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Table of Contents
ORIGINAL ARTICLE
Year : 2021  |  Volume : 14  |  Issue : 4  |  Page : 184-190

Fasting insulin-lipid index - A novel insulin resistance index with better cardiovascular risk predictability in type 2 diabetes mellitus


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

Date of Submission22-May-2021
Date of Decision13-Jun-2021
Date of Acceptance27-Jun-2021
Date of Web Publication11-Jan-2022

Correspondence Address:
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/hmj.hmj_28_21

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  Abstract 


Introduction: Insulin resistance (IR) is an independent cardiovascular risk factor. Aim: The study aimed at comparing a potential novel marker, fasting insulin-lipid (FILIP) index, with homoeostatic model assessment of IR (HOMA-IR) and McAuley's index, in terms of IR quantitation and cardiovascular risk predictability. Methods: The study involved 70 individuals (35 females and 35 males) who were previously diagnosed with Type 2 diabetes mellitus. Ethical approval was obtained from the Institution's Ethical Review Committee. Fasting plasma insulin, fasting plasma glucose, plasma triglyceride (TG) and high-density lipoprotein-cholesterol (HDL-C) were determined with appropriate techniques. QRISK 3 was obtained from a validated calculator. FILIP index, HOMA-IR and McAuley's index were obtained from the recommended formulae. SPSS version 22 was used for data analysis. Pearson's correlation was used to compare the indices. Bland–Altman plots comparing FILIP index with HOMA-IR and McAuley's index were done. Receiver operating characteristic (ROC) curve analysis was done to determine the cardiovascular risk predictability of the indices. Results: The mean age was 53.34 ± 9.57 years. FILIP index significantly correlated with HOMA-IR (r = 0.514; P < 0.0001) and McAuley's index (r = −0.830; P < 0.0001). The Bland–Altman plots between FILIP index and HOMA-IR as well as between FILIP index and McAuley's index showed reasonably acceptable limits of difference. ROC curve analysis in determining cardiovascular risk predictability showed that FILIP index had the highest area under curve. Conclusion: FILIP index is a simple derivative of fasting insulin multiplied by TG-HDL-C ratio. As a surrogate marker of IR, FILIP index compared well with HOMA-IR and McAuley's index. In Type 2 diabetes mellitus, FILIP index is a better predictor of cardiovascular risk when compared with HOMA-IR and McAuley's index.

Keywords: Cardiovascular diseases, diabetes mellitus type 2, fasting insulin-lipid index, insulin resistance biomarker, insulin resistance


How to cite this article:
Azeez TA. Fasting insulin-lipid index - A novel insulin resistance index with better cardiovascular risk predictability in type 2 diabetes mellitus. Hamdan Med J 2021;14:184-90

How to cite this URL:
Azeez TA. Fasting insulin-lipid index - A novel insulin resistance index with better cardiovascular risk predictability in type 2 diabetes mellitus. Hamdan Med J [serial online] 2021 [cited 2022 Jan 20];14:184-90. Available from: http://www.hamdanjournal.org/text.asp?2021/14/4/184/335377




  Introduction Top


Insulin resistance (IR) is a condition where the physiological responses to insulin are impaired.[1] It is the attenuated action of insulin at the cellular and subcellular level manifesting with a myriad of clinical features.[2] There are reduced glucose disposal and increased cardiovascular risk in insulin-resistant persons. The global prevalence of IR among adults is 15.5%–46.5%.[3] Studies have reported a critical role of IR in the pathogenesis of Type 2 diabetes mellitus.[4],[5],[6] The cluster of clinical and biochemical abnormalities commonly found in insulin-resistant people is called metabolic syndrome.[7] These abnormalities include glucose intolerance, obesity, hypertension and dyslipidaemia.

Epidemiologically and clinically, it is imperative to be able to measure IR due to its central role in cardiovascular diseases. Hyperinsulinaemic euglycaemic clamp is regarded as the gold standard technique for quantifying IR.[8] However, it is expensive, cumbersome and time-consuming, which makes it impracticable in day-to-day clinical practice. This brought about the concept of the surrogate indices for quantifying IR. Some of these indices are derived from fasting insulin, glucose and/or lipids parameters, while others are derived from dynamic tests such as the oral glucose tolerance test and intravenous glucose tolerance test.[8] Examples of the former are homoeostatic model assessment of IR (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), McAuley's index and fasting glucose-insulin ratio while examples of the latter are Avignon index, Matsuda index, Gutt index, Belfiore index and Stumvoll index. Due to their complexities and technicalities, the derivatives of dynamic tests are not suitable for clinical usage.[8]

