|Year : 2021 | Volume
| Issue : 2 | Page : 51-58
Metabolic syndrome among Nigerians with type 2 diabetes mellitus: A comparative study of the diagnostic criteria
Taoreed A Azeez, Jokotade Adeleye, Enigbokan A Omololu, Bolaji Adejimi, John S Oladapo
Endocrinology Unit, Department of Medicine, University College Hospital, Ibadan, Nigeria
|Date of Submission||09-Aug-2021|
|Date of Acceptance||15-Jan-2022|
|Date of Web Publication||28-Feb-2022|
Taoreed A Azeez
Endocrinology Unit, Department of Medicine, University College Hospital, Ibadan.
Source of Support: None, Conflict of Interest: None
Background: Metabolic syndrome is associated with increased cardiovascular death. The objectives of this study were to find the frequency of metabolic syndrome among Nigerians with type 2 diabetes and to compare the modified National Cholesterol Education Program on the detection, evaluation, and treatment of high blood cholesterol in adults—Adults Treatment Panel III (NCEP ATP III) criteria and the International Diabetes Federation (IDF) criteria. Materials and Methods: The study involved 134 participants. Sixty-seven were cases with type 2 diabetes, whereas the rest were the controls without type 2 diabetes. Ethical approval was granted by the Institutional Ethics Review Committee. Anthropometric, clinical, and laboratory parameters were obtained using standard protocols. Data were analyzed with SPSS version 22. Means were compared with Student’s t-test, whereas proportions were compared with Pearson’s χ2 test. Point biserial correlation was used to determine the association between the dichotomous variables and interval variables. Agreement between the criteria was tested with Cohen’s kappa test. Results: Type 2 diabetes was associated with a higher prevalence of hypertension and truncal obesity. The frequency of metabolic syndrome was lower with the IDF criteria compared with the modified NCEP criteria (65.7% vs. 71.6%). Although there was a strong agreement between the IDF and the modified NCEP criteria (κ=0.862; P<0.0001), the IDF criteria missed 8.3% of diabetic individuals diagnosed with metabolic syndrome by the modified NCEP criteria. Cardiovascular risk is better predicted when the modified NCEP criteria were used to diagnose metabolic syndrome. Conclusion: Metabolic syndrome is very common among Nigerians with type 2 diabetes, and it is better diagnosed with the modified NCEP ATP III criteria.
Keywords: Cardiovascular risk, comparative study, diabetes mellitus type 2, diagnosis criteria, metabolic syndrome, Nigeria
|How to cite this article:|
Azeez TA, Adeleye J, Omololu EA, Adejimi B, Oladapo JS. Metabolic syndrome among Nigerians with type 2 diabetes mellitus: A comparative study of the diagnostic criteria. J Cardio Diabetes Metab Disord 2021;1:51-8
|How to cite this URL:|
Azeez TA, Adeleye J, Omololu EA, Adejimi B, Oladapo JS. Metabolic syndrome among Nigerians with type 2 diabetes mellitus: A comparative study of the diagnostic criteria. J Cardio Diabetes Metab Disord [serial online] 2021 [cited 2022 May 17];1:51-8. Available from: http://www.jcdm.org/text.asp?2021/1/2/51/338610
| Introduction|| |
A syndrome is a constellation of symptoms and signs attributed to a disease. It is a term that suggests that the occurrence of certain clinical manifestations is not just due to chance. In 1988, an American Endocrinologist, Gerald Reaven, described a cluster of clinical features, which were considered to be linked together and insulin resistance was posited to be the central theme of the cluster. He called the cluster “syndrome X.” Since then, there has been disagreement among researchers and experts on the definition, nomenclature, and criteria for the syndrome. Apart from insulin resistance, the other components of the syndrome include elevated blood pressure, dyslipidemia, glucose intolerance, and central obesity.
In 1998, the World Health Organization (WHO) Consultation Group came up with a definition based on the presence of obesity and certain metabolic abnormalities. The syndrome became more commonly referred to as “metabolic syndrome.” The WHO criteria for the diagnosis of metabolic syndrome are shown in [Table 1].
In 1999, shortly after the publication of the WHO criteria for metabolic syndrome, the European Group for the Study of Insulin Resistance (EGIR) reported their own criteria for metabolic syndrome. The term favored by the group was “insulin resistance syndrome.” The EGIR required an elevated plasma insulin > 75th percentile before the diagnosis of metabolic syndrome could be made. In addition, the focus was on abdominal obesity, whereas the presence of diabetes and microalbuminuria was dropped.
