|ORIGINAL RESEARCH REPORT
|Year : 2021 | Volume
| Issue : 2 | Page : 90-97
Surrogate markers and predictors of insulin resistance in Nigeria patients with Type 2 diabetes mellitus: A cross sectional study
Ifedayo Adeola Odeniyi1, Ukamaka Beatrice Odife2, Olufemi Adetola Fasanmade1, Augustine Efedaye Ohwovoriole1
1 Department of Medicine, College of Medicine, University of Lagos, Surulere; Department of Medicine, Lagos University Teaching Hospital, Idi-Araba, Lagos, Nigeria
2 Department of Medicine, Lagos University Teaching Hospital, Idi-Araba, Lagos, Nigeria
|Date of Submission||02-Jun-2020|
|Date of Acceptance||15-Jul-2020|
|Date of Web Publication||24-Apr-2021|
Dr. Ifedayo Adeola Odeniyi
Department of Medicine, College of Medicine, University of Lagos, PMB 12003, Surulere, Lagos
Source of Support: None, Conflict of Interest: None
Background: This study set out to identify and compare surrogate markers of insulin resistance (IR) in males and females and compare the prevalence of IR using different surrogate markers. Methods: The study was an analytical cross-sectional hospital-based study among Nigerians with Type 2 diabetes mellitus (T2DM). A total of 234 (131 T2DM and 103 healthy) persons were invited to participate in the study. One hundred and ninety-nine persons completed the study. The following anthropometric measurements were taken (height [m], weight [kg], waist circumference [cm], and hip circumference [cm]). IR score (IRS) was derived using these six measurements: fasting basal insulin, fasting C-peptide, glucose-insulin ratio, quantitative insulin sensitivity check index, homeostasis model assessment (HOMA-IR), HOMA-c-peptide. Results: Using an IRS of >11 as criteria for IR, 52 (41.6%) of T2DM, and 21 (28.4%) of the control group were found to have IR. Forty-five percent of the DM females and 36.4% of DM males had IR, respectively, whereas 31.8% of the control females and 23.3% of control males had IR, respectively. Visceral Adiposity Index (VAI) had the best predictive value with the highest area under the receiver operating characteristic curve (0.648). Conclusion: The prevalence of IR is nonsignificantly higher in females than males. VAI is the best surrogate marker to predict the presence of IR among the male study participants, while waist circumference is the best surrogate marker to predict the presence of IR among the female study participants.
Keywords: Diabetes mellitus, insulin resistance, Nigerians, prevalence, surrogate markers
|How to cite this article:|
Odeniyi IA, Odife UB, Fasanmade OA, Ohwovoriole AE. Surrogate markers and predictors of insulin resistance in Nigeria patients with Type 2 diabetes mellitus: A cross sectional study. J Clin Sci 2021;18:90-7
|How to cite this URL:|
Odeniyi IA, Odife UB, Fasanmade OA, Ohwovoriole AE. Surrogate markers and predictors of insulin resistance in Nigeria patients with Type 2 diabetes mellitus: A cross sectional study. J Clin Sci [serial online] 2021 [cited 2021 May 17];18:90-7. Available from: https://www.jcsjournal.org/text.asp?2021/18/2/90/314448
| Introduction|| |
Insulin resistance (IR) is a pathological state characterized by the deficient response of body tissues to insulin action, leading to metabolic and hemodynamic disturbances. There is an inadequate response by insulin-sensitive tissues (liver, skeletal muscle, and adipose tissue) to circulating levels of insulin. IR is associated with several chronic diseases such as hypertension, diabetes mellitus (DM), ischemic heart disease, and others. The identification of patients with IR has preventive and therapeutic significance. There are many methods available for the estimation of IR. These methods range from complex techniques to simple indices. The highly technical hyperinsulinaemic euglycemic clamp (HEC) and the simpler, frequently sampled intravenous glucose tolerance test are the most reliable methods available for estimating IR. The diagnosis of IR using the standard methods are too cumbersome for routine clinical use.
