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Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk

Diabetology & Metabolic Syndrome volume 16, Article number: 304 (2024) Cite this article

Abstract

Objective

Our aim was to examine the correlation between CVDs and various anthropometric and biochemical indices in the Iranian population.

Methods

9704 healthy individuals without CVD aged 35–65 were enrolled in our study. The anthropometric indices including Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Body Roundness Index (BRI), Waist to Hip Ratio (WHR), Weight-adjusted Waist Index (WWI), Conicity Index (C-Index), A Body Shape Index (ABSI), Waist to Height Ratio (WHtR), Body Surface Area (BSA), Body Mass Index (BMI), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI) were calculated. The biochemical indices including Cardiac Risk Ratio (CRR), Atherogenic Index of Plasma (AIP), Triglycerides-Glucose Index (TyG), Cardiac Risk Index (CRI), Atherogenic Coefficient (AC), and high-sensitivity C-Reactive Protein (hs-CRP) were investigated. The association of the above indices with CVD was analyzed using logistic regression (LR) and the decision tree (DT) models.

Results

The LR showed age, hs-CRP, AIP, AVI, LAP, and TyG had significant associations with CVDs in men (p-value < 0.002). Also, age, hs-CRP, LAP, TyG, BRI, VAI, and CRR had significant associations with CVDs in women (p-value < 0.002). The DT showed 95% of men with age > = 48, AIP > = 0.94, TyG > = 9.71, and AVI > = 14.24 had CVDs. Also, 97% of women with age > = 54, TyG > = 8.33, and hs-CRP > = 36.69 had CVDs.

Conclusion

Age, TyG, AIP, AVI, hs-CRP and LAP were the best predictors of CVD in men. Moreover, age, TyG, hs-CRP and BAI were the best indicators of CVD in women.

Graphical Abstract

Introduction

Cardiovascular diseases (CVDs) has an important impact on health services in both developed and low-income countries by being the leading cause of premature death [1]. Traditional risk factors for CVD, such as type 2 diabetes mellitus, smoking, hypertension, and dyslipidemia have led to the development of risk prediction models and to major developments in therapy. However, up to 20% of patients with CVD have no conventional risk factors, and 40% have only one [2]. The performance of such strategies in a cost-effective manner is restricted by the limited predictive value of the current risk-assessment models. In this study, we discuss ongoing risk biomarkers and indices to enhance the current risk-stratification metrics for CVD and improve the selection of individuals for preventative strategies [3]. Annual deaths from CVDs are expected to increase to 23.6 million by 2030 [4, 5]. Treatments for CVDs may include pharmacological or surgical procedures which are associated with high costs and side effects [6, 7]. Since prevention is better than cure, it is the most appropriate approach to deal with this widespread health issue [6]. Because CVD is a multifactorial disease, the management of its rapid growth has been challenging [4]. The risk factors for CVDs mainly include aging, genetic susceptibility, smoking, obesity, hyperlipidemia, low level of physical activity, and high blood pressure [8, 9]. Some of the aforementioned risk factors such as age and genetics are non-modifiable but there is still room for change in the management of the modifiable risk factors [4].

Obesity generally defines as excess fat accumulation in the body [10]. Pro-inflammatory cytokines and adipokines that are produced by fat tissue, especially visceral adipose tissue, induce the formation of atherosclerotic plaques and cause cardiovascular dysfunction [11,12,13]. Commonly, obesity is described by body mass index (BMI) [14]. BMI is a simple index that is associated with a higher probability of developing CVD, although it cannot distinguish body fat from muscle (lean tissue) nor can it determine the body fat distribution [14,15,16,17]. In comparison to BMI, waist circumference (WC) is a more suitable index of fat tissue distribution and is positively associated with visceral adiposity [18]. However, using WC alone may not provide accurate information as it can vary depending on the anatomical site of measurement or between different races [19, 20]. Additionally, it is easily influenced by variations in an individual’s height and weight [21]. The waist-to-height ratio (WHtR) has been found to be more effective than BMI and WC in identifying visceral obesity and has a strong association with several cardiometabolic factors [22]. Compared to the traditional anthropometric indices, new indicators have recently gained interest in the literature. Some of these indices include the body roundness index (BRI), abdominal volume index (AVI), a body shape index (ABSI), weight-adjusted waist index (WWI), lipid accumulation product (LAP), visceral adiposity index (VAI), conicity index (C-index), body surface area (BSA), waist-to-hip ratio (WHR), and body adiposity index (BAI). Nevertheless, it is not clear whether these new indices are superior to the traditional ones. Furthermore, the association of these indices with the risk of developing CVDs has not been well-established in large cohorts [7, 23,24,25,26,27].

Dyslipidemia is a well-known risk factor for CVDs and is defined as high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), low high-density lipoprotein cholesterol (HDL-C), and high triglyceride (TG) levels [28]. However, previous studies indicated that LDL-C and HDL-C alone are not suitable screening markers for atherosclerosis risk [29]. Therefore, this issue requires the discovery of new biochemical indices in the prediction of CVD. The triglyceride glucose index (TyG) is a novel marker that is identified as an authentic substitute for insulin resistance [30]. Also, it is an independent risk factor for CVD [31]. Other new indexes include the Cardiac Risk Ratio (CRR), Atherogenic Coefficient (AC), Atherogenic Index of Plasma (AIP), and Cardiac Risk Index (CRI) which have received a lot of attention due to their outstanding role in the CVD diagnosis [29, 32,33,34,35,36,37]. C-reactive protein (CRP) is another factor that plays a noteworthy role in the incidence of CVD [38]. It is produced in the liver and increases during infection or inflammation. Recent studies have shown that a rise in serum CRP level is directly related to an increased risk of CVDs [39]. However, few studies with a large sample and long-term follow-up have investigated the relationship of these indicators with CVD prediction in the Iranian population. Additionally, it is not clear which of these new biochemical indicators can better predict CVD.

