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 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.

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.

Association of Adherence to the Mediterranean Diet with All-Cause Mortality in Subjects with Heart Failure.

We investigated the associations of adherence to the Mediterranean diet with all-cause and cardiovascular mortality in patients with heart failure. We analyzed the National Health and Nutrition Examination Survey (NHANES) participants from 1999 to 2010, with their vital status confirmed through to the end of 2011. The alternate Mediterranean Diet Index (aMED) was used to assess study participants' adherence to the Mediterranean diet according to information on dietary questionnaires. We conducted weighted Cox proportional hazards regression models to determine the associations of adherence to the Mediterranean diet (aMED ≥ median vs.

Associations of Adherence to the DASH Diet and the Mediterranean Diet With All-Cause Mortality in Subjects With Various Glucose Regulation States.

BACKGROUND AND AIMS: A dietary pattern concordant with either the Dietary Approaches to Stop Hypertension (DASH) diet or the Mediterranean diet has been associated with a lower risk of all-cause mortality in general population. We investigated the associations of adherence to the DASH diet and the Mediterranean diet with all-cause mortality across three glucose regulation states (normal glucose tolerance, prediabetes, and diabetes) using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: Data from the NHANES participants from 1999 to 2010, including their vital status linked to the National Death Index through the end of 2011, were analyzed. Adherence to the DASH diet and the Mediterranean diet was assessed using the DASH score and the alternative Mediterranean Diet Index (aMED), respectively. Weighted Cox proportional hazards regression models were used to compare the hazard ratios for the associations of adherence (diet score >median vs. ≤ median) to the DASH diet and the Mediterranean diet with all-cause mortality. RESULTS: A total of 28,905 participants were analyzed, and 2,598 of them had died after a median follow-up of 6.3 years. The median DASH score and aMED were 2 and 3, respectively. Adherence to the Mediterranean diet (aMED >3 vs. ≤ 3), but not the DASH diet, was associated with a lower risk of all-cause mortality (adjusted HR 0.74, 95% CI 0.66-0.83, p < 0.001) in the overall population. The findings were consistent across the three glucose regulation states. A joint effect of aMED >3 and DASH score >2 (adjusted HR 0.71, 95% CI 0.52-0.99, p = 0.042) was noted in participants with diabetes. CONCLUSIONS: Adherence to the Mediterranean diet (aMED >median) was associated with reduced all-cause mortality in a general population. For people with diabetes, a dietary pattern concordant with both the DASH diet and the Mediterranean diet (DASH score >median and aMED >median) was associated with a lower risk of mortality.

HbA1c trajectory and cardiovascular outcomes: an analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.

BACKGROUND: We investigated the associations between glycated hemoglobin (HbA1c) trajectories and cardiovascular outcomes using data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. METHODS: We used HbA1c values within the first 2 years of treatment for modeling with a latent class growth model. Groups of HbA1c trajectories were modeled separately in the standard (group 1-group 4) and intensive (group 5-group 8) treatment arms. The primary outcome in the ACCORD study was a composite of non-fatal myocardial infarction, non-fatal stroke, or death from cardiovascular causes. Effects of HbA1c trajectories on cardiovascular outcomes were analyzed using a Cox-proportional hazard model. RESULTS: Baseline HbA1c levels for the eight trajectories (group 1-group 8) were 7.8 ± 0.8, 8.2 ± 0.9, 9.3 ± 1.1, 9.6 ± 1.2, 7.8 ± 0.7, 10.1 ± 0.8, 8.3 ± 0.7, and 9.5 ± 1.1%, respectively. The respective values after 2 years of treatment were 7.0 ± 0.6, 7.7 ± 0.7, 8.5 ± 0.9, 10.3 ± 1.3, 6.2 ± 0.4, 6.5 ± 0.6, 7.2 ± 0.6, and 8.5 ± 1.1%. After a median follow-up of 4.8 years, group 5 and group 6 had similar outcomes compared with group 1 (reference group). In contrast, group 3, group 4, and group 8 had higher risks of the primary composite outcome compared with group 1. CONCLUSION: HbA1c trajectory was associated with cardiovascular outcomes in type 2 diabetes patients with high cardiovascular risk.

FTO Obesity Variant-Exercise Interaction on Changes in Body Weight and BMI: The Taiwan Biobank Study.

