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Table of Contents
Year : 2018  |  Volume : 1  |  Issue : 2  |  Page : 74-81

Gender-Specific profiles of cardiovascular disease in type 2 diabetes mellitus: A cross-sectional study

1 Department of Medical Biochemistry, Faculty of Medicine, Ain Shams University, Cairo, Egypt; Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
2 Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
3 College of Medicine Research Center, King Saud University, Riyadh, Saudi Arabia
4 Obesity Research Center, College of Medicine, King Saud University; Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia

Date of Web Publication6-Jun-2018

Correspondence Address:
Reem M Sallam
Department of Medical Biochemistry, Faculty of Medicine, Ain Shams University, 11381 Abbassia, Cairo

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/JNSM.JNSM_34_18

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Context: Cardiovascular disease (CVD) is a chronic macrovascular complication of diabetes mellitus (DM). Factors unique to a group of patients might imply specific differences in the manifestation and/or severity of type 2 DM (T2DM) and CVD. Increasing our knowledge of these factors is critical in designing more robust preventive and/or management approaches for such groups. Aims: The aim of this work is to investigate the gender differences among diabetic patients with and without CVD. Settings and Design: T2DM patients (64 men and 50 women) were recruited and subdivided according to the presence or absence of CVD as a complication to diabetes. Subjects and Methods: Biochemical measurements (glucose, insulin, HbA1c, lipid profile, and liver and kidney function tests), complete blood count, prothrombin and activated partial thromboplastin times, platelet aggregation, tissue factor pathway inhibitor, and plasminogen activator inhibitor-1 (PAI-1) were assessed. Platelet activation was assessed by flow cytometry and aggregation assay. Statistical Analysis Used: Microsoft Excel and SPSS were used for data analysis. Results: Among the assessed parameters, changes in anthropometry, platelet indices, and PAI-1 were detected. Age, body weight, body mass index, and systolic blood pressure (BP) were significantly higher in women with CVD than in those without. Conclusions: The critical association between patients' weight and BP and the development of CVD particularly in diabetic women emphasizes on the need to intensify the efforts for better management of obesity and hypertension specifically among diabetic Saudi women to minimize their CVD risk.

Keywords: Cardiovascular disease, diabetes mellitus, female, type 2

How to cite this article:
Sallam RM, Z. Alayoubi SM, Al-Daghri NM, Alhammad AA, Alfadda AA. Gender-Specific profiles of cardiovascular disease in type 2 diabetes mellitus: A cross-sectional study. J Nat Sci Med 2018;1:74-81

How to cite this URL:
Sallam RM, Z. Alayoubi SM, Al-Daghri NM, Alhammad AA, Alfadda AA. Gender-Specific profiles of cardiovascular disease in type 2 diabetes mellitus: A cross-sectional study. J Nat Sci Med [serial online] 2018 [cited 2023 Mar 23];1:74-81. Available from: https://www.jnsmonline.org/text.asp?2018/1/2/74/233818

Reem M. Sallam, Samha M. Z. Alayoubi
FNx01These authors have equally contributed to this study

  Introduction Top

Worldwide rise in the prevalence of type 2 diabetes mellitus (T2DM) is parallel to the increasing prevalence of obesity within the general population.[1],[2] Moreover, obese individuals with T2DM are at higher risk for diabetic complications than their nonobese counterparts.[3] This phenotype is clearly exemplified in Saudi Arabia and is alarming as it imposes a heavy burden, both from the disease and from its devastating complications and comorbidities.[1],[3]

Cardiovascular disease (CVD) is a chronic macrovascular complication of DM.[4] Cellular and molecular factors such as chronic inflammation, cell dysfunction, and tissue damage secondary to chronic hyperglycemia, dyslipidemia, and oxidative stress are among the underlying mechanisms for such vascular complication.[5],[6]

Factors unique to a group of patients such as their ethnic background, age group, or gender might imply specific differences in the manifestation and/or severity of chronic noncommunicable diseases including T2DM and CVD.[7] Increasing our knowledge of the molecular bases of these factors will help in designing more robust approach for such groups.

