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Patients at risk of chronic kidney disease (CKD) must be identified early and precisely in order to prevent complications, save lives, and limit expenditures for patients and health systems. This study aimed to develop a simple, high-precision machine learning model to identify individuals at risk of developing CKD in the near future, using a novel metabolic index with or without creatinine. This retrospective cohort study used data from the MJ medical record database collected between 2001 and 2015 in Taiwan. We used Cox hazard regression to identify potential predictors, including the novel metabolic index, for use as variables in the models. To develop a machine learning-based CKD risk model with fewer variables, we performed several experimental analyses to combine interacting variables into subsets. Those subsets were used to train three models, random forest, logistic regression, and XGBoost, with or without adding creatinine. The study included 12,189 participants, 20% with and 80% without CKD. The most important conventional predictors of CKD are age and gender. The novel metabolic index, TyG-Index, TG/HDL-ratio and VAI, had stronger predictive power than the conventional risk factors. Without including creatinine data, the XGBoost provided the best predictive performance. After adding creatinine, the performance of all the models was excellent, outperforming both conventional indicators and existing clinical algorithms for CKD. Using novel metabolic index in machine learning-based CKD risk prediction can accurately identify individuals at risk of diagnosis with CKD in the next year, with or without including creatinine. [ABSTRACT FROM AUTHOR]

Unhealthy diet and inappropriate lifestyle contribute to an imbalance in cardiometabolic profiles among postmenopausal women. This research aimed to analyze the association between dietary pattern and changes in cardiovascular risk factors among postmenopausal Taiwanese women using binary logistic regression. This cross-sectional study involved 5689 postmenopausal Taiwanese women aged 45 years and above, and the data were obtained from Mei Jau Health Management Institution database between 2001 and 2015. The cardiovascular risk dietary pattern characterized by high intakes of processed food, rice/flour products, organ meat, and sauce was derived by reduced rank regression. Participants in the highest quartile of the cardiovascular risk dietary pattern were more likely to have high levels of systolic blood pressure (OR = 1.29, 95% CI 1.08–1.53), diastolic blood pressure (OR = 1.28, 95% CI 1.01–1.62), atherogenic index of plasma (OR = 1.26, 95% CI 1.06–1.49), triglycerides (OR = 1.38, 95% CI 1.17–1.62), and fasting blood glucose (Q3: OR = 1.45, 95% CI 1.07–1.97). However, this dietary pattern was not correlated with total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and C-reactive protein. Therefore, adherence to the cardiovascular risk dietary pattern increases the risk of having higher levels of blood pressure, triglycerides, fasting blood glucose in postmenopausal Taiwanese women. [ABSTRACT FROM AUTHOR]

(1) Background: Nutritional intake is fundamental to human growth and health, and the intake of different types of nutrients and micronutrients can affect health. The content of the diet affects the occurrence of disease, with the incidence of many diseases increasing each year while the age group at which they occur is gradually decreasing. (2) Methods: An artificial intelligence model for precision nutritional analysis allows the user to enter the name and serving size of a dish to assess a total of 24 nutrients. A total of two AI models, including semantic and nutritional analysis models, were integrated into the Precision Nutritional Analysis. A total of five different algorithms were used to identify the most similar recipes and to determine differences in text using cosine similarity. (3) Results: This study developed two models to form a precision nutrient analysis model. The 2013–2016 Taiwan National Nutrition Health Status Change Survey (NNHS) was used for model verification. The model's accuracy was determined by comparing the results of the model with the NNHS. The results show that the AI model has very little error and can significantly improve the efficiency of the analysis. (4) Conclusions: This study proposed an Intelligence Precision Nutrient Analysis Model based on a digital data collection framework, where the nutrient intake was analyzed by entering dietary recall data. The AI model can be used as a reference for nutrition surveys and personal nutrition analysis. [ABSTRACT FROM AUTHOR]

