A gluco-metabolic risk index with cardiovascular risk stratification potential in patients with coronary artery disease

Association between Cardiometabolic risk factor and responsiveness to vitamin D supplementation: a new approach using artificial neural network analysis

BACKGROUND

There are increasing data highlighting the effectiveness of vitamin D supplementation in the treatment of vitamin D deficiency. But individuals vary in their responsiveness to vitamin D supplementation. In this study, the association between several cardiometabolic risk factors and the magnitude of response to vitamin D supplementation (change in vitamin D level) was investigated using a novel artificial neural networks (ANNs) approach.

METHODS

Six hundred eight participants aged between 12 to 19 years old were recruited to this prospective interventional study. Nine vitamin D capsules containing 50,000 IU vitamin D/weekly were given to all participants over the 9 week period. The change in serum 25(OH) D level was calculated as the difference between post-supplementation and basal levels. Suitable ANNs model were selected between different algorithms in the hidden and output layers and different numbers of neurons in the hidden layer. The major determinants for predicting the response to vitamin D supplementation were identified.

RESULTS

The sigmoid in both the hidden and output layers with 4 hidden neurons had acceptable sensitivity, specificity and accuracy, assessed as the area under the ROC curve, was determined in our study. Baseline serum vitamin D (30.4%), waist to hip ratio (10.5%), BMI (10.5%), systolic blood pressure (8%), heart rate (6.4%), and waist circumference (6.1%) were the most important factors in predicting the response to serum vitamin D levels.

CONCLUSION

We provide the first attempt to relate anthropometric specific recommendations to attain serum vitamin D targets. With the exception of cardiometabolic risk factors, the relative importance of other factors and the mechanisms by which these factors may affect the response requires further analysis in future studies (Trial registration: IRCT201509047117N7; 2015-11-25; Retrospectively registered).

The relationship between neuropsychological function and responsiveness to vitamin D supplementation using artificial neural networks

BACKGROUND

Vitamin D has recently attracted interest for its pleiotropic effects. Vitamin D supplements are a potentially important public health intervention, but the response to supplementation varies between individuals.

AIM

We aimed to assess the association between several neuropsychological parameters and the magnitude of response to vitamin D supplementation using an artificial neural network method.

METHODS

Neuropsychological function was assessed in 619 participants using standard questionnaires. The study participants received vitamin D capsules containing 50,000 IU vitamin D per week over 9 weeks. To assess the relationship between responsiveness to vitamin D supplements and the impact on these neuropsychological parameters, the best-performing artificial neural network algorithms were selected from a combination of different transfer functions in hidden and output layers and variable numbers of hidden layers (between two and 50). The performance of the artificial neural network algorithm was assessed by receiver operating characteristic analysis and variables of importance were identified.

RESULTS

The artificial neural network algorithm with sigmoid transfer function in both hidden and output layers could predict responsiveness to vitamin D supplementation effectively. The sensitivity and specificity were between 0.60 and 0.70 and 0.66 and 0.70, respectively. Cognitive abilities (42.5%), basal vitamin D (21.3%), body mass index (9.5%), and daytime sleepiness (8%) are the most widely used variables to predict changes in serum vitamin D levels.

CONCLUSIONS

Cognitive abilities status and baseline 25-hydroxyvitamin D are important novel modifiers of the enhancement in circulating 25-hydroxyvitamin D after vitamin D supplementation.

The Effects of Vitamin D Supplementation on Glycemic Control, Lipid Profiles and C-Reactive Protein Among Patients with Cardiovascular Disease: a Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background:

Insulin resistance, dyslipidemia and chronic inflammation are important risk factors for cardiovascular diseases (CVD). Hence, vitamin D supplementation might be an appropriate approach to decrease the complications of CVD. This systematic review and meta-analysis aimed to determine the effects of vitamin D supplementation on glycemic control, lipid profiles, and C-reactive protein among patients with coronary artery disease.

Methods:

Two independent authors systematically searched online databases including EMBASE, Scopus, Pub- Med, Cochrane Library, and Web of Science until 20th September 2018. Cochrane Collaboration risk of bias tool was applied to assess the methodological quality of included trials. The heterogeneity among the included studies was assessed using Cochran’s Q test and I-square (I2) statistic. Data were pooled using a random-effects model and weighted mean difference (WMD) was considered as the overall effect size.

