|Year : 2019 | Volume
| Issue : 2 | Page : 43-48
Markers of insulin resistance and their performance in adult Nigerians with metabolic syndrome
Ifeoma Christiana Udenze1, Idowu A Taiwo2, Joseph Babatunde Minari2, Casmir E Amadi3
1 Department of Clinical Pathology, College of Medicine, University of Lagos, Lagos, Nigeria
2 Department of Cell Biology and Genetics, Faculty of Science, University of Lagos, Lagos, Nigeria
3 Department of Medicine, College of Medicine, University of Lagos, Lagos, Nigeria
|Date of Web Publication||10-Jul-2019|
Dr. Ifeoma Christiana Udenze
Department of Clinical Pathology, College of Medicine, University of Lagos, Lagos
Source of Support: None, Conflict of Interest: None
Background: Insulin resistance (IR), which is a state of deficient response to normal insulin levels, is associated with metabolic abnormalities of dyslipidemia, glucose intolerance, obesity, and hypertension, parameters which define metabolic syndrome (MS). Markers of IR could be useful tools to predict MS.
Aim: This study aims to evaluate the performance of surrogate markers of IR in predicting MS in nondiabetic adult Nigerians.
Settings and Design: This cross-sectional, analytical study was conducted in Lagos, Nigeria.
Subjects and Methods: One hundred and forty-one apparently healthy adult Nigerians aged between 40 and 80 years were consecutively recruited. MS was defined according to the harmonized MS criteria. Data were collected using a questionnaire, and fasting blood samples were collected for analysis.
Statistical Analysis: The data were analyzed using the IBM SPSS statistical package. Statistical significance was set at P < 0.05.
Results: The mean values of Homeostatic Model for Insulin Assessment-IR (HOMA-IR), HOMA-beta cell (HOMA-B), and Quantitative Insulin Sensitivity Check Index (QUICKI), but not fasting glucose-insulin ratio (FGIR), significantly distinguished individuals with and without MS components (P = 0.0001). HOMA-IR and QUICKI, but not HOMA-B and FGIR, had significant correlations with the components of MS (P = 0.041). QUICKI had the largest area under the receiver operating characteristic curve for predicting MS, with a sensitivity of 90% and a specificity of 40%, at a cutoff value of 0.324 (P < 0.05).
Conclusion: QUICKI performed better than HOMA-IR, HOMA-B, and FGIR in predicting MS in apparently healthy adult Nigerians.
Keywords: Fasting glucose-insulin ratio, Homeostatic Model for Insulin Assessment-Insulin Resistance, Homeostatic Model for Insulin Assessment-beta cell and Quantitative Insulin Sensitivity Check Index, insulin resistance, metabolic syndrome, reference interval
|How to cite this article:|
Udenze IC, Taiwo IA, Minari JB, Amadi CE. Markers of insulin resistance and their performance in adult Nigerians with metabolic syndrome. Int J Non-Commun Dis 2019;4:43-8
|How to cite this URL:|
Udenze IC, Taiwo IA, Minari JB, Amadi CE. Markers of insulin resistance and their performance in adult Nigerians with metabolic syndrome. Int J Non-Commun Dis [serial online] 2019 [cited 2020 Sep 27];4:43-8. Available from: http://www.ijncd.org/text.asp?2019/4/2/43/262470
| Introduction|| |
Insulin resistance (IR) is defined as decreased response to normal concentrations of circulating insulin with decreased insulin-stimulated peripheral glucose utilization and failure to suppress free fatty acid release from adipose tissue and hepatic glucose production. IR, therefore, triggers a cascade of metabolic abnormalities including cardiometabolic risk factors of dyslipidemia (high triglyceride and low high-density lipoprotein [HDL]-cholesterol), hypertension, raised fasting glucose, and central obesity. The clustering of these risk factors for cardiovascular disease and type 2 diabetes mellitus, which occur together more often than by chance alone, is known as metabolic syndrome (MS). The presence of MS signifies increased risk for atherosclerotic cardiovascular disease (CVD).
IR increases with age; increasing age is associated with decreased glucose tolerance as manifested by a reduction in glucose-mediated B-cell insulin release and deficient insulin-mediated skeletal muscle glucose disposal, creating a state of IR., IR plays a pivotal role in the pathogenesis of age-related diseases of CVD, MS, and type 2 diabetes, and the assessment of IR status may have a role in risk stratification and inform treatment options.