Despite the premenopausal female gender protection against CVD, DM reduces such protection. This may be partly explained by the diabetes-related metabolic changes and consequent shifts in vascular risk profiles.[8] In a recent cross-sectional study, the risk of developing CVD was increased 2–3 folds in premenopausal diabetic women as compared to their nondiabetic counterpart.[9] The factors responsible for this finding have not been fully investigated. However, several contributing elements such as increased body weight, hypertension, and dyslipidemia are among the conventional factors known to play key roles in the incidence of CVD in general since they are all components of the metabolic syndrome (MetS). Some of these factors, such as waist circumference and high-density lipoprotein-cholesterol (HDL-C), are known to have gender-specific cutoff values.[10],[11] However, whether individual components of MetS contribute differently to the precipitation of CVD in men versus women has not been fully assessed. Since the majority of cardiovascular events are precipitated by vessel occlusion caused by thrombus on a ruptured atherosclerotic plaque, platelet dysfunction in T2DM patients may contribute to their increased CVD risk.[12],[13],[14] Likewise, factors contributing to maintaining the blood vessels' integrity and patency such as fibrinolysis and endothelial cell function may need further investigation regarding possible gender differences.[15]

The present work is designed to be a pilot study aiming to investigate the gender difference's influence in a group of adult T2DM Saudi men and women, both without and with CVD.

  Subjects and Methods Top

This is a cross-sectional study. General medical information was extracted from the archived patients' files. Ethical approval from the Institutional Review Board was obtained before the study. Informed consent was obtained from all individual participants included in the study. One hundred and fourteen adult Saudi patients with T2DM were recruited (males: n = 64, age range: 20–70 years old, average age ± standard deviation [SD]: 46.5 years old ± 9.4 and females: n = 50, age range: 16–80 years old, average age ± SD: 48.9 years old ± 12.4). Inclusion criteria included adult age, Saudi nationality, and T2DM, with or without CVD. CVD was defined as a history of coronary heart disease manifested by myocardial infarction, angina pectoris, or heart failure; cerebral ischemia manifested by stroke or transient ischemic attack; or peripheral arterial disease manifested by intermittent claudication. Exclusion criteria included acute chest pain, renal failure, pregnancy, liver diseases, and consumption of medications that could affect the coagulation or fibrinolytic systems.

Participants were divided into two groups based on gender. Each group was further subdivided according to the presence of CVD as a complication to T2DM: (T2DM men without complications [n = 28], T2DM men with CVD complications [n = 36], T2DM women without complications [n = 32], and T2DM women with CVD complications [n = 18]). Fasting venous blood was drawn and processed taking precautions that avoid platelet activation. Ethylenediaminetetraacetic acid-whole blood was used for flow cytometry and for the complete blood count (CBC). Citrated whole blood was used to assess platelet aggregation, and citrated plasma was used to determine prothrombin time (PT) and activated partial thromboplastin time (APTT). The remaining citrated plasma was stored at −80° C for enzyme-linked immunosorbent assay (ELISA). Serum was used for biochemical measurements including fasting glucose (FG), fasting insulin, and HbA1c. Homeostasis model assessment for insulin resistance (HOMA-IR) was calculated.[16] Lipid profile including total cholesterol, triacylglycerol (TG), low-density lipoprotein-cholesterol (LDL-C), and HDL-C; liver function tests including aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), alkaline phosphatase (ALP), bilirubin, total protein, and albumin; and renal function tests including serum creatinine and blood urea nitrogen were all assessed. The creatinine clearance was estimated by applying Cockroft-Gault formula.[17] The biochemical tests were performed using the Dimension Xpand plus autoanalyzer (Siemens Healthcare Diagnostics, USA). Insulin was measured by electrochemiluminescence using a Cobas e411 immunoanalyzer (Roche, USA).

Hemostatic and fibrinolytic parameters, in addition to measuring the platelet aggregation function and the platelet cell surface expression of activation markers, were assessed. Tests were performed within 3 h after venipuncture. Adenosine diphosphate (ADP; from Chrono-log, final concentration 10 μmol/L)-induced platelet aggregation in whole blood was assessed using a Chrono-Log aggregometer (Model 570VS equipped with Chrono-log AGGRO/LINK ® Software). Changes in electrical impedance in ohms (Ω) was quantified.[18]

