A pilot study of the effects of crocin on high-density lipoprotein cholesterol uptake capacity in patients with metabolic syndrome: A randomized clinical trial

Serum Zinc and Copper Concentrations and Dyslipidemia as Risk Factors of Cardiovascular Disease in Adults: Data Mining Techniques

This study aimed to examine the relationship between serum cholesterol levels and the ratio of zinc (Zn) and copper (Cu) in the blood serum and the incidence of cardiovascular disease (CVD). In Phase I of the study, 9704 individuals between the age of 35 and 65 years were recruited. Phase II of the cohort study comprised 7561 participants who completed the 10-year follow-up. The variables which were measured at the baseline of the study included gender, age, systolic blood pressure (SBP), diastolic blood pressure (DBP); biochemical parameters including serum Cu, Zn, copper-zinc ratio (Cu/Zn), zinc-copper ratio (Zn/Cu); fasted lipid profile consisting of triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL) as well as fasting serum glucose, and triglycerides-glucose (TyG) index. Decision tree (DT) and logical regression (LR) models were applied to examine the relationship between the aforementioned factors and CVD. CVD was diagnosed in 837 individuals (378 males and 459 females) out of 7561 participants. According to the LR models, SBP, TC, HDL, age, Zn/Cu, and TyG index for males and SBP, age, TyG index, HDL, TC, Cu/Zn, and Cu for females had the highest correlation with CVD (p-value ≤ 0.033). Based on the DT algorithm, 88% of males with SPB < 129.66 mmHg, younger age (age < 53 years), TyG index < 9.53, 173 ≤ TC < 187 mg/dL, and HDL ≥ 32 mg/dL had the lowest risk of CVD. Also, 98% of females with SBP < 128 mmHg, TyG index < 9.68, age < 44, TC < 222 mg/dL, and HDL ≥ 63.7 mg/dL had the lowest risk of CVD. It can be concluded that the Zn/Cu for men and Cu/Zn for women, along with dyslipidemia and SBP, could significantly predict the risk of CVD in this cohort from northeastern Iran.

Prediction of cardiovascular disease risk by serum zinc and copper concentrations and anthropometric measurements

INTRODUCTION

We aimed to investigate the association between cardiovascular disease (CVD) and various anthropometric indices, as well as the serum levels of copper (Cu) and zinc (Zn), copper-zinc ratio (Cu/Zn ratio) and zinc-copper ratio (Zn/Cu ratio), in a large population sample from northeastern Iranian.

METHOD

9704 individuals aged 35 to 65 were enrolled in the first phase of the study. After a 10-year follow-up, 7560 participants were enrolled into the second phase. The variables used in this study included demographic characteristics, such as gender and age; biochemical parameters including: serum Zn, Cu, Cu/Zn ratio, and Zn/Cu ratio; anthropometric parameters including: waist circumference (WC), body mass index (BMI), and waist-to-hip ratio (WHR). The relationship between the aforementioned indices and CVD was examined using decision tree (DT) and logistic regression (LR) models.

RESULTS

A total of 837 individuals were diagnosed with CVD among the 7560 participants. LR analysis showed that BMI, age, WH zinc-copper ratio (Zn/Cu ratio), and serum Zn/Cu ratio were significantly associated the development of CVD in men, and WHR, age, BMI, serum Cu, and Cu/Zn ratio in women. DT analysis showed that, age was the most important predictor of CVD in both genders. 71% of women, older than 49 years, with a WHR≥ 0.89, serum Cu< 75 (µg/dl), BMI≥ 22.93 (kg/m2), and serum Cu≥ 14 (µg/dl), had the highest risk of CVD. In men, among those who were ≥ 53 years, with a WHR≥ 0.98, serum Zn/Cu ratio< 1.69, and BMI≥ 22.30, had the highest risk of CVD.

CONCLUSION

Among Iranian adult population, BMI, age, and WHR were one of the predictors of CVD for both genders. The Zn/Cu ratio was CVD predictor for men while Cu/Zn ratio was CVD predictor for women.