Other simpler hormone-based methods of measuring IR like the homeostasis model assessment (HOMA),, quantitative insulin sensitivity check index (QUICKI), fasting insulin (FI), fasting glucose to insulin ratio (GIR) have been found to correlate with IR assessed by the glucose clamp technique. These simpler methods, however, present important limitations related to cost and technical expertise to be used routinely in clinical settings. There is, therefore, a need to employ surrogate markers to make the diagnosis of IR.
Surrogate markers of IR are measures that can detect IR when the use of a reference diagnostic technique is impracticable or not cost-effective. Several simple surrogate markers of IR have been proposed. These comprise anthropometric, biochemical, and combination of anthropometric and biochemical measures. Examples are serum bilirubin, lipid accumulation product (LAP) index, Visceral Adiposity Index (VAI), waist circumference (WC), waist-to-height ratio, and triglycerides to HDL-cholesterol (HDL-C) ratio.,,, Some of these simple surrogate markers have been reported to correlate strongly with direct measurements of IR. There is also debate on which of the simple measurements best predict IR and the surrogate markers which perform better in males and females.
The prevalence rate of IR of 19.4% to 26% has been reported in apparently healthy adults in Nigeria,,, while prevalence rates of IR of 40% and 95.5% have been reported among T2DM participants in Nigeria., The gold standard method to measure IR, the HEC technique, is unsuitable for clinical studies because it is invasive, expensive, time-consuming, and technically demanding. Other reference methods for measuring IR all have their advantages and limitations, especially as regards the ease of application in routine clinical practice. These limitations have led to the need for simpler and readily available surrogate markers of IR for easy assessment of IR in routine clinical practice.
This study set out to identify and compare surrogate markers of IR in males and females with Type 2 DM (T2DM) and to also compare the prevalence of IR using different surrogate markers.
| Methods|| |
Approval from the Lagos University Teaching Hospital Health Research and Ethics Committee (HREC) was obtained before the commencement of the study.
The study site was a tertiary hospital situated in South Western Nigeria. The study was an analytical cross-sectional hospital-based case–control study. Participants with T2DM were recruited from the Diabetes Clinic of the Lagos University Teaching Hospital. The diabetes clinic register was used to identify T2DM participants who were not on insulin treatment and whose age group fell within the inclusion criteria. Control participants who did not have hypertension or diabetes aged 30–60 years were selected among staff and patients attending the General Outpatient Clinic. The purpose of this study and the procedures involved was explained to them. Those who accepted to participate were invited to the Endocrinology Laboratory of the Department of Medicine of the Hospital. Participants were instructed to report between 8 am and 10 am after an overnight fast of 10–12 h on the appointment date.
T2DM participants were selected using simple random sampling. The eligible participants were recruited from the diabetes clinic register using computer generated random number. Control participants who met the inclusion criteria were selected using stratified random sampling. The control participants were age-matched, and hence, they were stratified into age groups. The participants were recruited into the study after signing the informed consent form.
The sample size was calculated using the Kish formula (N = Z2Pq/d2). where N is the desired sample size, Z is the standard deviation set at 1.96 correspondings to 95% confidence interval, P is the population of T2DM patients estimated to have IR (assuming an unknown prevalence rate of 50% of IR in T2DM subjects in Nigeria). Q is 1.0 – P and d is the degree of accuracy required to set at 0.10 (10%). Allowing for 20% attrition, 120 participants with T2DM were in the study group, whereas 60 participants without DM were in the control group. Hence, the total sample size was 180.
Inclusion criteria for those with DM include those with T2DM aged between 30 and 60 years (these ages are the predominant age group attending the clinic) who gave consent to participate. The following DM subjects were excluded from the study. Those with T1DM (those patients who have low c-peptide and/or have been on insulin since diagnosis), T2DM (patients on oral antidiabetic drugs) currently or previously on insulin therapy, those outside the age range of 30 and 60 years, presence of liver disease, kidney disease, and sickle cell disease.
