• Users Online: 62
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
Year : 2015  |  Volume : 2  |  Issue : 3  |  Page : 134-141

Clinical significance of glycated hemoglobin testing in obese subjects attending a tertiary hospital at Calabar, Nigeria

1 Department of Medical Laboratory Science, University of Calabar, P.M.B. 1115 Calabar, Cross River State, Nigeria
2 Department of Medical Microbiology, University of Abuja Teaching Hospital, P.M.B. 228 Gwagwalada, Nigeria

Date of Submission29-Jan-2015
Date of Acceptance21-May-2015
Date of Web Publication3-Sep-2015

Correspondence Address:
Idris Abdullahi Nasir
Department of Medical Microbiology, University of Abuja Teaching Hospital, P.M.B. 228 Gwagwalada, FCT Abuja
Login to access the Email id

DOI: 10.4103/2384-5147.164422

Rights and Permissions

Background: Glycated hemoglobin (HbA1c) has been suggested to be a reliable alternative to fasting plasma glucose in diagnosing hyperglycemia and monitoring glycemic control especially in individuals with type II diabetes mellitus (T2DM). Obesity has been largely incriminated to be a major risk factor for T2DM. Aim: This study aimed to investigate the relationship between body mass index (BMI), anthropometric measurements and glycated hemoglobin (HBA1c) in obese subjects attending the University of Calabar Teaching Hospital, Calabar. Materials and Methods: This was a prospective comparative study that which compared the levels of glycated hemoglobin in 70 obese and 30 nonobese control participants. Whole blood HbA1c was quantified using kits from Pointe scientific Inc. USA. The method was controlled and validated using Pointe™ control reagents from the manufacturer. Results: The mean HbA1c, systolic blood pressure (SBP) and diastolic blood pressure (DBP), waist circumference, and waist-to-hip ratio for obese participants were; 6.13 ± 2.76%, 128.14 ± 12.65 mmHg, 88.56 ± 11.87 mmHg, 106.90 ± 13.52 and 0.87 ± 0.072, respectively. These values were significantly higher than those of the nonobese control subjects with HbA1c, SBP and DBP of 5.34 ± 1.15, 114.00 ± 7.24 mmHg and 88.56 ± 11.87 mmHg (P < 0.05). A significant mean difference was observed between all the classes of obesity (Class I-III) and the control in all the parameters. A positive correlation between BMI, anthropometric measurements and HbA1c was observed in obese participants (r = 0.341 P < 0.05). Conclusion: The findings from this study indicated that obese individuals have higher risk of developing T2DM if appropriate interventions are not considered. Glycated hemoglobin may be used as a reliable, feasible, and fairly accurate tool for screening and assessing blood glycemic control in obese subjects who are at risk of developing T2DM.

Keywords: Body mass index and calabar, diabetes mellitus, glycated hemoglobin, obesity

How to cite this article:
Emeribe AU, Elochukwu AC, Nasir IA, Bassey IE, Udoh EA. Clinical significance of glycated hemoglobin testing in obese subjects attending a tertiary hospital at Calabar, Nigeria . Sub-Saharan Afr J Med 2015;2:134-41

How to cite this URL:
Emeribe AU, Elochukwu AC, Nasir IA, Bassey IE, Udoh EA. Clinical significance of glycated hemoglobin testing in obese subjects attending a tertiary hospital at Calabar, Nigeria . Sub-Saharan Afr J Med [serial online] 2015 [cited 2020 Apr 9];2:134-41. Available from: http://www.ssajm.org/text.asp?2015/2/3/134/164422

  Introduction Top

Obesity as defined by World Health Organization (WHO) is a condition in which there is excessive fat accumulation in the body, to the extent that the health and well-being of the individual is adversely affected. [1]

Obesity is the second leading cause of preventable death after smoking worldwide with increasing prevalence in adults and children, authorities view it as one of the most serious public health problems of the 21 st century. [2]

