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Review

Gender-Related Factors in Medication Adherence for Metabolic and Cardiovascular Health

by
Vittorio Venditti
1,†,
Enrico Bleve
1,†,
Susanna Morano
1 and
Tiziana Filardi
2,*
1
Department of Experimental Medicine, “Sapienza” University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
2
Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, Via di Val Cannuta, 247, 00166 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to the work.
Metabolites 2023, 13(10), 1087; https://doi.org/10.3390/metabo13101087
Submission received: 20 September 2023 / Revised: 5 October 2023 / Accepted: 12 October 2023 / Published: 17 October 2023
(This article belongs to the Special Issue Lipid Metabolism in Obesity and Diabetes 2023)
Versions Notes

Abstract

:
This review explores the impact of gender on medication adherence in the context of metabolic and cardiovascular diseases. Optimal adherence to medication is crucial for achieving treatment goals and preventing adverse outcomes in chronic diseases. The review examines specific conditions such as type 2 diabetes, hypercholesterolemia, arterial hypertension, cardiovascular diseases, and heart failure. In type 2 diabetes, female sex, younger age, new drug prescription, non-white ethnicity, low education level, and low income were identified as predictors of non-adherence. Depressive disorders were also found to influence adherence. In hypercholesterolemia, women exhibited poorer adherence to statin therapy compared to men, with statin-related side effects and patient perception being significant factors. Adherence to anti-hypertensive therapy showed conflicting results, with studies reporting both higher and lower adherence in women. Limited evidence suggests that women may have poorer adherence after acute myocardial infarction and stroke. Regarding heart failure, adherence studies have shown inconsistent findings. The reasons for gender differences in medication adherence are multifactorial and include sociodemographic, disease-related, treatment-related, and psychological factors. This review emphasizes the need for further research to better understand these differences and develop gender-customized interventions that can improve medication adherence and reduce the burden of metabolic and cardiovascular diseases.

1. Introduction

The European Commission defines medication adherence as “the process by which patients take their medications as prescribed”. This process consists of three main phases: initiation, which is the period between prescription and the first dose administration; implementation, which measures the extent of adherence to the prescribed dose; and discontinuation, which refers to the cessation of therapy [1]. Obstacles to adherence can occur at any of these three phases. In fact, a significant number of patients never start a new therapy after it is prescribed [2]. On the other hand, implementation can be affected by both involuntary behaviors, such as negligence and inattention due to cognitive impairment, and voluntary actions to alter the timing and dosage of prescriptions. Although many people use the terms adherence and persistence interchangeably, they have different connotations. Persistence refers to the time between initiation and the last dose before discontinuation, and non-persistence is considered the most common cause of reduced adherence [1].
Good adherence is essential for preventing adverse outcomes and achieving therapeutic targets in chronic diseases. Therefore, accurately estimating the degree of adherence is fundamental. However, assessing patients’ adherence can be challenging for healthcare providers due to the widespread problems faced by healthcare systems worldwide, including resource and time constraints.
Various methods, both direct and indirect, can be used to determine adherence, but they are rarely employed in clinical practice due to their high costs, complexity, and lack of accuracy. Indirect methods, such as interviews, questionnaires, pill counts, and prescription refill data, are undoubtedly cheaper, less complex, and more accessible. On the other hand, direct methods, which are more reliable but complex and expensive, include drug administration supervised by physicians, electronic tools that record pill removal from packaging, digital sensors that track pill ingestion, and measurement of drug metabolites in body fluids [3].
Identifying risk factors for poor adherence plays a crucial role in the management of chronic diseases. The most significant risk factors can be categorized into four main areas: demographic factors (gender, age, education, social background), healthcare system issues or patient-physician relationships, treatment issues (therapy complexity, side effects), comorbidity (polypharmacy and other diseases), and subjective factors (consciousness, awareness of therapy benefits) [4].
It is now commonly understood, and relatively recent knowledge, that gender may influence the medication adherence process. The growing interest in sex-related approaches may pave the way for improving adherence and the management of chronic diseases through personalized treatments.
Furthermore, it is important to properly define the distinct meanings of “sex” and “gender”, which are often used interchangeably. Sex refers to the biological component regulated by sex hormones, while gender is a more complex characteristic that results from interactions between individuals and their surrounding environment [5,6]. Both sex and gender have implications for patients’ attitudes and subsequently impact disease outcomes. They are involved in various aspects of diseases, including epidemiology, pathophysiology, therapy, and outcomes.
Today, a completely new clinical approach to metabolic and cardiovascular diseases (CVD) that takes into account gender disparities is required.
The aim of this review is to analyze and present all available evidence regarding sex and gender differences in adherence to therapies for metabolic and cardiovascular diseases. Special attention will be given to emerging explanations for gender disparities. This review aims to provide clinicians with a better understanding of the clinical course of diseases and, in turn, improve the current clinical approach through gender-driven personalized treatments.

2. Medication Adherence in Metabolic and Cardiovascular Diseases

2.1. Type 2 Diabetes

According to the recent pandemic proportions of diabetes in the last decades, it is reported that almost 500 million people around the globe are affected by this chronic disease. In particular, type 2 diabetes (T2D) is estimated to cause the majority of all cases, approximately 90% [7]. Given these numbers, it can be easily understood why currently there is an increase in diabetes prevalence and mortality, with more than 4 million deaths claimed to be caused by diabetes. Several epidemiological studies have reported few sex and gender differences in T2D prevalence. In particular, a higher T2D rate has been observed in adult men compared to women of the same age [8,9], although there is an opposite trend concerning the diabetes-related mortality rate [7].
It is well established that patients with T2D have an increased risk for CVD compared to non-diabetic patients. However, the burden of CVD seems to differ greatly between men and women with T2D, and the excess cardiovascular (CV) risk observed in non-diabetic men compared to women is markedly reduced in the context of diabetes [10]. Moreover, prospective studies and meta-analyses suggest that there is a higher relative risk of CVD in T2D female patients [10,11,12,13]. Additionally, female patients show a higher risk of chronic kidney disease and end-stage renal disease compared to diabetic males [14,15,16,17], as well as a greater risk of cognitive impairment and cancer [18,19].
It is still unknown how the female gender could be responsible for the higher risk of both macro- and microvascular T2D complications. Among others, the higher body mass index (BMI) detected at the time of T2D diagnosis and the more frequent atypical presentations of coronary heart disease (CHD) in women can be taken into account [20,21,22]. Furthermore, different treatment strategies and drug metabolism are also contributors [23]. Additionally, a worse control of glycemia, lipid levels, and blood pressure has been reported in women compared to men, despite equal or even increased intensity of treatments [22,24,25,26,27].
Concerning disease management, women are more likely to achieve glycemic targets later than men after T2D diagnosis [28,29]. Both differences in drug efficacy and side effects are called into question for these outcomes. However, current studies about treatment outcomes between men and women still lack good female representation and are therefore unreliable [30,31].
Despite biological factors and different drug efficacy, medication adherence can be implicated in poor outcomes in females. Available records highlight that women have lower access to healthcare facilities due to social, cultural, and psychological issues [32,33]. A recent study has observed that Italian women have more difficulties in accessing diabetes care units compared to men [34]. Additionally, a lower number of T2D female patients are treated with antihyperglycemic agents in any age group compared to men [35]. When treated, women are also less frequently prescribed hypoglycemic agents with demonstrated cardio-renal protection [35]. All these data reveal significant sex disparities in T2D management, which may be causes of disease progression. Indeed, low medication adherence can have a powerful impact on morbidity and mortality in the management of chronic diseases [36,37,38,39,40,41].
Several observational studies, mostly retrospective, indicate the main determinants of non-adherence to antidiabetic treatment. On average, medication adherence was mainly related to clinical, sociodemographic, and system-level factors.
A retrospective study enrolling patients treated with oral antidiabetic agents reported an average percentage of medication adherence of 69%. The main predictors of low adherence were female sex, younger age, new drug prescription, low education level, and low social status [42]. A retrospective cohort analysis that evaluated medication adherence in T2D patients with newly prescribed oral antihyperglycemic agents showed quite similar results. This time, the predictors of low adherence were female sex, younger age, and non-white ethnicity. Additionally, adherence differed among the various types of drugs prescribed, being higher for metformin, while the non-adherence rate varied across other oral agents [43]. Data from real-world studies reported that female sex is an independent predictor of low medication adherence for both sulfonylureas and glucagon-like receptor agonists [44,45]. An even lower adherence is reported when patients are prescribed insulin. The percentage of adherence was 43% in patients newly prescribed basal insulin therapy, with the younger female patients showing the highest non-adherence rate [46,47].
Different studies have attempted to pinpoint adherence determinants of long-term oral antihyperglycemic therapy [48]. Indeed, adherence seems to be above 50% after three years from prescription and is higher in male patients (average age range of 50–60 years) and in therapy schemes involving more than three medications [48]. In addition to that, a longer disease duration (more than five years) has been reported as a predictor of good adherence, according to a recent large retrospective study [49].
A large analysis of medical claims involving patients treated for diabetes and CVD showed that women had lower medication adherence, were treated with more drugs, and were less likely to obtain guidelines-based prescriptions [50]. Thus, an observational Italian study reported that the percentage of patients with T2D over 65 years was higher in women compared with men (26.1% vs. 21.5%), highlighting that female T2D patients might have a higher clinical complexity due to their older age at the time of diagnosis [35]. The coexistence of multiple chronic diseases also seems to decrease medication adherence. In a large retrospective analysis, the coexistence of hypertension besides diabetes lowered the level of adherence compared to patients who only suffered from diabetes [51]. Similarly, the coexistence of diabetic complications appears to be another contributor to low adherence [52].
A meta-analysis of 22 studies revealed a gender gap in medication adherence to antihyperglycemic therapy. Depression, younger age, and female sex predicted low adherence [53]. Psychological disorders are common in T2D patients, with almost 30% experiencing depression [54,55]. Major depressive disorder rates are higher in diabetic patients, especially females, leading to significant consequences on metabolic control [56]. Diabetes distress affects patients’ self-management and clinical outcomes more than depression. [57,58,59]. This disorder indicates a whole range of feelings concerning comorbidities, complications, self-care, a sense of guilt, worries about hypoglycemia, or medical prescriptions. A recent study showed that healthcare professionals could help motivate patients, with women being more motivated than men when physicians used empathic communication [60]. In a randomized controlled trial, using informatics tools or educational printed items improved satisfaction and medication education [61,62]. Physicians’ effective communication is essential in helping patients and improving adherence, especially for patients with lower education or social background. Women showed more social barriers, leading to lower self-care adherence 63]. Conversely, perceived support was consistently related to better self-efficacy in women but not in men, even though men reported higher levels of support [63].
Another contributor to clinical outcomes in T2D patients is socioeconomic status. A recent systematic review and meta-analysis evaluated several studies showing that employment can lead to non-adherence to T2D treatment [64]. Thus, gender disparities may have an economic effect in terms of medication adherence and costs. In a large US study, a strong association was found between female sex and low medication adherence regarding costs: women were indeed more inclined than men to turn down medical prescriptions or delay medication replacements [65].
Over the last decades, the so-called “urban diabetes” has become a growing concern in wealthier areas of the globe [66]. Research should focus on better understanding how social background and gender can affect medication adherence.
In conclusion, reaching glycemic goals and controlling cardiovascular risk factors are well-known keys to diabetes care. Recently, the main clinical improvements in diabetes have resulted from the use of updated evidence-based standards of care and therapeutic algorithms, which are effective in reducing both mortality and costs. However, emerging barriers affecting diabetic clinical goals and contributing to a higher risk of diabetic complications and mortality need to be addressed. Among these, low medication adherence is recognized as one of the major determinants of poorer outcomes in diabetes management. Evidence and data from new studies have identified female gender as an independent predictor of low adherence to antidiabetic agents, as indicated by higher rates of worse clinical outcomes among female patients. Although the causes of this gender disparity are not completely understood to date, it is likely that a complex interplay of biological, clinical, sociodemographic, and psychological factors is involved. Strategies to improve medication adherence in T2D should consider these factors and adopt a personalized approach, taking into account the specific needs and challenges faced by women with diabetes. Empowering patients, providing effective communication, addressing psychological well-being, and addressing social determinants of health are crucial components in optimizing medication adherence and improving outcomes in T2D management (Table 1).