Apparently, healthy persons aged 30–60 years who gave consent to participate were included as control participants. In contrast, persons with DM, impaired fasting glucose, hypertension, age outside the range of 30 and 60 years, liver disease, kidney disease, and sickle cell disease were excluded.
The following independent variables were obtained or measured as appropriate. The sex and age (in years) were obtained from the participants using the data collection questionnaire. The following anthropometric measurements were taken (height [m], weight [kg], WC [cm] and hip circumference [cm]). On the day of the appointment, completion of the focused history section of the study protocol was done. Weight was measured with an electronic weighing scale (subjects were without shoes and on light clothing) to the nearest 0.1 kg. Height was measured with a portable stadiometer to the nearest 0.1 m. Using a nonstretch tape, the WC was taken midway between the inferior margin of the lower ribs and the iliac crest in a horizontal plane to the nearest 0.1 cm at the end of normal expiration. Using a nonstretch flexible tape, the hip circumference was taken around the maximum circumference of the buttocks in a horizontal plane to the nearest 0.1 cm. Blood pressure measurement was done using a digital sphygmomanometer in both the sitting and standing positions using the appropriate sized cuff after the participants are well relaxed for 5 min.
Derived independent indices are body mass index (BMI) (kg/m2), VAI, and waist-hip ratio (WHR), LAP index. VAI score can be calculated as described by Amato et al. using the following sex-specific equations:
LAP is a simple index for estimating lipid over accumulation among adults described by Kahn which is computed from WC (WC, cm and triglycerides (TGs, mmol/l): (WC-65) × TG (men) and (WC-58) × TG (women).
The fasting serum total cholesterol, HDL-C, LDL cholesterol, triglyceride, and bilirubin, all in mmol/l were measured in the laboratory. Plasma total cholesterol levels were estimated using 0.2 ml of plasma-based on the modified method of Liebermann-Burchard. In a fasting state, between 08.00 am and 10.00 am, 14 ml of venous blood was collected from the antecubital fossa of each subject. The blood collected was shared into lithium heparin (5 ml), fluoride oxalate (3 ml), EDTA (3 ml), and plain sample bottles (3 ml) for the relevant tests. The whole blood in lithium heparin bottles was centrifuged at a speed of 3000 revolutions per minute (rpm) for 10 min. The plasma-derived was stored at −20°C for bilirubin, lipids, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase analysis. The whole blood in fluoride oxalate bottles was centrifuged at a speed of 3000 rpm for 10 min. The plasma-derived was stored at −20°C for glucose analysis. The whole blood in the plain bottle was kept at room temperature to clot before they were centrifuged at a speed of 3000 rpm for 10 min. The serum-derived was stored at −80°C for insulin and c-peptide analysis. About 2 ml of the whole blood was stored at a temperature of between 2°C and 8°C for glycated hemoglobin assay for no longer than a week. Two hundred µl of plasma was used for plasma total and direct bilirubin assays. The vanadate oxidation method was employed for the assay. One milliliter of plasma was used for plasma using the Trinder glucose oxidase method. Serum insulin was estimated using the ELISA technique with 25 µL of serum.
IR score (IRS) was derived using these six measurements; Fasting basal insulin, fasting C-peptide (FCP), glucose-insulin ratio (GIR), QUICKI, HOMA-IR, HOMA-c-peptide to increase reliability because the score is more robust than using only one reference method [Table 1]. A total score of 18 was derived using the first and third interquartile values of the six different reference standards methods as the cutoff. IR is defined as any score greater than the third quartile of the total IRS, i.e., scores >11/18.
