Association between serum uric acid and large-nerve fiber dysfunction in type 2 diabetes: a cross-sectional study

Increased Systemic Immune-Inflammation Index Was Associated with Type 2 Diabetic Peripheral Neuropathy: A Cross-Sectional Study in the Chinese Population

Background

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been demonstrated to be associated with type 2 diabetes mellitus (T2DM) and its vascular complications, however, the relation between SII and diabetic peripheral neuropathy (DPN) has been never reported. We aimed to explore whether SII is associated with DPN in Chinese population.

Methods

A cross-sectional study was conducted among 1460 hospitalized patients with T2DM. SII was calculated as the platelet count × neutrophil count/lymphocyte count, and its possible association with DPN was investigated by correlation and multivariate logistic regression analysis, and subgroup analyses.

Results

Patients with higher SII quartiles had higher vibration perception threshold and prevalence of DPN (all P<0.01), and SII was independently positively associated with the prevalence of DPN (P<0.01). Multivariate logistic regression analysis showed that the risk of prevalence of DPN increased progressively across SII quartiles (P for trend <0.01), and participants in the highest quartile of SII was at a significantly increased risk of prevalent DPN compared to those in the lowest quartile after adjustment for potential confounding factors (odds rate: 1.211, 95% confidence intervals 1.045-1.404, P<0.05). Stratified analysis revealed positive associations of SII quartiles with risk of prevalent DPN only in men, people less than 65 years old, with body mass index <24 kg/m2, duration of diabetes >5 years, hypertension, dyslipidaemia, poor glycaemic control, and estimated glomerular filtration rate <90 mL/min/1.73 m2 (P for trend <0.01 or P for trend <0.05). The receiver operating characteristic curve analysis revealed that the optimal cut-off point of SII for predicting DPN was 617.67 in patients with T2DM, with a sensitivity of 45.3% and a specificity of 73%.

Conclusion

The present study showed that higher SII is independently associated with increased risk of DPN, and SII might serve as a new risk biomarker for DPN in Chinese population.

Uric acid en route to gout

Gout and hyperuricemia (HU) have generated immense attention due to increased prevalence. Gout is a multifactorial metabolic and inflammatory disease that occurs when increased uric acid (UA) induce HU resulting in monosodium urate (MSU) crystal deposition in joints. However, gout pathogenesis does not always involve these events and HU does not always cause a gout flare. Treatment with UA-lowering therapeutics may not prevent or reduce the incidence of gout flare or gout-associated comorbidities. UA exhibits both pro- and anti-inflammation functions in gout pathogenesis. HU and gout share mechanistic and metabolic connections at a systematic level, as shown by studies on associated comorbidities. Recent studies on the interplay between UA, HU, MSU and gout as well as the development of HU and gout in association with metabolic syndromes, non-alcoholic fatty liver disease (NAFLD), and cardiovascular, renal and cerebrovascular diseases are discussed. This review examines current and potential therapeutic regimens and illuminates the journey from disrupted UA to gout.

Association of Elevated Serum Uric Acid with Nerve Conduction Function and Peripheral Neuropathy Stratified by Gender and Age in Type 2 Diabetes Patients

Background: The relationship between serum uric acid (SUA) level and diabetic peripheral neuropathy (DPN) remains controversial. We aimed to investigate the association between SUA level and DPN and evaluate the effects of SUA level on nerve conduction function via electromyography in patients with type 2 diabetes (T2DM), stratified by gender and age. Methods: This cross-sectional study included 647 inpatients with T2DM from the First Affiliated Hospital of Wenzhou Medical University between February 2017 and October 2020. The diagnosis of DPN was confirmed according to the Toronto Expert Consensus. Clinical data, SUA level, and nerve conduction parameters were obtained from electronic medical records. Results: A total of 647 patients with T2DM were included, and 471 patients were diagnosed with DPN. The level of SUA was higher in the DPN group than in the Non-DPN group (330.58 ± 99.67 vs. 309.16 ± 87.04, p < 0.05). After adjustment, a higher SUA level was associated with the presence of DPN [odds ratio (OR) 1.003, 95% confidence interval (CI), 1.001−1.005; p = 0.017]. The area under the curve for the prediction of DPN was 0.558 (95% CI, 0.509−0.608; p = 0.022), and the optimized cut-off of SUA level was 297.5 µmol/L. The SUA > 297.5 µmol/L level was independently associated with DPN in the male subgroup (OR 2.507, 95% CI, 1.405−4.473; p = 0.002) rather than in the female subgroup. Besides, SUA > 297.5 µmol/L was independently associated with DPN in the younger subgroup (age < 65 years) (OR 2.070, 95% CI, 1.278−3.352; p = 0.003) rather than in the older subgroup. In multiple linear regression analysis, SUA was significantly correlated with certain nerve conduction study parameters in the all patients group, and was also observed in the male and younger subgroups. Conclusions: Elevated SUA was independently associated with poorer nerve conduction functions, and hyperuricemia was also significantly associated with a higher risk of developing DPN in T2DM patients, especially in male and younger patients.

