Risk Predictors of High Uric Acid Levels Among Patients with Type-2 Diabetes

Association between Polycystic Ovarian Syndrome, Impaired Kidney Function and Hyperuricaemia: A Systematic Review and Meta-analysis

Background

Polycystic ovarian syndrome (PCOS) is a gynaecological problem affecting women within reproductive age, accompanied by several metabolic anomalies, thus leading to alteration in kidney function and hyperuricaemia. Due to the high prevalence of cardiometabolic factors in PCOS, there is a need to anticipate an increased number of kidney impairments amongst these women.

Objectives

This review aims to investigate the potential link between PCOS, impaired kidney function, and elevated uric acid levels. By elucidating this association, we hope to provide clinicians with a tool to stratify the risk of kidney disease in women diagnosed with PCOS, based on readily available kidney function parameters.

Materials and Methods

The recommendations used for the analysis were outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Subsequently, eligible studies were identified using several databases (MEDLINE, ProQuest and EBSCOhost) between 1996 and 2022, with a total of 13 studies included. Serum uric acid, serum creatinine, as well as estimated glomerular filtration rate (eGFR) were evaluated as the outcome of interest. Quality assessment for cohort, case-control and cross-sectional studies was conducted utilising the Newcastle-Ottawa Scale, while Review Manager 5.4 was utilised for meta-analysis.

Results

Uric acid was significantly higher in women with PCOS (mean difference [MD] = 0.70, 95% confidence interval [CI] [0.45-0.95], P < 0.00001). Meanwhile, serum creatinine and eGFR were statistically similar in each group (MD = 0.08, 95% CI [-0.05-0.21], P = 0.22 and MD = 3.54, 95% CI [-4.53-11.61], P = 0.39, respectively).

Interpretation

This review showed that PCOS was significantly associated with elevated uric acid. However, no significant difference was found between eGFR and creatinine levels compared to healthy controls. Routine uric acid assessment in PCOS patients is recommended as a simple tool for risk stratification.

Limitations

No body mass index (BMI) subgroup analysis was done due to limited BMI reporting in our included studies. Quantitative analysis of all kidney function parameters was also limited by sparse data on urea and albumin.

PROSPERO Registration Number

CRD42023410092 (02 April 2023).

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

Hip circumference has independent association with the risk of hyperuricemia in middle-aged but not in older male patients with type 2 diabetes mellitus

BACKGROUND

Obesity and type 2 diabetes mellitus (T2DM) are risk factors for hyperuricemia. However, which anthropometric indices can better predict incident hyperuricemia in patients with T2DM remains inconsistent. This study aimed to examine the associations between hyperuricemia and different anthropometric indices in middle-aged and older male patients with T2DM.

METHODS

In this retrospective study, a total of 1447 middle-aged (45-65 years, n = 791) and older (≥ 65 years, n = 656) male patients with T2DM were collected from December 2015 to January 2020 at Shanghai Xinhua Hospital. Hyperuricemia was defined as a serum uric acid level above 7.0 mg/dL. Weight, height, waist circumference (WC) and hip circumference (HC) were measured by trained nurses at visit.

RESULTS

The median uric acid level of subjects was 5.6 (interquartile ranges: 4.7-6.7) mg/dl, and 279 (19.3%) were hyperuricemia, with 146 (18.5%) in the middle-aged group, and 133 (20.3%) in the older group. After adjusting for age, duration of T2DM, fasting plasma glucose and insulin, homeostasis model assessment-β, aspartate aminotransferase, triglycerides, high-density lipoprotein cholesterol and estimated glomerular filtration rate, body mass index (BMI), WC, HC, and waist-to-height ratio (WHtR) were associated with a higher risk of hyperuricemia in both middle-aged and older group (P < 0.05). After further adjusting for BMI and WC, HC still showed a positive relationship with the risk of hyperuricemia (Odds Ratio = 1.51, 95% confidence intervals: 1.06-2.14) in the middle-aged group, but such relationship was not found in the older group. Moreover, according to receiver operating characteristic analysis, the optimal cutoff value was 101.3 cm of HC for hyperuricemia screening in the middle-aged male patients with T2DM.

CONCLUSION

In middle-aged male patients with T2DM, more attention should be paid to HC with the cutoff value of 101.3 cm in clinical practice for early recognition of individuals with a high risk of hyperuricemia for targeted guidance on disease prevention, such as community screening.

A predictive model for hyperuricemia among type 2 diabetes mellitus patients in Urumqi, China

BACKGROUND

Patients with type 2 diabetes Mellitus (T2DM) are more likely to suffer from a higher uric acid level in blood-hyperuricemia (HUA). There are no conclusive studies done to predict HUA among T2DM patients. Therefore, this study aims to explore the risk factors of HUA among T2DM patients and finally suggest a model to help with its prediction.

