Coronary microvascular dysfunction in gestational diabetes: Insights on possible mechanism from a large institutional registry

Coronary microvascular dysfunction in gestational diabetes: Insights on possible mechanism from a large institutional registry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coronary microvascular dysfunction in gestational diabetes: Insights on possible mechanism from a large institutional registry Mustafa Caliskan, Mumtaz Takir, Ahmet Eren Caliskan, Fatma Betul Celik, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194002/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted 8 You are reading this latest preprint version Abstract Background Gestational diabetes is associated with an increased risk of coronary artery disease and adverse cardiovascular events in later life. Coronary microvascular dysfunction is a common precursor of coronary after disease. Although microvascular dysfunction is common in GDM, the exact mechanisms remain unknown. Aim To study the associations between coronary flow reserve (CFR) with common biomarkers of hyperglycemia, insulin resistance, inflammation and oxidative stress. Methods Measurement of CFR was performed noninvasively using echocardiography in all patients. Results Patients with a low CFR (≤ 2.5) had higher HbA1c%, HOMA-IR, triglyceride-glucose index, uric acid, and total and non-HDL cholesterol as compared to those with a normal CFR (> 2.5). On univariate analysis, there was strong evidence favoring an association between CFR with HbA1c% (r=-0.32,p < 0.001,BF 10 :785) and uric acid (r=-0.29,p < 0.001,BF 10 :113) and moderate to strong evidence for HOMA-IR (r=-0.21,p = 0.006,BF 10 :20). Final linear regression model included HbA1c% (β=-0.31,p < 0.001); HOMA-IR (β=-0.25,p = 0.001); uric acid (β=-0.19,p = 0.007) and waist circumference (β = 0.25,p < 0.001). Conclusions GDM patients with higher HbA1c%, HOMA-IR, hyperuricemia and lower waist circumference are at a higher risk for coronary microvascular dysfunction. However, most of the variance in CFR is not related to any biomarkers and a past history of GDM rappears as the most likely cause for reduced CFR. Gestational diabetes coronary artery disease coronary microvascular dysfunction Figures Figure 1 Figure 2 Figure 3 Highlights Gestational diabetes is associated with coronary artery disease in later life. Coronary microvascular dysfunction is common in patients with a history of gestational diabetes. Present findings show hemoglobin A1c%, HOMA-IR and uric acid are associated with coronary flow reserve. Abnormal glucose tolerance, insulin resistance and hyperuricemia are risk markers for microvascular dysfunction. Most of the variance in coronary flow reserve cannot be explained with these biomarkers. Introduction Gestational diabetes (GDM) is a major complication of the pregnancy and is observed in up to 17–20% of pregnancies worldwide (1). Although the impaired glucose tolerance associated with GDM is temporary, a history of GDM confers an increased risk for developing prediabetes, diabetes and cardiovascular disease (2–5). Even in those that remain normoglycemic after birth, a prior diagnosis of GDM is associated with a doubling of the risk for coronary artery disease (CAD) as assessed with coronary after calcium score (6). The mechanisms that link GDM with the increased risk of CAD is incompletely understood, but GDM-related endothelial dysfunction (ED) appears as a major factor that increases the risk of future CAD (7,8). A major manifestation of ED in the coronary circulation is abnormal coronary flow reserve (CFR), which assesses the ability of coronary microvasculature to increase blood supply to the myocardium under maximal vasodilatation (9). Hyperglycemia is an established risk factor for coronary microvascular dysfunction, and our study group as well as others have previously demonstrated an impaired coronary flow reserve in GDM patients (10–12). Although various measures of abnormal glucose tolerance appears to correlate with the degree of reduction in CFR in these previous studies, a detailed analysis on the possible mechanisms underlying coronary microvascular dysfunction in GDM is still unavailable. In the present work, we aimed to understand possible associations between coronary microvascular dysfunction with hyperglycemia, hyperinsulinemia, inflammation and oxidative stress in patients with GDM. As a secondary aim, we sought to understand the strength of evidence that supports these associations using Bayesian methods. Methods Subjects Present study was as retrospective observational study done using data from the institutional registry that enrolled patients older than 18 years with chest pain in the absence of epicardial coronary artery disease. Per registry protocols, subjects were only included in the registry after exclusion of epicardial coronary disease with stress imaging, CT angiography or invasive angiography. For this particular analysis, we scanned the database for women with a previous history of GDM in at least one previous pregnancy. Exclusion criteria were active smoking, active pregnancy, GDM within one year of inclusion in the registry, established diabetes, chronic or acute liver disease, kidney disease, hypertension (patients under hypertensive treatment or with blood pressure > 140/90 mmHg), any cardiomyopathy, significant valve disease, atrial fibrillation or any other persistent arrhythmia, previous epicardial CAD (myocardial infarction, coronary angioplasty, coronary bypass surgery, or more than 30% epicardial coronary artery stenosis on coronary angiography), resting wall motion abnormalities and severe pulmonary disease. After applying these criteria, 166 out of 1743 cases were deemed eligible for inclusion. Patients’ demographic, clinical, imaging and laboratory parameters were recorded using electronic registry records. The study was conducted according to the 1975 Declaration of Helsinki and its subsequent revisions. All subject gave their informed consent prior to enrollment to the registry. An ethical approval was obtained from the Medeniyet University ethics committee, document number 2020/0472. Laboratory analyses Per registry protocols, blood was withdrawn from all participants after overnight fasting and obtained specimens were delivered to the laboratory within 30 minutes of collection. Complete blood counts were studied using an automated Coulter analyzer. Other biochemical tests were done as defined before. Triglyceride-glucose index (TGI, Eq. 1 ) and Homeostasis model assessment of insulin resistance (HOMA-IR, Eq. 2 ) were calculated as described previously (13,14): Eq1. Ln (fasting triglycerides in mg/dl x fasting glucose in mg/dl / 2) Eq2. HOMA-IR = (Fasting plasma insulin × Fasting plasma glucose) / 22.5. Transthoracic echocardiography and measurement of coronary flow reserve All echocardiographic measurements for the registry were performed using an echocardiography platform equipped with a phased-array transducer (GE Vivid 6, GE Healthcare, Piscataway, NJ) by an experienced cardiologist who was blind to clinical data. A conventional echocardiographic examination was done prior to the measurement of coronary flow measurements, and left ventricular end-diastolic and end-systolic diameters and volumes, as well as inter ventricular septal and posterior wall thickness were recorded. Left ventricular mass was calculated using Deveraux formula and left ventricular ejection fraction was calculated using modified Simpson method (15). Coronary flow velocity reserve, a derivative of CFR that can be measured with transthoracic Doppler echocardiography was measured as previously described using intravenous dipyridamole infusion (0.56 mg/kg over 4 min). If the acceleration in heart rate wasn’t enough (10% increase from the baseline), another dose of dipyridamole (0.28 mg/kg over a 2-min period) was administrated. The mid-distal part of the LAD was studied using the S5-1 probe, and the LAD artery was visualized by color Doppler flow mapping guidance in the modified parasternal view. For color Doppler echocardiography, the velocity range was defined as 8.9 to 24.0 cm/sec. Blood flow velocity was measured using pulsed-wave Doppler echocardiography, using a sample volume of 3 to 4 mm, placed on the color signal in the distal LAD. The ultrasound beam direction was aligned with the distal LAD flow. The angle was kept small and no correction was applied. Coronary diastolic peak velocities were measured at baseline and after dipyridamole by averaging the highest three Doppler signals for each measurement. CFR was defined as the ratio of hyperemic to baseline diastolic peak velocities and the cut-off value for CFR was taken as 2.5. The heart rate was monitored continuously during the patient examination, and blood pressure was recorded at baseline and during hyperemia using an automatic arm sphygmomanometer. For the present analysis, all stored data pertinent to GDM were re-reviewed by an investigator blinded to clinical data and repeat measurements were done to calculate CFR for each individual patient. To determine intra-observer variability of the coronary flow measurements, repeat measurements from the stored pulsed-wave Doppler images were done by the same investigator who made the initial measurements. Using the two sets of measurements, intraclass correlation coefficient for CFR was calculated. Statistical analyses Continuous parameters were given as mean ± SD or as median and interquartile range, while categorical variables were given as percentages. Patterns of distribution were analyzed using visual inspection of histograms, QQ plots and Shapiro Wilk tests. For continuous variables, differences between groups of patients with a CFR ≤ 2.5 and CFR > 2.5 were analyzed using Student’s t or Mann-Whitney U tests. Correlation analyses were done using Pearson test or Spearman’s rho. Four linear regression models were used to assess the association between clinical, echocardiographic and laboratory parameters with CFR. At each step, parameters that were found as significant predictors of CFR were included in the latter model. First model included parameters of glucose intolerance (fasting glucose, HbA1c%, HOMA-IR and TGI), while second model introduced demographic and clinical parameters (age, body mass index, waist circumference, systolic blood pressure, heart rate) and the third model introduced lipid parameters (total cholesterol and non-HDL cholesterol), markers of inflammation and oxidative stress (C-reactive protein and uric acid) and hemoglobin, while the fourth model included echocardiographic variables (left ventricular ejection fraction and mass). Due to the significant collinearity; body mass index (BMI), diastolic blood pressure and LDL cholesterol were excluded from the models. Conversions were made for HOMA-IR and C-reactive protein due to the skewed distribution of both parameters. A final model incorporating all significant predictors were used to calculate the adjusted R 2 as a measure of variance of CFR that can be explained by these variables. Additional Bayesian analyses were performed to understand the strength of evidence favoring a particular association between a parameter and CFR. Bayesian correlation analyses with Pearson test or Kendall’s tau were used to assess the associations between