CONTEXT: Gene-exercise interaction on cross-sectional body mass index (BMI) has been extensively studied and is well established. However, gene-exercise interaction on changes in body weight/BMI remains controversial. OBJECTIVE: To examine the interaction between the FTO obesity variant and regular exercise on changes in body weight/BMI. PARTICIPANTS: Taiwan Biobank participants aged 30-70 years (N = 20 906) were examined at both baseline and follow-up visit (mean follow-up duration: 3.7 years). MAIN OUTCOME MEASURES: The interaction between the FTO obesity variant rs1421085 and regular exercise habit (no exercise, ≤20 metabolic equivalent of tasks (METs)/week exercise, >20 METs/week exercise) on changes in body weight/BMI. RESULTS: Individuals with the risk allele of rs1421085 gained more weight and increased BMI than those without the risk allele if they did not exercise. In contrast, individuals with the risk allele gained less weight and BMI if they exercised regularly, indicating an interaction between rs1421085 and regular exercise habit (P = .030 for Δbody weight and P = .034 for ΔBMI). The effect of exercise on maintaining body weight was larger in those with the risk allele of rs1421085. When we focused on individuals without regular exercise at baseline, individuals with the risk allele again tended to lose more weight than those with a nonrisk allele if they had acquired an exercise habit by the follow-up visit. CONCLUSION: The beneficial effect of exercise is greater in individuals genetically prone to obesity due to the interaction between the FTO obesity variant rs1421085 and regular exercise on changes in body weight and BMI.

A cross-sectional analysis of the association between sleep duration and osteoporosis risk in adults using 2005-2010 NHANES.

Controversy remains regarding the relationship between bone health and sleep. In the literature, the effect of sleep on bone density in the clinical setting varies depending on the definition of normal sleep duration, sleep quality, selected population, and diagnostic tools for bone density. The aim of this study was to examine the association between bone mineral density (BMD)assessed by dual-energy X-ray absorptiometry and sleep duration/quality in the defined adult population from the National Health and Nutrition Examination Survey (NHANES) (a national household survey) within a 6-year period (2005-2010) and explore age differences. The basic variables, metabolic diseases, and bone density in the femoral neck as determined through dual-energy X-ray absorptiometry, were segregated, and analyzed according to different sleep durations (1-4, 5-6,7-8, and > 9 h/day) and sleep quality using multinomial regression models. A total of 12,793 subjects were analyzed. Our results reveal that women aged > 50 years with sleep duration < 5 h/day had a 7.35 (CI 3.438-15.715) odds of osteoporosis than those in other groups. This analysis is based on a nationally representative sample using survey and inspection data and clarifies the relationship between bone density and the effect of the combination of sleep quality and duration.

Associations of low-carbohydrate and low-fat intakes with all-cause mortality in subjects with prediabetes with and without insulin resistance.

BACKGROUND & AIMS: We investigated the associations of low-carbohydrate and low-fat diets with all-cause mortality in people with prediabetes according to insulin resistance status using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: We analyzed the NHANES participants with prediabetes from 2005 to 2008, and their vital status was linked to the National Death Index through the end of 2011. Low-carbohydrate and low-fat diets were defined as ≦40% and ≦30% of calories from carbohydrate and fat, respectively. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to determine insulin resistance. Weighted Cox proportional hazards regression models were used to compare the hazard ratios for the associations of low-carbohydrate and low-fat diets with all-cause mortality. RESULTS: Among the 1687 participants with prediabetes, 96 of them had died after a median follow-up of 4.5 years. Participants with a HOMA-IR >3.0 had an increase in all-cause mortality compared with those who had a HOMA-IR ≦3.0 (HR 1.797, 95% CI 1.110 to 2.909, p = 0.019). Participants with ≦40% of calories from carbohydrate and >30% from fat (3.75 per 1000 person-years) had a lower all-cause mortality rate compared with those who had >40% from carbohydrate and >30% from fat (10.20 per 1000 person-years) or >40% from carbohydrate and ≦30% from fat (8.09 per 1000 person-years), with statistical significance observed in those who had a HOMA-IR ≦3.0. CONCLUSIONS: A low-carbohydrate intake (≦40%) was associated with a lower all-cause mortality rate in people with prediabetes.

Association of diurnal calorie trajectory with all-cause mortality: Findings from the National Health and Nutrition Examination Survey.

