Utility of Serum Albumin for Predicting Incident Metabolic Syndrome according to Hyperuricemia

Dissecting the risk factors for hyperuricemia in vegetarians in Taiwan

BACKGROUND

Vegetarian diets have been shown to lower the risks of hyperuricemia and gout. Little is known about the risk factors of hyperuricemia in vegetarians.

METHODS

This community-based retrospective case-control study was conducted to establish prediction models for hyperuricemia. From September 5, 2005, to December 31, 2016, 7331 adult vegetarians were recruited at Taipei Tzu Chi Hospital. Hyperuricemia was defined as a serum uric acid concentration greater than 7 mg/dL.

RESULTS

There were 593 (8.1%) vegetarians with hyperuricemia and 6738 (91.9%) without hyperuricemia. We stepwise built up three models for predicting hyperuricemia in vegetarians. The full model (model 3) has the highest area under the receiver operating characteristic curve (AUROC, 85.52%). Additionally, the AUROC of model 3 is 77.97% and 84.85% in vegetarians with or without prior gout history, respectively. Moreover, male gender, hyperlipidemia, body mass index, and serum albumin are independent risk factors for hyperuricemia in vegetarians. In contrast, estimated glomerular filtration rate and proteinuria are independently associated with lower risks of hyperuricemia in vegetarians.

CONCLUSION

Our study revealed that risk factors for hyperuricemia, which includes clinical characteristics, account for more than 85% of discriminatory performance in Taiwanese vegetarians. This model may be helpful for monitoring and preventing hyperuricemia in the population.

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.

Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke

Malnutrition and autonomic dysfunction are associated with poor outcomes, mortality, and psychological problems after stroke. Relevant laboratory biomarkers include serum albumin, prealbumin, and transferrin. Heart rate variability (HRV), a noninvasive measurement, can objectively measure autonomic nervous system (ANS) function. The relationship between HRV and nutritional biomarkers in stroke patients has not been studied. This study aimed to examine the relationship between nutritional biomarkers and HRV parameters in stroke patients. We retrospectively recruited 426 patients with subacute stroke who were examined for nutritional biomarkers, such as serum albumin, prealbumin, and transferrin, and underwent 24 h ambulatory Holter electrocardiography. Patients were divided into groups according to their nutritional biomarker status. Differences in HRV parameters between nutritional biomarker-deficient and normal groups were assessed. Pearson's correlation and multiple regression analyses were used to verify the relationship between HRV parameters and nutritional biomarkers. HRV parameters were significantly lower in the nutritional biomarker-deficient groups. In addition, there was a significant association between HRV parameters and nutritional biomarkers. Serum albumin, prealbumin, and transferrin levels were associated with ANS function, as measured by HRV, and their deficiency may be a predictive factor for the severity of ANS dysfunction in stroke patients.

Synergistic Interaction between Hyperuricemia and Abdominal Obesity as a Risk Factor for Metabolic Syndrome Components in Korean Population

BACKGROUND

The present study investigated the role of synergistic interaction between hyperuricemia and abdominal obesity as a risk factor for the components of metabolic syndrome.

METHODS

We performed a cross-sectional study using the data of 16,094 individuals from the seventh Korean National Health and Nutrition Examination Survey (2016 to 2018). The adjusted odds ratios of metabolic syndrome and its components were analyzed by multivariate logistic regression analysis. The presence of synergistic interaction between hyperuricemia and abdominal obesity was evaluated by calculating the additive scales-the relative excess risk due to interaction, attributable proportion due to interaction, and synergy index (SI).

RESULTS

There was a synergistic interaction between hyperuricemia and abdominal obesity in hypertriglyceridemia (men: SI, 1.39; 95% confidence interval [CI], 1.01 to 1.98; women: SI, 1.61; 95% CI, 1.02 to 2.69), and low high-density lipoprotein cholesterol (HDL-C) (men: SI, 2.03; 95% CI, 1.41 to 2.91; women: SI, 1.70; 95% CI, 1.05 to 2.95). There was no significant synergistic interaction between hyperuricemia and abdominal obesity for the risk of high blood pressure (men: SI, 1.22; 95% CI, 0.85 to 1.77; women: SI, 1.53; 95% CI, 0.79 to 2.97), and hyperglycemia (men: SI, 1.03; 95% CI, 0.72 to 1.47; women: SI, 1.39; 95% CI, 0.75 to 2.57).

CONCLUSION

Hyperuricemia and abdominal obesity synergistically increased the risk of hypertriglyceridemia and low HDL-C in both sexes.

Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review

Childhood cancer survivors (CCS) are at increased risk to develop metabolic syndrome (MetS), diabetes, and cardiovascular disease. Common criteria underestimate adiposity and possibly underdiagnose MetS, particularly after abdominal radiotherapy. A systematic literature review and meta-analysis on the diagnostic and predictive value of nine newer MetS related biomarkers (adiponectin, leptin, uric acid, hsCRP, TNF-alpha, IL-1, IL-6, apolipoprotein B (apoB), and lipoprotein(a) [lp(a)]) in survivors and adult non-cancer survivors was performed by searching PubMed and Embase. Evidence was summarized with GRADE after risk of bias evaluation (QUADAS-2/QUIPS). Eligible studies on promising biomarkers were pooled. We identified 175 general population and five CCS studies. In the general population, valuable predictive biomarkers are uric acid, adiponectin, hsCRP and apoB (high level of evidence), and leptin (moderate level of evidence). Valuable diagnostic biomarkers are hsCRP, adiponectin, uric acid, and leptin (low, low, moderate, and high level of evidence, respectively). Meta-analysis showed OR for hyperuricemia of 2.94 (age-/sex-adjusted), OR per unit uric acid increase of 1.086 (unadjusted), and AUC for hsCRP of 0.71 (unadjusted). Uric acid, adiponectin, hsCRP, leptin, and apoB can be alternative biomarkers in the screening setting for MetS in survivors, to enhance early identification of those at high risk of subsequent complications.

