Introduction

Male Breast cancer (MBC) is a rare condition, it accounts for less than 1% of all breast cancer in Spain and worldwide, as with Female Breast cancer (FBC) its incidence rate is steadily rising on an annual basis1,2,3. Due to its low prevalence, there is a lacking in prospective randomized clinical studies; historically male patients have been excluded from breast cancer clinical trials. Our current knowledge of MBC come from small retrospective studies, this is one of the reasons why MBC has been traditionally considered an analogous clinical entity to menopausal FBC4,5.

All the above has hampered to obtain useful and realistic knowledge regarding MBC characteristics and management, currently most treatment recommendations are extrapolated from FBC guidelines6. Nonetheless, there is an increasing evidence that MBC embodies its own distinct clinical identity, which requires its own particular considerations4,5,7. In fact, two specific histological subtypes have been described in MBC8, further supporting the aforementioned.

Most of the efforts for a better understanding of MBC have been focused to characterize sociodemographic, life-style and clinical differences between MBC and FBC patients and how those differences are associated to survivability and mortality. There are certain features of MBC well described in the literature. For instance, MBC patients tend to be diagnosed at older age, with a more advance tumour stage, with higher number of co-existing pathologies and with hormone-positive phenotype in comparison with their female counterparts. In reality, most of MBC patients are usually progesterone positive, estrogen positive and epidermal growth factor receptor negative. This hormone profile is associated with poorer prognosis2,3,9,10,11.

Breast cancer is a complex multifactorial disease, and as such, there seem to be genomic factors modulating its appearance and evolution. For instances, mutations in BRCA2 and BRCA1 are presented in around 16% of all MBC cases, increasing an 8% and a 2% respectively the risk of MBC development versus the general population with wild type genes with just 0.1%. There are other found genes that may influence appearance and evolution of breast cancer in a lighter way12. Other risk factor worthy to be mentioned are family history of breast cancer, strengthening the genomic background relevance; breast cancer in closed-degree relative has been associated with a high increased odds of MBC13.

Regarding treatment, MBC patients seem to mostly undergo mastectomy and they tend to be nonadherent to adjuvant endocrine therapy14,15. Nevertheless, it appears there are certain surgical treatments with better outcomes, but all of them have similar survival rates comparing with FBC once adjusted by treatment9,10,16,17.

On the other hand, available data of survivability in MBC patients suggest they have worse results than FBC in general, most likely due to the older age of diagnosis, the higher burden of multimorbidity and the advance tumour stage18,19,20. Remarkably, little is known about associated comorbidities to MBC, which may influence heavily survivability; and which ones differ from the general population or its female counterpart, information relevant enough to modulate the treatment itself of MBC patients. As far as we know up to the date, only Zoorob et al.(2019) have tried to elucidate this association and the differences between MBC and FBC patients in a set list of 20 chronic pathologies21. Other few studies have described the frequency of small fixed lists of chronic conditions without determining associations3,9,22, and lastly, in the vast majority of cases when talking about multimorbidity in MBC patients they choose some kind of index to assess disease number or multimorbidity burden without further disaggregation8,9,14,16,17,20,23,24,25,26. Despite the lack of knowledge in this matter, characterizing comorbidities associated to MBC patients is fundamental for a better management of their health and increasing chances of survival in their complex situation, as previous studies have widely seen that high multimorbidity burden leads to poorer cancer outcomes.

Bearing all above in mind, this study aimed to analyze the association of breast cancer with other comorbidities and to compare those associated comorbidities to those found in a general population of men without breast cancer and in a population of female breast cancer patients.

Methods

Study design and population

We conducted a retrospective, observational study in the EpiChron Cohort, which contains anonymized sociodemographic and clinical information of all users of the public health care system in the Spanish region of Aragon. Detailed description and data-linkage method of the EpiChron Cohort can be found elsewhere27. The total general population for this study comprised all individuals with at least one year of activity in the Health System and at least one chronic condition between 2010 and 2019 (1,136,624 individuals; 529,683 men and 606,941 women), almost 90% of the total population of Aragon region (1.3 M individuals).

For the breast cancer cohorts, all prevalent cases of breast cancer between 2010 and 2019 in men (105 men) and women (11,657 women) aged 18 years and older were selected. Patients with breast cancer were identified as those with an active diagnosis of breast cancer during the study period recorded in their primary or hospital electronic health records. All codes under the 24 code (Cancer of breast) of the Clinical Classification Software (CCS) were selected. Specifically, the following ICD9 diagnosis codes were considered by CCS: 174.0, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.8, 174.9, 175.0, 175.9, 233.0 and V103. No other inclusion or exclusion criteria were applied.

