Sex Disparity in the Association of Metabolic Syndrome with Cognitive Impairment.

Background/Objectives: Metabolic syndrome (MS) is a constellation of several cardiometabolic risk factors. We investigated sex disparity in the associations between MS and cognitive impairment using cross-sectional data from Taiwan Biobank. Methods: We determined the associations of MS and its five components with cognitive impairment (mini-mental state examination, MMSE < 24) and the five domains of MMSE using logistic regression analyses. Results: A total of 7399 men and 11,546 women were included, and MS was significantly associated with cognitive impairment only in women (adjusted OR 1.48, 95% CI 1.29-1.71, p = 0.001) (p for interaction 0.005). In women, the association with MS was significant in orientation (adjusted OR 1.21, 95% CI 1.07-1.37, p = 0.003), memory (adjusted OR 1.12, 95% CI 1.01-1.25, p = 0.034) and design copying (adjusted OR 1.41, 95% CI 1.23-1.62, p = 0.001) (p value for interaction 0.039, 0.023, and 0.093, respectively). Among the components of MS, a large waist circumference (adjusted OR 1.25, 95% CI 1.08-1.46, p = 0.003), high fasting glucose (adjusted OR 1.16, 95% CI 1.00-1.34, p = 0.046), and low HDL cholesterol (adjusted OR 1.16, 95% CI 1.00-1.34, p = 0.049) were significantly associated with cognitive impairment in women. Conclusions: Our findings suggest that sex has a significant influence on the association between MS and cognitive dysfunction, especially in orientation and memory.

Interaction between type 2 diabetes polygenic risk and physical activity on cardiovascular outcomes.

AIM: The beneficial effects of exercise on reducing the risk of cardiovascular disease are established. However, the potential interaction between genetic risk for type 2 diabetes and physical activity on cardiovascular outcomes remains elusive. We aimed to investigate the effect of type 2 diabetes genetic risk-physical activity interaction on cardiovascular outcomes in individuals with diabetes. METHODS: Using the UK Biobank cohort, we investigated the effect of type 2 diabetes genetic risk-physical activity interaction on 3-point and 4-point major adverse cardiovascular events (MACE), in 25,701 diabetic participants. We used a polygenic risk score for type 2 diabetes (PRS_T2D) as a measure of genetic risk for type 2 diabetes. RESULTS: We observed significant interaction between PRS_T2D and physical activity on cardiovascular outcomes (3-point MACE: P trend for interaction = 0.0081; 4-point MACE: P trend for interaction = 0.0037). Among participants whose PRS_T2D was in the first or second quartile, but not in the third or fourth quartile, each 10 metabolic equivalents (METs) hours per week of physical activity decreased the risk of 3-point or 4-point MACE. Furthermore, restricted cubic spline analysis indicated that intense physical activity (>80 METs hours per week, which was self-reported by 12.7% of participants) increased the risk of cardiovascular outcomes among participants whose PRS_T2D was in the fourth quartile. Subgroup analysis suggested that negative impact of intense physical activity was observed only in non-insulin users. CONCLUSIONS: The beneficial effect of physical activity on cardiovascular outcomes were disappeared among those with high genetic risk for type 2 diabetes.

The beneficial effects of exercise on reducing the risk of cardiovascular disease are established. However, whether genetic risk for type 2 diabetes influences the effect of physical activity on cardiovascular outcomes in individuals with diabetes remains elusive. We aimed to investigate interaction between genetic risk for type 2 diabetes and physical activity on major adverse cardiovascular events in individuals with diabetes. The beneficial effect of physical activity on cardiovascular outcomes were disappeared among diabetic individuals with high genetic risk for type 2 diabetes, due to significant gene-environment interaction; in this subpopulation, intense physical activity was associated with increased risk of cardiovascular outcomes. Personalized exercise recommendations tailored to avoid excessively intense exercise, in combination with genetic screening of high-risk individuals, would be required.

Effects of Citrus depressa Hayata juice on high-fat diet-induced obesity in HBV transgenic mice.