Therefore, in this large cohort study we are going to investigate the association of aforementioned anthropometric indices (BAI, WHR, BRI, AVI, WWI, C-Index, WHtR, ABSI, BSA, BMI, LAP and VAI) and biochemical factors (CRR, AIP, TyG, CRI, AC, and hs-CRP) with the risk of developing CVDs.

Material and methods

Study population

An extensive prospective cohort study was part of the MASHAD study on stroke and heart atherosclerosis disorder [40]. Based on the populace of Mashhad, a city located in the northeast of Iran in 2015, the individuals were selected from three regions of the city applying stratified cluster random sampling scheme. Nine sites were created in each region that were centered on Mashhad Healthcare Center divisions. Eligible families were provided with an information brochure about the study by the local population authorities. Following the identification of subjects in this age range, they were contacted to set up a physical examination conducted formally appointment (start in 2007). 82% of the people who answered agreed to take part in the study. Then, A nurse along with two certified health care took notes and collected demographic, anthropometric, and biochemical data. Coronary artery disease, stroke, and PAD were exclusion criteria, along with cancer, chronic kidney disease, and prevalent CVD. The MASHAD cohort study sought to determine the 6-year risk factors and CVD risk among the Mashhad population. Subjects were followed up for CVDs incidence every three years. All patients who reported suspected CVDs during the follow-up years were contacted and consulted with a cardiologist. During the 6 years that the study was followed up, among the 9704 cases that took part in the study. Finally, 235 individuals were confirmed to have been diagnosed with CVDs (myocardial infarction affected 29, unstable angina affected 118, stable angina affected 63, and cardiovascular death affected 25). The remainder of the participants were regarded as subjects who did not have CVD (9469). Flow chart of the paper is depicted in Fig. 1.

Fig. 1
figure 1

Flow chart of this study

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The number of individuals who developed CVDs during the follow-up period was much smaller than the number of people who did not. In other words, only 2.4% of people have the disease during the follow-up period. In this case, we are dealing with a data set called "Unbalanced Dataset", which is very common in such studies and occurs when the number of observations in one category is much less or more than in other categories. Thus, in the logistic regression (LR) and decision tree (DT) models, the unbalanced data set was transformed into a balanced model using an algorithm that has been common in this type of studies in the past. In order to transform the unbalanced data set into a balanced one, a Bayesian theory-based approach was used [41]. Incidence of CVDs was investigated after receiving written informed consent from participants. After removing the missing data from each measured variable, the data were analyzed on a balanced dataset and finally with 9529 data as illustrated in Fig. 1. Mashhad University of Medical Sciences' Ethics Committee approved the study in compliance with Code of Ethics IR.MUMS.MEDICAL.REC.1402.023.

Diagnosis of cardiovascular diseases (CVDs)

A physical examination was performed by an expert cardiologist during the 6-year follow-up to determine if the participants have a history of heart disease or not. Participants were evaluated in terms of CVD according to the major adverse cardiovascular events (MACE) criteria which comprised nonfatal cardiovascular disease including stable angina, unstable angina, myocardial infarction (MI), PCI, CABG, HF stroke, PE and DVT along with death from cardiovascular death was defined as death from MI, chronic ischemic heart disease, heart failure, fatal arrhythmia, cerebrovascular, pulmonary thromboembolism, PAD and sudden cardiac arrest [42, 43]. The cardiologist conducted other investigations if they were uncertain about the diagnosis of CVD, which included echocardiography, ETT, radioisotope interventions, and stress echocardiography.

Data collection and anthropometric measurements

Demographic characteristics (such as age and gender), anthropometric information (such as height, weight, BMI, etc.) and biochemical information (such as TG, TC, glucose, etc.) were documented in this database. At the start of the research, all of these variables were measured as previously mentioned [44].

Anthropology measurements were measured by a nurse who was registered. Light clothes and no shoes were asked of participants to measure their weight and height. A stationary stadiometer calibrated to the nearest 0.1 cm was applied to measure height in centimeters, and an electronic scale was applied to measure weight to the nearest 0.1 kg. Then, BMI was used based on the definition of dividing weight (kg) by the square of height (m). The World Health Organization (WHO) defines overweight as having a 25 < BMI < = 29.9 kg/m2 and obese as having a BMI > = 30 kg/m2 [45]. Anthropometric indices were calculated by using the formulas given in Table 1.

Table 1 The measurement of anthropometric and biochemical indices
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Subjects were asked to stand and exhale as part of the measurement process for hip circumference (HC) and WC. The HC was determined by measuring the greatest circumference between the crotch and the iliac crest. All measurements were taken with a retractable tape meter. The calculation of WHR was done by dividing WC by HC. WHTR was calculated by multiplying WC (m) by height (m). According to WHO, men with truncal obesity are referred to as WHR > = 0.95 while women with truncal obesity are referred to as WHR > = 0.8. Men and women, respectively, are classified as having high WC if it is 94 cm or higher, according to International Diabetes Federation (IDF) protocols.

Blood sampling and biochemical measurements

According to standard protocol, the first phase of the study in 2007 consisted of collecting blood samples after a 12–14 h overnight fast from 8 to 10 a.m. in a sitting position, all participants' antecubital veins were measured. Then, the serum and plasma were separated into six aliquots (0.5 ml) by centrifuging at room temperature, the samples should be stored immediately. The MASHAD study biobank was the destination for them in the next stage. The biochemical measurements and indices used in this paper were high-sensitivity C-reactive protein (hs-CRP), HDL, LDL, TC, TG, CRR, AIP, LAP, TyG, CRI, VAI, and AC. Table 1 provided the formulas for calculating biochemical indices. The discussion of laboratory measurements and cut-offs has been detailed [40].

