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Table of Contents
ORIGINAL ARTICLE
Year : 2021  |  Volume : 4  |  Issue : 3  |  Page : 70-74

Diabesity lipid index: A potential novel marker of 10-year cardiovascular risk


Department of Medicine, Endocrinology Unit, University College Hospital, Ibadan, Nigeria

Date of Submission01-May-2021
Date of Decision28-May-2021
Date of Acceptance01-Jun-2021
Date of Web Publication20-Jul-2021

Correspondence Address:
Dr. Taoreed Adegoke Azeez
Department of Medicine, Endocrinology Unit, University College Hospital, Ibadan
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2589-9686.321923

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  Abstract 


INTRODUCTION: Diabetes mellitus is a major cardiovascular risk factor. To put appropriate preventive measures in place, there is a need to estimate the 10-year cardiovascular risk. Most of the available risk estimators are cumbersome while some are inaccurate in estimating the risk for individuals with Type 2 diabetes. This study aimed to describe the diabesity lipid index (DLI), a composite index for predicting 10-year cardiovascular risk in Type 2 diabetes.
METHODS: The study design was a cross-sectional study involving 70 individuals living with Type 2 diabetes mellitus. Ethical approval and informed consent were obtained for the study. Body mass index and waist circumference were determined for each participant. Fasting plasma glucose, fasting lipid profile, and glycated hemoglobin (HbA1c) were also measured. Atherogenic index of plasma (AIP), DLI, and QRISK 3 were calculated using the appropriate formulae. Receiver operating characteristics (ROC) curve analysis was performed for DLI.

DLI = HbA1c (%) x Waist circumference (cm) / HDL -- C (mg/dl)

RESULTS: The mean age of the subjects was 53.34 ± 9.57 years. The median duration of diabetes was 11.50 years. Hypertension, dyslipidemia, and truncal obesity were found in 70%, 65.7%, and 64.3% of the participants, respectively. About 38.6% had sub-optimal glycemic control. There was a statistically significant positive correlation between 10-year cardiovascular risk using QRISK 3 and DLI (r = 0.317; P = 0.008). Moreover, a ROC curve analysis done showed that the area under curve was 0.72 (95% confidence interval 0.56–0.85; P = 0.032). The sensitivity and specificity of using this cut-off value to define high cardiovascular risk were 87.5% and 79.2%, respectively
CONCLUSION: DLI is a simple estimator of 10-year cardiovascular risk among individuals with Type 2 diabetes mellitus. It compares favorably with AIP, a previously validated cardiovascular risk estimator.

Keywords: 10-year cardiovascular risk, cardiovascular disease, diabesity lipid index, QRISK 3, Type 2 diabetes mellitus


How to cite this article:
Azeez TA. Diabesity lipid index: A potential novel marker of 10-year cardiovascular risk. Vasc Invest Ther 2021;4:70-4

How to cite this URL:
Azeez TA. Diabesity lipid index: A potential novel marker of 10-year cardiovascular risk. Vasc Invest Ther [serial online] 2021 [cited 2021 Dec 6];4:70-4. Available from: https://www.vitonline.org/text.asp?2021/4/3/70/321923




  Introduction Top


Cardiovascular disease is the most common cause of mortality worldwide.[1] According to the World Health Organization (WHO), about 17.9 million people died from cardiovascular disease in 2020, accounting for about 31% of the causes of mortality.[2] It is estimated that by the year 2030, the number of deaths would have risen to 23.6 million.[3] Despite the intensified global efforts at curbing the menace of cardiovascular disease, the prevalence of traditional cardiovascular risk factors such as diabetes mellitus, hypertension, dyslipidemia, and obesity is continuously rising in both developed and developing countries.[4] This has necessitated the development of various models for predicting the risk of cardiovascular disease.

Cardiovascular risk estimation is important because it serves as an evidence-based decision-making tool and a simplified way of communicating risk to the general population.[5] The risk estimator assesses the total burden of cardiovascular risk as an absolute value since people who suffer from cardiovascular disease tend to have multiple cardiovascular risk factors occurring together.[6] These models are usually multifactorial and cumbersome yet clinicians are expected to be able to estimate the cardiovascular risk of people. The various 10-year cardiovascular risk calculators include Framingham risk score, atherosclerotic cardiovascular disease (ASCVD) score, QRISK, Joint British Society risk calculator 3 (JBS 3), and WHO risk chart.[7] Other documented risk estimators are Prospective Cardiovascular Munster Study score, United Kingdom Prospective Diabetes Study risk engine, Reynolds risk score, ASSIGN score, and Systemic Coronary Risk Evaluation.