The incidence and prevalence of obesity is rising globally, in 2008, about 1.5 billion adults, 20 years and older were overweight, of this 1.5 billion overweight adults, over 200 million men and nearly 300 million women were obese. [3] In overall, more than one-tenth of the world's population is obese, and nearly 4 million children under the age of 5 were overweight in 2010. [4]

In Nigeria, early data in the middle and later part of last century suggested a low prevalence, however, recent reports from various studies have documented of overweight individuals ranged from 20.3% to 35.1%, while the prevalence of obesity ranged from 8.1% to 22.2%. [5] A meta-analysis review carried out in 2007 provided a prevalence of obesity of 10.0%. [6] Women were more likely to be obese than men, with odds ratios of 3.16 and 4.79 in urban and rural areas, respectively. Thus obesity, a disease previously thought to have low prevalence in Nigeria because of its association with wealth and affluence has surprisingly risen over the last decade to level that constitute an epidemic. [7] This increase has been attributed to rapid and unplanned urbanization, changes from local dietary pattern to Western style diet which is driven by the proliferation of fast food outlets in major cities across the country. [7]

The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. [6] Also genetic factors, environmental factors, diet, medication, psychological factors, lifestyle preferences, and cultural environment seem to play a major role in the rising prevalence of obesity worldwide. [7]

Obesity poses a major risk to serious diet-related noncommunicable diseases including diabetes mellitus, cardiovascular diseases, hypertension, dyslipidemia, stroke, gallbladder disease, osteoarthritis, sleep apnea, and certain forms of cancer such as ovary, breast, and colon cancer. [8]

However, a combination of energy restriction, exercise, behavioral modifications, drugs and recently surgery plays the greatest role in the management of the obesity problems. [8] But for any significant progress to be made in the prevention of obesity, a public health approach is urgently needed. [9] Obesity is the leading determinant of dyslipidemia and diabetes mellitus. [9]

Body mass index (BMI) is used to classify overweight and obesity and it is defined as a person's weight in kilograms divided by the square of his/her height in meters (kg/m 2 ). [10] Based on WHO classification, a BMI of <18.5 kg/m 2 is classified as underweight, a BMI of 18.5-24.9 kg/m 2 is classified as normal weight, a BMI of 25.0-29.9 kg/m 2 is classified as overweight, a BMI of 30.0-34.9 kg/m 2 is classified as Class I obesity, BMI of 35.0-39.9 kg/m 2 is classified as Class II obesity and BMI of ≥40.0 kg/m 2 is classified as Class III obesity. [1]

HbA1c is the term used to describe the formation of a hemoglobin compound produced when glucose (a reducing sugar) reacts with the amino group of hemoglobin (a protein). The glucose molecule attaches nonenzymatically to the hemoglobin to form a ketoamine. The rate of formation is directly proportional to the plasma glucose concentrations. Because the average red blood cell lives approximately 120 days, the glycated hemoglobin level at any time reflects the average blood glucose level over the previous 2-3 months. Therefore, measuring the glycated hemoglobin provides the clinician with a time-average picture of the patient's blood glucose concentration over the past 3 months. [11]

Fasting plasma glucose and the oral glucose tolerance test (OGTT) are considered to be appropriate tests for diagnosing prediabetes and/or diabetes while OGTT is also considered an appropriate test for assessing diabetes risk in patients with impaired fasting glucose. [12] As an alternative to these methods, an International Expert Committee, including representatives of the American Diabetes Association (ADA), the International Diabetes Federation (IDF), and the European Association for the Study of Diabetes, recently recommended evaluating HbA1c, with a cut-off point of ≥6.5% to diagnose diabetes. [13],[14] This strategy was endorsed and adopted by the ADA in 2010. [14]

