Search Results (40)

Background/Objectives: Recently, the term metabolic dysfunction-associated steatotic liver disease (MASLD) has replaced non-alcoholic fatty liver disease (NAFLD). Through analysis of the trends and disparities regarding cardiovascular disease (CVD) among individuals with MASLD, identifying the leading cause of death in this population is crucial. Methods: We conducted a cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) III (1988–1994) and NHANES 2017–2020 data. MASLD was identified by using clinical profiles and liver ultrasonography to exclude other liver diseases. We estimated the prevalence of CVD among individuals with MASLD and calculated the prevalence ratios for those with and without MASLD. Results: In 2017–2020, MASLD affected 31.2% or 61.9 million US adults, and 17.0% (95% confidence interval: 13.7–20.3%) of these individuals had CVD. The absolute CVD prevalence in individuals with MASLD doubled from that in the NHANES III cohort, which was 8.7% (6.4%, 10.9%). These increases were especially notable among older adults, non-Hispanic whites, and those with higher education and income. Individuals with MASLD had a higher prevalence of total CVD than those without MASLD, even after adjusting for socioeconomic and metabolic factors. These differences were more pronounced in younger age groups. Conclusions: This study revealed a doubled 30-year trend in CVD prevalence among adults with MASLD in the US. Sociodemographic disparities emphasize the need for tailored screening, prevention, and policy measures to address gaps and promote cardiovascular health in this population. Full article

Metabolic dysfunction associated steatotic liver disease (MASLD) has been associated with increased risks of all-cause and cardiovascular disease (CVD) mortality. Identification of modifiable risk factors that may contribute to higher risks of mortality could facilitate targeted and intensive intervention strategies in this population. This study aims to examine whether the magnesium depletion score (MDS) is associated with all-cause and CVD mortality among individuals with MASLD or metabolic and alcohol associated liver disease (MetALD). Methods: A total of 3802 participants with MASLD or MetALD were followed up over a median of 26 years in the National Health and Nutrition Examination Survey (NHANES) III cohort. The MDS was calculated by aggregating four factors influencing the reabsorption capability of the kidneys. The associations between MDS and all-cause, CVD, and cancer mortality were quantified using Cox proportional hazard regression models. Results: In the combined MASLD + MetALD cohort, a higher MDS (>2) was associated with increased all-cause mortality (HR, 2.52; 95%CI, 1.77–3.61; p-trend < 0.0001) and CVD mortality (HR, 3.01; 1.87–4.86; p-trend < 0.0001) compared to MDS = 0; this association became stronger among participants who did not meet the estimated average requirement level of Mg intake (2.72; 1.69–4.37; p-trend = 0.0014) and those with a Fibrosis-4 index (FIB-4) < 1.3 (2.95; 1.69–5.15; p-trend = 0.0006). Conclusions: In individuals with MASLD or MetALD, higher MDS, indicative of worse global Mg status, was associated with an increased risk of all-cause and CVD mortality. Correcting global Mg deficiency in high-risk MASLD/MetALD patients may have long-term health benefits. Full article

Objective: To assess the complementary role of the Body Mass Index (BMI) and Edmonton Obesity Staging System (EOSS) in predicting all-cause and cause-specific mortality in people living with overweight and obesity (PLwOW/O). Methods: A longitudinal analysis of prospectively collected data from the 1999–2018 cycles of the National Health and Nutrition Examination Survey (NHANES) was conducted. The association between BMI, EOSS, and mortality was evaluated through Cox regression models, adjusted for confounders. Results: The analysis included 36,529 subjects; 5329 deaths occurred over a median follow-up of 9.1 years (range: 0–20.8). An increased mortality risk was observed for obesity class II and III (HR = 1.21, 95% CI 1.08–1.36, p = 0.001 and HR = 1.58, 95% CI 1.39–1.80, p < 0.001; compared to overweight), and for EOSS stage 2 and 3 (HR = 1.36, 95% CI 1.16–1.58, p < 0.001 and HR = 2.66, 95% CI 2.26–3.14, p < 0.001; compared to stage 0/1). The prognostic role of BMI was more pronounced in younger patients, males, and non-Black individuals, while that of EOSS was stronger in women. Both BMI and EOSS independently predicted cardiovascular- and diabetes-related mortality. EOSS stage 3 was the only predictor of death from malignancy or renal causes. Conclusions: BMI and EOSS independently predict all-cause and cause-specific mortality in PLwOW/O. Their integrated use seems advisable to best define the obesity-related mortality risk. Full article