Statistical analysis was performed using the Statistical Package for the Social Sciences, SPSS version 20 (IBM, Armonk, New York, United States). Summary data are presented as mean, median, standard deviation, confidence intervals, proportions, charts, tables, and figures. Comparison of the clinical, anthropometric, and biochemical variables between T2DM and control groups was made using nonparametric (Wilcoxon) inferential statistical test. The mean differences in measurements between the T2DM and control groups were analyzed using Mann–Whitney U test. Spearman's rank correlation coefficient was used to determine the association between surrogate markers of IR and components of the metabolic syndrome, glycemia, and beta-cell function due to their skewed distributions. Values of the correlation coefficient (rho), 0.80–1.0 signifies a very strong linear relationship, 0.50–0.79 signifies strong relationship, 0.30–0.49 signifies moderate relationship, 0.10–0.29 signifies weak relationship while 0.01–0.09 signifies a very weak linear relationship. Categorical variables such as age and sex were analyzed with the Chi-squared test.
Operational definition of terms
- DM – Fasting plasma glucose ≥7 mmol/L (126 mg/dl) or 2 h postprandial ≥11.1 mmol/L (200 mg/dl)
- T1DM – For this study, this referred to patients who were diagnosed when they were <30 years and required insulin alone for survival and have been on insulin since diagnosis
- T2DM – For the purpose of this study, subjects on the dietary control of blood glucose or on oral glucose lowering agents only or in combination with insulin for control of hyperglycemia were considered having Type 2 diabetes as opposed to subjects requiring insulin alone for survival and diagnosed when they were <30 years.
- FI – For this study, the presence of IR using FI was defined using values greater than the third interquartile values of the control participants' values (>8.03 µU/ml) as cut off.
- FCP – For this study, the presence of IR using FCP was defined using values greater than the third interquartile values of the control participants' values (>2.09 ng/ml) as cutoff.
- GIR – For the purpose of this study, the presence of IR using GIR was defined using values less than the first interquartile values of the control participants' values (<12.0) as cut off.
- Well-controlled glycemia – FPG of 70–130 mg/dl or 2 hPGL of <180 mg/dl or HbA1C <6.5%
- Poorly controlled glycemia – FPG of >130 mg/dl or 2 hPGL of >180 mg/dl or HbA1C ≥6.5%
- Homeostasis Model Assessment of Insulin Resistance Indices – For this study, the presence of IR using HOMA-IR and HOMA-IR (CP) was defined using values greater than the third interquartile values of the control participants values (>1.82 and >1.6 respectively) as cutoff.
IR – IR was defined by the study derived IRS using six different reference standards methods of measuring IR (HOMA-IR, HOMA-IR (CP), FI, FCP, fasting glucose to FI ratio and QUICKI). A total score of 18 was derived using the first and third interquartile values of the six different reference standards methods as the cutoff. IR was defined as any score greater than the third quartile of the total IRS, i.e., scores >11/18Quantitative Insulin Sensitivity Index (QUICKI): For this study, the presence of IR using QUICKI was defined using values less than the first interquartile values of the control participants' values (<0.491) as cut off.
| Results|| |
A total of 234 (131 T2DM and 103 healthy) persons were invited to participate in the study. Out of the 131 T2DM participants, six were excluded for not meeting the inclusion criteria and/or for having incomplete results. Of the 103 control participants, 29 were excluded for not meeting the inclusion criteria.
The reproducibility of serum insulin assay was determined. The coefficients of variation (CV) of serum insulin were within expected ranges for low (8.1%), medium (4.9%), and high (5.8%) concentrations on intra assay. The CV of serum insulin was within expected ranges on low (8.5%), medium (5.7%), and high (6.9%) concentrations for interassay. The reproducibility of serum c-peptide assay. The CV of serum C-peptide was within expected ranges for low (3.6%), medium (3.9%), and high (2.6%) concentrations on intra assay. The CV of serum c-peptide was within expected ranges for low (10.8%), medium (6.9%), and high (9.8%) concentrations on inter assays.