There are several arguments in favor of using HbA1c instead of fasting blood sugar tests. One is that unless a specimen is taken to the laboratory right away (or the serum is separated from the plasma) then there is a drop in glucose levels from the time a sample is obtained to the time of processing, leading to inaccuracy. Furthermore, there is more variability week to week in fasting glucose levels than in HbA1c levels. Fasting glucose varies with time of day, with stress and many other factors while HbA1c is more integrated measurement of average glucose. Conversely, people who have all sorts of kidney diseases or chronic infections will have abnormal red cell survival and will tend to have lowered HbA1c interfering with the use of HbA1c in understanding their glucose exposure, this is also obtainable in people with hemoglobinopathies and iron deficiency anemia. Especially in Africa. [14]

Epidemiological evidence suggests that elevated HbA1c is associated with cardiovascular and ischemic heart disease risk. [15] Both obesity and physical inactivity are considered to play important roles in the prevention and treatment of diabetes, with the ADA [16] recommending that people with HbA1c of 5.7-6.4% undergo moderate weight loss (7% of initial body mass), as well as increasing physical activity to at least 150 min/week of moderate activity.

This present study aimed to quantify red cells' glycated hemoglobin and obtain anthropometric data from the three classes of obesity based on their BMI and to determine if there is a significant statistical relationship between anthropometric measurements, BMI and Hb1Ac between obese and nonobese participants.

  Materials and methods Top

Selection of Subjects

A total number of 70 obese subject (BMI ≥30 kg/m 2 ) of which consisted of 30 males and 40 females within the age range of 20-45 years participated as the test group and 30 apparently healthy nonobese subjects (BMI 18.5-24.9 kg/m 2 ) of which consisted of 10 males and 20 females participated as control group and were within the same age range as the test group.

A structured questionnaire was used to which get data on each individual details of his/her health, family health history, age, sex, occupation, physical activity, eating habit, and whether or not on medication that may affect tests results. Subjects reported in the morning after an approximate of 12-h overnight fast.

Ethics Statement

This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the human research and Ethics Committee of University of Calabar Teaching Hospital all the participants gave their written informed consent for inclusion before they participated in the study. All data were analyzed anonymously throughout the study.

Exclusion Criteria

Participants with known family history of diabetes and hypertension were excluded.

Anthropometric Measurements

Anthropometric measurements included height, weight, waist circumference (WC), and hip circumference (HC). Weight and height were measured with the subjects wearing light clothing's and without shoes. Weight was measured to the nearest kilogram using a balanced scale, height was measured to the nearest meters using a wall-mounted ruler, with the subjects' barefoot, standing with feet together and with head, shoulder, buttocks, and heels touching the wall. WC and HC were measured to the nearest 0.1 cm using a flexible but in elastic measuring tape, while the subjects were standing relaxed WC was taken midway between the costal margin and the iliac crest in the mid-auxiliary line around the gluteal region.

Body mass index was calculated for each subject as the ratio of body weight (in kg) and squared height (in meters), BMI (kg/m 2 ) was used as the index of total (general) obesity. Waist-to-hip ratio (WHR) was calculated by dividing the measurement of the waist (cm) by that of the hip (cm) and was used together with the WC as the index of central obesity.

Sample Collection

Five milliliters of blood was dispensed into a 0.08 mL of dipotassium ethylene diamine tetra-acetic bottle for glycated hemoglobin estimation. Standard method of Trivelli et al.[17] was used for glycated hemoglobin estimation. The samples were processed within 24 h of collection.

Quantitative Determination of Glycosylated Hemoglobin in Blood by Cation Exchange Resin (Method of Trivelli)[17]


A hemolyzed preparation of the whole blood is mixed continuously for 5 min with a weak binding cation-exchange resin. During this time, HbA 0 binds to the resin. After the mixing period, a filter is used to separate the supernatant containing the glycosylated hemoglobin from the resin. The binding is temperature dependent which necessitates the inclusion of a standard in each run. The percentage glycosylated hemoglobin is determined by measuring the absorbance at 415 nm (405-420 nm acceptable) of the glycosylated hemoglobin fraction and the total hemoglobin fraction. The ratio of the two absorbances gives the percent hemoglobin.