individual parameters with CFR. Finally, a Bayesian multivariable linear regression model including predictors found to be significant in Model IV was used to assess the overall evidence favoring an association between this final model and CFR. Results Mean age of the subjects were 33.6 ± 4.3, with a mean BMI of 27.4 ± 4.4 kg/m2 and a mean waist circumference of 85.6 ± 7.3 cm. Median coronary flow reserve of the study participants were 2.46 (2.16–2.75). Mean hemoglobin A1c% (HbA1c%) and median TGI and HOMA-IR were 5.25 ± 0.4, 8.65 (8.26–8.90) and 2.65 (1.65–3.32), respectively. Intraclass correlation coefficient for intra-observer variability of the CFR measurement was 0.946. Table 1 summarizes key demographic, clinical, laboratory and imaging differences between the study groups. Apart from the differences between measures of insulin resistance and hyperglycemia, including HOMA-IR, TGI and HbA1c% (Fig. 1 ), there were significant differences between the groups in terms of total cholesterol, non-HDL cholesterol and uric acid. Table 1 Demographic, clinical, laboratory and imaging findings for patients with a coronary flow reserve ≤ 2.5 as compared to those with a coronary flow reserve > 2.5. P values below 0.05 were given in bold. LDL: Low-density lipoprotein, HDL: High-density lipoprotein, HOMA-IR: Homeostatic model assessment for insulin resistance, GFR: Glomerular filtration rate. Characteristic Coronary flow reserve > 2.5 (n = 78) Coronary flow reserve ≤ 2.5 (n = 88) P value Age (years) 33.0 ± 4.2 34.2 ± 4.3 0.07 Body mass index (kg/m 2 ) 28.1 ± 5.0 26.7 ± 3.7 0.21 Waist circumference (cm) 86.9 ± 7.7 84.5 ± 6.7 0.09 Systolic blood pressure (mmHg) 118.0 ± 11.5 119.0 ± 11.5 0.59 Diastolic blood pressure (mmHg) 74.9 ± 8.1 74.7 ± 8.0 0.99 Heart rate (bpm) 73.4 ± 8.3 76.1 ± 9.1 0.03 Hemoglobin (g/dl) 13.5 ± 1.6 13.4 ± 1.2 0.91 Total cholesterol (mg/dl) 182.0 ± 28.3 191.0 ± 29.3 0.04 LDL-cholesterol (mg/dl) 111.0 ± 24.2 116.0 ± 27.8 0.17 Non-HDL cholesterol (mg/dl) 133.0 ± 29.3 144.0 ± 32.2 0.04 Fasting glucose (mg/dl) 91.8 ± 7.1 92.9 ± 8.2 0.40 Hemoglobin A1c (%) 5.1 ± 0.4 5.4 ± 0.4 < 0.001 Triglyceride glucose index 8.5 (8.2–8.8) 8.8 (8.4–9.0) 0.003 HOMA-IR 2.4 (1.6–3.0) 2.8 (1.7–3.4) 0.03 Creatinine (mg/dl) 0.83 ± 0.16 0.81 ± 0.19 0.43 Estimated GFR (ml/min/1.73 m 2 ) 98.3 ± 20.7 99.0 ± 20.0 0.87 C-reactive protein (mg/dl) 1.4 (0.8–3.2) 2.0 (1.1–4.0) 0.06 Uric acid (mg/dl) 4.7 ± 0.6 5.0 ± 0.8 0.02 Left ventricular end-diastolic volume (ml) 95.5 ± 17.3 93.1 ± 17.5 0.38 Left ventricular end-systolic volume (ml) 31.3 ± 7.7 29.3 ± 6.8 0.18 Left ventricular ejection fraction (%) 67.3 ± 4.4 68.3 ± 5.7 0.22 Left ventricular mass (g) 134.0 ± 25.8 131.0 ± 29.5 0.22 Coronary flow velocity at rest (cm/s) 24.0 (21.0–26.0) 27.4 (24.0–33.0) < 0.001 Peak coronary flow velocity (cm/s) 66.5 (58.3–73.8) 60.0 (52.0 (71.0) 0.002 Correlation analyses On correlation analyses, CFR showed significant correlations with age (r=-0.18, p = 0.02), heart rate (r=-0.23, p = 0.003), HbA1c% (r=-0.32, p < 0.001), HOMA-IR (r=-0.21, p = 0.006), TGI (r=-0.18, p = 0.02) and uric acid (r=-0.29, p < 0.001). There was a positive correlation between CFR and BMI (r = 0.14, p = 0.07) as well as between CFR and waist circumference (r = 0.15, p = 0.06), but neither finding reached statistical significance. Both BMI (r = 0.18, p = 0.02) and waist circumference (r = 0.34, p < 0.001) showed significant positive correlations with HOMA-IR, while BMI (r = 0.19, p = 0.02) but not waist circumference (r = 0.09, p = 0.27) had a significant correlation with HbA1c%. Scatter plots for bivariate correlations between CFR and HbA1c%, HOMA-IR, uric acid and waist circumference were given in Fig. 2 . Multiple linear regression analyses After introducing all studied variables in a stepwise and clustered method, the final model included 4 variables: HbA1c% (β=-0.31, 95%CI:-0.45 — -0.17, p < 0.001); HOMA-IR (β=-0.25, 95%CI:-0.40 — -0.10, p = 0.001); uric acid (β=-0.19, 95%CI:-0.33 — -0.06, p = 0.007) and waist circumference (β = 0.25, 95%CI:0.11 — 0.40, p < 0.001) (Table 2 ). This final model explained 23.2% of the overall variance of CFR in the sample (p < 0.001). Table 2 Multiple linear regression models for prediction of coronary flow reserve. Numbers show standardized estimates and 95% confidence intervals for standardized estimates. Variables that showed a significant association with the coronary flow reserve were given in bold and these variables were included in the subsequent models. HbA1c%: Hemoglobin A1c, TGI: Triglyceride glucose index, HDL: High-density lipoprotein, LV, Left ventricle. Characteristic Model I Model II Model III Model IV Glucose (mg/dl) 0.15 (-0.01 — 0.31) HbA1c (%) -0.34 (-0.49 — -0.19) -0.30 (-0.43 — -0.16) -0.27 (-0.41 — -0.13) -0.32 (-0.45 — -0.17) HOMA-IR -0.22 (-0.22 — -0.06) -0.23 (-0.38 — -0.08) -0.23 (-0.39 — -0.08) -0.23 (-0.38 — -0.07) TGI -0.11 (-0.26 — 0.04) Age (y) -0.14 (-0.27 — -0.01) -0.13 (-0.27 — 0.01) Waist circumference (cm) 0.30 (0.15 — 0.44) 0.27 (0.11 — 0.42) 0.29 (0.13 — 0.43) Systolic blood pressure (mmHg) -0.08 (-0.23 — 0.06) Heart rate (bpm) -0.17 (-0.31 — -0.03) -0.14 (-0.28 — 0.00) Total cholesterol (mg/dl) -0.04 (-0.43 — 0.37) Non-HDL cholesterol (mg/dl) -0.03 (-0.43 — 0.37) Hemoglobin (g/dl) 0.10 (-0.03 — 0.24) Creatinine (mg/dl) 0.07 (-0.07 — 0.22) C-reactive protein (mg/dl) 0.03 (-0.12 — 0.18) Uric acid (mg/dl) -0.19 (-0.33 — -0.04) -0.18 (-0.33 — -0.04) LV ejection fraction (%) -0.02 (-0.16 — 0.12) LV mass (g) -0.12 (-0.27 — 0.02) Bayesian analyses On univariate linear correlation analyses for parameters included in the final multiple regression model, there was very strong evidence supporting an association between CFR and HbA1c (BF 10 : 785) and between CFR and uric acid (BF 10 : 113). There was strong evidence supporting a correlation between CFR and HOMA-IR (BF 10 : 20), while there was no evidence supporting a correlation between waist circumference and CFR (BF 10 : 0.55). For variables not included in the final model, there were no evidence supporting an association between CFR and either age (BF 10 : 1.3) or with TGI (BF 10 : 0.50). The results for Bayesian linear regression model including 4 variables (HbA1c, HOMA-IR, uric acid and waist circumference) were given in Table 3 and a summary of posterior coefficients for the variables included in the model were provided in Fig. 3 . The r 2 for this model was 0.25, and there was very strong evidence for a difference between model and null hypothesis (BF 10 : 5.05 x 10 6 ) and strong evidence for a change from the prior (BF M : 71). Table 3 Bayesian multiple regression model. Bayesian multiple regression model that included the 4 variables that were found as independent predictors of coronary flow reserve in the multiple linear regression analysis. BF: Bayesian factor, HOMA-IR: Homeostatic model assessment for insulin resistance. 95% Credible Interval Coefficient Mean SD BF inclusion Lower Upper Hemoglobin A1c% -0.2923 0.06855 1277.05 -0.42770 -0.1570 HOMA-IR -0.2136 0.06719 25.66 -0.34625 -0.0809 Uric acid -0.0980 0.03703 7.52 -0.17114 -0.0249 Waist circumference 0.0129 0.00392 31.81 0.00515 0.0206 Discussion Gestational diabetes is an important sex-specific cardiovascular risk factor that is linked to an increased risk of CAD even in the absence of overt diabetes in later life. Present study focused on the possible mechanisms that may explain the association of GDM with coronary microvascular dysfunction, which is a key precursor of coronary artery disease. The main takeaways of the present study are as follows: i) both hyperglycemia and insulin resistance are predictors of a lower CFR, ii) uric acid, which is the final product of purine metabolism in humans and is a biomarker of oxidative stress, is associated with coronary flow reserve and iii) although waist circumference had a positive correlation with measures of insulin resistance, particularly HOMA-IR, a higher waist circumference was not associated with a lower CFR and iv) only a quarter of variance in flow reserve can be explained using these 4 variables, and as such either unobserved factors or the transient hyperglycemia during pregnancy is the potential cause for coronary microvascular dysfunction in most patients with GDM. There is a 67–98% increase in the relative risk of cardiovascular disorders in patients with GDM, with CAD and CAD-associated complications such as myocardial infarction being primarily response for this increased risk (16,17). Even in those did not developed pre diabetes or diabetes after pregnancy, GDM is associated with a twofold increase in CAD risk, as shown in the recent analysis of the Coronary Artery Risk Development in Young Adults (CARDIA) study (4). Both fasting glucose and HOMA-IR were associated with an increased coronary calcium score in this latter analysis, which fits well with the current findings as we have observed a strong association between hyperglycemia, insulin resistance and a reduced coronary flow reserve (4). Abnormal glucose tolerance and insulin resistance are both strongly related to endothelial dysfunction and CAD, and it is therefore not surprising to observe either HbA1c or HOMA-IR as predictors of CFR (18,19). Unlike the aforementioned analysis of the CARDIA study, present study included those with prediabetes (i.e. fasting glucose > 100 mg/dl and/or HbA1c > 5.7%) and some of the risk might be attributable to development of hyperglycemia after pregnancy rather than a history of GDM (4). However, present findings also suggest that only a quarter of the variance in CFR can be explained in the final model that included HbA1c and HOMA-IR, and as such much of the association between GDM and coronary microvascular dysfunction could not be explained solely with persisting hyperglycemia or hyperinsulinemia. Therefore, it is reasonable to consider that a history of transient hyperglycemia per se may have long-lasting effects on coronary microvasculature, which is accentuated in the presence of persistent hyperglycemia. Uric acid is the terminal product of purine metabolism in humans and high uric acid concentrations are associated with coronary artery disease and cardiovascular mortality (20,21). Although very high levels of uric acid is toxic to cardiomyocytes in animal models, direct cardiotoxicity is unlikely in humans as uric acid production is much lower (22,23). Hyperuricemia is a marker of over activation of xanthine oxidase (XO), which is the terminal enzyme that converts hypoxanthine to uric acid in two steps (24). In addition to production of uric acid, XO leads to formation to reactive oxygen species and provides an alternative source of nitric oxide in the endothelium (23–25). It has been suggested that this last action of XO explains the link between high uric acid concentrations and cardiovascular disorders, given that reduction of uric acid by inhibiting XO does not lead to a reduction in cardiovascular mortality (26). Although present study could not provide causal inferences for the observed association between uric acid and CFR, it is nonetheless reasonable to consider that patients with GDM and lower CFR have a greater degree of reduction in endothelial nitric oxide synthesis capability as nitric oxide is the primary determinant of microvascular dysfunction. This would lead to the sequestration of XO in