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Although men are more prone to developing cardiovascular disease (CVD) than women, risk factors for CVD, such as nicotine abuse and diabetes mellitus, have been shown to be more detrimental in women than in men.
We developed a method to systematically investigate population-wide electronic health records for all possible associations between risk factors for CVD and other diagnoses. The developed structured approach allows an exploratory and comprehensive screening of all possible comorbidities of CVD, which are more connected to CVD in either men or women.
Based on a population-wide medical claims dataset comprising 44 million records of inpatient stays in Austria from 2003 to 2014, we determined comorbidities of acute myocardial infarction (AMI; International Classification of Diseases, Tenth Revision [ICD-10] code I21) and chronic ischemic heart disease (CHD; ICD-10 code I25) with a significantly different prevalence in men and women. We introduced a measure of sex difference as a measure of differences in logarithmic odds ratios (ORs) between male and female patients in units of pooled standard errors.
Except for lipid metabolism disorders (OR for females [ORf]=6.68, 95% confidence interval [CI]=6.57-6.79, OR for males [ORm]=8.31, 95% CI=8.21-8.41), all identified comorbidities were more likely to be associated with AMI and CHD in females than in males: nicotine dependence (ORf=6.16, 95% CI=5.96-6.36, ORm=4.43, 95% CI=4.35-4.5), diabetes mellitus (ORf=3.52, 95% CI=3.45-3.59, ORm=3.13, 95% CI=3.07-3.19), obesity (ORf=3.64, 95% CI=3.56-3.72, ORm=3.33, 95% CI=3.27-3.39), renal disorders (ORf=4.27, 95% CI=4.11-4.44, ORm=3.74, 95% CI=3.67-3.81), asthma (ORf=2.09, 95% CI=1.96-2.23, ORm=1.59, 95% CI=1.5-1.68), and COPD (ORf=2.09, 95% CI 1.96-2.23, ORm=1.59, 95% CI 1.5-1.68). Similar results could be observed for AMI.
Although AMI and CHD are more prevalent in men, women appear to be more affected by certain comorbidities of AMI and CHD in their risk for developing CVD.
Despite the overall higher prevalence of cardiovascular disease (CVD) in men, the gender gap in CVD narrows with age, especially postmenopause [
Some risk factors for CVD are associated with excess risk in 1 sex but not the other. A series of meta-analyses identified smoking [
A more comprehensive quantification of SDs in AMI or chronic ischemic heart disease (CHD) risk in association with other comorbidities, such as respiratory and renal diseases, is still needed. This analysis aimed to fill this knowledge gap by identifying potential gender gaps in comorbidities associated with AMI or CHD and by determining the extent of age-/menopause-related differences in the gender gaps.
Both the terms “woman/man” and “female/male” are used in this paper as we investigate SDs. However, through our study design, we cannot rule out an influence of gender aspects (in addition to sex-specific aspects) on disease risk. For the purpose of this study and in line with the previous literature [
Medical claims data of the entire Austrian population were examined with a structured approach to analyze comorbidity networks for female and male patients. This database contains approximately 44 million records, containing for each in-hospital stay in Austria from 1997 until 2014 the patient’s ID, age, date of admission, date of discharge, primary diagnosis, secondary diagnoses, and type of release. The age of patients is given at a resolution of 5 years. The reason for the hospital admission is given by the primary diagnosis. Conditions that coexist at the time of admission are secondary diagnoses. In this study, we considered primary and secondary diagnoses as equally relevant. All diagnoses are recorded in the form of level 3 International Classification of Diseases, Tenth Revision (ICD-10) codes, a medical classification system by the World Health Organization (WHO). This study concentrated on 1080 different ICD-10 codes ranging from A00 to N99. We only extracted the subset of patients who did not have any hospital stays during the 6 years from 1997 to 2002.
The total number of patients in the database is 8,996,916. After extracting the described subset of patients, the total number of patients for this analysis was 3,758,634 (51% women, 49% men). To compare changes that might occur before and after (peri-)menopause, we conducted additional analyses comparing patient groups with ages being above or below the cutoff age of 50 years. The total number of all diagnoses recorded in the selected dataset was 36,358,201 (50.14% diagnoses of female patients, 49.86% diagnoses of male patients). The 5 most frequent diagnoses were hypertension (I10), CHD (I25), type 2 diabetes mellitus (E11), atrial fibrillation and flutter (I48), lipid metabolism disorder (E78) (female: I10, malignant neoplasm of breast [C50], other disorders of urinary system [N39], I25, E11; male: I10, I25, E11, E78, COPD [J44]).