2.2. Hypercholesterolaemia

Hypercholesterolemia is a major risk factor for cardiovascular disease (CVD). Its prevalence is constantly rising worldwide, including in high-middle- and low-income countries [67]. High levels of low-density lipoprotein cholesterol (LDL-c) are estimated to be responsible for 3.78 million CV deaths and 0.61 million cerebrovascular deaths [68]. Notably, among all other CV risk factors, lipid alterations account for the majority of attributable risk for a first myocardial infarction in 49.5% of men and 47.1% of women, highlighting the predominant association between dyslipidemia and this disease [69]. Menopause and older age lead to a reduction in sex differences in lipid levels between men and women [70,71]. Specifically, only premenopausal women show a better lipid profile characterized by lower levels of LDL-c and higher levels of high-density lipoprotein cholesterol (HDL-c) compared to men [72].
Strong evidence from epidemiological studies and randomized controlled trials (RCTs) supports a logarithmic relationship between LDL-c variations and CVD risk [73,74,75]. It is well-established that there is a causal relationship between LDL-c and CVD, and LDL-c reduction therapy effectively reduces CVD risk [76]. Statins are the first-line pharmacological therapy for dyslipidemia [77]. Numerous meta-analyses have shown that statin use, both in primary and secondary prevention, is associated with a significant reduction in CV morbidity and mortality [73,78,79]. Notably, a large meta-analysis comparing statin therapy versus control and less intensive statin therapy found that each 1 mmol/L reduction in LDL-c achieved by statin therapy was associated with a 23% decline in the incidence of acute myocardial infarction (AMI), 20% reduction in CV death, 17% reduction in stroke, and 10% reduction in overall mortality over a period of 5 years [73].
Previous studies have debated the effectiveness of statins between men and women, particularly in the case of primary prevention [80,81]. This concern arose due to the relatively low percentage of women included in clinical trials investigating the CV efficacy of statins [82]. Typically, women tend to develop coronary artery disease 10 years later than men, which may partly explain their under-representation in clinical trials that primarily enrolled elderly patients [77]. As a result, the efficacy of statins in women has been questioned due to the limited number of gender-specific analyses [83]. The Cholesterol Treatment Trialists Collaboration analyzed 22 trials (174,149 participants, 27% women) to evaluate the effects of statin therapy on cardiovascular outcomes in both primary and secondary prevention for both men and women. After adjusting for confounding factors, the statistical analysis showed a similar reduction in major vascular events for both men and women, even in those with a predicted 5-year risk lower than 10%, suggesting equal efficacy of statins in both sexes [79].
Despite the demonstrated benefits of statin therapy, adherence to its prescription is not always optimal. The highest rate of discontinuation occurs soon after prescription and treatment adherence is estimated to be 50% at six months and 25% after one year [84]. Similarly, long-term adherence is not fully satisfactory, with discontinuation rates of 33% in primary prevention and 18% in secondary prevention observed in clinical trials after 5 years of treatment [85,86]. Suboptimal adherence to statin therapy has a significant impact on the incidence of CV events and mortality. Non-adherent patients have shown an increased risk of 1.22 to 5.26 for CV events and 1.25 to 2.54 for mortality in most observational studies [87]. Moreover, non-adherence is associated with a twofold higher risk of CV events and fourfold increased rates of stroke and death [88]. Various predictor factors have been identified, including socio-demographic factors (gender, age, ethnicity, income, education, costs), therapy-related factors (adverse events, statin type, and intensity, polypharmacotherapy), lifestyle factors (alcohol abuse), and patient perception (unawareness of the beneficial effects of treatment, medical distrust) [89] (Table 1).
Several observational studies and meta-analyses have attributed gender as a key factor influencing adherence to statin therapy, with evidence of poorer adherence among women compared to men. A recent Italian cohort study enrolled patients initiating statin therapy. After one year, the discontinuation rate was high in both sexes. Specifically, only 19% of women and 27% of men had a proportion of days covered (PDC, the ratio between the number of days when the medication is taken and the total number of days during the follow-up) higher than 80% (indicating optimal adherence) at one year. The gender difference was partly attenuated by age, as the male group had a higher mean PDC in all age groups up to 90 years. However, a higher percentage of male subjects with optimal adherence was observed only until 70 years, after which the proportion was higher in women [90].
A large meta-analysis evaluating 53 studies (including cohort studies, cross-sectional studies, and a few RCTs) found a higher percentage of non-adherent patients to statin therapy among women (53% of women and 50% of men). Female gender increased the risk of non-adherence by 10%. This excess risk was confirmed by studies that included multivariable models adjusting for other variables such as socioeconomic status, ethnicity, and indication for treatment (primary or secondary prevention). Additionally, non-white ethnicity was 53% more likely to be non-adherent compared to white ethnicity [91]. Another meta-analysis of 22 cohort studies reported that women were 7% more likely to be non-adherent than men. Furthermore, younger patients (under 50 years old) and older patients (over 70 years) were less adherent to therapy compared to those aged 50–65 years, indicating a U-shaped association between age and adherence. Other factors, such as higher income, secondary prevention, and comorbidities such as diabetes and hypertension, were associated with higher adherence [92]. A more recent meta-analysis, including 19 studies enrolling only primary prevention patients (two RCTs and mainly cohort and cross-sectional studies), confirmed higher adherence to statin therapy in men. In addition, obesity was associated with non-adherence only in women. Furthermore, a sex-dependent correlation between adherence and education was reported. Higher education was associated with higher adherence only in studies enrolling more than 50% men. Conversely, higher education was a predictor of low adherence in studies enrolling more than 50% women. This could be related to the different awareness of the risk of developing a CV event, mainly due to the widespread assumption that women have a lower CV risk compared to men. Diabetes and hypertension, higher income, previous smoking habits, and white ethnicity were predictors of good adherence. On the other hand, depression, alcohol abuse, and high-dose statins were correlated with non-adherence [93]. A large meta-analysis including only patients older than 65 years found different predictors of non-adherence, such as female gender, non-white ethnicity, current smoking habits, copayment, newly prescribed statins, primary prevention, depression, lower income, and polypharmacotherapy. In contrast, diabetes was associated with better adherence [94].
Many studies have found that statin-related side effects are a common cause of poor adherence [93,95,96]. Muscle symptoms are the most frequent adverse effects of statins. In RCTs, the occurrence of side effects, including statin-associated muscle symptoms (SAMS), was similar between the statin and placebo groups [97,98], and its prevalence is estimated to be around 7–29% in real-life settings [99,100,101]. Female sex is a known risk factor for SAMS, which significantly contributes to statin discontinuation [102] and might explain the association between female gender and poor adherence. Other factors that can worsen adherence include unawareness of the beneficial effects of statins, lack of knowledge about their mechanism of action, medical distrust, and lack of patient-physician communication [95,96].
Different strategies are useful for improving statin adherence, including better patient awareness, medical support, and doctor–patient relationship. Lastly, compelling evidence links female gender to poor adherence to lipid-lowering treatments. Variables such as socio-demographic factors, treatment-related factors, patient behavior, and perception-related factors are key contributors to medication adherence. Further research is needed to elucidate the correlation between these factors and their role in medication adherence (Table 1).

2.3. Arterial Hypertension

Hypertension is one of the major modifiable risk factors for cardiovascular disease (CVD) and a leading cause of mortality globally. Its prevalence is continually rising worldwide, although low- and middle-income countries have a more pronounced increase compared to higher-income countries [103,104]. In 2015, the global prevalence of high blood pressure was 1.13 billion, and the age-standardized prevalence was estimated to be 24% in men and 20% in women. Even though blood pressure (BP) is higher in younger male patients, this trend is inverted after 60 years of age, as the average increase in BP is greater in women [105]. Higher-income countries are associated with the lowest overall rate of women affected by hypertension. On the other hand, sub-Saharan Africa is associated with the highest prevalence of women with high blood pressure, mostly related to different lifestyle habits (diet and physical activity) among various socio-cultural contexts [103,106].
Hypertension is independently and linearly associated with CV morbidity and mortality at all ages and among all ethnic groups [107,108,109]. In 2015, hypertension-related CVD, hemorrhagic stroke, and ischemic stroke made hypertension the leading cause of disability and premature death, affecting almost 10 million people worldwide [110]. Additionally, hypertension is an independent risk factor for chronic kidney disease and end-stage renal disease [111]. Strong evidence from clinical trials showed that appropriate control of hypertension reduced the burden of CVD. Pharmacological intervention, combined with lifestyle education, is frequently required in hypertensive patients and is associated with a significant reduction in CV risk and mortality.
A large meta-analysis of randomized controlled trials (RCTs) showed that a 10 mmHg reduction in systolic BP and a 5 mmHg reduction in diastolic BP were associated with a reduction of 20% in all major CV events, 10–15% in all-cause mortality, 35% in stroke, 20% in coronary events, and 40% in heart failure, independently of age, gender, CV risk score, baseline BP values, and comorbidities such as diabetes and chronic kidney failure [112].
Despite the evidence, hypertension control remains far from optimal worldwide, and awareness of the disease is still limited. Real-life data show that BP goals are reached in less than 20% of all treated patients, whereas 80% of patients reached BP goals in clinical trials [113]. Additionally, real-life data show different evidence on BP treatment and outcomes between sexes compared to clinical trials [114,115].
An analysis from the National Health and Nutrition Examination Survey Mortality (NHANES) 1999–2004, which included patients taking antihypertensive medication, highlighted important differences between genders in BP treatment and control. When adjusted for age, ethnicity, and comorbidities, women with high BP were more frequently treated but were less likely to achieve BP goals, especially systolic BP, particularly at older ages and in the presence of comorbidities such as CVD, stroke, and chronic kidney disease. Partial BP control, especially at older ages, might explain part of the worse CV outcomes that affect women [116]. Diabetic women were more likely to be prescribed diuretics and angiotensin receptor blockers (ACE-Is). Furthermore, women with chronic kidney disease were less frequently treated with angiotensin receptor blockers (ARBs) compared to men [117].
Low adherence, along with suboptimal medical prescription and physician inertia, plays a crucial role in poor disease control [118,119]. The discontinuation of antihypertensive drugs is estimated to be up to 50% after one year, and low adherence to treatment may be responsible for more than 50% of resistant hypertension [120,121]. Poor adherence has an evident impact on CV outcomes, hospital admissions, and healthcare costs [122,123]. Conversely, different studies have demonstrated that better adherence to antihypertensive therapy is associated with improved outcomes. A large Italian prospective study showed that adherent patients had a 37% reduced risk of CV and cerebrovascular events compared to patients interrupting treatment over a six-year period [124]. Furthermore, a population-based cohort study including numerous patients on primary prevention starting antihypertensive treatment showed that high adherence was associated with a 56% decreased risk of a first CV event [125].
Identifying the most relevant risk factors associated with non-adherence is crucial to improving BP control and reducing the global burden of hypertension. Various determinants influence adherence to antihypertensive therapy, including socio-demographic factors (sex, age, ethnicity, income, and education), drug-related factors (acute or chronic adverse effects), clinical factors (presence of comorbidities leading to polypharmacotherapy and depressive disorders), patient’s disease and treatment awareness and knowledge, and factors related to the patient-physician relationship [126]. Detecting non-adherence is complicated due to the complex interplay among different contributors.
The scientific community has shown a growing interest in investigating the complex link between sex/gender and medication adherence, including hypertension therapy. Numerous observational studies, mainly based on pharmacy claims, have investigated hundreds of thousands of patients from different geographic areas to identify any correlation between sex differences and antihypertensive treatment compliance. However, the emerged data are inconclusive, showing opposite findings.
A large Italian population-based study that enrolled new users of antihypertensive drugs (50% women) showed that 30% of patients reported at least one episode of therapy interruption during a one-year follow-up. Males were associated with better adherence (53% vs. 42%), a 10% lower risk of discontinuation, and higher persistence, independently of age and type of medication. However, no difference emerged when patients with worse comorbidity status and taking drug combinations were compared [127]. Another Italian large cohort study, enrolling newly prescribed antihypertensive medication patients, demonstrated a lower rate of discontinuation in men, older patients, and patients on glucose-lowering medication with CVD or renal diseases. Conversely, depressive disorders and dementia were associated with a higher risk of discontinuation. Diuretic therapy was linked to the highest risk of interruption among drug classes [128].
A large Dutch population-based study found comparable results, with female sex being associated with a lower rate of adherence to antihypertensive therapy one year after its prescription [129]. A recent study collected urine samples from 174 patients (48% females) with poor BP control to evaluate medication adherence, despite the use of three or more BP-lowering medications. The overall non-adherence rate was 40%, and women had a three times higher probability of being non-adherent compared to men, after adjusting for confounders. Furthermore, a positive independent association between the number of medications and non-adherence was observed [130].
On the contrary, other observational studies showed lower adherence in men. A retrospective study observed factors such as male sex, dementia, history of stroke, and polypharmacotherapy to be associated with lower adherence [131]. Similarly, Friedman et al. studied drug adherence in a large sample of Canadian elderly patients initiating BP-lowering treatment. Female sex, absence of comorbidities, and high income were associated with higher compliance with treatment. Among drug classes, ACE-Is had the greatest rates of compliance, while beta-blockers had the worst [132]. Additionally, in a large US population-based cohort study of individuals older than 65 years who were newly prescribed antihypertensive medication the overall rate of low-intermediate adherence, measured as PDC, was around 40%. Factors such as female sex, non-Hispanic white ethnicity, use of more than one antihypertensive drug, and the presence of diabetes or dyslipidemia were associated with higher adherence [125]. A Swedish observational cohort study enrolled patients who received antihypertensive therapy for the first time. The data showed a low rate of treatment continuation both at one-year and two-year evaluations (57% and 43%, respectively). Risk factors for discontinuation included male sex, younger age, lower systolic BP at prescription, and lower income, with no difference observed between drug classes [133].
A recent meta-analysis collected data from 82 studies to evaluate adherence to BP-lowering medication using self-report or pharmacy refill prescription-based methods. After adjusting for confounding factors, no relation between sex and medication adherence was observed. These results were consistent across different geographic areas and adherence assessment methods. A subgroup analysis demonstrated higher adherence in men only in older age groups (>65 years) and studies adopting self-report methods for adherence assessment [134].
In conclusion, the role of sex as a determinant of medication adherence to antihypertensive treatment is still not fully established. The controversial data might be, at least partially, related to different methodological biases, such as the heterogeneity of methods selected for assessing adherence, differences in characteristics and cultures of the populations included, and discrepancies in the inclusion and conclusion criteria. Therefore, further research is still needed to clarify this issue (Table 1).

