|Year : 2021 | Volume
| Issue : 1 | Page : 61-64
Severe obesity associated with hyperglycemia and abdominal fats in metabolic syndrome patients
Khansa Ibrahim Musa1, Mariam Abbas Ibrahim1, Mai Abderahman Al Masri2, Amar Mohamed Ismail3
1 Department of Clinical Chemistry, College of Medical Laboratory Science, Sudan University of Science and Technology, Khartoum, Sudan
2 Department of Molecular Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
3 Department of Biochemistry and Molecular Biology, Faculty of Science and Technology, Al-Neelain University, Khartoum, Sudan
|Date of Submission||25-Jul-2020|
|Date of Decision||05-Oct-2020|
|Date of Acceptance||18-Nov-2020|
|Date of Web Publication||26-Mar-2021|
Amar Mohamed Ismail
Department of Biochemistry and Molecular Biology, Faculty of Science and Technology, Al-Neelain University, Khartoum
Source of Support: None, Conflict of Interest: None
Background: Obesity is a major public problem in developed and developing countries associated with a high mortality rate. Herein, we determined the relationships between severe obesity, lipid profile, blood glucose in metabolic syndrome (MS) patients. Materials and Methods: In a cross-sectional 215 MS patients, ages ranged from 37 to 84 were randomly selected. Body mass index and waist circumference (WC) were estimated. Fasting serum lipid profile and plasma blood glucose were measured. Results: In total, 132 (61.4%) were female, 151 (70.2%) were obese, 64 (29.8%) were sever obese, and 143 (66.5%) had WC ≥ 110. Chi-square analyses show that severe obesity was significantly associated with increased WC and hyperglycemia with (odds ratio [OR] = 2.230 and 2.400) and (P = 0.019 and 0.005), respectively. The severe obesity in females was two-fold increased than males (OR = 1.93, P = 0.028). Conclusion: Severe obesity associated with central obesity and hyperglycemia in MS patients. Moreover, females at higher risk to have severe obesity.
Keywords: Body mass index, lipids, metabolic syndrome, severe obesity, small dense-low-density lipoprotein
|How to cite this article:|
Musa KI, Ibrahim MA, Al Masri MA, Ismail AM. Severe obesity associated with hyperglycemia and abdominal fats in metabolic syndrome patients. Saudi J Health Sci 2021;10:61-4
|How to cite this URL:|
Musa KI, Ibrahim MA, Al Masri MA, Ismail AM. Severe obesity associated with hyperglycemia and abdominal fats in metabolic syndrome patients. Saudi J Health Sci [serial online] 2021 [cited 2021 Jul 23];10:61-4. Available from: https://www.saudijhealthsci.org/text.asp?2021/10/1/61/311955
| Introduction|| |
Obesity is a basic component of metabolic syndrome (MS), which characterized by multiple health problems and a higher mortality rate worldwide., Several studies suggest that pandemic levels of obesity-induced type 2 diabetes mellitus (DM) will be reached by 2030 in developed and developing countries. The incidence of obesity is increasing due to changes in lifestyle, socioeconomics, and stress. Sudan not far, therefore the clinical practitioners observed a higher prevalence of obesity over the past few decades. Since abdominal obesity associated with insulin resistance, increase triglycerides (TG), reduction in high-density lipoproteins (HDL), and hypertension (MS components). Many shreds of evidence showing that high body mass index (BMI) leads to elevation of the MS components, and thus increase the prevalence of MS in adolescents and adults.,,, Strong relationship between BMI, low-density lipoprotein (LDL), and HDL levels were reported previously by several studies., Recently, small dense-LDL (sd-LDL) levels are associated with low HDL levels, high TG, atherosclerosis, inflammation, type 2 DM, and consequently MS. The mechanism is due to the lower affinity binding of the sd-LDL with LDL receptors; therefore, higher circulation levels were oxidized by free radicals, which serve as proatherogenic factors. However, the relationship between obesity, circulating lipids, the glucose level in MS patients has not been investigated yet; therefore little will do, to tackle the relationship between severe obesity and MS components in our population. The purpose of this study was to find out the relationships between severe obesity, lipid profile, sd-LDL, and blood glucose in MS patients.