Statistical analysis

The data was analyzed using SAS JMP Pro version 13 (SAS Institute Inc., Cary, NC) and SPSS version 22 (Armonk, NY: IBM Corp.). Mean ± standard deviation (SD) and number with frequency (%) were applied to describe quantitative and qualitative data, respectively. Chi-square test was utilized to measure the relationship between the measured parameters. Variables continuous were compared utilizing a student’s t-test as well. DT and LR methods were employed to examine data. In LR, there is a presentation of the probability of placing every record in the target groups [46,47,48]. A statistical difference can be inferred when the two-tailed p-value is < 0.05. Data mining schemes such as LR and DT have been utilized for analyzing the data.

Logistic regression (LR) modelling

LR is a popular model to investigate the association between several predictor parameters (either continuous or categorical) and binary outcomes in public health, medicine, etc. [46, 47].

The response variable, \({Y}_{i}\), should be marked with either 0 or 1 depending on the outcome of the response. The X vector represents the covariates linked to the response variable and the regression coefficients that correspond to it. As follows, we can investigate the relationship between the binary response and covariates variable:

$$logit\{E({Y}_{i})\}=logit\{Pr({Y}_{i}=1| {\varvec{X}},{\varvec{\beta}})\} = {{\varvec{\beta}}}^{{\varvec{T}}}{\varvec{X}}.$$

The important advantage of applying the LR is that it can show the inverse or the direct association between the binary response and input variables.

Decision tree (DT) modelling

In the late twentieth century, data mining emerged as one of the techniques used in artificial intelligence. Data mining is the process of extracting hidden knowledge from large amounts of data. The classification of data is a crucial issue for researchers in this process [47, 49, 50]. Classification problems can be analyzed using various methods [49]. DT has many applications in medical fields [48, 51,52,53]. These fields are heavily influenced by its clarity, simplicity, and ability to extract simple and understandable rules [49]. DT is comprised of component nodes and branches. All records are divided into two or more distinct subsets, resulting in a root node, the first point. The root node and leaf nodes can be connected to the internal nodes in the tree structure through top and bottom connections. When it comes to dividing records into target groups, the final outcomes of the tree are displayed by leaf nodes, which are the third nodes. Branches in the tree indicate how likely it is to place records in target groups from both the internal nodes and root node [54]. DT technique utilizes the Gini impurity index to select the best variable.

$$Gini\left(D\right)=1-\sum_{i=1}^{m}{P}_{i}^{2}$$

where \({P}_{i}\) is the probability that a record in D belongs to the class \({C}_{i}\) and is estimated by |\({C}_{i}\),D|/|D|.

Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy, specificity, and precision for applied methods. Also, the confusion matrix of the methods was given.

Results

Characteristics of the study population

The clinical and demographic characteristics of the study population are summarized in Table 2. Mean ± standard deviation (SD) and frequency (%) were utilized to describe both quantitative and qualitative variables. The follow-up period found that 235 individuals (2.4%) developed CVDs while 9469 (97.6%) were non-CVDs. In general, there was a higher female population in the non-CVD group however male population was higher in CVD group. CVD cases had higher mean age compared to non-CVD cases in both male and female cases (56.09 ± 6.91 and 54.01 ± 6.88 vs. 48.32 ± 8.45 and 47.47 ± 8.05, p-value < 0.001).

Table 2 Clinical characteristics at the baseline of Mashhad stroke and heart atherosclerotic disorder (MASHAD) study used in this paper
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The association between anthropometric and biochemical indices and cardiovascular diseases (CVDs) using logistic regression (LR) model

Based on the reported results from the data mining analysis for men (Table 3), age, hs-CRP, BMI, VAI, BAI, and LAP was linked to CVDs in a significant way (p-value = < 0.006). Among the analyzed factors, CVD development was the most strongly related to age, hs-CRP, and LAP (LogWorth column). Our LR model showed that CVDs were not significantly correlated with other investigated parameters. Table 3 (OR column) presents the unit odds ratios that are determined by the significant factor. Age, hs-CRP, BMI, VAI, BAI, and LAP were significantly associated with CVDs. Among these parameters, age and VAI are the main risk factors for CVDs, according to research (OR: 1.08, (95% CI 1.12, 1.14) and 1.31 (1.18, 1.45), respectively).

Table 3 Parameter estimates of the LR model for CVD in male and female
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Based on the reported results from the data mining analysis for women (Table 3), age, hs-CRP, LAP, CRR, TyG, and BRI were linked to CVDs in a significant way (p-value = < 0.004). Among the analyzed factors, CVD development was the most strongly related to Age, hs-CRP, LAP, CRR, and TyG (LogWorth column). Our LR model showed that CVDs were not significantly associated with other investigated parameters. Table 3 (OR column) presents the unit odds ratios that are determined by the significant factor. Age, hs-CRP, LAP, TyG, BRI, and CRR were significantly correlated with CVDs. Among these parameters, TyG and age are the main risk factors for CVDs, according to research (OR: 1.66, (95% CI 1.31, 2.10) and 1.08 (1.07, 1.09), respectively). Table 5 has the confusion matrixes of the models that can be used to evaluate the model.

The association between anthropometric and biochemical indices and cardiovascular diseases (CVDs) using decision tree (DT) model

Figure 2 demonstrates the DT training's results in males, which include anthropometric and biochemical indices. Five layers of risk factors for CVDs were categorized according to the DT algorithm's evaluation. The DT model prioritizes the root variable as the most important variable, with the following variables at the next levels of significance. The most important factor in CVD development risk is age, which is followed by AIP and TyG, as shown in Fig. 2. Participants with age below 48 had higher CVD rates compared to those with younger age according to the DT model in males. In the subgroup with younger age < 48 and lower TyG (TyG < 8.44), 99% of the participants did not have CVDs diagnosed (lowest risk of CVDs). Meanwhile, among those with older age > = 48, AIP > = 0.94, TyG > = 9.71, and AVI > = 14.24, 95% of individuals were determined as CVDs (highest risk of CVD). Table 4 displays the specific rules for CVDs that the DT algorithm creates.