Modulating effects of crocin on lipids and lipoproteins: Mechanisms and potential benefits

Dyslipidemia poses a significant risk to cardiovascular health in both diabetic and non-diabetic individuals. Therefore, it is crucial to normalize lipid homeostasis in order to prevent or minimize complications associated with dyslipidemia. However, pharmacological interventions for controlling lipid metabolism often come with adverse effects. As an alternative, utilizing herbal-based agents, which typically have fewer side effects, holds promise. Crocin, a naturally occurring nutraceutical, has been shown to impact various intracellular pathways, reduce oxidative stress, and alleviate inflammatory processes. Recent evidence suggests that crocin may also confer lipid-related benefits and potentially contribute to the normalization of lipid homeostasis. However, the specific advantages and the cellular pathways involved are not yet well understood. In this review, we present the latest findings regarding the lipid benefits of crocin, which could be instrumental in preventing or reducing disorders associated with dyslipidemia. Additionally, we explore the potential cellular mechanisms and pathways that mediate these lipid benefits.

Decreased plasma miR-140-3p is associated with coronary artery disease

Background

Although many circulating miRNAs (c-miRNAs) are associated with coronary artery disease (CAD), they are far from being the biomarker for CAD diagnosis or risk prediction. Therefore, novel c-miRNAs discovery and validation are still required, especially evaluating their prediction capacity.

Objectives

Identify novel CAD-related c-miRNAs and evaluate its risk prediction capacity for CAD. Methods: miRNAs associated with CAD were preliminarily investigated in three paired samples representing pre-CAD stage and CAD stage of three female individuals using the Applied Biosystems miRNA TaqMan® Low-Density Array (TLDA). Then, the candidate miRNAs were further verified in an independent case-control study including 129 CAD patients and 76 controls, and their potential practical value in prediction for CAD was evaluated using a machine learning (ML) algorithm. The accuracy of classification and prediction was assessed with the area under the receiver operating characteristic curve (AUC).

Results

TLDA analysis shows that miR-140-3p decreased significantly in CAD-stage (FC = -3.01, P = 0.007). Further study shows that miR-140-3p was significantly lower in CAD group [1.26 (0.68, 2.01)] than in control group [2.07 (1.19, 3.21)] (P < 0.001) and independently associated with CAD (P < 0.001). The addition of miR-140-3p to the variables including smoking history, HDL-c, and APOA1 improved the accuracy of classification by logistic regression and of prediction for CAD by ML models. The ML models built with miR-140-3p and HDL-c, respectively, had a similar prediction accuracy. The feature importance of miR-140-3p and HDL-c in the ML models was also similar. Decision curve analysis showed that miR-140-3p and HDL-c had almost identical net benefits.

Conclusion

Reduced levels of miR-140-3p is linked to CAD, and it is possible to use the plasma level of miR-140-3p as a means of evaluating the risk of CAD.

The relationship between anthropometric indices and the presence of hypertension in an Iranian population sample using data mining algorithms

Hypertension (HTN) is a common chronic condition associated with increased morbidity and mortality. Anthropometric indices of adiposity are known to be associated with a risk of HTN. The aim of this study was to identify the anthropometric indices that best associate with HTN in an Iranian population. 9704 individuals aged 35-65 years were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. Demographic and anthropometric data of all participants were recorded. HTN was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, and/ or a diastolic blood pressure (DBP) ≥ 90 mmHg on two subsequent measurements, or being treated with oral drug therapy for BP. Data mining methods including Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF) were applied. Of 9704 participants, 3070 had HTN, and 6634 were normotensive. LR showed that body roundness index (BRI), body mass index (BMI) and visceral adiposity index (VAI) were significantly associated with HTN in both genders (P < 0.0001). BRI showed the greatest association with HTN (OR = 1.276, 95%CI = (1.224, 1.330)). For BMI we had OR = 1.063, 95%CI = (1.047, 1.080), for VAI we had OR = 1.029, 95%CI = (1.020, 1.038). An age < 47 years and BRI < 4.04 was associated with a 90% probability of being normotensive. The BF indicated that age, sex and BRI had the most important role in HTN. In summary, among anthropometric indices the most powerful indicator for discriminating hypertensive from normotensive patients was BRI.