| Materials and Methods|| |
This was a cross-sectional hospital-based study carried out in Khartoum State, from December 2016 to November 2018. The local committee of Sudan University of Science and Technology and the Ministry of Health approved this study. After was obtaining the informed consent, 215 patients, ages ranged from 37 to 84 were included. The participants were already diagnosed according to the International Diabetes Federation criteria. The patients were attending for routine investigation and monitoring at different hospitals (Zinam Diabetic Centre, Gaber-Aboeliz, Rebate Teaching Hospital). Clinical data and study variables were evaluated. Patients with renal diseases, liver diseases, chronic inflammation, severe illness, amputation, and pregnant women were excluded. Fasting venipuncture blood sample was collected under aseptic condition. BMI was calculated using (Weight Kg/Height m2) formula, and waist circumference (WC) were estimated using tap meter at middle inferior. Severe obesity is defined as BMI ≥ 35, whereas obesity is BMI < 35. Serum was obtained after centrifugation at 3000 rpm, and fluoride oxalate was used for plasma blood glucose. Fasting lipid profile and blood glucose were measured.
Estimation of lipid profile
Brief according to the manufacturer, fasting blood glucose and lipid profile (Total cholesterol, TG, HDL-cholesterol [HDL-C], and LDL-cholesterol [LDL-C]) levels were measured by enzymatic methods, using an Automated Chemistry Analyzer (Mindray BS-380)., Serum sdLDL levels were estimated using direct sandwich enzyme-linked immunosorbent assay.
Statistical analysis was performed using the Statistical Package for the Social Science (SPSS) software version 21.0 (SPSS Inc., Chicago, IL, USA). Demographic variables were expressed as number and percentage, whereas cut-off values were adjusted. A Chi-square test was used to identify risk factors associated with severe obesity (BMI ≥ 35) compared to obesity (BMI < 35). Results expressed as percentages (%), odds ratio (OR), confidence interval, and P ≤ 0.05 was considered statistically significant.
| Results|| |
The demographic characteristics results showed that the percentage of severe obesity is 29.8% in Sudanese MS patients, whereas obese is 70.2%. Meanwhile, 143 (66.5%) had central obesity with WC ≥ 110 and 72 (33.5) with <110. Of 215 patients, 132 (61.4%) were female and 83 (38.6) were male. Age group more ≥55 were found to be 111 (51.6%) and <55 were 104 (48.4%). Of MS patients, 120 (55.8) had hyperglycemia and 95 (44.2%) had normoglycemia. Moreover, 81 (37.8%) had circulated sd-LDL ≥ 0.5 and 134 (62.2%) had <0.5, the results are presented in [Table 1].
Chi-square analyses found that there were significant associations between severe obesity, gender, WC, and fasting blood glucose with (P = 0.028, 0.019 and 0.005) and (OR = 1.932, 2.230, and 2.400), respectively. Whereas no associations were observed between severe obesity, cholesterol, triglyceride, HDL-C, LDL-C, and sd-LDL with (P = 0.631, 0.203, 0.064, 0.071, 0.714) and (OR = 1.172, 0.675, 1.965, 0.272, 1.260) receptively, the results are presented in [Table 2].
|Table 2: Percentages and adjusted odd ratios (95% confidence interval) of gender, age, waist circumference, and lipid profile for comparison of severe obesity (body mass index≥35) with obesity (body mass index<35)|
Click here to view
| Discussion|| |
The difficulty in the investigation of MS prevalence is due to different selection criteria. Although obesity is a basic MS component, it was causing all MS complications. Meanwhile, severe obesity was found to be an independent risk factor for morbidity and mortality in MS patients. In our population, severe obesity with MS components has not been investigated yet. Therefore, this study was conducted to determine whether severe obesity associated with blood glucose, sd-LDL, and lipid profile in MS patients.