The present study investigated the potential anti-obesity properties of Citrus depressa Hayata (CDH) juice in HBV transgenic mice, as well as the impact of fermentation on the effectiveness of the juice. The results revealed that fermentation increased the levels of polyphenols and hesperidin in CDH juice. The animal study demonstrated that both juices were effective in mitigating the weight gain induced by a high-fat diet by correcting metabolic parameter imbalances, reducing hepatic lipid accumulation, and reversing hepatic immune suppression. Furthermore, fermented juice exhibited superior efficacy in managing body weight and inhibiting the expansion of white adipose tissue (WAT). Fermented juice significantly enhanced adiponectin production and PPARγ expression in WAT, while also reducing hypertrophy. This study offers valuable insights into the potential role of CDH juices in combating obesity associated with high fat consumption and underscores the promise of CDH juice as a functional beverage.

Effects of AST-120 on mortality in patients with chronic kidney disease modeled by artificial intelligence or traditional statistical analysis.

Chronic kidney disease (CKD) imposes a substantial burden, and patient prognosis remains grim. The impact of AST-120 (AST-120) on the survival of CKD patients lacks a consensus. This study aims to investigate the effects of AST-120 usage on the survival of CKD patients and explore the utility of artificial intelligence models for decision-making. We conducted a retrospective analysis of CKD patients receiving care in the pre-end-stage renal disease (ESRD) program at Taichung Veterans General Hospital from 2000 to 2019. We employed Cox regression models to evaluate the relationship between AST-120 use and patient survival, both before and after propensity score matching. Subsequently, we employed Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) models to assess their performance in predicting AST-120's impact on patient survival. Among the 2584 patients in our cohort, 2199 did not use AST-120, while 385 patients received AST-120. AST-120 users exhibited significantly lower mortality rates compared to non-AST-120 users (13.51% vs. 37.88%, p < 0.0001) and a reduced prevalence of ESRD (44.16% vs. 53.17%, p = 0.0005). Propensity score matching at 1:1 and 1:2 revealed no significant differences, except for dialysis and all-cause mortality, where AST-120 users exhibited significantly lower all-cause mortality (p < 0.0001), with a hazard ratio (HR) of 0.395 (95% CI = 0.295-0.522). This difference remained statistically significant even after propensity matching. In terms of model performance, the XGBoost model demonstrated the highest accuracy (0.72), specificity (0.90), and positive predictive value (0.48), while the logistic regression model showed the highest sensitivity (0.63) and negative predictive value (0.84). The area under the curve (AUC) values for logistic regression, DNN, and XGBoost were 0.73, 0.73, and 0.69, respectively, indicating similar predictive capabilities for mortality. In this cohort of CKD patients, the use of AST-120 is significantly associated with reduced mortality. However, the performance of artificial intelligence models in predicting the impact of AST-120 is not superior to statistical analysis using the current architecture and algorithm.

The association between bone density of lumbar spines and different daily protein intake in different renal function.

BACKGROUND: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. METHODS: We studied the database of the National Health and Nutrition Examination Survey, 2005-2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8-1.0, 1.0-1.2, and >1.2 g/kg/day). RESULTS: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8-1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062-1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057-1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015-1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051-1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098-1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8-1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. CONCLUSIONS: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis.

A low-protein diet should be encouraged in patients with CKD, but protein is essential for bone health. In this study, we showed that a low-protein diet did not affect lumbar bone density. Therefore, in the care of CKD, a low-protein diet is beneficial for renal function and without harm to lumbar bone health.

The joint effects of physical activity and sleep duration on risk of osteoporosis in Taiwanese adult population: The Taiwan Biobank Study.