Mean and visit-to-visit variability of glycemia and left ventricular diastolic dysfunction: A longitudinal analysis of 3025 adults with serial echocardiography

OBJECTIVE

We aimed to determine the mean glucose thresholds to increase the risk of left ventricular diastolic dysfunction (LVDD) and whether visit-to-visit variability of fasting plasma glucose (FPG) and glycated hemoglobin (A1C) could independently increase the risk in a cohort with serial echocardiography.

METHODS

This was a 3.5-year (range, 0.5-8.3) retrospective longitudinal cohort study of 3025 adults (age, 55.15 ± 7.6 years; without diabetes, n = 2755) with LV ejection fraction > 50% by serial echocardiography between 2006 and 2016. Mean, standard of deviation (SD) and coefficient of variation (CV) of FPG and A1C obtained from three consecutive measurements preceding the first echocardiography. The definition of LVDD in this study was primarily based on early peak mitral inflow velocity and early diastolic mitral annulus motion velocity.

RESULTS

LVDD developed in 611/3025 subjects (20.2%). Cox proportional hazard models showed increased adjusted hazard ratios (HRs) for incident LVDD in the highest quartile of FPG-mean (HR 1.76, 95% confidence interval [CI]; 1.36-2.30), FPG-SD (HR 1.63, 95% CI; 1.27-2.09), FPG-CV (HR 1.47, 95% CI; 1.15-1.89), and A1C-mean (HR 1.83, 95% CI; 1.41-2.38) versus the lowest quartile, which was consistent even in subjects without diabetes. Mean glucose thresholds for the increased risk were below the lower limits for pre-diabetes.

CONCLUSIONS

In terms of mean glycemia, LVDD may be initiated in the earliest diabetic continuum, and such changes could be measurable within several years. Visit-to-visit variability of FPG, but not that of A1C, predicted accelerated development of LVDD.

Association between dairy product consumption and hyperuricemia in an elderly population with metabolic syndrome

BACKGROUND AND AIMS

The prevalence of hyperuricemia has increased substantially in recent decades. It has been suggested that it is an independent risk factor for weight gain, hypertension, hypertriglyceridemia, metabolic syndrome (MetS), and cardiovascular disease. Results from epidemiological studies conducted in different study populations have suggested that high consumption of dairy products is associated with a lower risk of developing hyperuricemia. However, this association is still unclear. The aim of the present study is to explore the association of the consumption of total dairy products and their subtypes with the risk of hyperuricemia in an elderly Mediterranean population with MetS.

METHODS AND RESULTS

Baseline cross-sectional analyses were conducted on 6329 men/women (mean age 65 years) with overweight/obesity and MetS from the PREDIMED-Plus cohort. Dairy consumption was assessed using a food frequency questionnaire. Multivariable-adjusted Cox regressions were fitted to analyze the association of quartiles of consumption of total dairy products and their subtypes with the prevalence of hyperuricemia. Participants in the upper quartile of the consumption of total dairy products (multiadjusted prevalence ratio (PR) = 0.84; 95% CI: 0.75-0.94; P-trend 0.02), low-fat dairy products (PR = 0.79; 95% CI: 0.70-0.89; P-trend <0.001), total milk (PR = 0.81; 95% CI: 0.73-0.90; P-trend<0.001), low-fat milk (PR = 0.80; 95% CI: 0.72-0.89; P-trend<0.001, respectively), low-fat yogurt (PR = 0.89; 95% CI: 0.80-0.98; P-trend 0.051), and cheese (PR = 0.86; 95% CI: 0.77-0.96; P-trend 0.003) presented a lower prevalence of hyperuricemia. Whole-fat dairy, fermented dairy, and yogurt consumption were not associated with hyperuricemia.

CONCLUSIONS

High consumption of total dairy products, total milk, low-fat dairy products, low-fat milk, low-fat yogurt, and cheese is associated with a lower risk of hyperuricemia.

Evaluation of serum uric acid levels in patients with rosacea

Rosacea is an inflammatory skin disease with a chronic course. Although the pathogenesis of rosacea is not completely understood, it is regarded as an inflammatory process. The aim of the present study was to evaluate uric acid (UA) levels in patients with rosacea and to detect the correlation of UA levels with disease activity. A total of 61 patients with rosacea and 64 sex- and age-matched controls were included in the study. Demographic characteristics, medical history, and dermatological examination of the patient and control groups were recorded. Concentrations of serum UA and C-reactive protein (CRP) were evaluated and compared in both groups. This study included 61 patients with rosacea (39 females, 22 males, median age = 30 years) and 64 age- and sex-matched controls. Metabolic syndrome was significantly more common in patients with rosacea than in the control group. Patients with rosacea had significantly higher body mass index (BMI) values compared with those of controls. Serum UA and CRP values were significantly higher in the rosacea group than values in the control group. There was no statistically significant correlation between serum UA level and clinical rosacea severity. This study suggests that rosacea is not only a skin-related disease but also an inflammatory disease that can be related to higher uric acid levels, BMI values, and metabolic syndrome. It may be recommended that clinicians pay careful attention to the clinical follow-up of these patients to avoid missed associated comorbidities.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.