White blood cell and platelet distribution widths are associated with hypertension: data mining approaches

In this paper, we are going to investigate the association between Hypertension (HTN) and routine hematologic indices in a cohort of Iranian adults. The data were obtained from a total population of 9704 who were aged 35-65 years, a prospective study was designed. The association between hematologic factors and HTN was assessed using logistic regression (LR) analysis and a decision tree (DT) algorithm. A total of 9704 complete datasets were analyzed in this cohort study (N = 3070 with HTN [female 62.47% and male 37.52%], N = 6634 without HTN [female 58.90% and male 41.09%]). Several variables were significantly different between the two groups, including age, smoking status, BMI, diabetes millitus, high sensitivity C-reactive protein (hs-CRP), uric acid, FBS, total cholesterol, HGB, LYM, WBC, PDW, RDW, RBC, sex, PLT, MCV, SBP, DBP, BUN, and HCT (P < 0.05). For unit odds ratio (OR) interpretation, females are more likely to have HTN (OR = 1.837, 95% CI = (1.620, 2.081)). Among the analyzed variables, age and WBC had the most significant associations with HTN OR = 1.087, 95% CI = (1.081, 1.094) and OR = 1.096, 95% CI = (1.061, 1.133), respectively (P-value < 0.05). In the DT model, age, followed by WBC, sex, and PDW, has the most significant impact on the HTN risk. Ninety-eight percent of patients had HTN in the subgroup with older age (≥58), high PDW (≥17.3), and low RDW (<46). Finally, we found that elevated WBC and PDW are the most associated factor with the severity of HTN in the Mashhad general population as well as female gender and older age.

Association between biochemical and hematologic factors with COVID-19 using data mining methods

BACKGROUND AND AIM

Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection.

METHOD

A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied.

RESULTS

In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors.

CONCLUSION

The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Serum uric acid to high-density lipoprotein ratio as a novel indicator of inflammation is correlated with the presence and severity of metabolic syndrome: A large-scale study

INTRODUCTION

We investigated the association of serum uric acid to high-density lipoprotein ratio (UHR) with the presence and severity of metabolic syndrome (MetS) among MASHAD cohort participants.

METHODS

In this cross-sectional study, according to International Diabetes Federation criteria, the cohort participants were divided into MetS (+) and MetS (-) groups. MetS (+) were classified into Group 1 (those with 3 MetS criteria), Group 2 (those with 4 MetS criteria) and Group 3 (those with 5 MetS criteria). UHR was compared among the groups.

RESULTS

Data related to 9637 subjects including 3824 MetS (+) and 5813 MetS (-) were analysed. The mean UHR was significantly higher (p < .001) in the MetS (+) group compared with the MetS (-) group. UHR increased as the MetS severity increased (p < .001). ROC analysis revealed that UHR greater than 9.5% has 89.07% sensitivity and 77.03% specificity in differentiating MetS (-) from MetS (+) subjects.

CONCLUSION

Among MASHAD cohort study participants, a significant association between UHR and MetS was found. Furthermore, there is an increase in UHR as the severity of MetS increases. Registration number of MASHAD cohort study: 85134.

Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches

OBJECTIVE

To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults.

METHOD

For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF).

RESULTS

Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status.

CONCLUSION

The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.

Applying data mining techniques to predict vitamin D deficiency in diabetic patients

Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.

The effects of low-fat dairy products fortified with 1500 IU vitamin D3 on serum liver function biomarkers in adults with abdominal obesity: a randomized controlled trial

INTRODUCTION

Vitamin D deficiency has been reported to affect liver function biomarkers. This study was aimed to investigate the effect of consuming vitamin D fortified low-fat dairy products on liver function tests in adults with abdominal obesity.

METHODS

This total blinded randomized controlled trial was undertaken on otherwise healthy abdominally obese adults living in Mashhad, Iran. Milk and yogurt were fortified with 1500 IU vitamin D3 nano-capsules. Participants were randomized to receive fortified milk (n = 73), plain milk (n = 73), fortified yogurt (n = 69), and plain yogurt (n = 74) for 10 weeks. Blood samples were taken at baseline and at the end of the study to assess serum levels of vitamin D, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase (ALP), and Gamma glutamyl transferase.