Most studies investigating the association between physical activity and osteoporosis prevention only focused on specific types of physical activity. This study's evidence regarding the combined effects or interaction of sleep duration and physical activity. The findings emphasize the role of sleep duration and physical activity in association with osteoporosis. PURPOSE: The associations between physical activity, sleep duration, and prevalent osteoporosis in Taiwanese adults were studied in this cross-sectional study. METHODS: The Taiwan Biobank enrolled a community-based cohort of ~ 120,000 volunteers (as of April 30, 2020) between 30 and 76 years of age with no history of cancer. Amongst, bone mineral density (BMD) measures by dual-energy X-ray absorptiometry (DXA) were available in 22,402 participants. After excluding individuals who had no complete data of BMI (n = 23), MET score (n = 207), T-score (n = 8,826), and sleep duration (n = 16), 13,330 subjects were included as the primary cohort. Univariate and multivariable regression analyses were performed to determine the associations between the presence of osteoporosis, physical activity level, sleep duration, and other variables. RESULTS: The results showed that after adjustment, subjects with physical activity < 20 METs/week and ≥ 20 METs/week (aOR = 1.017 and 0.767, respectively) were associated with risk of osteoporosis than those with zero MET. The odds of osteoporosis were not significantly lower in subjects who slept for ≥ 8 h/day (aOR = 0.934,p=0.266). In addition, compared to short sleepers with no physical activity, adults with increased physical activity ≥ 20 METs/week and sleep ≥ 8 h/day had a significantly lowest likelihood of osteoporosis (aOR = 0.702). Those with medium physical activity (< 20 METs/week) plus average sleep duration (6.5-8 h/day) did not have significant higher odds of osteoporosis (aOR = 1.129,p=0.151). CONCLUSION: The findings emphasize the joint role of sleep duration and physical activity in association with osteoporosis. Adults with high physical activity plus high sleep hours have the highest BMD and lowest risk of osteoporosis.

Risk factors and mortality of SARS-CoV-2 reinfection during the Omicron era in Taiwan: A nationwide population-based cohort study.

BACKGROUND: Prior to 2022, Taiwan had effectively contained the domestic COVID-19 epidemic. However, during 2022, the country encountered multiple large outbreaks of COVID-19, with patients experiencing their first or second infection (reinfection) were both predominantly caused by the Omicron variant. Data are lacking on the risk factors and mortality of COVID-19 reinfection in Omicron era. METHODS: In this retrospective population-based cohort study, we recruited COVID-19 patients with their first episode confirmed between April 1, 2022 and June 11, 2022. A reinfection patient was defined as an individual who infected again by SARS-CoV-2 with an interval of more than 90 days. Demographic characteristics, severity of underlying diseases, and vaccination status were adjusted to identify risk factors for reinfection and to further evaluate the hazard of all-cause mortality within 30 days between reinfection and non-reinfection patients. RESULTS: There were 28,588 reinfection patients matched with 142,940 non-reinfection patients included in this study. We found that being female, younger in age, having more severe underlying diseases, and not being fully vaccinated against COVID-19 were risk factors for reinfection. After adjusting for confounding factors, reinfection patients were at a significantly higher risk of all-cause mortality within 30 days (aHR = 4.29, 95% CI: 3.00-6.12, p < 0.001) comparing with non-reinfection patients. CONCLUSION: During the SARS-CoV-2 Omicron era, reinfection patients were observed to have an increased risk of all-cause mortality. To reduce the disease burden and minimize the risk of reinfection, it is crucial for vulnerable patients to receive full vaccination and adhere to recommended precautions.

Bioactive phytochemicals from the tubers of Bletilla striata Rchb.f.

Thirty-four phytochemicals were isolated from dry tubers of Bletilla striata Rchb.f. The compounds were classified as bibenzyls 1-14, dihydrophenanthrenes 15, 17, 20, 21, phenanthrenes 16, 18, 19, simple benzoids 22-24, a fatty acid 25, glucosyloxybenzyl 2-isobutylmalates 26-32, and glucosyloxybenzyl cinnamates 33, 34. Compounds 1-4, 7, 8, 11, 12, and 16 inhibited melanogenesis (17.96-55.27%) induced by α-MSH in B16F10 cells at 10-40 µM. However, compounds 9, 10, 17, 18, and 21 exhibited significant cytotoxicity against melanomas, with IC50 values of 12-34 µM. Additionally, compounds 15, 17, 19, 20, 23, 31, and 33 reduced the ROS generation induced by H2O2 in HaCaT cells at 6.25-100 µM. In particular, compounds 15, 19, and 20 strongly inhibited ROS generation, with IC50 values of 2.15-9.48 µM. Consequently, compounds 1-4, 7-12, and 15-21 may be the strongest leads to follow in B. striata extract for further research on the skin disorders, hyperpigmentation, melanoma, and ageing.

