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.

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.

Aging Affects Insulin Resistance, Insulin Secretion, and Glucose Effectiveness in Subjects with Normal Blood Glucose and Body Weight.

AIM: Several studies have demonstrated that factors including diabetes, including insulin resistance (IR), glucose effectiveness (GE), and the first and second phase of insulin secretion (FPIS, SPIS) could easily be calculated using basic characteristics and biochemistry profiles. Aging is accompanied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging in the different phases of insulin secretion (ISEC), i.e., the first and second phase of insulin secretion (FPIS, SPIS), and glucose effectiveness (GE). METHODS: In total, 169 individuals (43 men and 126 women) recruited from the data bank of the Meei-Jaw (MJ) Health Screening Center and Cardinal Tien Hospital Data Access Center between 1999 and 2008, with a similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. The IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS, and GE). The equations that are used to calculate the DF in the present study were built and published by our group. RESULTS: The age of the participants ranged from 18 to 78 years. Men had higher FPIS but lower HDL-C levels than women (2.067 ± 0.159, 1.950 ± 0.186 µU/min and 1.130 ± 0.306, 1.348 ± 0.357 mmol/dl, accordingly). The results of the Pearson correlation revealed that age was negatively related to the IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). At the same time, the FPIS was also only found to be negatively correlated with age in females (r = -0.238, p = 0.003), but there was no difference in the SPIS and age among both genders. CONCLUSIONS: We have found that in Chinese subjects with a normal FPG level (90 mg/dL) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to the IR and GE among both genders. Only the FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest, are GE, FPIS, and IR. These results should be interpreted with caution because of the small sample size.

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.

Sesamol Attenuates Renal Inflammation and Arrests Reactive-Oxygen-Species-Mediated IL-1ß Secretion via the HO-1-Induced Inhibition of the IKKα/NFκB Pathway In Vivo and In Vitro.

Chronic nephritis leads to irreversible renal fibrosis, ultimately leading to chronic kidney disease (CKD) and death. Macrophage infiltration and interleukin 1ß (IL-1ß) upregulation are involved in inflammation-mediated renal fibrosis and CKD. Sesamol (SM), which is extracted from sesame seeds, has antioxidant and anti-inflammatory properties. We aimed to explore whether SM mitigates macrophage-mediated renal inflammation and its underlying mechanisms. ApoE-/- mice were subjected to 5/6 nephrectomy (5/6 Nx) with or without the oral gavage of SM for eight weeks. Blood and urine samples and all the kidney remnants were collected for analysis. Additionally, THP-1 cells were used to explore the mechanism through which SM attenuates renal inflammation. Compared with the sham group, the 5/6 Nx ApoE-/- mice exhibited a significant increase in the macrophage infiltration of the kidneys (nephritis), upregulation of IL-1ß, generation of reactive oxygen species, reduced creatinine clearance, and renal fibrosis. However, the administration of SM significantly alleviated these effects. SM suppressed the H2O2-induced secretion of IL-1ß from the THP-1 cells via the heme oxygenase-1-induced inhibition of the IKKα-NF-κB pathway. SM attenuated renal inflammation and arrested macrophage accumulation by inhibiting IKKα, revealing a novel mechanism of the therapeutic effects of SM on renal injury and offering a potential approach to CKD treatment.

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.

Apolipoprotein C3-Rich Low-Density Lipoprotein Induces Endothelial Cell Senescence via FBXO31 and Its Inhibition by Sesamol In Vitro and In Vivo.

Premature endothelial senescence decreases the atheroprotective capacity of the arterial endothelium. Apolipoprotein C3 (ApoC3) delays the catabolism of triglyceride-rich particles and plays a critical role in atherosclerosis progression. FBXO31 is required for the intracellular response to DNA damage, which is a significant cause of cellular senescence. Sesamol is a natural antioxidant with cardiovascular-protective properties. In this study, we aimed to examine the effects of ApoC3-rich low-density lipoprotein (AC3RL) mediated via FBXO31 on endothelial cell (EC) senescence and its inhibition by sesamol. AC3RL and ApoC3-free low-density lipoproteins (LDL) (AC3(-)L) were isolated from the plasma LDL of patients with ischemic stroke. Human aortic endothelial cells (HAECs) treated with AC3RL induced EC senescence in a dose-dependent manner. AC3RL induced HAEC senescence via DNA damage. However, silencing FBXO31 attenuated AC3RL-induced DNA damage and reduced cellular senescence. Thus, FBXO31 may be a novel therapeutic target for endothelial senescence-related cardiovascular diseases. Moreover, the aortic arch of hamsters fed a high-fat diet with sesamol showed a substantial reduction in their atherosclerotic lesion size. In addition to confirming the role of AC3RL in aging and atherosclerosis, we also identified AC3RL as a potential therapeutic target that can be used to combat atherosclerosis and the onset of cardiovascular disease in humans.