Platelet surface expression of activation markers using flow cytometry was performed according to the manufacturer's instructions. All reagents and monoclonal antibodies (MoAbs) were from BD (USA). Lysis of red blood cells (RBCs) and labeling of cell surface receptors on the platelets and monocytes were performed. MoAbs used included: FITC-conjugated CD63 (for activation-dependent platelet membrane protein), PerCP-conjugated CD61 (for constitutively expressed platelet membrane protein), and PE-conjugated CD14 (for constitutively expressed monocyte membrane protein). Equal concentrations of isotype-matched similarly conjugated nonimmune mouse IgG were used as negative control. Following cell labeling and fixation, using 2% paraformaldehyde, platelets were identified by flow cytometry based on their light scatter properties and immunostaining with anti-CD61 MoAb. Activated platelets were then quantified by assessing the percent platelets immunostained with anti-CD63 MoAb. Monocytes were identified by their light-scatter properties and immunostaining with anti-CD14 MoAb. Platelet-monocyte aggregates were identified by their double immunostaining with both anti-CD14 and anti-CD61 MoAbs.

Circulating levels of tissue factor pathway inhibitor both total and free (TFPI; total and free) and of plasminogen activator inhibitor-1 (PAI-1) were measured in citrated plasma using ELISA (Diagnostica Stago, France) following the manufacturer's instructions. The intra- and inter-assay coefficients of variation for the ELISA kits used were as follows; total TFPI kit: <5 and <8%, free TFPI kit: <6 and <10%, and PAI-1 kit: 6.53 and 8.69%, respectively.

Microsoft Excel and IBM SPSS Statistics for Windows, Version 22.0. (Armonk, NY: IBM Corp) were used to compare both groups and subgroups. Central values and the data spread were described as mean ± SD and/or standard error of the mean. Student's t-test or Mann–Whitney U-test was used to compare groups. Spearman's rank correlation coefficient or Spearman's rho was used to determine correlation coefficient between various variables. P ≤ 0.05 was considered statistically significant.

  Results Top

Analysis of the anthropometric, biochemical, and hematological data of the recruited T2DM patients is demonstrated in [Table 1]. Both men and women subjects were of matching age. Although there was a trend for a higher average body weight among men, the average body mass index (BMI) values in both genders were matching. Similarly, blood pressure (BP) average values were comparable in both genders. Biochemical measurements analysis has demonstrated no difference in FG or fasting insulin levels among both genders. On the other hand, men were on average more insulin resistant than women as indicated by the HOMA-IR index. Insulin resistance indices showed a wide dispersion of values in both genders as apparent from the large SD values. Both genders were on average maintaining more or less acceptable glycemic control for T2DM patients as demonstrated by the HbA1c%. Similarly, the lipid measures have demonstrated no differences among both genders. Significantly lower erythrocyte parameters were demonstrated in women relative to men. On the other hand, no intergender significant differences were detected in total white blood cells' count, platelet count and volume, or PT and APTT.
Table 1: Anthropometric, biochemical, and hematological parameters of type 2 diabetes mellitus men and women

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Analyzing the participant's anthropometric data after categorizing them according to the presence or absence of CVD has demonstrated gender-related and CVD-related differences as shown in [Table 2] and [Table 3]. Within both genders, the average age was significantly higher in the group with CVD complications as compared to the group with no complications. While only within the women group, the body weight, BMI, and systolic BP (SBP) were significantly higher in the presence of CVD.
Table 2: Anthropometric parameters of type 2 diabetes mellitus men and women, without and with cardiovascular disease

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Table 3: Percentage of type 2 diabetes mellitus women in each of the World Health Organization bodyweight category

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Among the T2DM women without CVD complication, 53% were premenopausal, while 47% were postmenopausal [Figure 1]a, 31% had SBP >135 mmHg, and none had diastolic BP (DBP) ≥85 mmHg. On the other hand, in the group of T2DM women with CVD, 27% were premenopausal, while 73% were postmenopausal [Figure 1]b, 44% of this group had SBP >135 mmHg, and 17% had DBP ≥85 mmHg.
Figure 1: Percentage of pre- and post-menopausal type 2 diabetes mellitus women with no cardiovascular disease (a) and with cardiovascular disease (b)