The original version of the HOMA-IR was first described in 1985 by Matthews et al. by using a computer-solved mathematical model.[9] Since then, HOMA-IR has been revised. In addition, previous studies have shown that it is a reliable marker of IR.[10],[11],[12] Due to its simplicity and wide validation, HOMA-IR is the most commonly used marker of IR in hospital-based research studies.[13] McAuley's index was first documented by McAuley et al. in 2001.[14] A cross-sectional study done to compare the various surrogate indices of IR showed that McAuley's index had the highest area under curve (AUC), accuracy, positive predictive value and negative predictive value in the study.[15] In another study, Ascaso et al. also posited that McAuley's index had the highest sensitivity and sensitivity when compared with other indices of IR.[16] Another study also demonstrated a strong correlation between McAuley's index and other common surrogate indices of IR such as HOMA-IR and QUICKI.[17] From the various cited studies, it can be concluded that HOMA-IR and the McAuley's index are reliable indices of IR.

Cardiovascular disease remains the leading cause of deaths globally.[18] Therefore, it is of extreme importance to be able to estimate the long-term cardiovascular risk of an adult so as to modify the risk factors and prevent mortality. This has led to the development of various cardiovascular risk assessment tools such as the World Health Organization cardiovascular risk prediction chart, Framingham Risk Score, atherosclerotic cardiovascular disease risk score, QRISK score, the United Kingdom Prospective Diabetes Study risk engine, Prospective Cardiovascular Munster Study (PROCAM) score and SCORE.[19],[20]

The QRISK score was first described in 2007 by Hippisley-Cox et al.[21] It has undergone repeated modifications and the latest version, QRISK 3 score, was released in 2017.[22] QRISK score has been documented to correlate strongly with the widely validated Framingham Risk Score.[21] In fact, another study showed that the QRISK score had better 10-year cardiovascular risk predictability compared with the Framingham Risk Score.[23] A study done in sub-Sahara Africa demonstrated a correlation between QRISK 3 and lipid indices among Black Africans with Type 2 diabetes mellitus.[24] This implies that QRISK 3 could be used reliably to estimate 10-year cardiovascular risk in Type 2 diabetes mellitus, particularly among Black Africans.

Objective

The study aimed at describing a novel index called fasting insulin-lipid (FILIP) index and its usefulness as a simple surrogate marker of IR. The study also purposed to find out if FILIP could predict 10-year cardiovascular risk of patients with Type 2 diabetes better than the common IR indices such as HOMA-IR and McAuley's index.


  Methods Top


The study design was a cross-sectional study. The sample size was 70 adults. The sample size was based on the availability of consenting Type 2 diabetes patients. The participants were attendees of a specialist diabetes clinic who were previously diagnosed with Type 2 diabetes using the American Diabetes Association criteria.[25] Ethical approval was granted by the ethical committee of the Institute Advanced Medical Research and Training with the reference number NHREC/05/01/2008a. The ethical approval number for the study was UI/EC/17/0284. In addition, each participant gave written informed consent before recruitment into the study. Inclusion criteria included adults, 30 years and above, previously diagnosed with Type 2 diabetes mellitus. Exclusion criteria were Type 1 diabetes mellitus, pregnancy and hospital admissions within 3 months to the study.

Fasting blood sample, following overnight fast of 8–12 h, was taken and assayed for fasting insulin, fasting plasma glucose and fasting lipid profile. Fasting plasma glucose was determined by the widely validated glucose oxidase enzymatic method using Dialab Glucose assay kit.[26] The sample was collected in a fluoride oxalate bottle and kept on ice. Analytical run on the sample was carried out using the Landwind C100plus AutoChemistry Analyser (Accurex Biomedical, Mumbai, Maharashtra, India). The intra-assay and inter-assay coefficients of variation were 2.98% and 3.02%, respectively. Fasting plasma triglycerides (TGs) sample was taken in an ethylene diamine tetraacetic acid (EDTA) bottle, which was centrifuged appropriately to obtain the plasma. Analytical run on the plasma sample was carried out using the Landwind C100plus AutoChemistry Analyser. The coefficient of variation was 3.31%. High-density lipoprotein-cholesterol (HDL-C) sample was collected in the same EDTA bottle as TG and was equally analysed using the enzymatic method run on the same Landwind C100plus AutoChemistry Analyser. The coefficient of variation was 1.74%.