METHOD

In this retrospective research, all the date were collected between March 2017 and October 2019 in the Medical Laboratory Center of the First Affiliated Hospital of Xinjiang Medical University. The information included sociodemographic factors, blood routine index, thyroid function indicators and serum biochemical markers. The least absolute shrinkage and selection operator (LASSO) and multivariate binary logistic regression were performed to screen the risk factors of HUA among T2DM patients in blood tests, and the nomogram was used to perform and visualise the predictive model. The receiver operator characteristic (ROC) curve, internal validation, and clinical decision curve analysis (DCA) were applied to evaluate the prediction performance of the model.

RESULTS

We total collected the clinical date of 841 T2DM patients, whose age vary from 19-86. In this study, the overall prevalence of HUA in T2DM patients was 12.6%. According to the result of LASSO-logistic regression analysis, sex, ethnicity, serum albumin (ALB), serum cystatin C (CysC), serum inorganic phosphorus (IPHOS), alkaline phosphatase (ALP), serum bicarbonate (CO2) and high-density lipoprotein (HDLC) were included in the HUA risk prediction model. The nomogram confirmed that the prediction model fits well (χ2 = 5.4952, P = 0.704) and the calibration curve indicates the model had a good calibration. ROC analysis indicates that the predictive model shows the best discrimination ability (AUC = 0.827; 95% CI: 0.78-0.874) whose specificity is 0.885, and sensitivity is 0.602.

CONCLUSION

Our study reveals that there were 8 variables that can be considered as independent risk factors for HUA among T2DM patients. In light of our findings, a predictive model was developed and clinical advice was given on its use.

Serum uric acid/creatinine ratio and free androgen index are synergistically associated with increased risk of polycystic ovary syndrome in obese women

BACKGROUND

Features of metabolic syndrome such as abdominal obesity, insulin resistance, hypertension and dyslipidemia are commonly encountered in polycystic ovary syndrome (PCOS). Recent evidence has suggested an association between high serum uric acid/creatinine (UA/Cr) ratio and metabolic syndrome however, no studies have investigated this association in PCOS. The current study was conducted to investigate the relationship between UA/Cr ratio and PCOS and to identify whether UA/Cr ratio and free androgen index (FAI) have an additive interaction for detection of PCOS risk in obese women.

METHODS

This study enrolled 40 obese women with PCOS and 40 control women with regular menstrual cycles matched for age and body mass index (BMI). Anthropometric measurements, fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), lipids profile, luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol, dehydroepiandrosterone sulfate (DHEAS), sex hormone binding globulin (SHBG), total testosterone, free androgen index (FAI), UA/Cr ratio were assessed.

RESULTS

Serum UA/Cr ratio was significantly higher in obese women with PCOS than in non-PCOS women. UA/Cr ratio was correlated with BMI, waist and neck circumferences, blood pressure, fasting insulin, HOMA-IR, lipids, LH/FSH, estradiol, DHEAS, total testosterone, FAI and SHBG. UA/Cr ratio and FAI were independent risk factors for PCOS in obese women however, the addictive interaction between UA/Cr ratio and FAI had a higher fold risk (OR: 4.3, 95% CI, 3.4-7.58) and a more significance (P = 0.002) for determination of PCOS.

CONCLUSION

Serum UA/Cr ratio combined with FAI can exert an additive or synergistic impact on prediction of PCOS in obese women.

Association Between Triglyceride-Glucose Index and Serum Uric Acid Levels: A Biochemical Study on Anthropometry in Non-Obese Type 2 Diabetes Mellitus Patients

PURPOSE

The triglyceride-glucose index (TyG) is positively correlated with serum uric acid (SUA) in patients with type 2 diabetes mellitus (T2DM). However, whether this relationship exists in non-obese T2DM patients remains unknown. The study investigated the relationship between TyG and SUA in Chinese non-obese T2DM patients and examined the prognostic value of TyG in hyperuricemia (HUA).

PATIENTS AND METHODS

In total, 719 T2DM patients who were not obese were enrolled from among those who visited the Hebei General Hospital. The patients were categorized into groups according to their SUA levels. The relationship between TyG and clinical parameters was examined through correlation analysis. To consider covariates and examine the independent impact of TyG on HUA, logistic regression was performed. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of TyG and homeostasis model assessment of insulin resistance (HOMA-IR) for HUA.

RESULTS

The HUA prevalence was 12.10%. TyG was statistically different among the four SUA groups, with lower TyG levels in the Q1, Q2, and Q3 groups than that in the Q4 group. TyG was positively correlated with SUA (r = 0.176, P < 0.001). Logistic regression exhibited that TyG and SUA were independently correlated (OR = 2.427, 95% CI = 1.134-5.195, P = 0.022) even after adjustment for confounding factors. The ROC curve showed that the predictive value of TyG for HUA was higher than that of HOMA-IR (AUROC = 0.613, P = 0.001).

CONCLUSION

TyG was positively correlated with SUA in non-obese T2DM patients. TyG may better predict HUA in non-obese T2DM patients than HOMA-IR.