The clinical and anthropometric characteristics of T2DM and control participants are shown in [Table 2]. There were 55 (44%) males and 70 (56%) females in the T2DM group and 30 (40.5%) males and 44 (59.5%) females in the control group (χ2 = 0.026; P = 0.714).
|Table 2: Clinical and anthropometric characteristics of study participants|
Click here to view
The biochemical characteristics of the study participants are shown in [Table 3]. The mean HbA1c of the participants with T2DM was 6.7% and 5% in control. Poor glycemic control (HbA1c > 6.5%) was present in 57 (45.6%) of the T2DM participants. Dyslipidemia was present in 105 (84%) of the T2DM subjects.
Frequency of insulin resistance
Using an IRS of >11 as criteria for IR, 52 (41.6%) of T2DM and 21 (28.4%) of the control group were found to have IR, as shown in [Table 4]. The proportion of IR was higher in the females compared to the males in both groups. Forty-five-point seven percent of the DM females and 36.4% of DM males had IR, respectively, while 31.8% of the control females and 23.3% of control males had IR, respectively. This is shown in [Figure 1].
|Figure 1: Prevalence of insulin resistance among study participants by sex using the Insulin Resistance Score. 52 (41.6%) of the diabetes mellitus subjects had insulin resistance while 21 (28.4%) of the control group had insulin resistance (χ2 = 13.07; P = 0.001). The prevalence of insulin resistance among male and female diabetes mellitus subjects were 20 (36.4%) and 32 (45.7%) respectively (χ2 = 0.815; P = 0.367) while the prevalence among male and female controls were 7 (23.3%) and 14 (31.8%) respectively (χ2 = 0.632; P = 0.427). The prevalence was nonsignificantly higher in the females compared to the males in both groups|
Click here to view
|Table 4: Prevalence of insulin resistance among study participants by different criteria|
Click here to view
Surrogate markers as predictors of insulin resistance
The predictive performance of the different surrogate markers in diagnosing IR was determined using the IRS as a reference in the receiver operating characteristic (ROC) analysis. Using ROC analysis, an area under the curve (AUC) value of one signifies that the test is perfectly accurate, while an AUC value of 0.5 indicates that the test performs equal to chance. For all male participants, VAI had the best predictive value among all the surrogate markers with the highest area under the ROC analysis curve (0.648). The THR had the highest predictive value (0.558) among the biochemical markers, while WHtR had the highest predictive value (0.602) among the anthropometric markers. For all female participants, WC performed better than all the other surrogate markers with the highest area under the ROC analysis curve (0.658). The THR had the highest predictive value (0.594) among the biochemical markers, while LAP had the highest predictive value (0.585) among the combined markers.
For T2DM male subjects, VAI had the best predictive value among all the other surrogate markers with the highest area under the ROC analysis curve (0.550). This was followed by THR (0.547) [Table 5]. As shown in [Table 5], for T2DM female participants, WC performed better than all the other surrogate markers with the highest area under the ROC analysis curve (0.532). The THR had the highest predictive value (0.507) among the biochemical markers, while LAP had the highest predictive value (0.503) among the combined markers. For control male participants, WHtR had the best predictive value among all the other surrogate markers with the highest area under the ROC analysis curve (0.748). THR had the highest predictive value (0.550) among the biochemical markers, while VAI had the highest predictive value (0.720) among the combined markers [Table 5]. As shown in [Table 5], for control female participants, WC performed better than all the other surrogate markers with the highest area under the ROC analysis curve (0.550), as shown in [Table 5].
|Table 5: Comparison of performance of surrogate markers in diagnosing insulin resistance among diabetes mellitus and control Participants using ROC analysis|
Click here to view
| Discussion|| |
In this study, the enzyme-linked immunosorbent assay (ELISA) method was used for the assay of insulin and c-peptide. The ELISA method has high sensitivity and specificity. The assays for insulin and c-peptide were done in duplicates, and the mean values were taken to improve the precision of the assay. The reproducibility of results for insulin and c-peptide assays met international recommended standards as by low to moderate CVs.