the endothelium to provide an alternative source for nitric oxide, hence causing overproduction of uric acid (27). Thus, it is unlikely that targeting uric acid or XO to restore microvascular function or prevent cardiovascular complications of GDM would succeed, given that uric acid is possibly a marker and not to cause of CAD in these patients. An interesting finding observed in the present study is the association of BMI and waist circumference with CFR. A higher BMI is a known predictor of CAD in the overall population as well as in patients with GDM (4,28). However, present results indicate that there was a trend towards a higher CFR in those with a higher waist circumference, and waist circumference was a predictor of higher CFR when included in the final model. Similar findings were also observed when waist circumference was replaced with BMI, although the effect size was lower (data not given in the manuscript). Although CAD is more common in patients with obesity, there are multiple observations suggesting that the prognosis of CAD is actually better in patients with obesity (29). There is some preclinical data to suggest that this so-called “obesity paradox” have a true biological basis as circulating progenitor cell counts are higher in obese patients with or without CAD, but the clinical validity of these results are uncertain (30,31). Although it is tempting to associate present findings with the “obesity paradox” observed in other patients with CAD, caution should be exercised as obesity is a well-known risk factor for endothelial dysfunction (32). Moreover, the concept of “obesity paradox” may be more noise than signal as the survival “benefit” of obese patients disappear when anthropomorphic indices other than body-mass index is used (33). Thus, present findings regarding waist circumference (or BMI) should be interpreted with caution until more evidence supporting or refuting this association becomes available. From a clinical perspective, present findings suggest that patients with a past history of GDM and higher HbA1c, HOMA-IR or uric acid levels have worse coronary microvascular dysfunction and such patients might be at a higher risk for CAD. Nonetheless, much of the reduction in CFR could not be explained with the common parameters measured in this study and therefore associated with either other factors not included in the present work or associated with the transient hyperglycemia during pregnancy. Given that most parameters commonly measured in clinical practice were included in the present study, it is reasonable to ascribe most risk to a past history of GDM until more data emerges to refine risk stratification for GDM, while considering those with hyperglycemia, hyperinsulinemia and/or hyperuricemia at a higher risk for microvascular dysfunction and CAD. As aforementioned, it is reasonable to consider present findings on waist circumference as noise rather than signal until more data emerges. Present study have several strengths and limitations. Although the analysis is retrospective in nature, the data was obtained from a registry and the sample size was relatively large as compared to similar studies. Our study group have a long track record of performing echocardiographic measurement of CFR, thus increasing the reliability of the present results (11,34). Although there are several studies showing the relationship between reduced CFR, CAD and cardiovascular mortality; CFR is nonetheless an intermediary and not all patients with a reduced CFR would be at risk for more clinically significant cardiovascular events (35). As with all observational studies, only variables that were recorded could be analyzed and therefore some associations would invariably be missed. This is particularly true for inflammation, as only C-reactive protein was recorded and contrary to what is expected, we have not found an association between inflammation and CFR. Conclusions Patients with a past history of GDM and concurrent hyperglycemia, insulin resistance and hyperuricemia have worse coronary microvascular function as assessed with CFR. Contrary to what would be expected, CFR was higher those with an increased waist circumference despite these patients having more insulin resistance, but this finding should not be regarded as valid until more data emerges. Finally, as clinical predictors can only explain only a fraction of variance in CFR, coronary microvascular function cannot be reliably estimated using clinical or laboratory data. Thus, a history of GDM itself should be regarded as a risk determinant for coronary microvascular dysfunction, until tools for further risk refinement becomes available. Declarations Declaration of Interests Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The authors received no financial support for the research and/or authorship of this article. Author Contribution MC, AEC, MT : study design, echocardiographic examination, date collection and writing , FBC, LC, EE and TSG: prepared figure and study design, statistic References Vandorsten JP, Dodson WC, Espeland MA, Grobman WA, Guise JM, Mercer BM, Minkoff HL, Poindexter B, Prosser LA, Sawaya GF, Scott JR, Silver RM, Smith L, Thomas A, Tita AT. NIH consensus development conference: diagnosing gestational diabetes mellitus. NIH Consens State Sci Statements. 2013 Mar 6;29(1):1-31. PMID: 23748438. Prados M, Flores-Le Roux JA, Benaiges D, Llauradó G, Chillarón JJ, Paya A, Pedro-Botet J. Previous Gestational Diabetes Increases Atherogenic Dyslipidemia in Subsequent Pregnancy and Postpartum. Lipids. 2018 Apr;53(4):387-392. doi: 10.1002/lipd.12040. Epub 2018 May 6. PMID: 29732563. Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009 May 23;373(9677):1773-9. doi: 10.1016/S0140-6736(09)60731-5. PMID: 19465232. Gunderson EP, Lewis CE, Tsai AL, Chiang V, Carnethon M, Quesenberry CP Jr, Sidney S. A 20-year prospective study of childbearing and incidence of diabetes in young women, controlling for glycemia before conception: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Diabetes. 2007;56:2990–2996. doi: 10.2337/db07-1024 Gunderson EP, Chiang V, Pletcher MJ, Jacobs DR, Quesenberry CP, Sidney S, Lewis CE. History of gestational diabetes mellitus and future risk of atherosclerosis in mid-life: the Coronary Artery Risk Development in Young Adults study. J Am Heart Assoc. 2014 Mar 12;3(2):e000490. doi: 10.1161/JAHA.113.000490. PMID: 24622610; PMCID: PMC4187501. Gunderson EP, Sun B, Catov JM, Carnethon M, Lewis CE, Allen NB, Sidney S, Wellons M, Rana JS, Hou L, Carr JJ. Gestational Diabetes History and Glucose Tolerance After Pregnancy Associated With Coronary Artery Calcium in Women During Midlife: The CARDIA Study. Circulation. 2021 Mar 9;143(10):974-987. doi: 10.1161/CIRCULATIONAHA.120.047320. Epub 2021 Feb 1. PMID: 33517667; PMCID: PMC7940578. Rao R, Sen S, Han B, Ramadoss S, Chaudhuri G. Gestational diabetes, preeclampsia and cytokine release: similarities and differences in endothelial cell function. Adv Exp Med Biol. 2014;814:69-75. doi: 10.1007/978-1-4939-1031-1_6. PMID: 25015801. Knock GA, McCarthy AL, Lowy C, Poston L. Association of gestational diabetes with abnormal maternal vascular endothelial function. Br J Obstet Gynaecol. 1997 Feb;104(2):229-34. doi: 10.1111/j.1471-0528.1997.tb11051.x. PMID: 9070145. Kelshiker MA, Seligman H, Howard JP, Rahman H, Foley M, Nowbar AN, Rajkumar CA, Shun-Shin MJ, Ahmad Y, Sen S, Al-Lamee R, Petraco R; Coronary Flow Outcomes Reviewing Committee. Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis. Eur Heart J. 2022 Apr 19;43(16):1582-1593. doi: 10.1093/eurheartj/ehab775. Erratum in: Eur Heart J. 2023 Jan 1;44(1):27. PMID: 34849697; PMCID: PMC9020988. Nahser PJ Jr, Brown RE, Oskarsson H, Winniford MD, Rossen JD. Maximal coronary flow reserve and metabolic coronary vasodilation in patients with diabetes mellitus. Circulation. 1995 Feb 1;91(3):635-40. doi: 10.1161/01.cir.91.3.635. PMID: 7828287. Caliskan M, Turan Y, Caliskan Z, Gullu H, Ciftci FC, Avci E, Duran C, Kostek O, Telci Caklili O, Koca H, Kulaksizoglu M. Previous gestational diabetes history is associated with impaired coronary flow reserve. Ann Med. 2015;47(7):615-23. doi: 10.3109/07853890.2015.1099719. Epub 2015 Nov 9. PMID: 26555575. Ozyildirim S, Barman HA, Dogan O, Ersanli MK, Dogan SM. The Relationship between Coronary Flow Reserve and the TyG Index in Patients with Gestational Diabetes Mellitus. Medicina (Kaunas). 2023 Oct 12;59(10):1811. doi: 10.3390/medicina59101811. PMID: 37893529; PMCID: PMC10608421. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985 Jul;28(7):412-9. doi: 10.1007/BF00280883. Fritz J, Brozek W, Concin H, et al. The Triglyceride-Glucose Index and Obesity-Related Risk of End-Stage Kidney Disease in Austrian Adults. JAMA Netw Open. 2021 Mar 1;4(3):e212612. doi: 10.1001/jamanetworkopen.2021.2612. Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P, Muraru D, Picard MH, Rietzschel ER, Rudski L, Spencer KT, Tsang W, Voigt JU. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015 Jan;28(1):1-39.e14. doi: 10.1016/j.echo.2014.10.003. PMID: 25559473. Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019 Jun;62(6):905-914. doi: 10.1007/s00125-019-4840-2. Epub 2019 Mar 7. PMID: 30843102. Grandi SM, Filion KB, Yoon S, Ayele HT, Doyle CM, Hutcheon JA, Smith GN, Gore GC, Ray JG, Nerenberg K, Platt RW. Cardiovascular Disease-Related Morbidity and Mortality in Women With a History of Pregnancy Complications. Circulation. 2019 Feb 19;139(8):1069-1079. doi: 10.1161/CIRCULATIONAHA.118.036748. Erratum in: Circulation. 2019 Aug 27;140(9):e544. doi: 10.1161/CIR.0000000000000718. PMID: 30779636. Wasserman DH, Wang TJ, Brown NJ. The Vasculature in Prediabetes. Circ Res. 2018 Apr 13;122(8):1135-1150. doi: 10.1161/CIRCRESAHA.118.311912. PMID: 29650631; PMCID: PMC5901903. Muniyappa R, Sowers JR. Role of insulin resistance in endothelial dysfunction. Rev Endocr Metab Disord. 2013 Mar;14(1):5-12. doi: 10.1007/s11154-012-9229-1. PMID: 23306778; PMCID: PMC3594115. von Lueder TG, Girerd N, Atar D, Agewall S, Lamiral Z, Kanbay M, High-Risk Myocardial Infarction Database Initia- tive Investigators. Serum uric acid is associated with mortality and heart failure hospitalizations in patients with complicated myocardial infarction: findings from the High-Risk Myocar- dial Infarction Database Initiative. Eur J Heart Fail 2015;17:1144–51. Verma S, Ji Q, Bhatt DL, Mazer CD, Al-Omran M, Inzucchi SE, Wanner C, Ofstad AP, Zwiener I, George JT, Zinman B, Fitchett D. Association between uric acid levels and cardio-renal outcomes and death in patients with type 2 diabetes: A subanalysis of EMPA-REG OUTCOME. Diabetes Obes Metab. 2020 Jul;22(7):1207-1214. doi: 10.1111/dom.13991. Epub 2020 Mar 28. PMID: 32030863; PMCID: PMC7317186. Tan Z, Dai T, Zhong X, Tian Y, Leppo MK, Gao WD. Preservation of cardiac contractility after long-term therapy with oxypurinol in post-ischemic heart failure in mice. Eur J Pharmacol. 2009 Oct 25;621(1-3):71-7. doi: 10.1016/j.ejphar.2009.08.033. Epub 2009 Sep 6. PMID: 19737552. Packer M. Uric Acid Is a Biomarker of Oxidative Stress in the Failing Heart: Lessons Learned from Trials With Allopurinol and SGLT2 Inhibitors. J Card Fail. 2020 Nov;26(11):977-984. doi: 10.1016/j.cardfail.2020.08.015. Epub 2020 Sep 3. PMID: 32890737. Hille R, Nishino T. Flavoprotein structure and mechanism. 