The Effect of Schwann Cells/Schwann Cell-Like Cells on Cell Therapy for Peripheral Neuropathy.

Peripheral neuropathy is a common neurological issue that leads to sensory and motor disorders. Over time, the treatment for peripheral neuropathy has primarily focused on medications for specific symptoms and surgical techniques. Despite the different advantages of these treatments, functional recovery remains less than ideal. Schwann cells, as the primary glial cells in the peripheral nervous system, play crucial roles in physiological and pathological conditions by maintaining nerve structure and functions and secreting various signaling molecules and neurotrophic factors to support both axonal growth and myelination. In addition, stem cells, including mesenchymal stromal cells, skin precursor cells and neural stem cells, have the potential to differentiate into Schwann-like cells to perform similar functions as Schwann cells. Therefore, accumulating evidence indicates that Schwann cell transplantation plays a crucial role in the resolution of peripheral neuropathy. In this review, we summarize the literature regarding the use of Schwann cell/Schwann cell-like cell transplantation for different peripheral neuropathies and the potential role of promoting nerve repair and functional recovery. Finally, we discuss the limitations and challenges of Schwann cell/Schwann cell-like cell transplantation in future clinical applications. Together, these studies provide insights into the effect of Schwann cells/Schwann cell-like cells on cell therapy and uncover prospective therapeutic strategies for peripheral neuropathy.

Kansuinine A Ameliorates Atherosclerosis and Human Aortic Endothelial Cell Apoptosis by Inhibiting Reactive Oxygen Species Production and Suppressing IKKß/IκBα/NF-κB Signaling.

Reactive oxygen species (ROS)-induced vascular endothelial cell apoptosis is strongly associated with atherosclerosis progression. Herein, we aimed to examine whether Kansuinine A (KA), extracted from Euphorbia kansui L., prevents atherosclerosis development in a mouse model and inhibits cell apoptosis through oxidative stress reduction. Atherosclerosis development was analyzed in apolipoprotein E-deficient (ApoE-/-) mice fed a high-fat diet (HFD) using Oil Red O staining and H&E staining. Human aortic endothelial cells (HAECs) were treated with KA, followed by hydrogen peroxide (H2O2), to investigate the KA-mediated inhibition of ROS-induced oxidative stress and cell apoptosis. Oil Red O staining and H&E staining showed that atherosclerotic lesion size was significantly smaller in the aortic arch of ApoE-/- mice in the HFD+KA group than that in the aortic arch of those in the HFD group. Further, KA (0.1-1.0 µM) blocked the H2O2-induced death of HAECs and ROS generation. The H2O2-mediated upregulation of phosphorylated IKKß, phosphorylated IκBα, and phosphorylated NF-κB was suppressed by KA. KA also reduced the Bax/Bcl-2 ratio and cleaved caspase-3 expression, preventing H2O2-induced vascular endothelial cell apoptosis. Our results indicate that KA may protect against ROS-induced endothelial cell apoptosis and has considerable clinical potential in the prevention of atherosclerosis and cardiovascular diseases.

Sesamol Ameliorates Renal Injury-Mediated Atherosclerosis via Inhibition of Oxidative Stress/IKKα/p53.

Patients with chronic kidney disease (CKD) are at an increased risk of premature death due to the development of cardiovascular disease (CVD) owing to atherosclerosis-mediated cardiovascular events. However, the mechanisms linking CKD and CVD are clear, and the current treatments for high-risk groups are limited. In this study, we aimed to examine the effects of sesamol, a natural compound extracted from sesame oil, on the development of atherosclerosis in a rodent CKD model, and reactive oxygen species-induced oxidative damage in an endothelial cell model. ApoE-/- mice were subjected to 5/6 nephrectomy (5/6 Nx) and administered sesamol for 8 weeks. Compared with the sham group, the 5/6 Nx ApoE-/- mice showed a significant increase in malondialdehyde levels and Oil Red O staining patterns, which significantly decreased following sesamol administration. Sesamol suppressed H2O2-induced expression of phospho-IKKα, p53, and caspase-3. Our results highlight the protective role of sesamol in renal injury-associated atherosclerosis and the pathological importance of oxidative stress burden in CKD-CVD interaction.