Age-related differences in understanding pronominal reference in sentence comprehension: An electrophysiological investigation.

This study aimed to investigate how age affects the ability to comprehend sentence meaning, specifically how individuals resolve pronouns to their corresponding nouns. The study included 34 young participants (20-29 years old) and 34 older participants (60-81 years old). The participants were presented with sentences containing two characters and a third-person singular pronoun. Stereotypical genders associated with character names were manipulated such that the pronoun had either one, two, or no possible antecedents, rendering the pronoun referentially unambiguous, ambiguous, or mismatched, respectively. Consistent with the prior findings on preserved syntactic processing with advanced age, event-related potential data time-locked to the critical pronouns showed a P600 effect to mismatched pronouns regardless of age. These results indicate that older adults, like their younger counterparts, have a strong preference for readily available antecedents. When the pronoun was ambiguous, younger adults showed a typical Nref effect-a sustained anterior negativity associated with elaborative inferencing to search for the referent. Older adults did not exhibit this effect, suggesting a reduction in elaborative processes for establishing coherence. Nevertheless, the Nref response to ambiguous pronouns was observed in a subset of older adults, who also showed a Nref instead of P600 response to mismatched pronouns. Overall, individuals who elicited the Nref response to ambiguous pronouns were associated with a higher level of print exposure, suggesting that life-long reading experience may help to counteract age-related decline. Together, these findings help characterize the differential effects of aging on pronominal understanding and provide initial electrophysiological evidence of the protective benefit of print exposure on language processing in the aging population. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study.

Background: Insulin resistance (IR) is associated with diabetes mellitus, cardiovascular disease (CV), and mortality. Few studies have used machine learning to predict IR in the non-diabetic population. Methods: In this prospective cohort study, we trained a predictive model for IR in the non-diabetic populations using the US National Health and Nutrition Examination Survey (NHANES, from JAN 01, 1999 to DEC 31, 2012) database and the Taiwan MAJOR (from JAN 01, 2008 to DEC 31, 2017) database. We analysed participants in the NHANES and MAJOR and participants were excluded if they were aged <18 years old, had incomplete laboratory data, or had DM. To investigate the clinical implications (CV and all-cause mortality) of this trained model, we tested it with the Taiwan biobank (TWB) database from DEC 10, 2008 to NOV 30, 2018. We then used SHapley Additive exPlanation (SHAP) values to explain differences across the machine learning models. Findings: Of all participants (combined NHANES and MJ databases), we randomly selected 14,705 participants for the training group, and 4018 participants for the validation group. In the validation group, their areas under the curve (AUC) were all >0.8 (highest being XGboost, 0.87). In the test group, all AUC were also >0.80 (highest being XGboost, 0.88). Among all 9 features (age, gender, race, body mass index, fasting plasma glucose (FPG), glycohemoglobin, triglyceride, total cholesterol and high-density cholesterol), BMI had the highest value of feature importance on IR (0.43 for XGboost and 0.47 for RF algorithms). All participants from the TWB database were separated into the IR group and the non-IR group according to the XGboost algorithm. The Kaplan-Meier survival curve showed a significant difference between the IR and non-IR groups (p < 0.0001 for CV mortality, and p = 0.0006 for all-cause mortality). Therefore, the XGboost model has clear clinical implications for predicting IR, aside from CV and all-cause mortality. Interpretation: To predict IR in non-diabetic patients with high accuracy, only 9 easily obtained features are needed for prediction accuracy using our machine learning model. Similarly, the model predicts IR patients with significantly higher CV and all-cause mortality. The model can be applied to both Asian and Caucasian populations in clinical practice. Funding: Taichung Veterans General Hospital, Taiwan and Japan Society for the Promotion of Science KAKENHI Grant Number JP21KK0293.