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Analyzing fasting insulin and glucose, HOMA-IR, HBA1c, and lipid profile after categorizing the recruited participants, according to the presence or absence of CVD, has not revealed any significant difference in both genders. However, among these measures, the presence of CVD was associated with a lower trend in HDL-C average level and a higher trend in TG level (P > 0.05) in the group of T2DM women [Figure 2]. A significant positive correlation was detected between insulin level and TG level in T2DM women (when women with and without CVD were combined in one group) (correlation coefficient (ρ) = 0.586, and P = 0.02). This was not detected in T2DM men group. A positive trend was maintained between insulin and TG levels in T2DM women with CVD, although it did not reach statistical significance.
Figure 2: Fasting lipid profile in diabetes mellitus men and women with or without cardiovascular disease. Data are presented as mean ± standard error of the mean (SEM). SEM values are shown as vertical error bars. Abbreviations used: DM: diabetic; T. Chol: Total cholesterol; HDL-C: High-density lipoprotein-cholesterol; LDL-C: Low-density lipoprotein-cholesterol; TG: Triacylglycerol

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Analysis of liver enzymes measurements as liver function tests has shown that T2DM women had significantly higher levels of circulating ALP and ALT than T2DM men [Table 1] and [Figure 3]. Within the same gender, the occurrence of CVD was not associated with a statistically significant difference in liver function tests (P > 0.05) [Figure 4] or in kidney function tests (data not shown).
Figure 3: Liver function tests in diabetes mellitus men and women. Data are presented as mean ± standard error of the mean (SEM). SEM values are shown as vertical error bars. Student t-test significance: *P ≤ 0.05, ***P ≤ 0.001. GGT: γ-glutamyl transferase; ALP: Alkaline phosphatase; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase

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Figure 4: Liver function tests in diabetes mellitus men and women with or without cardiovascular disease. Data are presented as mean ± standard error of the mean (SEM). SEM values are shown as vertical error bars. GGT: g-glutamyl transferase; ALP: Alkaline phosphatase; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase

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Only T2DM men demonstrated CVD-related significant change in the hemoglobin concentration and hematocrit value, both were significantly lower in T2DM men with CVD relative to those without CVD (P = 0.026 and P = 0.042, respectively).

The hemostatic parameters, PT and APTT, assessed revealed no gender-related or CVD-related statistical significant differences [Table 1].

The CBC showed a trend of higher platelet count in T2DM women relative to T2DM men, although this trend did not reach statistical significance [Table 1]. However, the platelet count difference was more apparent using the flow cytometric cell surface expression of CD61, where the average ± SD of the percentage of CD61-positive particles was 19.87% ± 15.85 (men) and 28.03% ± 16.14 (women) (P = 0.019).

There was a significant difference in platelet count only in female group categorized according to the presence of CVD, where lower platelets' count was found in T2DM female with CVD as compared to those without CVD (percentage of CD61-positive particles was 33.3% ±16.1 [without CVD] and 18.1% ±16.2 [with CVD] P = 0.016).

No statistically significant difference was observed in the ADP-induced platelet aggregation, basal activation level of circulating platelets by flow cytometry, and the ELISA measurements of the hemostatic parameter (total and free TFPI) (data not shown). Circulating levels of PAI-1, on the other hand, showed a trend of increasing levels among T2DM men with CVD as compared to those without CVD (P = 0.08). This was not observed among T2DM women. Significant positive correlation between PAI-1 and body weight was detected among the group of all T2DM patients (men and women combined) having CVD (ρ = 0.396, P = 0.045). No such correlation was found when we analyzed the T2DM men and women separately nor when we subcategorized each gender based on the presence of CVD. PAI-1 levels in all T2DM patients (both genders and regardless of the presence or absence of CVD) were positively correlated with the BMI (ρ = 0.258, P = 0.05). Interestingly, only in T2DM women group (regardless of the presence or absence of CVD) circulating PAI-1 levels positively correlate with fasting insulin (ρ = 0.867, P < 0.001), with HOMA-IR (ρ = 0.860, P < 0.001), and with fasting TG (ρ = 0.360, P = 0.024).

  Discussion Top

The present work demonstrates gender-related differences in certain parameters in T2DM patients when assessing the insulin resistance index, liver enzymes, RBCs parameters, and platelets count. Only in T2DM women was there a significant positive correlation between fasting insulin and TG and between PAI-1 levels and each of the following parameters: fasting insulin, HOMA-IR, and TG. Both genders showed CVD-related significant difference in age, where older age is more associated with the occurrence of CVD in T2DM. T2DM women, in particular, showed CVD-related significant differences in their body weight, BMI, and SBP.