Standard measurements of IR include the euglycemic hyperinsulinemic clamp, the hyperglycemic clamp, and the insulin suppression test., These in vivo techniques which are direct measures of insulin sensitivity are technically demanding and invasive, requiring insulin infusion and repeated blood sampling and the continuous adjustment of glucose infusions. These methods are impractical in large epidemiological studies and in clinical settings, creating a need for the use of surrogate markers. Surrogate markers of IR include the Homeostatic Model for Insulin Assessment (HOMA), which provided equations for estimating IR (HOMA-IR) and beta cell function (HOMA-B) from simultaneous fasting measures of insulin and glucose levels, by mathematical modeling of the normal physiological dynamics of insulin and glucose. The Quantitative Insulin Sensitivity Check Index (QUICKI) derived from logarithmically transformed fasting plasma glucose and insulin levels has proven to be a first-rate index of IR compared with the clamp methods. The fasting glucose-insulin ratio (FGIR) is another surrogate marker which has been employed in a number of studies as an index of IR and insulin sensitivity., These markers have been validated against standard measures of insulin sensitivity, but their performance in certain clinical states such as MS has not been evaluated in adult Nigerians.
| Subjects and Methods|| |
This was a cross-sectional, analytical study of 141 apparently healthy adult male and female Nigerians aged between 40 and 80 years who were recruited from an urban population in Lagos, Nigeria. Announcement for enrollment was made, and individuals who responded to the call, met the inclusion criteria, and gave written informed consent were consecutively recruited into the study. The Institutional Review Board of the College of Medicine, University of Lagos, approved the study protocol.
MS is defined as a combination of at least three of the following five criteria: abdominal circumference ≥102 cm in males or ≥88 cm in females, HDL-cholesterol ≮1.03 mmol/L (≮40 mg/dL) in males or ≮1.3 mmol/L (≮50 mg/dL) in females, triglycerides ≥1.7 mmol/L (≥150 mg/dL), blood pressure ≥130/85 mmHg or the patient receiving hypotensive treatment, and fasting glycemia >6.1 mmol/L (>110 mg/dL).
Individuals who were not ambulant, pregnant women, and institutionalized individuals were excluded from the study. Individuals with diabetes were also excluded from the study.
The eligible study participants were counseled on the objectives of the study and the study protocol. The study was conducted according to the guidelines laid down in the Declaration of Helsinki, and the Institutional Review Board of the University of Lagos approved the protocol. Each participant completed an interviewer-administered, anonymous, standardized questionnaire. The participants then underwent anthropometric measurements and blood pressure measurements. Abdominal obesity was determined by measurement of the waist circumference. The measurement was taken at the end of several consecutive natural breaths, at a level parallel to the floor, and at the midpoint between the top of the iliac crest and the lower margin of the last palpable rib in the mid-axillary line. The hip circumference was measured at a level parallel to the floor, at the largest circumference of the buttocks.
General obesity was determined by the body mass index. Weight was measured to the nearest 0.1 kg and height to the nearest 0.1 m.
Blood pressure was determined using the Accoson's mercury sphygmomanometer (cuff size 15 cm × 43 cm). The participants were seated and rested for 5 min before measurement. Systolic blood pressure was taken at the first Korotkoff sound and diastolic blood pressure at the fifth Korotkoff sound.
The study participants reported on the morning of the study after an overnight (10–12 h) fast. Venous blood was collected for fasting glucose, lipid profile, and insulin measurements.
Fasting glucose and lipid profile were estimated from lithium heparin plasma on a biochemistry autoanalyzer, cobas C 311 (Roche Diagnostics GmbH D-68298 Mannheim, Germany). Fasting insulin was determined from serum using reagents from Biovendor Laboratories (62100 Brno, Czech Republic) by an enzyme-linked immunoassay technique on Acurex Plate Read (Acurex Diagnostics, Ohio, USA, 419-872-4775).
IR was assessed using HOMA-IR, HOMA-B, QUICKI index, and FGIR and calculated thus: HOMA-IR = fasting insulin (μIU/mL) × fasting glucose (mmol/L)/22.5. HOMA-%B = fasting insulin (μIU/ml) × 100/fasting glucose (mmol/L). QUICKI = 1/[log (insulin μU/mL) + log (glucose mg/dL) and FGIR = fasting glucose (mmol/L) × fasting insulin (μIU/ml).
