Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.

INTRODUCTION: The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. METHODS: We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). RESULTS: Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. CONCLUSION: In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.

BACKGROUND: The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships. AIM: To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS. METHODS: 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods. RESULTS: Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS. CONCLUSION: Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Redrawing Urokinase Receptor (uPAR) Signaling with Cancer Driver Genes for Exploring Possible Anti-Cancer Targets and Drugs.

During tumorigenesis, urokinase (uPA) and uPA receptor (uPAR) play essential roles in mediating pathological progression in many cancers. To understand the crosstalk between the uPA/uPAR signaling and cancer, as well as to decipher their cellular pathways, we proposed to use cancer driver genes to map out the uPAR signaling. In the study, an integrated pharmaceutical bioinformatics approach that combined modulator identification, driver gene ontology networking, protein targets prediction and networking, pathway analysis and uPAR modulator screening platform construction was employed to uncover druggable targets in uPAR signaling for developing a novel anti-cancer modality. Through these works, we found that uPAR signaling interacted with 10 of 21 KEGG cancer pathways, indicating the important role of uPAR in mediating intracellular cancerous signaling. Furthermore, we verified that receptor tyrosine kinases (RTKs) and ribosomal S6 kinases (RSKs) could serve as signal hubs to relay uPAR-mediated cellular functions on cancer hallmarks such as angiogenesis, proliferation, migration and metastasis. Moreover, we established an in silico virtual screening platform and a uPAR-driver gene pair rule for identifying potential uPAR modulators to combat cancer. Altogether, our results not only elucidated the complex networking between uPAR modulation and cancer but also provided a paved way for developing new chemical entities and/or re-positioning clinically used drugs against cancer.

Gamma-Glutamyltransferase Is a Predictor for Future Changes of Diabetogenic Factors in Aged Chinese-A Four-Year Follow-Up Study.

Glucose homeostasis in the body is determined by four diabetes factors (DFs): insulin resistance (IR), glucose effectiveness (GE), and the two phases of insulin secretion-first phase (FPIS) and second phase (SPIS). Previous research points to a correlation between elevated levels of gamma-glutamyl transferase (γGT) and an increased risk of type 2 diabetes. This study investigates the relationship between γGT and the four DFs in older Chinese individuals. This study involved 2644 men and 2598 women, all of whom were relatively healthy Chinese individuals aged 60 years or more. The DFs were calculated using formulas developed by our research, based on demographic data and factors related to metabolic syndrome. Pearson's correlation was utilized to assess the relationship between γGT and the four DFs. The findings suggested a positive correlation between γGT and IR, FPIS, and SPIS, but a negative correlation with GE in men. Among women, only SPIS and GE were significantly correlated with γGT. The factors showed varying degrees of correlation, listed in descending order as follows: GE, SPIS, FPIS, and IR. This study confirms a significant correlation between γGT and DFs in this population, highlighting the noteworthy role of GE.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

Changes in insulin resistance, glucose effectiveness, and first and second phases of insulin secretion in women aged 45-60 years old in Taiwan.

BACKGROUND: In women after menopause, the incidence of diabetes mellitus increases. Increased insulin resistance (IR), decreased glucose effectiveness (GE), and the first and second phases of insulin secretion (FPIS and SPIS), are the four most important factors that trigger glucose intolerance and diabetes (diabetogenic factor [DF]). In the cross-sectional study, we enrolled nondiabetic women between the ages of 45 and 60 years to observe the changes in DFs during the perimenopausal period and to elucidate the underlying mechanisms of diabetes in menopausal women. METHODS: We randomly enrolled 4194 women who underwent health checkups. Using demographic and biochemical data, IR, FPIS, SPIS, and GE were calculated using previously published equations. The relationship between the DFs and age was evaluated using a simple correlation. RESULTS: Body mass index, blood pressure, fasting plasma glucose, low-density lipoprotein cholesterol, triglyceride, and SPIS were higher, and GE was lower in older women (≥52 years old). A significant decrease in GE and increased SPIS were observed with age. However, no changes were observed in IR or FPIS. CONCLUSION: The IR and FPIS did not change during perimenopause. Increased SPIS may compensate for the decrease in GE, which is probably one of the reasons for the higher incidence of diabetes in menopausal women.