The National Institute of Health and Clinical Excellence published the QRISK score in 2010 and recommended its usage in the United Kingdom claiming that it was superior to the Framingham risk score as a cardiovascular risk prediction tool.[8] Since the first publication, the QRISK score has undergone series of reviews and the latest edition, QRISK 3 was released in 2017.[9] The QRISK score has been validated in different ethnic groups and races.[10] In terms of the ability to predict cardiovascular disease (myocardial infarction, angina, coronary heart disease, stroke, and transient ischaemic stroke) risk, a large prospective study reported that QRISK performed similar to the Framingham risk score and better than the WHO risk chart, ASCVD score and JBS 3 score.[7],[8] Cardiovascular disease risk, as defined by Collins and Altman, is the propensity to develop the first myocardial infarction, angina, coronary heart disease, stroke, and transient ischemic stroke.[8]

Composite lipid indices such as the atherogenic index of plasma (AIP), atherogenic coefficient, Castelli's risk Index I and II as well as CHOLindex, have been found to be better independent predictors of cardiovascular risk than the absolute lipid parameters.[11],[12],[13] AIP has been demonstrated to have the strongest correlation with 10-year cardiovascular risk when compared with other lipid indices.[11],[14] However, cardiovascular risk factors tend to occur in clusters and this requires multiple entries to use the risk estimators making them very complex and cumbersome to use. There is a need for a composite index in Type 2 diabetes which is simple to use but which correlates strongly with 10-year cardiovascular risk.

Objectives

This study aimed to describe the diabesity lipid index (DLI), a composite index for predicting 10-year cardiovascular risk in Type 2 diabetes. Moreover, the correlation of DLI with a validated 10-year cardiovascular risk calculator was to be compared with that of AIP which is an established predictor of cardiovascular risk.


  Methods Top


The study design was a cross-sectional study. Seventy individuals (35 males and 35 females) living with Type 2 diabetes mellitus were involved in the study. The participants were being managed for Type 2 diabetes in a tertiary hospital in Nigeria on an outpatient basis. Diabetes mellitus was diagnosed using the American Diabetes Association diagnostic criteria.[15] Ethical approval for the study was granted by the institutional ethical committee. Participants also gave written consent to participate in the study. Individuals who were 30 years and above, with no hospital admission 3 months before the study or inexplicable weight loss were recruited into the study.

Weight was measured in Kilograms using a D-339 Detecto Eye-level Physician Beam Scale (made in the USA). Height was measured in meters with a portable stadiometer using standard protocols. Body mass index (BMI) was calculated using the formula below:[16]

BMI = Weight (kg)/height2 (m2).

Waist circumference was measured in centimeters using a flexible inelastic tape and following the recommended WHO protocol.[17] Blood pressure was measured following the American Heart Association recommendation.[18]

After 8–12 h of overnight fast, samples for fasting lipid profile and fasting plasma glucose were taken. High-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), and total cholesterol (TC) were determined using enzymatic methods run on Landwind Auto-chemistry analyzer (made by Accurex Biomedical, Mumbai, India). Low-density lipoprotein-cholesterol was calculated from the Fridewald formula.[19] Fasting plasma glucose was measured using enzymatic methods run on Landwind Auto-chemistry analyzer. Glycated hemoglobin (HbA1c) was determined using high-performance liquid chromatography technique. QRISK 3 was obtained using a validated online calculator.[20]

AIP was determined using the formula below:

AIP = Log (TG/HDL-C)

DLI was calculated using the formula below. HbA1c, waist circumference, and HDL-cholesterol have all being independently associated with increased risk of cardiovascular risk and have been incorporated into various cardiovascular risk calculators.[21],[22],[23]



Data were analyzed using the Statistical Package for the Social Sciences software version 22 (made by IBM, New York, USA). Continuous variables are presented as mean ± standard deviation if normally distributed and as median ± interquartile range if not normally distributed, while the categorical variables are presented as frequencies and percentages. Correlation between AIP as well as DLI with QRISK 3 was determined using Pearson's correlation. P < 0.05 was taken as being statistically significant.