Hydroxychloroquine might reduce risk of incident endometriosis in patients with systemic lupus erythematosus: A retrospective population-based cohort study.

BACKGROUND: SLE, which is common in women, is commonly treated with HCQ, an anti-inflammation medication. Reproductive-age women with SLE are prone to be impacted by endometriosis. This study analyzes the relationship between HCQ and endometriosis patients with SLE in order to determine whether HCQ is effective for treating the latter. METHODS: This population-based, retrospective cohort study analyzed the SLE risk in a cohort of newly diagnosed SLE patients with endometriosis during 2000 through 2013. Controls were selected at a 1:2 ratio through age-matching using the greedy algorithm. The Cox proportional hazard model was used to analyze the association between HCQ use and endometriosis incidence. Four different Cox regression models were used. Lastly, sensitivity analysis with PSOW and IPW was implemented to evaluate the hazard ratio (HR) of endometriosis after exposure with HCQ. RESULTS: In the cohort where age and sex matched high and low HCQ dosage, the average follow-up time was about 1 year. The cohort's overall incidence rates of endometriosis were 44.54 and 90.03 per 100000 person-month for high and low dosage respectively. The high dose group's conditional hazard ratio (aHR) for incidental endometriosis was 0.482 (CI = 0.191 to 1.213). The incidence rate and Kaplan-Meir curves of endometriosis were consistent with the results for the cohort. CONCLUSION: This study demonstrated that SLE patients continuously treated with HCQ have a lower risk of developing endometriosis. Clinically, HCQ can be beneficial for endometriosis patients with SLE.

Poncirin suppresses lipopolysaccharide (LPS)-induced microglial inflammation and ameliorates brain ischemic injury in experimental stroke in mice.

BACKGROUND: Based on accumulating evidence, excessive activation of microglia-mediated inflammatory responses plays an essential role in ischemic stroke. Poncirin (Pon) exerts anti-hyperalgesic, anti-osteoporotic and anti-tumor effects on various diseases. However, the roles of Pon in microglial activation and the underlying mechanism have not been elucidated. This study aimed to explore whether Pon inhibits lipopolysaccharide (LPS)-induced microglial neuroinflammation and protects against brain ischemic injury in experimental stroke in mice. METHODS: Primary microglia cells were prepared from the cerebral cortices of 1- to 2-day-old C57BL/6J mice. Murine BV2 cells and primary microglia were stimulated with LPS and the effects of a non-cytotoxic concentration of Pon on LPS-stimulated pro-inflammatory factors were measured using real-time PCR and enzyme-linked immunosorbent assays (ELISAs). Western blot analyses were used for mechanistic studies. In an in vivo study, 8-week-old male C57BL/6J mice were subjected to focal cerebral ischemia through middle cerebral artery occlusion (MCAO). Pon (30 mg/kg, i.p.) or the same volume of saline was administered after the MCAO model was established, and the infarct volume was evaluated using 2,3,5-triphenyltetrazolium chloride (TTC) staining. We also evaluated animal behaviours, the expression of pro-inflammatory cytokines and microglial activation in the ischemic hemisphere. RESULTS: Pon prevented the release of nitric oxide (NO), prostaglandin E2 (PGE2), interleukin (IL)-1ß, IL-6 and tumor necrosis factor-alpha (TNF-α) in both BV2 cells and primary microglia stimulated with LPS. The inhibitory effects of Pon were associated with the regulation of the ERK1/2, JNK and nuclear factor kappa B (NF-κB) signaling pathways. In mice that underwent MCAO, Pon administration decreased the lesion size and improved neurological deficits. Furthermore, Pon attenuated the production of inflammatory cytokines mainly by restraining microglial activation after ischemic stroke. CONCLUSIONS: Based on the findings from the present study, Pon provides neuroprotection through its anti-inflammatory effects on microglia and it may be a useful treatment for ischemic stroke.

Combined LDL and VLDL Electronegativity Correlates with Coronary Heart Disease Risk in Asymptomatic Individuals.

