

Author(s):

Sang-Min Oh ◽

Jin-Su Shin ◽

Il-Koo Kim ◽

Jae-Seung Moon ◽

Jung-Ho Kim ◽

...

Keyword(s):

Bone Formation ◽

Rna Sequencing ◽

Normal Mouse ◽

Target Genes ◽

Hypoxia Inducible Factor ◽

Diabetic Mouse ◽

Differentially Expressed ◽

Diabetic Patients ◽

Diabetic Mice ◽

Hypoxia Inducible Factor 1Α

AbstractPatients with uncontrolled diabetes are susceptible to implant failure due to impaired bone metabolism. Hypoxia-Inducible Factor 1α (HIF-1α), a transcription factor that is up-regulated in response to reduced oxygen condition during the bone repair process after fracture or osteotomy, is known to mediate angiogenesis and osteogenesis. However, its function is inhibited under hyperglycemic conditions in diabetic patients. The aim of this study is to evaluate the effects of exogenous HIF-1α on bone formation around implants by applying HIF-1α to diabetic mice via a novel PTD-mediated DNA delivery system. Smooth surface implants (1mm in diameter; 2mm in length) were placed in the both femurs of diabetic and normal mice. HIF-1α and placebo gels were injected to implant sites of the right and left femurs, respectively: Normal mouse with HIF-1α gel (NH), Normal mouse with placebo gel (NP), Diabetic mouse with HIF-1α gel (DH), and Diabetic mouse with placebo gel (DP). RNA sequencing was performed 4 days after surgery. Based on RNA sequencing, Differentially Expressed Genes (DEGs) were identified and HIF-1α target genes were selected. Histologic and histomorphometric results were evaluated 2 weeks after the surgery. The results showed that bone-to-implant contact (BIC) and bone volume (BV) were significantly greater in the DH group than the DP group (p < 0.05). A total of 216 genes were differentially expressed in DH group compared to DP group. On the other hand, there were 95 DEGs in the case of normal mice. Twenty-one target genes of HIF-1α were identified in diabetic mice through bioinformatic analysis of DEGs. Among the target genes, NOS2, GPNMB, CCL2, CCL5, CXCL16 and TRIM63 were manually found to be associated with wound healing-related genes. In conclusion, local administration of HIF-1α via PTD may help bone formation around the implant and induce gene expression more favorable to bone formation in diabetic mice.

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Oxidized low-density lipoprotein (oxLDL) triggers hypoxia-inducible factor-1α (HIF-1α) accumulation via redox-dependent mechanisms

Blood ◽

10.1182/blood-2002-09-2711 ◽

2003 ◽

Vol 101 (12) ◽

pp. 4847-4849 ◽

Cited By ~ 82

Author(s):

Vladimir A. Shatrov ◽

Vadim V. Sumbayev ◽

Jie Zhou ◽

Bernhard Brüne

Keyword(s):

Reporter Gene ◽

Low Density Lipoprotein ◽

Hypoxia Inducible Factor ◽

Density Lipoprotein ◽

Luciferase Reporter ◽

Low Density ◽

Mrna Levels ◽

Oxidized Low Density Lipoprotein ◽

Human Macrophages ◽

Hypoxia Inducible Factor 1Α

Abstract Oxidized low-density lipoprotein (oxLDL) and macrophages play a central role in atherosclerosis. Here, we obtained evidence that oxLDL induced hypoxia-inducible factor-1α (HIF-1α) protein accumulation in human macrophages (Mono-Mac-6) under normoxia. HIF-1α accumulation was attenuated by pretreatment with the antioxidant N-acetyl-L-cysteine (NAC), the nitric oxide (NO) donor S-nitrosoglutathione (GSNO), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase inhibitors such as diphenyleniodonium (DPI) or 4-(2-aminoethyl)-benzenesulfonyl fluoride (AEBSF), thus implicating the contribution of oxLDL-generated reactive oxygen species (ROS). Whereas oxLDL did not modulate HIF-1α mRNA levels, experiments with cycloheximide pointed to a translational mechanism in oxLDL action. HIF-1–dependent luciferase reporter gene analysis underscored HIF-1 transactivation. Our results indicate that oxLDL induced HIF-1α accumulation and HIF-1–dependent reporter gene activation in human macrophages via a redox-mediated pathway. This finding may suggest a role of HIF-1 in atherosclerosis and oxLDL-induced pathogenesis.

