The relationship between vitamin K and peripheral arterial disease.
Vissers Linda E T,Dalmeijer Geertje W,Boer Jolanda M A,Verschuren W M Monique,van der Schouw Yvonne T,Beulens Joline W J
Atherosclerosis
BACKGROUND AND AIMS:A high dietary intake of vitamin K1 (phylloquinone) and vitamin K2 (menaquinones) is thought to decrease cardiovascular disease risk by reducing vascular calcification. The objective of this study is to explore if there is a relationship between phylloquinone and menaquinones intake and risk of PAD. METHODS:We investigated the association between intake of phylloquinone and menaquinones with PAD in a prospective cohort with 36,629 participants. Occurrence of PAD was obtained by linkage to national registries. Baseline intake of phylloquinone and menaquinones was estimated using a validated food-frequency questionnaire. Multivariate Cox regression was used to estimate adjusted hazard ratio's for the association. RESULTS:During 12.1 years (standard deviation 2.1 years) of follow-up, 489 incident cases of PAD were documented. Menaquinones intake was associated with a reduced risk of PAD with a hazard ratio (HR) of 0.71, 95% CI; 0.53-0.95 for the highest versus lowest quartile. A stronger association was observed (p interaction 0.0001) in participants with hypertension (HRQ4 versus Q1 0.59; 95% CI 0.39-0.87) or diabetes (HRQ4 versus Q1 0.56; 95% CI 0.18-1.91), though confidence intervals were wide in the small (n = 530) diabetes stratum. Phylloquinone intake was not associated with PAD risk. CONCLUSIONS:High intake of menaquinones was associated with a reduced risk of PAD, at least in hypertensive participants. High intake of phylloquinone was not associated with a reduced risk of PAD.
10.1016/j.atherosclerosis.2016.07.915
Cadmium and peripheral arterial disease: gender differences in the 1999-2004 US National Health and Nutrition Examination Survey.
American journal of epidemiology
Gender differences in the association of blood and urine cadmium concentrations with peripheral arterial disease (PAD) were evaluated by using data from 6,456 US adults aged ≥40 years who participated in the 1999-2004 National Health and Nutrition Examination Survey. PAD was defined as an ankle-brachial blood pressure index of <0.9 in at least one leg. For men, the adjusted odds ratios for PAD comparing the highest with the lowest quintiles of blood and urine cadmium concentrations were 1.82 (95% confidence interval (CI): 0.82, 4.05) and 4.90 (95% CI: 1.55, 15.54), respectively, with a progressive dose-response relation and no difference by smoking status. For women, the corresponding odds ratios were 1.19 (95% CI: 0.66, 2.16) and 0.56 (95% CI: 0.18, 1.71), but there was evidence of effect modification by smoking: among women ever smokers, there was a positive, progressive dose-response relation; among women never smokers, there was a U-shaped dose-response relation. Higher blood and urine cadmium levels were associated with increased prevalence of PAD, but women never smokers showed a U-shaped relation with increased prevalence of PAD at very low cadmium levels. These findings add to the concern of increased cadmium exposure as a cardiovascular risk factor in the general population.
10.1093/aje/kwq172
Differences in vitamin D status as a possible contributor to the racial disparity in peripheral arterial disease.
