Efficacy of urinary glucose for diabetes screening: a reconsideration

Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study

BACKGROUND AND OBJECTIVE

Early identification of post-stroke cognitive impairment (PSCI) is an important challenge for clinicians. In this study, we aimed to build a machine learning-based prediction model for PSCI and uncover potential risk factors to support clinical decision-making.

MATERIALS AND METHODS

We collected features of 96 patients with acute ischemic stroke and measured cognitive impairment using the Mini-Mental State Examination. Three common machine learning algorithms, including support vector machine, Gaussian naive Bayes, and logistic regression, were used to build clinical prediction models for PSCI. The area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, negative prediction value, positive prediction value, accuracy, and model fitting effect were used to evaluate the predictive performance of the models and further determine the clinical prediction rules.

RESULTS

In this study, the logistic regression model showed the best performance with an AUROC of 0.86, which was higher than the values of the other two models. Moreover, the logistic regression model showed high sensitivity (0.82), specificity (0.83), negative prediction value (0.88), positive prediction value (0.75), and accuracy (0.83). This work identified the top nine factors in importance ranking as predictors of PSCI. Among them, age and urine glucose were significantly associated with PSCI (P < 0.05).

CONCLUSIONS

Machine learning algorithms may be useful in the prediction of PSCI, especially logistic regression algorithms. In the present study, aging and hyperglycemia were independent risk factors for PSCI, and the cognition of such patients should be carefully addressed in clinical practice screening work.

Dose-response relationship between body mass index and hypertension: A cross-sectional study from Eastern China

Background

A high body mass index (BMI) increases the risk of hypertension. However, little is known about the dose-dependent association between BMI and hypertension. Therefore, this study investigated the prevalence of hypertension in 7568 subjects from the Jiangsu Province, Eastern China, and analyzed the dose-response relationship between BMI and hypertension risk.

Methods

The eligible subjects completed a structured questionnaire and clinical biochemical indicators were measured according to standardized protocols. Multivariate logistic regression models were used to evaluate the association between BMI and hypertension. Restricted cubic spline (RCS) analysis was used to analyze the dose-response relationship between BMI and hypertension risk. Moreover, sensitivity analysis was performed to verify the robustness of our findings.

Results

The prevalence of hypertension was 35.3 % in the total population. BMI was significantly associated with systolic and diastolic blood pressure. The fully-adjusted odds ratio (OR) with 95 % confidence interval (CI) for hypertension was 1.17 (1.15, 1.19) for every 1 kg/m2 increase in BMI. Furthermore, the OR (95 % CI) for hypertension in the highest BMI group (Obesity) was 4.14 (3.45, 4.96) after adjusting for covariates compared with the normal group. Multivariable adjusted RCS analysis showed a positive and linear dose-response relationship between BMI and hypertension risk both in male and female populations (all P for non-linearity > 0.05).

Conclusion

Our study demonstrated a positive and linear dose-response relationship between BMI and the risk of hypertension. The results of this study provide evidence for BMI-related clinical interventions to reduce the risk of hypertension.

Universal Fully Integrated Wearable Sensor Arrays for the Multiple Electrolyte and Metabolite Monitoring in Raw Sweat, Saliva, or Urine

Fully integrated wearable sensors are capable of dynamically, directly, and independently tracking biomarkers in raw noninvasive biofluids without any other equipment or accessories by integrating the unique on-body monitoring feature with the special complete functional implementation attribute. Sweat, saliva, and urine are three important noninvasive biofluids, and changes in their biomarkers hold great potential for revealing physiological conditions. However, it is still a challenge to design single fully integrated wearable sensor arrays (FIWSAs) that are universally able to concurrently measure electrolytes and metabolites in three of the most common noninvasive biofluids including sweat, saliva, and urine. Here, we propose the first single universal FIWSAs for wirelessly, noninvasively, and simultaneously measuring various metabolites (i.e., uric acid) and electrolytes (i.e., Na⁺ and H⁺) in raw sweat, saliva, or urine under subjects' exercise by integrating the specifically designed microfluidic, sensing, and electronic modules in a seamless manner. We evaluate its utility for noninvasive gout management in healthy subjects and in gout patients through a purine-rich meal challenge and with a medicine-treatment control, respectively. Noninvasive monitoring of multiple electrolytes and metabolites in a variety of raw noninvasive biofluids via such single universal FIWSAs may enrich the understanding of the biomarkers' levels in the body and would also facilitate self-health management.

Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China

BACKGROUND

Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose.

METHODS

A sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes.

RESULTS

After adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively.

CONCLUSIONS

The TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes.