2.4. Cardiovascular Diseases

CVD is the leading cause of mortality worldwide, responsible for 17.9 million deaths each year [135]. Currently, more than three-quarters of these deaths are related to coronary heart disease (CHD) and stroke [135]. Traditionally, CVD has been considered more prevalent among men due to their higher incidence of CV events and mortality [136]. However, female cardiovascular risk seems to be delayed by approximately 10 years, and morbidity and mortality differences between sexes tend to diminish in older age, particularly for stroke [136]. Interestingly, more women than men die from CVD, largely due to their longer lifespan [77]. Conventional CVD risk factors have varying impacts on men and women, contributing to the sex and gender disparities in CV outcomes. For instance, women who smoke have a 25% higher risk of developing CHD compared to men who smoke [137]. Moreover, compelling evidence shows that diabetes has a greater impact on CV morbidity and mortality in women [138,139]. Female patients are reported to be less likely to achieve blood pressure (BP) targets and are more frequently undertreated than men. However, despite these differences, no clear gender disparities regarding the risk of adverse outcomes related to hypertension have been observed [116,117]. In addition to traditional risk factors, sex-related issues such as gestational diabetes and gestational hypertension play a critical role in increasing CV risk in women [140,141]. Furthermore, women have been noted to have poorer disease awareness, less social support, and a higher prevalence of depressive disorders. All these factors are believed to limit women’s access to care and widen sex inequalities [142]. Moreover, low socioeconomic status poses a greater additional CV risk in women compared to men [143].
Female CHD exhibits distinct pathophysiological features. Acute ischemia in women is commonly secondary to non-occlusive coronary lesions caused by microvascular damage [144], and acute myocardial infarction (AMI) typically manifests without ST elevation. Women also present with different clinical manifestations, including atypical symptoms such as weakness, dyspepsia, epigastralgia, dyspnea, and shoulder or back pain, which may contribute to delayed diagnosis and intervention [145]. Furthermore, in most studies, female patients have a higher risk of bleeding and vascular complications after percutaneous coronary intervention (PCI) [146,147]. Women also have a higher prevalence of atypical stroke symptoms, such as loss of consciousness, urinary incontinence, and swallowing difficulties [148]. Clear sex disparities have not emerged in studies focusing on acute treatment outcomes after stroke [149]. Randomized controlled trials (RCTs) evaluating evidence-based medications for CVD show equal efficacy in both sexes. However, the relatively low number of female participants enrolled in these studies limits the interpretation of their results [150,151,152].
It is important to consider that screening and management of CVD present sex disparities. In particular, women are less likely to be assessed for CV risk in primary care. They are also less frequently prescribed evidence-based medications recommended by current guidelines, such as beta-blockers, ACE inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), statins, and antiplatelet agents, for secondary prevention [153,154,155,156]. Furthermore, female patients less frequently achieve BP and lipid goals one year after AMI and experience more hospital readmissions than men [157]. Inequalities in healthcare may explain worse outcomes in women.
Optimal adherence is necessary to maximize the efficacy of evidence-based medications and to avoid poor CV outcomes. However, evidence shows that adherence to CV medication is far from optimal, leading to increased morbidity [158], mortality [159], and healthcare costs [158]. A meta-analysis of 20 studies, including 376,162 patients (51% female) in both primary and secondary prevention, evaluated adherence to seven drug classes (aspirin, ACE-Is, ARBs, beta-blockers, calcium channel blockers, thiazide diuretics, and statins). The data revealed an overall adherence rate of 57%, which did not exceed 50% and 66% for primary and secondary prevention, respectively [160]. Another meta-analysis examining 44 studies and 197,819 patients (23% on secondary prevention) investigated the effect of adherence to different drug classes (statins, antihypertensives, antiplatelet agents, antihyperglycemic medications, and other vascular agents) on CV events and all-cause mortality. Good adherence to medication was observed in only 60% of patients. Importantly, optimal adherence was associated with a 20% reduction in CVD and a 35% reduction in all-cause mortality [161].
Currently, limited data are available on sex inequalities in adherence to evidence-based medication regimens prescribed after AMI and stroke events. The few available studies demonstrate poorer adherence in women compared to men. In a recent retrospective study evaluating adherence to chronic pharmacological therapy (antiplatelet agents, statins, beta-blockers, ACE-Is, or ARBs) six months after discharge for a first AMI in 25,779 patients, overall adherence rates were 78% for statins, 59% for antiplatelet agents, 63% for ACE-Is/ARBs, and 50% for beta-blockers. However, full adherence was observed in only a quarter of patients, and women were 25% less likely to be adherent compared to men to evidence-based combined regimens post-AMI. Comorbidities and older age were predictive factors for low adherence [162]. Another Italian population-based cohort study evaluated adherence to antiplatelet agents, ACE-Is/ARBs, beta-blockers, and statins one year after AMI. The overall adherence rates were 90.5% for antiplatelet agents, 60% for beta-blockers, 78.1% for ACE-Is/ARBs, and 77.8% for statins [163].
A meta-analysis examining 44 studies (23% of patients with known CVD) evaluated the effect of various drug classes (statins, antihypertensives, antiplatelet agents, antihyperglycemic agents, and other vascular agents) on CV events and all-cause mortality. Only 60% of cases exhibited good adherence to evidence-based medication regimens. Importantly, optimal adherence was associated with a 20% reduction in CVD and a 35% reduction in all-cause mortality [161]. Currently, limited data are available on sex inequalities in adherence to evidence-based prescriptions after AMI or stroke. In general, most studies show poorer adherence in women compared to men. A recent Italian population-based retrospective study analyzed adherence to chronic medications (antiplatelet agents, statins, beta-blockers, ACE-Is, or ARBs) six months after discharge for a first AMI. The comprehensive adherence rates were 78% for statins, 69% for antiplatelet agents, 63% for ACE-Is/ARBs, and 50% for beta-blockers. However, only a quarter of patients were consistent with evidence-based combined regimens post-AMI, and female sex was associated with a 25% reduction in adherence compared to males, after adjusting for confounders. Additionally, factors such as older age and the presence of other comorbidities predicted lower adherence [162].
Authors from another Italian population-based cohort study investigated adherence to evidence-based pharmacological therapy, including antiplatelet agents, ACE-Is/ARBs, beta-blockers, and statins, one year after AMI. At the time of discharge, women were older and showed a worse comorbidity status compared to men. The overall adherence rates were 90.5% for antiplatelet medication, 60% for beta-blockers, 78.1% for ACE-Is/ARBs, and 77.8% for statins. After adjusting for confounders, women were 16% less likely to be adherent than men [163]. These findings were observed for both single drug classes and combined therapy. Older age was again a significant predictor of lower adherence [163]. Adherence to secondary prevention medications (antiplatelet agents, statins, beta-blockers, ACE-Is, or ARBs) and attendance of cardiac rehabilitation were recently examined six months and one year after discharge for acute coronary syndrome. After adjusting for confounders, women were more likely to be non-adherent to cardiac rehabilitation programs and showed a 35% increased risk of developing another major CV event after six months. After one year, women were less likely to be consistent with secondary prevention medications compared to men [164].
In a large retrospective cohort study, adherence to beta-blockers, ACE-Is/ARBs, and statins was investigated in patients after AMI. The overall adherence estimates one year after discharge were 66% for beta-blockers, 63% for ACE-Is/ARBs, and 66% for statins. Moreover, black women, and to a lesser extent, white women, had lower adherence to ACE-Is/ARBs, beta-blockers, and statins compared to white men one year after evaluation [165]. This trend was confirmed in a more recent retrospective cohort study of 52,672 patients, which found greater adherence to evidence-based drug prescriptions (antiplatelet agents/anticoagulants, beta-blockers, ACE-Is/ARBs) in male patients one year after AMI [166]. Along these lines, female gender and older age were significant predictors of non-adherence to secondary prevention therapies in previous studies [167,168].
On the other hand, sex differences in medication adherence after ischemic stroke are still insufficiently studied. A cohort study of patients older than 65 years evaluated adherence to antiplatelet therapy three years after a first ischemic stroke. The data showed that more than one-quarter of patients were not adherent, and women were 25% less likely to be persistent with therapy compared to men. Interestingly, in this case, older age (≥75 years) and other comorbidities such as diabetes were associated with better adherence to therapy [169].
The reasons for these sex differences in medication adherence after acute coronary syndrome and stroke are still largely unknown. Various factors, including sociodemographic, disease-related, treatment-related, and others, are believed to play a crucial role. Currently, there is growing interest in identifying potentially modifiable contributors, such as patients’ awareness, beliefs, and perceptions toward treatment, social support, and mood disorders (Table 1).
It should be highlighted that CVD is a multifaceted condition, ensuing from a rather intricate interplay of major and minor risk factors. Noteworthy, risk factors often coexist, being differently modulated by sex and gender. In this context, the relative impact of a single risk factor on CVD outcomes is rather complex to assess, as well as the effect of gender on multiple risk factors’ management in clinical practice. The coexistence of multiple chronic diseases seems to decrease medication adherence. Specifically, there are hints that female sex is associated with less adherence to medication schemes that target multiple risk factors. However, most studies have focused on adherence to medications after AMI (antihypertensive therapy, statins, antiplatelet agents). As regards the coexistence of diabetes and CVD, women treated for diabetes and CVD seem to have lower medication adherence compared to men [50], although further studies are needed to confirm this trend.

2.5. Heart Failure

Heart Failure (HF) is a widespread condition affecting 25 million people worldwide [170]. During the last decades, a slight reduction in age-standardized incidence has been observed. However, its overall prevalence has progressively increased, conceivably due to population aging and better survival rates after diagnosis [171]. Moreover, there has been a linear parallel increase in the prevalence of associated comorbidities, such as diabetes, hypertension, and CVD. These data suggest that the clinical presentation of patients with HF is becoming more complex, negatively affecting prognosis and mortality and further imposing a heavy burden on health services [171].
Women represent nearly half of the patients with HF [172]. Notably, a sex dimorphism in the clinical presentation of HF has been extensively described. In fact, women tend to be older than men at the time of diagnosis and are often associated with a poorer quality of life, a more complicated clinical phenotype, and severe and atypical symptoms [173]. Specifically, women have a two-fold increased risk of being affected by HF with preserved ejection fraction (HFpEF). On the other hand, men are more prone to suffer from HF with reduced ejection fraction (HFrEF). These findings suggest different etiologies and pathophysiological patterns between the sexes [174]. Particularly, hypertension is a common cause of HF in female patients, commonly leading to concentric cardiac hypertrophy, diastolic dysfunction, and HFpEF. In contrast, HF in men is more frequently associated with an ischemic etiology, which implies eccentric cardiac hypertrophy, dilatation, and reduced left ventricular EF [150].
Overall, mortality tends to be higher in males than in women, as a result of lower EF and a more frequent coexistence of CHD in men [175,176]. ACE-Is or an ARB and eventually added beta-blockers are part of the current evidence-based therapy for HF. A mineralocorticoid receptor antagonist (MRA) is added in patients with HFrEF with still uncontrolled symptoms. Diuretics are recommended in the presence of signs and symptoms of congestion. Sacubitril/valsartan is used as a replacement for ACE-I in patients with HFrEF with persistent symptoms despite optimal treatment with an ACE-I, beta-blocker, and MRA. Ivabradine is a treatment option in patients with left ventricular EF ≤ 35%, with sinus rhythm, and a heart rate ≥ 70 bpm despite treatment with a beta-blocker, ACE-I (or ARB), and MRA, or in patients who are unable to tolerate or have contraindications for beta-blocker treatment. Digoxin may be considered in symptomatic patients in sinus rhythm, despite treatment with an ACE-I (or ARB), beta-blocker, and MRA [177].
Recently, the anti-hyperglycemic class of Sodium-glucose co-transporter-2 (SGLT2) inhibitors has been shown to reduce the risk of hospitalization for HF in diabetic patients and also in patients without diabetes [178,179]. Current available data show sex differences in drug safety and efficacy [150]. In particular, considering relevant sex disparities in pharmacokinetics and pharmacodynamics profiles, women develop drug side effects more frequently [180]. For instance, the development of cough and angioedema ACE-Is seems to be higher in women than in men [181]. Additionally, diuretic therapy more frequently leads to electrolyte disorders in women compared to men [180]. Taking into account drug efficacy, it is noteworthy that women’s proportion in RCTs, as well as in preliminary studies for drug development and dose assessing, is scarce, generally not exceeding 20–30% [150,151,152]. Furthermore, sex-stratified analyses are either not performed or underpowered to detect significant differences.
Patients with HF have a poor prognosis and high mortality. In fact, the 1-year all-cause mortality rate is estimated to be around 6.4%, while the combination of mortality or hospitalization within 1 year is 14.5% [182]. The aforementioned evidence-based HF pharmacological treatment has been widely demonstrated to reduce adverse outcomes in large RCTs [183]. Additionally, adequate self-care measures, mostly changes in dietary habits, weight and fluid monitoring, and optimal medication adjustments, can lead to a significant improvement in prognosis if adopted [184].
Like other chronic disorders, adherence to HF drug prescriptions plays a crucial role in achieving treatment goals and reducing the burden of the disease. It has been observed that each 10% increase in the proportion of days covered (PDC) significantly reduces hospital admissions and all-cause mortality [185]. Accordingly, two large meta-analyses evaluated the efficacy of several types of intervention to improve medication adherence, such as training/education, reminder tools, technical measures, and medical support. The results showed a significant reduction in mortality by 2–11% and in hospitalization by 10–21% [186,187].
Evidence about sex/gender differences in adherence to evidence-based HF treatment is still limited. Only a few studies have investigated this issue, leading to conflicting results. A large cohort study involving patients with HF (47.9% women), newly prescribed with an evidence-based HF drug regimen, studied adherence to therapy. Notably, men were significantly less likely than women to be adherent one year after initiation [188]. Moreover, in a retrospective cohort study, women were more adherent to ACEIs/ARBs therapy after their first hospital admission for HF. In addition, a larger number of comorbidities was associated with a higher adherence to these drugs. Conversely, adherence to beta-blockers was not influenced by these factors [189]. Similarly, in a population-based study enrolling patients treated with conventional medications for HF (41% female), males were more likely to be non-adherent to ACE-Is/ARBs compared to women, but this relationship between sex and adherence was not observed for other drug classes [190]. Another large retrospective study reported lower adherence to HF treatment in male patients [191].
On the other hand, Granger et al. found opposite results by analyzing adherence in patients with HF enrolled in the Candesartan in Heart Failure Assessment of Mortality and Morbidity (CHARM) trial (n = 7599, 31.5% women). Particularly, women were less adherent compared to men after adjusting for confounders, and this difference was even more marked when considering women younger than 75 years. This trend remained significant both in the candesartan and in the placebo arm. Of note, women were prescribed a higher number of drugs even though fewer evidence-based medications were adopted, as beta-blockers were less prescribed, while the use of calcium blockers was more common among them compared to men [192]. Similarly, in a sample of 236 patients with HF (35.2% women), other authors reported higher adherence to ACE-Is in men compared to women six months after hospital discharge [193]. However, a recent retrospective study including 25,776 patients with HF (45% women) did not find any difference between men and women in adherence to medication [194].
In addition, adherence to self-care recommendations (weight monitoring, fluid and sodium restriction, and physical activity) has shown conflicting data about its association with sex. In a cross-sectional study, adherence to self-care recommendations was evaluated in a sample of 310 patients with HF (64.2% women). Men were significantly more adherent compared to women after adjustment for confounders. The absence of comorbidities and a high level of knowledge of the disease resulted in other predictors of good adherence [195]. Some authors reported similar results [196,197], while other authors observed no significant sex differences [198,199].
Overall, the evidence that examined the effect of sex/gender on adherence to HF therapy is still insufficient to draw firm conclusions. In consideration of the relevant impact of medication adherence on HF outcomes, further research is needed on this issue (Table 1).

3. Conclusions

It is, therefore, crucial to assess adherence levels in clinical settings using reliable and cost-effective tools and to identify risk factors for non-adherence through large-scale studies. This approach aims to achieve complete adherence to treatment and successful management of chronic diseases (Table 2).
Interest in understanding the impact of sex and gender on medication adherence and identifying modifiable barriers, including cognitive, mood-related, and psychosocial factors, has grown in recent decades. Emerging evidence reveals a sex dimorphism in medication adherence, which could partly explain higher rates of poor outcomes in women compared to men for certain chronic conditions. Studies and meta-analyses demonstrate that being female is an independent predictor of non-adherence to antidiabetic medications, lipid-lowering therapy, and evidence-based medication regimens after acute myocardial infarction (AMI). However, data regarding sex disparities in hypertension, heart failure (HF), and stroke is limited and conflicting due to the scarcity of available studies.
The underlying reasons for this dimorphism in medication adherence are largely unknown. Several factors, such as biological, treatment-related, psychosocial, socioeconomic, cognitive, and mood-related aspects, and their complex interplay, may contribute to sex disparities. Women are more likely to experience drug side effects. Feasible solutions to overcome this barrier include the adequate implementation of treatment plans and therapeutic interchange. Moreover, numerous studies have identified complex medication regimens as negative predictors of adherence. Therefore, utilizing combination pills and long-acting drugs, and avoiding complex drug regimens, could be a useful approach to address treatment-related issues. Notably, women appear to have lower awareness of their cardiovascular risk, harbor more negative perceptions and beliefs about diseases and treatment, receive less social support, and experience higher rates of depressive disorders. Additionally, the common role of women as caregivers within the family context might negatively impact their self-care (Table 2).
Despite limited studies investigating the role of these factors and lacking definitive conclusions, healthcare providers often overlook these variables. Routine assessment of these factors can effectively help overcome adherence barriers and improve outcomes. Factors such as patients’ unawareness, misperception of treatment benefits, and psychological barriers could significantly benefit from improvements in the doctor–patient relationship and enhanced medical support. Given these findings from observational studies, it is essential to conduct intervention studies with a gender-centered design and appropriate sample sizes. This approach will help identify effective solutions for promoting adherence in a clinical setting (Table 2). Increasing awareness and knowledge about sex and gender disparities can lead to more gender-customized interventions and tailored clinical approaches, thereby significantly improving outcomes and substantially reducing the burden of metabolic and cardiovascular diseases (Table 3).

