Explore the vast range of titles available.

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent globally and includes chronic liver diseases ranging from simple steatosis to nonalcoholic steatohepatitis (NASH). The neutrophil-to-albumin ratio (NPAR) is a cost-effective, readily available biomarker of inflammation used to assess cancer and cardiovascular disease prognosis, and it may be of predictive value in NAFLD. This study was to evaluate the associations between the NPAR, the neutrophil-to-lymphocyte ratio (NLR), and the presence of NAFLD or advanced liver fibrosis, and to assess the predictive value of the NPAR in NAFLD in a nationally representative database. This population-based, cross-sectional, retrospective study analyzed the secondary data of adults with NAFLD or advanced liver fibrosis extracted from the National Health and Nutrition Examination Survey (NHANES) database 2017–2018. NHANES participants with complete information of vibration-controlled transient elastography (VCTE) and controlled attenuation parameter (CAP) were enrolled. A logistic regression analysis was used to determine the associations between the variables in the participants with and without NAFLD or advanced liver fibrosis. The mean values of the lymphocyte counts, neutrophil counts, NPAR, aspartate aminotransaminase (AST), alanine aminotransaminase (ALT), total cholesterol, triglycerides, and HbA1c were significantly higher in the participants with NAFLD than in those without NAFLD or advanced liver fibrosis. The mean blood albumin levels of the subjects without NAFLD or advancing fibrosis were considerably greater than those of the individuals with these conditions. The mean values of the NLR, NPAR, AST, ALT, triglycerides, lymphocyte count, neutrophil count, and HbA1c were significantly higher in patients with advanced fibrosis than in those without advanced fibrosis. A multivariate analysis showed that per unit increases in both the NLR and NPAR were significantly associated with an increased risk of developing NAFLD, while neither the NLR nor NPAR was significantly associated with higher odds of advanced fibrosis. In conclusion, the novel biomarker NPAR demonstrates a good association with NAFLD, along with participants’ clinical characteristics, in a nationwide population. The NPAR may serve as a biomarker for NAFLD and help clinicians refine the diagnosis and treatment of chronic liver disease.

In the general population, there is little evidence of a link between blood urea nitrogen (BUN) and long-term mortality. The goal of this study was to explore whether higher BUN concentration is a predictor of cardiovascular disease (CVD) and all-cause mortality. From 1999 to 2006, the National Health and Nutrition Examination Survey (NHANES) included 17,719 adult individuals. Death outcomes were ascertained by linkage to the database records through 31 December 2015. The Cox proportional hazard regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD and all-cause mortality in individuals. We also performed stratified analyses based on age, gender, drinking, smoking, history of hypertension and diabetes. During a mean follow-up 11.65 years, a total of 3628 deaths were documented, of which 859 were due to CVD. Participants with higher BUN had a higher risk of CVD and all-cause death compared to those with lower BUN. After multifactor adjustment for demographics, major lifestyle factors, and hypertension and diabetes history, higher BUN levels compared with lower levels were significantly associated with higher risk of CVD (HR: 1.48 [1.08, 2.02], P-trend < 0.001) and all-cause mortality (HR: 1.48 [1.28, 1.72], P-trend < 0.001). In subgroup analyses, we found that the trend in the association of BUN with the risk of death remained strong in female subjects. Greater BUN levels were linked to higher CVD and all-cause mortality in the NHANES of American adults. The importance of BUN in predicting death is supported by our research.