4. Xanthine oxidase and xanthine dehydrogenase. FASEB J. 1995 Aug;9(11):995-1003. PMID: 7649415. Peleli M, Zollbrecht C, Montenegro MF, Hezel M, Zhong J, Persson EG, et al. Enhanced XOR activity in eNOS-deficient mice: effects on the nitrate-nitrite-NO pathway and ROS homeostasis. Free Radic Biol Med 2016;99:472–84. Kanbay M, Afsar B, Siriopol D, Dincer N, Erden N, Yilmaz O, Sag AA, Kuwabara M, Cherney D, Rossignol P, Ortiz A, Covic A. Effect of Uric Acid-Lowering Agents on Cardiovascular Outcome in Patients With Heart Failure: A Systematic Review and Meta-Analysis of Clinical Studies. Angiology. 2020 Apr;71(4):315-323. doi: 10.1177/0003319719897509. Epub 2020 Jan 31. PMID: 32000517. Cantu-Medellin N, Kelley EE. Xanthine oxidoreductase-catalyzed reactive species generation: A process in critical need of reevaluation. Redox Biol. 2013 Jun 10;1(1):353-8. doi: 10.1016/j.redox.2013.05.002. PMID: 24024171; PMCID: PMC3757702. Fadl H, Magnuson A, Östlund I, Montgomery S, Hanson U, Schwarcz E. Gestational diabetes mellitus and later cardiovascular disease: a Swedish population based case-control study. BJOG. 2014 Nov;121(12):1530-6. doi: 10.1111/1471-0528.12754. Epub 2014 Apr 25. PMID: 24762194; PMCID: PMC4232923. Uretsky S, Messerli FH, Bangalore S, Champion A, Cooper-Dehoff RM, Zhou Q, Pepine CJ. Obesity paradox in patients with hypertension and coronary artery disease. Am J Med. 2007 Oct;120(10):863-70. doi: 10.1016/j.amjmed.2007.05.011. PMID: 17904457. Graziani F, Leone AM, Basile E, Cialdella P, Tritarelli A, Bona RD, Liuzzo G, Nanni G, Iaconelli A, Iaconelli A, Mingrone G, Biasucci LM, Crea F. Endothelial progenitor cells in morbid obesity. Circ J. 2014;78(4):977-85. doi: 10.1253/circj.cj-13-0976. Epub 2014 Feb 27. PMID: 24572586. Mehta A, Meng Q, Li X, Desai SR, D'Souza MS, Ho AH, Islam SJ, Dhindsa DS, Almuwaqqat Z, Nayak A, Alkhoder AA, Hooda A, Varughese A, Ahmad SF, Mokhtari A, Hesaroieh I, Sperling LS, Ko YA, Waller EK, Quyyumi AA. Vascular Regenerative Capacity and the Obesity Paradox in Coronary Artery Disease. Arterioscler Thromb Vasc Biol. 2021 Jun;41(6):2097-2108. doi: 10.1161/ATVBAHA.120.315703. Epub 2021 Apr 15. PMID: 33853349; PMCID: PMC8147702. Cooke JP. Endotheliopathy of Obesity. Circulation. 2020 Jul 28;142(4):380-383. doi: 10.1161/CIRCULATIONAHA.120.047574. Epub 2020 Jul 27. PMID: 32718250; PMCID: PMC7391057. Butt JH, Petrie MC, Jhund PS, Sattar N, Desai AS, Køber L, Rouleau JL, Swedberg K, Zile MR, Solomon SD, Packer M, McMurray JJV. Anthropometric measures and adverse outcomes in heart failure with reduced ejection fraction: revisiting the obesity paradox. Eur Heart J. 2023 Apr 1;44(13):1136-1153. doi: 10.1093/eurheartj/ehad083. PMID: 36944496; PMCID: PMC10111968. Kul Ş, Güvenç TS, Baycan ÖF, Çelik FB, Çalışkan Z, Çetin Güvenç R, Çiftçi FC, Caliskan M. Combined past preeclampsia and gestational diabetes is associated with a very high frequency of coronary microvascular dysfunction. Microvasc Res. 2021 Mar;134:104104. doi: 10.1016/j.mvr.2020.104104. Epub 2020 Nov 13. PMID: 33189732. Kelshiker MA, Seligman H, Howard JP, Rahman H, Foley M, Nowbar AN, Rajkumar CA, Shun-Shin MJ, Ahmad Y, Sen S, Al-Lamee R, Petraco R; Coronary Flow Outcomes Reviewing Committee. Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis. Eur Heart J. 2022 Apr 19;43(16):1582-1593. doi: 10.1093/eurheartj/ehab775. Erratum in: Eur Heart J. 2023 Jan 1;44(1):27. doi: 10.1093/eurheartj/ehac628. PMID: 34849697; PMCID: PMC9020988. Additional Declarations No competing interests reported. Supplementary Files copyrighttransferform.docx Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted Editorial decision: Revision requested 20 Sep, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 26 Jul, 2025 Submission checks completed at journal 26 Jul, 2025 First submitted to journal 23 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7194002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493326898,"identity":"d4c81f17-50c3-49a1-acbd-236619b187a2","order_by":0,"name":"Mustafa Caliskan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYFAC5oYDDAeA9AFmECkhQ4QWRpgWtgSQFh6itDBAtPAYgLiEtfC3H2w8+OOMXR7f7TOfX92oseBhYD98dAM+LRJnEhsO89xILpY8l7vNOucY0GE8aWk38GkxYABqYfjAnLjhDO824xw2oBYJHjP8WvgfNhz88aEeqIXnmXHOP2K0SCQ2HOC5cRikhflxbhsRWiRuPAT65czxxJln2MyYc/skeNgI+YW/P/nwxx/HqhP7zjA//pzzrU6On/3wMbxakAGbBJgkVjkIMH8gRfUoGAWjYBSMHAAAhxRRfRLOPAEAAAAASUVORK5CYII=","orcid":"","institution":"Istanbul Medeniyet University Göztepe Süleyman Yalçın City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Caliskan","suffix":""},{"id":493326900,"identity":"ea5fef8d-190c-4c24-8410-7bd2605b70cf","order_by":1,"name":"Mumtaz Takir","email":"","orcid":"","institution":"Istanbul Medeniyet University Göztepe Süleyman Yalçın City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mumtaz","middleName":"","lastName":"Takir","suffix":""},{"id":493326901,"identity":"84329228-7f5c-4537-8fdf-195f62d5875c","order_by":2,"name":"Ahmet Eren Caliskan","email":"","orcid":"","institution":"Bezmialem University Medical Faculty","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"Eren","lastName":"Caliskan","suffix":""},{"id":493326902,"identity":"135d2272-3a52-4fdf-931e-9ce658d64eee","order_by":3,"name":"Fatma Betul Celik","email":"","orcid":"","institution":"Istanbul Medeniyet University Göztepe Süleyman Yalçın City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"Betul","lastName":"Celik","suffix":""},{"id":493326903,"identity":"052e207e-b43f-4fbd-aeeb-8f794cbddb59","order_by":4,"name":"Lutfullah Castur","email":"","orcid":"","institution":"Istanbul Medeniyet University Göztepe Süleyman Yalçın City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lutfullah","middleName":"","lastName":"Castur","suffix":""},{"id":493326904,"identity":"1663fe1a-db14-41cf-80f2-5f748054b52f","order_by":5,"name":"Erhan Eken","email":"","orcid":"","institution":"Istanbul Medeniyet University Göztepe Süleyman Yalçın City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Erhan","middleName":"","lastName":"Eken","suffix":""},{"id":493326905,"identity":"9b3fbc93-76cc-462d-9b06-a02e34d17d23","order_by":6,"name":"Tolga Sinan Guvenc","email":"","orcid":"","institution":"Istinye University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tolga","middleName":"Sinan","lastName":"Guvenc","suffix":""}],"badges":[],"createdAt":"2025-07-23 08:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7194002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7194002/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10554-025-03549-w","type":"published","date":"2025-10-23T16:16:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88229354,"identity":"bda4ecc4-11b0-43ae-b715-ae0883e8ed42","added_by":"auto","created_at":"2025-08-04 09:12:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279336,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing fasting glucose (A), Hemoglobin A1c% (B), Homeostatic Model Assessment for Insulin Resistance (C) and triglyceride-glucose index (D) in subgroups of patients with a coronary flow reserve \u0026gt;2.5 and ≤2.5. Boxes show median values and interquartile ranges, while squares within the boxes show mean values. HbA1c: Hemoglobin A1c, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194002/v1/f641cabf80bc33db2bee7529.jpg"},{"id":88229362,"identity":"346afd64-cc90-4317-a5ba-ac0b0c53f343","added_by":"auto","created_at":"2025-08-04 09:12:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":436057,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing correlations between coronary flow reserve with Hemoglobin A1c% (A), Homeostatic Model Assessment for Insulin Resistance (B), waist circumference (C) and uric acid (D). Area given around HbA1c: Hemoglobin A1c, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194002/v1/e05c0e4b6c37f3823477db48.jpg"},{"id":88229352,"identity":"3c76b138-a9fe-4a28-99b3-6b1f04b4b80f","added_by":"auto","created_at":"2025-08-04 09:12:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67037,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior coefficients for variables included in the Bayesian multiple linear regression analysis. Error bars show 95% credible intervals. HbA1c: Hemoglobin A1c, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7194002/v1/dce5111ab7a546082d609584.jpg"},{"id":94490686,"identity":"4596773d-885f-45ed-b64c-58bda1f382ca","added_by":"auto","created_at":"2025-10-27 17:13:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1804359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194002/v1/88b6c154-21b5-4697-8ea9-598168a9273f.pdf"},{"id":88229761,"identity":"9d08bbce-6da2-45c6-88f6-e912a87484a9","added_by":"auto","created_at":"2025-08-04 09:20:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11716,"visible":true,"origin":"","legend":"","description":"","filename":"copyrighttransferform.docx","url":"https://assets-eu.researchsquare.com/files/rs-7194002/v1/d639e925dcbbbe46ed32955f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCoronary microvascular dysfunction in gestational diabetes: Insights on possible mechanism from a large institutional registry\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eGestational diabetes is associated with coronary artery disease in later life.\u003c/li\u003e\n \u003cli\u003eCoronary microvascular dysfunction is common in patients with a history of gestational diabetes.\u003c/li\u003e\n \u003cli\u003ePresent findings show hemoglobin A1c%, HOMA-IR and uric acid are associated with coronary flow reserve.\u003c/li\u003e\n \u003cli\u003eAbnormal glucose tolerance, insulin resistance and hyperuricemia are risk markers for microvascular dysfunction.\u003c/li\u003e\n \u003cli\u003eMost of the variance in coronary flow reserve cannot be explained with these biomarkers.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eGestational diabetes (GDM) is a major complication of the pregnancy and is observed in up to 17\u0026ndash;20% of pregnancies worldwide (1). Although the impaired glucose tolerance associated with GDM is temporary, a history of GDM confers an increased risk for developing prediabetes, diabetes and cardiovascular disease (2\u0026ndash;5). Even in those that remain normoglycemic after birth, a prior diagnosis of GDM is associated with a doubling of the risk for coronary artery disease (CAD) as assessed with coronary after calcium score (6). The mechanisms that link GDM with the increased risk of CAD is incompletely understood, but GDM-related endothelial dysfunction (ED) appears as a major factor that increases the risk of future CAD (7,8).\u003c/p\u003e\u003cp\u003eA major manifestation of ED in the coronary circulation is abnormal coronary flow reserve (CFR), which assesses the ability of coronary microvasculature to increase blood supply to the myocardium under maximal vasodilatation (9). Hyperglycemia is an established risk factor for coronary microvascular dysfunction, and our study group as well as others have previously demonstrated an impaired coronary flow reserve in GDM patients (10\u0026ndash;12). Although various measures of abnormal glucose tolerance appears to correlate with the degree of reduction in CFR in these previous studies, a detailed analysis on the possible mechanisms underlying coronary microvascular dysfunction in GDM is still unavailable.