A Nomogram for Predicting the Possibility of Peripheral Neuropathy in Patients with Type 2 Diabetes Mellitus

Background and Purpose: Diabetic peripheral neuropathy (DPN) leads to ulceration, noninvasive amputation, and long-term disability. This study aimed to develop and validate a nomogram for forecasting the probability of DPN in type 2 diabetes mellitus patients. Methods: From February 2017 to May 2021, 778 patients with type 2 diabetes mellitus were included in this study. We confirmed the diagnosis of DPN according to the Toronto Expert Consensus. Patients were randomly divided into a training cohort (n = 519) and a validation cohort (n = 259). In the training cohort, univariate and multivariate logistic regression analyses were performed, and a simple nomogram was built using the stepwise method. The receiver operating characteristic (ROC), calibration curve, and decision curve analysis were computed in order to validate the discrimination and clinical value of the nomogram model. Results: About 65.7% and 72.2% of patients were diagnosed with DPN in the training and validation cohorts. We developed a novel nomogram to predict the probability of DPN based on the parameters of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine. The areas under the curves (AUCs) of the nomogram model were 0.763 in the training cohort and 0.755 in the validation cohort. The calibration plots revealed well-fitted accuracy between the predicted and actual probability in the training and validation cohorts. Decision curve analysis confirmed the clinical value of the nomogram. In subgroup analysis, the predictive ability of the nomogram model was strong. Conclusions: The nomogram of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine may assist clinicians with the early identification of DPN in patients with type 2 diabetes mellitus.

Prevalence and Risk Factors for Peripheral Neuropathy in Chinese Patients With Gout

Objective

Peripheral neuropathies (PNs) are a group of disorders that affect the peripheral nervous system. PN in gout has been scarcely described. This study is conducted to determine the prevalence and related risk factors of PN, as assessed by vibration perception threshold (VPT) in patients with gout.

Methods

A total of 442 patients were included in the cross-sectional study. The VPT values were measured by using the Biothesiometer sensory quantitative tester on each patient. The VPT value of either limb higher than 15 V was considered abnormal and is considered to have PN. The univariate and multivariate logistic regression models were used to identify risk factors for PN in patients with gout.

Results

We included 442 patients with gout, 97.5% men, 26.9% tophaceous gout, mean age 45.5 ± 15.2 years, and 7.4 ± 4.6 years of disease duration. The prevalence of PN in patients with gout was 11.1%. Patients in the abnormal VPT group were older, had a longer gout duration, and had significantly higher levels of waist circumference, waist-to-hip ratio, systolic blood pressure (SBP), and erythrocyte sedimentation rate (ESR), as compared to patients in the normal vibration group (P < 0.05). The univariate logistic regression analysis demonstrated that there was a significant association between abnormal VPT and age, duration of gout, SBP, C-reactive protein, ESR, presence of tophi, and estimated glomerular filtration rate (eGFR) in all the subjects (P < 0.05). The multivariate logistic regression analysis indicated that age (odds ratio, 1.094) and presence of tophi (odds ratio, 1.048) were independent risk factors for PN in patients with gout.

Conclusion

The abnormal VPT was significantly correlated with age and presence of tophi in patients with gout and the VPT level may be useful as a screening tool for assessment of PN in gout.

Why Does Hyperuricemia Not Necessarily Induce Gout?

Hyperuricemia is a risk factor for gout. It has been well observed that a large proportion of individuals with hyperuricemia have never had a gout flare(s), while some patients with gout can have a normuricemia. This raises a puzzle of the real role of serum uric acid (SUA) in the occurrence of gout flares. As the molecule of uric acid has its dual effects in vivo with antioxidant properties as well as being an inflammatory promoter, it has been placed in a delicate position in balancing metabolisms. Gout seems to be a multifactorial metabolic disease and its pathogenesis should not rely solely on hyperuricemia or monosodium urate (MSU) crystals. This critical review aims to unfold the mechanisms of the SUA role participating in gout development. It also discusses some key elements which are prerequisites for the formation of gout in association with the current therapeutic regime. The compilation should be helpful in precisely fighting for a cure of gout clinically and pharmaceutically.