Fig. 2
figure 2

Decision tree for CVD in men

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Table 4 Detailed rules based on DT model for men and women
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In Fig. 3, female anthropometric and biochemical indices are shown as a result of DT training. As demonstrated in Fig. 3, age holds the greatest importance in predicting CVD development risk, followed by TyG, BRI, and hs-CRP. According to the DT model, individuals who are 54 years or older had higher CVD rates compared to those who are younger. In the subgroup with younger age < 54 and 7.81 < = TyG < = 8.10, 99% of the participants did not have CVDs diagnosed (lowest risk of CVDs). Meanwhile, among those with older age > = 54, TyG > = 8.33, and hs-CRP > = 36.69, 97% of individuals were determined as CVDs (highest risk of CVD Table 4 displays the specific rules for CVDs that the DT algorithm creates. Table 5 contains the confusion matrixes of the models for evaluation. This study is summarized as a graphic abstract in Fig. 4.

Fig. 3
figure 3

Decision tree for CVD in women

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Table 5 Performance indices of the LR model for men and women
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Discussion

In this article, we studied the importance of several anthropometric and biochemical indices in predicting the likelihood of developing CVDs. In order to identify the most contributing indices, we used LR and DT. The LR models indicated that in both genders the most important risk factor was age which was followed by hs-CRP. The third index based on the degree of importance in developing CVDs was LAP in females and AIP in males. Based on the ORs for males AIP, TyG and age and for females TyG, BRI, VAI and age significantly increased the chance of developing CVDs. Our findings from DT demonstrated that age was placed as the first root in both genders followed by TyG and AIP as the second root for males and only TyG as the second root for females.

From all of the findings of this study, it can be implied that the results of different models of analysis were in line with each other. For instance, the factors that indicated a strong correlation with the development of CVDs in one model also demonstrated the same effect in the other models. Interestingly, Age and TyG were placed in the highest levels of importance in all of the results.

The LR has been used in various studies for the prediction of CVDs with high accuracy [55, 56]. The generalizability of LR models can be understood by comparing the area under the curve (AUC) in the training and the testing groups [57]. Furthermore, the amount of AUC in the training group is a good representer of diagnostic accuracy in which the AUC of 100% shows perfect accuracy, > = 90% shows outstanding, 80–90% represents excellent, 70–80% is acceptable and values below 50% are interpreted as no discrimination [57]. According to Table 5, the AUC of training and testing groups in had insignificant differences and the AUCs of training groups were in the range of 70–80%. Therefore, our LR analysis represents a generalizable model with acceptable accuracy.

DT modeling has emerged as one of the practical tools for prediction because of the simplicity in interpreting the results, the potential to include nonlinear relationships and the creation of rules [58]. While looking at the DT results, it should be taken into account that the first root has the greatest importance and then the second and the third roots which stand on lower levels. The generalizability of DT models can be understood by comparing the area under the curve (AUC) in the training and the testing groups. As stated in Table 5, the AUC difference between the training and testing group of the DT model is small in both genders. Since the AUCs of training groups in both genders are above 80% the DT model has excellent accuracy.

Our results from the LR model showed that AIP significantly increased the likelihood of CVDs by almost 5 times in males while the highest increase in females belonged to TyG which significantly doubled the chance. Also, in the second root of our DT model, TyG > = 8.44 and AIP > = 0.94 were placed for males and TyG > = 8.33 in one branch and TyG > = 8.1 in another branch for females. Similar to our results, Sadeghi et al. and Kim et al. concluded that AIP could be an independent factor in predicting cardiovascular events [59, 60]. Cai et al. observed an independent association between AIP and acute coronary syndrome, but it should be noted that only a small proportion of their study belonged to females (7%) [35]. Liu et al. conducted a systematic review and meta-analysis that included 12 cohort studies, a potential for positive association was demonstrated between TyG and CVD incidence [61]. Other studies concluded that TyG amount above 8 may indicate an increased chance of CVDs and other mechanisms such as increased insulin resistance, increased atherosclerosis, and inflammation may contribute to this outcome [32, 62, 63].

An interesting finding from our LR model was depicted in LAP ORs in both genders which implied a significant 1–2% decrease in the chance of CVDs. Also, LAP was placed in the third branch of our DT model in males. Although the majority of studies proposed a predictive characteristic of LAP in CVDs [64, 65], a study conducted on the Iranian population with normal BMIs reached statistically insignificant results for the relation between LAP and cardiovascular events [25]. Because WC plays a critical role in calculating LAP, our controversial result may be due to the similarity of WC in our study population of healthy individuals and CVDs.

According to our LR model, age was a significant factor in CVD incidence in both males and females. Moreover, our findings in the DT model depicted age as the first root in both genders (Figs. 2 and 3). In the females group the cut-off for age was 54 while in the males group this cut-off was equal to 48 which is aligned with previous literature that declared men generally develop CVDs at a younger age compared to females [66]. The underlying mechanisms that lead to this result could be the better lipid profile of females at younger ages, lowering of blood pressure as the result of a more potent anti-inflammatory immune profile, different body size distribution, and high levels of estrogen in younger females [67,68,69,70].

Based on our LR and DT models, the AVI (Abdominal Volume Index) only performed well in men for the occurrence of CVDs. Research conducted on a Chinese population revealed that the AVI was not the most effective predictor for coronary heart disease risk in both genders [71]. Although, the AVI has shown to be a promising predictor for other chronic diseases such as metabolic syndrome and diabetes mellitus because of its direct relationship with insulin resistance [72, 73]. Therefore, AVI might also possess a predicting characteristic for CVDs too.