This study examined the correlation of dietary patterns with components of metabolic syndrome (MetS) and inflammation among middle-aged and older adults with MetS in Taiwan. This cross-sectional study used data from the Mei Jau International Health Management Institution in Taiwan between 2004 and 2013. A total of 26,016 subjects aged 35 years and above were selected for analysis. MetS was defined according to the International Diabetes Federation. Three dietary patterns were identified by principal component analysis. High intake of a meat-instant food dietary pattern (rich in animal protein, saturated fat, sweets, sodium, and food additives) was positively associated with components of MetS and C-reactive protein (CRP), while high intake of a vege-seafood dietary pattern (rich in dietary fiber, vitamins, minerals, and unsaturated fat) or a cereal-dairy dietary pattern (rich in dietary fiber, antioxidants, phytochemicals, complex carbohydrate, prebiotics, and probiotics) was inversely associated with components of MetS and CRP. Our findings suggested that intake of a vege-seafood dietary pattern or a cereal-dairy dietary pattern decreased the risk of developing MetS and inflammation among middle-aged and older adults with MetS. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three year in Taiwan. It is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. This is a population-based study from NHIRD. A higher performance pancreatic cancer prediction model has been established. This predictive model can improve the awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated. (1) Background: Cancer has been the leading cause of death in Taiwan for 39 years, and among them, pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three years. While the incidence rate of pancreatic cancer is ranked at the bottom of the top 10 cancers, the survival rate is very low. Pancreatic cancer is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. Early screening is important for the treatment of pancreatic cancer. Only a few studies have designed predictive models for pancreatic cancer. (2) Methods: The Taiwan Health Insurance Database was used in this study, covering over 99% of the population in Taiwan. The subset sample was not significantly different from the original NHIRD sample. A machine learning approach was used to develop a predictive model for pancreatic cancer disease. Four models, including logistic regression, deep neural networks, ensemble learning, and voting ensemble were used in this study. The ROC curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. (3) Results: The AUC of the LR model was higher than the other three models in the external testing set for all three of the factor combinations. Sensitivity was best measured by the stacking model for the first factor combinations, and specificity was best measured by the DNN model for the second factor combination. The result of the model that used only nine factors (third factor combinations) was equal to the other two factor combinations. The AUC of the previous models for the early assessment of pancreatic cancer ranged from approximately 0.57 to 0.71. The AUC of this study was higher than that of previous studies and ranged from 0.71 to 0.76, which provides higher accuracy. (4) Conclusions: This study compared the performances of LR, DNN, stacking, and voting models for pancreatic cancer prediction and constructed a pancreatic cancer prediction model with accuracy higher than that of previous studies. This predictive model will improve awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated. [ABSTRACT FROM AUTHOR]

Background and objectives: Insulin resistance (IR) is frequently associated with chronic low-grade inflammation and has an important role as a mediator in the development of liver disease. Thus, this study aimed to explore the relationship between two indexes of IR and abnormal liver function parameters. Materials and Methods: This cross-sectional study obtained data of 41,510 men and 92,357 women aged ≥30 years from a private health screening institute in Taiwan. Two IR indexes namely triglyceride-glucose (TyG) index and triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio were used to examine their relationship to predict abnormal liver function parameters (aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP)). Results: Positive trend was shown for the association of TyG index in the highest quintile (Q5) and risk of high AST (OR = 1.45, 95% CI: 1.33–1.57), high ALT (OR = 1.85, 95% CI: 1.73–1.97), high GGT (OR = 2.04, 95% CI: 1.93–2.15), and high ALP (OR = 1.13, 95% CI: 1.07–1.19) compared with the median quintile (Q3) in the fully adjusted model. Similarly, participants in the Q5 of the TG/HDL-C ratio were associated with 1.38 (95% CI: 1.27–1.49), 1.71 (95% CI: 1.61–1.82), 1.75 (95% CI: 1.66–1.84), and 1.21 (1.16–1.27) odds for having high AST, ALT, GGT, and ALP respectively. The AUC (95% CI) value of the TyG index for predicting high AST, high ALT, and high GGT was 0.699 (0.692–0.705), 0.738 (0.734–0.742), and 0.752 (0.749–0.755), respectively. Meanwhile, the AUC (95% CI) of the TG/HDL-C ratio for predicting high AST, high ALT, and high GGT was 0.680 (0.673–0.686), 0.738 (0.734–0.742), 0.734 (0.731–0.738), respectively. Conclusions: Our study supported that the TyG index and TG/HDL-C ratio may be useful as non-invasive methods to predict the existence of impaired liver function in the early stage. [ABSTRACT FROM AUTHOR]