Results:

A total of eight trials (305 participants in the intervention group and 325 in placebo group) were included in the current meta-analysis. Pooling effect sizes from studies revealed a significant reduction in fasting glucose (WMD): -15.67; 95% CI: -29.32, -2.03), insulin concentrations (WMD: -3.53; 95% CI: -4.59, -2.46) and homeostatic model assessment of insulin resistance (WMD: -1.07; 95% CI: -1.49, -0.66), and significant increase in the quantitative insulin-sensitivity check index (WMD: 0.02; 95% CI: 0.01, 0.03) following the administration of vitamin D. In addition, pooled analysis revealed a significant increase in serum HDL-cholesterol concentrations following vitamin D therapy (WMD: 3.08; 95% CI: 1.42, 4.73). Additionally, vitamin D supplementation significantly reduced C-reactive protein (CRP) levels (WMD: -0.75; 95% CI: -1.28, -0.23).

Conclusion:

This meta-analysis demonstrated the beneficial effects of vitamin D supplementation on improving glycemic control, HDL-cholesterol and CRP levels among patients with CVD, though it did not affect triglycerides, total- and LDL-cholesterol levels.

Predicting body fat percentage based on gender, age and BMI by using artificial neural networks

In the human body, the relation between fat and fat-free mass (muscles, bones etc.) is necessary for the diagnosis of obesity and prediction of its comorbidities. Numerous formulas, such as Deurenberg et al., Gallagher et al., Jackson and Pollock, Jackson et al. etc., are available to predict body fat percentage (BF%) from gender (GEN), age (AGE) and body mass index (BMI). These formulas are all fairly similar and widely applicable, since they provide an easy, low-cost and non-invasive prediction of BF%. This paper presents a program solution for predicting BF% based on artificial neural network (ANN). ANN training, validation and testing are done by randomly divided dataset that includes 2755 subjects: 1332 women (GEN = 0) and 1423 men (GEN = 1), with AGE from 18 to 88 y and BMI from 16.60 to 64.60 kg/m(2). BF% was estimated by using Tanita bioelectrical impedance measurements (Tanita Corporation, Tokyo, Japan). ANN inputs are: GEN, AGE and BMI, and output is BF%. The predictive accuracy of our solution is 80.43%. The main goal of this paper is to promote a new approach to predicting BF% that has same complexity and costs but higher predictive accuracy than above-mentioned formulas.

A primary estimation of the cardiometabolic risk by using artificial neural networks

Estimation of the cardiometabolic risk (CMR) has a leading role in the early prevention of atherosclerosis and cardiovascular diseases. The CMR estimation can be separated into two parts: primary estimation (PE-CMR) that includes easily-obtained, non-invasive and low-cost diagnostic methods and secondary estimation (SE-CMR) involving complex, invasive and/or expensive diagnostic methods. This paper presents a PE-CMR solution based on artificial neural networks (ANN) as it would be of great interest to develop a procedure for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete SE-CMR tests only on them. ANN inputs are values obtained by using PE-CMR methods, i.e. primary risk factors: gender, age, waist-to-height ratio, body mass index, systolic and diastolic blood pressures. ANN output is cmr-coefficient obtained from the number of disturbances in biochemical indicators, i.e. secondary risk factors: HDL-, LDL- and total cholesterol, triglycerides, glycemia, fibrinogen and uric acid. ANN training and testing are done by dataset that includes 1281 persons. The accuracy of our solution is 82.76%.

Mortality prediction in the elderly by an easily measured metabolic index

OBJECTIVE

Hyperglycaemia enhances the risk of cardiovascular events and death, while high-density lipoprotein cholesterol (HDLc) is protective. Information on these associations among the elderly population is scanty. We applied a cardiometabolic risk index (CMRI) based on HDLc and fasting plasma glucose (FPG) in an elderly Swedish population.

METHODS

In total, 432 75-year-olds were followed for 10-year mortality. The impact of risk factors on survival was analysed using Cox regression.

RESULTS

HDLc (mmol/l; median and interquartile range) was 1.6 (1.3-2.0) in women and 1.4 (1.2-1.5) in men, while FPG was 5.9 (5.5-6.6) and 5.9 (5.5-6.5). Some 89 persons were at high risk according to CMRI, and 163 persons died. FPG was related to mortality in women (HR; 95% CI: 1.23; 1.10-1.37) and there was a similar trend in men (1.08; 1.00-1.17; p = 0.061). Increasing HDLc was protective in men (0.38; 0.19-0.77) but not in women (0.77; 0.45-1.29). CMRI was related to mortality in both genders even after adjustment for established risk factors (1.79; 1.14-2.79; p = 0.011).

CONCLUSIONS

The CMRI helps identify elderly subjects at risk and may serve as a cost-effective risk prediction tool.