Recent studies have shown that the role of inflammation in the development of atherosclerosis is very important. In recent decades, it has been revealed that hs-CRP is a valuable and independent factor in predicting atherothrombotic and other cardiovascular events [74, 75]. Our results also confirm these findings. Hs-CRP had a significant role in CVD prediction in both genders based on our LR method. Although it was not placed in any of the top 2 branches of the DT model in males and females.

Our two models showed that BRI (Body Roundness Index)) was meaningfully associated with CVD incidence only in females. Same to our results, a study carried out by Wang et al. concluded that BRI was one of the best indicators for coronary heart disease only in females [27]. Moreover, a cohort study indicated that BRI was associated with the risk of developing CVD, and this association was more evident in individuals younger than 55 years [76].

The identified indices could play a pivotal role in refining preventive strategies for CVDs by providing a more individualized risk assessment. For instance, TyG and AIP, indicators associated with lipid and glucose metabolism, could highlight individuals at higher risk due to metabolic dysfunctions even before conventional markers indicate an issue [77]. Recognizing these indices could support more tailored lifestyle modifications, emphasizing targeted dietary adjustments, physical activity, and glucose management strategies specific to each individual's metabolic profile. Additionally, since some indices like VAI and BRI reflect body fat distribution and insulin sensitivity, they could encourage healthcare professionals to place a greater focus on body composition rather than solely on BMI, urging patients toward specific physical activities and nutritional plans that impact visceral fat [78]. By integrating these novel indices into routine assessments, healthcare providers could offer proactive, data-informed guidance to patients, potentially enhancing the efficacy of lifestyle modifications for CVDs prevention and promoting early intervention for those at increased risk.

The usage of data mining methods for investigating the predicting role of novel biochemical and anthropometric indices with CVDs risk in a large-scale population-based sample should be noted as a strong characteristic of this study. Also, our study has some limitations that are worth noting. One of them is that we did not consider genetic and epigenetic issues. Also, we did not examine people over 65 and under 35 years old in our study. The lack of account for genetic and epigenetic variations could have influenced the results, potentially affecting the response to interventions or baseline risk. Limiting the age range could affect the generalizability of the results, potentially overlooking early-onset risk factors or age-related changes. Addressing these gaps in future studies would enhance the applicability of this paper. Also, there was not any information about the cardiovascular medications that patients have taken (statin, aspirin, etc.).

Conclusion

Considering the results of our both machine learning models, TyG, AIP, AVI, hs-CRP and LAP were the best predictors of CVD in men. Moreover, TyG, hs-CRP and BAI were the best indicators of CVD in women. Given these differences in determining the best predictors of CVDs between males and females, gender should also be considered in clinical practice. One of the interesting findings of this study is that TyG has very effective role in predicting CVD in both genders. Due to its ease of measurement and availability in clinical settings, more research should be conducted to determine whether TyG may be a reliable index for predicting cardiovascular events along with current predictors.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

References

  1. Teo KK, Rafiq T. Cardiovascular risk factors and prevention: a perspective from developing countries. Can J Cardiol. 2021;37(5):733–43.

    Article  PubMed  Google Scholar 

  2. Hozawa A, Folsom AR, Sharrett AR, Chambless LE. Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects—atherosclerosis risk in communities study. Arch Intern Med. 2007;167(6):573–9.

    Article  PubMed  Google Scholar 

  3. Wang J, Tan G-J, Han L-N, Bai Y-Y, He M, Liu H-B. Novel biomarkers for cardiovascular risk prediction. J Geriatric Cardiol JGC. 2017;14(2):135.

    CAS  Google Scholar 

  4. Francula-Zaninovic S, Nola IA. Management of measurable variable cardiovascular disease’ risk factors. Curr Cardiol Rev. 2018;14(3):153–63.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mendis S, Puska P, Norrving B. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization; 2011.

    Google Scholar 

  6. Kottke TE, Faith DA, Jordan CO, Pronk NP, Thomas RJ, Capewell S. The comparative effectiveness of heart disease prevention and treatment strategies. Am J Prev Med. 2009;36(1):82–8.

    Article  PubMed  Google Scholar 

  7. Kumar A, Siddharth V, Singh SI, Narang R. Cost analysis of treating cardiovascular diseases in a super-specialty hospital. PLoS ONE. 2022;17(1): e0262190.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ciumărnean L, Milaciu MV, Negrean V, Orășan OH, Vesa SC, Sălăgean O, et al. Cardiovascular risk factors and physical activity for the prevention of cardiovascular diseases in the elderly. Int J Environ Res Public Health. 2021. https://doi.org/10.3390/ijerph19010207.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Psaltopoulou T, Hatzis G, Papageorgiou N, Androulakis E, Briasoulis A, Tousoulis D. Socioeconomic status and risk factors for cardiovascular disease: impact of dietary mediators. Hellenic J Cardiol. 2017;58(1):32–42.

    Article  PubMed  Google Scholar 

  10. Huai P, Liu J, Ye X, Li W-Q. Association of central obesity with all cause and cause-specific mortality in US adults: a prospective cohort study. Front Cardiovasc Med. 2022;9:25.

    Article  Google Scholar 

  11. De Rosa R, Vasa-Nicotera M, Leistner DM, Reis SM, Thome CE, Boeckel J-N, et al. Coronary atherosclerotic plaque characteristics and cardiovascular risk factors-insights from an optical coherence tomography study. Circ J. 2017;81(8):1165–73.

    Article  PubMed  Google Scholar 

  12. Carbone S, Shah K, Van Tassell B. Obesity and diastolic heart failure: is inflammation the link. Transl Med. 2013;3(03): e124.