Author Contributions

Conceptualization, S.M. and T.F.; PubMed search, V.V. and E.B.; writing—draft preparation, T.F., V.V. and E.B.; writing—review and editing, S.M. and T.F.; supervision and critical revision, S.M. and T.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vrijens, B.; De Geest, S.; Hughes, D.A.; Przemyslaw, K.; Demonceau, J.; Ruppar, T.; Dobbels, F.; Fargher, E.; Morrison, V.; Lewek, P.; et al. A new taxonomy for describing and defining adherence to medications. Br. J. Clin. Pharmacol. 2012, 73, 691–705. [Google Scholar] [CrossRef] [PubMed]
  2. Vrijens, B.; Vincze, G.; Kristanto, P.; Urquhart, J.; Burnier, M. Adherence to prescribed antihypertensive drug treatments: Longitudinal study of electronically compiled dosing histories. BMJ 2008, 336, 1114–1117. [Google Scholar] [CrossRef] [PubMed]
  3. Hamrahian, S.M. Medication Non-adherence: A Major Cause of Resistant Hypertension. Curr. Cardiol. Rep. 2020, 22, 133. [Google Scholar] [CrossRef] [PubMed]
  4. World Health Organization. Definig Adherence 2003. Available online: https://www.who.int/chp/knowledge/publications/adherence_Section1.pdf (accessed on 8 June 2021).
  5. Schiebinger, L.; Stefanick, M.L. Gender Matters in Biological Research and Medical Practice. J. Am. Coll. Cardiol. 2016, 67, 136–138. [Google Scholar] [CrossRef]
  6. Clayton, J.A.; Tannenbaum, C. Reporting Sex, Gender, or Both in Clinical Research? JAMA 2016, 316, 1863–1864. [Google Scholar] [CrossRef]
  7. International Diabetes Federation (IDF). Diabetes Atlas 2019. Available online: https://www.diabetesatlas.org/en/resources/ (accessed on 8 June 2021).
  8. Lipscombe, L.L.; Hux, J.E. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: A population-based study. Lancet 2007, 369, 750–756. [Google Scholar] [CrossRef]
  9. Li, J.; Ni, J.; Wu, Y.; Zhang, H.; Liu, J.; Tu, J.; Cui, J.; Ning, X.; Wang, J. Sex Differences in the Prevalence, Awareness, Treatment, and Control of Diabetes Mellitus among Adults Aged 45 Years and Older in Rural Areas of Northern China: A Cross-Sectional, Population-Based Study. Front. Endocrinol. 2019, 10, 147. [Google Scholar] [CrossRef]
  10. Prospective Studies Collaboration; Asia Pacific Cohort Studies Collaboration. Sex-specific relevance of diabetes to occlusive vascular and other mortality: A collaborative meta-analysis of individual data from 980,793 adults from 68 prospective studies. Lancet Diabetes Endocrinol. 2018, 6, 538–546. [Google Scholar] [CrossRef]
  11. Emerging Risk Factors Collaboration; Sarwar, N.; Gao, P.; Seshasai, S.R.; Gobin, R.; Kaptoge, S.; Di Angelantonio, E.; Ingelsson, E.; Lawlor, D.A.; Selvin, E.; et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet 2010, 375, 2215–2222. [Google Scholar] [CrossRef]
  12. Kanaya, A.M.; Grady, D.; Barrett-Connor, E. Explaining the sex difference in coronary heart disease mortality among patients with type 2 diabetes mellitus: A meta-analysis. Arch. Intern. Med. 2002, 162, 1737–1745. [Google Scholar] [CrossRef]
  13. Wenger, N.K. Coronary heart disease in women: Highlights of the past 2 years--stepping stones, milestones and obstructing boulders. Nat. Clin. Pract. Cardiovasc. Med. 2006, 3, 194–202. [Google Scholar] [CrossRef] [PubMed]
  14. Shepard, B.D. Sex differences in diabetes and kidney disease: Mechanisms and consequences. Am. J. Physiol. Renal Physiol. 2019, 317, F456–F462. [Google Scholar] [CrossRef] [PubMed]
  15. Shen, Y.; Cai, R.; Sun, J.; Dong, X.; Huang, R.; Tian, S.; Wang, S. Diabetes mellitus as a risk factor for incident chronic kidney disease and end-stage renal disease in women compared with men: A systematic review and meta-analysis. Endocrine 2017, 55, 66–76. [Google Scholar] [CrossRef] [PubMed]
  16. Earle, K.A.; Ng, L.; White, S.; Zitouni, K. Sex differences in vascular stiffness and relationship to the risk of renal functional decline in patients with type 2 diabetes. Diabetes Vasc. Dis. Res. 2017, 14, 304–309. [Google Scholar] [CrossRef]
  17. Cobo, G.; Hecking, M.; Port, F.K.; Exner, I.; Lindholm, B.; Stenvinkel, P.; Carrero, J.J. Sex and gender differences in chronic kidney disease: Progression to end-stage renal disease and haemodialysis. Clin. Sci. 2016, 130, 1147–1163. [Google Scholar] [CrossRef]
  18. Chatterjee, S.; Peters, S.A.E.; Woodward, M.; Arango, S.M.; Batty, G.D.; Beckett, N.; Beiser, A.; Borenstein, A.R.; Crane, P.K.; Haan, M.N.; et al. Type 2 Diabetes as a Risk Factor for Dementia in Women Compared with Men: A Pooled Analysis of 2.3 Million People Comprising More Than 100,000 Cases of Dementia. Diabetes Care 2016, 39, 300–307. [Google Scholar] [CrossRef]
  19. Ohkuma, T.; Peters, S.A.E.; Woodward, M. Sex differences in the association between diabetes and cancer: A systematic review and meta-analysis of 121 cohorts including 20 million individuals and one million events. Diabetologia 2018, 61, 2140–2154. [Google Scholar] [CrossRef]
  20. Wenger, N.K. Clinical presentation of CAD and myocardial ischemia in women. J. Nucl. Cardiol. 2016, 23, 976–985. [Google Scholar] [CrossRef]
  21. Raparelli, V.; Morano, S.; Franconi, F.; Lenzi, A.; Basili, S. Sex Differences in Type-2 Diabetes: Implications for Cardiovascular Risk Management. Curr. Pharm. Des. 2017, 23, 1471–1476. [Google Scholar] [CrossRef]
  22. Huebschmann, A.G.; Huxley, R.R.; Kohrt, W.M.; Zeitler, P.; Regensteiner, J.G.; Reusch, J.E.B. Sex differences in the burden of type 2 diabetes and cardiovascular risk across the life course. Diabetologia 2019, 62, 1761–1772. [Google Scholar] [CrossRef]
  23. Ostan, R.; Monti, D.; Gueresi, P.; Bussolotto, M.; Franceschi, C.; Baggio, G. Gender, aging and longevity in humans: An update of an intriguing/neglected scenario paving the way to a gender-specific medicine. Clin. Sci. 2016, 130, 1711–1725. [Google Scholar] [CrossRef] [PubMed]
  24. Wright, A.K.; Kontopantelis, E.; Emsley, R.; Buchan, I.; Mamas, M.A.; Sattar, N.; Ashcroft, D.; Rutter, M.K. Cardiovascular Risk and Risk Factor Management in Type 2 Diabetes Mellitus. Circulation 2019, 139, 2742–2753. [Google Scholar] [CrossRef] [PubMed]
  25. Tavaglione, F.; Filardi, T.; Fallarino, M.; Mandosi, E.; Turinese, I.; Rossetti, M.; Lenzi, A.; Morano, S. The SNP rs9677 of VPAC1 gene is associated with glycolipid control and heart function in female patients with type 2 diabetes: A follow-up study. Nutr. Metab. Cardiovasc. Dis. 2016, 26, 109–113. [Google Scholar] [CrossRef] [PubMed]
  26. Peters, S.A.E.; Muntner, P.; Woodward, M. Sex Differences in the Prevalence of, and Trends in, Cardiovascular Risk Factors, Treatment, and Control in the United States, 2001 to 2016. Circulation 2019, 139, 1025–1035. [Google Scholar] [CrossRef]
  27. Penno, G.; Solini, A.; Bonora, E.; Fondelli, C.; Orsi, E.; Zerbini, G.; Trevisan, R.; Vedovato, M.; Gruden, G.; Laviola, L.; et al. Gender differences in cardiovascular disease risk factors, treatments and complications in patients with type 2 diabetes: The RIACE Italian multicentre study. J. Intern. Med. 2013, 274, 176–191. [Google Scholar] [CrossRef]
  28. Choe, S.-A.; Kim, J.Y.; Ro, Y.S.; Cho, S.-I. Women are less likely than men to achieve optimal glycemic control after 1 year of treatment: A multi-level analysis of a Korean primary care cohort. PLoS ONE 2018, 13, e0196719. [Google Scholar] [CrossRef]
  29. Sia, H.-K.; Kor, C.-T.; Tu, S.-T.; Liao, P.-Y.; Chang, Y.-C. Predictors of treatment failure during the first year in newly diagnosed type 2 diabetes patients: A retrospective, observational study. PeerJ 2021, 9, e11005. [Google Scholar] [CrossRef]
  30. Kautzky-Willer, A.; Harreiter, J. Sex and gender differences in therapy of type 2 diabetes. Diabetes Res. Clin. Pract. 2017, 131, 230–241. [Google Scholar] [CrossRef]
  31. Clemens, K.K.; Woodward, M.; Neal, B.; Zinman, B. Sex Disparities in Cardiovascular Outcome Trials of Populations with Diabetes: A Systematic Review and Meta-analysis. Diabetes Care 2020, 43, 1157–1163. [Google Scholar] [CrossRef]
  32. Bird, C.E.; Fremont, A.M.; Bierman, A.S.; Wickstrom, S.; Shah, M.; Rector, T.; Horstman, T.; Escarce, J.J. Does quality of care for cardiovascular disease and diabetes differ by gender for enrollees in managed care plans? Womens Health Issues 2007, 17, 131–138. [Google Scholar] [CrossRef]
  33. Vaccarino, V.; Rathore, S.S.; Wenger, N.K.; Frederick, P.D.; Abramson, J.L.; Barron, H.V.; Manhapra, A.; Mallik, S.; Krumholz, H.M.; National Registry of Myocardial Infarctions. Sex and Racial Differences in the Management of Acute Myocardial Infarction, 1994 through 2002. N. Engl. J. Med. 2005, 353, 671–682. [Google Scholar] [CrossRef]
  34. Associazione Medici Diabetologi. Nuovi Annali AMD 2020. Available online: https://aemmedi.it/nuovi-annali-amd-2020/ (accessed on 8 June 2021).
  35. Società Italiana di Diabetologia. Osservatorio ARNO 2019. Available online: https://www.siditalia.it/clinica/linee-guida-societari/send/80-linee-guida-documenti-societari/5025-rapporto-arno-diabete-2019 (accessed on 8 June 2021).
  36. Kim, Y.-Y.; Lee, J.-S.; Kang, H.-J.; Park, S.M. Effect of medication adherence on long-term all-cause-mortality and hospitalization for cardiovascular disease in 65,067 newly diagnosed type 2 diabetes patients. Sci. Rep. 2018, 8, 12190. [Google Scholar] [CrossRef] [PubMed]
  37. Pednekar, P.; Heller, D.A.; Peterson, A.M. Association of Medication Adherence with Hospital Utilization and Costs among Elderly with Diabetes Enrolled in a State Pharmaceutical Assistance Program. J. Manag. Care Spec. Pharm. 2020, 26, 1099–1108. [Google Scholar] [CrossRef] [PubMed]
  38. Kennedy-Martin, T.; Boye, K.S.; Peng, X. Cost of medication adherence and persistence in type 2 diabetes mellitus: A literature review. Patient Prefer. Adherence 2017, 11, 1103–1117. [Google Scholar] [CrossRef] [PubMed]
  39. E Curtis, S.; Boye, K.S.; Lage, M.J.; García-Perez, L.-E. Medication adherence and improved outcomes among patients with type 2 diabetes. Am. J. Manag. Care 2017, 23, e208–e214. [Google Scholar]
  40. Beernink, J.M.; Oosterwijk, M.M.; Khunti, K.; Gupta, P.; Patel, P.; van Boven, J.F.; Heerspink, H.J.L.; Bakker, S.J.; Navis, G.; Nijboer, R.M.; et al. Biochemical Urine Testing of Medication Adherence and Its Association with Clinical Markers in an Outpatient Population of Type 2 Diabetes Patients: Analysis in the DIAbetes and LifEstyle Cohort Twente (DIALECT). Diabetes Care 2021, 44, 1419–1425. [Google Scholar] [CrossRef]
  41. Brunton, S.A.; Polonsky, W.H. Hot Topics in Primary Care: Medication Adherence in Type 2 Diabetes Mellitus: Real-World Strategies for Addressing a Common Problem. J. Fam. Pract. 2017, 66 (Suppl. S4), S46–S51. [Google Scholar]
  42. Kirkman, M.S.; Rowan-Martin, M.T.; Levin, R.; Fonseca, V.A.; Schmittdiel, J.A.; Herman, W.H.; Aubert, R.E. Determinants of Adherence to Diabetes Medications: Findings from a Large Pharmacy Claims Database. Diabetes Care 2015, 38, 604–609. [Google Scholar] [CrossRef]
  43. McGovern, A.; Hinton, W.; Calderara, S.; Munro, N.; Whyte, M.; de Lusignan, S. A Class Comparison of Medication Persistence in People with Type 2 Diabetes: A Retrospective Observational Study. Diabetes Ther. 2018, 9, 229–242. [Google Scholar] [CrossRef]
  44. Iglay, K.; Qiu, Y.; Steve Fan, C.P.; Li, Z.; Tang, J.; Laires, P. Risk factors associated with treatment discontinuation and down-titration in type 2 diabetes patients treated with sulfonylureas. Curr. Med. Res. Opin. 2016, 32, 1567–1575. [Google Scholar] [CrossRef]
  45. Malmenas, M.; Bouchard, J.R.; Langer, J. Retrospective real-world adherence in patients with type 2 diabetes initiating once-daily liraglutide 1.8 mg or twice-daily exenatide 10 mug. Clin Ther. 2013, 35, 795–807. [Google Scholar] [CrossRef] [PubMed]
  46. Zhou, F.L.; Xie, L.; Pan, C.; Wang, Y.; Vaidya, N.; Ye, F.; Preblick, R.; Meneghini, L. Relationship between treatment persistence and A1C trends among patients with type 2 diabetes newly initiating basal insulin. Diabetes Obes. Metab. 2018, 20, 1298–1301. [Google Scholar] [CrossRef] [PubMed]
  47. Rathmann, W.; Czech, M.; Franek, E.; Kostev, K. Treatment persistence in the use of basal insulins in Poland and Germany. Int. J. Clin. Pharmacol. Ther. 2017, 55, 119–125. [Google Scholar] [CrossRef] [PubMed]
  48. Horii, T.; Momo, K.; Yasu, T.; Kabeya, Y.; Atsuda, K. Determination of factors affecting medication adherence in type 2 diabetes mellitus patients using a nationwide claim-based database in Japan. PLoS ONE 2019, 14, e0223431. [Google Scholar] [CrossRef]
  49. Xu, N.; Xie, S.; Chen, Y.; Li, J.; Sun, L. Factors Influencing Medication Non-Adherence among Chinese Older Adults with Diabetes Mellitus. Int. J. Environ. Res. Public Health 2020, 17, 6012. [Google Scholar] [CrossRef]
  50. Manteuffel, M.; Williams, S.; Chen, W.; Verbrugge, R.R.; Pittman, D.G.; Steinkellner, A. Influence of Patient Sex and Gender on Medication Use, Adherence, and Prescribing Alignment with Guidelines. J. Women’s Health 2014, 23, 112–119. [Google Scholar] [CrossRef]