Visfatin and Retinol Binding Protein-4 in Young-Onset Type 2 Diabetes Mellitus.

Background and Objectives: The prevalence of type 2 diabetes mellitus in adolescents has increased rapidly in recent decades. However, the role of adipokines on pathophysiology in young-onset type 2 diabetes mellitus (YDM) is not clear. In this article, we explored the relationships between the adipokines (visfatin and retinol binding protein 4 (RBP4)) and metabolic syndrome (MetS) components in both YDM and late-onset type 2 diabetes mellitus (ODM). Materials and Methods: There were 36 patients with YDM (23.6 ± 4.8 years) and 36 patients with ODM (54.3 ± 10.1 years) enrolled. Visfatin, RBP4, and MetS components were measured. The relationships between visfatin, RBP4 and MetS components were assessed in YDM and ODM. Results: The visfatin, but not the RPB4 level, was significantly higher in YDM than in ODM. After adjusting for age and body mass index, visfatin was not related to any MetS components except that there was a negative correlation with fasting plasma glucose (FPG). As for RPB4, triglyceride was found to be positively and FPG negatively related to RBP4 in YDM. However, in ODM, the only positive relationship that existed was between RBP4 and diastolic blood pressure. Conclusions: In conclusion, both visfatin and RBP4 had certain roles in diabetes and MetS although their relationships were different in YDM and ODM. Further studies are needed to explore their physiological and pathological effects in glucose metabolism.

Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes.

Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.

Urokinase Plasminogen Activator Deficiency Aggravates Cationic Bovine Serum Albumin-Induced Membranous Nephropathy Through T Helper Cell Type 2-Prone Immune Response in Mice.

Urokinase plasminogen activator (uPA) is a crucial activator of the fibrinolytic system that modulates tissue remodeling, cancer progression, and inflammation. However, its role in membranous nephropathy (MN) remains unclear. To clarify this issue, an established BALB/c mouse model mimicking human MN induced by cationic bovine serum albumin (cBSA), with a T helper cell type 2-prone genetic background, was used. To induce MN, cBSA was injected into Plau knockout (Plau-/-) and wild-type (WT) mice. The blood and urine samples were collected to measure biochemical parameters, such as serum concentrations of immunoglobulin (Ig)G1 and IgG2a, using enzyme-linked immunoassay. The kidneys were histologically examined for the presence of glomerular polyanions, reactive oxygen species (ROS), and apoptosis, and transmission electron microscopy was used to examine subepithelial deposits. Lymphocyte subsets were determined using flow cytometry. Four weeks post-cBSA administration, Plau-/- mice exhibited a significantly higher urine protein-to-creatine ratio, hypoalbuminemia, and hypercholesterolemia than WT mice. Histologically, compared to WT mice, Plau-/- mice showed more severe glomerular basement thickening, mesangial expansion, IgG granular deposition, intensified podocyte effacement, irregular thickening of glomerular basement membrane and subepithelial deposits, and abolishment of the glycocalyx. Moreover, increased renal ROS levels and apoptosis were observed in Plau-/- mice with MN. B-lymphocyte subsets and the IgG1-to-IgG2a ratio were significantly higher in Plau-/- mice after MN induction. Thus, uPA deficiency induces a T helper cell type 2-dominant immune response, leading to increased subepithelial deposits, ROS levels, and apoptosis in the kidneys, subsequently exacerbating MN progression in mice. This study provides a novel insight into the role of uPA in MN progression.

A randomized trial on the effect of transcutaneous electrical nerve stimulator on glycemic control in patients with type 2 diabetes.