The criterion for the identification of IR used in this study was derived from an IRS using six different hormone-based methods of measuring IR (HOMA-IR, HOMA-IR (CP), FI, FCP, fasting glucose to FI ratio and QUICKI). This is the first study to our knowledge in Nigeria to derive and utilize IRS in the identification of IR. The six different methods correlate well with IR assessed by the glucose clamp technique, which is considered the gold standard method., The combined scoring system method has a better correlation with the clamp-derived IR compared HOMA-IR., The IRS is thus more specific in identifying IR.
Female subjects form the bulk of the participants in this study. A similar pattern of female preponderance in the diabetes clinic was noted by Lawan et al. in Kano (M: F = 1: 1.43). In a combined multicentre study carried out by Uloko et al. involving seven different tertiary health institutions covering the six geopolitical regions of Nigeria (Kano, Enugu, Port Harcourt, Lagos, Ibadan, and Abuja), similar pattern of female preponderance was also noted (60.6% versus 39.4%). However, Desalu et al. in Ado-Ekiti and Yola, Nigeria noted a marked male predominance in their study (61.4% of males versus 38.6% of females).
This difference in health-seeking behavior may explain the findings of a marked gender difference. Women are more likely to have more time to attend appointments during the clinic opening hours and to wait at the clinic for long periods than their male counterparts.
The mean HbA1c found in this study was lower compared to the previous report of a mean of 10.5% reported among T2DM subjects in this center. The reason could be due to improvement in patients care, which is linked to better use of guidelines and treatment protocols. The proportion of subjects with poor glycaemic control seen in this study is not at variance with what obtains in other centers across the country, which ranged from 46% to 64%.,
The prevalence of IR in this study was 41.6% and 28.4% in T2DM participants and controls, respectively. The findings in this study are in tandem with the findings of Reaven et al., who reported that 25% of normal individuals in the general European population are insulin resistant. Gezawa et al. in Northern Eastern, Nigeria, and Raimi et al. in South Western, Nigeria found IR in 25% and 26% of apparently healthy adults in their studies, respectively. However, other workers have reported a lower prevalence of IR, 12.1%, and 19.4% among the normal population.,
The prevalence of IR among T2DM subjects in this study of about 42% using IRS is similar to the 40% rate reported by Bakari et al. from northern Nigeria. However, others have reported much higher rates. Oli et al. reported 95.5% among T2DM subjects, while Bonora et al. found a rate almost twice that of our finding.
The prevalence of IR was nonsignificantly higher in the females compared to the males in both DM and control participants. Raimi et al. also found a higher prevalence of IR in females in their study, but Oli et al. did not find gender differences in the prevalence of IR. The preponderance of females in both Raimi's et al. study, and this study may have affected the results. The differences in the prevalence rates of IR in the different studies may be related to different criteria used in identifying IR. The other studies used HOMA-IR alone, while this study used a score combining six different standard methods of measuring IR, including HOMA-IR.,,, When HOMA-IR alone was used to define IR in this study, a higher prevalence of about 80% similar to that reported by Bonora et al. was noted. Even in the studies where HOMA-IR was used, arbitrary cutoff values were used to define IR. Gezawa et al. used a lower cutoff of 1.24, Bakari et al. and Oli et al. used a higher cut off of 2 to define IR, while this study used a cut off of 1.82. The differences in the age and BMI of the studied population could also have attributed to the differences in the prevalence of IR in the different studies. Bakari et al. carried out their study in a younger population with a lower mean age (49.4 ± 9 years) and BMI (24.9 ± 4.4 kg/m2) while Reaven et al. carried out their own study in an older population with a higher mean age (56 ± 4 years) and BMI (27.9 ± 0.8 kg/m2). However, Oli et al. carried out their study in the population of T2DM subjects with similar mean age used in this study (53.6 years versus 53 years). The differences in ethnicities could also have attributed to the differences in the prevalence of IR in different studies. Reaven et al. carried out their study in Caucasians while other mentioned studies were carried out in different parts of Nigeria.,,