Executive Function and Diabetes: A Clinical Neuropsychology Perspective

Objective

Diabetes is a global public health concern. Management of diabetes depends on successful implementation of strategies to alleviate decline in executive functions (EFs), a characteristic of diabetes progression. In this review, we describe recent research on the relationship between diabetes and EF, summarize the existing evidence, and put forward future research directions and applications.

Methods

Herein, we provide an overview of recent studies, to elucidate the relationship between DM and EF. We identified new screening objectives, management tools, and intervention targets for diabetes management. We also discuss the implications for clinical practice.

Results

In both types 1 and 2 diabetes mellitus (DM), hyperglycemia substantially impairs EF in people of all age groups and ethnicities. Hypoglycemia can similarly impair EF. Interestingly, a decline in EF contributes to DM progression. Glucose dysregulation and EF decline exacerbate each other in a vicious cycle: poor blood glucose control, impaired EF, diabetes management task failure, then back to poor blood glucose control. Many pathophysiological indexes (e.g., obesity, metabolic index, inflammatory and immune factors), neuropsychological indexes (e.g., compliance, eating habits, physical exercise, sleep, and depression), and genetic factors are changed by this pathological interaction between DM and EF. These changes can provide insight into the pathophysiological mechanisms of diabetes-related EF decline.

Conclusion

Further studies, including large-scale prospective and randomized controlled trials, are needed to elucidate the mechanism of the interaction between diabetes and EF and to develop novel strategies for breaking this cycle.

The 100 Top-Cited Systematic Reviews/Meta-Analyses on Diabetic Research

OBJECTIVE

The objective of this study was to analyze the 100 top-cited systematic reviews/meta-analyses on diabetic research.

METHODS

The Science Citation Index Expanded database was searched to identify top-cited studies on diabetic research up to March 4^th, 2020. Studies were analyzed using the following characteristics: citation number, publication year, country and institution of origin, authorship, topics, and journals.

RESULTS

The 100 top-cited diabetic systematic reviews/meta-analyses were published in 43 different journals, with Diabetes Care having the highest numbers (n = 17), followed by The Journal of the American Medical Association (n = 14) and Lancet (n = 9). The majority of studies are published in the 2000s. The number of citations ranged from 2197 to 301. The highest number of contributions was from the USA, followed by England and Australia. The leading institution was Harvard University. The hot topic was a risk factor (n = 33), followed by comorbidity (n = 27).

CONCLUSIONS

The 100 top-cited systematic reviews/meta-analyses on diabetic research identify impactful authors, journals, institutes, and countries. It will also provide the most important references to evidence-based medicine in diabetes and serve as a guide to the features of a citable paper in this field.

Retrospective Analysis of the Correlation between Uric Acid and Thyroid Hormone in People with Normal Thyroid Function

OBJECTIVE

This study adopts the method of retrospective analysis to collect general information and laboratory results of physical examination population, hoping to clarify the correlation between uric acid and thyroid hormone.

METHODS

The subjects of the study were healthy subjects who underwent physical examination at the Sir Run Run Shaw Hospital affiliated to the Medical College of Zhejiang University from January 2016 to December 2018. Demographic information and medical history of all subjects were recorded through an electronic health system. Serum uric acid (SUA) was grouped by quartiles. Statistical analyses were performed with R version 3.5.1.

RESULTS

A total of 48,526 subjects were included in the analysis. Gender ratio, age, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, FBG, HbA1c, TG, HDL-C, ALT, AST, FT3, FT4, and TSH were significantly different among the uric acid groups. The regression coefficients of SUA in the TSH, FT3, and FT4 regression models were B = 1.000 (95% CI 1.000-1.000, p = 0.009), B = 0.999 (95% CI 0.999-0.999, p < 0.001), and B = 1.001 (95% CI 1.001-1.001,  p < 0.001), respectively. There was a significant dose-dependent relationship between FT4, FT3, and SUA gradient.

CONCLUSIONS

Under normal thyroid function, there were significant differences in TSH, FT3, and FT4 between groups with different uric acid levels. Uric acid levels were linearly correlated with FT3 and FT4, but not with TSH.