Enzyme-linked immunosorbent assay (ELISA) technique was employed for fasting plasma insulin. Cell Biolab Human Insulin ELISA kit (California, USA) was used for the analysis. The intra-assay and inter-assay coefficients of variations were 4.83% and 5.74%, respectively. Glycated haemoglobin (HbA1c) was determined using high-performance liquid chromatography method. Automated glycohaemoglobin analyser (Bio-Rad 220-0212, Hercules, California, USA) was used. Coefficient of variation was 1.74%.

HOMA-IR, McAuley's index and FILIP index were calculated using the appropriate formulae as shown below.[27]





QRISK 3 was obtained using a validated online calculator.[28] QRISK 3 score below 10 was considered low risk while values ≥ 10 were considered as intermediate/high risk.[29]

Data analysis was carried out using the Statistical Package for the Social Sciences software (SPSS) version 22 (IBM, New York, USA). Quantitative variables were presented as mean ± standard deviation. Correlation was done with Pearson's correlation. Bland–Altman plots between FILIP index and HOMA-IR as well as between FILIP index and McAuley's index were done and the limits of agreement were determined. Receiver operating characteristic (ROC) analysis which compared QRISK 3 categories and FILIP index, HOMA IR and McAuley's inde was done. The area under curve (AUC) was determined with 95% confidence interval (CI). P < 0.05 was considered statistically significant. Fasting plasma glucose (FPG) in mmol/L was converted to mg/dl by multiplying with 18. TG in mmol/L was converted to mg/dl by multiplying with 88.5.


  Results Top


Seventy adults participated in the study and 50% of them were males. [Table 1] shows the parameters of the participants. The mean age falls within the middle-age group. Short-term (represented by FPG) and long-term (represented by HbA1c) glycaemic controls were averagely good. The fasting lipid parameters were also not significantly deranged in the participants. [Table 2] shows the correlation between FILIP index and other indices of IR. There were significant positive correlation between FILIP index and HOMA-IR (r = 0.514; P < 0.0001) as well as a significant negative correlation between FILIP index and McAuley's index (r = −0.830; P < 0.0001). As expected, there was also a significant negative correlation between HOMA-IR and McAuley's index (r = −0.717; P < 0.0001). [Figure 1] shows the linear regression line between FILIP index and HOMA-IR, while [Figure 2] shows the linear regression line between FILIP index and McAuley's index.
Table 1: The parameters of the study participants

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Table 2: Correlation between fasting insulin-lipid index and homoeostatic mode assessment of insulin resistance with McAuley's index

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Figure 1: Linear regression line between fasting insulin-lipid index and homoeostatic model assessment of insulin resistance

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Figure 2: Linear regression line between fasting insulin-lipid index and McAuley's index

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[Figure 3] shows the Bland–Altman plot involving FILIP index and HOMA-IR. The reference lines were the bias (red line) and 95% CI (green line). The graph shows that the bias is minimal and the limit of difference is reasonable.
Figure 3: Bland–Altman plot (fasting insulin-lipid index and homoeostatic model assessment of insulin resistance)

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[Figure 4] shows the Bland–Altman plot involving FILIP index and McAuley's index. The reference lines indicate the bias (red line) and 95% CI (green line). Similarly, the graph shows that the bias is minimal and the limit of difference is reasonable.
Figure 4: Bland–Altman plot (fasting insulin-lipid index and McAuley's index)

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[Figure 5] shows the ROC curves of FILIP index, HOMA-IR and McAuley's index using QRISK 3 categories of low and intermediate/high risk. [Table 3] shows the AUC of each index. Interestingly, the novel index, FILIP index, has a higher AUC than the established indices HOMA-IR and McAuley's index. The predictability of 10-year cardiovascular risk into low and intermediate/high, using FILIP index, was statistically significant whereas the established indices (HOMA-IR and McAuley's index) are poor predictors of cardiovascular risk categories.
Figure 5: Receiver operating characteristic curves of the insulin resistance indices using QRISK 3 categories

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Table 3: Area under curve of each insulin resistance index

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  Discussion Top


The study showed a statistically significant correlation between the novel FILIP index and HOMA-IR (r = 0.514; P < 0.0001). Since HOMA-IR is an established and widely validated surrogate marker of IR, it can be inferred that FILIP index could also be a good marker of IR.[30] In addition, FILIP index showed a statistically significant stronger correlation with another validated surrogate index of IR, McAuley's index (r = −0.830; P < 0.0001).[15] This might be explained by the fact that FILIP index, HOMA-IR and McAuley's index are all derived from the fasting plasma insulin. Moreover, the stronger correlation of FILIP index with McAuley's index might be due to the fact that both FILIP index and McAuley's index are derivatives of fasting TGs as well as the fasting plasma insulin, unlike HOMA-IR which is not derived from fasting TG.