Transcutaneous electrical nerve stimulator (TENS) has been demonstrated to be beneficial in glycemic control in animal models, but its application in humans has not been well studied. We randomly assigned 160 patients with type 2 diabetes on oral antidiabetic drugs 1:1 to the TENS study device (n = 81) and placebo (n = 79). 147 (92%) randomized participants (mean [SD] age 59 [10] years, 92 men [58%], mean [SD] baseline HbA1c level 8.1% [0.6%]) completed the trial. At week 20, HbA1c decreased from 8.1% to 7.9% in the TENS group (- 0.2% [95% CI - 0.4% to - 0.1%]) and from 8.1% to 7.8% in the placebo group (- 0.3% [95% CI - 0.5% to - 0.2%]) (P = 0.821). Glycemic variability, measured as mean amplitude of glycemic excursion (MAGE) at week 20 were significantly different in the TENS group vs. the placebo group (66 mg/dL [95% CI 58, 73] vs. 79 mg/dL [95% CI 72, 87]) (P = 0.009). Our study provides the clinical evidence for the first time in humans that TENS does not demonstrate a statistically significant HbA1c reduction. However, it is a safe complementary therapy to improve MAGE in patients with type 2 diabetes.

Elevated alanine aminotransferase is associated with biphasic insulin secretion in the healthy elderly Han Chinese population.

OBJECTIVE: To evaluate the relationship between alanine transaminase (ALT) level and biphasic insulin secretion (BPIS) in healthy elderly Han Chinese individuals. METHODS: This cross-sectional study enrolled healthy elderly participants aged ≥60 years that were part of a health examination programme. In order to explore the correlation and severity of the clinical condition, those with any possible confounding factors known to affect insulin secretion or liver function were excluded from the study. BPIS was calculated using an equation developed previously by this research team. RESULTS: This study enrolled 39 845 healthy elderly individuals (19 058 males and 20 787 females). Participants were stratified into four quartile groups according to their ALT level. In both males and females, the increasing ALT quartiles (ordinal variable) were associated with greater values of log-transformed first-phase insulin secretion (FPIS) and second-phase insulin secretion (SPIS). The correlation and the linear regression model showed that increasing ALT level was significantly correlated with higher log-transformed FPIS and SPIS. CONCLUSIONS: ALT was positively correlated with BPIS in a healthy elderly population in both men and women. Elevated ALT may serve as an indicating factor for developing metabolic syndrome and type 2 diabetes mellitus in healthy elderly individuals.

Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.

Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.

The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

Prevalence and risk factors for latent tuberculosis among diabetes patients in Taiwan: A cross-sectional study.

INTRODUCTION: Diabetes mellitus (DM) is a known risk factor for tuberculosis (TB), leading to an approximate three-fold higher risk of developing active TB. However, epidemiological studies on the prevalence of latent TB infection (LTBI) in DM patients are lacking. In this study, we investigated the presence of LTBI and determined risk factors for LTBI in DM patients. METHODOLOGY: We conducted a cross-sectional study at Taipei Medical University-Shuang Ho Hospital in northern Taiwan. The study population comprised DM patients (aged 20-70 years) attending a metabolism outpatient clinic between February 2011 and February 2013, excluding patients who were suspected or confirmed to have active TB. Venous blood samples were drawn from patients to detect LTBI using the QuantiFERON-TB Gold In-Tube (QFT-GIT) method. RESULTS: We enrolled 1120 patients with DM. The QFT-GIT showed positive results for 241 people (21.5%) and negative results for 879 people (78.5%). The mean age at QFT-GIT positivity was 58.2 years, which was significantly dissimilar to the mean age at QFT-GIT negativity, which was 55.0 years (p < 0.001). Multivariate logistic regression indicated that the trend of QFT-GIT positivity increased after the age of 50 years. Effective glycemic control did not differ significantly between QFT-GIT-positive and -negative patients. Moreover, men were predominant were predominant in both QFT-GIT-positive and -negative patients. CONCLUSIONS: More than one-fifth of DM patients have LTBI. Among the DM patients, those older than 50 years may have a higher risk of LTBI. Moreover, effective glycemic control did not differ significantly in patients with LTBI.