Additionally, a small matched sample of male patients without breast cancer diagnosis (210 men) was selected to compare disease association.

Ethical considerations

The Clinical Research Ethics Committee of Aragón (CEICA) approved this study (PI17/0024). CEICA waived the need of obtaining the informed consent due to the use of anonymized data and the epidemiological approach used. This research was performed in accordance with the Declaration of Helsinki.

Study variables

For all MBC patients, we analysed age at breast cancer diagnosis, residency (urban/rural), all chronic diseases that the patient had registered in the studied period, number of chronic diseases at diagnosis and multimorbidity state (yes/no).

The diagnosis codes were originally coded according to the International Classification of Primary Care, 2nd edition (ICPC-2) or the International Classification of Diseases, 9th edition (ICD-9), depending on the source of the data (primary or hospital care). In order to reduce the number of different diagnosis codes, we transformed the ICPC-2 codes into ICD-9 codes using the conversion developed by the Navarra Institute of Public and Labour Health (8th edition)28, then we used the Agency for Healthcare Research and Quality’s Clinical Classification Software to group ICD-9 codes, particularly we used the single level category29. Then, we filtered out all non-chronic conditions using the Chronic Condition Indicator software tool, resulting in 161 chronic mutually exclusive categories30. Multimorbidity was defined as the presence of two or more chronic diseases after this reclassification process. For all populations, prevalence of the different CCS codes was analysed.

Statistical analysis

For the descriptive analysis, we presented continuous data by their mean and standard deviation and categorical data by their frequency and proportion.

For the characterization of comorbidities, firstly no matching by age was performed in the comparison with the FBC population, so odds ratios (OR) were calculated by using the whole population without breast cancer for their respective sex, the comorbidity results were compared between MBC and FBC. For the second comparison, a matching procedure was performed in a ratio 1:2, using birth year as matching variable, MBC patients were matched with male patients without breast cancer, a small population of 315 individuals in total. Conditional logistic regression models were used to calculate OR and their confidence interval of prevalence for each comorbidity according to the presence or absence of breast cancer in each population. OR were adjusted by age. All p-values were two-sided and adjusted by age, results with adjusted p-values < 0.05 were considered statistically significant. All the analysis was made in STATA 12.0.

Data visualization was performed using Microsoft Excel version 1808; for the frequency tables and using ggplot2 v3.4.2 in R v4.3.0; for the bar plots.

Results

Characteristics of male breast cancer patients

Between 2010 and 2019, 105 MBC patients were identified in EpiChron Cohort, their mean age at diagnosis was 65.62 ± 12.02 years [Mean ± Standard deviation]. Half of all MBC patients had died during the study period and 60 of them (57.14%) lived in an urban area. Almost all of them (more than 90%) were over 45 years old and with multimorbidity. Notoriously, more than half of all MBC patients (52.38%) had six or more chronic conditions at breast cancer diagnosis time and, in general, the mean number of chronic diseases presented in the population was 5.7 ± 3.17, without considering breast cancer diagnosis (Table 1).

Table 1 Socio-demographic and clinical characteristics of men with cancer of breast between 2010 and 2019 from the EpiChron Cohort.
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Most frequent comorbidities in breast cancer patients

The most frequent comorbidities for breast cancer patients are shown in Fig. 1. “Hypertension”, “Genitourinary symptoms and ill-defined conditions” and “Delirium, dementia, and amnestic and other cognitive disorders” were shared similarly in proportion between male and female breast cancer populations. Nevertheless, in MBC patients there were some pathologies more frequently presented, such as “Disorders of lipid metabolism”, “Other nutritional, endocrine, and metabolic disorders”, “Diabetes Mellitus”, “Cardiac dysrhythmia”, “Other ear and sense organ disorders”, “Coagulation and haemorrhagic disorders” and “Acute myocardial infraction”. On the other hand, FBC patients tended to suffer more “Anxiety disorders”, “Osteoarthritis”, “Thyroid disorders”, “Depression and mood disorders”, “Osteoporosis”, “Headache; including migraine”, “Spondylosis; intervertebral disc disorders; other back problems” and “Menopausal disorders”. Around 15% of MBC cases suffered from “Hyperplasia of prostate” (all prevalences can be found in Table 2).

Fig. 1
figure 1

Most frequent comorbidities in prevalent male breast cancer (MBC) and female breast cancer (FBC) patients older than 18 years old between 2010 and 2019. Results expressed as percentage of the total population of MBC patients (105 cases) and FBC patients (11,657 cases).