QRISK 3 score <10 is considered low risk while values ≥10 is considered as high risk. This formed the basis for doing the receiver operating characteristics (ROC) curve analysis for DLI. Area under curve (AUC) <7 is considered as having low diagnostic accuracy, while AUC of 0.7–0.9 and >0.9 are considered as having intermediate and high diagnostic accuracy, respectively, as adapted from a previously published article.[24]

Definition of terms

Dyslipidemia--TC >200 mg/dl and/or total glyceride >150 mg/dl and/or HDL-Cholesterol <40 mg/dl in males or <50 mg/dl in females.[25]

Hypertension-Systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg on at least 2 occasions and/or presently on treatment for hypertension.[26]

Obesity-BMI ≥ 30.0.[27]

Overweight-BMI values of 25–29.9.[27]

Suboptimal glycemic control-long term- HbA1c above 7%.[28]

Suboptimal glycemic control-short term Fasting plasma glucose concentration above 130 mg/dl.[28]

Truncal obesity-Waist circumference >94 cm in men or >80 cm in women.[27]


  Results Top


The mean age of the subjects is 53.34 ± 9.57 years. The median duration of diabetes was 11.50 years with an interquartile range of 4.6–16.2 years. [Table 1] shows the baseline characteristics of the study participants. [Table 2] shows the frequency of cardiovascular risk factors among the participants. [Table 3] depicts the correlation between QRISK 3 and AIP as well as DLI. It shows that the correlation of QRISK 3 with AIP (r = 0.368; P = 0.002), which is a validated marker of cardiovascular risk, is comparable with that of DLI and QRISK 3 (r = 0.317; P = 0.008)
Table 1: Clinical and biochemical characteristics of the participants

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Table 2: Frequencyofcardiovascularriskfactors

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Table 3: CorrelationbetweenQRISK3,atherogenicindexofplasma,anddiabesitylipidindex

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The ROC curve analysis of DLI is shown in [Figure 1] below. The AUC is 0.72 (95% confidence interval [CI] 0.56–0.85; P = 0.032). The cut-off value of predicting high 10-year cardiovascular risk with DLI is 10.5. In other words, DLI > 10.5 predicts a high 10-year cardiovascular risk for Type 2 diabetes patients. At the cut-off DLI value of 10.5, sensitivity is 87.5%, while specificity is 79.2%
Figure 1: Receiver operating characteristics curve analysis for diabesity lipid index cut-off value

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  Discussion Top


The average age of the participants (53.34 ± 9.57 years) falls within the middle age group. Previous studies have shown that Type 2 diabetes mellitus is most prevalent in this age group.[29],[30] Physical inactivity, obesity, hypertension, and other associated risk factors for Type 2 diabetes mellitus are also most common in this age group and this may explain why diabetes is most prevalent in this age group

In addition, this study showed a constellation of cardiovascular risk factors such as dysglycaemia, hypertension, dyslipidemia, and truncal obesity occurring together. About 2/3rd of the participants have hypertension, dyslipidemia, and truncal obesity, in addition to the background Type 2 diabetes mellitus. This constellation of cardiovascular risk factors is termed metabolic syndrome. Other studies have reported similar findings among individuals living with Type 2 diabetes mellitus.[31],[32],[33] Insulin resistance, which is also a core pathophysiologic mechanism in the development of Type 2 diabetes mellitus, is believed to be responsible for this phenomenon.[34]

There was a statistically significant positive although the weak correlation between QRISK 3 and DLI (r = 0.317; P = 0.008). This is not surprising because HbA1c, HDL, and the presence of obesity are also considered in the estimation of QRISK 3 and this may explain the statistically significant association between DLI and QRISK 3. Similarly, as demonstrated in previous studies, this study also found a statistically significant positive correlation between QRISK 3 and AIP (r = 0.368; P = 0.002).[11],[12] More importantly, there was a statistically significant positive correlation between AIP and DLI (r = 0.684; P = 0.000). HDL-C is an important component in both AIP and DLI formulae and this may be the reason behind the significant correlation between the two indices.