In 2001, in the executive summary of the third report of the National Cholesterol Education Program on the detection, evaluation, and treatment of high blood cholesterol in adults—Adults Treatment Panel III (NCEP ATP III), the criteria for the diagnosis of metabolic syndrome were described. The panel did not believe that insulin resistance was mandatory for the development of metabolic syndrome; rather, abdominal obesity was seen as the main culprit, hence they favored the term “metabolic syndrome.” Also, waist circumference was adopted to quantify abdominal obesity instead of the waist–hip ratio. Moreover, plasma triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were considered as independent criteria. The NCEP ATP III criteria are depicted in [Table 2].
In 2005, due to the profuse controversies generated from the definition of metabolic syndrome by various bodies, the International Diabetes Federation (IDF) came up with a global consensus definition for metabolic syndrome. It also established central obesity as the focal point in metabolic syndrome. Waist circumference was also used in quantifying central obesity, but the cut-off points became ethnic- and gender-specific. The Europoids cut-off is adopted for Africans, and it is shown in [Table 3].
Following the publication of the IDF criteria, the modified NCEP ATP III criteria came on board. The modified version agreed with the IDF criteria, and the fixed waist circumference shown in [Table 2] was dropped for the ethnic-based waist circumference. However, despite the modification, abdominal obesity continues to be seen as a component rather than a prerequisite for the diagnosis of metabolic syndrome, and this distinguishes the modified NCEP ATP III criteria from the IDF criteria.
Insulin resistance is defined as the attenuated biological response to the usual amount of insulin. Despite the various controversies, many authors still aver that insulin resistance is the underlying mechanism of insulin resistance which results from an interplay of genetic and environmental factors. Previous studies have also shown an association between insulin resistance and the various components of metabolic syndrome. Insulin resistance cannot be directly measured clinically but there are surrogate markers for it. Among the surrogate markers of insulin resistance, the homeostatic model of assessment of insulin resistance (HOMA-IR) is the most commonly quoted.
Metabolic syndrome is a cluster of cardiovascular risk factors and is therefore associated with increased cardiovascular death. Cardiovascular risk is said to be 50–60% higher in people with metabolic syndrome when compared with those without metabolic syndrome. There are different cardiovascular risk calculators that have been documented in the literature. In the primary prevention of atherosclerotic cardiovascular disease, risk estimation is of crucial importance. The American Heart Association/American College of Cardiology (AHA/ACC) Atherosclerotic Cardiovascular Disease (ASCVD) risk score predicts 10-year cardiovascular risk of heart disease or stroke and is validated in many ethnic groups.
The study was aimed at the following:
- Comparing the profile of cardiovascular risk factors among Nigerians with and without type 2 diabetes mellitus,
- Comparing the prevalence of metabolic syndrome among Nigerians with type 2 diabetes using the modified NCEP ATP III and IDF criteria,
- Determining the association between metabolic syndrome diagnosed with the modified NCEP ATP III and IDF criteria among Nigerians with type 2 diabetes and HOMA-IR, waist circumference, and ASCVD risk score, and
- Estimating the extent of agreement between the modified NCEP ATP III and IDF criteria for the diagnosis of metabolic syndrome among Nigerians with type 2 diabetes.
| Materials and Methods|| |
The study design was an analytical cross-sectional study with a control group. One hundred and thirty-four participants were involved in the study. The cases were 67 individuals previously diagnosed with type 2 diabetes mellitus, and the controls were also 67 individuals without type 2 diabetes mellitus. The cases were the individuals selected by systematic random sampling from the type 2 diabetes mellitus patients attending the diabetes clinic of a public tertiary hospital in Southern Nigeria. The controls were apparently healthy individuals without type 2 diabetes mellitus selected randomly from the community. Potential controls were asked if they had ever been diagnosed with type 2 diabetes and were also screened for type 2 diabetes with FPG and glycated hemoglobin. Only those confirmed as not having type 2 diabetes were recruited into the study.
Inclusion criterion for the cases was prior diagnosis with type 2 diabetes (more than 6 months). Exclusion criteria were type 1 diabetes mellitus, on-going active weight loss program, metabolic decompensation, hospital admission in the preceding 3 months to recruitment, pregnancy, and prior diagnosis of cardiovascular disease (stroke, myocardial infarction, and peripheral arterial disease). Inclusion criterion for the controls was the absence of type 2 diabetes mellitus. The exclusion criteria were similar to those of the cases earlier stated.
