Sex difference in the risk for exercise-induced albuminuria correlates with hemoglobin A1C and abnormal exercise ECG test findings

We analyzed data that was collected between January 2012 to September 2016 at the Tel-Aviv Medical Center Inflammation Survey, a registered databank of the Israeli Ministry of Justice [24‐27]. The Tel-Aviv Medical Center Inflammation Survey encompasses a large cohort of subjects who attended the Tel-Aviv Medical Center for routine annual checkups (including a physician’s interview and examination, blood and urine tests, and an exercise stress test). The study was approved by the local Ethics committee and informed consent was obtained from all participants.

For the purpose of this study, urine samples for albumin and creatinine measurements were collected from participants before and after an exercise stress test was performed according to the Bruce protocol. A total of 3712 subjects were included in our database. We excluded 185 subjects due to lack of urinary tests (before or after the exercise test), and another 342 subjects without valid exercise tests results (test performed on a bicycle instead of a treadmill or an incomplete test). After these exclusions, the final cohort comprised 3185 participants. Another 156 individuals had no valid measurements of urinary creatinine concentration before or after the exercise, therefore, the values shown in Table 1 include only 3029 subjects.

Blood samples were drawn upon the participants’ arrival to the center after a 12-h overnight fast. Only after the morning blood test participants were allowed to drink, and after the exercise test they were invited to eat breakfast. Urine tests were analyzed by an ADVIA chemistry analyzer (Siemens Healthcare Diagnostics, Tarrytown, NY, USA), which is an improved albumin detection method with an extended analytic range of 3–420 mg/l and coefficient of variation of 2% [28]. It is accepted that a test for UACR is the standard method for assessing proteinuria using a spot examination, as advised by National Kidney Foundation Kidney Disease Outcomes Quality Initiative guidelines [29]. Therefore, we performed calculation of the UACR with a standard cutoff of 30 mg/g for moderate albuminuria. eGFR values were calculated using the CKD-EPI formula [30].

Separation of A1C from non-glycated hemoglobin of whole blood samples in EDTA was done by Tosoh’s G7 HPLC (Tosoh Bioscience, Inc. San Francisco, CA, USA). The concentrations of glycated hemoglobin (A1C) and the concentration of total hemoglobin were measured, and the ratio reported as % A1C. A1C levels were categorized into three states, healthy; <5.7%, pre-diabetic; 5.7–6.4% and diabetic; >6.5% according to the American Diabetes Association (ADA) guidelines [31].‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Evaluation and diagnosis of metabolic syndrome (and it’s components) was performed based on the joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity [32]. Briefly, elevated waist circumference was defined as ≥94 cm (37 in.) in men and ≥80 (31.5 in.) in women, as recommended for individuals of European and Middle Eastern descent. Elevated triglycerides were defined as ≥150 mg/dl (1.7 mmol/l) or on drug treatment for elevated triglycerides. Reduced high-density lipoprotein-cholesterol (HDL) was defined as <40 mg/dl (1.0 mmol/l) in men and <50 mg/dl (1.3 mmol/l) in women. Elevated blood pressure was defined as ≥130 mmHg for systolic blood pressure or ≥85 mmHg for diastolic blood pressure or on antihypertensive drug treatment in a patient with a history of hypertension. Elevated fasting glucose was defined as ≥100 mg/dl (5.55 mmol/l). The diagnosis of metabolic syndrome was based on the existence of at least three abnormal findings out of the five mentioned above.

Exercise ECG stress test was preformed according to the Bruce protocol. ECG results were manually reviewed on the spot by a cardiologist. The remarks were filled into the patient’s file and were categorized into two groups. The first was labeled “negative exercise ECG findings”, stating that there were no significant findings in the patient’s test. The second group was titled “positive exercise ECG findings”; stating that the patient’s test demonstrated irregular findings possibly indicating myocardial ischemia or cardiac arrhythmia. These findings included ST segment depressions in variating lengths and position, T wave abnormality and incidence of ventricular premature contractions (VPCs).

All participants were asked if they had any previous diagnosis of diabetes or cardiac disease as part of their medical history questioner. 160 Patients (133 males and 27 females) with reported history of ischemic heart disease were excluded from the ECG stress test analysis.

All continuous variables are displayed as means (SD) for normally distributed variables or median [interquartile range (IQR)] for variables with abnormal distribution., whereas categorical variables are displayed as numbers (%) of patients within each group. The different biomarkers in men and women were compared by a Student’s t test for normally distributed variables and by the Mann–Whitney U-test for non-normally distributed variables. To assess associations among categorical variables, we used a χ²-test. The values before and after the exercise test were compared using the Wilcoxon signed ranks test.

A one-way ANOVA test was used to evaluate the difference in ΔEiA according to number of metabolic syndrome components.

A general linear univariate model was used to evaluate the estimated effect of specified covariates: age, sex, body mass index, metabolic equivalents (METs) achieved during the exercise stress test, eGFR and A1C levels using Parameter’s estimates; B (with 95% confidence intervals) for ΔEiA. Multivariate binary logistic regression was used to evaluate the odds ratio of positive ECG findings among subjects according to the presence of ΔEiA > 13 (mg/g) in men and women. The model was further adjusted for the following specified covariates: age > 46, BMI > 30, METs score, waist circumference, HDL, BP, glucose and triglycerides. p values of <0.05 were considered statistically significant. The IBM SPSS Statistics 22.0 statistical package was used to perform all statistical analyses (IBM Corporation, Armonk, New York, USA).

Men were older, had higher BMI, METs score during the exercise test, blood pressure measurements, LDL, A1C and fasting glucose levels alongside lower levels of HDL (Table 1). At rest, men presented with lower UACR values compared to women; 2.9 (1.3–6.8) vs. 4.8 (1.9–11.2) mg/g for median (IOR), p < 0.001. Following exercise UACR values were similar; 7.1 (2.5–22.3) vs. 7.9 (2.9–22.3) mg/g for median (IQR), p = 0.306. The change in UACR values (ΔEiA) was significantly higher in men compared to women; 3.1 (0.1–14.7) vs. 1.7 (−0.8 to −10.7) mg/g for median (IQR).

General linear univariate model to predict baseline UACR, showed significant effect of eGFR, but no significant effect of sex, age, A1C levels, BMI or METs components (Table 2).

When searching for predictors of ΔEiA, we found a significant effect of sex, A1C and the interaction between them along with BMI, METs and baseline UACR (Table 2). Specifically, our data show that women with an A1C > 6.5% are at increased risk for higher ΔEiA even after controlling for age, BMI, eGFR, baseline UACR and METs; B = 95.37, 95% CI 49.34–141.39, p < 0.001 (Table 2; Fig. 2b).

A ΔEiA > 13 mg/g was defined as a cutoff level based on it being the lower bound of the top quartile of the cohort [75% of the cohort demonstrated ΔEiA < 13.6 (mg/g)]. ΔEiA > 13 mg/g was a significant predictor of positive (i.e. abnormal) exercise ECG findings in men and women, with women demonstrating much higher rates (OR = 2.691, p = 0.001 vs. OR = 1.511 p = 0.036, respectively). Among men, fasting glucose levels, Blood pressure and age were also observed as significant predictors (Fig. 3).

The positive predictive value (PPV) of ΔEiA > 13 mg/g in predicting positive ECG findings was higher in women compared to men (38 and 27% respectively). The negative predictive value (NPV) was high among the entire cohort NPV = 81% (82% in men and 77% in women).