The most electronegative constituents of human plasma LDL (i.e., L5) and VLDL (i.e., V5) are highly atherogenic. We determined whether the combined electronegativity of L5 and V5 (i.e., L5 + V5) plays a role in coronary heart disease (CHD). In 33 asymptomatic individuals (ages 32-64), 10-year hard CHD risk correlated with age (r = 0.42, p = 0.01). However, in age-adjusted analyses, 10-year hard CHD risk correlated with L5 + V5 plasma concentration (r = 0.43, p = 0.01) but not age (p = 0.74). L5 + V5 plasma concentration was significantly greater in the group with high CHD risk (39.4 ± 22.0 mg/dL; n = 17) than in the group with low CHD risk (16.9 ± 14.8 mg/dL; n = 16; p = 0.01). In cultured human aortic endothelial cells, L5 + V5 treatment induced significantly more senescence-associated-ß-Gal activity than did equal concentrations of L1 + V1 (n = 4, p < 0.001). To evaluate the in vivo relevance of these findings, we fed ApoE-/- and wild-type mice with a high-fat diet and found that plasma LDL, VLDL, and LDL + VLDL from ApoE-/- mice exhibited significantly greater electrophoretic mobility than did wild-type counterparts (n = 6, p < 0.01). The increased electronegativity of LDL and VLDL in ApoE-/- mice was accompanied by increased aortic lipid accumulation and cellular senescence (n = 6, p < 0.05). Clinical trials are warranted to test the predictive value of L5 + V5 concentration in patients with CHD.

Increased electronegativity of high-density lipoprotein in uremia patients impairs its functional properties and is associated with the risk of coronary artery disease.

BACKGROUND AND AIMS: Uremia patients have impaired high-density lipoprotein (HDL) function and a high risk of coronary artery disease (CAD). Increased lipoprotein electronegativity can compromise lipoprotein function, but the effect of increased HDL electronegativity on HDL function and its association with CAD in uremia patient are not clear. We aimed to assess HDL electronegativity and various properties of HDL in uremia patients and investigate whether electronegative HDL is a risk factor for CAD in these individuals. METHODS: HDL from 60 uremia patients and 43 healthy controls was separated into 5 subfractions (H1H5) with increasing electronegativity by using anion-exchange chromatography. Lipoprotein content was analyzed by gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight-mass spectrometry. HDL anti-oxidant, anti-apoptosis and cholesterol efflux activities were examined by fluorescence-based assays. RESULTS: The percentage of H5 HDL (H5%) was significantly higher in uremia patients than in controls (p < 0.001). The concentration of apolipoprotein (Apo) AI was lower and apolipoprotein modifications were more prevalent in uremia HDL subfractions than in control HDL subfractions. Carbamylation of ApoAI and ApoCIII was increased in more electronegative HDL subfractions from uremia patients. Anti-oxidant activity, anti-apoptotic activity, and cholesterol efflux capability were reduced in HDL subfractions from uremia patients when compared with control HDL subfractions. Multiple logistic regression analysis showed that H5% was associated with CAD risk in uremia patients. CONCLUSIONS: In HDL of uremia patients, increased electronegativity is accompanied by compositional changes and impaired function. Our findings indicate that increased H5% is associated with increased CAD risk in uremia patients.

The Association of Flap Endonuclease 1 Genotypes with the Susceptibility of Endometriosis.

BACKGROUND/AIM: Flap endonuclease 1 (FEN1), a protein with multiple functions in genome stability maintenance, is important in cancer prevention. The two functional germline variants of FEN1, rs174538 and rs4246215, regarding cancer susceptibility have been reported in lung, breast, liver, esophageal, gastric, colorectal cancer, glioma and leukemia, but not endometriosis. In this study, we firstly aimed at evaluating the contribution of FEN1 genotypes to endometriosis risk in a representative Taiwan population. MATERIALS AND METHODS: In total, 153 patients with endometriosis and 636 non-cancer healthy controls were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. RESULTS: The genotypes of FEN1 rs174538, but not those of rs4246215, were differently distributed between the endometriosis and control groups. In detail, the AA of FEN1 rs174538 genotypes were significantly less frequently found among endometriosis patients than among controls (odds ratio [OR]=0.43, 95% confidence interval [CI]=0.24-0.78, p=0.0125). The A allele at FEN1 rs174538 was also significantly less frequent among cases than controls (OR=0.65, 95%CI=0.50-0.86, p=0.0021). As for age of first menarche, those with first menarche at the age >12.8 carrying the FEN1 rs174538 AA genotype conferred lower OR of 0.29 (95%CI=0.11-0.78, p=0.0381) for endometriosis. Regarding the full pregnancy status, those without having had a full-term pregnancy carrying the FEN1 rs174538 AA genotype were of lower risk (ORs=0.12, 95%CI=0.03-0.53, p=0.0050). CONCLUSION: The FEN1 rs174538 A allele is a novel protective biomarker for endometriosis and this genotype may have interactions with age- and hormone-related factors on the development of endometriosis.