Background Healthy dietary patterns have been negatively associated with methylation-based measures of biological age, yet previous investigations have been unable to establish the relationship between them and biological aging assessed through blood chemistry-based clinical biomarkers. We sought to assess the associations of 4 dietary metrics with 4 measures of biological age. Methods Among 16,666 participants in NHANES 1999–2018, 4 dietary metrics [Dietary inflammatory index (DII), Dietary approaches to stop hypertension index (DASH), Alternate mediterranean diet score (aMED), and Healthy eating index-2015 (HEI-2015)] were calculated through the ‘dietaryindex’ R package. Twelve blood chemistry parameters were utilized to compute 4 indicators of biological age [homeostatic dysregulation (HD), allostatic load (AL), Klemera–Doubal method (KDM), and phenotypic age (PA)]. Binomial logistic regression models and restricted cubic spline (RCS) regression were employed to evaluate the associations. Results All 4 dietary metrics were significantly associated with biological age acceleration or deceleration. In comparison to the lowest DII, the odds ratios (ORs) for accelerated HD, AL, KDM, and PA were 1.25 (1.08,1.45), 1.29 (1.11,1.50), 1.34 (1.08,1.65), and 1.61 (1.39,1.87) for the highest. The multivariable-adjusted ORs of the highest quartile of DASH, aMED, and HEI-2015 were 0.85 (0.73,0.97), 0.88 (0.74,1.04), and 0.84 (0.74,0.96) for HD, 0.64 (0.54,0.75), 0.61 (0.52,0.72), and 0.70 (0.59,0.82) for AL, 0.68 (0.54,0.85), 0.62 (0.50,0.76), and 0.71 (0.58,0.87) for KDM, and 0.50 (0.42,0.59), 0.64 (0.54,0.76), and 0.51 (0.44,0.58) for PA when compared with the lowest level. The findings were validated by the best-fitting dose-response curves for the associations. Among participants consuming dietary supplements ( P interaction < 0.05), the positive effects of a healthy dietary pattern on biological aging were more pronounced. Systemic immune inflammation index (SII) and atherogenic index of plasma (AIP) were identified as being involved in and mediating the associations. Conclusions Biological aging assessed through blood chemistry-based clinical biomarkers is negatively associated with diet quality. The anti-aging benefits of improving the diet may be due to its ability to reduce inflammation and lower blood lipids.

Background Low back pain is the leading cause of productivity loss, imposes a significant economic burden on the patients and society. Oxidative stress is considered a critical factor in the complex pathophysiological process and pathogenic mechanism of low back pain. Adjustment dietary pattern can effectively increase antioxidant biomarkers levels within the body to reduce oxidative stress. The composite dietary antioxidant index (CDAI) serves a reliable scoring system for quantifying the potential dietary antioxidant capacity of daily diets. Objective We aim to investigate the potential association between CDAI and low back pain, in order to enhance the management of low back pain through dietary guidance. Methods This study included 17,682 participants from the National Health and Nutrition Examination Survey (NHANES) 1999–2000, 2001–2002, 2003–2004 and 2009–2010. The weighted logistic regression model was used to investigate the association between CDAI and low back pain, while restricted cubic spline (RCS) was employed to examine non-linear trend and cutoffs. Results After adjusting for all confounders, the results showed that there was no significant association between CDAI and low back pain. However, individuals in the highest quartile of CDAI exhibited an 11.7% less likelihood of experiencing a low back pain than those in the lowest quartile (OR = 0.883; 95% CI [0.787,0.991], P  = 0.034), and the trend test was also significant (P for trend < 0.001). RCS indicated a linear relationship between CDAI and low back pain (P for non-linear = 0.876). Gender subgroup analysis showed that this negative association was significant in the female population (OR = 0.983; 95% CI [0.968, 0.998], P  = 0.027), and females in the highest quartile of CDAI were 19.7% less likely to suffer low back pain than those in the lowest quartile (OR = 0.803; 95% CI [0.682,0.945], P  = 0.008). Additionally, the changes in zinc (OR = 1.009; 95% CI [1.002, 1.016], P  = 0.015) and selenium (OR = 0.379; 95% CI [0.164, 0.875], P  = 0.023) per milligram were independently associated with low back pain. Conclusion The fully adjusted model showed no significant association between CDAI and low back pain, but it was significant in quartiles. Meanwhile, subgroup analysis by gender revealed a negative association between CDAI and low back pain in the female population. Additionally, the findings of this study also suggested that the antioxidant diets should be studied in a dietary pattern context.