Waist circumference was measured with an inelastic tape measure as the horizontal abdominal girth at the midpoint between the lowest rib and the iliac rest after tidal expiration. The WHO standard protocol on waist circumference measurement was carefully followed. Using the American Heart Association blood pressure measurement guidelines, each participant had his/her systolic blood pressure (SBP) as well as the diastolic blood pressure (DBP) measured with a sphygmomanometer (Accoson brand, made in England).
A fasting blood sample was obtained from each participant. FPG was determined using the enzymatic method run on an Autochemistry analyzer (Accurex Biomedical, Mumbai, India). Fasting plasma triglyceride and HDL-C were also measured using the appropriate enzymatic method. Fasting plasma insulin was assayed using the enzyme-linked immunosorbent assay (ELISA) technique with Cell Biolab Human Insulin ELISA kit (CA, USA). The recommended high-performance liquid chromatography method was adopted in measuring the glycated hemoglobin (HbA1c). Automated glycohemoglobin analyzer (Bio-Rad 220-0212, Hercules, CA, USA) was used for this purpose.
HOMA-IR was calculated using the standard formula given subsequently. Insulin resistance was defined as HOMA-IR > 2 as defined by a previous study involving Nigerians.
The 10-year cardiovascular risk score was determined using the ASCVD risk estimator on the American College of Cardiology website. The ASCVD score below 5.0 was taken as “low risk,” whereas the ASCVD score ≥ 5.0 was taken as “intermediate/high risk.” Metabolic syndrome was defined using the modified NCEP ATP III and IDF criteria, respectively.,
Statistical analysis was performed with the Statistical Package for Social Sciences (SPSS) version 22. Continuous variables were summarized as mean ± standard deviation, whereas nominal variables were summarized as frequencies and percentages. The association between the presence or absence of metabolic syndrome (using the modified NCEP ATP III and IDF criteria) and interval variables (ASCVD risk score, waist circumference, and HOMA-IR) was determined using point biserial correlation. The means of continuous variables were compared with the independent sample Student’s t-test. The frequencies of nominal variables were compared with Pearson’s χ2 test. Agreement between two dichotomous nominal variables was determined using Cohen’s kappa statistic. Cohen’s kappa coefficient (κ) was interpreted as shown in [Table 4]. A P <0.05 was considered as being statistically significant.
The Institutional Ethics Review Committee (with the reference number NHREC/05/01/2008a) granted ethical approval for the study. The reference number of the approval is UI/EC/17/0284. After thorough explanation, the potential participants gave written consent for participation in the study and publication of the findings before they were recruited into the study.
| Results|| |
The characteristics of the participants are shown in [Table 5]. The mean age of the cases was 53.21 ± 9.71, and it was not significantly different from that of the controls (P=0.792). The gender distribution of both the cases and the controls was the same with females accounting for 50.7%. Male cases with type 2 diabetes had a higher waist circumference compared with male controls without type 2 diabetes (P<0.0001) but this was not reproducible in females (P=0.158). The subjects had a significantly higher SBP compared with that of the controls (P=0.002).
Interestingly, this study did not show any significant difference in TG and HDL-C between cases with type 2 diabetes and controls without the disease. Insulin resistance and 10-year cardiovascular risk were significantly higher among the cases compared with those of the controls (P<0.0001).
The frequency of metabolic syndrome among individuals living with diabetes was slightly higher using the modified NCEP ATP III criteria (71.6%) than using the IDF criteria (65.7%). As expected, irrespective of the criteria used, metabolic syndrome was significantly common in the participants with type 2 diabetes mellitus when compared with controls without type 2 diabetes mellitus (P<0.0001).