Diet is a modifiable lifestyle factor that could impact the development of Metabolic Syndrome (MetS) and its components. MetS prevalence is high and diet quality is suboptimal among older African American women. MetS has been associated with many individual food groups, however, emerging research suggests that analyzing overall diet quality provides insight into the synergistic effects of food groups on health outcomes. In the current cross-sectional study, we examined the relationship between diet quality and MetS, and investigated associations between diet quality and MetS components among older African American women. This study was based on 357 African American women between 45 and 65 years from the NHANES 2011–2018 datasets. This analysis utilized the NCEP ATP III (2001) criteria for women to diagnose MetS. MetS was dichotomized in addition to a MetS z-score being calculated for each participant using a sex- and race-specific equation. Participants’ diet quality was measured using the HEI-2015. Linear and logistic regressions were performed to assess the association between HEI-2015 diet quality and metabolic syndrome and its components. 65% of African American women aged 45–65 in the NHANES 2011–2018 had MetS. Study participants had an average HEI-2015 score of 55.4 out of 100. As HEI-2015 quartiles increased, the mean MetS z-score decreased (p-value: 0.0011). Age-adjusted models demonstrated statistically significant inverse relationships between HEI-2015 and waist circumference (β: −0.217; 95% CI: −0.372, −0.063), systolic blood pressure (β: −0.215; 95% CI: −0.359, −0.072), blood glucose (β: −0.344; 95% CI: −0.681, −0.0066), and triglycerides (β: −0.652; 95% CI: −1.05, −0.251). Significant associations could not be established between MetS and diet quality, assessed with the HEI-2015, among African American women aged 45–65 enrolled in NHANES 2011–2018. However, statistically significant relationships were observed between increased HEI-2015 scores and lowered risks of abdominal obesity, hyperglycemia, hypertriglyceridemia, and systolic hypertension. The findings of this study affirm the necessity of public health strategies to improve diet quality among African-American women which could help to reduce their risks of chronic diseases. Full article

Background: Subclinical myocardial injury (SCMI) is associated with an increased risk of poor cardiovascular disease (CVD) outcomes. Understanding the underlying risk factors for SCMI is crucial for the prevention and management of CVD. We hypothesized that atherogenic dyslipidemia, a combination of high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C), is associated with an increased risk of SCMI. Methods: This analysis from the third National Health and Nutrition Examination Survey (NHANES-III) included 7093 participants (age 59.3 ± 13.4 years, 52.8% women, and 49.4% White) free of CVD. Atherogenic dyslipidemia was defined as TG ≥ 150 mg/dL and HDL-C < 40 mg/dL in men or <50 mg/dL in women. A validated electrocardiographic-based cardiac infarction injury score (CIIS) ≥ 10 was considered positive for SCMI. Multivariable logistic regression analysis was used to examine the association of different combinations of TG and HDL-C groups, including atherogenic dyslipidemia with SCMI. Results: About 22.5% (n = 1594) of participants had atherogenic dyslipidemia, and 26.3% (n = 1862) had SCMI. Compared to participants with normal TG and normal HDL-C, those with atherogenic dyslipidemia had a higher prevalence of SCMI (31.2% vs. 23.9%, p-value < 0.001). In a multivariable logistic regression model, atherogenic dyslipidemia was associated with the highest odds of SCMI followed by high TG/normal HDL-C, then low HDL-C/normal TG [OR (95% CI): 131 (1.14, 1.52), 1.13 (0.97, 1.33), and 1.01 (0.86, 1.20), respectively). Conclusions: Atherogenic dyslipidemia is associated with a higher risk of SCMI, which highlights the role of nontraditional risk factors in the development of subclinical CVD. Full article

Background: Although the link between lead exposure and patterns of cardiovascular disease (CVD) has been reported, its association with silent myocardial infarction (SMI) remains unexplored. We aimed to assess the association between blood lead levels (BLLs) and SMI risk. Methods: We included 7283 (mean age 56.1 ± 2.52 years, 52.5% women) participants free of CVD from the Third National Health and Nutrition Examination Survey. BLL was measured using graphite-furnace atomic absorption spectrophotometry. SMI was defined as ECG evidence of myocardial infarction (MI) without history of MI. The association between SMI and BLLs was examined using multivariable logistic regression. Results: SMI was detected in 120 participants with an unweighted prevalence of 1.65%. Higher BLL correlated with higher SMI prevalence across BLL tertiles. In multivariable-adjusted models, participants in the third BLL tertile had more than double the odds of SMI (OR: 3.42, 95%CI: 1.76–6.63) compared to the first tertile. Each 1 µg/dL increase in BLL was linked to a 9% increase in SMI risk. This association was consistent across age, sex, and race subgroups. Conclusions: Higher BLLs are associated with higher odds of SMI in the general population. These results underscore the significance of the ongoing efforts to mitigate lead exposure and implement screening strategies for SMI in high-risk populations. Full article