The present study identified that severe obesity associated with WC (accumulation of abdominal fats) and hyperglycemia in MS patients. Moreover, the frequency of severe obesity was found to be 29.8%, whereas females had a higher risk of severe obesity than males. Previous studies have found that obesity is a major component of MS; furthermore, obesity is more common in females compared to males.,, The difference may be due to fat distribution, physical activity, cultural habits, and hormonal variations., Other studies report that abdominal fats play a vital role in developing of MS., In fact, central obesity is a risk factor for insulin resistance, which leads to a lower level of HDL, an elevated level of TG, hyperglycemia, and the development of MS. Therefore, losing abdominal fats is important for reducing morbidity associated with increased WC, especially in females. From the evidence that impaired blood glucose is one of three components of MS, several studies suggested that obesity, especially central obesity-induced insulin resistance, leads to impaired fasting blood glucose since insulin resistance associated with hyperglycemia in MS patients.,, Concurrent with the previous study, severely obese patients had a higher prevalence of type 2 DM. Our results revealed that severe obesity was an independent risk factor for hyperglycemia in MS patients. Therefore, reducing body weight could be a useful intervention to minimize the complications related to hyperglycemia, such as retinopathy, nephropathy, and neuropathy in our population. Indeed, substantial evidence indicated that maintaining a healthy body weight has a beneficial effect on insulin resistance and hyperglycemia. Despite the mechanisms of obesity-induced insulin resistance and hyperglycemia are complex and not fully understood. Some studies suggest that fatty acids released from adiposities prevent glucose uptake by insulin-dependent cells.,
To the best of our knowledge, this is the first study that investigated the relationship between severe obesity and lipid profiles in MS patients. Although no associations were observed between severe obesity and lipid profile (Cholesterol, TG, HDL-C, LDL-C, and sd-LDL), which justified by the narrow range of BMI in this population 30–40. Therefore, the contradictory findings attributed to the previous report that all classes of obesity (obesity, severe and morbid obesity) were associated with metabolic abnormalities of lipids.,, Moreover, all participants in this study were receiving different treatments for MS components; therefore, it is difficult to identify the potential effect of these confounders, thus considered as a limitation of this study. Meanwhile, the study had other limitations; firstly, this was a descriptive hospital-based study, which is missing MS patients who not a referral to hospitals. Second, we did not measure the insulin resistance index, which might help in the explanation of the relationship between severe obesity and hyperglycemia in MS patients. Finally, the study is not including the third class of obesity (morbid obesity), which is an MS patient who had BMI >40. Therefore, a further cohort study is needed.
| Conclusion|| |
The data of the present study suggests that severe obesity is significantly associated with abdominal fats and hyperglycemia in MS patients. Moreover, females at a higher risk to have severe obesity than males. Therefore, severe obesity is an independent risk factor for hyperglycemia and thus related complications in MS patients. Intervention protocols such as physical activity and losing weight are needed for our population.
KI. MA, MA, and AM study design and methodology. KI writing of the original draft. MA and AM reviewing and editing. MA, MA, and AM supervision. The authors have read the manuscript and agreed to the submission.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Al-Bachir M, Bakir MA. Predictive value of body mass index to metabolic syndrome risk factors in Syrian adolescents. J Med Case Rep 2017;11:170.
Hailemariam S, Melak T, Abebe M. Waist circumference cutoff point determination for defining metabolic syndrome in type 2 diabetes mellitus in Ethiopia. EJIFCC 2019;30:48-58.
Martyn JA, Kaneki M, Yasuhara S. Obesity-induced insulin resistance and hyperglycemia: Etiologic factors and molecular mechanisms. Anesthesiology 2008;109:137-48.
Liu W, Lin R, Liu A, Du L, Chen Q. Prevalence and association between obesity and metabolic syndrome among Chinese elementary school children: A school-based survey. BMC Public Health 2010;10:780.