[Table 6] indicates the point biserial correlation between the presence of metabolic syndrome among the individuals with type 2 diabetes mellitus (using either the modified NCEP ATP III criteria or the IDF criteria) and HOMA-IR as well as 10-year cardiovascular risk (using the ASCVD risk score). Among Nigerians living with type 2 diabetes mellitus, those diagnosed with metabolic syndrome using the modified NCEP ATP III criteria were likely to have a higher ASCVD risk score (P=0.04). However, this could not be said for those diagnosed with the IDF criteria. However, there was a significant association between metabolic syndrome and waist circumference, irrespective of the criteria used (P<0.0001).
|Table 6: Point biserial correlation between cardiovascular risk and metabolic syndrome|
Click here to view
[Table 7] shows the level of agreement between the diagnostic criteria for metabolic syndrome. There is almost perfect agreement between using the IDF criteria and the modified NCEP ATP III criteria in diagnosing metabolic syndrome (κ=0.862; P<0.0001). Also, there was a fair agreement between metabolic syndrome diagnosed with the modified NCEP ATP III criteria and the presence of intermediate/high cardiovascular risk (κ=0.213; P=0.029).
|Table 7: Level of agreement among the diagnostic criteria and cardiovascular risk|
Click here to view
| Discussion|| |
This study showed a higher frequency of cardiovascular risk factors such as truncal obesity and elevated systolic blood pressure among individuals living with type 2 diabetes compared with controls who did not have type 2 diabetes. In another study involving Nigerians with type 2 diabetes mellitus, the researchers posited that cardiovascular risk factors were more common among the individuals with type 2 diabetes when compared with those without type 2 diabetes. Several authors in the past have noted this cluster of risk factors and that was the genesis of the studies on metabolic syndrome.
Previous studies have also documented a higher frequency of elevated SBP among individuals with diabetes when compared with the general population, just as it was found in the present study., Type 2 diabetes causes arterial stiffening, especially in the presence of suboptimal metabolic control, and this is believed to be one of the links between type 2 diabetes and elevated SBP. Other mechanisms that have been documented as the plausible explanation for a higher frequency of elevated SBP among individuals with diabetes are endothelial dysfunction, sodium retention, sympathetic overactivity, renin–angiotensin–aldosterone activation, and nephropathy. However, as hypothesized by some authors, the relationship between type 2 diabetes and hypertension is more reciprocal than causal.
As noted in the present study, truncal obesity, represented by weight circumference, was higher among individuals with type 2 diabetes, and similar observation has been reported from previous studies. Abdominal distribution of fat is a better determinant of the risk of developing type 2 diabetes than the total body fat mass. Truncal obesity is characterized with increased inflammatory cytokines and non-esterified fatty acids as well as hormonal dysregulation, which are thought to be the underlying processes leading to insulin resistance and eventually type 2 diabetes mellitus.
Quite striking was the observation, in this study, that the HDL-C and fasting triglycerides of those with or without type 2 diabetes mellitus were not significantly different. In the general population, an epidemiological study quoted by Laakso has demonstrated that the lipid profiles of individuals with type 2 diabetes were not remarkably different from those of the general population. However, our study was a hospital-based study and previous hospital-based studies have documented a significantly lower HDL-C and higher triglycerides (a cluster sometimes called diabetic dyslipidemia) among individuals with type 2 diabetes., Nevertheless, Zheng et al. posited that the association between hypertriglyceridemia and type 2 diabetes is better appreciated in the presence of poor glycemic control. As the participants in this present study had averagely good glycemic control, evidenced by the HbA1c (6.99 ± 0.73%), it would not be out of place to get a triglyceride and HDL-C profile that is not remarkably different from those of the controls without type 2 diabetes.
As expected, HOMA-IR, a marker of insulin resistance, was higher among the cases with type 2 diabetes when compared with that of the controls. Insulin resistance is a core pathophysiological pathway in the development of type 2 diabetes mellitus. Also, this study showed a significantly higher 10-year cardiovascular risk score among Nigerians with type 2 diabetes when compared with those without type 2 diabetes. This is not surprising because type 2 diabetes is associated with a concurrent cluster of other cardiovascular risk factors such as obesity and hypertension, as demonstrated in this study and other previous studies., Again, insulin resistance has been suggested as a potential culprit behind this observation.
Going by the results of this study, the prevalence of metabolic syndrome in people living with type 2 diabetes depends on the diagnostic criteria used. The frequency of metabolic syndrome, using the modified NCEP ATP III criteria, was 71.6%. However, the frequency was slightly lower (65.7%) when IDF criteria were applied. A previous study done among Nigerians with type 2 diabetes mellitus patients was 66.7%. However, while the previous study used the conventional NCEP ATP III criteria, this present study used the modified NCEP ATP III criteria and this may account for the lower frequency in the previous study (66.7% vs. 71.6%) as it has been suggested that the modified criteria have a better performance. In support of this assertion, a study done in Ethiopia that used the modified NCEP ATP III criteria reported a prevalence of 70.1%, which is quite similar to 71.6% found in the present study. The frequency of metabolic syndrome among type 2 diabetes mellitus patients diagnosed with the IDF criteria in this present study (65.7%) is quite similar to what was found in a previous study in Nigeria that also used the IDF criteria (63.6%).