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Table 2 Prevalence of chronic comorbidities in men with cancer of breast between 2010 and 2019 from the EpiChron Cohort (n=105).
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In Tables 3 and 4 the results for men and women of the conditional logistic model after adjusting by age can be checked, nine pathologies showed modified risk chances in MBC patients in comparison to the general male population. MBC patients had less risk of “Hypertension” [0.62 (0.41–0.93)] [OR (95% CI)]. In contrast, these patients had more risk than male population of “Anxiety disorders” [1.65 (1.03–2.65)], “Asthma” [2.66 (1.23–5.77)], “Heart valve disorders” [2.99 (1.31–6.85)], “Aortic; peripheral; and visceral artery aneurysms” [3.39 (1.24–9.28)], “Epilepsy; convulsions” [3.43 (1.26–9.31)], “Osteoporosis” [4.26 (2.2–8.24)], “Other endocrine disorders” [7.77 (2.46–24.58)] and “Other diseases of veins and lymphatics” [23.12 (8.45–63.25)] (Tables 3 and 4). Women with FBC also had less risk of “Hypertension”, but this risk also decreased with other comorbidities like “Genitourinary symptoms and ill-defined conditions”, “Headache; including migraine” or “Congestive heart failure; nonhypertensive”.

Table 3 Logistic regression models were used to calculate odds ratios (OR) of prevalence for each comorbidity (dependent variable) according to the presence or absence of breast cancer in men (independent variable).
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Table 4 Logistic regression models were used to calculate odds ratios (OR) of prevalence for each comorbidity (dependent variable) according to the presence or absence of breast cancer in women (independent variable).
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Comparison with age-matched population

The aforementioned relationships were checked in a case-control sample to confirm their validity and reduce biases. For that purpose, a matched-by-birth-year population of male breast patients without breast cancer diagnoses were selected. Only four diseases remained associated in this scenario, two of them not previously associated, “Disorder of lipid metabolism” [1.65 (1.03–2.64)] and “Genitourinary symptoms and ill-defined conditions” [2.03 (1.07–3.87)]; and the other two had been already associated, “Anxiety disorders” [2.05 (1.09–3.87)] and “Osteoporosis” [3.58 (1.26–10.14)] (Table 5).

Table 5 Logistic regression models results for male breast cancer (MBC) patients in the year-of-birth matched cohort.
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Discussion

Our MBC population is mostly old with high number of chronic conditions, which is in accordance with previously seen in the literature, independently if we are referring to a Western or Eastern country3,5,10,11,14,18,20,21,23,26,31. The fact that they have a greater number of comorbidities is a factor to be taken into account, since a greater number of comorbidities has been related to a worse prognosis14, along with other factors like poor access to health care, older age and black race11,18,19.

The difference in the prevalence of comorbidities between MBC and FBC was observed previously3,11,22. Men are described to have more comorbidities than women3,11,21, and some diseases like diabetes, chronic obstructive pulmonary disease (COPD), chronic kidney disease and liver cirrhosis are more frequent in men, whereas female patients are more likely to suffer from autoimmune diseases22. The higher rate of comorbidities in MBC cases may be associated to the higher median age of MBC cases comparing with FBC3.

Between 2010 and 2019, the five most prevalent comorbidities in men in our cohort were “Disorders of lipid metabolism”, “Hypertension”, “Other nutritional; endocrine; and metabolic disorders”, “Depression and mood disorders”, “Diabetes Mellitus” and “Anxiety disorders”, Shimomura et al. observed similar results to us, with more prevalence of diabetes and cardiovascular disease3. Furthermore, our results seem to be in accordance with those from Zoorob et al. Despite using only a set of 20 chronic conditions; their top five most prevalent comorbidities were the same as ours, except for the presence of “Arrythmia” instead of “Other nutritional disorders”21.

From this descriptive analysis, there seem to be a difference at least in the nature of the most frequent diseases between MBC and FBC; MBC patients tend to suffer more of cardiometabolic diseases in contrast with FBC patients who presented more hormone-, bone- and mental comorbidities. It might be useful for a proper management of their diseases to act in tailored manner regarding the most common MBC patient’s comorbidities, as they are thought to impact the overall risk of developing second primary cancer22.