\u003c/p\u003e\u003cp\u003eIn the present work, we aimed to understand possible associations between coronary microvascular dysfunction with hyperglycemia, hyperinsulinemia, inflammation and oxidative stress in patients with GDM. As a secondary aim, we sought to understand the strength of evidence that supports these associations using Bayesian methods.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSubjects\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePresent study was as retrospective observational study done using data from the institutional registry that enrolled patients older than 18 years with chest pain in the absence of epicardial coronary artery disease. Per registry protocols, subjects were only included in the registry after exclusion of epicardial coronary disease with stress imaging, CT angiography or invasive angiography. For this particular analysis, we scanned the database for women with a previous history of GDM in at least one previous pregnancy. Exclusion criteria were active smoking, active pregnancy, GDM within one year of inclusion in the registry, established diabetes, chronic or acute liver disease, kidney disease, hypertension (patients under hypertensive treatment or with blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;140/90 mmHg), any cardiomyopathy, significant valve disease, atrial fibrillation or any other persistent arrhythmia, previous epicardial CAD (myocardial infarction, coronary angioplasty, coronary bypass surgery, or more than 30% epicardial coronary artery stenosis on coronary angiography), resting wall motion abnormalities and severe pulmonary disease. After applying these criteria, 166 out of 1743 cases were deemed eligible for inclusion.\u003c/p\u003e\u003cp\u003ePatients\u0026rsquo; demographic, clinical, imaging and laboratory parameters were recorded using electronic registry records. The study was conducted according to the 1975 Declaration of Helsinki and its subsequent revisions. All subject gave their informed consent prior to enrollment to the registry. An ethical approval was obtained from the Medeniyet University ethics committee, document number 2020/0472.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePer registry protocols, blood was withdrawn from all participants after overnight fasting and obtained specimens were delivered to the laboratory within 30 minutes of collection. Complete blood counts were studied using an automated Coulter analyzer. Other biochemical tests were done as defined before. Triglyceride-glucose index (TGI, \u003cem\u003eEq.\u0026nbsp;1\u003c/em\u003e) and Homeostasis model assessment of insulin resistance (HOMA-IR, \u003cem\u003eEq.\u0026nbsp;2\u003c/em\u003e) were calculated as described previously (13,14):\u003c/p\u003e\u003cp\u003e\u003cem\u003eEq1.\u003c/em\u003e Ln (fasting triglycerides in mg/dl x fasting glucose in mg/dl / 2)\u003c/p\u003e\u003cp\u003e\u003cem\u003eEq2.\u003c/em\u003e HOMA-IR = (Fasting plasma insulin \u0026times; Fasting plasma glucose) / 22.5.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTransthoracic echocardiography and measurement of coronary flow reserve\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll echocardiographic measurements for the registry were performed using an echocardiography platform equipped with a phased-array transducer (GE Vivid 6, GE Healthcare, Piscataway, NJ) by an experienced cardiologist who was blind to clinical data. A conventional echocardiographic examination was done prior to the measurement of coronary flow measurements, and left ventricular end-diastolic and end-systolic diameters and volumes, as well as inter ventricular septal and posterior wall thickness were recorded. Left ventricular mass was calculated using Deveraux formula and left ventricular ejection fraction was calculated using modified Simpson method (15). Coronary flow velocity reserve, a derivative of CFR that can be measured with transthoracic Doppler echocardiography was measured as previously described using intravenous dipyridamole infusion (0.56 mg/kg over 4 min). If the acceleration in heart rate wasn\u0026rsquo;t enough (10% increase from the baseline), another dose of dipyridamole (0.28 mg/kg over a 2-min period) was administrated. The mid-distal part of the LAD was studied using the S5-1 probe, and the LAD artery was visualized by color Doppler flow mapping guidance in the modified parasternal view. For color Doppler echocardiography, the velocity range was defined as 8.9 to 24.0 cm/sec. Blood flow velocity was measured using pulsed-wave Doppler echocardiography, using a sample volume of 3 to 4 mm, placed on the color signal in the distal LAD. The ultrasound beam direction was aligned with the distal LAD flow. The angle was kept small and no correction was applied. Coronary diastolic peak velocities were measured at baseline and after dipyridamole by averaging the highest three Doppler signals for each measurement. CFR was defined as the ratio of hyperemic to baseline diastolic peak velocities and the cut-off value for CFR was taken as 2.5. The heart rate was monitored continuously during the patient examination, and blood pressure was recorded at baseline and during hyperemia using an automatic arm sphygmomanometer.\u003c/p\u003e\u003cp\u003eFor the present analysis, all stored data pertinent to GDM were re-reviewed by an investigator blinded to clinical data and repeat measurements were done to calculate CFR for each individual patient. To determine intra-observer variability of the coronary flow measurements, repeat measurements from the stored pulsed-wave Doppler images were done by the same investigator who made the initial measurements. Using the two sets of measurements, intraclass correlation coefficient for CFR was calculated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eContinuous parameters were given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or as median and interquartile range, while categorical variables were given as percentages. Patterns of distribution were analyzed using visual inspection of histograms, QQ plots and Shapiro Wilk tests. For continuous variables, differences between groups of patients with a CFR\u0026thinsp;\u0026le;\u0026thinsp;2.5 and CFR\u0026thinsp;\u0026gt;\u0026thinsp;2.5 were analyzed using Student\u0026rsquo;s t or Mann-Whitney U tests. Correlation analyses were done using Pearson test or Spearman\u0026rsquo;s rho. Four linear regression models were used to assess the association between clinical, echocardiographic and laboratory parameters with CFR. At each step, parameters that were found as significant predictors of CFR were included in the latter model. First model included parameters of glucose intolerance (fasting glucose, HbA1c%, HOMA-IR and TGI), while second model introduced demographic and clinical parameters (age, body mass index, waist circumference, systolic blood pressure, heart rate) and the third model introduced lipid parameters (total cholesterol and non-HDL cholesterol), markers of inflammation and oxidative stress (C-reactive protein and uric acid) and hemoglobin, while the fourth model included echocardiographic variables (left ventricular ejection fraction and mass). Due to the significant collinearity; body mass index (BMI), diastolic blood pressure and LDL cholesterol were excluded from the models. Conversions were made for HOMA-IR and C-reactive protein due to the skewed distribution of both parameters. A final model incorporating all significant predictors were used to calculate the adjusted R\u003csup\u003e2\u003c/sup\u003e as a measure of variance of CFR that can be explained by these variables.\u003c/p\u003e\u003cp\u003eAdditional Bayesian analyses were performed to understand the strength of evidence favoring a particular association between a parameter and CFR. Bayesian correlation analyses with Pearson test or Kendall\u0026rsquo;s tau were used to assess the associations between individual parameters with CFR. Finally, a Bayesian multivariable linear regression model including predictors found to be significant in Model IV was used to assess the overall evidence favoring an association between this final model and CFR.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMean age of the subjects were 33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3, with a mean BMI of 27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 kg/m2 and a mean waist circumference of 85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3 cm. Median coronary flow reserve of the study participants were 2.46 (2.16\u0026ndash;2.75). Mean hemoglobin A1c% (HbA1c%) and median TGI and HOMA-IR were 5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, 8.65 (8.26\u0026ndash;8.90) and 2.65 (1.65\u0026ndash;3.32), respectively. Intraclass correlation coefficient for intra-observer variability of the CFR measurement was 0.946.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes key demographic, clinical, laboratory and imaging differences between the study groups. Apart from the differences between measures of insulin resistance and hyperglycemia, including HOMA-IR, TGI and HbA1c% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), there were significant differences between the groups in terms of total cholesterol, non-HDL cholesterol and uric acid.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic, clinical, laboratory and imaging findings for patients with a coronary flow reserve\u0026thinsp;\u0026le;\u0026thinsp;2.5 as compared to those with a coronary flow reserve\u0026thinsp;\u0026gt;\u0026thinsp;2.5. P values below 0.05 were given in bold. LDL: Low-density lipoprotein, HDL: High-density lipoprotein, HOMA-IR: Homeostatic model assessment for insulin resistance, GFR: Glomerular filtration rate.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoronary flow reserve\u0026thinsp;\u0026gt;\u0026thinsp;2.5 (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoronary flow reserve\u0026thinsp;\u0026le;\u0026thinsp;2.5 (n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182.0\u0026thinsp;\u0026plusmn;\u0026thinsp;28.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-cholesterol (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-HDL cholesterol (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144.0\u0026thinsp;\u0026plusmn;\u0026thinsp;32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting glucose (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin A1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride glucose index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.5 (8.2\u0026ndash;8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.8 (8.4\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4 (1.6\u0026ndash;3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (1.7\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstimated GFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (0.8\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.1\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular end-diastolic volume (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular end-systolic volume (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular ejection fraction (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular mass (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary flow velocity at rest (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.0 (21.0\u0026ndash;26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.4 (24.0\u0026ndash;33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak coronary flow velocity (cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.5 (58.3\u0026ndash;73.