Urokinase plasminogen activator induces epithelial-mesenchymal and metastasis of pancreatic cancer through plasmin/MMP14/TGF-ß axis, which is inhibited by 4-acetyl-antroquinonol B treatment.

BACKGROUND: The current standard therapy for metastatic pancreatic cancer is ineffective, necessitating a new treatment approach for prognosis improvement. The urokinase-plasmin activator (uPA) is a critical factor in epithelial-mesenchymal transition (EMT) and cancer metastasis, but its underlying mechanisms in pancreatic cancer remains elusive. METHODS: We investigated uPA expression in our pancreatic cancer cohort. A bioinformatics approach was used to further determine the role of uPA in pancreatic cancer. We employed MiaPaCa-2 and PANC-1 cell lines to investigate how uPA regulates EMT and metastasis in pancreatic cancer and present a novel approach aimed at inhibiting uPA in pancreatic cancer. RESULTS: We observed that higher uPA mRNA expression was significantly associated with overall-poor survival and progression-free survival in pancreatic cancer. uPA was highly expressed in tumor tissue. Gene set enrichment analysis revealed a positive association between uPA mRNA expression and EMT and transforming growth factor ß (TGF-ß) signaling pathways. Moreover, shRNA-mediated uPA gene knockdown reduced plasmin, MMP14, and TGF-ß activation, leading to the inhibition of PANC-1 cells' EMT marker expression, migration, invasion, and cell viability. Notably, 4-acetyl-antroquinonol B (4-AAQB) treatment suppressed MiaPaCa-2 and PANC-1 cell migratory and invasive abilities by inhibiting the uPA/MMP14/TGF-ß axis through upregulation of miR-181d-5p. In the xenograft mouse model of orthotropic pancreatic cancer, 4-AAQB treatment has reduced tumor growth and metastasis rate by deactivating uPA and improving the survival of the mice model. CONCLUSION: Accordingly, to extent of our knowledge and previous studies, we demonstrated that 4-AAQB is an anti Pan-Cancer drug, and may inhibit pancreatic cancer EMT and metastasis and serve as a new therapeutic approach for patients with late-stage pancreatic cancer.

Reduced Expression of Urokinase Plasminogen Activator in Brown Adipose Tissue of Obese Mouse Models.

In recent decades, the obesity epidemic has resulted in morbidity and mortality rates increasing globally. In this study, using obese mouse models, we investigated the relationship among urokinase plasminogen activator (uPA), metabolic disorders, glomerular filtration rate, and adipose tissues. Two groups, each comprised of C57BL/6J and BALB/c male mice, were fed a chow diet (CD) and a high fat diet (HFD), respectively. Within the two HFD groups, half of each group were euthanized at 8 weeks (W8) or 16 weeks (W16). Blood, urine and adipose tissues were collected and harvested for evaluation of the effects of obesity. In both mouse models, triglyceride with insulin resistance and body weight increased with duration when fed a HFD in comparison to those in the groups on a CD. In both C57BL/6J and BALB/c HFD mice, levels of serum uPA initially increased significantly in the W8 group, and then the increment decreased in the W16 group. The glomerular filtration rate declined in both HFD groups. The expression of uPA significantly decreased in brown adipose tissue (BAT), but not in white adipose tissue, when compared with that in the CD group. The results suggest a decline in the expression of uPA in BAT in obese m models as the serum uPA increases. There is possibly an association with BAT fibrosis and dysfunction, which may need further study.

The relationship of irisin with metabolic syndrome components and insulin secretion and resistance in schoolchildren.