Background Information on the influences of daily eating frequency (DEF) and nighttime fasting duration (NFD) on biological aging is minimal. Our study investigated the potential associations of DEF and NFD with accelerated aging. Methods Out of 24212 participants in NHANES 2003–2010 and 2015–2018, 4 predicted age metrics [homeostatic dysregulation (HD), Klemera–Doubal method (KDM), phenoAge (PA), and allostatic load (AL)] were computed based on 12 blood chemistry parameters. Utilizing 24-h dietary recall, DEF was measured by the frequency of eating occurrences, while NFD was determined by assessing the timing of the initial and final meals throughout the day. Weighted multivariate linear regression models and restricted cubic spline (RCS) were utilized to examine the associations. Results Compared to DEF of ≤ 3.0 times, subjects with DEF ≥ 4.6 times demonstrated lower KDM residual [β: -0.57, 95% confidence-interval (CI): (-0.97, -0.17)] and PA residual [β: -0.47, 95% CI: (-0.69, -0.25)]. In comparison to NFD between 10.1 and 12.0 h, individuals with NFD ≤ 10.0 h were at higher HD [β: 0.03, 95% CI: (0.01, 0.04)], KDM residual [β: 0.34, 95% CI: (0.05, 0.63)], and PA residual [β: 0.38, 95% CI: (0.18, 0.57)]. Likewise, those with NFD ≥ 14.1 h also had higher HD [β: 0.02, 95% CI: (0.01, 0.04)] and KDM residual [β: 0.33, 95% CI: (0.03, 0.62)]. The results were confirmed by the dose–response relationships of DEF and NFD with predicted age metrics. Lactate dehydrogenase (LDH) and globulin (Glo) were acknowledged as implicated in and mediating the relationships. Conclusions DEF below 3.0 times and NFD less than 10.0 or more than 14.1 h were independently associated with higher predicted age metrics.

Evidence-based dietary advice regarding meats (including beef), requires accurate assessment of beef and other red meat intakes across life stages. Beef intake is subject to misclassification due to the use of broad categories such as “red and processed meat”. In the current study, intake trends for total beef (i.e., any beef type) and specific beef types (fresh lean, ground, processed) among Americans participating in the National Health and Nutrition Examination Survey (NHANES) 2001–2018 (n = 74,461) were characterized and usual intake was assessed using NHANES 2011–2018 (n = 30,679). The usual intake amounts of beef were compared to those of relevant protein food subgroups modeled in the Healthy U.S.-Style Dietary Pattern (HDP) reported in the 2020–2025 Dietary Guidelines for Americans (DGA). Total per capita beef consumption declined an average of 12 g (p < 0.0001) for ages 2–18 years and 5.7 g (p = 0.0004) for ages 19–59 years per 2-yr NHANES cycle, over the 18-year timeframe, while remaining unchanged for Americans aged 60+ years. On a per capita basis, Americans aged 2 years and older consumed 42.2 g (1.5 ounces) of total beef per day. Fresh lean beef per capita consumption was 33.4 g (1.2 ounces) per day. Per capita intake was similar across all age groups and below the daily HDP modeled amount of 3.7 ounce equivalents for the “Meats, Poultry, Eggs” (MPE) subgroup, while approximately 75% of beef consumers’ intakes of total beef was within HDP modeling. Evidence from intake trends suggests beef is not overconsumed by the majority of Americans but rather within the amounts for MPE and red meat modeled in the HDP of the DGA at the 2000-calorie level.

Background Cardiovascular disease (CVD) poses a significant challenge to global public health. Dietary intervention therapy offers high cost-effectiveness for treating CVD. Currently, there is limited research on the dietary niacin intake and survival of CVD patients. This study aims to examine the association of dietary niacin intake with long-term survival in people with CVD. Methods A nationally representative sample of 4,377 diabetes subjects was drawn from the NHANES (National Health and Nutrition Examination Survey) data collected between 2003 and 2018. Dietary niacin intake in this study represents either the average of the two recalls or the value from one recall (if only one recall was available for a participant). Weighted Cox proportional hazards regression models were used to calculate hazard ratios (HRs) and 95% CIs to examine the associations between dietary niacin intake and the risk of all-cause and CVD mortality. Results After adjustment for multiple covariates, HRs and 95% CIs in model 3 indicated that participants in the highest quartile (Quartile 4) of dietary niacin intake were at lower risk for all-cause mortality (HR = 0.74, 95% CI: 0.60–0.90, P for trend = 0.010) and CVD mortality (HR = 0.67, 95% CI:0.51–0.89, P for trend = 0.020). Conclusion Higher dietary niacin intake may be associated with a reduced risk of all-cause and cardiovascular disease mortality among CVD patients. Additionally, significant interactions were found between dietary niacin intake and BMI as well as vitamin B12 subgroups.