    Article  Google Scholar 

  13. Ballak DB, Stienstra R, Tack CJ, Dinarello CA, van Diepen JA. IL-1 family members in the pathogenesis and treatment of metabolic disease: Focus on adipose tissue inflammation and insulin resistance. Cytokine. 2015;75(2):280–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen G, Yan H, Hao Y, Shrestha S, Wang J, Li Y, et al. Comparison of various anthropometric indices in predicting abdominal obesity in Chinese children: a cross-sectional study. BMC Pediatr. 2019;19:1–7.

    Article  Google Scholar 

  15. Darroudi S, Soflaee SS, Hosseini ZS, Farmad MS, Mirshafiei H, Andalibi MSS, et al. The visceral adiposity index and lipid accumulation product as predictors of cardiovascular events in normal weight subjects. Clin Nutrit ESPEN. 2022;52:190–7.

    Article  Google Scholar 

  16. Dudina A, Cooney MT, Bacquer DD, Backer GD, Ducimetière P, Jousilahti P, et al. Relationships between body mass index, cardiovascular mortality, and risk factors: a report from the SCORE investigators. Eur J Cardiovasc Prev Rehabil. 2011;18(5):731–42.

    Article  PubMed  Google Scholar 

  17. Mansoori A, Hosseini ZS, Ahari RK, Poudineh M, Rad ES, Zo MM, et al. Development of data mining algorithms for identifying the best anthropometric predictors for cardiovascular disease: MASHAD cohort study. High Blood Pressure Cardiovasc Prev. 2023;30:243.

    Article  Google Scholar 

  18. Baioumi AYAA. Comparing measures of obesity: waist circumference, waist-hip, and waist-height ratios. Nutrition in the prevention and treatment of abdominal obesity. Amsterdam: Elsevier; 2019. p. 29–40.

    Google Scholar 

  19. Bosy-Westphal A, Booke C-A, Blöcker T, Kossel E, Goele K, Later W, et al. Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population. J Nutr. 2010;140(5):954–61.

    Article  CAS  PubMed  Google Scholar 

  20. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation. 2009;120(16):1640–5.

    Article  CAS  PubMed  Google Scholar 

  21. Calderón-García JF, Roncero-Martín R, Rico-Martín S, Nicolás-Jiménez D, Jorge M, López-Espuela F, et al. Effectiveness of body roundness index (BRI) and a body shape index (ABSI) in predicting hypertension: a systematic review and meta-analysis of observational studies. Int J Environ Res Public Health. 2021;18(21):11607.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wu L, Zhu W, Qiao Q, Huang L, Li Y, Chen L. Novel and traditional anthropometric indices for identifying metabolic syndrome in non-overweight/obese adults. Nutr Metab. 2021;18:1–10.

    Article  Google Scholar 

  23. Caitano Fontela P, Winkelmann ER, Nazario Viecili PR. Study of conicity index, body mass index and waist circumference as predictors of coronary artery disease. Rev Port Cardiol. 2017;36(5):357–64.

    Article  PubMed  Google Scholar 

  24. Ding C, Shi Y, Li J, Li M, Hu L, Rao J, et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: a prospective cohort study. Nutr Metab Cardiovasc Dis. 2022;32(5):1210–7.

    Article  CAS  PubMed  Google Scholar 

  25. Hosseinpanah F, Barzin M, Mirbolouk M, Abtahi H, Cheraghi L, Azizi F. Lipid accumulation product and incident cardiovascular events in a normal weight population: Tehran lipid and glucose study. Eur J Prev Cardiol. 2016;23(2):187–93.

    Article  PubMed  Google Scholar 

  26. Kouli GM, Panagiotakos DB, Kyrou I, Georgousopoulou EN, Chrysohoou C, Tsigos C, et al. Visceral adiposity index and 10-year cardiovascular disease incidence: The ATTICA study. Nutr Metab Cardiovasc Dis. 2017;27(10):881–9.

    Article  PubMed  Google Scholar 

  27. Wang F, Chen Y, Chang Y, Sun G, Sun Y. New anthropometric indices or old ones: which perform better in estimating cardiovascular risks in Chinese adults. BMC Cardiovasc Disord. 2018;18(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gebreegziabiher G, Belachew T, Mehari K, Tamiru D. Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia. PLoS ONE. 2021;16(2): e0243103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sun T, Chen M, Shen H, Fan L, Chen X, Wu J, et al. Predictive value of LDL/HDL ratio in coronary atherosclerotic heart disease. BMC Cardiovasc Disord. 2022;22(1):1–11.

    Article  Google Scholar 

  30. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc Diabetol. 2014;13(1):1–10.

    Article  Google Scholar 

  31. Park K, Ahn CW, Lee SB, Kang S, Nam JS, Lee BK, et al. Elevated TyG index predicts progression of coronary artery calcification. Diabetes Care. 2019;42(8):1569–73.

    Article  CAS  PubMed  Google Scholar 

  32. Huang R, Wang Z, Chen J, Bao X, Xu N, Guo S, et al. Prognostic value of triglyceride glucose (TyG) index in patients with acute decompensated heart failure. Cardiovasc Diabetol. 2022;21(1):88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Perini W, Snijder MB, Peters RJ, Kunst AE, van Valkengoed IG. Estimation of cardiovascular risk based on total cholesterol versus total cholesterol/high-density lipoprotein within different ethnic groups: the HELIUS study. Eur J Prev Cardiol. 2019;26(17):1888–96.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sujatha R, Kavitha S. Atherogenic indices in stroke patients: a retrospective study. Iran J Neurol. 2017;16(2):78–82.

    PubMed  PubMed Central  Google Scholar 

  35. Wu TT, Gao Y, Zheng YY, Ma YT, Xie X. Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids Health Dis. 2018;17(1):197.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Si Y, Fan W, Han C, Liu J, Sun L. Atherogenic index of plasma, triglyceride-glucose index and monocyte-to-lymphocyte ratio for predicting subclinical coronary artery disease. Am J Med Sci. 2021;362(3):285–90.