ABSTRACT: Irisin, a novel myokine, is believed to be the crucial factor in converting white adipose tissue to beige adipose tissue. For this paper, we studied the relationship among irisin and components of metabolic syndrome (MetS), and insulin secretion and resistance in schoolchildren of Taiwan.Subjects receiving routine annual health examination at elementary school were enrolled. Demographic data, anthropometry, MetS components, irisin, and insulin secretion and resistance were collected. Subjects were divided into normal, overweight, and obese groups for evaluation of irisin in obesity. Finally, the relationship between irisin and MetS was analyzed.There were 376 children (179 boys and 197 girls), aged 10.3 ±â€Š1.5 years, were enrolled. In boys, irisin levels were not associated with body mass index percentile, body fat, blood pressure, lipid profiles, insulin secretion or resistance. After adjusting for age, the irisin level in boys was negatively related to fasting plasma glucose (FPG) (r = -0.21, P = .006). In girls, after adjusting for age, the irisin levels were positively related only to FPG (r = 1.49, P = .038). In both genders, irisin levels were similar among normal, overweight, and obese groups, and between subjects with and without MetS.The irisin levels were not associated with MetS in either boys or girls. In girls, circulating irisin levels have a nonsignificant declining trend in overweight and obese girls. However, irisin levels were negatively related to FPG in boys and positively related to FPG in girls. The contrary relationship between irisin and FPG in boys and girls needs further exploration.

First-phase insulin secretion is positively correlated with alanine aminotransferase in young adults.

BACKGROUND: Type 2 diabetes (T2D) is known to be one of the most prevalent diseases, and its prevalence is significantly associated with age and metabolic syndrome (MetS). Few studies have been conducted on liver function, MetS and insulin secretion among young adults. OBJECTIVES: In the present study, we explored the relationship between the liver function enzyme - alanine aminotransferase (ALT) - and first-phase insulin secretion (FPIS) among young adults. MATERIAL AND METHODS: There were 22,971 men and 28,740 women, aged 18-27 years, assigned to subgroups according to the presence of MetS and quartiles of ALT values. Simple correlation was applied to evaluate their relationship. The difference between the slopes of these relationships and FPIS were statistically analyzed with Chris's calculator. RESULTS: Most values for metabolic parameters, including ALT and FPIS, were determined to be relatively high in individuals with MetS. By contrast, individuals with MetS had lower high-density-lipoprotein cholesterol (HDL-C) counts and FPIS. Similar results were observed in the quartiles of ALT. Significant positive results were also found in the linear model. Depending on the ALT level, the slope change of FPIS still demonstrated a positive correlation between ALT and FPIS. This correlation was stronger for men than for women. CONCLUSIONS: A positive correlation between ALT and FPIS exists among young adults. Moreover, this correlation was stronger for men than for women. Both the cause and the effect require further investigation.

Predictors of abnormality in thallium myocardial perfusion scans for type 2 diabetes.

Type 2 diabetes mellitus (T2DM) increases coronary artery disease (CAD) risk. In this study, we used T2DM clinical variables to predict abnormality in thallium-201 myocardial perfusion scans (Th-201 scans). These clinical variables were summed stress score (SSS), summed rest score, and summed difference score (SDS), with data obtained from 368 male and 428 female participants with T2DM. Multiple linear regression results were as follows. In male participants, body mass index (BMI) and creatinine (Cr) were associated with SSS (ß = 0.224, p < 0.001; ß = 0.140, p = 0.022, respectively), and only BMI was associated with SDS (ß = 0.174, p = 0.004). In female participants, BMI and high-density lipoprotein cholesterol level were associated with SSS (ß = 0.240, p < 0.001; ß = - 0.120, p = 0.048, respectively), and only BMI was correlated with SDS (ß = 0.123, p = 0.031). Our multivariate logistic regression indicated that in male and female participants, BMI was the only independent indicator of high SSS (SSS ≥ 9). In this study, we demonstrated that male patients have a higher SSS and SDS than female patients do in Th-201 scans for T2DM in a Chinese population. For male and female patients, BMI was the strongest predictor of abnormality in Th-201 scans. Our results can help clinicians identify patients with T2DM at high risk of CAD.