The data were analyzed using IBM Statistical Software for Social Sciences (SPSS Inc., Chicago, IL) version 20.0.. The Chi-squared test, independent Student's t-test, Spearman's correlation, and receiver operating characteristic (ROC) curve were employed for the analysis. Statistical significance was set at P < 0.05.
| Results|| |
One hundred and forty-one apparently healthy adult Nigerians participated in this study. Nearly 15.6% of participants had MS, whereas 84.4% did not have MS. Almost 36.8% of the participants were male, whereas 63.2% were female. The age range was 40–80 years, with a mean of 52.29 ± 9.42 years.
[Table 1] shows the sociodemographic data of the study participants. The sociodemographic characteristics of the study participants were evaluated by age, gender, ethnicity, educational level, and religion. All the variables were evenly distributed across groups, showing no statistically significant differences.
|Table 1: Sociodemographic data of the study participants with metabolic syndrome by age, gender, ethnicity, educational level, and religion|
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Majority of the study participants were aged between 40 and 60 years old, representing 68.2% with MS and 79.1% without MS. More females than males had MS, 63.2% compared to 51.7%, though the difference was not statistically significant. The major ethnic groups that participated in the study were Yoruba and Igbo and both were evenly distributed in the groups, with and without MS. There was no statistically significant difference in the levels of education and religious affiliation between the groups with and without MS.
[Table 2] shows the reference intervals for the markers of IR, FGIR, HOMA-IR, HOMA-B, and QUICKI in adult Nigerians aged 40–80 years of age.
|Table 2: Reference interval for markers of insulin resistance such as fasting glucose-insulin ratio, Homeostatic Model Assessment of Insulin Resistance, Homeostatic Model Assessment of Insulin Resistance-β, and Quantitative Insulin Sensitivity Check Index in adult Nigerians aged 40-80 years|
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The reference interval for the study population defines the 2.5% and 97.5% boundaries that define the normal limits for markers of IR in an apparently healthy (with and without MS) adult Nigerian population aged 40–80 years.
[Table 3] compares the mean values of markers of IR, i.e., FGIR, HOMA-IR, HOMA-B, and QUICKI, in between individuals without any MS components and those with one or more components.
|Table 3: Mean values of markers of insulin resistance such as fasting glucose-insulin ratio, Homeostatic Model Assessment of Insulin Resistance, Homeostatic Model Assessment of Insulin Resistance-β, and Quantitative Insulin Sensitivity Check Index and their correlation in individuals with and without metabolic syndrome components|
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Markers of IR, i.e., HOMA-IR, HOMA-B, FGIR, and QUICKI, were compared in the presence and absence of MS components. HOMA-IR, HOMA-B, and QUICKI were significantly different between the groups. HOMA-IR and HOMA-B were significantly higher in the group with the presence of MS components, showing that increased values of HOMA-IR and HOMA-B indicate increased IR. QUICKI was significantly lower in the group with the presence of MS components, showing that increased values of QUICKI indicate lower insulin-resistant state.
Of all the markers of IR evaluated, HOMA-IR and QUICKI showed statistically significant relationship with a number of MS components. HOMA-IR had a positive and significant correlation with a number of MS components, showing that higher values of HOMA-IR correlate with a higher number of MS components. QUICKI had a negative and significant correlation with a number of MS components, showing that higher values of QUICKI correlate with a lower number of MS components.
[Figure 1] shows the performance of markers of IR, i.e., FGIR, HOMA-IR, HOMA-B, and QUICKI, in the study participants in diagnosing MS.
|Figure 1: The receiver operating characteristic curve of markers of insulin resistance; fasting glucose-insulin ratio, Homeostatic Model Assessment of Insulin Resistance, Homeostatic Model Assessment of Insulin Resistance-β, and Quantitative Insulin Sensitivity Check Index in the study participants in metabolic syndrome|
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[Table 4] shows the characteristics of the markers of IR, i.e., FGIR, HOMA-IR, HOMA-B, and QUICKI, with respect to area under the ROC curve (AUC), confidence intervals, sensitivity, and specificity, in diagnosing MS.