Associations of Long-Term Physical Activity Trajectories With All-Cause Mortality in a General Population.

Objectives: We investigated the associations of mean levels of leisure-time physical activity (LTPA) and latent LTPA trajectories with all-cause mortality risk. Methods: Trajectories of LTPA were established using group-based trajectory analysis with a latent class growth model in a population-based cohort between 1996 and 2014. A Cox-proportional hazard model was conducted to examine the associations of LTPA quintiles and LTPA trajectories with all-cause mortality. Results: A total of 21,211 participants (age 18-90 years) were analyzed (median follow-up 16.8 years). The study participants were divided into five groups according to percentiles of LTPA (<20th, 20th-<40th, 40th-<60th, 60th-<80th, ≥80th) and LTPA trajectories (low/stable, medium/stable, increasing, decreasing, and fluctuating), respectively. Participants with a decreasing trajectory did not have a significantly lower risk of all-cause mortality despite having the highest baseline level of LTPA. In contrast, participants with a medium/stable (HR 0.84, 95% CI 0.72-0.98, p = 0.031) or an increasing (HR 0.57, 95% CI 0.33-0.97, p = 0.037) trajectory had a significantly lower risk of all-cause mortality. Conclusion: Promotion of maintaining stable LTPA is beneficial for public health and survival.

Risk of all-cause mortality in subjects with varying degrees of renal impairment with or without dietary protein restriction.

BACKGROUND AND OBJECTIVE: Low-protein diet (less than 0.8g/kg/day) has been practiced in the management of chronic kidney disease (estimated glomerular filtration rate [eGFR]<60ml/min/1.73m2 or urine albumin-to-creatinine ratios [UACR] ≥30mg/g) for decades. However, its effect on all-cause mortality is unclear. We investigated the association between a low-protein intake and all-cause mortality in subjects with varying degrees of renal impairment. MATERIALS AND METHODS: We analyzed participants in the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2010. They were divided into four groups according to their eGFR (≥60 or <60ml/min/1.73m2) and UACR (≥30 or <30mg/g). Daily protein intake of the NHANES participants could be assessed using information from the dietary interview questionnaires. The mortality data was retrieved by linking to the National Death Index till the end of 2011. The hazard ratios for all-cause mortality were evaluated by the weighted Cox proportional hazards regression models. RESULTS: A total of 8093 participants were analyzed. During a median follow-up of 4.7 years, participants with UACR≥30mg/g (with or without eGFR<60ml/min/1.73m2) had a higher risk of all-cause mortality compared with those having UACR<30mg/g and eGFR≥60ml/min/1.73m2 (reference group). The higher risk of mortality in participants with UACR≥30mg/g was consistently observed in those with or without a low-protein intake. CONCLUSIONS: A low-protein intake was not associated with a lower risk of all-cause mortality in subjects with varying degrees of renal impairment.

Adherence to the dietary approaches to stop hypertension diet and all-cause mortality in patients with a history of heart failure.

Background and aims: We investigated the association of adherence to the Dietary Approaches to Stop Hypertension (DASH) diet with all-cause mortality in patients with a history of heart failure. Methods: We analyzed data from the National Health and Nutrition Examination Survey (NHANES). Dietary information was obtained from a 24-h dietary recall interview. Adherence to the DASH diet was assessed using the DASH score. The primary outcome was all-cause mortality which was confirmed by the end of 2011. Weighted Cox proportional hazards regression models were used to determine the hazard ratios and 95% CI for the association of the DASH score and all-cause mortality with multivariate adjustment. Results: The median DASH score was 2 among the 832 study participants. There were 319 participants who died after a median follow-up duration of 4.7 years. A higher DASH score (>2 vs. ≤ 2) was not associated with a decrease in the risk of all-cause mortality (adjusted HR 1.003, 95% CI 0.760-1.323, p = 0.983). With respect to the components of the DASH score, a lower sodium intake was not associated with a decreased risk of mortality (adjusted HR 1.045, 95% CI 0.738-1.478, p = 0.803). Conclusion: A higher DASH score (>2 vs. ≤ 2) was not associated with all-cause mortality in patients with heart failure.