RESULTS

A total of 289 participants completed the study (54% female). The groups were homogenous in terms of age, sex, weight, energy intake, and physical activity level (p-value > 0.05). After the trial, vitamin D serum levels were significantly increased in both groups receiving fortified products (both p < 0.001). There was a significant time*group effect only in serum ALP (p < 0.001).

CONCLUSION

Consumption of dairy products fortified by 1500 IU vitamin D3 might have detrimental effects on serum levels of some liver enzymes in individuals with abdominal obesity. Further studies needed to determine these effects and underlying mechanisms.

TRIAL REGISTRATION

IRCT20101130005280N27 .

Association of baseline and changes in adiponectin, homocysteine, high-sensitivity C-reactive protein, interleukin-6, and interleukin-10 levels and metabolic syndrome incidence: Tehran lipid and glucose study

Background

Metabolic syndrome (MetS) is accompanied by chronic low-grade inflammation, and inflammatory markers like high-sensitivity C-reactive protein(hs-CRP), interleukin-6(IL-6), and homocysteine(Hcy) contribute to inflammation, obesity, and insulin resistance. Adiponectin(AdipoQ) and interleukin-10(IL-10) are anti-inflammatory markers that play protective roles in MetS. This study aimed to investigate the association between these biochemical marker changes and MetS in a sample of the Tehranian population during six years of follow-up.

Methods

In this longitudinal study, 340 adults at baseline and after a six-year follow-up, aged ≥18 years, were selected randomly from the Tehran Lipid and Glucose Study (TLGS). MetS was defined according to the Joint Interim Statement (JIS) criteria. Individuals were categorized into four groups based on their MetS status at baseline and follow-up: 1) non-MetS: participants who did not have MetS at both baseline and follow-up; 2) incident MetS: participants who did not have MetS at baseline but developed MetS during the follow-up ; 3) recovery MetS: participants who had MetS at baseline but no longer had MetS during the follow-up; 4) persistent MetS: participants who had MetS both at baseline and follow-up.

Results

The mean follow-up time was 6.1 years. There were 176 subjects in the non-MetS group, 35 in the incident MetS group, 41 in the recovery MetS group, and 88 in the persistent MetS group. Increases in the levels of both hs-CRP 1.40 (95% CI: 1.15, 1.71, p = 0.001) and IL-6 1.09 (95% CI: 1.03, 1.17, p = 0.004) significantly increased the odds of the incident and persistent MetS, respectively. The area under the ROC curve (AUC) was more than 0.69 (p < 0.000) for hs-CRP in predicting MetS incidence and more than 0.86 (p < 0.000) for IL-6 in predicting MetS persistence.

Conclusion

After a six-year average follow-up, hs-CRP and IL-6 levels were deemed more reliable predictors of MetS incidence and persistence, respectively.

Association of dietary and blood inflammatory indicators with depression, anxiety, and stress in adults with vitamin D deficiency

BACKGROUND

There is growing evidence that vitamin D may be related to mental health. The aim of the current study was to investigate the association of dietary and blood inflammatory factors with mental health disorders in subjects with vitamin D deficiency, shedding further light on the complex interplay of these conditions.

METHOD

In this cross-sectional study, 306 subjects completed the validated Depression, Anxiety, and Stress Scale questionnaire to evaluate their depression, anxiety, and stress scores. Dietary inflammatory index (DII) and healthy eating index (HEI) were calculated using a validated 65-item food frequency questionnaire. Blood samples were taken and vitamin D, cytokine, and hs-CRP levels were measured using enzyme-linked immunosorbent assay kits. Platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR) were calculated using standard laboratory methods.

RESULTS

The subjects were divided into two groups based on their vitamin D levels: a vitamin D < 20 μg/dl group (N = 257) and a vitamin D ≥ 20  μg/dl group (N = 49). Between group analysis revealed that only DII (p = 0.015), platelet (p = 0.04), and hs-CRP (p = 0.015) were significantly different. In adults with vitamin D levels below 20 μg/dl, NLR and DII were significantly higher in subjects with anxiety (p < 0.05), and this relationship remained significant only for NLR after adjusting for age and sex. Additionally, PLR and HEI were significantly different in depressed compared to non-depressed subjects, and this association remained significant only for HEI after adjusting for age and sex.