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.0 (52.0 (71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn correlation analyses, CFR showed significant correlations with age (r=-0.18, p\u0026thinsp;=\u0026thinsp;0.02), heart rate (r=-0.23, p\u0026thinsp;=\u0026thinsp;0.003), HbA1c% (r=-0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HOMA-IR (r=-0.21, p\u0026thinsp;=\u0026thinsp;0.006), TGI (r=-0.18, p\u0026thinsp;=\u0026thinsp;0.02) and uric acid (r=-0.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a positive correlation between CFR and BMI (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;=\u0026thinsp;0.07) as well as between CFR and waist circumference (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.06), but neither finding reached statistical significance. Both BMI (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.02) and waist circumference (r\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed significant positive correlations with HOMA-IR, while BMI (r\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.02) but not waist circumference (r\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.27) had a significant correlation with HbA1c%. Scatter plots for bivariate correlations between CFR and HbA1c%, HOMA-IR, uric acid and waist circumference were given in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultiple linear regression analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter introducing all studied variables in a stepwise and clustered method, the final model included 4 variables: HbA1c% (β=-0.31, 95%CI:-0.45 \u0026mdash; -0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); HOMA-IR (β=-0.25, 95%CI:-0.40 \u0026mdash; -0.10, p\u0026thinsp;=\u0026thinsp;0.001); uric acid (β=-0.19, 95%CI:-0.33 \u0026mdash; -0.06, p\u0026thinsp;=\u0026thinsp;0.007) and waist circumference (β\u0026thinsp;=\u0026thinsp;0.25, 95%CI:0.11 \u0026mdash; 0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This final model explained 23.2% of the overall variance of CFR in the sample (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple linear regression models for prediction of coronary flow reserve. Numbers show standardized estimates and 95% confidence intervals for standardized estimates. Variables that showed a significant association with the coronary flow reserve were given in bold and these variables were included in the subsequent models. HbA1c%: Hemoglobin A1c, TGI: Triglyceride glucose index, HDL: High-density lipoprotein, LV, Left ventricle.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel III\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel IV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e0.15 \u003c/p\u003e\u003cp\u003e(-0.01 \u0026mdash; 0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.34\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e (-0.49 \u0026mdash; -0.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.30 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.43 \u0026mdash; -0.16)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.27 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.41 \u0026mdash; -0.13)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.32 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.45 \u0026mdash; -0.17)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.22 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.22 \u0026mdash; -0.06)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.23 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.38 \u0026mdash; -0.08)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.23 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.39 \u0026mdash; -0.08)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.23 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.38 \u0026mdash; -0.07)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTGI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.11 \u003c/p\u003e\u003cp\u003e(-0.26 \u0026mdash; 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.14 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.27 \u0026mdash; -0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.13 \u003c/p\u003e\u003cp\u003e(-0.27 \u0026mdash; 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.30 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(0.15 \u0026mdash; 0.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.27 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(0.11 \u0026mdash; 0.42)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.29 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(0.13 \u0026mdash; 0.43)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.08 \u003c/p\u003e\u003cp\u003e(-0.23 \u0026mdash; 0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart rate (bpm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.17 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.31 \u0026mdash; -0.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14 \u003c/p\u003e\u003cp\u003e(-0.28 \u0026mdash; 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal cholesterol (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.04 \u003c/p\u003e\u003cp\u003e(-0.43 \u0026mdash; 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-HDL cholesterol (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e (-0.43 \u0026mdash; 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemoglobin (g/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10 \u003c/p\u003e\u003cp\u003e(-0.03 \u0026mdash; 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCreatinine (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07 \u003c/p\u003e\u003cp\u003e(-0.07 \u0026mdash; 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-reactive protein (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03 \u003c/p\u003e\u003cp\u003e(-0.12 \u0026mdash; 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUric acid (mg/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.19 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.33 \u0026mdash; -0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.18 \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(-0.33 \u0026mdash; -0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLV ejection fraction (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.02 \u003c/p\u003e\u003cp\u003e(-0.16 \u0026mdash; 0.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLV mass (g)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12 \u003c/p\u003e\u003cp\u003e(-0.27 \u0026mdash; 0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBayesian analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn univariate linear correlation analyses for parameters included in the final multiple regression model, there was very strong evidence supporting an association between CFR and HbA1c (BF\u003csub\u003e10\u003c/sub\u003e: 785) and between CFR and uric acid (BF\u003csub\u003e10\u003c/sub\u003e: 113). There was strong evidence supporting a correlation between CFR and HOMA-IR (BF\u003csub\u003e10\u003c/sub\u003e: 20), while there was no evidence supporting a correlation between waist circumference and CFR (BF\u003csub\u003e10\u003c/sub\u003e: 0.55). For variables not included in the final model, there were no evidence supporting an association between CFR and either age (BF\u003csub\u003e10\u003c/sub\u003e: 1.3) or with TGI (BF\u003csub\u003e10\u003c/sub\u003e: 0.50).\u003c/p\u003e\u003cp\u003eThe results for Bayesian linear regression model including 4 variables (HbA1c, HOMA-IR, uric acid and waist circumference) were given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and a summary of posterior coefficients for the variables included in the model were provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The r\u003csup\u003e2\u003c/sup\u003e for this model was 0.25, and there was very strong evidence for a difference between model and null hypothesis (BF\u003csub\u003e10\u003c/sub\u003e: 5.05 x 10\u003csup\u003e6\u003c/sup\u003e) and strong evidence for a change from the prior (BF\u003csub\u003eM\u003c/sub\u003e: 71).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBayesian multiple regression model. Bayesian multiple regression model that included the 4 variables that were found as independent predictors of coronary flow reserve in the multiple linear regression analysis. BF: Bayesian factor, HOMA-IR: Homeostatic model assessment for insulin resistance.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95% Credible Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBF\u003csub\u003einclusion\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin A1c%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.2923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1277.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.42770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.1570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.2136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.34625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.17114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGestational diabetes is an important sex-specific cardiovascular risk factor that is linked to an increased risk of CAD even in the absence of overt diabetes in later life. Present study focused on the possible mechanisms that may explain the association of GDM with coronary microvascular dysfunction, which is a key precursor of coronary artery disease. The main takeaways of the present study are as follows: i) both hyperglycemia and insulin resistance are predictors of a lower CFR, ii) uric acid, which is the final product of purine metabolism in humans and is a biomarker of oxidative stress, is associated with coronary flow reserve and iii) although waist circumference had a positive correlation with measures of insulin resistance, particularly HOMA-IR, a higher waist circumference was not associated with a lower CFR and iv) only a quarter of variance in flow reserve can be explained using these 4 variables, and as such either unobserved factors or the transient hyperglycemia during pregnancy is the potential cause for coronary microvascular dysfunction in most patients with GDM.