FILIP index is a novel biomarker, and there are no previous studies on the index to relate with. However, previous studies on new biomarkers of IR often correlated the new biomarker with established markers to prove the reliability and validity of the new marker.[31] It is of crucial importance to demonstrate the validity of a new diagnostic index like the FILIP index because it gives an assurance on its clinical usage. This necessitated the comparison of FILIP index with established indices such as HOMA-IR and the McAuley's index using the Bland–Altman's plot.

The novel FILIP index is better than some established indices such as HOMA-IR and McAuley's index because the novel marker (FILIP index) makes use of the widely cited fasting TG: HDL-C ratio into its equation. Several previous authors have documented a strong association between TG:HDL-C ratio and IR.[32],[33],[34] In fact, both fasting TG and HDL-C are considered as independent criteria for diagnosing IR syndrome using the National Cholesterol Education Programme Adult Treatment Panel III criteria.[35] This infers that an IR equation, like the novel FILIP index, incorporating the two lipid parameters might be a better marker of IR than the IR indices using just one of the two lipid parameters.[35]

Bland–Altman plot is a recommended method of comparing the performance of a novel index with an established index to establish the validity of the new index.[36] Hence, the novel FILIP index was independently compared with two established indices, HOMA-IR and McAuley's index, so as to ascertain the validity of the new index. The study showed that when FILIP index and HOMA-IR were compared, using the Bland–Altman plot, the limit of agreement between the two was reasonable. A similar finding was found between HOMA-IR and FILIP index. These findings, therefore, suggest that FILIP index, as a comparable surrogate marker of IR, is comparable with HOMA-IR and McAuley's index. This might be due to the fact that FILIP index is a mathematical derivative of fasting insulin and lipid parameters which have all being independently shown to be associated with IR.[34],[37]

IR is an independent cardiovascular risk factor.[38] Consequently, it is expected that a surrogate marker of IR should also be able to predict risk of cardiovascular disease. This study showed that in terms of predictability of cardiovascular risk, using QRISK 3 categories, FILIP index is a better predictor compared with HOMA-IR and McAuley's index. This might be due to HDL-C which is present in the FILIP index equation but absent in both HOMA-IR and McAuley's index equations. HDL-C has been reported in several previous studies to be an independent cardiovascular risk factor.[39],[40],[41]

FILIP index compares favourably with HOMA-IR and McAuley's index as a marker of IR and as a predictor of cardiovascular risk. However, in view of the small sample size in this study, the generalizability would be further enhanced by studying a larger sample of individuals with Type 2 diabetes mellitus. Vasileiou et al. posited that sample size insufficiency could greatly impair the generalizability of the findings in a study.[42] Bujang and Adnan also suggested that determining the minimum sample size for a novel marker in specificity and sensitivity studies could be challenging, and this has led to the development of different software and formulae for that purpose.[43] This study employed the individuals willing to participate in the study, and this raised some concerns on the generalizability of the findings. It is however worthy of note that a previous study that also reported a novel index of IR termed “metabolic score of IR” also involved 68 individuals living with Type 2 diabetes mellitus.[44]

Limitations

The sample size was rather small which might affect the findings and their significance. Similarly, a hospital-based study, as compared with community-based study, has its inherent limitations.


  Conclusion Top


FILIP index is a simple derivative of fasting insulin and lipid parameters. It is a novel surrogate marker of IR. It is comparable with established surrogate markers such as HOMA-IR and McAuley's index. Importantly, FILIP index can predict cardiovascular risk even better than HOMA-IR and McAuley's index in individuals living with Type 2 diabetes mellitus. Further studies would be needed to validate the reliability of this potential novel marker of IR.

Ethical clearance

Ethical approval was granted by the ethical committee of the Institute Advanced Medical Research and Training with the reference number NHREC/05/01/2008a. The ethical approval number for the study was UI/EC/17/0284. Furthermore, the recruited participants gave written informed consent to partake in the study.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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