|Table 4: Characteristics of the markers of insulin resistance such as fasting glucose-insulin ratio, Homeostatic Model Assessment of Insulin Resistance, Homeostatic Model Assessment of Insulin Resistance-α, and Quantitative Insulin Sensitivity Check Index, with respect to area under the receiver operating characteristic curve, confidence intervals, sensitivity, and specificity, in diagnosing metabolic syndrome|
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At a cutoff value of − 0.966, HOMA-IR had the narrowest AUC, at 100% sensitivity and zero specificity, followed by HOMA-B, with an AUC of 0.589, at a cutoff value of 3.090, a sensitivity of 74%, and a specificity of 40%. FGIR, at a cutoff value of 0.477, had an AUC of 0.611, a sensitivity of 95%, and a specificity of 40%. While QUICKI had the highest AUC of 0.722, at a cutoff value of 0.324 and a sensitivity and specificity of 90% and 40%, respectively.
| Discussion|| |
All parameters taken together, this study returns QUICKI as the best surrogate marker for IR in an adult population of Nigerians with MS. Mean values for QUICKI were significantly lower in individuals with one or more components of MS, moderately but significantly correlated with MS, and had the largest AUC at a sensitivity of 90% albeit at a specificity of 40%.
The gold standard method for measuring IR is the euglycemic hyperinsulinemic clamp. This is a direct method of estimating IR. It is, however, technically demanding and invasive, requiring insulin infusion and repeated blood sampling with continuous adjustment of glucose infusion. The euglycemic hyperinsulinemic clamp method is impractical in large epidemiological studies and in clinical settings, creating a need for the use of surrogate markers.
A recent review of surrogate markers of IR focused on the pros and cons of these markers. HOMA-IR, HOMA-B, FGIR, and QUICKI are surrogate markers derived from fasting steady-state conditions and have the advantage of being inexpensive, quantitative tools that can be easily applied in almost every setting, including epidemiological studies, large clinical trials, clinical research investigations, and clinical practice. A significant drawback is that the physiologic state assumption in their derivation formulae is no longer obeyed in insulin-resistant nondiabetic controls, with inappropriately high glucose and inappropriately low insulin levels., Log transformation of both fasting insulin and glucose in the QUICKI equation, can maintain linear correlations with direct measures of IR in this clinically important state of nondiabetic individuals who are insulin resistant.,
The performance of different markers of IR has been assessed in different subpopulations and in different clinical states. HOMA-IR, at a cutoff of 2.5, had a sensitivity of >70% and a specificity of >60% for diagnosing MS in Indian adolescents. HOMA-IR was also useful in diagnosing MS in Korean children. FGIR has shown good predictive value for IR and MS in women with polycystic ovarian syndrome., In nondiabetic adult populations, QUICKI has been shown to predict impaired glucose tolerance, normal glucose tolerance, and type 2 diabetes with an AUC of 0.654, 0.686, and 0.715, respectively, in a large prospective cohort study of healthy adults, consisting of combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study. These values are similar to the value of 0.722 obtained from this study, for predicting MS using QUICKI. Results from the above Cohort studies showed that QUICKI ranked 3rd, 5th, and 5th in AUC in predicting impaired glucose tolerance, normal glucose tolerance, and type 2 diabetes, respectively, preceded only by more direct measures of IR. Findings from our study further validate reports in literature that QUICKI is among the most thoroughly evaluated and validated surrogate index for IR and can predict MS, especially in nondiabetic adult population.
This study is limited by its cross-sectional design and by the lack of standardization of insulin assays between laboratories, which makes it impossible to use surrogate markers to define universal cutoff points for IR. Future directions should focus on standardization of insulin assays across laboratories to make way for the application of surrogate markers of IR in clinical practice.
| Conclusion|| |
QUICKI, a surrogate marker of IR, has shown consistency in detecting IR in an adult, nondiabetic Nigerian population, by consistently being able to distinguish between individuals, with and without MS components; correlating with the components of the syndrome; and having the largest AUC for detecting MS, compared to HOMA-IR, HOMA-B, and FGIR. At a cutoff value of 0.324, QUICKI detected MS with 90% sensitivity although with a low sensitivity of 40%.