The prevalence of metabolic syndrome is significantly higher among patients with type 2 diabetes when compared with the general population whether the modified NCEP ATP III criteria (71.6% vs. 17.9%; P<0.0001) or the IDF criteria (65.7% vs. 10.4%; P<0.0001) were used in making the diagnosis. A study done in Nigeria, using the IDF criteria for metabolic syndrome in the general population without type 2 diabetes mellitus, found a prevalence of 8.8% which is comparable to 10.4% documented in the present study. Another study done in Nigeria that used the NCEP ATP III criteria to diagnose metabolic syndrome in apparently healthy individuals not previously diagnosed with type 2 diabetes reported a prevalence rate of 12.1%, which is lower than 17.9% found in this present study. This may be because while the previous study used the old NCEP ATP II criteria, the present study used the modified NCEP ATP III criteria which has been shown to have a better performance.
Using point biserial correlation, there was no statistically significant association between HOMA-IR (a marker of insulin resistance) and the presence of metabolic syndrome in type 2 diabetes mellitus whether the IDF criteria (P=0.810) or the modified NCEP ATP III criteria (P=0.909) were used. There is now a paradigm shift in what is believed to be the core component of metabolic syndrome. It is now thought that waist circumference, or truncal obesity, is more important than insulin resistance in the diagnosis of metabolic syndrome, and this informed the IDF criteria which insist on the presence of increased waist circumference as a prerequisite for the diagnosis of metabolic syndrome. Interestingly, this present study found a significant association between metabolic syndrome, whether the modified NCEP ATP III or IDF criteria were used, and waist circumference (P<0.0001). Previous studies have also demonstrated an association between metabolic syndrome and waist circumference.,
This study showed a significant association between metabolic syndrome and 10-year cardiovascular risk score only when the modified NCEP ATP III criteria were used in diagnosing metabolic syndrome (P=0.04), although the strength of the association was weak. This association was not demonstrable using the IDF criteria. This is in keeping with the hypothesis by previous researchers that the modified NCEP ATP III criteria have a better performance than the IDF criteria.
This study was able to demonstrate an almost perfect agreement between using NCEP ATP II and IDF criteria in the diagnosis of metabolic syndrome among Nigerians with type 2 diabetes mellitus (κ=0.862; P<0.0001). It is still worthy of note that the IDF criteria missed 8.3% of the participants with type 2 diabetes, which met the NCEP ATP criteria. However, neither NCEP ATP criteria (κ=0.143; P=0.116) nor the IDF criteria (κ=0.144; P=0.273) had a significant agreement with insulin resistance (using HOMA-IR). Again, this is in agreement with the hypothesis that insulin resistance is not a prerequisite in the diagnosis of metabolic syndrome. This study also found a fair but significant agreement between metabolic syndrome diagnosed with the modified NCEP ATP criteria (κ=0.213; P=0.029) and intermediate-to-high cardiovascular risk (using ASCVD risk categories), but this was not found with metabolic syndrome diagnosed with the IDF criteria. This suggests that the modified NCEP ATP III criteria predict cardiovascular risk much better than the IDF criteria.
| Limitations|| |
A larger sample size would be an advantage in this kind of study. The recruited cases with type 2 diabetes mellitus were already being managed in a multidisciplinary setting, which may make some of the findings different from what is obtainable in the community.
| Conclusion|| |
Cardiovascular risk factors are common in people living with type 2 diabetes. The prevalence of metabolic syndrome among Nigerians with type 2 diabetes is slightly lower if IDF criteria are used than if the modified NCEP ATP III criteria are used. Using the modified NCEP ATP criteria to diagnose metabolic syndrome predicts cardiovascular risk better than using the IDF criteria. IDF criteria compare well with the modified NCEP ATP III criteria, but it still misses out some people. The modified NCEP ATP III criteria appear to be a better diagnostic tool for metabolic syndrome among Nigerians with type 2 diabetes mellitus.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
Ethical approval and consent to participate
Ethical approval was granted by the Ethics 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. Also, the recruited participants gave written informed consent to partake in the study.