While diet and lifestyle are independently implicated in the etiology of liver disease, the interaction of diet and lifestyle may be more helpful for determining the risk of liver abnormality. Thus, our study aimed to evaluate the interaction between the dietary pattern associated with liver biomarkers and lifestyle factors among Taiwanese adults with abnormal liver enzymes. A liver-associated dietary pattern, generated using reduced rank regression, was characterized by high intake of soy sauce or other dips, sugar sweetened beverages, and preserved and processed foods, but low intake of seafood, fruits, eggs, and dark-colored vegetables. In the fully adjusted model, liver-associated dietary patterns or unhealthy concordance lifestyle factors were associated with an increased risk of having liver function abnormality (OR = 1.08, 95% CI: 1.04, 1.12 and OR = 1.42, 95% CI: 1.31, 1.53, respectively). Moreover, the interaction between liver-associated dietary pattern and unhealthy concordance lifestyle factors showed more significant correlation, with an elevated risk of abnormal liver function (OR = 2.14, 95% CI: 2.02, 2.26). Therefore, our study suggests that participants who have a strong liver-associated dietary pattern along with unhealthy concordance lifestyles are likely to have increased odds of abnormal liver function. [ABSTRACT FROM AUTHOR]

Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue's error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%. [ABSTRACT FROM AUTHOR]

We investigated the association between dietary patterns and serum hepatic enzyme levels in adults with dyslipidemia and impaired fasting glucose in Taiwan. A total of 15,005 subjects (5452 men and 9553 women) aged 35–69 years were selected. Two major dietary patterns were identified by principal component analysis: Western dietary pattern and Mediterranean dietary pattern. Subjects in the highest quartile (Q4) of the Western dietary pattern showed an increased risk of elevated serum alanine aminotransferase (ALT) levels (OR: 1.24, 95% CI: 1.06–1.45, p-trend = 0.01). Fur-thermore, in the highest quartile of the Western dietary pattern, subjects with high waist circum-ference were observed to have a greater risk for developing abnormal serum ALT levels compared to those in the lowest quartile (Q1) (OR: 1.43, 95% CI: 1.04–1.97, p-trend = 0.01). In the highest quartile of the Western dietary pattern, only women were at an increased risk for having abnormal serum ALT levels (OR: 1.28, 95% CI: 1.04–1.59, p-trend = 0.03). By contrast, in the highest quartile of the Mediterranean dietary pattern, only men were at a reduced risk for having abnormal serum gamma-glutamyl transferase (GGT) levels (OR: 0.72, 95% CI: 0.53–0.97, p-trend = 0.048). We report a positive association between the Western dietary pattern and abnormal serum ALT levels. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is the leading cause of death in Taiwan. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health In-surance Research Database (NHIRDB) was used to determine the diseases and symptoms associ-ated with lung cancer, and a 10-year probability deep neural network prediction model for lung cancer was developed. The proposed model could allow patients with a high risk of lung cancer to receive an earlier diagnosis and support the physicians' clinical decision-making. As a result, a total of 13 diseases were selected as the predicting factors. The proposed model showed an accuracy of 85.4%, a sensitivity of 72.4% and a specificity of 85%, as well as an 87.4% area under ROC (95%, 0.8604–0.8885) model precision. Based on data analysis and deep learning, our prediction model discovered some features that had not been previously identified by clinical knowledge. This study tracks a decade of clinical diagnostic records to identify possible symptoms and comorbidities of lung cancer, allows early prediction of the disease, and assists more patients with early diagnosis. Cancer is the leading cause of death in Taiwan. According to the Cancer Registration Report of Taiwan's Ministry of Health and Welfare, a total of 13,488 people suffered from lung cancer in 2016, making it the second-most common cancer and the leading cancer in men. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health Insurance Research Database (NHIRDB) was used to determine the diseases and symptoms associated with lung cancer, and a 10-year probability deep neural network prediction model for lung cancer was developed. The proposed model could allow patients with a high risk of lung cancer to receive an earlier diagnosis and support the physicians' clinical decision-making. The study was designed as a cohort study. The subjects were patients who were diagnosed with lung cancer between 2000 and 2009, and the patients' disease histories were back-tracked for a period, extending to ten years before the diagnosis of lung cancer. As a result, a total of 13 diseases were selected as the predicting factors. A nine layers deep neural network model was created to predict the probability of lung cancer, depending on the different pre-diagnosed diseases, and to benefit the earlier detection of lung cancer in potential patients. The model is trained 1000 times, the batch size is set to 100, the SGD (Stochastic gradient descent) optimizer is used, the learning rate is set to 0.1, and the momentum is set to 0.1. The proposed model showed an accuracy of 85.4%, a sensitivity of 72.4% and a specificity of 85%, as well as an 87.4% area under ROC (AUROC) (95%, 0.8604–0.8885) model precision. Based on data analysis and deep learning, our prediction model discovered some features that had not been previously identified by clinical knowledge. This study tracks a decade of clinical diagnostic records to identify possible symptoms and comorbidities of lung cancer, allows early prediction of the disease, and assists more patients with early diagnosis. [ABSTRACT FROM AUTHOR]