Analytical Method

Kits for the quantitative determination of glycated hemoglobin were from Pointe scientific Inc. USA. All analyses were conducted in accordance with the manufacturers' instructions. More so, methods were controlled and validated using Pointe™ control reagents from the kit manufacturer.

Statistical Analysis

The generated data were systematically analyzed as appropriate for means, standard deviation (SD), Student's t-test, Pearson's correlation analysis and analysis of variance (ANOVA) on Microsoft excel and SPSS (statistical package for social sciences Version 20, California Inc., USA). Results were presented as the mean ΁ SD. A two-sided P < 0.05 was considered statistically significant for t-test (used to determine the differences between the groups) and Pearson's correlation analysis was used to determine the inter-variable associations of the various groups.

  Results Top

Anthropometric parameters and HbA1c were determined in 70 obese subjects (BMI 30.0 kg/m 2 and above) and 30 nonobese control subject (BMI 18.5-24.9 kg/m 2 ).

[Table 1] shows the mean age, anthropometric parameter, HbA1c and blood pressure in obese subjects and control subjects. The result revealed that the mean value of BMI, anthropometric measurements, HbA1c were significantly higher in obese participant when compared to the control participant (P < 0.05).
Table 1: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese subjects and control group

Click here to view

Obese subjects were further divided into three classes using BMI, Class I when BMI is between 30 and 34.9 kg/m 2 , Class II when BMI is between 35 and 39.9 kg/m 2 and above. The different anthropometric parameters, HbA1c and blood pressure were compared alongside that of the control group using one-way ANOVA. These comparisons are shown in [Table 2]. Results from table showed that a significant statistical difference exists between the four group for BMI, WC, HC, WHR, HbA1c, systolic blood pressure (SBP), diastolic blood pressure (DBP) (P < 0.05).
Table 2: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure of the three classes of obesity alongside the control using one-way ANOVA

Click here to view

[Table 3] shows the comparison of anthropometric parameters, HbA1c, and blood pressure in obese Class I and control group. BMI, HbA1c, SBP, DBP were significantly higher in obese Class I group when compared with that of the control group (P < 0.05).
Table 3: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class I group and control group

Click here to view

[Table 4] shows the comparison of anthropometric parameters, HbA1c and blood pressure in obese Class I and obese Class II groups. BMI, SBP showed a significant higher difference in obese Class II group when compared to Class I group (P < 0.05). No significant difference exists in HbA1c, and DBP between these two groups (P > 0.05).
Table 4: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class I group and obese class II groups

Click here to view

[Table 5] shows the comparison of anthropometric parameters, HbA1c and blood pressure in obese Class I and obese Class III groups. Obese Class III group showed significantly higher BMI, HbA1c, SBP, DBP when compared to obese Class I group (P < 0.05).
Table 5: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class I group and obese class III groups

Click here to view

[Table 6] shows the comparison of anthropometric parameters, HbA1c, and blood pressure in obese Class II and control group. BMI, HbA1c, SBP, DBP showed a significant higher difference in obese Class II group when compared to control group (P < 0.05).

[Table 7] shows the comparison of anthropometric parameters, HbA1c and blood pressure in obese Class II and obese Class III groups. No significant difference exists in HbA1c, SBP, DBP between the two groups (P > 0.05). There was a significant difference in BMI, between the two groups (P < 0.05).
Table 6: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class II group and control groups

Click here to view
Table 7: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class II group and obese class III groups

Click here to view

[Table 8] shows the comparison of anthropometric parameters, HbA1c, and blood pressure in obese Class III and control groups. BMI, HbA1c, SBP, DBP showed a significant higher difference in obese Class III group when compared to the control group (P < 0.05).

Correlation analysis was carried out for HbA1c and BMI in obese subjects. BMI correlated positively with HbA1c in these obese subjects (r = 0.393, P < 0.05) [Figure 1].
Figure 1: Correlation plot of glycated hemoglobin against body mass index in obese subjects

Click here to view
Table 8: Comparison of anthropometric parameters, glycated hemoglobin, and blood pressure in obese class III group and control group

Click here to view

  Discussion Top

Obesity-related metabolic disorders have not been adequately addressed due to a failure to distinguish the importance of general obesity or body fat distribution in relation to developing type II diabetes mellitus (T2DM) risk.

