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The association between carbohydrate quality index and conventional risk factors of cardiovascular diseases in an Iranian adult population

BMC Research Notes volume 17, Article number: 243 (2024) Cite this article

Abstract

Objective

Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Diet plays an important role among many risk factors for CVDs. The present study aimed to investigate the relationship between carbohydrate quality index (CQI) and conventional risk factors of CVDs in Iranian adults.

Results

A higher CQI was related to a higher intake of energy, fiber, whole grains, fruits, vegetables, nuts, legumes, and dairy products. Additionally, a significant negative association was observed between CQI and triglycerides (TG) (odds ratio (OR) = 0.85; 95% confidence interval (CI): 0.73–0.98, highest versus the lowest tertile, p for trend = 0.026) and non-high density lipoprotein cholesterol (non-HDL-C) (OR = 0.85; 95% CI: 0.75–0.96, highest versus the lowest tertile, p for trend = 0.012). No significant correlation was shown between CQI and other cardiovascular risk factors. The findings indicate that the CQI is inversely associated with TG and non-HDL-C. Further studies are proposed to confirm these findings.

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Introduction

Cardiovascular diseases (CVDs) are the most common cause of death worldwide [1]. By 2030, it is expected that about 23.6 million people will die from CVDs, mainly from stroke and heart disease [2]. In Iran, 46% of all deaths and 20–23% of the disease burden are reported to be caused by CVDs [3].

A poor diet combined with a sedentary lifestyle could lead to body fat accumulation, high blood pressure, hyperlipidemia, and insulin resistance [4], all of which are risk factors for cardiovascular morbidity [5]. Diet has been widely studied as a risk factor for major CVDs such as stroke and coronary heart disease. It is also associated with other cardiovascular risk factors, such as hypertension, obesity, and diabetes [6]. In observational studies, diets containing high glycemic index (GI) carbohydrates have been associated with decreased high-density lipoprotein cholesterol (HDL-C) concentrations, higher insulin resistance, and triglyceride (TG) concentrations [7]. Carbohydrates are the main part of the diet in Iran, and more than 60% of the calories come from carbohydrates [8].

Also, studies have shown an association between the consumption of sugar-sweetened beverages [9, 10], whole grains/fiber [11], and sugar [12] with the risk of CVDs. Recently, the carbohydrate quality index (CQI) has been suggested as an indicator for assessing dietary carbohydrate quality. This index includes factors such as GI, whole grains, total fiber intake, and solid or liquid carbohydrates [13]. Studies have illustrated that a higher CQI is related to a lower risk of abdominal and general obesity [14,15,16], as well as metabolic syndrome [17].

To the best of our knowledge, no cross-sectional study has previously evaluated the association between CQI and risk factors of CVDs in Iranian adults. Also, according to the studies conducted on the importance of CQI with CVD risk factors, the present study aimed to investigate the association between CQI and conventional risk factors of CVDs in an Iranian adult population.

Methods

Study design, Study population

This cross-sectional study was conducted on 10,663 participants between 40 and 70 years old who participated in the Kharameh cohort. This cohort is a part of the Prospective Epidemiological Research Studies in Iran (PERSIAN) cohort and was conducted from 2014 to 2017 [18, 19]. The census method was used to include qualified subjects in this study. In the PERSIAN cohort, physical activity, demographic information, medical history, and smoking status were gathered. Biochemical variables such as fasting blood sugar (FBS), HDL-C, low-density lipoprotein cholesterol (LDL-C), TG, total cholesterol (TC), diet, as well as height, weight, hip circumference (HC), waist circumference (WC), systolic blood pressure, and diastolic blood pressure were measured.

The main inclusion criteria for the Kharameh cohort study included having Iranian nationality, living in Kharameh, and an age range from 40 to 70 years. Furthermore, the subjects who had one or more types of diseases (n = 4,015) and had an intake of energy more than 4200 kcal or less than 800 kcal (n = 31), as well as those with missing data (n = 6), were omitted. This study was confirmed by Shiraz University of Medical Sciences, Fars, Iran (IR.SUMS.REC.1399.1115).

Dietary intake assessment

A 130-item food frequency questionnaire (FFQ) was used to obtain food intake. The validity of this FFQ has been assessed among the Iranian population [18]. The value of each food item in the FFQ was converted to grams. Nutritionist IV software (version 7.0; N-Squared Computing, Salem, OR, USA) was used to compute energy, micronutrients, and macronutrients.