The Application of Wearable Glucose Sensors in Point-of-Care Testing

Diabetes and its complications have become a worldwide concern that influences human health negatively and even leads to death. The real-time and convenient glucose detection in biofluids is urgently needed. Traditional glucose testing is detecting glucose in blood and is invasive, which cannot be continuous and results in discomfort for the users. Consequently, wearable glucose sensors toward continuous point-of-care glucose testing in biofluids have attracted great attention, and the trend of glucose testing is from invasive to non-invasive. In this review, the wearable point-of-care glucose sensors for the detection of different biofluids including blood, sweat, saliva, tears, and interstitial fluid are discussed, and the future trend of development is prospected.

Factors Associated with Poor Glycemic Control Amongst Rural Residents with Diabetes in Korea

(1) Background: Glycemic control is an effective way to reduce the cardiovascular complications of diabetes. The purpose of this study was to identify the factors associated with poor glycemic control amongst rural residents with diabetes in Korea. (2) Methods: This cross-sectional analysis was conducted amongst a total of 522 participants who had completed baseline health examinations for the Korean Genome and Epidemiology Study (KoGES) Rural Cohort from 2005 to 2011. The subjects were divided into two groups: the good glycemic control group (GCG) (glycosylated hemoglobin (HbA1C) < 7%) and the poor GCG (HbA1C ≥ 7%). Logistic regression was used to examine the role of sociodemographics, health-related behavior, comorbidity and diabetes-related and clinical factors in poor glycemic control amongst rural residents with diabetes. (3) Results: In total, 48.1% of participants were in the poor GCG. Poor GCG was significantly associated with drinking (odds ratio (OR) = 0.42, 95% CI = 0.24-0.71), lack of regular physical activity (OR = 1.68, 95% CI = 1.03-2.76), fasting blood glucose (FBG) > 130 mg/dL (OR = 7.80, 95% CI = 4.35-13.98), diabetes for > 7 years (OR = 1.79, 95% CI = 1.08-2.98), cholesterol ≥ 200 mg/dL (OR = 1.73, 95% CI = 1.05-2.84) and positive urine glucose (OR = 6.24, 95% CI = 1.32-29.44). (4) Conclusion: Intensive glucose control interventions should target individuals amongst rural residents with diabetes who do not engage in regular physical activity, have been diagnosed with diabetes for more than seven years and who have high fasting-blood glucose, high cholesterol levels and glucose-positive urine.

Lipid Accumulation Product Combined With Urine Glucose Excretion Improves the Efficiency of Diabetes Screening in Chinese Adults

BACKGROUND

To compare the efficacy of lipid accumulation product (LAP) and urine glucose excretion (UGE) in predicting diabetes and evaluate whether the combination of LAP and UGE would help to improve the efficacy of using LAP alone or UGE alone in identifying diabetes.

METHODS

Data from 7485 individuals without prior history of diabetes who participated in a cross-sectional survey in Jiangsu, China, were analyzed. Each participant underwent an oral glucose-tolerance test. Operating characteristic curves (ROC) and logistic regression analyses were used to evaluate the performance of LAP and UGE in identification of newly diagnosed diabetes (NDM) and prediabetes (PDM).

RESULTS

For subjects with NDM, the area under the ROC curve was 0.72 for LAP and 0.85 for UGE, whereas for PDM, these values were 0.62 and 0.61, respectively. Furthermore, LAP exhibited a comparable sensitivity with UGE in detecting NDM (76.4% vs 76.2%, p = 0.31). In predicting PDM, LAP showed a higher sensitivity than UGE (66.4% vs 42.8%, p < 0.05). The combination of LAP and UGE demonstrated a significantly higher sensitivity than that of LAP alone and UGE alone for identification of NDM (93.6%) and PDM (80.1%). Moreover, individuals with both high LAP and high UGE had significantly increased risk of NDM and PDM than those with both low LAP and low UGE.

CONCLUSIONS

The combination of LAP and UGE substantially improved the efficacy of using LAP and using UGE alone in detecting diabetes, and may be a novel approach for mass screening in the general population.

The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P < 0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

Increased urinary glucose excretion is associated with a reduced risk of hyperuricaemia

AIM

To investigate the association of urinary glucose excretion with levels of serum uric acid in adults with newly diagnosed diabetes.

METHODS

A total of 597 people with newly diagnosed diabetes, confirmed in an oral glucose tolerance test, were included in the present study. The participants were divided into two groups: 142 participants with low urinary glucose excretion and 455 with high urinary glucose excretion. Demographic characteristics and clinical variables were evaluated. The association of urinary glucose excretion with uric acid was analysed using multivariable regression analysis.