We evaluated the interactive effects of nutrition education (NE) and lifestyle factors on kidney function parameters and cardiovascular risk factors among chronic kidney disease (CKD) patients. This cross-sectional cohort study recruited 2176 CKD stages 3–5 patients aged > 20 years from Integrated Chronic Kidney Disease Care Network, Shuang Ho Hospital, Taiwan between December 2008 and April 2019. The multivariable regression analysis was performed to investigate the interactive effects of NE with lifestyle factors on kidney function parameters and cardiovascular risk factors. Relative excess risk due to interaction (RERI) and attributable proportion (AP) were applied to assess additive interaction. Patients who were smoking or physically inactive but received NE had better estimated glomerular filtration rate (eGFR) (β: 3.83, 95% CI: 1.17–6.49 or β: 3.67, 95% CI: 2.04–5.29) compared to those without NE. Patients with smoking and NE significantly reduced risks for having high glycated hemoglobin A1c (HbA1c) by 47%, high low-density lipoprotein cholesterol (LDL-C) by 38%, and high corrected calcium (C-Ca) by 50% compared to those without NE. Moreover, NE and smoking or inactive physical activity exhibited an excess risk of high C-Ca (RERI: 0.47, 95% CI: 0.09–0.85 for smoking or RERI: 0.46, 95% CI: 0.01–0.90 and AP: 0.51, 95% CI: 0.03–0.99 for physical activity). Our study suggests that CKD patients who were enrolled in the NE program had better kidney function. Thus, NE could be associated with slowing kidney function decline and improving cardiovascular risk factors. [ABSTRACT FROM AUTHOR]