CONCLUSION

In subjects with vitamin D deficiency, increased levels of PLR, NLR, and DII were associated with depression and anxiety, while HEI was negatively associated with depression. These associations were not found in subjects with vitamin D levels ≥20 μg/dl.

Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2

BACKGROUND AND OBJECTIVE

Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease.

MATERIAL AND METHOD

This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection.

RESULT

Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection.

CONCLUSION

There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.

Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study

INTRODUCTION

Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist.

AIM

Investigating the association between CVDs and anthropometric measurements among Iranian adults.

METHODS

For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models.

RESULTS

During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%).

CONCLUSION

BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

HDL-cholesterol concentration and its association with coronary artery calcification: a systematic review and meta-analysis

BACKGROUND

Coronary artery calcification (CAC) is a potential risk marker of coronary atherosclerosis that has high specificity and sensitivity. However, the association between high-density lipoprotein cholesterol (HDL-C) concentration and CAC incidence and progression is controversial.

METHODS

PubMed, Embase, Web of Science, and Scopus were systematically searched to identify relevant observational studies up to March 2023 and assessed the methodological quality using Newcastle-Ottawa Scale (NOS) scale. Random-effects meta-analysis was used to estimate pooled odds ratios (OR) and 95% confidence interval considering heterogeneity across studies.

RESULTS

Of the 2,411 records, 25 cross-sectional (n = 71,190) and 13 cohort (n = 25,442) studies were included in the systematic review. Ten cross-sectional and eight cohort studies were not eligible and were omitted from the meta-analysis. A total of 15 eligible cross-sectional studies (n = 33,913) were included in the meta-analysis and pooled results revealed no significant association between HDL-C and CAC > 0, CAC > 10, or CAC > 100 [pooled OR: 0.99 (0.97, 1.01)]. Meta-analysis of the 5 eligible prospective cohort studies (n = 10,721) revealed no significant protective effect of high HDL-C against CAC > 0 [pooled OR: 1.02 (0.93, 1.13)].

CONCLUSIONS

According to this analysis of observational studies, high HDL-C levels were not found to predict protection against CAC. These results suggest HDL quality rather than HDL quantity is important for certain aspects of atherogenesis and CAC.

REGISTRATION NUMBER

CRD42021292077.

Evaluation of rs1748195 ANGPTL3 gene polymorphism in patients with angiographic coronary artery disease compared to healthy individuals

SUBJECT

The Angiopoietin-like 3 (ANGPTL3) gene has been reported to be associated with cardiovascular risk. This study is designed to compare the genetic variant (rs1748195) of the ANGPTL3 gene and the presence of a coronary artery occlusion of >50% in Iranian nation.

METHOD

In this study, 184 patients underwent angiography and 317 healthy individuals were evaluated for polymorphism of rs1748195 the ANGPTL3 gene using Tetra-ARMs PCR. Coronary patients who experience angiography were categorized into two groups: 54 patients who had an angiography indication for the first time and coronary occlusion was <50% (Angio-) and 134 patients who formerly underwent coronary stent implanting at least 1 month before with coronary occlusion of ≥50% that again have an angiography indication (Angio+). In addition, individuals with angio+ are categorized in two groups: (1) non-in-stent restenosis (NISR); patient with a patent stent (N = 92). (2) in-stent restenosis (ISR); in-stent stenosis >50% (N = 42).

RESULT

The fundamental of characteristics of our study design population was categorized based on undergoing angiography or not. In the present study, we investigated that the CC genotype, and also the A allele corresponding to rs1748195 at the ANGPTL3 gene loci, was associated with negative angiogram and directly related to the risk of coronary occlusion >50%. In contrast, this result was not significant in genotypes of ANGPTL3 between non-ISR and ISR groups.

CONCLUSION

The outcomes of this study showed that rs1748195 polymorphism at the ANGPTL3 gene loci is associated with an elevated risk for the existence of a coronary occlusion of >50%.

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

BACKGROUND

The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches.

METHODS

A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software.

RESULTS

Traditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p-value < 0.001) and in females, demispan and WC (p-value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI.

CONCLUSIONS

Our results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.