Waist circumference and WHR have been used as measures of central obesity, and BMI has been used as a measure of general obesity. Studies have indicated that central obesity might be more important. [18],[19] Central obesity has been associated with decreased glucose tolerance, alterations in glucose-insulin homeostasis, reduced metabolic clearance of insulin, and decreased insulin-stimulated glucose disposal. [20]

Findings from this investigations revealed that HC, WC, WHR, and mean HbA1c level were significantly higher in obese participants than those of nonobese counterpart. This was in consonance with reports of Martins et al. [21] and McGill et al., [22] obese participants had higher HbA1c levels than nonobese only when using the IDF cut-off points [23] In their correlation analysis, they also revealed positive relationship between WC and HbA1c. This observation suggests that the regional distribution of fat mass may be an equally valid predictor of HbA1c risk especially in older adults.

A significant mean difference for glycated hemoglobin levels was observed between all the classes of obesity (Class I, II and III) and the control in all the parameters. This was not in agreement with the findings of Incani et al. [24] who stated that glycated hemoglobin levels (HbA1c) identified diabetic participants of Class II and III obesity with less sensitivity due to limited time to develop the chronic hyperglycemia that is, necessary to affect HbA1c levels. It was further stated that the pathophysiological mechanisms that underlie severe obesity could differ from those present in Class I obesity. [25] Insulin resistance due to obesity invariably results to increased glycosylated hemoglobin in hyperglycemia. [26] The adipose tissue of the visceral fat in obesity responds to multiple signals to produce pro-inflammatory substances and adipokines resulting in altered lipid and glucose metabolism and oxidative stress. [27] The effects of these factors are not limited to the adipose tissue but may also affect skeletal muscle and liver.

Adipokines and inflammatory factors produced by the visceral fat which include interleukin (IL)-6, plasminogen activator inhibitor 1, tumor necrotic factor alpha (TNF-α) and angiotensin converting enzymes etc., may stimulate the recruitment of macrophages, which stimulate increased adipogenesis, with concomitant reduction in anti-inflammatory factors like adiponectin. These factors (IL-6 and TNF-α) are associated with obesity, insulin resistance and impaired insulin signaling in mature adipocytes.[28]

The positive correlation reported between BMI and glycated hemoglobin (HbA1c) was observed in obese subjects (r = 0.341 P < 0.05) is also in support of other studies who reported similar findings in adults ≥60 years, where diabetes is commonly associated with higher BMI and/or WC values. [29] More so, our study identified Class III obese subjects as diabetic and hypertensive. This is a clear demonstration that diabetes and hypertension are likely to be related to metabolic syndrome associated with obesity, a triad of insulin resistance, dyslipidemia, and elevated blood pressure, which is common in obese youth. [30] The risk of these serious health consequences has been shown to rise with an increase in (BMI) [31] but it is an excess of body fat in the abdomen, measured simply by WC that is, more indicative of the metabolic syndrome profile than BMI. [30],[32],[33] The underlying cause of the metabolic syndrome continues to challenge medical experts, but both insulin resistance and central obesity are considered significant factors. [34]

The use of HbA1c as a model of blood glycemic assessment could help identify participants at high risk of developing T2DM, the predictability can be improved by the inclusion of fasting blood glucose and lipid profile tests. Therefore, evaluating the relationship between HbA1c and lipid profile might be expected to help in the identification of people at high risk to T2DM and cardiovascular disorders. Findings from this study justify the need to encourage use of HbA1c testing in monitoring effects of control measures (such as health dietary habits, enhanced physical activities, antilipids and/or antiglycemics) on blood glucose metabolism.