Associations of insulin resistance and insulin secretion with bone mineral density and osteoporosis in a general population.

We investigated the associations of insulin resistance and ß-cell secretion with bone mineral density (BMD) and osteoporosis using data from the National Health and Nutrition Examination Survey. Data on BMD assessed using dual-energy x-ray absorptiometry from 5292 participants were analyzed. Insulin resistance and ß-cell secretion were assessed using the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and ß-cell function (HOMA-ß), respectively. We divided the study population into four groups according to HOMA-IR (<2 vs. ≥ 2) and HOMA-ß (<100 vs. ≥ 100). BMD and T score at the lumbar spine, hip joint, and femur were used for analyses. Osteoporosis was defined as a T score ≤ -2.5. Logistic regression analyses were conducted to examine the associations of HOMA-IR and HOMA-ß with osteoporosis, and the joint effects of HOMA-IR and HOMA-ß on osteoporosis. We found a positive association between HOMA-IR and osteoporosis in participants with a HOMA-ß ≥ 100 (OR 8.773, 95% CI 2.160-35.637, p=0.002 at the femoral neck). A negative association between HOMA-ß and osteoporosis was noted in those with a HOMA-IR <2 (OR 0.183, 95% CI 0.038-0.882, p=0.034 at the femoral neck). Compared with participants who had HOMA-IR <2 and HOMA-ß <100, those with HOMA-IR <2 and HOMA-ß ≥ 100 had a lower risk of osteoporosis (OR 0.126, 95% CI 0.020-0.805, p=0.032 at the femoral neck). In conclusion, the association between HOMA-ß and BMD/osteoporosis changed as HOMA-IR increased. HOMA-ß was negatively associated with osteoporosis when HOMA-IR <2. The association was not significant when HOMA-IR ≥ 2.

Lower All-Cause Mortality for Coronary Heart or Stroke Patients Who Adhere Better to Mediterranean Diet-An NHANES Analysis.

Consuming a Mediterranean-style diet (MED) is helpful for primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, few studies have compared mortality in ASCVD subjects with different degrees of adherence to the MED diet or have evaluated the contributions of individual diet components. We analyzed National Health and Nutrition Examination Survey (NHANES) participants with a history of coronary heart disease (CHD) or stroke (N = 2052) in a period from 1999 to 2010. Their individual vital status was linked to the National Death Index till the end of 2011. The level of adherence to the MED diet was quantified using a 9-point evaluation score (aMED score). Cox regression models were used to compare the different levels of adherence to the MED diet, and contributions of individual components of the MED diet on cardiovascular, cancer, and all-cause mortality. Among the 2052 subjects with CHD or stroke, 29.0% (596 of 2052) died after a median follow-up of 5.6 years. In Cox regression analysis, higher absolute aMED score (HR 0.798, p = 0.0079) or above median aMED score (score 4-9) (HR 0.646, p = 0.0013) was negatively associated with all-cause mortality. Among various components of the MED diet, intake of more whole grains or nuts was significantly associated with a lower all-cause mortality. In contrast, a higher aMED score was not associated with less cardiovascular mortality. In a secondary analysis that excluded deaths within 2 years of the NHANES study entry, the above median aMED score (score 4-9) was negatively associated with both all-cause and cardiovascular mortality. In conclusion, subjects with a history of CHD or stroke adhering better to the MED diet in the NHANES study had a lower all-cause mortality during follow-ups. Consuming more whole grains or nuts had a lower all-cause mortality. The protective effect of adherence to the MED diet on decreasing cardiovascular mortality was seen only after excluding those who died within first two years of the NHANES study entry.

Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach.