Consent for publication
Consent was taken from all participants.
Availability of data and material
Available, if required.
| References|| |
Nilsson PM, Tuomilehto J, Rydén L The metabolic syndrome—What is it and how should it be managed? Eur J Prev Cardiol 2019;26:33-46.
Reaven GM Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37:1595-607.
Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol 2018;17:122.
Parikh RM, Mohan V Changing definitions of metabolic syndrome. Indian J Endocrinol Metab 2012;16:7-12.
Balkau B, Charles MA Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 1999;16:442-3.
Kassi E, Pervanidou P, Kaltsas G, Chrousos G Metabolic syndrome: Definitions and controversies. BMC Med 2011;9:48.
Expert Panel on Detection E and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486-97.
Alexander CM, Landsman PB, Teutsch SM, Haffner SM; Third National Health and Nutrition Examination Survey (NHANES III); National Cholesterol Education Program (NCEP). NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes 2003;52:1210-4.
Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C; American Heart Association; National Heart, Lung, and Blood Institute. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on scientific issues related to definition. Circulation 2004;109:433-8.
Ford ES Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S. Diabetes Care 2005;28:2745-9.
Moy FM, Bulgiba A The modified NCEP ATP III criteria may be better than the IDF criteria in diagnosing metabolic syndrome among Malays in Kuala Lumpur. BMC Public Health 2010;10:678.
Azeez TA, Osundina MA Insulin resistance and non-alcoholic fatty liver disease: A review of the pathophysiology and the potential targets for drug actions | Azeez | Journal of the Medical Sciences (Berkala ilmu Kedokteran) [Internet] [cited May 6, 2021]. Available from: https://jurnal.ugm.ac.id/bik/article/view/59594. [Last accessed on 2021 May 5].
Roberts CK, Hevener AL, Barnard RJ Metabolic syndrome and insulin resistance: Underlying causes and modification by exercise training. Compr Physiol 2013;3:1-58.
Lteif AA, Han K, Mather KJ Obesity, insulin resistance, and the metabolic syndrome: Determinants of endothelial dysfunction in whites and blacks. Circulation 2005;112:32-8.
Singh B, Saxena A Surrogate markers of insulin resistance: A review. World J Diabetes 2010;1:36-47.
Udenze. Markers of insulin resistance and their performance in adult Nigerians with metabolic syndrome [Internet] [cited May 14, 2021]. Available from: https://www.ijncd.org/article.asp?issn=2468–8827;year=2019;volume=4;issue=2;spage=43;epage=48;aulast=Udenze. [Last accessed on 2021 May 5].
Tune JD, Goodwill AG, Sassoon DJ, Mather KJ Cardiovascular consequences of metabolic syndrome. Transl Res 2017;183:57-70.
Qiao Q, Gao W, Zhang L, Nyamdorj R, Tuomilehto J Metabolic syndrome and cardiovascular disease. Ann Clin Biochem 2007;44:232-63.
Lloyd-Jones DM, Braun LT, Ndumele CE, Smith SC Jr, Sperling LS, Virani SS, et al
. Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: A special report from the American Heart Association and American College of Cardiology. Circulation 2019;139:e1162-77.
Menon VP, Edathadathil F, Sathyapalan D, Moni M, Don A, Balachandran S, et al
. Assessment of 2013 AHA/ACC ASCVD risk scores with behavioral characteristics of an urban cohort in India: Preliminary analysis of Noncommunicable Disease Initiatives and Research at Amrita (NIRAM) study. Medicine (Baltimore) 2016;95:e5542.
Anurhada R, Hemacnandran S, Ruma D The waist circumference measurement: A simple method for assessing the abdominal obesity. J Clin Diagn Res 2012;6:1510-3.
Antoniolli LP, Nedel BL, Pazinato TC, de Andrade Mesquita L, Gerchman F Accuracy of insulin resistance indices for metabolic syndrome: A cross-sectional study in adults. Diabetol Metab Syndr 2018;10:65.
Akande TO, Adeleye JO, Kadiri S Insulin resistance in Nigerians with essential hypertension. Afr Health Sci 2013;13:655-60.
ASCVD Risk Estimator [Internet] [cited June 3, 2021]. Available from: https://tools.acc.org/ldl/ascvd_risk_estimator/index.html#!/calulate/estimator/. [Last accessed on 2021 May 7].