Financial Support and Sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

World Health Organization, Department of NonCommunicable Disease Surveillance. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications; 1999. Available from: http://www.whqlibdoc.who.int/hq/1999/WHO_NCD_NCS_99.2.pdf.  Back to cited text no. 1
Metcalf BS, Hosking J, Frémeaux AE, Jeffery AN, Voss LD, Wilkin TJ. BMI was right all along: Taller children really are fatter (implications of making childhood BMI independent of height) EarlyBird 48. Int J Obes (Lond) 2011;35:541-7.  Back to cited text no. 2
Lieb W, Sullivan LM, Harris TB. Plasma leptin levels and incidence of heart failure, cardiovascular disease, and total mortality in elderly individuals. Br Med J 2009;32:612-6.  Back to cited text no. 3
Martinelli CE, Keogh JM, Greenfield JR, Henning E, van der Klaauw AA, Blackwood A, et al. Obesity due to melanocortin 4 receptor (MC4R) deficiency is associated with increased linear growth and final height, fasting hyperinsulinemia, and incompletely suppressed growth hormone secretion. J Clin Endocrinol Metab 2011;96:E181-8.  Back to cited text no. 4
Chukwuonye II, Chuku A, John C, Ohagwu KA, Imoh ME, Isa SE, et al. Prevalence of overweight and obesity in adult Nigerians - A systematic review. Diabetes Metab Syndr Obes 2013;6:43-7.  Back to cited text no. 5
Abubakari AR, Bhopal RS. Systematic review on the prevalence of diabetes, overweight/obesity and physical inactivity in Ghanaians and Nigerians. Public Health 2008;122:173-82.  Back to cited text no. 6
Lawoyin TO, Asuzu MC, Kaufman J, Rotimi C, Owoaje E, Johnson L, et al. Prevalence of cardiovascular risk factors in an African, urban inner city community. West Afr J Med 2002;21:208-11.  Back to cited text no. 7
Nedeltcheva AV, Kilkus JM, Imperial J, Schoeller DA, Penev PD. Insufficient sleep undermines dietary efforts to reduce adiposity. Ann Intern Med 2010;153:435-41.  Back to cited text no. 8
Murray PG, Read A, Banerjee I, Whatmore AJ, Pritchard LE, Davies RA, et al. Reduced appetite and body mass index with delayed puberty in a mother and son: Association with a rare novel sequence variant in the leptin gene. Eur J Endocrinol 2011;164:521-7.  Back to cited text no. 9
Wijga AH, Scholtens S, Bemelmans WJ. Comorbidities of obesity in school children: A cross-sectional study in the PIAMA birth cohort. Med Sport Sci J 2010;10:184.  Back to cited text no. 10
Sidorenkov G, Haaijer-Ruskamp FM, de Zeeuw D, Denig P. A longitudinal study examining adherence to guidelines in diabetes care according to different definitions of adequacy and timeliness. PLoS One 2011;6:e24278.  Back to cited text no. 11
American Diabetes Association. Standards of medical care in diabetes - 2009. Diabetes Care 2009;32 Suppl 1:S13-61.  Back to cited text no. 12
Pani LN, Korenda L, Meigs JB, Driver C, Chamany S, Fox CS, et al. Effect of aging on A1C levels in individuals without diabetes: Evidence from the Framingham Offspring Study and the National Health and Nutrition Examination Survey 2001-2004. Diabetes Care 2008;31:1991-6.  Back to cited text no. 13
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2010;33 Suppl 1:S62-9.  Back to cited text no. 14
Gao L, Matthews FE, Sargeant LA, Brayne C, MRC CFAS. An investigation of the population impact of variation in HbA1c levels in older people in England and Wales: From a population based multi-centre longitudinal study. BMC Public Health 2008;8:54.  Back to cited text no. 15
American Diabetes Association. Standards of medical care in diabetes-2011. Diabetes Care 2011;34:S11-61.  Back to cited text no. 16
Trivelli LA, Ranney HM, Lai HT. Hemoglobin components in patients with diabetes mellitus. N Engl J Med 1971;284:353-7.  Back to cited text no. 17