Total GI was calculated by the following formula: GI multiplied by available carbohydrates divided by total available carbohydrates. Available carbohydrates are equal to total carbohydrates (derived from the table of food composition of the United States Department of Agriculture (USDA)) except fiber [20].

CQI was determined by summing these four criteria: GI, dietary fiber intake, the ratio of whole grains to total grains, and the ratio of solid carbohydrates to total carbohydrates (solid and liquid). Total grains include refined grains, whole grains, and their products. Individuals were classified into quintiles according to the intake of each part and achieved a value from 1 to 5 based on each quintile. The lowest quintile of fiber, the ratio of whole grains to total grains, and the ratio of solid carbohydrates to total carbohydrates achieved 1 point, and the highest group achieved 5 points. As for GI, the lowest quintile received 5 points and the highest quintile received 1 point. To compute the CQI score, all four groups were summed up (from 4 to 20 points). A higher score means better carbohydrate quality [21, 22].

Anthropometric and biochemical assessments

Weight, height, HC, WC, and blood pressure of the subjects were measured in such a way that height without shoes and weight while wearing light clothing were evaluated. The precision of measuring weight, HC, and WC was all 0.1 cm. Then, the body mass index (BMI) was calculated. After ten minutes of rest in a sedentary position, the blood pressure of the participants was measured using a standard German sphygmomanometer. After 14 h of fasting, a 20 mL sample of blood was taken from each subject. The levels of TG, HDL-C, and TC were evaluated using an enzymatic method. The level of LDL-C was determined using Friedwald’s formula [23].

Statistical analysis

Other Variables

Demographic characteristics (gender, level of education, and age) were gathered by a checklist. Physical activity (time of exercise, sleep, and work) was assessed by using a questionnaire [24]. After calculating the metabolic equivalent of the task (MET) for each activity [25], the total MET for every participant was computed [24].

We used SPSS software (version 26.0) to analyze the data. The Kolmogorov-Smirnov test was used to assess the normality of the data. A p-value < 0.05 was considered as the level of significance. Chi-square tests and one-way analysis of variance (ANOVA) were used to compare the categorical and continuous variables, respectively. The Kruskal-Wallis test was used to compare the intake of nutrients and foods among tertiles of CQI. Three multivariate logistic regression models were used to evaluate the association between CVD risk factors across the tertile of CQI, adjusted for age, gender, education, physical activity, and BMI.

Results

Of the 10,663 participants, data from 4,015 individuals were excluded due to having diabetes, CVDs, hypertension, or other diseases, and six individuals due to missing data. Additionally, data from 31 participants with energy intake ≤ 800 or ≥ 4200 kcal/day were not included (Fig. 1).

Fig. 1
figure 1

Flow diagram of the study

Full size image

The baseline characteristics of the study population are presented in Table 1. In the last tertiles of CQI compared to the first tertiles, age (P < 0.001) and physical activity (P < 0.001) showed a significant increase. On the other hand, the percentage of men participating in the study was significantly reduced (P = 0.004). No significant difference was observed for other variables.

Table 1 Baseline characteristics of the study participants
Full size table

Also, a higher CQI was related to a higher energy intake, fiber, whole grains, fruits, vegetables, nuts, legumes, and dairy products (P < 0.001 for all). Moreover, a higher CQI was associated with a lower intake of protein, carbohydrate, fat, cholesterol, saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), refined grains, meats, processed meats, sweets, and sugar-sweetened beverages (P < 0.001) (Table 2). After adjusting for energy intake, the results remained significant.

Table 2 Comparison of nutrient and food intakes among the tertiles of carbohydrate quality index
Full size table

As shown in Table 3, in the crude model, there were no significant differences in WC, FBS, LDL-C, HDL-C, and LDL-C to HDL-C ratio among the CQI tertiles. However, an inverse association between TG (Ptrend= 0.011), non-HDL (Ptrend= 0.028), and CQI was observed in the highest tertiles of CQI compared to the lowest tertiles in the crude model. Also, after adjusting for confounders, in Model 1, individuals in the highest CQI tertile had greater odds of increasing WC than those in the first tertile (odds ratio (OR) = 1.14; 95% confidence interval (CI): 1.01–1.29, Ptrend = 0.028). However, no significant association between CQI and WC was seen in the fully adjusted model (OR = 0.97; 95% CI: 0.82–1.15, Ptrend= 0.821). Also, in the adjusted model, participants in the last tertiles of CQI compared to the first tetiles had lower odds for TG (OR = 0.85; 95% CI: 0.73–0.98, Ptrend = 0.026) and non-HDL-C (OR = 0.85; 95% CI: 0.75–0.96, Ptrend= 0.012) abnormalities.