  51. Jankowska-Polańska, B.; Świątoniowska-Lonc, N.; Karniej, P.; Polański, J.; Tański, W.; Grochans, E. Influential factors in adherence to the therapeutic regime in patients with type 2 diabetes and hypertension. Diabetes Res. Clin. Pract. 2021, 173, 108693. [Google Scholar] [CrossRef]
  52. Demoz, G.T.; Wahdey, S.; Bahrey, D.; Kahsay, H.; Woldu, G.; Niriayo, Y.L.; Collier, A. Predictors of poor adherence to antidiabetic therapy in patients with type 2 diabetes: A cross-sectional study insight from Ethiopia. Diabetol. Metab. Syndr. 2020, 12, 62. [Google Scholar] [CrossRef]
  53. Choi, Y.J.; Smaldone, A.M. Factors Associated with Medication Engagement among Older Adults with Diabetes: Systematic Review and Meta-Analysis. Diabetes Educ. 2018, 44, 15–30. [Google Scholar] [CrossRef]
  54. Ali, S.; Stone, M.A.; Peters, J.L.; Davies, M.; Khunti, K. The prevalence of co-morbid depression in adults with Type 2 diabetes: A systematic review and meta-analysis. Diabet. Med. 2006, 23, 1165–1173. [Google Scholar] [CrossRef]
  55. Egede, L.E.; Ellis, C. Diabetes and depression: Global perspectives. Diabetes Res. Clin. Pract. 2010, 87, 302–312. [Google Scholar] [CrossRef] [PubMed]
  56. Fisher, L.; Skaff, M.M.; Mullan, J.T.; Arean, P.; Glasgow, R.; Masharani, U. A longitudinal study of affective and anxiety disorders, depressive affect and diabetes distress in adults with Type 2 diabetes. Diabet. Med. 2008, 25, 1096–1101. [Google Scholar] [CrossRef] [PubMed]
  57. Perrin, N.E.; Davies, M.J.; Robertson, N.; Snoek, F.J.; Khunti, K. The prevalence of diabetes-specific emotional distress in people with Type 2 diabetes: A systematic review and meta-analysis. Diabet. Med. 2017, 34, 1508–1520. [Google Scholar] [CrossRef] [PubMed]
  58. Fisher, L.; Mullan, J.T.; Arean, P.; Glasgow, R.E.; Hessler, D.; Masharani, U. Diabetes Distress but Not Clinical Depression or Depressive Symptoms Is Associated with Glycemic Control in Both Cross-Sectional and Longitudinal Analyses. Diabetes Care 2010, 33, 23–28. [Google Scholar] [CrossRef] [PubMed]
  59. Aronson, B.D.; Sittner, K.J.; Walls, M.L. The Mediating Role of Diabetes Distress and Depressive Symptoms in Type 2 Diabetes Medication Adherence Gender Differences. Health Educ. Behav. 2020, 47, 474–482. [Google Scholar] [CrossRef]
  60. Bhaloo, T.; Juma, M.; Criscuolo-Higgins, C. A solution-focused approach to understanding patient motivation in diabetes self-management: Gender differences and implications for primary care. Chronic Illn. 2018, 14, 243–255. [Google Scholar] [CrossRef]
  61. Heisler, M.; Choi, H.; Palmisano, G.; Mase, R.; Richardson, C.; Fagerlin, A.; Montori, V.M.; Spencer, M.; An, L.C. Comparison of community health worker-led diabetes medication decision-making support for low-income Latino and African American adults with diabetes using e-health tools versus print materials: A randomized, controlled trial. Ann. Intern. Med. 2014, 161 (Suppl. S10), S13–S22. [Google Scholar] [CrossRef]
  62. Hofer, R.; Choi, H.; Mase, R.; Fagerlin, A.; Spencer, M.; Heisler, M. Mediators and Moderators of Improvements in Medication Adherence. Health Educ. Behav. 2017, 44, 285–296. [Google Scholar] [CrossRef]
  63. Mansyur, C.L.; Rustveld, L.O.; Nash, S.G.; Jibaja-Weiss, M.L. Social factors and barriers to self-care adherence in Hispanic men and women with diabetes. Patient Educ. Couns. 2015, 98, 805–810. [Google Scholar] [CrossRef]
  64. Shahabi, N.; Fakhri, Y.; Aghamolaei, T.; Hosseini, Z.; Homayuni, A. Socio-personal factors affecting adherence to treatment in patients with type 2 diabetes: A systematic review and meta-analysis. Prim. Care Diabetes 2023, 17, 205–220. [Google Scholar] [CrossRef]
  65. Bhuyan, S.S.; Shiyanbola, O.; Deka, P.; Isehunwa, O.O.; Chandak, A.; Huang, S.; Wang, Y.; Bhatt, J.; Ning, L.; Lin, W.J.; et al. The Role of Gender in Cost-Related Medication Nonadherence among Patients with Diabetes. J. Am. Board Fam. Med. 2018, 31, 743–751. [Google Scholar] [CrossRef] [PubMed]
  66. Cities Changing Diabetes. Available online: https://www.citieschangediabetes.com/ (accessed on 8 June 2021).
  67. Pirillo, A.; Casula, M.; Olmastroni, E.; Norata, G.D.; Catapano, A.L. Global epidemiology of dyslipidaemias. Nat. Rev. Cardiol. 2021, 18, 689–700. [Google Scholar] [CrossRef] [PubMed]
  68. Institute for Health Metrics and Evaluation. Global Health Data Exchange. GBD Results Tool. Available online: http://ghdx.healthdata.org/gbd-results-tool (accessed on 8 June 2021).
  69. Yusuf, S.; Hawken, S.; Ôunpuu, S.; Dans, T.; Avezum, A.; Lanas, F.; McQueen, M.; Budaj, A.; Pais, P.; Varigos, J.; et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. Lancet 2004, 364, 937–952. [Google Scholar] [CrossRef] [PubMed]
  70. Wang, Q.; Ferreira, D.L.S.; Nelson, S.M.; Sattar, N.; Ala-Korpela, M.; Lawlor, D.A. Metabolic characterization of menopause: Cross-sectional and longitudinal evidence. BMC Med. 2018, 16, 17. [Google Scholar] [CrossRef] [PubMed]
  71. de Kat, A.C.; Dam, V.; Onland-Moret, N.C.; Eijkemans, M.J.C.; Broekmans, F.J.M.; van der Schouw, Y.T. Unraveling the associations of age and menopause with cardiovascular risk factors in a large population-based study. BMC Med. 2017, 15, 2. [Google Scholar] [CrossRef]
  72. Leutner, M.; Göbl, C.; Wielandner, A.; Howorka, E.; Prünner, M.; Bozkurt, L.; Harreiter, J.; Prosch, H.; Schlager, O.; Charwat-Resl, S.; et al. Cardiometabolic Risk in Hyperlipidemic Men and Women. Int. J. Endocrinol. 2016, 2016, 2647865. [Google Scholar] [CrossRef]
  73. Cholesterol Treatment Trialists’ (CTT) Collaboration; Baigent, C.; Blackwell, L.; Emberson, J.; Holland, L.E.; Reith, C.; Bhala, N.; Peto, R.; Barnes, E.H.; Keech, A.; et al. Efficacy and safety of more intensive lowering of LDL cholesterol: A meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010, 376, 1670–1681. [Google Scholar] [CrossRef]
  74. Emerging Risk Factors Collaboration; Di Angelantonio, E.; Gao, P.; Pennells, L.; Kaptoge, S.; Caslake, M.; Thompson, A.; Butterworth, A.S.; Sarwar, N.; Wormser, D.; et al. Lipid-related markers and cardiovascular disease prediction. JAMA 2012, 307, 2499–2506. [Google Scholar]
  75. Silverman, M.G.; Ference, B.A.; Im, K.; Wiviott, S.D.; Giugliano, R.P.; Grundy, S.M.; Braunwald, E.; Sabatine, M.S. Association Between Lowering LDL-C and Cardiovascular Risk Reduction among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. JAMA 2016, 316, 1289–1297. [Google Scholar] [CrossRef]
  76. Baigent, C.; Keech, A.; Kearney, P.M.; Blackwell, L.; Buck, G.; Pollicino, C.; Kirby, A.; Sourjina, T.; Peto, R.; Collins, R.; et al. Efficacy and safety of cholesterol-lowering treatment: Prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 2005, 366, 1267–1278. [Google Scholar]
  77. Mach, F.; Baigent, C.; Catapano, A.L.; Koskinas, K.C.; Casula, M.; Badimon, L.; Chapman, M.J.; De Backer, G.G.; Delgado, V.; Ference, B.A.; et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. 2020, 41, 111–188. [Google Scholar] [CrossRef] [PubMed]
  78. Cholesterol Treatment Trialists’ (CTT) Collaborators; Mihaylova, B.; Emberson, J.; Blackwell, L.; Keech, A.; Simes, J.; Barnes, E.H.; Voysey, M.; Gray, A.; Collins, R.; et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: Meta-analysis of individual data from 27 randomised trials. Lancet 2012, 380, 581–590. [Google Scholar] [PubMed]
  79. Cholesterol Treatment Trialists’ (CTT) Collaborators; Fulcher, J.; O’Connell, R.; Voysey, M.; Emberson, J.; Blackwell, L.; Mihaylova, B.; Simes, J.; Collins, R.; Kirby, A.; et al. Efficacy and safety of LDL-lowering therapy among men and women: Meta-analysis of individual data from 174,000 participants in 27 randomised trials. Lancet 2015, 385, 1397–1405. [Google Scholar] [PubMed]
  80. Abramson, J.; Wright, J.M. Are lipid-lowering guidelines evidence-based? Lancet 2007, 369, 168–169. [Google Scholar] [CrossRef]
  81. Petretta, M.; Costanzo, P.; Perrone-Filardi, P.; Chiariello, M. Impact of gender in primary prevention of coronary heart disease with statin therapy: A meta-analysis. Int. J. Cardiol. 2010, 138, 25–31. [Google Scholar] [CrossRef]
  82. Khan, S.U.; Khan, M.Z.; Raghu Subramanian, C.; Riaz, H.; Khan, M.U.; Lone, A.N.; Khan, M.S.; Benson, E.M.; Alkhouli, M.; Blaha, M.J.; et al. Participation of Women and Older Participants in Randomized Clinical Trials of Lipid-Lowering Therapies: A Systematic Review. JAMA Netw. Open. 2020, 3, e205202. [Google Scholar] [CrossRef]
  83. Blauwet, L.A.; Hayes, S.N.; McManus, D.; Redberg, R.F.; Walsh, M.N. Low rate of sex-specific result reporting in cardiovascular trials. Mayo Clin. Proc. 2007, 82, 166–170. [Google Scholar] [CrossRef]
  84. Benner, J.S.; Glynn, R.J.; Mogun, H.; Neumann, P.J.; Weinstein, M.C.; Avorn, J. Long-term Persistence in Use of Statin Therapy in Elderly Patients. JAMA 2002, 288, 455–461. [Google Scholar] [CrossRef]
  85. Downs, J.R.; Clearfield, M.; Weis, S.; Whitney, E.; Shapiro, D.R.; Beere, P.A.; Langendorfer, A.; Stein, E.A.; Kruyer, W.; Gotto, A.M., Jr. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels: Results of AFCAPS/TexCAPS. Air Force/Texas Coronary Atherosclerosis Prevention Study. JAMA 1998, 279, 1615–1622. [Google Scholar] [CrossRef]
  86. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: A randomised placebo-controlled trial. Lancet 2002, 360, 7–22. [Google Scholar] [CrossRef]
  87. De Vera, M.A.; Bhole, V.; Burns, L.C.; Lacaille, D. Impact of statin adherence on cardiovascular disease and mortality outcomes: A systematic review. Br. J. Clin. Pharmacol. 2014, 78, 684–698. [Google Scholar] [CrossRef] [PubMed]
  88. Gehi, A.K.; Ali, S.; Na, B.; Whooley, M.A. Self-reported Medication Adherence and Cardiovascular Events in Patients with Stable Coronary Heart DiseaseThe Heart and Soul Study. Arch. Intern. Med. 2007, 167, 1798–1803. [Google Scholar] [CrossRef] [PubMed]
  89. Ingersgaard, M.V.; Andersen, T.H.; Norgaard, O.; Grabowski, D.; Olesen, K. Reasons for Nonadherence to Statins—A Systematic Review of Reviews. Patient Prefer. Adherence 2020, 14, 675–691. [Google Scholar] [CrossRef] [PubMed]
  90. Olmastroni, E.; Boccalari, M.T.; Tragni, E.; Rea, F.; Merlino, L.; Corrao, G.; Catapano, A.L.; Casula, M. Sex-differences in factors and outcomes associated with adherence to statin therapy in primary care: Need for customisation strategies. Pharmacol. Res. 2020, 155, 104514. [Google Scholar] [CrossRef] [PubMed]
  91. Lewey, J.; Shrank, W.H.; Bowry, A.D.; Kilabuk, E.; Brennan, T.A.; Choudhry, N.K. Gender and racial disparities in adherence to statin therapy: A meta-analysis. Am. Heart J. 2013, 165, 665–678.e1. [Google Scholar] [CrossRef]
  92. Mann, D.M.; Woodward, M.; Muntner, P.; Falzon, L.; Kronish, I. Predictors of nonadherence to statins: A systematic review and meta-analysis. Ann Pharmacother. 2010, 44, 1410–1421. [Google Scholar] [CrossRef]
  93. Hope, H.F.; Binkley, G.M.; Fenton, S.; Kitas, G.D.; Verstappen, S.M.M.; Symmons, D.P.M. Systematic review of the predictors of statin adherence for the primary prevention of cardiovascular disease. PLoS ONE 2019, 14, e0201196. [Google Scholar] [CrossRef]
  94. Ofori-Asenso, R.; Jakhu, A.; Curtis, A.J.; Zomer, E.; Gambhir, M.; Jaana Korhonen, M.; Nelson, M.; Tonkin, A.; Liew, D.; Zoungas, S. A Systematic Review and Meta-analysis of the Factors Associated with Nonadherence and Discontinuation of Statins among People Aged >/=65 Years. J. Gerontol. A Biol. Sci. Med. Sci. 2018, 73, 798–805. [Google Scholar] [CrossRef]
  95. Chee, Y.; Chan, V.; Tan, N. Understanding patients’ perspective of statin therapy: Can we design a better approach to the management of dyslipidaemia? A literature review. Singap. Med. J. 2014, 55, 416–421. [Google Scholar] [CrossRef]
  96. Ju, A.; Hanson, C.S.; Banks, E.; Korda, R.; Craig, J.C.; Usherwood, T.; MacDonald, P.; Tong, A. Patient beliefs and attitudes to taking statins: Systematic review of qualitative studies. Br. J. Gen. Pract. 2018, 68, e408–e419. [Google Scholar] [CrossRef]
  97. Ridker, P.M.; Danielson, E.; Fonseca, F.A.; Genest, J.; Gotto, A.M., Jr.; Kastelein, J.J.; Koenig, W.; Libby, P.; Lorenzatti, A.J.; MacFadyen, J.G.; et al. Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein. N. Engl. J. Med. 2008, 359, 2195–2207. [Google Scholar] [CrossRef] [PubMed]
  98. Kashani, A.; Foody, J.M.; Wang, Y.; Krumholz, H.M.; Phillips, C.O.; Mangalmurti, S.; Ko, D.T.; Rosenberg, L.; Uretsky, S.; Brewster, L.M.; et al. Response to Letters Regarding Article, “Risks Associated with Statin Therapy: A Systematic Overview of Randomized Clinical Trials”. Circulation 2006, 116, 9. [Google Scholar] [CrossRef]
  99. Bruckert, E.; Hayem, G.; Dejager, S.; Yau, C.; Bégaud, B. Mild to Moderate Muscular Symptoms with High-Dosage Statin Therapy in Hyperlipidemic Patients —The PRIMO Study. Cardiovasc. Drugs Ther. 2005, 19, 403–414. [Google Scholar] [CrossRef] [PubMed]