Intergender differences in platelets count and volume were previously demonstrated in healthy population where higher platelets count and lower mean platelet volume were reported in healthy women as compared to healthy men.[19] In our study, an association between platelet count and the presence of CVD among T2DM women was demonstrated by the significant decrease in the percentage of particles positive for CD61 in the presence of CVD. Although this finding needs further confirmation on a larger sample size, a possible explanation might be the increased consumption of platelets due to the thrombotic events. Platelet aggregation, on the other hand, was not affected in the group of T2DM patients with CVD; a finding that might be related to the observed large interindividual variation in platelets' aggregation response. It is not uncommon to find conflicting results in the literature in this regard, i.e., the correlation between platelet aggregation response and platelet indices.[19] Such ambiguity in describing platelet contribution to the occurrence of CVD in T2DM seems to be the result of the complex nature of the platelets' activity, and their integration with other factors involved in maintaining normal blood flow. We suggest that it is the balance between two states; namely platelet responsiveness upon activation and platelet's basal-state of activity; that leads the T2DM patient to manifest specific vascular complication versus another. Additional complexity possibly exists, namely the interindividual variation, not only in platelets count and functions but also in various other hemostatic/thrombotic factors. Results demonstrated in the current work indicate that measuring both platelet indices (count and volume) and platelet function, perhaps by more than one approach; for instance, measuring platelet microparticles' level and activity,[20] or assessing activation-induced intraplatelet calcium mobilization, in addition to platelet aggregation; in larger cohort may provide a better understanding of the platelet status in T2DM.

Impaired fibrinolysis, as detected in the present work as well as in others,[21] signals a hazardous situation with high risk of precipitating thrombovascular complications in uncontrolled T2DM patients at any time point, especially in those patients with other risk factors as dyslipidemia and atherosclerosis. Factors such as hyperinsulinemia and obesity might partially contribute to the impaired fibrinolysis. A few years ago, Stegenga et al. have demonstrated that hyperinsulinemia inhibits fibrinolysis irrespective of glucose concentration and that hyperglycemia stimulates coagulation irrespective to insulin concentration.[21] This is in line with our findings of significant positive correlation between PAI-1, which is not only a marker for the fibrinolytic system activity but also for inflammation and for endothelial dysfunction and both fasting insulin concentrations and insulin resistance index in T2DM. This correlation seems to be contributed more by the female group of T2DM patients, since the significant association observed in the whole T2DM group becomes statistically more significant when the female group was separately analyzed, while the association becomes no longer significant when the male group was separately analyzed. Since the majority of the circulating PAI-1 is contributed by adipose tissue, obese patients are expected to have higher levels of PAI-1.[22],[23] Indeed, results obtained from the present study confirm this and show a strong association between obesity measures (body weight and BMI) and circulating levels of PAI-1. This association is found to be related to the presence of diabetic complications but is not gender related. Alternatively, based on a study showing that high PAI-1 levels predicted the risk of T2DM and that this predictive ability extended after a long-term follow-up period of 9 years,[24] one can postulate that, in a population at risk of developing diabetes (such as the offspring of T2DM), elevated PAI-1 level may be an independent marker for predicting the disease. Further studies are needed to test this postulation.

CVD is uncommon in premenopausal women, particularly in the absence of other risk factors.[25] This is confirmed in the present study, and it highlights the protective roles played by the female reproductive hormonal profile and the favorable lipid profile in the premenopausal period. This might also reflect the unfavorable inflammatory milieu of obesity, since T2DM women with CVD were on average more obese, and hence more exposed to the inflammatory cytokines/adipokines secreted by the adipose tissue than those without the CVD complications.[26]

Insulin resistance, DM, and dyslipidemia are key components of the MetS.[27] In accordance with data reported by Jax et al.,[28] who have shown a statistically significant positive correlation between fasting TG – while not total cholesterol nor HDL-C – and fasting insulin levels in T2DM complicated with CVD, our data demonstrated similar finding among T2DM women both controlled and complicated. A positive trend was maintained between insulin and TG levels in the group of T2DM women with CVD complications, although it did not reach statistical significance. This might be explained by the natural history of T2DM. The appearance of complications represents a late stage in the disease progress, where the pancreas is unable to compensate for the peripheral insulin resistance by secreting more insulin.[29] Nevertheless, the association between CVD as a macrovascular complication of T2DM and both hyperglycemia and hyperinsulinemia is not fully understood, and recent evidence suggests that it is the peripheral insulin resistance state that increases the risk for CVD rather than the insulin level per se. Hyperinsulinemia and insulin resistance are also key factors in the mechanism of atherosclerosis and hence have been the focus of intense studies for almost four decades. Several research groups have demonstrated that elevated fasting plasma insulin levels were strongly linked to enhanced atherosclerosis, and that they are associated with hypertension and obesity, both are risk factors for CVD. These studies were recently analyzed in a systematic review by Kelly et al.[30]