Table 3 Association between cardiovascular disease risk factors and carbohydrate quality index in the crude and multivariable-adjusted models
Full size table

Discussion

The present study showed a significant inverse association between TG and non-HDL-C with CQI. No significant association was observed regarding WC, FBS, LDL-C, HDL-C, and LDL-C to HDL-C ratio with the dietary CQI.

The results showed that people with a higher CQI had a higher energy intake. On the other hand, the percentage of total energy derived from carbohydrates in the last tertile was significantly lower than in the first tertile (58.8% vs. 73.2%, respectively). However, the dietary sources of carbohydrates were significantly different in individuals with a higher CQI compared to those with a lower CQI. People with a higher CQI consumed more fruits, vegetables, legumes, whole grains, and fiber, while their consumption of refined grains, sweets, and sugar-sweetened beverages was lower.

As mentioned earlier, CQI was inversely associated with TG. Our findings are in line with previous studies. A study on people with metabolic syndrome showed that the higher tertile of CQI was inversely associated with TG [26]. Moreover, a case-control study by Suara et al. indicated that CQI was negatively related to TG in people with type 2 diabetes mellitus (T2DM) [17]. TG levels have been illustrated to increase with elevating GI and glycemic load (GL) [27]. Therefore, consuming low GI and GL foods can help reduce TG. A low GL food pattern includes legumes, fruits, vegetables, and whole grains [28]. As the results of the study showed, in the higher tertiles of CQI, there was an increase in the intake of foods containing certain carbohydrates such as whole grains, vegetables, dairy products, and legumes. Therefore, the association between CQI and TG can be attributed to the higher intake of foods with low GL. In a cohort study, serum TG level was found to be an independent determinant of cardiovascular risk in Asians [29]. A study showed that in people whose TG concentration was less than 89 mg/dL (< 1 mmol/L) compared to people whose TG concentration was ≥ 5.00 mmol/L, the hazard ratios for myocardial infarction were 1.6 and 3.4, respectively [30]. So, TG can be considered one of the important risk factors in the occurrence of CVDs.

The findings did not show a significant association between CQI and WC. A study by Suara et al. among patients with T2DM and metabolic syndrome revealed that a higher CQI was negatively associated with WC [17]. Another study by Suara et al. also indicated that a higher CQI was associated with a lower WC in women [14]. The difference in sample size and statistical population can be the reason for the difference in the results of the present study compared to the mentioned study.

In the current study, there was no association between CQI and parameters such as FBS, LDL-C, HDL-C, and LDL-C to HDL-C ratio. Previous studies confirmed the present study’s findings. A study by Suara et al. demonstrated no significant association between HDL-C, FBS, LDL-C, LDL-C to HDL-C ratio, and CQI [17]. Also, Martínez-González et al. revealed that there was no significant association between LDL-C and HDL-C with CQI [26].

Moreover, the findings showed that the higher CQI was inversely associated with non-HDL-C. To our knowledge, there were no studies on the relationship between CQI and non-HDL-C. In the current study, in the last tertile of CQI, the intake of carbohydrates and refined carbohydrates was lower than in the first tertile. A study by Meng et al. reported that diets containing refined carbohydrates increased serum concentrations of non-HDL-C compared to unrefined or simple carbohydrates [31]. Also, a study by Sondike et al. showed that a low-carbohydrate diet could improve non-HDL-C in a randomized clinical trial for 12 weeks [32]. Therefore, based on the studies mentioned above, the inverse association between CQI and non-HDL-C can be attributed to reduced consumption of refined grains and carbohydrates. Non-HDL-C is applied to measure the cholesterol of atherogenic lipoproteins containing apo B [33]. So, non-HDL-C was as useful as LDL-C in evaluating the risk of atherogenic CVD and was preferable to LDL-C in people with mild to moderate hypertriglyceridemia [34]. Also, a study by Lu et al. reported that non-HDL-C was a helpful indicator in estimating CVD in patients with T2DM [35]. As a result, in the present study, despite the lack of association between LDL-C, HDL-C, and LDL-C to HDL-C ratio and CQI, consuming a high-quality carbohydrate diet with a reduction in non-HDL-C can help prevent CVDs.