This study noted that some surrogate markers could predict the presence of IR with different predictive values in both male and female subjects. The combined marker, VAI was found to be the best predictor of IR in all-male participants, while WC was found to the best predictor in females. The finding in this study is in agreement with the report by Amato et al. who noted a positive relationship between VAI and IR among the study group. The findings in this study are also in agreement with studies done in Maiduguri, Nigeria, and Pakistan, which noted a significant positive relationship between WC and IR among the study group., The disparity in adipose tissue and lipid distribution between men and women could be the reason for the varying differences in the predictive values of the different surrogate markers in both sexes. Men have been reported to have higher triglycerides and total cholesterol levels and lower HDL-levels compared to women.,
This study is not without limitations. IR in this study was derived from a combination of six different hormonal-based methods rather than the HEC method, which is considered the gold standard. The clamp technique is an expensive and cumbersome technique. The six different methods used however, had been shown to have a good correlation with the HEC method individually. Surrogate markers such as WC and serum triglyceride to HDL-C ratio are known to vary among different ethnic groups. The results of the study relating to these values may, therefore, not be applicable to other populations.
| Conclusion|| |
The prevalence of IR among T2DM participants is high. The prevalence of IR is nonsignificantly higher in females than males. The surrogate marker, which predicts IR varies according to sex. VAI was shown to be the best surrogate marker to predict the presence of IR among the male study participants, while WC was noted to be the best surrogate marker to predict the presence of IR among the female study participants.
IR should be assessed and treated in routine clinical practise as its treatment impacts on clinical outcome. Different surrogate markers can be reliably used to assess the presence of IR in different sexes. VAI can be used to assess for IR in males, while WC can be used to assess for IR in females.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Schenk S, Saberi M, Olefsky JM. Insulin sensitivity: Modulation by nutrients and inflammation. J Clin Invest 2008;118:2992-3002.
DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am J Physiol 1979;237:E214-23.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Teacher DF, Turner RC. Homeostasis model assessment: Insulin resistance and beta cell function from fasting plasma glucose and insulin concentration in man. Diabetologia 1985;28:412-9.
Polonsky KS, Rubenstein AH. C-peptide as a measure of the secretion and hepatic extraction of insulin. Pitfalls and limitations. Diabetes 1984;33:486-94.
Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al
. Quantitative insulin sensitivity check index: A simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85:2402-10.
Singh B, Saxena A. Surrogate markers of insulin resistance: A review. World J Diabetes 2010;1:36-47.
Lin LY, Kuo HK, Hwang JJ, Lai LP, Chiang FT, Tseng CD, et al
. Serum bilirubin is inversely associated with insulin resistance and metabolic syndrome among children and adolescents. Atherosclerosis 2009;203:563-8.
Lee S, Bacha F, Gungor N, Arslanian SA. Waist circumference is an independent predictor of insulin resistance in black and white youths. J Pediatr 2006;148:188-94.
Ejike CE. Lipid accumulation product and waist-to-height ratio are predictors of the metabolic syndrome in Asia. J. Rural Trop Public Health 2011;10:101-5.
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;33:920-2.
Lorenzo C, Haffner SM, Stancakova A, Laakso M. Relation of direct and surrogate measures of insulin resistance to cardiovascular risk factors in non-diabetic Finnish offspring of Type 2 diabetic individuals. J Clin Endocrinol Metab 2010;95:5082-90.
Bakari AG, Onyemelukwe GC. Insulin resistance in Type 2 diabetic Nigerians. Int J Diabetes Metabolism 2005;13:24-7.
Gezawa ID, Puepet FH, Mubi MB, Haliru I, Bakki B, Yusuph H et al
. Anthropometric correlates of insulin resistance: A study of healthy Nigerian adults in Maiduguri. Kanem J Med Sci 2010;4:14-8.