Our results are consistent with the general consensus that patients with T2DM, who also manifest the MetS or its main components carry a higher risk of CVD complications than those who have T2DM alone and are, otherwise well-controlled.[31] The link between various T2DM-associated complications/comorbidities includes dietary factors, metabolic, endothelial/vascular dysfunction, and kidney diseases leading to sodium retention, glomerular hyperfiltration, and proteinuria.[32],[33] Moreover, recent large cohort prospective studies have demonstrated that obesity is strongly associated with an increased risk of T2DM, hypertension, and dyslipidemia,[34] and that it is a strong and independent predictor of death from CVD among women.[35],[36] In fact, studies have confirmed that there is no healthy pattern of increased weight and that on the long term, even the recently described category of “obese but metabolically healthy” individuals are still at an increased risk of CVD and death from any cause, relative to “lean and metabolically healthy” individuals. This was recently published in systematic reviews and meta-analyses.[37],[38] The current work supports this notion and emphasizes that it is critically important to control body weight, especially among women suffering from T2DM.

Certain interesting findings reported in the current work are worthy for targeting in future studies. For instance, a liver imaging technique looking at the prevalence of fatty liver among diabetic female with and without CVD, as opposed to male participants might explain the intergender difference in the liver function tests reported in the present study. In addition, measuring the hemoglobin and hematocrit values in a larger cohort of diabetic population of both genders in the presence or absence of cardiovascular complications might help in better understanding for related finding of the present work. This can also be related to recent works that demonstrated a strong association between RBC indices and CVD.[39],[40]

The present study has strength points and some limitations. One limitation is imposed by the small sample size. Using the BMI as the obesity marker, as it represents total body weight and not specifically fat mass and central obesity, may be considered a limitation. However, this was based on the general consensus that if BMI is ≥30 kg/m 2, central obesity can be assumed and measures of central obesity need not to be measured.[31] Further limitation was due to the performed platelets' function assessment method, namely the ADP-induced platelet aggregation, which although informative, has shown a wide individual variation. Increasing the sample size and using an additional platelet functional assessment method is suggested for future analysis. On the other hand, focusing on confirmed cases of T2DM without and with CVD complication among men and women and highlighting the factors differentiating these subgroups are clearly strength points. The conductance of this study among Saudi adult men and women was meant to obtain meaningful pilot study data for a population that is not well-studied yet, allowing for assessing ethnicity- and/or gender-related characteristic in future studies. This is of particular significance in parameters such as circulating level of PAI-1 which was recently reported to manifest ethnicity-related differences.[23],[41]

  Conclusions Top

Results of the current study demonstrated several gender- and CVD-related differences among adult T2DM patients. Obesity, impaired fibrinolysis, and platelet count represent factors associated with the presence of cardiovascular complication, particularly among T2DM women. A link between PAI-1, obesity, insulin resistance, and atherogenic lipid profile among T2DM is also demonstrated. Further studies are needed to better understand the altered thrombotic milieu. Stringent control of obesity and BP is critical in the management of T2DM patients in general and among women in particular.

Financial support and sponsorship

This study was financially supported by King Abdul-Aziz City for Science and Technology (grant # LGP-12-9) and The Strategic Technologies Program of the National Plan for Sciences and Technology and Innovation in Kingdom of Saudi Arabia (Grant No. 08-MED 513-02 and Grant No. 11-MED1907-02).

Conflicts of interest

There are no conflicts of interest.