  100. Cohen, J.D.; Brinton, E.A.; Ito, M.K.; Jacobson, T.A. Understanding Statin Use in America and Gaps in Patient Education (USAGE): An internet-based survey of 10,138 current and former statin users. J. Clin. Lipidol. 2012, 6, 208–215. [Google Scholar] [CrossRef]
  101. Zhang, H.; Plutzky, J.; Skentzos, S.; Morrison, F.; Mar, P.; Shubina, M.; Turchin, A. Discontinuation of statins in routine care settings: A cohort study. Ann. Intern. Med. 2013, 158, 526–534. [Google Scholar] [CrossRef]
  102. Stroes, E.S.; Thompson, P.D.; Corsini, A.; Vladutiu, G.D.; Raal, F.J.; Ray, K.K.; Roden, M.; Stein, E.; Tokgozoglu, L.; Nordestgaard, B.G.; et al. Statin-associated muscle symptoms: Impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management. Eur. Heart J. 2015, 36, 1012–1022. [Google Scholar] [CrossRef]
  103. Mills, K.T.; Bundy, J.D.; Kelly, T.N.; Reed, J.; Kearney, P.M.; Reynolds, K.; Chen, J.; He, J. Abstract 16828: Global Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-based Studies from 90 Countries. Circulation 2016, 132, 441–450. [Google Scholar] [CrossRef]
  104. Collaboration NCDRF. Worldwide trends in blood pressure from 1975 to 2015: A pooled analysis of 1479 population-based measurement studies with 19.1 million participants. Lancet 2017, 389, 37–55. [Google Scholar] [CrossRef]
  105. Dorans, K.S.; Mills, K.T.; Liu, Y.; He, J. Trends in Prevalence and Control of Hypertension According to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Guideline. J. Am. Heart Assoc. 2018, 7, e008888. [Google Scholar] [CrossRef]
  106. Ibrahim, M.M.; Damasceno, A. Hypertension in developing countries. Lancet 2012, 380, 611–619. [Google Scholar] [CrossRef]
  107. Brown, D.W.; Giles, W.H.; Greenlund, K.J. Blood Pressure Parameters and Risk of Fatal Stroke, NHANES II Mortality Study. Am. J. Hypertens. 2007, 20, 338–341. [Google Scholar] [CrossRef] [PubMed]
  108. Lawes, C.M.; Rodgers, A.; Bennett, D.A.; Parag, V.; Suh, I.; Ueshima, H.; MacMahon, S.; Asia Pacific Cohort Studies Collaboration. Blood pressure and cardiovascular disease in the Asia Pacific region. J. Hypertens. 2003, 21, 707–716. [Google Scholar] [PubMed]
  109. Vishram, J.K.; Borglykke, A.; Andreasen, A.H.; Jeppesen, J.; Ibsen, H.; Jorgensen, T.; Broda, G.; Palmieri, L.; Giampaoli, S.; Donfrancesco, C.; et al. Impact of age on the importance of systolic and diastolic blood pressures for stroke risk: The MOnica, Risk, Genetics, Archiving, and Monograph (MORGAM) Project. Hypertension 2012, 60, 1117–1123. [Google Scholar] [CrossRef] [PubMed]
  110. Forouzanfar, M.H.; Liu, P.; Roth, G.A.; Ng, M.; Biryukov, S.; Marczak, L.; Alexander, L.; Estep, K.; Abate, K.H.; Akinyemiju, T.F.; et al. Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 mm Hg, 1990–2015. JAMA 2017, 317, 165–182. [Google Scholar] [CrossRef] [PubMed]
  111. Lewington, S.; Clarke, R.; Qizilbash, N.; Peto, R.; Collins, R.; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: A meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002, 360, 1903–1913. [Google Scholar]
  112. Thomopoulos, C.; Parati, G.; Zanchetti, A. Effects of blood pressure lowering on outcome incidence in hypertension. 1. Overview, meta-analyses, and meta-regression analyses of randomized trials. J. Hypertens. 2014, 32, 2285–2295. [Google Scholar] [CrossRef]
  113. World Health Organization. Hypertension Fact Sheets 2021. Available online: https://www.who.int/news-room/fact-sheets/detail/hypertension (accessed on 8 June 2021).
  114. Gueyffier, F.; Boutitie, F.; Boissel, J.-P.; Pocock, S.; Coope, J.; Cutler, J.; Ekbom, T.; Fagard, R.; Friedman, L.; Perry, M.; et al. Effect of antihypertensive drug treatment on cardiovascular outcomes in women and men. A meta-analysis of individual patient data from randomized, controlled trials. The INDANA Investigators. Ann. Intern. Med. 1997, 126, 761–767. [Google Scholar] [CrossRef]
  115. Oparil, S.; Miller, A.P. Gender and blood pressure. J. Clin. Hypertens. 2005, 7, 300–309. [Google Scholar] [CrossRef]
  116. Gu, Q.; Burt, V.L.; Paulose-Ram, R.; Dillon, C.F. Gender Differences in Hypertension Treatment, Drug Utilization Patterns, and Blood Pressure Control among US Adults with Hypertension: Data from the National Health and Nutrition Examination Survey 1999–2004. Am. J. Hypertens. 2008, 21, 789–798. [Google Scholar] [CrossRef]
  117. Wong, N.D.; Thakral, G.; Franklin, S.S.; L’italien, G.J.; Jacobs, M.J.; Whyte, J.L.; Lapuerta, P. Prevention and Rehabilitation: Preventing heart disease by controlling hypertension: Impact of hypertensive subtype, stage, age, and sex. Am. Heart J. 2003, 145, 888–895. [Google Scholar] [CrossRef]
  118. Krousel-Wood, M.; Joyce, C.; Holt, E.; Muntner, P.; Webber, L.S.; Morisky, D.E.; Frohlich, E.D.; Re, R.N. Predictors of decline in medication adherence: Results from the cohort study of medication adherence among older adults. Hypertension 2011, 58, 804–810. [Google Scholar] [CrossRef] [PubMed]
  119. Gale, N.K.; Greenfield, S.; Gill, P.; Gutridge, K.; Marshall, T. Patient and general practitioner attitudes to taking medication to prevent cardiovascular disease after receiving detailed information on risks and benefits of treatment: A qualitative study. BMC Fam. Pract. 2011, 12, 59. [Google Scholar] [CrossRef] [PubMed]
  120. Jung, O.; Gechter, J.L.; Wunder, C.; Paulke, A.; Bartel, C.; Geiger, H.; Toennes, S.W. Resistant hypertension? Assessment of adherence by toxicological urine analysis. J. Hypertens. 2013, 31, 766–774. [Google Scholar] [CrossRef] [PubMed]
  121. Gwadry-Sridhar, F.H.; Manias, E.; Lal, L.; Salas, M.; Hughes, D.A.; Ratzki-Leewing, A.; Grubisic, M. Impact of Interventions on Medication Adherence and Blood Pressure Control in Patients with Essential Hypertension: A Systematic Review by the ISPOR Medication Adherence and Persistence Special Interest Group. Value Health 2013, 16, 863–871. [Google Scholar] [CrossRef]
  122. Sokol, M.C.; McGuigan, K.A.; Verbrugge, R.R.; Epstein, R.S. Impact of Medication Adherence on Hospitalization Risk and Healthcare Cost. Med. Care 2005, 43, 521–530. [Google Scholar] [CrossRef]
  123. Simpson, S.H.; Eurich, D.T.; Majumdar, S.R.; Padwal, R.S.; Tsuyuki, R.T.; Varney, J.; Johnson, J.A. A meta-analysis of the association between adherence to drug therapy and mortality. BMJ 2006, 333, 15. [Google Scholar] [CrossRef]
  124. Corrao, G.; Parodi, A.; Nicotra, F.; Zambon, A.; Merlino, L.; Cesana, G.; Mancia, G. Better compliance to antihypertensive medications reduces cardiovascular risk. J. Hypertens. 2011, 29, 610–618. [Google Scholar] [CrossRef]
  125. Yang, Q.; Chang, A.; Ritchey, M.D.; Loustalot, F. Antihypertensive Medication Adherence and Risk of Cardiovascular Disease among Older Adults: A Population-Based Cohort Study. J. Am. Heart Assoc. 2017, 6, e006056. [Google Scholar] [CrossRef]
  126. Burnier, M. Drug adherence in hypertension. Pharmacol. Res. 2017, 125 Pt B, 142–149. [Google Scholar] [CrossRef]
  127. Rea, F.; Mella, M.; Compagnoni, M.M.; Cantarutti, A.; Merlino, L.; Mancia, G.; Corrao, G. Women discontinue antihypertensive drug therapy more than men. Evidence from an Italian population-based study. J. Hypertens. 2020, 38, 142–149. [Google Scholar] [CrossRef]
  128. Mancia, G.; Zambon, A.; Soranna, D.; Merlino, L.; Corrao, G. Factors involved in the discontinuation of antihypertensive drug therapy: An analysis from real life data. J. Hypertens. 2014, 32, 1708–1715; discussion 1716. [Google Scholar] [CrossRef] [PubMed]
  129. Erkens, J.A.; Panneman, M.M.J.; Klungel, O.H.; van den Boom, G.; Prescott, M.F.; Herings, R.M.C. Differences in antihypertensive drug persistence associated with drug class and gender: A PHARMO study. Pharmacoepidemiol. Drug Saf. 2005, 14, 795–803. [Google Scholar] [CrossRef] [PubMed]
  130. Kulkarni, S.; Rao, R.; Goodman, J.D.H.; Connolly, K.; O’Shaughnessy, K.M. Nonadherence to antihypertensive medications amongst patients with uncontrolled hypertension: A retrospective study. Medicine 2021, 100, e24654. [Google Scholar] [CrossRef]
  131. Tajeu, G.S.; Kent, S.T.; Kronish, I.M.; Huang, L.; Krousel-Wood, M.; Bress, A.P.; Shimbo, D.; Muntner, P. Trends in Antihypertensive Medication Discontinuation and Low Adherence among Medicare Beneficiaries Initiating Treatment from 2007 to 2012. Hypertension 2016, 68, 565–575. [Google Scholar] [CrossRef] [PubMed]
  132. Friedman, O.; McAlister, F.A.; Yun, L.; Campbell, N.R.; Tu, K.; Canadian Hypertension Education Program Outcomes Research Taskforce. Antihypertensive Drug Persistence and Compliance among Newly Treated Elderly Hypertensives in Ontario. Am. J. Med. 2010, 123, 173–181. [Google Scholar] [CrossRef] [PubMed]
  133. Qvarnstrom, M.; Kahan, T.; Kieler, H.; Brandt, L.; Hasselstrom, J.; Bostrom, K.B.; Manhem, K.; Hjerpe, P.; Wettermark, B. Persistence to antihypertensive drug classes: A cohort study using the Swedish Primary Care Cardiovascular Database (SPCCD). Medicine 2016, 95, e4908. [Google Scholar] [CrossRef]
  134. Biffi, A.; Rea, F.; Iannaccone, T.; Filippelli, A.; Mancia, G.; Corrao, G. Sex differences in the adherence of antihypertensive drugs: A systematic review with meta-analyses. BMJ Open 2020, 10, e036418. [Google Scholar] [CrossRef]
  135. World Health Organization. Cariovascular Diseases (CVDs) Fact Sheets 2017. Available online: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) (accessed on 8 June 2021).
  136. Bots, S.H.; A E Peters, S.; Woodward, M. Sex differences in coronary heart disease and stroke mortality: A global assessment of the effect of ageing between 1980 and 2010. BMJ Glob. Health 2017, 2, e000298. [Google Scholar] [CrossRef]
  137. Huxley, R.R.; Woodward, M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: A systematic review and meta-analysis of prospective cohort studies. Lancet 2011, 378, 1297–1305. [Google Scholar] [CrossRef]
  138. Peters, S.A.E.; Huxley, R.R.; Woodward, M. Diabetes as risk factor for incident coronary heart disease in women compared with men: A systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events. Diabetologia 2014, 57, 1542–1551. [Google Scholar] [CrossRef]
  139. A E Peters, S.; Huxley, R.R.; Woodward, M. Diabetes as a risk factor for stroke in women compared with men: A systematic review and meta-analysis of 64 cohorts, including 775,385 individuals and 12,539 strokes. Lancet 2014, 383, 1973–1980. [Google Scholar] [CrossRef]
  140. Kramer, C.K.; Campbell, S.; Retnakaran, R. Gestational diabetes and the risk of cardiovascular disease in women: A systematic review and meta-analysis. Diabetologia 2019, 62, 905–914. [Google Scholar] [CrossRef] [PubMed]
  141. Lo, C.C.W.; Lo, A.C.Q.; Leow, S.H.; Fisher, G.; Corker, B.; Batho, O.; Morris, B.; Chowaniec, M.; Vladutiu, C.J.; Fraser, A.; et al. Future Cardiovascular Disease Risk for Women with Gestational Hypertension: A Systematic Review and Meta-Analysis. J. Am. Heart Assoc. 2020, 9, e013991. [Google Scholar] [CrossRef] [PubMed]
  142. Goldstein, J.M.; Hale, T.; Foster, S.L.; Tobet, S.A.; Handa, R.J. Sex differences in major depression and comorbidity of cardiometabolic disorders: Impact of prenatal stress and immune exposures. Neuropsychopharmacology 2019, 44, 59–70. [Google Scholar] [CrossRef] [PubMed]
  143. Backholer, K.; A E Peters, S.; Bots, S.H.; Peeters, A.; Huxley, R.R.; Woodward, M. Sex differences in the relationship between socioeconomic status and cardiovascular disease: A systematic review and meta-analysis. J. Epidemiol. Community Health 2016, 71, 550–557. [Google Scholar] [CrossRef]
  144. Bairey Merz, C.N.; Pepine, C.J.; Walsh, M.N.; Fleg, J.L. Ischemia and No Obstructive Coronary Artery Disease (INOCA): Developing Evidence-Based Therapies and Research Agenda for the Next Decade. Circulation 2017, 135, 1075–1092. [Google Scholar] [CrossRef] [PubMed]
  145. Lichtman, J.H.; Leifheit, E.C.; Safdar, B.; Bao, H.; Krumholz, H.M.; Lorenze, N.P.; Daneshvar, M.; Spertus, J.A.; D’Onofrio, G. Sex Differences in the Presentation and Perception of Symptoms among Young Patients with Myocardial Infarction: Evidence from the VIRGO Study (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients). Circulation 2018, 137, 423–429. [Google Scholar] [CrossRef] [PubMed]
  146. Ahmed, B.; Piper, W.D.; Malenka, D.; VerLee, P.; Robb, J.; Ryan, T.; Herne, M.; Phillips, W.; Dauerman, H.L. Significantly improved vascular complications among women undergoing percutaneous coronary intervention: A report from the Northern New England Percutaneous Coronary Intervention Registry. Circ. Cardiovasc. Interv. 2009, 2, 423–429. [Google Scholar] [CrossRef]
  147. Jackson, E.A.; Moscucci, M.; Smith, D.E.; Share, D.; Dixon, S.; Greenbaum, A.; Grossman, P.M.; Gurm, H.S. The association of sex with outcomes among patients undergoing primary percutaneous coronary intervention for ST elevation myocardial infarction in the contemporary era: Insights from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2). Am. Heart J. 2011, 161, 106–112.e101. [Google Scholar] [CrossRef]