Moreover, a ROC curve analysis done showed that the AUC was 0.72 (95% CI 0.56–0.85; P = 0.032). The cut-off value for DLI in predicting high 10-year cardiovascular risk was 10.5. The sensitivity and specificity of using this cut-off value to define high cardiovascular risk were 87.5% and 79.2%, respectively.

Limitations

The main limitation of this study was the small sample size. A larger study would be needed to corroborate the findings of this study. Similarly, it was a hospital-based study but a community-based study may give a better picture of the index.


  Conclusion Top


There is a significant association between DLI and QRISK-3. Similarly, there is a strong association between DLI and AIP. AIP and QRISK 3 are extensively validated markers of 10-year cardiovascular risk. Therefore, DLI may be used as a marker of 10-year cardiovascular risk in Type 2 diabetes mellitus. A large prospective study would be needed to validate the observations in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Mc Namara K, Alzubaidi H, Jackson JK. Cardiovascular disease as a leading cause of death: How are pharmacists getting involved? Integr Pharm Res Pract 2019;8:1-11.  Back to cited text no. 1
    
2.
WHO. Cardiovascular Diseases. Available from: https://www.who.int/westernpacific/health-topics/cardiovascular-diseases. [Last accessed on 2021 May 07].  Back to cited text no. 2
    
3.
WHO. WHO | Global Status Report on Noncommunicable Diseases 2010. WHO. World Health Organization. Available from: http://www.who.int/nmh/publications/ncd_report2010/en/. [Last accessed on 2021 May 07].  Back to cited text no. 3
    
4.
Hajar R. Risk factors for coronary artery disease: Historical perspectives. Heart Views 2017;18:109-14.  Back to cited text no. 4
[PUBMED]  [Full text]  
5.
Borhanuddin B, Mohd Nawi A, Shah SA, Abdullah N, Syed Zakaria SZ, Kamaruddin MA, et al. 10-year cardiovascular disease risk estimation based on lipid profile-based and BMI-based Framingham risk scores across multiple sociodemographic characteristics: The Malaysian cohort project. ScientificWorldJournal 2018;2018:e2979206.  Back to cited text no. 5
    
6.
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J 2003;24:987-1003.  Back to cited text no. 6
    
7.
Garg N, Muduli SK, Kapoor A, Tewari S, Kumar S, Khanna R, et al. Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses. Indian Heart J 2017;69:458-63.  Back to cited text no. 7
    
8.
Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: Independent and external validation of an updated version of QRISK2. BMJ 2012;344:e4181.  Back to cited text no. 8
    
9.
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study. BMJ 2017;357:j2099.  Back to cited text no. 9
    
10.
Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations – Comparative analysis of risk algorithms in the RODAM study. Int J Cardiol 2018;254:310-5.  Back to cited text no. 10
    
11.
Olamoyegun MA, Oluyombo R, Asaolu SO. Evaluation of dyslipidemia, lipid ratios, and atherogenic index as cardiovascular risk factors among semi-urban dwellers in Nigeria. Ann Afr Med 2016;15:194-9.  Back to cited text no. 11
[PUBMED]  [Full text]  
12.
Guo Q, Zhou S, Feng X, Yang J, Qiao J, Zhao Y, et al. The sensibility of the new blood lipid indicator – Atherogenic index of plasma (AIP) in menopausal women with coronary artery disease. Lipids Health Dis 2020;19:27.  Back to cited text no. 12
    
13.
Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: Correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER (HDL)). Clin Biochem 2001;34:583-8.  Back to cited text no. 13
    
14.
Cai G, Shi G, Xue S, Lu W. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine (Baltimore) 2017;96:e8058.  Back to cited text no. 14
    
15.
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2004;27 Suppl 1:s5-10.  Back to cited text no. 15
    
16.
Misra A, Dhurandhar NV. Current formula for calculating body mass index is applicable to Asian populations. Nutr Diabetes 2019;9:3.  Back to cited text no. 16
    
17.
Ghesmaty Sangachin M, Cavuoto LA, Wang Y. Use of various obesity measurement and classification methods in occupational safety and health research: A systematic review of the literature. BMC Obes 2018;5:28.  Back to cited text no. 17
    
18.
Pickering Thomas G, Hall John E, Appel Lawrence J, Falkner Bonita E, John G, Hill Martha N, et al. Recommendations for blood pressure measurement in humans and experimental animals. Hypertension 2005;45:142-61.  Back to cited text no. 18
    