\u003c/p\u003e\u003cp\u003eThere is a 67\u0026ndash;98% increase in the relative risk of cardiovascular disorders in patients with GDM, with CAD and CAD-associated complications such as myocardial infarction being primarily response for this increased risk (16,17). Even in those did not developed pre diabetes or diabetes after pregnancy, GDM is associated with a twofold increase in CAD risk, as shown in the recent analysis of the Coronary Artery Risk Development in Young Adults (CARDIA) study (4). Both fasting glucose and HOMA-IR were associated with an increased coronary calcium score in this latter analysis, which fits well with the current findings as we have observed a strong association between hyperglycemia, insulin resistance and a reduced coronary flow reserve (4). Abnormal glucose tolerance and insulin resistance are both strongly related to endothelial dysfunction and CAD, and it is therefore not surprising to observe either HbA1c or HOMA-IR as predictors of CFR (18,19). Unlike the aforementioned analysis of the CARDIA study, present study included those with prediabetes (i.e. fasting glucose\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/dl and/or HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;5.7%) and some of the risk might be attributable to development of hyperglycemia after pregnancy rather than a history of GDM (4). However, present findings also suggest that only a quarter of the variance in CFR can be explained in the final model that included HbA1c and HOMA-IR, and as such much of the association between GDM and coronary microvascular dysfunction could not be explained solely with persisting hyperglycemia or hyperinsulinemia. Therefore, it is reasonable to consider that a history of transient hyperglycemia per se may have long-lasting effects on coronary microvasculature, which is accentuated in the presence of persistent hyperglycemia.\u003c/p\u003e\u003cp\u003eUric acid is the terminal product of purine metabolism in humans and high uric acid concentrations are associated with coronary artery disease and cardiovascular mortality (20,21). Although very high levels of uric acid is toxic to cardiomyocytes in animal models, direct cardiotoxicity is unlikely in humans as uric acid production is much lower (22,23). Hyperuricemia is a marker of over activation of xanthine oxidase (XO), which is the terminal enzyme that converts hypoxanthine to uric acid in two steps (24). In addition to production of uric acid, XO leads to formation to reactive oxygen species and provides an alternative source of nitric oxide in the endothelium (23\u0026ndash;25). It has been suggested that this last action of XO explains the link between high uric acid concentrations and cardiovascular disorders, given that reduction of uric acid by inhibiting XO does not lead to a reduction in cardiovascular mortality (26). Although present study could not provide causal inferences for the observed association between uric acid and CFR, it is nonetheless reasonable to consider that patients with GDM and lower CFR have a greater degree of reduction in endothelial nitric oxide synthesis capability as nitric oxide is the primary determinant of microvascular dysfunction. This would lead to the sequestration of XO in the endothelium to provide an alternative source for nitric oxide, hence causing overproduction of uric acid (27). Thus, it is unlikely that targeting uric acid or XO to restore microvascular function or prevent cardiovascular complications of GDM would succeed, given that uric acid is possibly a marker and not to cause of CAD in these patients.\u003c/p\u003e\u003cp\u003eAn interesting finding observed in the present study is the association of BMI and waist circumference with CFR. A higher BMI is a known predictor of CAD in the overall population as well as in patients with GDM (4,28). However, present results indicate that there was a trend towards a higher CFR in those with a higher waist circumference, and waist circumference was a predictor of higher CFR when included in the final model. Similar findings were also observed when waist circumference was replaced with BMI, although the effect size was lower (data not given in the manuscript). Although CAD is more common in patients with obesity, there are multiple observations suggesting that the prognosis of CAD is actually better in patients with obesity (29). There is some preclinical data to suggest that this so-called \u0026ldquo;obesity paradox\u0026rdquo; have a true biological basis as circulating progenitor cell counts are higher in obese patients with or without CAD, but the clinical validity of these results are uncertain (30,31). Although it is tempting to associate present findings with the \u0026ldquo;obesity paradox\u0026rdquo; observed in other patients with CAD, caution should be exercised as obesity is a well-known risk factor for endothelial dysfunction (32). Moreover, the concept of \u0026ldquo;obesity paradox\u0026rdquo; may be more noise than signal as the survival \u0026ldquo;benefit\u0026rdquo; of obese patients disappear when anthropomorphic indices other than body-mass index is used (33). Thus, present findings regarding waist circumference (or BMI) should be interpreted with caution until more evidence supporting or refuting this association becomes available.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, present findings suggest that patients with a past history of GDM and higher HbA1c, HOMA-IR or uric acid levels have worse coronary microvascular dysfunction and such patients might be at a higher risk for CAD. Nonetheless, much of the reduction in CFR could not be explained with the common parameters measured in this study and therefore associated with either other factors not included in the present work or associated with the transient hyperglycemia during pregnancy. Given that most parameters commonly measured in clinical practice were included in the present study, it is reasonable to ascribe most risk to a past history of GDM until more data emerges to refine risk stratification for GDM, while considering those with hyperglycemia, hyperinsulinemia and/or hyperuricemia at a higher risk for microvascular dysfunction and CAD. As aforementioned, it is reasonable to consider present findings on waist circumference as noise rather than signal until more data emerges.\u003c/p\u003e\u003cp\u003ePresent study have several strengths and limitations. Although the analysis is retrospective in nature, the data was obtained from a registry and the sample size was relatively large as compared to similar studies. Our study group have a long track record of performing echocardiographic measurement of CFR, thus increasing the reliability of the present results (11,34). Although there are several studies showing the relationship between reduced CFR, CAD and cardiovascular mortality; CFR is nonetheless an intermediary and not all patients with a reduced CFR would be at risk for more clinically significant cardiovascular events (35). As with all observational studies, only variables that were recorded could be analyzed and therefore some associations would invariably be missed. This is particularly true for inflammation, as only C-reactive protein was recorded and contrary to what is expected, we have not found an association between inflammation and CFR.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePatients with a past history of GDM and concurrent hyperglycemia, insulin resistance and hyperuricemia have worse coronary microvascular function as assessed with CFR. Contrary to what would be expected, CFR was higher those with an increased waist circumference despite these patients having more insulin resistance, but this finding should not be regarded as valid until more data emerges. Finally, as clinical predictors can only explain only a fraction of variance in CFR, coronary microvascular function cannot be reliably estimated using clinical or laboratory data. Thus, a history of GDM itself should be regarded as a risk determinant for coronary microvascular dysfunction, until tools for further risk refinement becomes available.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interests Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research and/or authorship of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMC, AEC, MT : study design, echocardiographic examination, date collection and writing , FBC, LC, EE and TSG: prepared figure and study design, statistic\u003c/p\u003e"},{"header":"References","content":"\n\u003col\u003e\n\u003cli\u003e Vandorsten JP, Dodson WC, Espeland MA, Grobman WA, Guise JM, Mercer BM, Minkoff HL, Poindexter B, Prosser LA, Sawaya GF, Scott JR, Silver RM, Smith L, Thomas A, Tita AT. NIH consensus development conference: diagnosing gestational diabetes mellitus. NIH Consens State Sci Statements. 2013 Mar 6;29(1):1-31. PMID: 23748438.\u003c/li\u003e\n\u003cli\u003e Prados M, Flores-Le Roux JA, Benaiges D, Llaurad\u0026oacute; G, Chillar\u0026oacute;n JJ, Paya A, Pedro-Botet J. Previous Gestational Diabetes Increases Atherogenic Dyslipidemia in Subsequent Pregnancy and Postpartum. Lipids. 2018 Apr;53(4):387-392. doi: 10.1002/lipd.12040. Epub 2018 May 6. PMID: 29732563.\u003c/li\u003e\n\u003cli\u003e Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009 May 23;373(9677):1773-9. doi: 10.1016/S0140-6736(09)60731-5. PMID: 19465232.\u003c/li\u003e\n\u003cli\u003e Gunderson EP, Lewis CE, Tsai AL, Chiang V, Carnethon M, Quesenberry CP Jr, Sidney S. A 20-year prospective study of childbearing and incidence of diabetes in young women, controlling for glycemia before conception: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Diabetes. 2007;56:2990\u0026ndash;2996. doi: 10.2337/db07-1024 \u003c/li\u003e\n\u003cli\u003e Gunderson EP, Chiang V, Pletcher MJ, Jacobs DR, Quesenberry CP, Sidney S, Lewis CE. History of gestational diabetes mellitus and future risk of atherosclerosis in mid-life: the Coronary Artery Risk Development in Young Adults study. J Am Heart Assoc. 2014 Mar 12;3(2):e000490. doi: 10.1161/JAHA.113.000490. PMID: 24622610; PMCID: PMC4187501.\u003c/li\u003e\n\u003cli\u003e Gunderson EP, Sun B, Catov JM, Carnethon M, Lewis CE, Allen NB, Sidney S, Wellons M, Rana JS, Hou L, Carr JJ. Gestational Diabetes History and Glucose Tolerance After Pregnancy Associated With Coronary Artery Calcium in Women During Midlife: The CARDIA Study. Circulation. 2021 Mar 9;143(10):974-987. doi: 10.1161/CIRCULATIONAHA.120.047320. Epub 2021 Feb 1. PMID: 33517667; PMCID: PMC7940578.\u003c/li\u003e\n\u003cli\u003e Rao R, Sen S, Han B, Ramadoss S, Chaudhuri G. Gestational diabetes, preeclampsia and cytokine release: similarities and differences in endothelial cell function. Adv Exp Med Biol. 2014;814:69-75. doi: 10.1007/978-1-4939-1031-1_6. PMID: 25015801.\u003c/li\u003e\n\u003cli\u003e Knock GA, McCarthy AL, Lowy C, Poston L. Association of gestational diabetes with abnormal maternal vascular endothelial function. Br J Obstet Gynaecol. 1997 Feb;104(2):229-34. doi: 10.1111/j.1471-0528.1997.tb11051.x. PMID: 9070145.\u003c/li\u003e\n\u003cli\u003e Kelshiker MA, Seligman H, Howard JP, Rahman H, Foley M, Nowbar AN, Rajkumar CA, Shun-Shin MJ, Ahmad Y, Sen S, Al-Lamee R, Petraco R; Coronary Flow Outcomes Reviewing Committee. Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis. Eur Heart J. 2022 Apr 19;43(16):1582-1593. doi: 10.1093/eurheartj/ehab775. Erratum in: Eur Heart J. 2023 Jan 1;44(1):27. PMID: 34849697; PMCID: PMC9020988.\u003c/li\u003e\n\u003cli\u003e Nahser PJ Jr, Brown RE, Oskarsson H, Winniford MD, Rossen JD. Maximal coronary flow reserve and metabolic coronary vasodilation in patients with diabetes mellitus. Circulation. 1995 Feb 1;91(3):635-40. doi: 10.1161/01.cir.91.3.635. PMID: 7828287.\u003c/li\u003e\n\u003cli\u003e Caliskan M, Turan Y, Caliskan Z, Gullu H, Ciftci FC, Avci E, Duran C, Kostek O, Telci Caklili O, Koca H, Kulaksizoglu M. Previous gestational diabetes history is associated with impaired coronary flow reserve. Ann Med. 2015;47(7):615-23. doi: 10.3109/07853890.2015.1099719. Epub 2015 Nov 9. PMID: 26555575.\u003c/li\u003e\n\u003cli\u003e Ozyildirim S, Barman HA, Dogan O, Ersanli MK, Dogan SM. The Relationship between Coronary Flow Reserve and the TyG Index in Patients with Gestational Diabetes Mellitus. Medicina (Kaunas). 