Comorbidities indicate the presence of more than 1 disease in the same person. In our analysis, we investigated all statistically significant co-occurring diseases.
Stratified analysis was performed to adjust for confounding variables (age, time period). The analyzed dataset was stratified by age (10-year age groups) and 6 time windows of 2 years each from 2003 to 2014 (2003-2004, 2005-2006, and so on), resulting in 48 strata for women and men. For each pair of diagnoses for each stratum, a contingency table was built. Contingency tables that contained a sufficient number of patients in each subgroup (>4) were used for computing relative risks (RRs) and the
By using the Cochran–Mantel–Haenszel method [
As a test statistic for SDs, we measured the differences of logarithmic ORs between male and female patients in units of pooled standard errors:
where ORm is the OR for males, ORf is the OR for females, SEm is the standard error of ORm, and SEf is the standard error of ORf.
To test for significant SDs, we tested the null hypothesis that an SD is measured from a normal distribution with zero mean to obtain the SD
We defined 5 significance levels of SDs:
Not significant
(SD<=2<==>
Weak
(2<SD<=3<==>.003<=
Substantial
(3<SD<=4<==>.00006<=
Strong
(4<SD<=5<==>.0001<=
Very strong
(5<SD<==>
We calculated the time difference for each pair of diagnoses (A and B) for every patient in the period 2003-2014. The time difference was defined as the difference between the time of the first diagnosis of A and the time of the first diagnosis of B. Patients were separated into 4 groups based on the time interval for each pair: (1) A and B were diagnosed during the same hospital stay and the time difference between A and B was (2) less than 3 months, (3) greater than 3 months and less than approximately 1 year (360 days), or, finally, (4) greater than approximately 1 year.
In each group and for each pair, we counted the number of patients who were first diagnosed with disease A and then disease B,
For all 4 above-defined time intervals, we calculated the ratio between the number of patients with the direction of “first A then B” relative to “first B then A” and the time order ratio TOR(A→B)=
A TOR(A→B) of <1 (>1) indicates that B (A) tends to occur before A (B). To see whether TOR(A→B) is significantly different from 1, we tested the null hypothesis that
We focused on the age group of 20-79 years, with a total of 2,716,967 patients (50.12% women, 49.88% men). As shown in
Baseline characteristics and prevalence (%) among all patients aged 20-79 years in Austria from 2003 to 2014.
Parameters and diagnoses | All | Female patients | Male patients |
All patients | 2,716,967 | 1,361,704 | 1,355,263 |
Age (years, mean±SEa) | 48.53±15.99 | 47.99±16.2 | 49.07±15.77 |
Number of hospital stays (mean±SE) | 3.04±4.92 | 3±4.82 | 3.08±5.01 |
Hospital days (mean±SE) | 17.52±45.83 | 16.65±44.38 | 18.38±47.3 |
Number of hospital diagnoses (mean±SE) | 4.28±4.83 | 4.12±4.66 | 4.44±4.99 |
Obesity and overweight (%) | 4.37 | 4.47 | 4.26 |
Disorders of lipoprotein metabolism and other lipidemias (%) | 8.46 | 7.33 | 9.62 |
Nicotine dependence (%) | 3.16 | 2.01 | 4.33 |
AMIb (%) | 2.02 | 1.42 | 2.64 |
CHDc (%) | 6.22 | 4.9 | 7.58 |
Asthma (%) | 1.3 | 1.28 | 1.31 |
COPDd (%) | 3.41 | 2.69 | 4.15 |
Respiratory failure (%) | 0.91 | 0.76 | 1.06 |
Diabetes mellitus (%) | 6.49 | 5.93 | 7.07 |
Acute kidney failure and CKDe (%) | 3.77 | 3.66 | 3.88 |
aSE: standard error.
bAMI: acute myocardial infarction.
cCHD: chronic ischemic heart disease.
dCOPD: chronic obstructive pulmonary disease.
eCKD: chronic kidney disease.