RESULTS

The low urinary glucose excretion group had a significantly higher prevalence of hyperuricaemia than the high urinary glucose excretion group. Moreover, urinary glucose excretion was negatively associated with uric acid level. The correlation remained significant after adjusting for potential confounders, including gender, age, fasting plasma glucose, 2-h plasma glucose and BMI. The results also showed that participants with high urinary glucose excretion were at decreased risk of hyperuricaemia (odds ratio 0.47, 95% CI 0.27-0.80; P = 0.006).

CONCLUSION

Urinary glucose excretion was independently associated with uric acid level in participants with newly diagnosed diabetes. In addition to lowering blood glucose, promoting urinary glucose excretion may also be an effective approach to reducing serum uric acid levels, especially for people with diabetes complicated with hyperuricaemia.

Increased waist-to-hip ratio is associated with decreased urine glucose excretion in adults with no history of diabetes

PURPOSE

Promoting urine glucose excretion (UGE) is an attractive approach for the treatment of diabetes. Obesity is associated with increased risk for type 2 diabetes. This study was aimed to investigate the association of waist-to-hip ratio (WHR), a simple measure of abdominal obesity, with UGE determined in subjects without previous history of diabetes.

METHODS

We studied the correlation of WHR with UGE in 7485 participants without previous history of diabetes. All participants were given a standard 75 g glucose solution. Clinical parameters and demographic characteristics were assessed. Multiple linear regression analysis and multivariate logistic regression analysis were performed to determine the association of WHR with UGE.

RESULTS

Individuals with high WHR (H-WHR) exhibited significantly lower UGE compared to those with low WHR (L-WHR), in either normal glucose tolerance group or pre-diabetes group. In newly diagnosed diabetes group, individuals with H-WHR also showed lower UGE than those with L-WHR; however, no statistical significance was observed. After adjustment for potential confounding factors, including age, genders, and blood glucose level, WHR was negatively associated with UGE (β = -250.901, 95% CI: -471.891 to -29.911, p = 0.026). However, no significant association was observed between BMI and UGE. Furthermore, multivariable logistic regression model showed that individuals with H-WHR were more likely to have low UGE (OR = 0.83, 95% CI: 0.71-0.97, p = 0.018).

CONCLUSIONS

Individuals with H-WHR were at risk for decreased UGE. This study suggests that WHR, but not BMI, might be an important determinant of UGE.

Associations of Insulin Levels and Insulin Resistance With Urine Glucose Excretion Independent of Blood Glucose in Chinese Adults With Prediabetes and Newly Diagnosed Diabetes

Several studies have demonstrated that renal glucose reabsorption is increased in patients with type 2 diabetes. However, the increased renal glucose reabsorption may contribute to the progression of hyperglycemia. Therefore, promoting urine glucose excretion (UGE) by suppression of renal glucose reabsorption is an attractive approach for the treatment of diabetes. Insulin resistance is identified as a major characteristic in the pathogenesis of type 2 diabetes. Thus, our aim was to evaluate the association of UGE with serum insulin levels and insulin resistance in subjects with glucose abnormalities, including prediabetes and newly diagnosed diabetes (NDD). The present study included 1129 subjects, 826 individuals with prediabetes and 303 individuals with NDD. Urine samples were collected within 2 h of oral glucose loading for the measurement of glucose. Fasting serum insulin was measured. Homeostatic model assessment of insulin resistance (HOMA-IR) was assessed. Multiple linear regression analysis and multivariate logistic regression analysis were performed to determine the association of UGE with insulin levels and HOMA-IR. A negative association between serum insulin levels and UGE was observed. The relationship remained significant after adjustment for potential confounders, including age, gender, blood pressure and glucose (β = -5.271, 95% CI: -9.775 to -0.767, p = 0.022). Furthermore, multivariable logistic regression model showed that increased insulin levels were associated with a decreased risk for high UGE after multivariable adjustment. In addition, similar correlation was also observed between HOMA-IR and UGE. HOMA-IR was negatively correlated with UGE after controlling for potential confounders. Moreover, an independent inverse relationship between HOMA-IR and the risk of high UGE was found (OR = 0.85, 95% CI: 0.78–0.93, p < 0.001). In conclusion, insulin levels and HOMA-IR were negatively correlated with UGE after adjusting for potential confounders. Subjects with increased insulin levels or IR were at a decreased risk of high UGE independent of blood glucose. The study suggests that insulin might affect UGE through other ways, in addition to the direct blood glucose-lowering effect, thereby resulting in reduced UGE.