Regarding the association analysis, initially nine diseases were linked to MBC. Nevertheless, most of them are presented in a very low number of MBC patients. “Hypertension”, “Anxiety disorder” and “Osteoporosis” account for 58, 22 and 10 cases respectively, greater MBC cohort numbers are needed to confirm these findings. Furthermore, after matching by year of birth, only four of those associations remained. “Disorder of lipid metabolism” and “Genitourinary symptoms and ill-defined conditions” were added to the before mentioned associations.

Firstly, “Hypertension” [0.62 (0.41–0.93)] was the only disease to have a negative association, this finding should be taken with special care, as to the low quantity of cases, lack of specific MBC literature and mix results of this association in FBC patients3,21,32 increases its uncertainty of an actual association. In fact, in our case once we matched by age, this association disappeared. The mechanisms proposed for the relationship between hypertension and breast cancer risk are the common pathophysiological pathway mediated by adipose tissue, which could cause chronic inflammation, the modification in the apoptosis caused by hypertension, and the use of calcium channel blockers32.

Secondly, breast cancer is well documented to generate psychological distress to female patients, depending on several factors, such as prognosis, tumour state, patient’s personal circumstance; their mental health might worsen in a lighter or heavier manner. A positive association of MBC with “Anxiety disorder” seems more than plausible and clear33. In addition, a study in the United States observed that the prevalence of depression was higher in MBC and FBC patients than in the general population, with a greater gradient between number of comorbidities and the odds of depression21. Nevertheless, FBC and MBC patients might react different to the psychological distress, as FBC patients appear to suffer less from “Anxiety disorders” and more from “Depression and mood disorders” than MBC patients, further concise research is needed.

Thirdly, bone health has been described to be negatively affected by endocrine therapy and adjuvant settings in certain cases in FBC and male prostate cancer patients with similar treatments, it is plausible that these increased chances of “Osteoporosis” in our MBC and FBC cohorts are cancer treatment-related comorbidity34, thus, actually associated, even though there is no possibility to determine if it is due to the disease or the treatment effect.

Fourthly, dyslipidaemia was more frequent in MBC patients, and it is important to control this disease properly because deregulation of lipid metabolism has been seen to increase proliferation, chemoresistance and invasiveness of breast cancer, leading to worsen outcomes and complications35,36,37. In addition, this deregulation generates an enhancing microenvironment for BC tumours proliferation and selection of chemoresistant cells35,37.

Lastly, “Genitourinary symptoms and ill-defined conditions” refers to a broad group of events related to the urinary tract, as with the case of “Osteoporosis” it might be related to BC medication and the older age of this kind of patients. The higher risk of genitourinary symptoms and MBC may be related to the associated risk between cancers of the breast and prostate, because BCRA2 mutations and estrogen treatment used in the prostate cancer38. Also, it has been showed that the incidence of urinary symptoms, like urinary incontinence, in patients with early-stage breast cancer may be higher than in the general population related to endocrine adjuvant therapy, and is questioned if the risk factors of developing urinary incontinence and breast cancer overlap39.

When compared to the age-matched population, the MBC cohort exhibited a similar pattern of comorbidities. This finding suggests that, from a comorbidity perspective, MBC patients may resemble to their non-cancerous counterparts of a similar age group. However, the presence of breast cancer itself may interact with these pre-existing conditions, potentially influencing treatment decisions and overall prognosis. Regarding prognosis, it is well-known that MBC patients have higher risk of cancer recurrence and similar overall survival than FBC patients, at least in later tumour stages40,41. Nevertheless, once comorbidities are taken into account, the recurrence, overall survival and breast-cancer specific mortality seems to worsen in FBC patients with high comorbidity burden, it is plausible this issue affect similarly to MBC patients42. Furthermore, it appears that specific comorbidities, such as anxiety and depression, are associated with poorer prognosis in all aspects (recurrence, overall survival and cancer-specific mortality)43. In regards of treatment, FBC patients with high comorbidity were associated with reduced usage of different treatments (mastectomy, lumpectomy + radiation, and chemotherapy) in regards of their tumour stage44. Additionally, pre-existing conditions not only might affect breast cancer treatment decision, but also the concomitant medication of those comorbidities might influence positively or negatively the prognosis of breast cancer45.

All these findings highlight the distinct characteristics of the MBC patient population, particularly regarding their high burden of comorbidities. The traditional approach of extrapolating treatment guidelines from FBC to MBC may not be optimal, given the differences in the associated comorbidity profiles. Our study underscores the need for a more tailored approach to managing MBC, considering the unique challenges posed by co-existing chronic conditions. Future research should focus on developing evidence-based treatment strategies that address the specific needs of the MBC population while accounting for their complex comorbidity landscape.