Background: Chronic kidney disease (CKD) is a complex syndrome without a definitive treatment. For these patients, insulin resistance (IR) is associated with worse renal and patient outcomes. Until now, no predictive model using machine learning (ML) has been reported on IR in CKD patients. Methods: The CKD population studied was based on results from the National Health and Nutrition Examination Survey (NHANES) of the USA from 1999 to 2012. The homeostasis model assessment of IR (HOMA-IR) was used to assess insulin resistance. We began the model building process via the ML algorithm (random forest (RF), eXtreme Gradient Boosting (XGboost), logistic regression algorithms, and deep neural learning (DNN)). We compared different receiver operating characteristic (ROC) curves from different algorithms. Finally, we used SHAP values (SHapley Additive exPlanations) to explain how the different ML models worked. Results: In this study population, 71,916 participants were enrolled. Finally, we analyzed 1,229 of these participants. Their data were segregated into the IR group (HOMA IR > 3, n = 572) or non-IR group (HOMR IR ≤ 3, n = 657). In the validation group, RF had a higher accuracy (0.77), specificity (0.81), PPV (0.77), and NPV (0.77). In the test group, XGboost had a higher AUC of ROC (0.78). In addition, XGBoost also had a higher accuracy (0.7) and NPV (0.71). RF had a higher accuracy (0.7), specificity (0.78), and PPV (0.7). In the RF algorithm, the body mass index had a much larger impact on IR (0.1654), followed by triglyceride (0.0117), the daily calorie intake (0.0602), blood HDL value (0.0587), and age (0.0446). As for the SHAP value, in the RF algorithm, almost all features were well separated to show a positive or negative association with IR. Conclusion: This was the first study using ML to predict IR in patients with CKD. Our results showed that the RF algorithm had the best AUC of ROC and the best SHAP value differentiation. This was also the first study that included both macronutrients and micronutrients. We concluded that ML algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD.

The effect of trajectory of serum uric acid on survival and renal outcomes in patients with stage 3 chronic kidney disease.

Uric acid (UA) is associated with renal disease and patient survival, but the causal associations remain unclear. Also, the longitudinal UA control (trajectory) is not well understood. We enrolled 808 subjects diagnosed with stage 3 chronic kidney disease from 2007 to 2017. We plotted the mean UA over a period of 6 months with a minimum requirement of 3 samples of UA. From the sampled points, we generated an interpolated line for each patient by joining mean values of UA levels over time. Using lines from all patients, we classified them into 3 groups of trajectories (low, medium, and high) through group-based trajectory modeling, and then we further separated them into either treatment or nontreatment subgroups. Due to multiple comparisons, we performed post hoc analysis by Bonferroni adjustment. Using univariate competing-risks regression, we calculated the competing risk analysis with subdistribution hazard ratio of possible confounders. All of the 6 trajectories appeared showed a gradual decline in function over time without any of the curves crossing over one another. For all-cause mortality risk, none of the variables (including age, gender, coronary arterial disease, cerebrovascular disease, diabetes mellitus, renin-angiotensin-aldosterone system inhibitors, trajectories of UA, and treatment of UA) were statistically significant. All 6 trajectories appeared as steady curves without crossovers among them over the entire period of follow-up. Patients with diabetes mellitus were statistically more likely to undergo dialysis. The only trend was seen in the on-treatment trajectories, which showed lower risks for dialysis compared to their nontreatment trajectories. There was no effect of UA control on survival. Initial treatment of UA is crucially important for UA control. However, the long-term effects on patients and renal survival appeared to be minor and without statistical significance.

Prediction of Mortality in Older Hospitalized Patients after Discharge as Determined by Comprehensive Geriatric Assessment.