  References Top

Scheen AJ, Van Gaal LF. Combating the dual burden: Therapeutic targeting of common pathways in obesity and type 2 diabetes. Lancet Diabetes Endocrinol 2014;2:911-22.  Back to cited text no. 1
Bhupathiraju SN, Hu FB. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res 2016;118:1723-35.  Back to cited text no. 2
Riobó Serván P. Obesity and diabetes. Nutr Hosp 2013;28 Suppl 5:138-43.  Back to cited text no. 3
Donaghue KC, Chiarelli F, Trotta D, Allgrove J, Dahl-Jorgensen K. Microvascular and macrovascular complications associated with diabetes in children and adolescents. Pediatr Diabetes 2009;10 Suppl 12:195-203.  Back to cited text no. 4
Bornfeldt KE. 2013 Russel Ross memorial lecture in vascular biology: Cellular and molecular mechanisms of diabetes mellitus-accelerated atherosclerosis. Arterioscler Thromb Vasc Biol 2014;34:705-14.  Back to cited text no. 5
Varga ZV, Giricz Z, Liaudet L, Haskó G, Ferdinandy P, Pacher P, et al. Interplay of oxidative, nitrosative/nitrative stress, inflammation, cell death and autophagy in diabetic cardiomyopathy. Biochim Biophys Acta 2015;1852:232-42.  Back to cited text no. 6
Sliwa K, Acquah L, Gersh BJ, Mocumbi AO. Impact of socioeconomic status, ethnicity, and urbanization on risk factor profiles of cardiovascular disease in Africa. Circulation 2016;133:1199-208.  Back to cited text no. 7
Dantas AP, Fortes ZB, de Carvalho MH. Vascular disease in diabetic women: Why do they miss the female protection? Exp Diabetes Res 2012;2012:570598.  Back to cited text no. 8
García NH, Pérez HA, Spence JD, Armando LJ. Risk of vascular disease in premenopausal women with diabetes mellitus. Clin Ther 2014;36:1924-34.  Back to cited text no. 9
World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. 8-11 December, 2008. Geneva: World Health Organization; 2011.  Back to cited text no. 10
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5.  Back to cited text no. 11
Webb DR, Gray LJ, Khunti K, Campbell S, Dallosso H, Davies MJ, et al. Contrasting cardiovascular risk profiles and prescribed cardio-protective therapies in newly-diagnosed type 2 diabetes identified through screening and standard practice. Diabetes Res Clin Pract 2011;91:280-5.  Back to cited text no. 12
Badimon L, Padró T, Vilahur G. Atherosclerosis, platelets and thrombosis in acute ischaemic heart disease. Eur Heart J Acute Cardiovasc Care 2012;1:60-74.  Back to cited text no. 13
Neergaard-Petersen S, Hvas AM, Kristensen SD, Grove EL. Platelets and antiplatelet therapy in patients with coronary artery disease and diabetes. Semin Thromb Hemost 2016;42:234-41.  Back to cited text no. 14
Mylotte D, Kavanagh GF, Peace AJ, Tedesco AF, Carmody D, O'Reilly M, et al. Platelet reactivity in type 2 diabetes mellitus: A comparative analysis with survivors of myocardial infarction and the role of glycaemic control. Platelets 2012;23:439-46.  Back to cited text no. 15
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC, et al. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.  Back to cited text no. 16
Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16:31-41.  Back to cited text no. 17
Sweeney JD, Hoernig LA, Fitzpatrick JE. Whole blood aggregation in von Willebrand disease. Am J Hematol 1989;32:190-3.  Back to cited text no. 18
Park BJ, Shim JY, Lee HR, Jung DH, Lee JH, Lee YJ, et al. The relationship of platelet count, mean platelet volume with metabolic syndrome according to the criteria of the American Association of Clinical Endocrinologists: A focus on gender differences. Platelets 2012;23:45-50.  Back to cited text no. 19
Zhang X, McGeoch SC, Johnstone AM, Holtrop G, Sneddon AA, MacRury SM, et al. Platelet-derived microparticle count and surface molecule expression differ between subjects with and without type 2 diabetes, independently of obesity status. J Thromb Thrombolysis 2014;37:455-63.  Back to cited text no. 20
Stegenga ME, van der Crabben SN, Blümer RM, Levi M, Meijers JC, Serlie MJ, et al. Hyperglycemia enhances coagulation and reduces neutrophil degranulation, whereas hyperinsulinemia inhibits fibrinolysis during human endotoxemia. Blood 2008;112:82-9.  Back to cited text no. 21