Diets could play an important role in testicular function, but studies on how adherence to the dietary patterns influences human testicular function in Asian countries are scarce. Herein, we examined the association between testosterone-related dietary patterns and testicular function among adult men in Taiwan. This cross-sectional study recruited 3283 men who attended a private medical screening program from 2009 to 2015. Testosterone-related dietary pattern was generated by the reduced rank regression (RRR) method. The association between adherence to quartile of dietary pattern scores with sex hormones (testosterone, follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2)) and sperm quality (sperm concentration (SC), total sperm motility (TSM), progressive motility (PRM), and normal sperm morphology (NSM)) were examined by multivariable linear regression. Hemoglobin (β = 0.57, p < 0.001), hematocrit (β = 0.17, p = 0.002), triglyceride (β = −0.84, p < 0.001), HDL-cholesterol (β = 3.58, p < 0.001), total cholesterol to HDL-cholesterol ratio (β = −0.78, p < 0.001), and uric acid (β = −10.77, p < 0.001) were highly correlated with testosterone levels. Therefore, these biomarkers were used to construct a testosterone-related dietary pattern. Highest adherence (Q4) to dietary pattern scores were negatively associated with lower testosterone in the pooled analysis (β = −0.89, p = 0.037) and normal-weight men (β = −1.48, p = 0.019). Likewise, men in the Q4 of the dietary pattern had lower SC (β = −5.55, p = 0.001) and NSM (β = −2.22, p = 0.007) regardless of their nutritional status. Our study suggesting that testosterone-related dietary pattern (rich in preserved vegetables or processed meat or fish, deep-fried foods, innards organs, rice or flour products cooked in oil, and dipping sauce, but low in milk, dairy products, legumes, or beans, and dark or leafy vegetables) was associated with a poor testicular function. [ABSTRACT FROM AUTHOR]

The use of image-based dietary assessments (IBDAs) has rapidly increased; however, there is no formalized training program to enhance the digital viewing skills of dieticians. An IBDA was integrated into a nutritional practicum course in the School of Nutrition and Health Sciences, Taipei Medical University Taiwan. An online IBDA platform was created as an off-campus remedial teaching tool to reinforce the conceptualization of food portion sizes. Dietetic students' receptiveness and response to the IBDA, and their performance in food identification and quantification, were compared between the IBDA and real food visual estimations (RFVEs). No differences were found between the IBDA and RFVE in terms of food identification (67% vs. 71%) or quantification (±10% of estimated calories: 23% vs. 24%). A Spearman correlation analysis showed a moderate to high correlation for calorie estimates between the IBDA and RFVE (r ≥ 0.33~0.75, all p < 0.0001). Repeated IBDA training significantly improved students' image-viewing skills [food identification: first semester: 67%; pretest: 77%; second semester: 84%) and quantification [±10%: first semester: 23%; pretest: 28%; second semester: 32%; and ±20%: first semester: 38%; pretest: 48%; second semester: 59%] and reduced absolute estimated errors from 27% (first semester) to 16% (second semester). Training also greatly improved the identification of omitted foods (e.g., condiments, sugar, cooking oil, and batter coatings) and the accuracy of food portion size estimates. The integration of an IBDA into dietetic courses has the potential to help students develop knowledge and skills related to "e-dietetics". [ABSTRACT FROM AUTHOR]

This study explored the association between dietary patterns and kidney function parameters in adults with metabolic syndrome in Taiwan. This cross-sectional study was undertaken in 56,476 adults from the health screening centers in Taiwan from 2001 to 2010. Dietary intake and dietary patterns were assessed using a food frequency questionnaire and principal component analysis, respectively. Blood urea nitrogen (BUN), creatinine, estimated glomerular filtration rate (eGFR), and uric acid were measured as clinical parameters of kidney function. Multivariate linear regression was conducted to explore the relationship between dietary patterns and kidney function parameters. After adjusting for confounders, the highest tertiles of the processed food–sweets dietary pattern and the meat–seafood–eggs dietary pattern were associated with increased BUN, creatinine, and uric acid but decreased eGFR (all adjusted p < 0.05). Meanwhile, the highest tertiles of the veggie–fruit–grains dietary pattern and the milk–dairy dietary pattern were associated with decreased BUN, creatinine, and uric acid but increased eGFR (all adjusted p < 0.05). A processed food–sweets dietary pattern or a meat–seafood–eggs dietary pattern is associated with worse kidney function parameters in adults with metabolic syndrome. In contrast, a veggie–fruit–grains dietary pattern or a milk–dairy dietary pattern is associated with better kidney function parameters. [ABSTRACT FROM AUTHOR]