The American journal of clinical nutrition
BACKGROUND:Racial differences in cardiovascular risk factors do not fully explain the higher prevalence of lower-extremity peripheral arterial disease (PAD) in black adults. OBJECTIVE:We sought to determine whether any of this excess risk may be explained by vitamin D status, which has been widely documented to be lower in blacks than in whites. DESIGN:This population-based cross-sectional study included 2987 white and 866 black persons aged >or=40 y from the 2001-2004 National Health and Nutrition Examination Survey. PAD was defined as an ankle-brachial pressure index of <0.90 in either leg. RESULTS:Mean (+/-SEM) 25-hydroxyvitamin D [25(OH)D] concentrations were significantly lower in black than in white adults (39.2 +/- 1.0 and 63.7 +/- 1.1 nmol/L, respectively; P < 0.001). Adjusted odds ratios for PAD decreased in a dose-dependent fashion with increasing quartiles of 25(OH)D in white adults [1.00 (referent), 0.86, 0.67, and 0.53; P for trend < 0.001]. In black adults, the association was nonlinear; models with cubic splines suggested evidence of greater odds for PAD and a trend for lower odds for PAD at the lowest and highest concentrations of 25(OH)D, respectively. After adjustment for racial differences in socioeconomic status and for traditional and novel risk factors, odds for PAD in black compared with white adults were reduced from 2.11 (95% CI: 1.55, 2.87) to 1.67 (1.11, 2.51). After additional adjustment for 25(OH)D, the odds were further reduced to 1.33 (0.84, 2.10). CONCLUSIONS:Racial differences in vitamin D status may explain nearly one-third of the excess risk of PAD in black compared with white adults. Additional research is needed to confirm these findings.
10.3945/ajcn.2008.26447
Inter-leg systolic blood pressure difference has been associated with all-cause and cardiovascular mortality: analysis of NHANES 1999-2004.
BMC public health
BACKGROUND:Inter-leg systolic blood pressure difference (ILSBPD) has emerged as a novel cardiovascular risk factor. This study aims to investigate the predictive value of ILSBPD on all-cause and cardiovascular mortality in general population. METHODS:We combined three cycles (1999-2004) of the National Health and Nutrition Examination Survey (NHANES) data. Levels of ILSBPD were calculated and divided into four groups based on three cut-off values of 5, 10 and 15mmHg. Time-to-event curves were estimated with the use of the Kaplan-Meier method, and two multivariable Cox proportional hazards regression models were conducted to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause and cardiovascular mortality associated with ILSBPD. RESULTS:A total of 6 842 subjects were included, with the mean (SD) age of 59.5 (12.8) years. By December 31, 2019, 2 544 and 648 participants were identified all-cause and cardiovascular mortality respectively during a median follow-up of 16.6 years. Time-to-event analyses suggested that higher ILSBPD was associated with increased all-cause and cardiovascular mortality (logrank, p < 0.001). Every 5mmHg increment of ILSBPD brings about 5% and 7% increased risk of all-cause and cardiovascular mortality, and individuals with an ILSBPD ≥ 15mmHg were significantly associated with higher incidence of all-cause mortality (HR 1.43, 95%CI 1.18-1.52, p < 0.001) and cardiovascular mortality (HR 1.73, 95%CI 1.36-2.20, p < 0.001) when multiple confounding factors were adjusted. Subgroup and sensitivity analysis confirmed the relationship. CONCLUSIONS:Our findings suggest that the increment of ILSBPD was significantly associated with higher risk of all-cause and cardiovascular mortality in general population.
10.1186/s12889-024-18508-8
Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018.
Frontiers in endocrinology
Background:Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community. Methods:The National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator. Results:There were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency. Conclusion:In this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations.
10.3389/fendo.2024.1327058
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.
Dinh An,Miertschin Stacey,Young Amber,Mohanty Somya D
BMC medical informatics and decision making
BACKGROUND:Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. METHODS:Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. RESULTS:The developed ensemble model for cardiovascular disease (based on 131 variables) achieved an Area Under - Receiver Operating Characteristics (AU-ROC) score of 83.1% using no laboratory results, and 83.9% accuracy with laboratory results. In diabetes classification (based on 123 variables), eXtreme Gradient Boost (XGBoost) model achieved an AU-ROC score of 86.2% (without laboratory data) and 95.7% (with laboratory data). For pre-diabetic patients, the ensemble model had the top AU-ROC score of 73.7% (without laboratory data), and for laboratory based data XGBoost performed the best at 84.4%. Top five predictors in diabetes patients were 1) waist size, 2) age, 3) self-reported weight, 4) leg length, and 5) sodium intake. For cardiovascular diseases the models identified 1) age, 2) systolic blood pressure, 3) self-reported weight, 4) occurrence of chest pain, and 5) diastolic blood pressure as key contributors. CONCLUSION:We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.
10.1186/s12911-019-0918-5