In the analysis at hand, the diagnosis of overweight and obesity was more prominently associated with AMI (ORf=3.36, 95% confidence interval [CI]=3.22-3.51 vs ORm=2.8, 95% CI=2.72-2.88,
Females showed a stronger association of diabetes mellitus with AMI and CHD compared to males (AMI: ORf=2.94, 95% CI=2.77-3.12 vs ORm=2.17, 95% CI=2.09-2.26,
There is a tendency that diabetes mellitus is diagnosed before AMI and CHD; see
Diabetes mellitus is typically diagnosed before AMI and CHD. We show the time
directionality (see Methods) for patients with a diagnosis of diabetes (E10-E14) and (a) AMI
(I21) and (b) CHD (I25). The larger the time difference between these two diagnoses, the
stronger the dominance of patients first having a diabetes mellitus diagnosis (TOR<1).
Significance levels of the TOR are indicated by asterisks (*
The increased risk for female patients with diabetes mellitus to develop AMI or CHD is a well-researched finding. Diabetes mellitus not only doubles the CVD risk but rather adds an additional 44% risk to females compared to males [
Female patients with acute kidney disease and CKD were more likely to be diagnosed with AMI and CHD, respectively, than male patients in our cohort (AMI: ORf=3.96, 95% CI=3.73-4.2 vs ORm=2.8, 95% CI=2.69-2.91,
Time directionality analysis for CKD. There is a tendency that patients are first diagnosed with (a) AMI and (b) CHD and then with CKD. Results are shown as in Figure 1 for diabetes. AMI: acute myocardial infarction; CHD: chronic ischemic heart disease; CKD: chronic kidney disease; TOR: time order ratio.
Nicotine abuse had a significantly higher associated AMI and CHD risk for female patients than male patients (AMI: ORf=10.14, 95% CI=9.66-10.64 vs ORm=6.68, 95% CI=6.51-6.84,
Female patients showed a stronger association of respiratory failure with AMI and CHD compared to male patients (AMI: ORf=3.11, 95% CI=2.78-3.48 vs ORm=2.24, 95% CI=2.09-2.4,
Time directionality analysis for respiratory failure. There is a tendency that patients are first diagnosed with (a) AMI and (b) CHD and then with respiratory failure. Results are shown in Figure 1 for diabetes mellitus. AMI: acute myocardial infarction; CHD: chronic ischemic heart disease; TOR: time order ratio.
Female patients had a significantly higher risk of having asthma and CHD (ORf=2.09, 95% CI=1.96-2.23 vs ORm=1.59, 95% CI=1.5-1.68,
When splitting patients into 2 age groups (20-49 years, 50-79 years) to account for potential differences before and after an age considered likely (peri-)menopausal, asthma and AMI and CHD were significantly more often connected in females in the age group of 50-79 years than in younger patients.
COPD and AMI or CHD were more likely to co-occur in female patients than in male patients (AMI: ORf=2.49, 95% CI=2.35-2.63 vs ORm=1.62, 95% CI=1.56-1.68,
Lipid metabolism disorders were associated with an excess risk for AMI and CHD in male than in female patients in our analysis (AMI: ORf=6.3, 95% CI=6.1-6.5 vs ORm=7.21, 95% CI=7.07-7.35,
The results of this analysis demonstrated that except for lipid metabolism disorders, the risk factors overweight and obesity, diabetes mellitus, acute kidney disease and CKD, nicotine dependence, respiratory failure, asthma, and COPD display a stronger connection to CHD and AMI in women than in men.