    Article  PubMed  Google Scholar 

  37. Yıldız A, Seçen Ö, Yıldız C, Çiçekçi M. Relationship between breast arterial calcification and lipid profile, plasma atherogenic index, Castelli’s risk index and atherogenic coefficient in premenopausal women. IJC Metab Endocr. 2016;11:19–22.

    Article  Google Scholar 

  38. Quispe R, Michos ED, Martin SS, Puri R, Toth PP, Al Suwaidi J, et al. High-sensitivity C-reactive protein discordance with atherogenic lipid measures and incidence of atherosclerotic cardiovascular disease in primary prevention: the ARIC study. J Am Heart Assoc. 2020;9(3): e013600.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ristagno G, Fumagalli F, Bottazzi B, Mantovani A, Olivari D, Novelli D, et al. Pentraxin 3 in cardiovascular disease. Front Immunol. 2019;10:823.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ghayour-Mobarhan M, Moohebati M, Esmaily H, Ebrahimi M, Parizadeh SMR, Heidari-Bakavoli AR, et al. Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. Int J Public Health. 2015;60:561–72.

    Article  PubMed  Google Scholar 

  41. Klein K, Hennig S, Paul SK. A bayesian modelling approach with balancing informative prior for analysing imbalanced data. PLoS ONE. 2016;11(4): e0152700.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Luepker RV, Murray DM, Jacobs DR Jr, Mittelmark MB, Bracht N, Carlaw R, et al. Community education for cardiovascular disease prevention: risk factor changes in the Minnesota Heart Health Program. Am J Public Health. 1994;84(9):1383–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Prineas RJ, Crow RS, Zhang Z-M. The Minnesota code manual of electrocardiographic findings. Berlin: Springer Science & Business Media; 2009.

    Google Scholar 

  44. Asadi Z, Ghazizadeh H, Yaghooti M, Bijari M, Khedmatgozar H, Dabagh AE, et al. Role of socio-demographic, lifestyle, psychological and anthropometric parameters on cardiovascular disease risk among Mashad cohort study population. 2020.

  45. Classification and Diagnosis of Diabetes. Standards of medical care in diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S13-s27.

    Google Scholar 

  46. David W, Hosmer SL, Rodney X. Sturdivant applied logistic regression. Hoboken: John Wiley & Sons Inc; 2013.

    Google Scholar 

  47. Mansoori A, Sahranavard T, Hosseini ZS, Soflaei SS, Emrani N, Nazar E, et al. Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis. Sci Rep. 2023;13(1):1–11.

    Article  Google Scholar 

  48. Saberi-Karimian M, Mansoori A, Bajgiran MM, Hosseini ZS, Kiyoumarsioskouei A, Rad ES, et al. Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements. J Clin Laboratory Anal. 2023;37:e24798.

    Article  CAS  Google Scholar 

  49. Zhong Y. The analysis of cases based on decision tree. 2016 7th IEEE international conference on software engineering and service science (ICSESS). IEEE. 2016.

  50. Mohammadi M, Mansoori A. A projection neural network for identifying copy number variants. IEEE J Biomed Health Inform. 2018;23(5):2182–8.

    Article  PubMed  Google Scholar 

  51. Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med. 2021;128: 104089.

    Article  PubMed  Google Scholar 

  52. Saberi-Karimian M, Safarian-Bana H, Mohammadzadeh E, Kazemi T, Mansoori A, Ghazizadeh H, et al. A pilot study of the effects of crocin on high-density lipoprotein cholesterol uptake capacity in patients with metabolic syndrome: a randomized clinical trial. BioFactors. 2021;47(6):1032–41.

    Article  CAS  PubMed  Google Scholar 

  53. Aghasizadeh M, Samadi S, Sahebkar A, Miri-Moghaddam E, Esmaily H, Souktanloo M, et al. Serum HDL cholesterol uptake capacity in subjects from the MASHAD cohort study: its value in determining the risk of cardiovascular endpoints. J Clin Lab Anal. 2021;35(6): e23770.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Song Y, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatr. 2015;27(2):130–5.

    Google Scholar 

  55. Ambrish G, Ganesh B, Ganesh A, Srinivas C, Dhanraj MK. Logistic regression technique for prediction of cardiovascular disease. Global Trans Proc. 2022;3(1):127–30.

    Article  Google Scholar 

  56. Karthick K, Aruna SK, Samikannu R, Kuppusamy R, Teekaraman Y, Thelkar AR. Implementation of a heart disease risk prediction model using machine learning. Comput Math Methods Med. 2022;2022:6517716.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–6.

    Article  PubMed  Google Scholar 

  58. Ramezankhani A, Pournik O, Shahrabi J, Khalili D, Azizi F, Hadaegh F. Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran lipid and glucose study. Diabetes Res Clin Pract. 2014;105(3):391–8.

    Article  PubMed  Google Scholar 

  59. Kim SH, Cho YK, Kim Y-J, Jung CH, Lee WJ, Park J-Y, et al. Association of the atherogenic index of plasma with cardiovascular risk beyond the traditional risk factors: a nationwide population-based cohort study. Cardiovasc Diabetol. 2022;21(1):81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Sadeghi M, Heshmat-Ghahdarijani K, Talaei M, Safaei A, Sarrafzadegan N, Roohafza H. The predictive value of atherogenic index of plasma in the prediction of cardiovascular events; a fifteen-year cohort study. Adv Med Sci. 2021;66(2):418–23.