Kumar S, Mukherjee S, Mukhopadhyay P, Pandit K, Raychaudhuri M, Sengupta N, et al. Prevalence of diabetes and impaired fasting glucose in a selected population with special reference to influence of family history and anthropometric measurements - the Kolkata policeman study. J Assoc Physicians India 2008;56:841-4.  Back to cited text no. 18
Daousi C, Casson IF, Gill GV, MacFarlane IA, Wilding JP, Pinkney JH. Prevalence of obesity in type 2 diabetes in secondary care: Association with cardiovascular risk factors. Postgrad Med J 2006;82:280-4.  Back to cited text no. 19
Vazquez G, Duval S, Jacobs DR Jr, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol Rev 2007;29:115-28.  Back to cited text no. 20
Martins RA, Jones JG, Cumming SP, Coelho e Silva MJ, Teixeira AM, Veríssimo MT. Glycated hemoglobin and associated risk factors in older adults. Cardiovasc Diabetol 2012;11:13.  Back to cited text no. 21
McGill HC Jr, McMahan CA, Herderick EE, Zieske AW, Malcom GT, Tracy RE, et al. Obesity accelerates the progression of coronary atherosclerosis in young men. Circulation 2002;105:2712-8.  Back to cited text no. 22
IDF: International Diabetes Federation; Diabetes Altas. 3 rd edition. Brussels: IDF; 2006. Available at http://www.ealtlas.idf.org/media [Last accessed on 2014 Dec 9].  Back to cited text no. 23
Incani M, Sentinelli F, Perra L, Pani MG, Porcu M, Lenzi A, et al. Glycated hemoglobin for the diagnosis of diabetes and prediabetes: Diagnostic impact on obese and lean subjects, and phenotypic characterization. J Diabetes Investig 2015;6:44-50.  Back to cited text no. 24
Sturm R. Increases in clinically severe obesity in the United States, 1986-2000. Arch Intern Med 2003;163:2146-8.  Back to cited text no. 25
Eckfeldt JH, Bruns DE. Another step towards standardization of methods for measuring haemoglobin A1c. Clin Chem 1997;43:1811-3.  Back to cited text no. 26
Gustafson B, Hammarstedt A, Andersson CX, Smith U. Inflamed adipose tissue: A culprit underlying the metabolic syndrome and atherosclerosis. Arterioscler Thromb Vasc Biol 2007;27:2276-83.  Back to cited text no. 27
Traish AM, Guay A, Feeley R, Saad F. The dark side of testosterone deficiency: I. Metabolic syndrome and erectile dysfunction. J Androl 2009;30:10-22.  Back to cited text no. 28
Kalyani RR, Saudek CD, Brancati FL, Selvin E. Association of diabetes, comorbidities, and A1C with functional disability in older adults: Results from the National Health and Nutrition Examination Survey (NHANES), 1999-2006. Diabetes Care 2010;33:1055-60.  Back to cited text no. 29
Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: Best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73:460-8.  Back to cited text no. 30
Lee IM, Manson JE, Hennekens CH, Paffenbarger RS Jr. Body weight and mortality. A 27-year follow-up of middle-aged men. JAMA 1993;270:2823-8.  Back to cited text no. 31
Ohlson LO, Larsson B, Svärdsudd K, Welin L, Eriksson H, Wilhelmsen L, et al. The influence of body fat distribution on the incidence of diabetes mellitus 13.5 years of follow-up of the participants in the study of men born in 1913. Diabetes 1985;34:1055-8.  Back to cited text no. 32
Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ, et al. Abdominal adiposity and coronary heart disease in women. JAMA 1998;280:1843-8.  Back to cited text no. 33
Carr DB, Utzschneider KM, Hull RL, Kodama K, Retzlaff BM, Brunzell JD, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes 2004;53:2087-94.  Back to cited text no. 34


  [Figure 1]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
   Materials and me...
   Article Figures
   Article Tables

 Article Access Statistics
    PDF Downloaded192    
    Comments [Add]    

Recommend this journal