Background: Klotho is an aging-related marker closely associated with a number of diseases. A growing body of evidence suggests that dietary factors and lifestyle habits can impact serum Klotho levels. The effect of dietary fiber, a key component of a healthy diet, on the body’s serum Klotho levels has not been fully elucidated. Objective: The aim of this study was to explore the relationship between dietary fiber intake and serum Klotho levels in people aged 40–79 years in the United States. Methods: A total of 11,282 participants were included in this study, all from the National Health and Nutrition Examination Survey from 2007 to 2016. Dietary fiber intake was assessed by uniformly trained interviewers using the 24 h dietary recall method. Serum Klotho was quantified using commercially available ELISA kits manufactured by IBL International, Japan. The relationship between dietary fiber intake and serum Klotho levels was analyzed using a multiple linear regression model. Subsequently, the non-linear dose–response relationship between the two was further explored using a restricted cubic spline (RCS) model. Results: After adjusting for potential confounders, serum Klotho levels increased by 1.9% (95% confidence interval [CI]: 0.8%, 3.0%) for each interquartile range increase in dietary fiber intake in all participants. Considering dietary fiber intake as a categorical variable, serum Klotho levels were found to be 4.7% higher in participants in the highest quartile of dietary fiber intake than in those in the lowest quartile (95% CI: 1.8%, 7.6%). RCS plots depicted a non-linear positive correlation between dietary fiber intake and serum Klotho levels. Subgroup analysis revealed that the relationship between dietary fiber intake and serum Klotho levels was more pronounced in older (percentage change: 7.0%; 95% CI: 2.5%, 11.7%) and overweight and obese participants (percentage change: 4.9%; 95% CI: 1.5%, 8.4%). Conclusions: The results of this study showed that dietary fiber intake was significantly associated with serum Klotho levels in participants. This finding is yet to be further confirmed by prospective studies.

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.

Background Gut microbes are important for the development of atherosclerotic cardiovascular disease (ASCVD), and the dietary index for gut microbiota (DI-GM), a new measure of gut flora-friendly diets, has not been systematically investigated in relation to ASCVD. Objective This study aimed to evaluate the correlation between DI-GM and the risk of ASCVD in American older adults, also to analyze the mediating role of body mass index (BMI). Methods Researchers selected 2234 elderly participants ≥ 65 years of age from the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2018 for a cross-sectional cohort study. Stratified analyses were taken based on DI-GM quartile. To achieve our research objectives, we employed logistic regression analysis, smooth curve fitting, interaction effects analysis, and mediation analysis. Results After adjusting for confounders, individuals with higher DI-GM had a significantly lower risk of ASCVD (highest quartile vs. lowest quartile OR = 0.73, 95% CI: 0.52–1.01, P < 0.001). DI-GM was linearly negatively associated with ASCVD (P = 0.13) and the association was stable in the diabetes subgroup (interaction P > 0.05), but age, gender and BMI may modify the association between DI-GM and ASCVD (interaction P < 0.05). BMI mediated 11.51% of the association between DI-GM and ASCVD (95% CI: 2.54%-54.1%, P = 0.016). Conclusion DI-GM is likely to be a promising indicator for the assessment of the risk of ASCVD, with BMI exhibiting a partial mediating effect in this association. Future studies should prioritize a comprehensive investigation of the underlying mechanisms by which DI-GM contributes to atherogenesis, with the aim of enhancing the efficacy of early prevention strategies for ASCVD.