    Article  PubMed  Google Scholar 

  61. Liu X, Tan Z, Huang Y, Zhao H, Liu M, Yu P, et al. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Angoorani P, Heshmat R, Ejtahed HS, Motlagh ME, Ziaodini H, Taheri M, et al. Validity of triglyceride-glucose index as an indicator for metabolic syndrome in children and adolescents: the CASPIAN-V study. Eat Weight Disord. 2018;23(6):877–83.

    Article  PubMed  Google Scholar 

  63. Kim MK, Ahn CW, Kang S, Nam JS, Kim KR, Park JS. Relationship between the triglyceride glucose index and coronary artery calcification in Korean adults. Cardiovasc Diabetol. 2017;16(1):108.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Taverna MJ, Martínez-Larrad MT, Frechtel GD, Serrano-Ríos M. Lipid accumulation product: a powerful marker of metabolic syndrome in healthy population. Eur J Endocrinol. 2011;164(4):559–67.

    Article  CAS  PubMed  Google Scholar 

  65. Zhao S, Ren Z, Yu S, Chi C, Tang J, Maimaitiaili R, et al. Association between lipid accumulation product and target organ damage in elderly population: the northern shanghai study. Clin Interv Aging. 2021;16:1769–76.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Peters SAE, Muntner P, Woodward M. Sex differences in the prevalence of, and trends in, cardiovascular risk factors, treatment, and control in the United States, 2001 to 2016. Circulation. 2019;139(8):1025–35.

    Article  CAS  PubMed  Google Scholar 

  67. Peters SA, Huxley RR, Woodward M. Diabetes as risk factor for incident coronary heart disease in women compared with men: a systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events. Diabetologia. 2014;57(8):1542–51.

    Article  PubMed  Google Scholar 

  68. Peters SAE, Woodward M. Sex differences in the burden and complications of diabetes. Curr Diab Rep. 2018;18(6):33.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women’s Health Initiative randomized controlled trial. JAMA. 2002;288(3):321–33.

    Article  CAS  PubMed  Google Scholar 

  70. Wang X, Magkos F, Mittendorfer B. Sex differences in lipid and lipoprotein metabolism: it’s not just about sex hormones. J Clin Endocrinol Metab. 2011;96(4):885–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Wang F, Chen Y, Chang Y, Sun G, Sun Y. New anthropometric indices or old ones: which perform better in estimating cardiovascular risks in Chinese adults. BMC Cardiovasc Disord. 2018;18(1):1–7.

    Article  Google Scholar 

  72. Lokpo SY, Amenyega W, Doe P, Osei-Yeboah J, Owiredu WKBA, Obirikorang C, et al. Abdominal volume index is a better predictor of visceral fat in patients with type 2 diabetes: a cross-sectional study in Ho municipality, Ghana. Alex J Med. 2022;58(1):85–91.

    Google Scholar 

  73. Quaye L, Owiredu WKBA, Amidu N, Dapare PPM, Adams Y. Comparative abilities of body mass index, waist circumference, abdominal volume index, body adiposity index, and Conicity index as predictive screening tools for metabolic syndrome among apparently healthy Ghanaian adults. J Obes. 2019;2019:8143179.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Boncler M, Wu Y, Watala C. The multiple faces of C-reactive protein—physiological and pathophysiological implications in cardiovascular disease. Molecules. 2019;24(11):2062.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Fu Y, Wu Y, Liu E. C-reactive protein and cardiovascular disease: from animal studies to the clinic. Exp Ther Med. 2020;20(2):1211–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wu M, Yu X, Xu L, Wu S, Tian Y. Associations of longitudinal trajectories in body roundness index with mortality and cardiovascular outcomes: a cohort study. Am J Clin Nutr. 2022;115(3):671–8.

    Article  PubMed  Google Scholar 

  77. Jafari A, Najafipour H, Shadkam M, Aminizadeh S. Evaluation of the novel three lipid indices for predicting five-and ten-year incidence of cardiovascular disease: findings from Kerman coronary artery disease risk factors study (KERCADRS). Lipids Health Dis. 2023;22(1):169.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral adiposity index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920–2.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the MASHAD cohort staff who have participated in running this study.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Amin Mansoori, Maryam Allahyari and Mobina Sadat Mirvahabi equally first author.

Authors and Affiliations

  1. Department of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

    Amin Mansoori

  2. Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Maryam Allahyari & Majid Ghayour-Mobarhan

  3. Department of Clinical Biochemistry, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Mobina Sadat Mirvahabi & Sahar Ghoflchi

  4. Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

    Davoud Tanbakuchi & Habibollah Esmaily

  5. International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran

    Farnoosh Azarian & Majid Ghayour-Mobarhan

  6. Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK

    Gordon Ferns

  7. Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

    Habibollah Esmaily

  8. Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Elahe Derakhshan-Nezhad

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  1. Amin Mansoori
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Contributions

Amin Mansoori: drafting the article, Maryam Allahyari: revising the article, Mobina Sadat Mirvahabi: conception, drafting the article, Davoud Tanbakuchi: data analyzing, Sahar Ghoflchi: data analyzing, conception, Elahe Derakhshan-Nezhad: revising the article, Farnoosh Azarian: drafting the article, Gordon Ferns: revising the article, Habibollah Esmaily: corresponding author, Majid Ghayour-Mobarhan: corresponding author.

Corresponding authors

Correspondence to Habibollah Esmaily or Majid Ghayour-Mobarhan.

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Ethics approval and consent to participate

All the participants consented to take part in the study by signing written informed consent. The study protocol was reviewed and all methods are approved by the Ethics Committee of Mashhad University of Medical Sciences with approval number IR.MUMS.MEDICAL.REC.1402.023. All methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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The authors declare no competing interests.

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Mansoori, A., Allahyari, M., Mirvahabi, M.S. et al. Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk. Diabetol Metab Syndr 16, 304 (2024). https://doi.org/10.1186/s13098-024-01516-4

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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