Personal health records (PHRs) have lots of benefits for things such as health surveillance, epidemiological surveillance, self-control, links to various services, public health and health management, and international surveillance. The implementation of an international standard for interoperability is essential to accessing personal health records. In Taiwan, the nationwide exchange platform for electronic medical records (EMRs) has been in use for many years. The Health Level Seven International (HL7) Clinical Document Architecture (CDA) was used as the standard of the EMRs. However, the complication of implementing CDA became a barrier for many hospitals to realize the standard EMRs. In this study, we implemented a Fast Healthcare Interoperability Resources (FHIR)-based PHR transformation process including a user interface module to review the contents of PHRs. We used "My Health Bank, MHB", a PHR data book developed and issued to all people by the Taiwan National Health Insurance, as the PHRs contents in this study. Network Time Protocol (NTP)/Simple Network Time Protocol (SNTP) was used in the security and user authentication mechanism when processing and applying personal health information. Transport Layer Security (TLS) 1.2 (such as HyperText Transfer Protocol Secure (HTTPS) was used for protection in data communication. User authentication is important in the platform. OAuth (OAuth 2.0) was used as a user authentication mechanism to confirm legitimate user access to ensure data security. The contents of MHB were analyzed and mapped to the FHIR, and then converted to FHIR format according to the mapping logic template. The function of format conversion was carried out by using ASP.NET. XPath and JSPath technologies filtered out specific information tags. The converted data structure was verified through an HL7 Application Programming Interface (HAPI) server, and a new JSON file was finally created. This platform can not only capture any PHR based on the FHIR format but also publish FHIR-based MHB records to any other platform to bridge the interoperability gap between different PHR systems. Therefore, our implementation/application with the automatic transformation from MHB to FHIR format provides an innovative method for people to access their own PHRs (MHB). No one has published a similar application like us using a nationwide PHR standard, MHB, in Taiwan. The application we developed will be very useful for a single person to use or for other system developers to implement their own standard PHR software. [ABSTRACT FROM AUTHOR]

Dyslipidemia, anemia, and inflammation are associated with declined kidney function. This study investigated the association of inflammatory dietary pattern with dyslipidemia, anemia, and kidney function biomarkers among middle-aged and older Taiwanese adults with declined kidney function. Biochemical data and food frequency questionnaire were obtained from 41,128 participants with estimated glomerular filtration rate (eGFR) <90 mL/min/1.73 m2 and positive urinary protein. Inflammatory dietary pattern was identified by reduced rank regression with C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (N/L) as response variables. Males had higher prevalence of dyslipidemia and higher inflammatory markers, but lower prevalence of anemia and lower eGFR levels compared to females. Inflammatory dietary pattern characterized with low intakes of seafood, grains, vegetables, and fruits but high intakes of meat, eggs, preserved/processed foods, and sugary drinks was associated with an increased risk of dyslipidemia by 21% in males and an increased risk of anemia by 28–47% in both genders. Furthermore, high consumption of inflammatory dietary pattern was associated with reduced eGFR (males β = −0.85, 95% CI −1.26 to −0.43, females β = −0.53, 95% CI −0.98 to −0.08) and increased N/L and/or CRP in both genders. In conclusion, inflammatory dietary pattern is positively associated with dyslipidemia, anemia, and decreased kidney function in middle-aged and older adults with declined kidney function. [ABSTRACT FROM AUTHOR]