  148. Gall, S.L.; Donnan, G.; Dewey, H.M.; Macdonell, R.; Sturm, J.; Gilligan, A.; Srikanth, V.; Thrift, A.G. Sex differences in presentation, severity, and management of stroke in a population-based study. Neurology 2010, 74, 975–981. [Google Scholar] [CrossRef]
  149. Carcel, C.; Woodward, M.; Wang, X.; Bushnell, C.; Sandset, E.C. Sex matters in stroke: A review of recent evidence on the differences between women and men. Front. Neuroendocr. 2020, 59, 100870. [Google Scholar] [CrossRef] [PubMed]
  150. Levinsson, A.; Dubé, M.-P.; Tardif, J.-C.; de Denus, S. Sex, drugs, and heart failure: A sex-sensitive review of the evidence base behind current heart failure clinical guidelines. ESC Heart Fail. 2018, 5, 745–754. [Google Scholar] [CrossRef] [PubMed]
  151. Romiti, G.F.; Recchia, F.; Zito, A.; Visioli, G.; Basili, S.; Raparelli, V. Sex and Gender-Related Issues in Heart Failure. Heart Fail. Clin. 2020, 16, 121–130. [Google Scholar] [CrossRef] [PubMed]
  152. Vaduganathan, M.; Tahhan, A.S.; Alrohaibani, A.; Greene, S.J.; Fonarow, G.C.; Vardeny, O.; Lindenfeld, J.; Jessup, M.; Fiuzat, M.; O’Connor, C.M.; et al. Do Women and Men Respond Similarly to Therapies in Contemporary Heart Failure Clinical Trials? JACC Heart Fail. 2019, 7, 267–271. [Google Scholar] [CrossRef]
  153. Hyun, K.K.; Redfern, J.; Patel, A.; Peiris, D.; Brieger, D.; Sullivan, D.; Harris, M.; Usherwood, T.; MacMahon, S.; Lyford, M.; et al. Gender inequalities in cardiovascular risk factor assessment and management in primary healthcare. Heart 2017, 103, 492–498. [Google Scholar] [CrossRef] [PubMed]
  154. Peters, S.A.; Colantonio, L.D.; Zhao, H.; Bittner, V.; Dai, Y.; Farkouh, M.E.; Monda, K.L.; Safford, M.M.; Muntner, P.; Woodward, M. Sex Differences in High-Intensity Statin Use Following Myocardial Infarction in the United States. J. Am. Coll. Cardiol. 2018, 71, 1729–1737. [Google Scholar] [CrossRef] [PubMed]
  155. Redfors, B.; Angerås, O.; Råmunddal, T.; Petursson, P.; Haraldsson, I.; Dworeck, C.; Odenstedt, J.; Ioaness, D.; Ravn-Fischer, A.; Wellin, P.; et al. Trends in Gender Differences in Cardiac Care and Outcome after Acute Myocardial Infarction in Western Sweden: A Report from the Swedish Web System for Enhancement of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART). J. Am. Heart Assoc. 2015, 4, e001995. [Google Scholar] [CrossRef]
  156. Zhao, M.; Vaartjes, I.; Graham, I.; Grobbee, D.; Spiering, W.; Klipstein-Grobusch, K.; Woodward, M.; Peters, S.A. Sex differences in risk factor management of coronary heart disease across three regions. Heart 2017, 103, 1587–1594. [Google Scholar] [CrossRef]
  157. Hambraeus, K.; Tydén, P.; Lindahl, B. Time trends and gender differences in prevention guideline adherence and outcome after myocardial infarction: Data from the SWEDEHEART registry. Eur. J. Prev. Cardiol. 2015, 23, 340–348. [Google Scholar] [CrossRef]
  158. Bitton, A.; Choudhry, N.K.; Matlin, O.S.; Swanton, K.; Shrank, W.H. The Impact of Medication Adherence on Coronary Artery Disease Costs and Outcomes: A Systematic Review. Am. J. Med. 2013, 126, 357.e7–357.e27. [Google Scholar] [CrossRef]
  159. Rasmussen, J.N.; Chong, A.; Alter, D.A. Relationship Between Adherence to Evidence-Based Pharmacotherapy and Long-term Mortality after Acute Myocardial Infarction. JAMA 2007, 297, 177–186. [Google Scholar] [CrossRef] [PubMed]
  160. Naderi, S.H.; Bestwick, J.P.; Wald, D.S. Adherence to Drugs That Prevent Cardiovascular Disease: Meta-analysis on 376,162 Patients. Am. J. Med. 2012, 125, 882–887.e1. [Google Scholar] [CrossRef] [PubMed]
  161. Chowdhury, R.; Khan, H.; Heydon, E.; Shroufi, A.; Fahimi, S.; Moore, C.; Stricker, B.; Mendis, S.; Hofman, A.; Mant, J.; et al. Adherence to cardiovascular therapy: A meta-analysis of prevalence and clinical consequences. Eur. Heart J. 2013, 34, 2940–2948. [Google Scholar] [CrossRef]
  162. Soldati, S.; Di Martino, M.; Castagno, D.; Davoli, M.; Fusco, D. In-hospital myocardial infarction and adherence to evidence-based drug therapies: A real-world evaluation. BMJ Open 2021, 11, e042878. [Google Scholar] [CrossRef] [PubMed]
  163. Kirchmayer, U.; Agabiti, N.; Belleudi, V.; Davoli, M.; Fusco, D.; Stafoggia, M.; Arcà, M.; Barone, A.P.; Perucci, C.A. Socio-demographic differences in adherence to evidence-based drug therapy after hospital discharge from acute myocardial infarction: A population-based cohort study in Rome, Italy. J. Clin. Pharm. Ther. 2012, 37, 37–44. [Google Scholar] [CrossRef]
  164. Hyun, K.; Negrone, A.; Redfern, J.; Atkins, E.; Chow, C.; Kilian, J.; Rajaratnam, R.; Brieger, D. Gender Difference in Secondary Prevention of Cardiovascular Disease and Outcomes Following the Survival of Acute Coronary Syndrome. Heart Lung Circ. 2021, 30, 121–127. [Google Scholar] [CrossRef]
  165. Lauffenburger, J.C.; Robinson, J.G.; Oramasionwu, C.; Fang, G. Racial/Ethnic and Gender Gaps in the Use of and Adherence to Evidence-Based Preventive Therapies among Elderly Medicare Part D Beneficiaries after Acute Myocardial Infarction. Circulation 2014, 129, 754–763. [Google Scholar] [CrossRef] [PubMed]
  166. Eindhoven, D.C.; Hilt, A.D.; Zwaan, T.C.; Schalij, M.J.; Borleffs, C.J.W. Age and gender differences in medical adherence after myocardial infarction: Women do not receive optimal treatment—The Netherlands claims database. Eur. J. Prev. Cardiol. 2018, 25, 181–189. [Google Scholar] [CrossRef]
  167. Tuppin, P.; Neumann, A.; Danchin, N.; Weill, A.; Ricordeau, P.; de Peretti, C.; Allemand, H. Combined secondary prevention after hospitalization for myocardial infarction in France: Analysis from a large administrative database. Arch. Cardiovasc. Dis. 2009, 102, 279–292. [Google Scholar] [CrossRef]
  168. Yan, A.T.; Yan, R.T.; Tan, M.; Huynh, T.; Soghrati, K.; Brunner, L.J.; DeYoung, P.; Fitchett, D.H.; Langer, A.; Goodman, S.G.; et al. Optimal medical therapy at discharge in patients with acute coronary syndromes: Temporal changes, characteristics, and 1-year outcome. Am. Heart J. 2007, 154, 1108–1115. [Google Scholar] [CrossRef]
  169. Wawruch, M.; Zatko, D.; Wimmer, G.; Luha, J.; Kuzelova, L.; Kukumberg, P.; Murin, J.; Hloska, A.; Tesar, T.; Kallay, Z.; et al. Factors Influencing Non-Persistence with Antiplatelet Medications in Elderly Patients after Ischaemic Stroke. Drugs Aging 2016, 33, 365–373. [Google Scholar] [CrossRef]
  170. Ambrosy, A.P.; Fonarow, G.C.; Butler, J.; Chioncel, O.; Greene, S.J.; Vaduganathan, M.; Nodari, S.; Lam, C.S.P.; Sato, N.; Shah, A.N.; et al. The global health and economic burden of hospitalizations for heart failure: Lessons learned from hospitalized heart failure registries. J. Am. Coll. Cardiol. 2014, 63, 1123–1133. [Google Scholar] [CrossRef] [PubMed]
  171. Conrad, N.; Judge, A.; Tran, J.; Mohseni, H.; Hedgecott, D.; Crespillo, A.P.; Allison, M.; Hemingway, H.; Cleland, J.G.; McMurray, J.J.V.; et al. Temporal trends and patterns in heart failure incidence: A population-based study of 4 million individuals. Lancet 2018, 391, 572–580. [Google Scholar] [CrossRef] [PubMed]
  172. Magnussen, C.; Niiranen, T.J.; Ojeda, F.M.; Gianfagna, F.; Blankenberg, S.; Vartiainen, E.; Sans, S.; Pasterkamp, G.; Hughes, M.; Costanzo, S.; et al. Sex-Specific Epidemiology of Heart Failure Risk and Mortality in Europe: Results from the BiomarCaRE Consortium. JACC Heart Fail. 2019, 7, 204–213. [Google Scholar] [CrossRef] [PubMed]
  173. Lenzen, M.J.; Rosengren, A.; Reimer, W.J.M.S.O.; Follath, F.; Boersma, E.; Simoons, M.L.; Cleland, J.G.F.; Komajda, M. Management of patients with heart failure in clinical practice: Differences between men and women. Heart 2008, 94, e10. [Google Scholar] [CrossRef] [PubMed]
  174. Eisenberg, E.; Di Palo, K.E.; Piña, I.L. Sex differences in heart failure. Clin. Cardiol. 2018, 41, 211–216. [Google Scholar] [CrossRef] [PubMed]
  175. Franke, J.; Lindmark, A.; Hochadel, M.; Zugck, C.; Koerner, E.; Keppler, J.; Ehlermann, P.; Winkler, R.; Zahn, R.; Katus, H.A.; et al. Gender aspects in clinical presentation and prognostication of chronic heart failure according to NT-proBNP and the Heart Failure Survival Score. Clin. Res. Cardiol. 2015, 104, 334–341. [Google Scholar] [CrossRef]
  176. Martínez-Sellés, M.; Doughty, R.N.; Poppe, K.; Whalley, G.A.; Earle, N.; Tribouilloy, C.; McMurray, J.J.; Swedberg, K.; Køber, L.; Berry, C.; et al. Gender and survival in patients with heart failure: Interactions with diabetes and aetiology. Results from the MAGGIC individual patient meta-analysis. Eur. J. Heart Fail. 2012, 14, 473–479. [Google Scholar] [CrossRef]
  177. Writing Committee Members; Yancy, C.W.; Jessup, M.; Bozkurt, B.; Butler, J.; Casey, D.E.; Colvin, M.M.; Drazner, M.H.; Filippatos, G.; Fonarow, G.C.; et al. 2016 ACC/AHA/HFSA Focused Update on New Pharmacological Therapy for Heart Failure: An Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 2016, 134, e282–e293. [Google Scholar] [CrossRef]
  178. McGuire, D.K.; Shih, W.J.; Cosentino, F.; Charbonnel, B.; Cherney, D.Z.I.; Dagogo-Jack, S.; Pratley, R.; Greenberg, M.; Wang, S.; Huyck, S.; et al. Association of SGLT2 Inhibitors with Cardiovascular and Kidney Outcomes in Patients with Type 2 Diabetes: A Meta-analysis. JAMA Cardiol. 2021, 6, 148–158. [Google Scholar] [CrossRef]
  179. McMurray, J.J.V.; Solomon, S.D.; Inzucchi, S.E.; Køber, L.; Kosiborod, M.N.; Martinez, F.A.; Ponikowski, P.; Sabatine, M.S.; Anand, I.S.; Bělohlávek, J.; et al. Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction. N. Engl. J. Med. 2019, 381, 1995–2008. [Google Scholar] [CrossRef] [PubMed]
  180. Rosano, G.M.; Lewis, B.; Agewall, S.; Wassmann, S.; Vitale, C.; Schmidt, H.; Drexel, H.; Patak, A.; Torp-Pedersen, C.; Kjeldsen, K.P.; et al. Gender differences in the effect of cardiovascular drugs: A position document of the Working Group on Pharmacology and Drug Therapy of the ESC: Figure 1. Eur. Heart J. 2015, 36, 2677–2680. [Google Scholar] [CrossRef]
  181. Tamargo, J.; Rosano, G.; Walther, T.; Duarte, J.; Niessner, A.; Kaski, J.; Ceconi, C.; Drexel, H.; Kjeldsen, K.; Savarese, G.; et al. Gender differences in the effects of cardiovascular drugs. Eur. Heart J.—Cardiovasc. Pharmacother. 2017, 3, 163–182. [Google Scholar] [CrossRef]
  182. Crespo-Leiro, M.G.; Anker, S.D.; Maggioni, A.P.; Coats, A.J.; Filippatos, G.; Ruschitzka, F.; Ferrari, R.; Piepoli, M.F.; Delgado Jimenez, J.F.; Metra, M.; et al. European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT): 1-year follow-up outcomes and differences across regions. Eur. J. Heart Fail. 2016, 18, 613–625. [Google Scholar] [CrossRef] [PubMed]
  183. Ponikowski, P.; Voors, A.A.; Anker, S.D.; Bueno, H.; Cleland, J.G.F.; Coats, A.J.S.; Falk, V.; Gonzalez-Juanatey, J.R.; Harjola, V.P.; Jankowska, E.A.; et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. Heart J. 2016, 37, 2129–2200. [Google Scholar] [PubMed]
  184. Jones, C.D.; Holmes, G.M.; DeWalt, D.A.; Erman, B.; Wu, J.R.; Cene, C.W.; Baker, D.W.; Schillinger, D.; Ruo, B.; Bibbins-Domingo, K.; et al. Self-reported recall and daily diary-recorded measures of weight monitoring adherence: Associations with heart failure-related hospitalization. BMC Cardiovasc. Disord. 2014, 14, 12. [Google Scholar] [CrossRef] [PubMed]
  185. Hood, S.R.; Giazzon, A.J.; Seamon, G.; Lane, K.A.; Wang, J.; Eckert, G.J.; Tu, W.; Murray, M.D. Association Between Medication Adherence and the Outcomes of Heart Failure. Pharmacotherapy 2018, 38, 539–545. [Google Scholar] [CrossRef]
  186. Unverzagt, S.; Meyer, G.; Mittmann, S.; Samos, F.-A.; Unverzagt, M.; Prondzinsky, R. Improving Treatment Adherence in Heart Failure. Dtsch. Aerzteblatt Online 2016, 113, 423–430. [Google Scholar] [CrossRef]
  187. Ruppar, T.M.; Cooper, P.S.; Mehr, D.R.; Delgado, J.M.; Dunbar-Jacob, J.M. Medication Adherence Interventions Improve Heart Failure Mortality and Readmission Rates: Systematic Review and Meta-Analysis of Controlled Trials. J. Am. Heart Assoc. 2016, 5, e002606. [Google Scholar] [CrossRef]
  188. Kayibanda, J.F.; Girouard, C.; Gregoire, J.P.; Demers, E.; Moisan, J. Adherence to the evidence-based heart failure drug treatment: Are there sex-specific differences among new users? Res. Soc. Adm. Pharm. 2018, 14, 915–920. [Google Scholar] [CrossRef]
  189. Lamb, D.A.; Eurich, D.T.; McAlister, F.A.; Tsuyuki, R.T.; Semchuk, W.M.; Wilson, T.W.; Blackburn, D.F. Changes in adherence to evidence-based medications in the first year after initial hospitalization for heart failure: Observational cohort study from 1994 to 2003. Circ. Cardiovasc. Qual. Outcomes 2009, 2, 228–235. [Google Scholar] [CrossRef] [PubMed]
  190. Dunlay, S.M.; Eveleth, J.M.; Shah, N.D.; McNallan, S.M.; Roger, V.L. Medication Adherence among Community-Dwelling Patients with Heart Failure. Mayo Clin. Proc. 2011, 86, 273–281. [Google Scholar] [CrossRef] [PubMed]
  191. Bagchi, A.D.; Esposito, D.; Kim, M.; Verdier, J.; Bencio, D. Utilization of, and Adherence to, Drug Therapy among Medicaid Beneficiaries with Congestive Heart Failure. Clin. Ther. 2007, 29, 1771–1783. [Google Scholar] [CrossRef] [PubMed]