Objective: Although several studies have found dietary intake is related to multiple myeloma (MM) and its precursor status risks, the role of one’s nutritional status has been ignored and its role in plasma cell neoplasm development is still unclear. This study aimed to explore the relationship between various clinical indices of nutritional status and the risk of monoclonal gammopathy of undetermined significance (MGUS) in the population. Methods: We selected 9520 participants from the NHANES III and NHANES 1999–2004 studies. Controlling nutritional status index (CONUT), prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI) and body mass index (BMI) were calculated as indices of nutritional status of the participants. Associations between nutritional indices and MGUS were investigated using multiple logistic regression, subgroup analysis, and an RCS model. Results: In our study, 266 participants had MGUS, with a prevalence of 2.79%. This study found that CONUT and PNI identified populations with poor nutritional status and had a significant positive correlation with the risk of MGUS. In multivariate logistic regression, compared with the lower CONUT score (<3) group, the OR for the group with higher scores (≥3) was 1.805 (95%CI: 1.271, 2.564). Compared with the lowest quartile group, the highest quartile PNI score group had an OR of 0.509 (95%CI: 0.290, 0.896). GNRI had no significant correlation with the risk of MGUS, with an OR of 0.737 (95%CI: 0.443, 1.227). Conclusion: This study found that older adults with CONUT and PNI scores indicating poorer nutrition had a higher risk of MGUS. Full article

Associations between liver enzymes or De Ritis ratio (DRR; aspartate aminotransferase (AST)/alanine aminotransferase (ALT)) and mortality stratified by non-alcoholic fatty liver disease (NAFLD), which have rarely been analyzed in previous studies, were investigated using the National Health and Nutrition Examination Survey (NHANES) III (1988–1994). Participants without risk factors for liver diseases other than NAFLD were linked with National Death Index records through 2019 (n = 11,385) and divided into two cohorts with or without NAFLD, based on ultrasound examination. Liver enzyme concentrations were categorized into sex-specific deciles and subsequently grouped (AST and ALT: 1–3, 4–9, 10; gamma glutamyltransferase (GGT): 1–8, 9–10). DRR was categorized into tertiles. Cox proportional hazards regression models adjusted for confounders were fitted to estimate associations with mortality. Compared with low levels, high GGT and DRR in participants with and without NAFLD had significantly higher hazard ratios for all-cause mortality. Compared with intermediate concentrations, low ALT showed higher all-cause mortality in participants with and without NAFLD, whereas low AST had higher HR in participants without NAFLD and high AST in those with NAFLD. Mortality was associated with liver enzymes or DRR in participants both with and without NAFLD, indicating that the relationship is not mediated solely by hepatocellular damage. Full article

Prescription opioid use among pregnant women has increased in recent years. Prenatal exposure to opioids and poor nutrition can both negatively impact maternal–fetal outcomes. The objective of this study was to characterize the nutrition and health status of reproductive-age women taking prescription opioids, compared to women not taking opioids. Using NHANES 1999–2018 data, non-pregnant women aged 20–44 years were classified as taking a prescription opioid in the last 30 days (n = 404) or unexposed controls (n = 7234). Differences in anthropometric, cardiovascular, hematologic, and micronutrient status indicators between opioid-exposed and unexposed women were examined. Opioid-exposed women were older, had lower income and education, and were more likely to be non-Hispanic White, to smoke, and to have chronic health conditions compared to unexposed women. In unadjusted analyses, several nutrition and health markers were significantly different between opioid exposure groups. After controlling for covariates, women taking opioids had higher odds of Class II (OR = 1.6, 95% CI = 1.1–2.3) or III obesity (OR = 1.6, 95% CI = 1.1–2.5), and lower levels of serum folate, iron, and transferrin saturation. Reproductive-age women taking prescription opioids may be at risk for poorer nutritional and cardiometabolic health. Future research is needed to explore whether nutritional status impacts maternal–fetal outcomes for women exposed to opioids during pregnancy. Full article

Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time. Full article

Background: Diabetes mellitus (DM) is a comorbidity commonly presenting with metabolic-dysfunction-associated fatty liver disease (MAFLD); however, few tests for interaction have been reported. Our target was to evaluate the prognostic implications of DM in patients with different forms of MAFLD. Methods: Using data from the Third National Health and Nutrition Examination Survey (NHANES III) in the United States, we screened 14,797 participants aged 20–74 who received ultrasound examinations from 1988–1994. Among them, 4599 patients met the diagnosis of MAFLD, and we defined mortality as the outcome event. Survival analysis of competitive risk events was performed using Cox regression and sub-distributed risk ratio (SHR). Results: During 21.1 years of follow-up, cardiovascular diseases seemed to be the most common cause of death among MAFLD patients. Of them, DM was present in 25.48% and was independently associated with increased risk of all-cause mortality (HRs: 1.427, 95% CIs: 1.256–1.621, p < 0.001) and cause-specific mortality (cardiovascular-related mortality (HRs: 1.458, 95% CIs: 1.117–1.902, p = 0.005), non-cardiovascular-related mortality (HRs: 1.423, 95% CIs: 1.229–1.647, p < 0.001), and non-cancer-related mortality (HRs: 1.584, 95% CIs: 1.368–1.835, p < 0.001), respectively). Surprisingly, this association was more significant for young patients (p-value for interaction <0.001). Moreover, DM had a greater risk of all-cause and cause-specific mortality among overweight and obese MAFLD patients (p-value for interaction <0.001). Conclusions: DM increased the risk of all-cause and cause-specific mortality (cardiovascular-related, non-cardiovascular-related, and non-cancer-related) in MAFLD patients, especially in younger patients with excess obesity. Full article