BACKGROUND & AIMS: The effect of diurnal variation in energy intake on mortality has not been reported. We investigated the effect of diurnal calorie trajectory on all-cause mortality using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: Participants in the NHANES from 1999 to 2010 were analyzed. We calculated daily energy intake and the two-hourly calorie intake according to dietary interview questionnaires, in which timing of meals, as well as energy and nutritional components of each food were recorded. The daily energy intake and the two-hourly calorie intake were divided by body weight to determine tertiles of daily energy intake and diurnal calorie trajectories, respectively. Three diurnal calorie trajectories (reference group, excess dinner, and high-calorie) were identified. The mortality data were linked to the National Death Index through the end of 2011. Cox proportional hazards models were used to compare the overall mortality among different groups. RESULTS: Among the 14,356 participants included in our analyses, 886 (6.2%) of them died after a median follow-up of 4.4 years. Daily energy intake tertiles were not associated with all-cause mortality in the fully adjusted model. In contrast, high-calorie trajectory was associated with a higher risk of mortality (hazard ratio 3.128, 95% CI 1.175 to 8.330, p = 0.024) compared with the reference group after adjustment for relevant factors. CONCLUSIONS: A diurnal high-calorie trajectory was associated with a higher risk of mortality, compared with the reference group. The effect of a large evening meal on mortality merits further investigation.

Effect of low-protein intake on all-cause mortality in subjects with an estimated glomerular filtration rate higher than 60 mL/min/1.73 m2 with or without albuminuria.

BACKGROUND: We aimed to investigate the effect of a low-protein intake on all-cause mortality in subjects with an estimated glomerular filtration rate (eGFR) ≧60 mL/min/1.73 m2 with or without albuminuria using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: We analysed participants in the NHANES from 2003 to 2010. We excluded participants with an eGFR less than 60 mL/min/1.73 m2 from the analyses. Low-protein intake was defined as a protein intake of less than 0.8 g/kg/day. The Healthy Eating Index 2010 was used to assess diet quality. The vital status of all participants in the NHANES was determined by linking to the National Death Index through the end of 2011. The hazard ratios (HRs) for the association of low-protein intake and mortality were determined using weighted Cox proportional hazards regression models. RESULTS: A total of 7730 participants were included in the analyses. After a median follow up of 4.7 years, 462 participants died. A low-protein intake was associated with a higher risk of mortality (HRs 1.394, 95% CI 1.121-1.734, P = .004) with adjustment for diet quality and relevant risk factors. The higher risk of mortality associated with a low-protein intake was consistent in subjects with or without albuminuria (P interaction .280). CONCLUSION: A protein intake of less than 0.8 g/kg/day was associated with a higher risk of mortality in subjects with an eGFR ≧60 mL/min/1.73 m2 , irrespective of whether they had albuminuria.

Approximate Mortality Risks between Hyperuricemia and Diabetes in the United States.

Aim: This study aimed to compare mortality risks across uric acid (UA) levels between non-diabetes adults and participants with diabetes and to investigate the association between hyperuricemia and mortality risks in low-risk adults. Methods: We analyzed data from adults aged >18 years without coronary heart disease and chronic kidney disease (n = 29,226) from the National Health and Nutrition Examination Survey (1999-2010) and the associated mortality data (up to December 2011). We used the Cox proportional hazards models to examine the risk of all-cause and cause-specific (cardiovascular disease (CVD) and cancer) mortality at different UA levels between adults with and without diabetes. Results: Over a median follow-up of 6.6 years, 2069 participants died (495 from CVD and 520 from cancers). In non-diabetes adults at UA ≥ 5 mg/dL, all-cause and CVD mortality risks increased across higher UA levels (p-for-trend = 0.037 and 0.058, respectively). The lowest all-cause mortality risk in participants with diabetes was at the UA level of 5-7 mg/dL. We set the non-diabetes participants with UA levels of <7 mg/dL as a reference group. Without considering the effect of glycemic control, the all-cause mortality risk in non-diabetes participants with UA levels of ≥7 mg/dL was equivalent to risk among diabetes adults with UA levels of <7 mg/dL (hazard ratio = 1.44 vs. 1.57, p = 0.49). A similar result was shown in CVD mortality risk (hazard ratio = 1.80 vs. 2.06, p = 0.56). Conclusion: Hyperuricemia may be an indicator to manage multifaceted cardiovascular risk factors in low-risk adults without diabetes, but further studies and replication are warranted.