An Assessment of HbA1c in Diabetes Mellitus and Pre-diabetes Diagnosis: a Multi-centered Data Mining Study

Is preoperative elevated glycated hemoglobin (HbA1c) a risk factor for postoperative shoulder stiffness after posterior-superior rotator cuff repair?

Background

Postoperative shoulder stiffness (POSS) affects a large number of patients undergoing rotator cuff repair (RCR). Diabetes may increase the risk of POSS. Preoperative glycated hemoglobin (HbA1c) is a convenient measure of glucose control in this group. The aim of the present study was to determine a relationship between preoperative HbA1c and POSS in patients undergoing postero-superior RCR.

Methods

Two hundred fifty patients with full-thickness postero-superior rotator cuffs who underwent RCR were followed for 6 months. Pre- and post-operative external rotation with arm by the side at 3 and 6 months were measured. Patient demographics, tear characteristics, preoperative HbA1c level, and surgical details were recorded. Patients with subscapularis tears, concomitant instability, partial thickness tears, arthritis, and irreparable rotator cuff tears were excluded. Univariate and multivariate logistic regression were used to determine the association between patient characteristics and POSS at 6 months.

Results

At the end of 6 months, 16% (41/250) of patients had POSS. Multivariate analysis demonstrated an elevated preoperative HbA1c level was a statistically significant predictor of POSS at 6 months (odds ratio 7.04, P < .01) after posterior superior RCR. Lower preoperative external rotation (P = .02) and female sex (P < .01) were also risk factors associated with POSS. Age, hand dominance, worker's compensation claim status, etiology, and size of the tear, surgical technique, and additional treatments were not statistically significant predictors.

Conclusion

Elevated preoperative HbA1c level is associated with POSS after RCR. Measuring HbA1c preoperatively may assist clinicians to identify patients at risk of POSS. HbA1c is a modifiable parameter that could then be optimized preoperatively in order to improve outcomes.

Hyperglycemia and glucose variability are associated with worse survival in mechanically ventilated COVID-19 patients: the prospective Maastricht Intensive Care Covid Cohort

BACKGROUND

Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients.

METHODS

We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c.

RESULTS

Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20-1.06; ß 11.2 mg/dl; 95% CI 3.6-19.1; P = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37-1.33; ß 15.3; 95%CI 6.7-23.9; P = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24-1.23 mmol/l) ; (ß 13.3; 95%CI 4.3-22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25-0.98) mmol/l) (ß 6.7 (95% ci 4.5-17.6) mg/dl)).

CONCLUSIONS

Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

MicroRNA-7a-5p ameliorates diabetic peripheral neuropathy by regulating VDAC1/JNK/c-JUN pathway

AIMS

The pathogenesis of diabetic peripheral neuropathy (DPN) is complex, and its treatment is extremely challenging. MicroRNA-7a-5p (miR-7a-5p) has been widely reported to alleviate apoptosis and oxidative stress in various diseases. This study aimed to investigate the mechanism of miR-7a-5p in DPN.

METHODS

DPN cell model was constructed with high-glucose-induced RSC96 cells. Cell apoptosis and viability were detected by flow cytometry analysis and cell counting kit-8 (CCK-8) assay respectively. The apoptosis and Jun N-terminal kinase (JNK)/c-JUN signalling pathway-related proteins expression were detected by Western blotting. The intracellular calcium content and oxidative stress levels were detected by flow cytometry and reagent kits. Mitochondrial membrane potential was evaluated by tetrechloro-tetraethylbenzimidazol carbocyanine iodide (JC-1) staining. The targeting relationship between miR-7a-5p and voltage-dependent anion-selective channel protein 1 (VDAC1) was determined by RNA pull-down assay and dual-luciferase reporter gene assay. The streptozotocin (STZ) rat model was constructed to simulate DPN in vivo. The paw withdrawal mechanical threshold (PTW) was measured by Frey capillary line, and the motor nerve conduction velocity (MNCV) was measured by electromyography.

RESULTS

MiR-7a-5p expression was decreased, while VDAC1 expression was increased in HG-induced RSC96 cells and STZ rats. In HG-induced RSC96 cells, miR-7a-5p overexpression promoted cell proliferation, inhibited apoptosis, down-regulated calcium release, improved mitochondrial membrane potential and repressed oxidative stress response. MiR-7a-5p negatively regulated VDAC1 expression. VDAC1 knockdown improved cell proliferation activity, suppressed cell apoptosis and mitochondrial dysfunction by inhibiting JNK/c-JUN pathway activation. MiR-7a-5p overexpression raised PTW, restored MNCV and reduced oxidative stress levels and nerve cell apoptosis in STZ rats.

CONCLUSION

MiR-7a-5p overexpression ameliorated mitochondrial dysfunction and inhibited apoptosis in DPN by regulating VDAC1/JNK/c-JUN pathway.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes

The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.

Ezetimibe attenuates experimental diabetes and renal pathologies via targeting the advanced glycation, oxidative stress and AGE-RAGE signalling in rats

The current in-vivo study was premeditated to uncover the protective role of ezetimibe (EZ) against advanced glycation endproducts (AGEs)-related pathologies in experimental diabetes. Our results showed that EZ markedly improved the altered biochemical markers of diabetes mellitus (DM) (FBG, HbA1c, insulin, microalbumin, and creatinine) and cardiovascular disease (in-vivo lipid/lipoprotein level and hepatic HMG-CoA reductase activity) along with diminished plasma carboxymethyl-lysine (CML) and renal fluorescent AGEs level. Gene expression study revealed that EZ significantly down-regulated the renal AGEs-receptor (RAGE), nuclear factor-κB (NFκB-2), transforming growth factor-β (TGF-β1), and matrix metalloproteinase-2 (MMP-2) mRNA expression, however, the neuropilin-1 (NRP-1) mRNA expression was up-regulated. In addition, EZ also maintained the redox status via decreasing the lipid peroxidation and protein-bound carbonyl content (CC) and increasing the activity of high-density lipoprotein (HDL)-associated-paraoxonase-1 (PON-1) and renal antioxidant enzymes as well as also protected renal histopathological features. We conclude that EZ exhibits antidiabetic and reno-protective properties in diabetic rats.