Several dimensional impairments regarding Comprehensive Geriatric Assessment (CGA) have been shown to be associated with the prognosis of older patients. The purpose of this study is to investigate mortality prediction factors based upon clinical characteristics and test in CGA, and then subsequently develop a prediction model to classify both short- and long-term mortality risk in hospitalized older patients after discharge. A total of 1565 older patients with a median age of 81 years (74.0−86.0) were consecutively enrolled. The CGA, which included assessment of clinical, cognitive, functional, nutritional, and social parameters during hospitalization, as well as clinical information on each patient was recorded. Within the one-year follow up period, 110 patients (7.0%) had died. Using simple Cox regression analysis, it was shown that a patient's Length of Stay (LOS), previous hospitalization history, admission Barthel Index (BI) score, Instrumental Activity of Daily Living (IADL) score, Mini Nutritional Assessment (MNA) score, and Charlson's Comorbidity Index (CCI) score were all associated with one-year mortality after discharge. When these parameters were dichotomized, we discovered that those who were aged ≥90 years, had a LOS ≥ 12 days, an MNA score < 17, a CCI ≥ 2, and a previous admission history were all independently associated with one-year mortality using multiple cox regression analyses. By applying individual scores to these risk factors, the area under the receiver operating characteristics curve (AUC) was 0.691 with a cut-off value score ≧ 3 for one year mortality, 0.801 for within 30-day mortality, and 0.748 for within 90-day mortality. It is suggested that older hospitalized patients with varying risks of mortality may be stratified by a prediction model, with tailored planning being subsequently implemented.

Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography.

Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass and strength. The examination of muscle mass must be carried in the clinic. However, the loss of muscle mass can be improved by rehabilitation that can be performed in non-medical environments. Electronic impedance myography (EIM) can measure some parameters of muscles that have the correlations with muscle mass and strength. The goal of this study is to use machine learning algorithms to estimate the total mass of thigh muscles (MoTM) with the parameters of EIM and body information. We explored the seven major muscles of lower limbs. The feature selection methods, including recursive feature elimination (RFE) and feature combination, were used to select the optimal features based on the ridge regression (RR) and support vector regression (SVR) models. The optimal features were the resistance of rectus femoris normalized by the thigh circumference, phase of tibialis anterior combined with the gender, and body information, height, and weight. There were 96 subjects involved in this study. The performances of estimating the MoTM used the regression coefficient (r2) and root-mean-square error (RMSE), which were 0.800 and 0.929, and 1.432 kg and 0.980 kg for RR and SVR models, respectively. Thus, the proposed method could have the potential to support people examining their muscle mass in non-medical environments.

Hyperuricemia and diabetes mellitus when occurred together have higher risks than alone on all-cause mortality and end-stage renal disease in patients with chronic kidney disease.

INTRODUCTION: Hyperuricemia and diabetes mellitus (DM) are associated with increased mortality risk in patients with chronic kidney disease (CKD). Here we aimed to evaluate the independent and joint risks of these two conditions on mortality and end stage kidney disease (ESKD) in CKD-patients. METHODS: This retrospective cohort study enrolled 4380 outpatients (with CKD stage 3-5) with mortality and ESKD linkage during a 7-year period (from 2007 to 2013). All-causes mortality and ESKD risks were analyzed by multivariable-adjusted Cox proportional hazards models (adjusted for age, sex, smoke, previous coronary arterial disease, blood pressure, and medications for hyperlipidemia, hyperuricemia and renin-angiotensin system inhibitors). RESULTS: Overall, 40.5% of participants had DM and 66.4% had hyperuricemia. In total, 356 deaths and 932 ESKD events occurred during the 7 years follow-up. With the multivariate analysis, increased risks for all-cause mortality were: hyperuricemia alone, HR = 1.48 (1-2.19); DM alone, and HR = 1.52 (1.02-2.46); DM and hyperuricemia together, HR = 2.12 (1.41-3.19). Similar risks for ESKD were: hyperuricemia alone, HR = 1.34 (1.03-1.73); DM alone, HR = 1.59 (1.15-2.2); DM and hyperuricemia together, HR = 2.46 (1.87-3.22). CONCLUSIONS: DM and hyperuricemia are strongly associated with higher all-cause mortality and ESKD risk in patients with CKD stage 3-5. Hyperuricemia is similar to DM in terms of risk for all-cause mortality and ESKD. DM and hyperuricemia when occurred together further increase both risks of all-cause mortality and ESKD.