Obesity predisposes to a multitude of comorbidities, many of which have a negative impact on CVD risk. As women are more likely to be obese [
Before menopause, the more favorable body fat distribution in the lower-body subcutaneous areas might mitigate CVD risk in females. The menopausal loss of ovarian hormones induces a redistribution of body fat to a more visceral, less favorable distribution [
Similar to overweight and obesity, females showed a stronger association of diabetes mellitus with AMI or CHD compared to males. The increased risk for female patients with diabetes mellitus to develop AMI or CHD in this study is a well-researched finding, as females with diabetes mellitus lose their “female protection” against CVD [
We found similar increased ORs in female patients with acute kidney disease and CKD who were also more likely to be diagnosed with AMI or CHD than in male patients in our cohort. The complex relationship between CKD and CVD probably results from overlapping risk factors and clustering of unspecific CVD risk factors, such as hypertension, diabetes mellitus, dyslipidemia, and CKD-specific factors (eg, anemia, volume overload) [
Concerning respiratory diseases, females had a 39% or 34% (54% or 31%) increased OR to be diagnosed with AMI or CHD, respectively, than males when they had respiratory failure (COPD). Respiratory failure was significantly more associated with AMI or CHD in women than in men in the age group of 50-79 years compared to younger patients. In younger patients, the effect was only visible in patients with CHD but not AMI. Furthermore, women had a significantly higher risk of having asthma and CHD but not asthma and AMI than men. Accordingly, asthma has been associated with a modest increase in CHD risk in females in a previous study [
Like in patients with asthma, COPD and AMI or CHD were more likely to co-occur in women than in men. A Finnish national health examination concluded that signs of obstruction in a spirometer at age 30–49 years appears to predict a major coronary event (adjusted HR=4.21) in women only. COPD is the fourth-leading cause of death globally; approximately 50% of those deaths can be attributed to a cardiovascular event (eg, myocardial infarction). With 9.23%, COPD is more prevalent in males than in females (6.95%) [
Lipid metabolism disorders were the only risk factor with an extensive gender gap in relative AMI and CHD risk associated with an excess risk for males in our analysis. Correspondingly, total cholesterol displayed a higher RR for CVD for men in a meta-analysis as well [
The analysis is based on a large dataset containing over 45,000,000 hospital diagnoses of the whole Austrian population from 1997 to 2014. The size of the hospital dataset is a clear strength; however, outpatient visits were not recorded. Patients had to have been admitted to a hospital at least once to be included in the analysis. As it is usually the case with medical claims data, our results are likely to be affected by missing diagnoses (in particular, diseases typically not treated in an inpatient setting) and wrong disease classifications. However, nonsystematic errors, for instance, randomly missing diagnoses, do not play a major role, as even if many data points would be missing, the larger the sample size, the more likely one is to still be able to statistically identify an existing correlation. This, of course, does not necessarily apply in the case of systematic errors in the data. Due to the character of this analysis, which is solely based on disease codes, we cannot rule out unobserved confounding factors related to gender aspects. Furthermore, repeated observations of patients over 12 years allowed us to perform a time directionality analysis to identify whether it is more likely that disease A increases the risk for diseases B or whether B is a risk factor for A. However, given the purely observational nature of our dataset, no statements on causality can be made based on this analysis. We chose age 50 as a cutoff for before and after (peri-)menopause as we did not have access to hormone levels or gynecological history; the unreliability of this strict cutoff is a limitation of this analysis.
Although all the discussed factors increase the risk for CVD in both sexes, nicotine abuse, diabetes mellitus, renal failure, obesity and overweight, and respiratory diseases were relatively more associated with AMI and CHD risk in women in this analysis. Only lipid metabolism disorders displayed the opposite relationship with AMI and CHD. As the inflammatory effect of sex hormones is believed to be a strong influencing factor for SDs in respiratory diseases, we hypothesized that these differences might be age related and change during menopause. Accordingly, SDs in the age group of over 50 years were more prominent than in under 50-year-olds. Further analyses, especially prospective studies, are needed to investigate this topic in detail. However, taken together, these results underline the importance of CVD-screening practices, specifically in women with the above-mentioned risk factors, and emphasize that physicians should be aware of the sex-specific excess risk for AMI and CHD associated with some but not all of their comorbidities.
The data that support the findings of this study are available from the Austrian Ministry of Health, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the Austrian Ministry of Health.
acute myocardial infarction
body mass index
chronic ischemic heart disease
confidence interval
chronic kidney disease
chronic obstructive pulmonary disease
cardiovascular disease
forced expiratory pressure in 1 s
high-density lipoprotein
hazard ratio
International Classification of Diseases, Tenth edition
low-density lipoprotein
odds ratio males
odds ratio females
sex difference P-value
relative risk
sex difference
standard error
ST-elevation myocardial infarction
time order ratio
very low-density lipoprotein
World Health Organization
Funding was received from the Vienna Science and Technology Fund (WWTF; MA16-045).
ED and CD wrote the manuscript and researched data. ED analyzed the data. AK-W, ML, and NH contributed to the discussion and reviewed/edited the manuscript. PK researched data, contributed to the methods, and reviewed/edited the manuscript. PK is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
All authors have no relevant conflicts of interest to disclose.