The authors acknowledge laboratory support from the Institute of Cardiogenetics, University of Luebeck, Germany.
Financial support and sponsorship
Research reported in this publication was supported by the Central Research Committee grant (Grant number 2017/15) of the University of Lagos, Nigeria.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Abel ED, O'Shea KM, Ramasamy R. Insulin resistance: Metabolic mechanisms and consequences in the heart. Arterioscler Thromb Vasc Biol 2012;32:2068-76.
Cabré JJ, Martín F, Costa B, Piñol JL, Llor JL, Ortega Y. Metabolic syndrome as a cardiovascular disease risk factor: Patients evaluated in primary care. BMC Public Health 2008;8:251.
Szoke E, Shrayyef MZ, Messing S, Woerle HJ, van Haeften TW, Meyer C, et al.
Effect of aging on glucose homeostasis: Accelerated deterioration of beta-cell function in individuals with impaired glucose tolerance. Diabetes Care 2008;31:539-43.
Reers C, Erbel S, Esposito I, Schmied B, Büchler MW, Nawroth PP, et al.
Impaired islet turnover in human donor pancreata with aging. Eur J Endocrinol 2009;160:185-91.
Chang AM, Halter JB. Aging and insulin secretion. Am J Physiol Endocrinol Metab 2003;284:E7-12.
Singh B, Saxena A. Surrogate markers of insulin resistance: A review. World J Diabetes 2010;1:36-47.
Singh Y, Garg MK, Tandon N, Marwaha RK. A study of insulin resistance by HOMA-IR and its cut-off value to identify metabolic syndrome in urban Indian adolescents. J Clin Res Pediatr Endocrinol 2013;5:245-51.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.
Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A. Repeatability characteristics of simple indices of insulin resistance: Implications for research applications. J Clin Endocrinol Metab 2001;86:5457-64.
Laakso M. How good a marker is insulin level for insulin resistance? Am J Epidemiol 1993;137:959-65.
Silfen ME, Manibo AM, McMahon DJ, Levine LS, Murphy AR, Oberfield SE. Comparison of simple measures of insulin sensitivity in young girls with premature adrenarche: The fasting glucose to insulin ratio may be a simple and useful measure. J Clin Endocrinol Metab 2001;86:2863-8.
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;120:1640-5.
World Health Organization. Waist circumference and waist-hip ratio: Report of a WHO expert consultation. Circulation 2000;5:34-9.
World Health Organization. Report of a WHO Expert Committee. Physical Status: The use and Interpretation of Anthropometry. WHO Technical Report Series, 854. Vol. 1. Geneva: World Health Organization; 1995. p. 51-6.
Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, et al.
Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the subcommittee of professional and public education of the American Heart Association council on high blood pressure research. Circulation 2005;111:697-716.
Haffner SM, Miettinen H, Stern MP. The homeostasis model in the San Antonio heart study. Diabetes Care 1997;20:1087-92.
Duncan MH, Singh BM, Wise PH, Carter G, Alaghband-Zadeh J. A simple measure of insulin resistance. Lancet 1995;346:120-1.
Legro RS, Finegood D, Dunaif A. A fasting glucose to insulin ratio is a useful measure of insulin sensitivity in women with polycystic ovary syndrome. J Clin Endocrinol Metab 1998;83:2694-8.
Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo
: Advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 2008;294:E15-26.
Quon MJ. Limitations of the fasting glucose to insulin ratio as an index of insulin sensitivity. J Clin Endocrinol Metab 2001;86:4615-7.
Cho J, Hong H, Park S, Kim S, Kang H. Insulin resistance and its association with metabolic syndrome in Korean children. Biomed Res Int 2017;2017:8728017.
Noel MW, Kao CN, Shinkai K, Pasch LA, Cedars MI. Markers of insulin resistance predict metabolic syndrome in women with polycystic ovary syndrome. J Endocrinol Diabetes Obes 2016;4:1092.
Hanley AJ, Williams K, Gonzalez C, D'Agostino RB Jr., Wagenknecht LE, Stern MP. Prediction of type 2 diabetes using simple measures of insulin resistance: Combined results from the San Antonio heart study, the Mexico city diabetes study, and the insulin resistance atherosclerosis study. Diabetes 2003;52:463-9.
[Table 1], [Table 2], [Table 3], [Table 4]