Alessi MC, Juhan-Vague I. Metabolic syndrome, haemostasis and thrombosis. Thromb Haemost 2008;99:995-1000.  Back to cited text no. 22
Barnard SA, Pieters M, De Lange Z. The contribution of different adipose tissue depots to plasma plasminogen activator inhibitor-1 (PAI-1) levels. Blood Rev 2016;30:421-9.  Back to cited text no. 23
Alessi MC, Nicaud V, Scroyen I, Lange C, Saut N, Fumeron F, et al. Association of vitronectin and plasminogen activator inhibitor-1 levels with the risk of metabolic syndrome and type 2 diabetes mellitus. Results from the D.E.S.I.R. prospective cohort. Thromb Haemost 2011;106:416-22.  Back to cited text no. 24
Ostadal B, Ostadal P. Sex-based differences in cardiac ischaemic injury and protection: Therapeutic implications. Br J Pharmacol 2014;171:541-54.  Back to cited text no. 25
Khera A, Vega GL, Das SR, Ayers C, McGuire DK, Grundy SM, et al. Sex differences in the relationship between C-reactive protein and body fat. J Clin Endocrinol Metab 2009;94:3251-8.  Back to cited text no. 26
Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37:1595-607.  Back to cited text no. 27
Jax TW, Peters AJ, Plehn G, Schoebel FC. Relevance of hemostatic risk factors on coronary morphology in patients with diabetes mellitus type 2. Cardiovasc Diabetol 2009;8:24.  Back to cited text no. 28
Ferrannini E, Mari A. B-cell function in type 2 diabetes. Metabolism 2014;63:1217-27.  Back to cited text no. 29
Kelly CT, Mansoor J, Dohm GL, Chapman WH 3rd, Pender JR 4th, Pories WJ, et al. Hyperinsulinemic syndrome: The metabolic syndrome is broader than you think. Surgery 2014;156:405-11.  Back to cited text no. 30
Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2010;375:181-3.  Back to cited text no. 31
DeMarco VG, Aroor AR, Sowers JR. The pathophysiology of hypertension in patients with obesity. Nat Rev Endocrinol 2014;10:364-76.  Back to cited text no. 32
Domingueti CP, Dusse LM, Carvalho MD, de Sousa LP, Gomes KB, Fernandes AP, et al. Diabetes mellitus: The linkage between oxidative stress, inflammation, hypercoagulability and vascular complications. J Diabetes Complications 2016;30:738-45.  Back to cited text no. 33
Schmiegelow MD, Andersson C, Køber L, Andersen SS, Norgaard ML, Jensen TB, et al. Associations between body mass index and development of metabolic disorders in fertile women – A nationwide cohort study. J Am Heart Assoc 2014;3:e000672.  Back to cited text no. 34
van Dam RM, Li T, Spiegelman D, Franco OH, Hu FB. Combined impact of lifestyle factors on mortality: Prospective cohort study in US women. BMJ 2008;337:a1440.  Back to cited text no. 35
Bruce SA. The association between central fat distribution and recurrent cardiovascular disease events in female survivors of nonfatal myocardial infarction. J Cardiovasc Nurs 2015;30:E15-22.  Back to cited text no. 36
Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann Intern Med 2013;159:758-69.  Back to cited text no. 37
Lin H, Zhang L, Zheng R, Zheng Y. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: A systematic review and meta-analysis: A PRISMA-compliant article. Medicine (Baltimore) 2017;96:e8838.  Back to cited text no. 38
Tziakas D, Chalikias G, Grapsa A, Gioka T, Tentes I, Konstantinides S, et al. Red blood cell distribution width: A strong prognostic marker in cardiovascular disease: Is associated with cholesterol content of erythrocyte membrane. Clin Hemorheol Microcirc 2012;51:243-54.  Back to cited text no. 39
Mallikethi-Reddy S, Briasoulis A, Akintoye E, Afonso L. Novel biomarkers with potential for cardiovascular risk reclassification. Biomarkers 2017;22:189-99.  Back to cited text no. 40
Kumari B, Srivastava S, Chatterjee T, Vardhan R, Tyagi T, Gupta N, et al. Study of associated genetic variants in Indian subjects reveals the basis of ethnicity related differences in susceptibility to venous thromboembolism. Thrombosis 2014;2014:182762.  Back to cited text no. 41


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3]


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