Strengths and limitations

Among the strengths of the study, the large sample size and the control of confounding factors can be mentioned. However, this study also had limitations. Due to the study’s cross-sectional nature, the mechanisms of the CQI effect on CVD risk markers could not be addressed. Additionally, an FFQ was used to assess the diet. This questionnaire has a recall bias. However, in extensive epidemiological studies, the FFQ is the most accessible and practical tool to assess eating habits. Additionally, it is worth noting that the data used in this study is from a cohort conducted between 2014 and 2017. Therefore, the use of outdated data may be a limitation of the current study.

Conclusions

In conclusion, the findings of the present study showed that the quality of dietary carbohydrates could influence the reduction of serum TG and non-HDL-C, both of which can be effective in the occurrence of CVDs. Therefore, consuming more fruits, vegetables, whole grains, fibers, and legumes while reducing the intake of refined grains, sweets, and sugar-sweetened beverages could improve these two markers. However, the findings indicated that the quality of dietary carbohydrates did not affect LDL-C, HDL-C, LDL-C to HDL-C ratio, FBS, and WC. Further studies are suggested to confirm the results of the current study.

Data availability and methods

Data are available through a reasonable request from the corresponding author.

Abbreviations

CI:

Confidence interval

CQI:

Carbohydrate quality index

CVDs:

Cardiovascular diseases

FBS:

Fasting blood sugar

FFQ:

Food frequency questionnaire

GI:

Glycemic index

GL:

Glycemic load

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HC:

Hip circumference

MUFAs:

Monounsaturated fatty acids

OR:

Odds ratio

PUFAs:

Polyunsaturated fatty acids

SFAs:

Saturated fatty acids

TC:

Total cholesterol

TG:

Triglycerides

T2DM:

Type 2 diabetes mellitus

WC:

Waist circumference

BMI:

Body mass index

MET:

Metabolic equivalent of task

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Acknowledgements

We sincerely thank all field investigators, staff, and participants of the present study and Shiraz University of Medical Sciences.

Funding

The authors received no financial support for this article’s research, authorship, and publication.

Author information

Authors and Affiliations

  1. Department of Nutrition and Biochemistry, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran

    Zainab Shateri

  2. Central Asian Center of Development Studies, New Uzbekistan University, 1 Movarounnahr Street, Tashkent, 100000, Uzbekistan

    Irodakhon Rasulova

  3. Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan

    Irodakhon Rasulova

  4. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

    Milad Rajabzadeh-dehkordi & Moein Askarpour

  5. Department of Community Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

    Milad Rajabzadeh-dehkordi & Shiva Faghih

  6. Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran

    Moein Askarpour

  7. Department of Epidemiology, School of Health and Nutrition, Shiraz University of Medical Sciences, Shiraz, Iran

    Abbas Rezaianzadeh

  8. Breast Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Masoumeh Ghoddusi Johari

  9. Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran

    Mehran Nouri

  10. Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Shiva Faghih

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Contributions

Z.S, I.R, M.R.D, M.A, M.G.J and M.N; Contributed to writing the first draft. M.N and M.A; Contributed to all data, statistical analysis, and interpretation of data. S.F. and A.R; Contributed to the research concept, supervised the work, and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Mehran Nouri or Shiva Faghih.

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Ethics approval and consent to participate

This study was approved by the medical research and ethics committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.1115) and the informed consents were completed by all participants and all experiments were performed in accordance with relevant guidelines and regulations.

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Shateri, Z., Rasulova, I., Rajabzadeh-dehkordi, M. et al. The association between carbohydrate quality index and conventional risk factors of cardiovascular diseases in an Iranian adult population. BMC Res Notes 17, 243 (2024). https://doi.org/10.1186/s13104-024-06897-3

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  • DOI: https://doi.org/10.1186/s13104-024-06897-3

Keywords

BMC Research Notes

ISSN: 1756-0500

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