The study determined the association of sleep duration and insomnia symptoms with the components of metabolic syndrome and inflammation in middle-aged and older adults with metabolic syndrome in Taiwan. This cross-sectional study used the database compiled in Taiwan between 2004–2013. A total of 26,016 volunteers aged 35 years and above were selected. Metabolic syndrome was defined according to the International Diabetes Federation. Compared with regular sleep duration (6–8 h/day), short (<6 h/day) or long sleep duration (>8 h/day) and insomnia symptoms significantly increased the odds ratios of high waist circumference, high blood pressure, low high-density lipoprotein-cholesterol, high triglycerides, high fasting blood glucose, and high C-reactive protein. Insomnia symptoms did not modify the effects of sleep duration on the components of metabolic syndrome and inflammation. Our study suggests that short or long sleep duration and insomnia symptoms may have an adverse effect on metabolic syndrome and inflammation. [ABSTRACT FROM AUTHOR]

Dietary patterns have been proposed to be related to dyslipidemia and hyperglycemia. This study investigated the correlation of dietary patterns with components of dyslipidemia and fasting plasma glucose (FPG) among young and middle-aged adults (aged between 20 and 50 years) with dyslipidemia and abnormal FPG in Taiwan. This cross-sectional study used the database compiled in Taiwan between 2001 to 2010. A total of 13,609 subjects aged between 20 and 50 years were selected. Dyslipidemia was defined primarily according to the National Cholesterol Education Program Adult Treatment Panel III guidelines with minor modification. Elevated FPG level was defined according to the American Diabetes Association. The factor analysis was conducted to identify three dietary patterns. Higher scores of the meat–convenience dietary pattern (high intake of deep-fried and processed food, sauces, sugar-added beverages, meat and organ meats, instant noodles, rice or flour cooked in oil, and eggs) had no association with components of dyslipidemia and abnormal FPG. Higher scores of the vegetables–fruits–seafood dietary pattern (high intake of vegetables, vegetables with oil or dressing, fruits, seafood, legumes, soy products, and rice or flour products) was inversely associated with hypercholesterolemia and positively associated with hyperglycemia. Higher scores of the dairy–complex carbohydrate dietary pattern (high intake of dairy products, milk, root crops, jam or honey, and whole grains) was inversely correlated with hypertriglycemia and low high-density lipoprotein cholesterol level. Our results support that the dietary pattern may have a role in the prevention and management of dyslipidemia and abnormal fasting plasma glucose. [ABSTRACT FROM AUTHOR]

Background and objectives: Insulin resistance (IR) is frequently associated with chronic low-grade inflammation and has an important role as a mediator in the development of liver disease. Thus, this study aimed to explore the relationship between two indexes of IR and abnormal liver function parameters. Materials and Methods: This cross-sectional study obtained data of 41,510 men and 92,357 women aged ≥30 years from a private health screening institute in Taiwan. Two IR indexes namely triglyceride-glucose (TyG) index and triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio were used to examine their relationship to predict abnormal liver function parameters (aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP)). Results: Positive trend was shown for the association of TyG index in the highest quintile (Q5) and risk of high AST (OR = 1.45, 95% CI: 1.33-1.57), high ALT (OR = 1.85, 95% CI: 1.73-1.97), high GGT (OR = 2.04, 95% CI: 1.93-2.15), and high ALP (OR = 1.13, 95% CI: 1.07-1.19) compared with the median quintile (Q3) in the fully adjusted model. Similarly, participants in the Q5 of the TG/HDL-C ratio were associated with 1.38 (95% CI: 1.27-1.49), 1.71 (95% CI: 1.61-1.82), 1.75 (95% CI: 1.66-1.84), and 1.21 (1.16-1.27) odds for having high AST, ALT, GGT, and ALP respectively. The AUC (95% CI) value of the TyG index for predicting high AST, high ALT, and high GGT was 0.699 (0.692-0.705), 0.738 (0.734-0.742), and 0.752 (0.749-0.755), respectively. Meanwhile, the AUC (95% CI) of the TG/HDL-C ratio for predicting high AST, high ALT, and high GGT was 0.680 (0.673-0.686), 0.738 (0.734-0.742), 0.734 (0.731-0.738), respectively. Conclusions: Our study supported that the TyG index and TG/HDL-C ratio may be useful as non-invasive methods to predict the existence of impaired liver function in the early stage.