19.
Knopfholz J, Disserol CC, Pierin AJ, Schirr FL, Streisky L, Takito LL, et al. Validation of the friedewald formula in patients with metabolic syndrome. Cholesterol 2014;2014:e261878.  Back to cited text no. 19
    
20.
QRISK3. Available from: https://qrisk.org/three/. [Last accessed on 2021 May 08].  Back to cited text no. 20
    
21.
Flint AJ, Rexrode KM, Hu FB, Glynn RJ, Caspard H, Manson JE, et al. Body mass index, waist circumference, and risk of coronary heart disease: A prospective study among men and women. Obes Res Clin Pract 2010;4:e171-81.  Back to cited text no. 21
    
22.
Hewing B, Moore KJ, Fisher EA. HDL and cardiovascular risk: Time to call the plumber? Circ Res 2012;111:1117-20.  Back to cited text no. 22
    
23.
Khaw KT, Wareham N. Glycated hemoglobin as a marker of cardiovascular risk. Curr Opin Lipidol 2006;17:637-43.  Back to cited text no. 23
    
24.
Li F, He H. Assessing the accuracy of diagnostic tests. Shanghai Arch Psychiatry 2018;30:207-12.  Back to cited text no. 24
    
25.
Sarfraz M, Sajid S, Ashraf MA. Prevalence and pattern of dyslipidemia in hyperglycemic patients and its associated factors among Pakistani population. Saudi J Biol Sci 2016;23:761-6.  Back to cited text no. 25
    
26.
Oparil S, Acelajado MC, Bakris GL, Berlowitz DR, Cífková R, Dominiczak AF, et al. Hypertension. Nat Rev Dis Primers 2018;4:18014.  Back to cited text no. 26
    
27.
Owolabi EO, Ter Goon D, Adeniyi OV. Central obesity and normal-weight central obesity among adults attending healthcare facilities in Buffalo City Metropolitan Municipality, South Africa: A cross-sectional study. J Health Popul Nutr 2017;36:54.  Back to cited text no. 27
    
28.
Oluma A, Abadiga M, Mosisa G, Etafa W. Magnitude and predictors of poor glycemic control among patients with diabetes attending public hospitals of Western Ethiopia. PLoS One 2021;16:e0247634.  Back to cited text no. 28
    
29.
Uloko AE, Musa BM, Ramalan MA, Gezawa ID, Puepet FH, Uloko AT, et al. Prevalence and risk factors for diabetes mellitus in Nigeria: A systematic review and meta-analysis. Diabetes Ther 2018;9:1307-16.  Back to cited text no. 29
    
30.
Adeloye D, Ige JO, Aderemi AV, Adeleye N, Amoo EO, Auta A, et al. Estimating the prevalence, hospitalisation and mortality from type 2 diabetes mellitus in Nigeria: A systematic review and meta-analysis. BMJ Open 2017;7:e015424.  Back to cited text no. 30
    
31.
Yadav D, Mahajan S, Subramanian SK, Bisen PS, Chung CH, Prasad GB. Prevalence of metabolic syndrome in type 2 diabetes mellitus using NCEP-ATPIII, IDF and WHO definition and its agreement in Gwalior Chambal region of Central India. Glob J Health Sci 2013;5:142-55.  Back to cited text no. 31
    
32.
Osei-Yeboah J, Owiredu WK, Norgbe GK, Yao Lokpo S, Gyamfi J, Alote Allotey E, et al. The prevalence of metabolic syndrome and its components among people with type 2 diabetes in the Ho Municipality, Ghana: A cross-sectional study. Int J Chronic Dis 2017;2017:8765804.  Back to cited text no. 32
    
33.
Osuji CU, Nzerem BA, Dioka CE, Onwubuya EI. Metabolic syndrome in newly diagnosed type 2 diabetes mellitus using NCEP-ATP III, the Nnewi experience. Niger J Clin Pract 2012;15:475-80.  Back to cited text no. 33
  [Full text]  
34.
Roberts CK, Hevener AL, Barnard RJ. Metabolic syndrome and insulin resistance: Underlying causes and modification by exercise training. Compr Physiol 2013;3:1-58.  Back to cited text no. 34
    


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