New Aspects of the Risk Assessment Guidelines: Practical Highlights, Scientific Evidence and Future Goals [Internet]. American College of Cardiology [cited June 3, 2021]. Available from: http%3a%2f%2fwww.acc.org%2flatest-in-cardiology%2farticles%2f2018%2f11%2f14%2f07%2f10%2fnew-aspects-of-the-risk-assessment-guidelines. [Last accessed on 2021 May 7].
McHugh ML Interrater reliability: The kappa statistic. Biochem Med (Zagreb) 2012;22:276-82.
Oda E Metabolic syndrome: Its history, mechanisms, and limitations. Acta Diabetol 2012;49:89-95.
Akalu Y, Belsti Y Hypertension and its associated factors among type 2 diabetes mellitus patients at Debre Tabor General Hospital, Northwest Ethiopia. DMSO 2020;13:1621-31.
Tsimihodimos V, Gonzalez-Villalpando C, Meigs JB, Ferrannini E Hypertension and diabetes mellitus: Coprediction and time trajectories. Hypertension 2018;71:422-8.
Ferrannini E, Cushman WC Diabetes and hypertension: The bad companions. Lancet 2012;380:601-10.
Cheung BM, Li C Diabetes and hypertension: Is there a common metabolic pathway? Curr Atheroscler Rep 2012;14:160-6.
Colussi G, Da Porto A, Cavarape A Hypertension and type 2 diabetes: Lights and shadows about causality. J Hum Hypertens 2020;34:91-3.
Båvenholm PN, Kuhl J, Pigon J, Saha AK, Ruderman NB, Efendic S Insulin resistance in type 2 diabetes: Association with truncal obesity, impaired fitness, and atypical malonyl coenzyme A regulation. J Clin Endocrinol Metab 2003;88:82-7.
Freemantle N, Holmes J, Hockey A, Kumar S How strong is the association between abdominal obesity and the incidence of type 2 diabetes? Int J Clin Pract 2008;62:1391-6.
Al-Goblan AS, Al-Alfi MA, Khan MZ Mechanism linking diabetes mellitus and obesity. Diabetes Metab Syndr Obes 2014;7:587-91.
Laakso M Cardiovascular disease in type 2 diabetes from population to man to mechanisms: The Kelly West Award Lecture 2008. Diabetes Care 2010;33:442-9.
Haase CL, Tybjærg-Hansen A, Nordestgaard BG, Frikke-Schmidt R HDL cholesterol and risk of type 2 diabetes: A Mendelian randomization study. Diabetes 2015;64:3328-33.
Zheng D, Dou J, Liu G, Pan Y, Yan Y, Liu F, et al
. Association between triglyceride level and glycemic control among insulin-treated patients with type 2 diabetes. J Clin Endocrinol Metab 2019;104:1211-20.
Azeez T, Osundina M Insulin resistance and non-alcoholic fatty liver disease: A review of the pathophysiology and the potential targets for drug actions. J Diabetes Obes 2020;52:365-76.
Adeva-Andany MM, Martínez-Rodríguez J, González-Lucán M, Fernández-Fernández C, Castro-Quintela E Insulin resistance is a cardiovascular risk factor in humans. Diabetes Metab Syndr 2019;13:1449-55.
Azeez TA Association between lipid indices and 10-year cardiovascular risk of a cohort of black Africans living with type 2 diabetes mellitus. J Adv Int Med 2021;10:38-42.
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.
Puepet FH, Uloko A, Akogu IY, Aniekwensi E Prevalence of the metabolic syndrome among patients with type 2 diabetes mellitus in urban North-Central Nigeria. Afr J Endocrinol Metab 2009;8:12-4.
Muazu. Metabolic syndrome and its associated factors among apparently “healthy” adults residing in rural settlements in Dutse, Northwestern Nigeria: A community-based study [Internet] [cited June 4, 2021]. Available from: https://www.jhrr.org/article.asp?issn=2394-2010;year=2019;volume=6;issue=3;spage=95;epage=101;aulast=Muazu;type=3. [Last accessed on 2021 May 7].
Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X, et al
. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring) 2006;14:727-36.
Gierach M, Gierach J, Ewertowska M, Arndt A, Junik R Correlation between body mass index and waist circumference in patients with metabolic syndrome. ISRN Endocrinol 2014;2014:514589.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]