Using the parametric g-formula, we estimated the 27-year risk of all-cause and specific causes of mortality under different potential interventions for blood lead (BLLs) and urinary cadmium (UCd) levels. We used data on 14,311 adults aged ≥20 years enrolled in the NHANES-III between 1988 and 1994 and followed up through 31 Dec 31 2015. Time and cause of death were determined from the National Death Index records. We used the parametric g-formula with pooled logistic regression models to estimate the relative and absolute risk of all-cause, cardiovascular, and cancer mortality under different potential threshold interventions for BLLs and UCd concentrations. Median follow-up was 22.5 years. A total of 5167 (36%) participants died by the end of the study, including 1550 from cardiovascular diseases and 1135 from cancer. Increases in BLLs and creatinine-corrected UCd levels from the 5th to the 95th percentiles were associated with risk differences of 4.17% (1.54 to 8.77) and 6.22% (4.51 to 12.00) for all-cause mortality, 1.52% (0.09 to 3.74) and 1.06% (−0.57 to 3.50) for cardiovascular disease mortality, and 1.32% (−0.09 to 3.67) and 0.64% (−0.98 to 2.80) for cancer mortality, respectively. Interventions to reduce historical exposures to lead and cadmium may have prevented premature deaths, especially from cardiovascular disease. Full article

The Black-White racial employment disparity and its link to mortality have demonstrated the health benefits obtained from employment. Further, racial/ethnic mortality disparities existing among men with different employment statuses have been previously documented. The purpose of this study was to examine the association between employment status and all-cause mortality among Black men. Data for the study was obtained from the National Health and Nutrition Examination Survey (NHANES) III 1988–1994 linked to the NHANES III Linked Mortality File. Cox proportional hazard models were specified to examine the association between health behaviors and mortality in Black men by employment status. Among those who were assumed alive (n = 1354), 41.9% were unemployed. In the fully adjusted model, unemployed Black men had an increased risk of all-cause mortality (Hazard Ratio [HR] 1.60, 95% confidence interval or CI [1.33, 1.92]) compared to Black men who were employed. These results highlight the impact of employment on all-cause mortality among unemployed Black men and underscore the need to address employment inequalities to reduce the mortality disparities among Black men. Full article

Studies have suggested an important role of dyslipidemia, a condition with alterations in blood lipid levels, in promoting an additional effect on periodontal breakdown. Thus, this study aimed to explore the theoretical pathways associated with dyslipidemia and periodontitis. We used data from 11,917 US adults with complete periodontal examinations participating in the Third National Health and Nutrition Examination Survey (NHANES III). Our hypothesis was tested using structural equation modelling (SEM). Dyslipidemia was defined according to the National Cholesterol Education Program (NCEP-ATP III) and periodontitis as a latent variable reflecting the shared variance of the number of surfaces with periodontal pocket depth [PPD] = 4 mm, PPD = 5 mm, PPD ≥ 6 mm, clinical attachment level [CAL] = 4 mm, CAL = 5mm, CAL ≥ 6 mm, and furcation involvement. The model also considered distal determinants (age, sex, and socioeconomic status) and proximal determinants (HbA1c, smoking and alcohol consumption, and obesity). The model showed sufficient global fit (Root Mean Squared Error of Approximation = 0.04, 90%CI = 0.04–0.05, Tucker–Lewis Index = 0.93, Comparative Fit Index = 0.95). Age, sex, socioeconomic status, obesity, and smoking were directly associated with periodontitis (p < 0.01). Dyslipidemia revealed a significant direct effect on periodontitis (standardized coefficient [SC] = 0.086, SE 0.027; p < 0.01), also mediated via an indirect pathway through HbA1c (SC = 0.021; SE 0.010; p = 0.02) and obesity (SC = 0.036; SE 0.012; p < 0.01) and resulted in a total effect on periodontitis. Dyslipidemia was associated with periodontitis through a direct pathway and indirectly through HbA1c and obesity in the US population. These results support the need for a multi-professional approach to tackling oral and noncommunicable diseases (NCDs), directed at their common risk factors. Full article

A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results. Full article