  192. Granger, B.B.; Ekman, I.; Granger, C.B.; Ostergren, J.; Olofsson, B.; Michelson, E.; McMurray, J.J.; Yusuf, S.; Pfeffer, M.A.; Swedberg, K. Adherence to medication according to sex and age in the CHARM programme. Eur. J. Heart Fail. 2009, 11, 1092–1098. [Google Scholar] [CrossRef]
  193. Roe, C.M.; Motheral, B.R.; Teitelbaum, F.; Rich, M.W. Compliance with and dosing of angiotensin-converting-enzyme inhibitors before and after hospitalization. Am. J. Health Pharm. 2000, 57, 139–145. [Google Scholar] [CrossRef]
  194. Gürgöze, M.T.; van der Galiën, O.P.; Limpens, M.A.; Roest, S.; Hoekstra, R.C.; Ijpma, A.S.; Brugts, J.J.; Manintveld, O.C.; Boersma, E. Impact of sex differences in co-morbidities and medication adherence on outcome in 25 776 heart failure patients. ESC Heart Fail. 2021, 8, 63–73. [Google Scholar] [CrossRef]
  195. Seid, M.A.; Abdela, O.A.; Zeleke, E.G. Adherence to self-care recommendations and associated factors among adult heart failure patients. From the patients’ point of view. PLoS ONE 2019, 14, e0211768. [Google Scholar] [CrossRef]
  196. Lee, K.S.; Moser, D.K.; Pelter, M.M.; Nesbitt, T.; Dracup, K. Self-care in rural residents with heart failure: What we are missing. Eur. J. Cardiovasc. Nurs. 2017, 16, 326–333. [Google Scholar] [CrossRef]
  197. Verena, R.; Stewart, S.; Pretorius, S.; Kubheka, M.; Lautenschläger, C.; Presek, P.; Sliwa, K. Medication adherence, self-care behaviour and knowledge on heart failure in urban South Africa: The Heart of Soweto study. Cardiovasc. J. Afr. 2010, 21, 86–92. [Google Scholar]
  198. Marti, C.N.; Georgiopoulou, V.V.; Giamouzis, G.; Cole, R.T.; Deka, A.; Tang, W.H.; Dunbar, S.B.; Smith, A.L.; Kalogeropoulos, A.P.; Butler, J. Patient-reported selective adherence to heart failure self-care recommendations: A prospective cohort study: The Atlanta Cardiomyopathy Consortium. Congest. Heart Fail. 2013, 19, 16–24. [Google Scholar] [CrossRef]
  199. van der Wal, M.H.; van Veldhuisen, D.J.; Veeger, N.J.G.M.; Rutten, F.H.; Jaarsma, T. Compliance with non-pharmacological recommendations and outcome in heart failure patients. Eur. Heart J. 2010, 31, 1486–1493. [Google Scholar] [CrossRef] [PubMed]
Table 1. Observations of non-adherence through all the conditions examined.
Table 1. Observations of non-adherence through all the conditions examined.
Type of ConditionObservations
Type 2 DiabetesWomen show low medication adherence to anti-hyperglycemic treatments. Depressive disorders and diabetes distress are significantly more common in female patients and seem to play a key role
Women with diabetes might greatly benefit from more structured and supportive educational programs, possibly involving multidisciplinary teams, aimed at overcoming barriers to medication adherence
DyslipidemiaNon-adherence is due to several factors (mainly socio-demographic and treatment-related) and appears to be more frequent in women
New treatment strategies are needed to improve adherence (association therapy, therapeutic interchange, increased medical support)
Arterial HypertensionWomen are less likely to achieve Blood Pressure targets
The contribution of sex as a determinant of medication adherence is still controversial
Cardiovascular DiseaseWorse outcomes in cardiovascular diseases among women could be associated with disparities in health assistance, including risk assessment and evidence-based medication prescription
Most studies are consistent with poorer adherence in women, but the reasons are largely unknown and involve a complex overlap between numerous factors
Heart FailureStudies that examined the effect of sex/gender on adherence to heart failure therapy are still insufficient to draw firm conclusions
In consideration of the relevant impact of medication adherence on heart failure outcomes, further research is needed on this issue
Table 2. Causes of reduced medication adherence and proposed strategies to improve adherence.
Table 2. Causes of reduced medication adherence and proposed strategies to improve adherence.
Causes of Non-AdherenceSuggested Strategies to Improve
Adherence
Complexity of treatment, polypharmacySingle pill administration
Patient’s misperceptionImprove patient awareness and
doctor–patient relationship
Lack of benefits in treatment or
immediacy of beneficial effects		Increase availability of medical support
Poor relationship patient-doctor
Psychological problems, cognitive
Impairment		Role of caregivers
Documented side effectsImplementation of treatment plan
Therapeutic interchange
Table 3. Studies that evaluated gender-related factors in medication adherence.
Table 3. Studies that evaluated gender-related factors in medication adherence.
Type 2 Diabetes
AuthorsYearMain Findings
Bird CE, et al. [32]2007Women have lower access to healthcare facilities due to social, cultural, and psychological issues
Fisher L, et al. [58]2010Diabetes distress affects patients’
self-management and clinical outcomes more than depression
Penno G, et al. [27]2013Women with type 2 diabetes have worse control of
glycemia, lipid levels, and blood pressure despite equal or increased
treatment intensity
Malmenas M, et al. [45]2013Female sex is an independent predictor of low
medication adherence for glucagon-like receptor agonists
Manteuffel M, et al. [50]2014Women have lower medication adherence, are
treated with more drugs, and are less likely to obtain guidelines-based
prescriptions
Kirkman MS, et al. [42]2015The main predictors of low adherence are female
sex, younger age, new drug prescription, low education level, and low social
status
Mansyur CL, et al. [63]2015Women show more social barriers and less
support, leading to lower self-care adherence
Iglay K, et al. [44]2016Female sex is an independent predictor of low
medication adherence for sulfonylureas
Brunton SA, et al. [41]2017Low adherence is associated with a higher
hospitalization rate and a negative impact on costs
Hofer R, et al. [62]2017There is a strong relationship between improved
satisfaction with medication knowledge and increased adherence
Kim YY, et al. [36]2018Low adherence to antihyperglycemic medications
is associated with an increased risk of all-cause mortality and
cardiovascular events
McGovern A, et al. [43]2018Adherence differs among various types of drugs
prescribed, being higher for metformin, while non-adherence rate varies
across other oral agents
Choi YJ, et al. [53]2018Younger age, female sex, and depression are
predictors of low adherence
Bhaloo T, et al. [60]2018Women are more motivated than men when
physicians use empathic communication
Bhuyan SS, et al. [65]2018Female sex is associated with low medication
adherence due to cost-related factors
Horii T, et al. [48]2019Adherence is higher in male patients and in
therapy schemes involving more than three medications
Xu N, et al. [49]2020Longer disease duration (more than five years)
is a predictor of good adherence
Demoz GT, et al. [52]2020The coexistence of diabetic complications is a
contributor to low adherence
Aronson BD, et al. [59]2020Diabetes distress and depressive disorders, more
frequent in females, have a role in low medication adherence, suggesting an implication in sex disparities
Beernink JM, et al. [40]2021Medication adherence is important to control
healthcare system costs arising from hospitalizations due to disease
progression and complications
Jankowska-Polanska B, et al. [51]2021The coexistence of hypertension alongside
diabetes lowers the level of adherence compared to patients who only suffer
from diabetes
Hypercholesterolaemia
Mann DM, et al. [92]2010Women were 7% more likely to be
non-adherent than men
Lewey J, et al. [91]2013Female gender
increased the risk of non-adherence by 10%
Stroes ES, et al. [102]2015Female sex is a
known risk factor for SAMS, which significantly contributes to statin
discontinuation
Ofori-Asenso R, et al. [94]2018Female gender
was associated with lower adherence to statin therapy among older patients
(>65 y.o.)
Hope HF, et al. [93]2019Male gender was
associated with higher adherence to statin therapy for primary prevention
Ingersgaard MV, et al. [89]2020Gender is one of
the main predictors of low adherence
Olmastroni E, et al. [67]2020Women showed
lower adherence to statin therapy after initiation
Arterial hypertension
Erkens JA, et al. [129]2005Female gender was associated with a lower rate
of adherence to antihypertensive therapy one year after its prescription
Brown DW, et al. [107]2007Women with high
blood pressure were more frequently treated but were less likely to achieve
blood pressure goals, especially in systolic blood pressure, particularly at
older ages and in presence of comorbidities such as CVD, stroke, and chronic
kidney disease
Friedman O, et al. [132]2010Female sex,
absence of comorbidities, and high income were associated with higher
compliance with antihypertensive treatment among elderly patients
Mancia G, et al. [128]2014Males showed
better adherence to blood pressure therapy and a 10% lower risk of
discontinuation
Tajeu, et al. [131]2016Male sex was one
of the risk factors of lower adherence to antihypertensive treatment
Qvarnstrom M, et al. [133]2016Male sex,
younger age, lower systolic blood pressure at prescription, and lower income
were related to lower adherence to antihypertensive treatment in newly
prescribed patients
Burnier M [126]2017Gender is among
determinants influencing adherence to antihypertensive therapy
Yang Q, et al. [125]2017Female sex,
non-Hispanic white ethnicity, use of more than one antihypertensive drug, and
the presence of diabetes or dyslipidemia were associated with higher
adherence
Rea F, et al. [127]2020Women were
associated with higher rates of antihypertensive therapy interruption after
first prescription
Biffi A, et al. [134]2020No relation
between sex and medication adherence was observed. A subgroup analysis showed
higher adherence in men only in older age groups (>65 y)
Cardiovascular Diseases
Kirchmayer U, et al. [163]2012The adherence rates were 90.5% for antiplatelet
agents, 60% for beta-blockers, 78.1% for ACE-Is/ARBs, and 77.8% for statins;
women were 16% less likely to be adherent than men
Lauffenburger JC, et al. [165]2014Black women, and
to a lesser extent, white women, had lower adherence to ACE-Is/ARBs,
beta-blockers, and statins compared to white men
Backholer K, et al. [143]2017Low
socioeconomic status poses a greater additional cardiovascular risk in women
compared to men
Goldstein JM, et al. [142]2019Women have
poorer disease awareness, less social support, and a higher prevalence of
depressive disorders, contributing to limited access to care and widening sex
inequalities
Carcel C, et al. [149]2020Clear sex
disparities have not emerged in studies focusing on acute treatment outcomes
after stroke
Soldati S, et al. [162]2021Comorbidities
and older age were predictive factors for low adherence
Hyun K, et al. [153]2021Women were less
likely to be consistent with secondary prevention medications compared to
men
Heart failure
Roe CM, et al. [193]2000Men showed higher adherence to ACEIs six months
after hospital discharge
Bagchi AD, et al. [191]2007Male patients
were less adherent to HF treatment
Lamb DA, et al. [189]2009Women were more
adherent to ACEIs/ARBs therapy after their first hospital admission for heart
failure
Granger BB, et al. [192]2009Women were less
adherent compared to men to HF treatment. This difference was more consistent
considering women younger than 75 years
Dunlay SM, et al. [190]2011Males were more
likely to be non-adherent to ACEIs/ARBs compared to women, but this
relationship between sex and adherence was not observed for other drug
classes
Kayibanda JF, et al. [188]2018Men were less
likely than women to be adherent one year after initiation of evidence base
HF drug regimen
Gurgoze MT, et al. [194]2021No Sex
difference in adherence to HF medication was found
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Venditti, V.; Bleve, E.; Morano, S.; Filardi, T. Gender-Related Factors in Medication Adherence for Metabolic and Cardiovascular Health. Metabolites 2023, 13, 1087. https://doi.org/10.3390/metabo13101087

AMA Style

Venditti V, Bleve E, Morano S, Filardi T. Gender-Related Factors in Medication Adherence for Metabolic and Cardiovascular Health. Metabolites. 2023; 13(10):1087. https://doi.org/10.3390/metabo13101087

Chicago/Turabian Style

Venditti, Vittorio, Enrico Bleve, Susanna Morano, and Tiziana Filardi. 2023. "Gender-Related Factors in Medication Adherence for Metabolic and Cardiovascular Health" Metabolites 13, no. 10: 1087. https://doi.org/10.3390/metabo13101087

APA Style

Venditti, V., Bleve, E., Morano, S., & Filardi, T. (2023). Gender-Related Factors in Medication Adherence for Metabolic and Cardiovascular Health. Metabolites, 13(10), 1087. https://doi.org/10.3390/metabo13101087

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