2023 Oct 12;59(10):1811. doi: 10.3390/medicina59101811. PMID: 37893529; PMCID: PMC10608421.\u003c/li\u003e\n\u003cli\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985 Jul;28(7):412-9. doi: 10.1007/BF00280883.\u003c/li\u003e\n\u003cli\u003e Fritz J, Brozek W, Concin H, et al. The Triglyceride-Glucose Index and Obesity-Related Risk of End-Stage Kidney Disease in Austrian Adults. JAMA Netw Open. 2021 Mar 1;4(3):e212612. doi: 10.1001/jamanetworkopen.2021.2612.\u003c/li\u003e\n\u003cli\u003e Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P, Muraru D, Picard MH, Rietzschel ER, Rudski L, Spencer KT, Tsang W, Voigt JU. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015 Jan;28(1):1-39.e14. doi: 10.1016/j.echo.2014.10.003. PMID: 25559473.\u003c/li\u003e\n\u003cli\u003e Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019 Jun;62(6):905-914. doi: 10.1007/s00125-019-4840-2. Epub 2019 Mar 7. PMID: 30843102.\u003c/li\u003e\n\u003cli\u003e Grandi SM, Filion KB, Yoon S, Ayele HT, Doyle CM, Hutcheon JA, Smith GN, Gore GC, Ray JG, Nerenberg K, Platt RW. Cardiovascular Disease-Related Morbidity and Mortality in Women With a History of Pregnancy Complications. Circulation. 2019 Feb 19;139(8):1069-1079. doi: 10.1161/CIRCULATIONAHA.118.036748. Erratum in: Circulation. 2019 Aug 27;140(9):e544. doi: 10.1161/CIR.0000000000000718. PMID: 30779636.\u003c/li\u003e\n\u003cli\u003e Wasserman DH, Wang TJ, Brown NJ. The Vasculature in Prediabetes. Circ Res. 2018 Apr 13;122(8):1135-1150. doi: 10.1161/CIRCRESAHA.118.311912. PMID: 29650631; PMCID: PMC5901903.\u003c/li\u003e\n\u003cli\u003e Muniyappa R, Sowers JR. Role of insulin resistance in endothelial dysfunction. Rev Endocr Metab Disord. 2013 Mar;14(1):5-12. doi: 10.1007/s11154-012-9229-1. PMID: 23306778; PMCID: PMC3594115.\u003c/li\u003e\n\u003cli\u003e von Lueder TG, Girerd N, Atar D, Agewall S, Lamiral Z, Kanbay M, High-Risk Myocardial Infarction Database Initia- tive Investigators. Serum uric acid is associated with mortality and heart failure hospitalizations in patients with complicated myocardial infarction: findings from the High-Risk Myocar- dial Infarction Database Initiative. Eur J Heart Fail 2015;17:1144\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003e Verma S, Ji Q, Bhatt DL, Mazer CD, Al-Omran M, Inzucchi SE, Wanner C, Ofstad AP, Zwiener I, George JT, Zinman B, Fitchett D. Association between uric acid levels and cardio-renal outcomes and death in patients with type 2 diabetes: A subanalysis of EMPA-REG OUTCOME. Diabetes Obes Metab. 2020 Jul;22(7):1207-1214. doi: 10.1111/dom.13991. Epub 2020 Mar 28. PMID: 32030863; PMCID: PMC7317186.\u003c/li\u003e\n\u003cli\u003e Tan Z, Dai T, Zhong X, Tian Y, Leppo MK, Gao WD. Preservation of cardiac contractility after long-term therapy with oxypurinol in post-ischemic heart failure in mice. Eur J Pharmacol. 2009 Oct 25;621(1-3):71-7. doi: 10.1016/j.ejphar.2009.08.033. Epub 2009 Sep 6. PMID: 19737552.\u003c/li\u003e\n\u003cli\u003e Packer M. Uric Acid Is a Biomarker of Oxidative Stress in the Failing Heart: Lessons Learned from Trials With Allopurinol and SGLT2 Inhibitors. J Card Fail. 2020 Nov;26(11):977-984. doi: 10.1016/j.cardfail.2020.08.015. Epub 2020 Sep 3. PMID: 32890737.\u003c/li\u003e\n\u003cli\u003e Hille R, Nishino T. Flavoprotein structure and mechanism. 4. Xanthine oxidase and xanthine dehydrogenase. FASEB J. 1995 Aug;9(11):995-1003. PMID: 7649415.\u003c/li\u003e\n\u003cli\u003e Peleli M, Zollbrecht C, Montenegro MF, Hezel M, Zhong J, Persson EG, et al. Enhanced XOR activity in eNOS-deficient mice: effects on the nitrate-nitrite-NO pathway and ROS homeostasis. Free Radic Biol Med 2016;99:472\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003e Kanbay M, Afsar B, Siriopol D, Dincer N, Erden N, Yilmaz O, Sag AA, Kuwabara M, Cherney D, Rossignol P, Ortiz A, Covic A. Effect of Uric Acid-Lowering Agents on Cardiovascular Outcome in Patients With Heart Failure: A Systematic Review and Meta-Analysis of Clinical Studies. Angiology. 2020 Apr;71(4):315-323. doi: 10.1177/0003319719897509. Epub 2020 Jan 31. PMID: 32000517.\u003c/li\u003e\n\u003cli\u003e Cantu-Medellin N, Kelley EE. Xanthine oxidoreductase-catalyzed reactive species generation: A process in critical need of reevaluation. Redox Biol. 2013 Jun 10;1(1):353-8. doi: 10.1016/j.redox.2013.05.002. PMID: 24024171; PMCID: PMC3757702.\u003c/li\u003e\n\u003cli\u003e Fadl H, Magnuson A, \u0026Ouml;stlund I, Montgomery S, Hanson U, Schwarcz E. Gestational diabetes mellitus and later cardiovascular disease: a Swedish population based case-control study. BJOG. 2014 Nov;121(12):1530-6. doi: 10.1111/1471-0528.12754. Epub 2014 Apr 25. PMID: 24762194; PMCID: PMC4232923.\u003c/li\u003e\n\u003cli\u003e Uretsky S, Messerli FH, Bangalore S, Champion A, Cooper-Dehoff RM, Zhou Q, Pepine CJ. Obesity paradox in patients with hypertension and coronary artery disease. Am J Med. 2007 Oct;120(10):863-70. doi: 10.1016/j.amjmed.2007.05.011. PMID: 17904457.\u003c/li\u003e\n\u003cli\u003e Graziani F, Leone AM, Basile E, Cialdella P, Tritarelli A, Bona RD, Liuzzo G, Nanni G, Iaconelli A, Iaconelli A, Mingrone G, Biasucci LM, Crea F. Endothelial progenitor cells in morbid obesity. Circ J. 2014;78(4):977-85. doi: 10.1253/circj.cj-13-0976. Epub 2014 Feb 27. PMID: 24572586.\u003c/li\u003e\n\u003cli\u003e Mehta A, Meng Q, Li X, Desai SR, D\u0026apos;Souza MS, Ho AH, Islam SJ, Dhindsa DS, Almuwaqqat Z, Nayak A, Alkhoder AA, Hooda A, Varughese A, Ahmad SF, Mokhtari A, Hesaroieh I, Sperling LS, Ko YA, Waller EK, Quyyumi AA. Vascular Regenerative Capacity and the Obesity Paradox in Coronary Artery Disease. Arterioscler Thromb Vasc Biol. 2021 Jun;41(6):2097-2108. doi: 10.1161/ATVBAHA.120.315703. Epub 2021 Apr 15. PMID: 33853349; PMCID: PMC8147702.\u003c/li\u003e\n\u003cli\u003e Cooke JP. Endotheliopathy of Obesity. Circulation. 2020 Jul 28;142(4):380-383. doi: 10.1161/CIRCULATIONAHA.120.047574. Epub 2020 Jul 27. PMID: 32718250; PMCID: PMC7391057.\u003c/li\u003e\n\u003cli\u003e Butt JH, Petrie MC, Jhund PS, Sattar N, Desai AS, K\u0026oslash;ber L, Rouleau JL, Swedberg K, Zile MR, Solomon SD, Packer M, McMurray JJV. Anthropometric measures and adverse outcomes in heart failure with reduced ejection fraction: revisiting the obesity paradox. Eur Heart J. 2023 Apr 1;44(13):1136-1153. doi: 10.1093/eurheartj/ehad083. PMID: 36944496; PMCID: PMC10111968.\u003c/li\u003e\n\u003cli\u003e Kul Ş, G\u0026uuml;ven\u0026ccedil; TS, Baycan \u0026Ouml;F, \u0026Ccedil;elik FB, \u0026Ccedil;alışkan Z, \u0026Ccedil;etin G\u0026uuml;ven\u0026ccedil; R, \u0026Ccedil;ift\u0026ccedil;i FC, Caliskan M. Combined past preeclampsia and gestational diabetes is associated with a very high frequency of coronary microvascular dysfunction. Microvasc Res. 2021 Mar;134:104104. doi: 10.1016/j.mvr.2020.104104. Epub 2020 Nov 13. PMID: 33189732.\u003c/li\u003e\n\u003cli\u003e Kelshiker MA, Seligman H, Howard JP, Rahman H, Foley M, Nowbar AN, Rajkumar CA, Shun-Shin MJ, Ahmad Y, Sen S, Al-Lamee R, Petraco R; Coronary Flow Outcomes Reviewing Committee. Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis. Eur Heart J. 2022 Apr 19;43(16):1582-1593. doi: 10.1093/eurheartj/ehab775. Erratum in: Eur Heart J. 2023 Jan 1;44(1):27. doi: 10.1093/eurheartj/ehac628. PMID: 34849697; PMCID: PMC9020988.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gestational diabetes, coronary artery disease, coronary microvascular dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-7194002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGestational diabetes is associated with an increased risk of coronary artery disease and adverse cardiovascular events in later life. Coronary microvascular dysfunction is a common precursor of coronary after disease. Although microvascular dysfunction is common in GDM, the exact mechanisms remain unknown.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAim\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo study the associations between coronary flow reserve (CFR) with common biomarkers of hyperglycemia, insulin resistance, inflammation and oxidative stress.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMeasurement of CFR was performed noninvasively using echocardiography in all patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients with a low CFR (\u0026le;\u0026thinsp;2.5) had higher HbA1c%, HOMA-IR, triglyceride-glucose index, uric acid, and total and non-HDL cholesterol as compared to those with a normal CFR (\u0026gt;\u0026thinsp;2.5). On univariate analysis, there was strong evidence favoring an association between CFR with HbA1c% (r=-0.32,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,BF\u003csub\u003e10\u003c/sub\u003e:785) and uric acid (r=-0.29,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,BF\u003csub\u003e10\u003c/sub\u003e:113) and moderate to strong evidence for HOMA-IR (r=-0.21,p\u0026thinsp;=\u0026thinsp;0.006,BF\u003csub\u003e10\u003c/sub\u003e:20). Final linear regression model included HbA1c% (β=-0.31,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); HOMA-IR (β=-0.25,p\u0026thinsp;=\u0026thinsp;0.001); uric acid (β=-0.19,p\u0026thinsp;=\u0026thinsp;0.007) and waist circumference (β\u0026thinsp;=\u0026thinsp;0.25,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGDM patients with higher HbA1c%, HOMA-IR, hyperuricemia and lower waist circumference are at a higher risk for coronary microvascular dysfunction. However, most of the variance in CFR is not related to any biomarkers and a past history of GDM rappears as the most likely cause for reduced CFR.\u003c/p\u003e","manuscriptTitle":"Coronary microvascular dysfunction in gestational diabetes: Insights on possible mechanism from a large institutional registry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 09:12:29","doi":"10.21203/rs.3.rs-7194002/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-20T08:05:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T09:40:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308832496731085097070462269278021341869","date":"2025-07-30T17:05:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314656589147436951910123877751798894429","date":"2025-07-30T09:45:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T09:35:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-26T09:11:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-26T09:10:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Cardiovascular Imaging","date":"2025-07-23T08:28:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"da84ee73-a187-406d-a7fa-bdc598bab3b7","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:38:01+00:00","versionOfRecord":{"articleIdentity":"rs-7194002","link":"https://doi.org/10.1007/s10554-025-03549-w","journal":{"identity":"the-international-journal-of-cardiovascular-imaging","isVorOnly":false,"title":"The International Journal of Cardiovascular Imaging"},"publishedOn":"2025-10-23 16:16:46","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2025-08-04 09:12:29","video":"","vorDoi":"10.1007/s10554-025-03549-w","vorDoiUrl":"https://doi.org/10.1007/s10554-025-03549-w","workflowStages":[]},"version":"v1","identity":"rs-7194002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194002","identity":"rs-7194002","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}