Raimi TH, Odusan OO, Fasanmade OA, Odewabi AO, Ajala MO, Oritogun K et al
. Insulin resistance in semi-Urban South Western Nigeria and It's relationship with indices of obesity. Nigerian Endocrine Practice 2012;6:1-3.
Oli, JM, Adeyemo AA, Okafor GO, Ofoegbu EN, Onyenekwe B, Chukwuka CJ, et al
. Basal insulin resistance and secretion in Nigerians with Type 2 diabetes mellitus. Metab Synd Related Disord 2009;7:595-600.
Kish L. A procedure for objective respondent selection within the Household. J Am Statist Assoc 1949;44:380-7.
Kahn HS. The 'lipid accumulation product' performs better than the body mass index for recognizing cardiovascular risk: A population-based comparison. BMC Cardiovasc Disord 2005;5:26-8.
Dancey C, Reidy J. Statistics without Maths for Psychology; using SPSS for Windows. London: Prentice Hall; 2004.
World Health Organization. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of WHO part 1 Geneva: World Health Organization; 1999.
American Diabetes Association. Screening for Type 2 Diabetes. Diabetes Care 2004;28:S11-4.
Stenman UH. Immunoassay standardization: Is it possible, who is responsible, who is capable? Clin Chem 2001;47:815-20.
Chesher D. Evaluating assay precision. Clin Biochem Rev 2008;29 Suppl 1:S23-6.
Hermans MP, Levy JC, Morris RJ, Turner RC. Comparision of tests of beta-cell function across a range of glucose tolerance from normal to diabetes. Diabetes 1999;48:1779-86.
Esposito K, Giugliano D. The metabolic syndrome and inflammation: Association or causation? Nutr Metab Cardiovasc Dis 2004;14:228-32.
Lawan A, Mohammed TB. Pattern of diabetic retinopathy in Kano, Nigeria Ann Afr Med 2012;11:75-79.
Uloko AE, Ofoegbu EN, Chinenye S, Fasanmade OA, Fasanmade AA, Ogbera AO, et al
. Profile of Nigerians with diabetes mellitus – Diabcare Nigeria study group (2008): Results of a multicenter study. Indian J Endocrinol Metab 2012;16:558-64.
Desalu OO, Salawu FK, Jimoh AK, Adekoya AO, Busari OA, Olokoba AB. Diabetic foot care: Self reported knowledge and practice among patients attending three tertiary hospital in Nigeria. Ghana Med J 2011;45:60-5.
Babwah F, Baksh S, Blake L, Cupid-Thuesday J, Hosein I, Sookhai A, et al
. The role of gender in compliance and attendance at an outpatient clinic for Type 2 diabetes mellitus in Trinidad. Rev Panam Salud Publica 2006;19:79-84.
Coker AO, Fasanmade OA. Quality of care for patients with Type 2 diabetes in Lagos University Teaching Hospital. Nig Quart J Hosp Med 2005;16:6-9.
Unadike BC, Eregie A, Ohwovoriole AE. Glycaemic control amongst persons with diabetes mellitus in Benin-City. Niger Med J 2010;51:164-6. [Full text]
Reaven GM, Hollenbeck CB, Chen YD. Relationship between glucose intolerance, insulin secretion, and insulin action in non-obese individuals with varying degrees of glucose intolerance. Diabetologia l989;32:52-5.
Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Targher G, et al
. Prevalence of insulin resistance in metabolic disorders: The bruneck study. Diabetes 1998;47:1643-9.
Nadeem A, Naveed AK, Hussain MM, Raza SI. Cut-off values of anthropometric indices to determine insulin resistance in Pakistani adults. J Pak Med Assoc 2013;63:1220-5.
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al
. Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735-52.
Seidell JC, Cigolini M, Charzewska J, Ellsinger BM, Björntorp P, Hautvast JG, et al
. Fat distribution and gender differences in serum lipids in men and women from four European communities. Atherosclerosis 1991;87:203-10.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]