Paley CA, Johnson MI. Abdominal obesity and metabolic syndrome: Exercise as medicine? BMC Sports Sci Med Rehabil 2018;10:7.
Arjona-Villicana RD, Herrera-Sanchez LF, Sumárraga-Ugalde CD, Alcocer-Gamboa MA. Relationship between body mass index and lipid profile in obese Mexican children and adolescents. Bol Med Hosp Infant Mex 2014;71:88-94.
Fairbrother U, Kidd E, Malagamuwa T, Walley A. Genetics of Severe Obesity. Curr Diab Rep 2018;18:85.
Milyani AA, Al-Agha AE. The effect of body mass index and gender on lipid profile in children and adolescents in Saudi Arabia. Ann Afr Med 2019;18:42-6.
] [Full text]
Singh O, Gupta M, Khajuria V. Lipid profile and its relationship with blood glucose levels in Metabolic Syndrome. Natl J Physiol Pharm Pharmacol 2015;5: 134-7.
Fan J, Liu Y, Yin S, Chen N, Bai X, Ke Q, et al
. Small dense LDL cholesterol is associated with metabolic syndrome traits independently of obesity and inflammation. Nutr Metab (Lond) 2019;16:7.
Sabir FM, Hassan DA, Elamin MI. Prevalence of metabolic syndrome among young Sudanese university students using three different criteria of WHO, IDF and NCEP-ATP III. Nurs Open Access 2016;2:1-4.
Rifai N, Bachorik PS, Albers JJ. Lipids, lipoproteins and apolipoprotiens. In: Burtis CA, Ashwood ER, editors. Tietz Textbook of Clinical Chemistry. 3rd
ed.. Philadelphia: W. B Saunders Company; 1999.p. 809-61.
CLSI. Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline-2edEdi, CLSI document EP5-A2. West Valley Road, Suite 1400, Wayne, PA 19087 USA: CLSI; 2008. p. 940.
Harikrishnan S, Sarma S, Sanjay G, Jeemon P, Krishnan MN, Venugopal K, et al
. Prevalence of metabolic syndrome and its risk factors in Kerala, South India: Analysis of a community based cross-sectional study. PLoS One 2018;13:e0192372.
Herath HM, Weerasinghe NP, Weerarathna TP, Amarathunga A. A comparison of the prevalence of the metabolic syndrome among Sri Lankan patients with type 2 diabetes mellitus using WHO, NCEP-ATP III, and IDF definitions. Int J Chronic Dis 2018;2018:1-8.
Weber KE, Fischl AF, Murray PJ, Conway BN. Effect of BMI on cardiovascular and metabolic syndrome risk factors in an Appalachian pediatric population. Diabetes Metab Syndr Obes 2014;7:445-53.
Slagter SN, van Waateringe RP, van Beek AP, van der Klauw MM, Wolffenbuttel BH, van Vliet-Ostaptchouk JV. Sex, BMI and age differences in metabolic syndrome: The Dutch lifelines cohort study. Endocr Connect 2017;6:278-88.
Yang YM, Shin BC, Son C, Ha IH. An analysis of the associations between gender and metabolic syndrome components in Korean adults: A national cross-sectional study. BMC Endocr Disord 2019;19:67.
Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis 2016;5:1-13.
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;2014:1-6.
Vinciguerra F, Baratta R, Farina MG, Tita P, Padova G, Vigneri R, et al
. Very severely obese patients have a high prevalence of type 2 diabetes mellitus and cardiovascular disease. Acta Diabetol 2013;50:443-9.
Chadt A, Scherneck S, Joost HG, Al-Hasani H. Molecular links between Obesity and Diabetes: “Diabesity.” In: Feingold KR, Anawalt B, Boyce A, Chrousos G, Dungan K, Grossman A, et al
., editors. South Dartmouth (MA): Endotext; 20.
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018;14:88-98.
[Table 1], [Table 2]