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Low Levels of Serum Fetuin-A and Retinol-Binding Protein 4 Correlate with Lipoprotein Subfractions in Morbid Obese and Lean Non-Diabetic Subjects

Life ◽

10.3390/life11090881 ◽

2021 ◽

Vol 11 (9) ◽

pp. 881

Author(s):

Hajnalka Lőrincz ◽

Imre Csige ◽

Mariann Harangi ◽

Anita Szentpéteri ◽

Ildikó Seres ◽

...

Keyword(s):

Binding Protein ◽

Density Lipoprotein ◽

Normal Weight ◽

Retinol Binding Protein ◽

Stepwise Multiple Regression ◽

Diabetic Patients ◽

Lipoprotein Subfractions ◽

Fetuin A ◽

Retinol Binding Protein 4 ◽

Morbid Obese

Background: Fetuin-A and retinol-binding protein 4 (RBP4) are secreted as both hepatokine and adipokine. These are involved in insulin resistance, obesity-related dyslipidemia, and atherosclerosis. To date, correlations of circulating fetuin-A and RBP4 with lipoprotein subfractions as well as high-density lipoprotein (HDL)-linked proteins have not been entirely investigated in morbid obese and lean non-diabetic subjects. Methods: One-hundred obese non-diabetic patients (body mass index, BMI: 42.5 ± 8.1 kg/m2) along with 32 gender and age-matched normal weight controls (BMI: 24.5 ± 2.5 kg/m2) were enrolled in our study. Serum fetuin-A and RBP4 were measured by ELISA. Lipoprotein subfractions were distributed by Lipoprint gelelectrophoresis. Results: Serum fetuin-A and RBP4 were unexpectedly lower in obese patients (p < 0.01 and p < 0.01, respectively) compared to controls and correlated with each other (r = 0.37; p < 0.001). Fetuin-A had positive correlations with HDL-C (r = 0.22; p = 0.02), apolipoprotein AI (apoAI) (r = 0.33; p < 0.001), very-low density lipoprotein (VLDL) subfraction (r = 0.18; p = 0.05), and large HDL subfraction levels (r = 0.3; p = 0.001) but did not show correlation with carbohydrate parameters in all subjects. RBP4 correlated positively with HDL-C (r = 0.2; p = 0.025), apoAI (r = 0.23; p = 0.01), VLDL subfraction (r = 0.37; p < 0.001), intermediate HDL subfraction (r = 0.23; p = 0.01), and small HDL subfraction (r = 0.21; p = 0.02) concentrations, as well as C-peptide levels in overall participants. Backward stepwise multiple regression analysis showed that serum fetuin-A concentration is best predicted by RBP4 and large HDL subfraction. In model 2, VLDL subfraction was the independent predictor of serum RBP4 level. Conclusions: Our data may indicate a potential role of fetuin-A and RBP4 in impaired lipoprotein metabolism associated with obesity.

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Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative Predictor

Journal of Diabetes Research ◽

10.1155/2021/5521493 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Seyyed Amir Yasin Ahmadi ◽

Razieh Shirzadegan ◽

Nazanin Mousavi ◽

Ermia Farokhi ◽

Maryam Soleimaninejad ◽

...

Keyword(s):

Logistic Regression ◽

Diabetic Foot ◽

Logistic Regression Model ◽

Density Lipoprotein ◽

Foot Ulcer ◽

Diabetic Foot Ulcer ◽

Hdl Cholesterol ◽

Diabetic Patients ◽

Final Model ◽

Negative Predictor

Objectives. Although the risk factors for diabetic neuropathy and diabetic foot ulcer have been detected, there was no practical modeling for their prediction. We aimed to design a logistic regression model on an Iranian dataset to predict the probability of experiencing diabetic foot ulcers up to a considered age in diabetic patients. Methods. The present study was a statistical modeling on a previously published dataset. The covariates were sex, age, body mass index (BMI), fasting blood sugar (FBS), hemoglobin A1C (HbA1C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), insulin dependency, and statin use. The final model of logistic regression was designed through a manual stepwise method. To study the performance of the model, an area under receiver operating characteristic (AUC) curve was reported. A scoring system was defined according to the beta coefficients to be used in logistic function for calculation of the probability. Results. The pretest probability for the outcome was 30.83%. The final model consisted of age ( β 1 = 0.133 ), BMI ( β 2 = 0.194 ), FBS ( β 3 = 0.011 ), HDL ( β 4 = − 0.118 ), and insulin dependency ( β 5 = 0.986 ) ( P < 0.1 ). The performance of the model was definitely acceptable ( AUC = 0.914 ). Conclusion. This model can be used clinically for consulting the patients. The only negative predictor of the risk is HDL cholesterol. Keeping the HDL level more than 50 (mg/dl) is strongly suggested. Logistic regression modeling is a simple and practical method to be used in the clinic.

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