Abstract
Aims
Emerging evidence suggests sex-specific differences in platelet biology and clinical responses to antiplatelet agents. Light transmission aggregometry (LTA) represents the historical gold standard for the assessment of platelet reactivity but is influenced by pre-analytical and analytical variables. We analysed a large dataset of patients undergoing LTA using a standardized methodology to investigate the impact of sex on platelet reactivity with or without antiplatelet therapy.
Methods and results
Between 2004 and 2022, 11,913 patients sequentially underwent LTA assessments following stimulation with adenosine diphosphate (ADP) (2 µM), collagen (2 µg/mL), arachidonic acid (AA, 0.5 mM), and epinephrine (10 µM). After applying study entry criteria, 5687 patients were included: 428 healthy volunteers (HV, F = 273; M = 155), 1055 controls (CTR; F = 725; M = 330), 3289 aspirin-treated patients (ASA; F = 2058; M = 1231), 430 clopidogrel-treated patients (CLOP; F = 272; M = 158), and 485 patients on dual antiplatelet therapy (DAPT; F = 166; M = 319). Within each group, results were analysed and compared between males and females.
Females exhibited significantly greater platelet reactivity in response to ADP compared to males in the HV (P = 0.004), CTR (P < 0.0001), ASA (P < 0.0001), and CLOP (P < 0.018) groups, but not in the DAPT group. Among aspirin-treated patients, females showed increased platelet reactivity (P < 0.0001) in response to collagen, compared with males.
Conclusion
Females exhibit heightened baseline ADP-dependent platelet reactivity and a diminished response to aspirin and clopidogrel monotherapy compared to males.
Keywords: aspirin, Clopidogrel, Antiplatelet therapy, Sex, P2Y12 inhibitors
Introduction
Antiplatelet agents, such as aspirin and P2Y12 inhibitors (i.e. clopidogrel, prasugrel, and ticagrelor), used as monotherapy or in combination (dual antiplatelet therapy, DAPT), play a pivotal role in the primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD).1 However, their use is inherently associated with an increased risk of bleeding, which carries significant prognostic implications and, in certain circumstances, may outweigh their therapeutic benefits.2
Growing evidence highlights substantial variability in individual responses to antiplatelet therapy, which significantly influences the risk-benefit balance of these treatment.3 This variability in response to antiplatelet therapy is particularly pronounced in clopidogrel-treated patients, but may also occur with other antiplatelet agents.4 Patients with poor responsiveness to antiplatelet therapy, resulting in high platelet reactivity (HPR), face an increased risk of thrombotic events compared to responders.3 Conversely, patients with heightened responsiveness, leading to low platelet reactivity (LPR), are at greater risk of bleeding complications without additional reductions in thrombotic risk.3 This variability underscores the limitations of a ‘one-size-fits-all’ approach and highlights the need for personalized strategies to optimize the efficacy and safety of antiplatelet agents.5 To this extent, international consensus documents recommend the selective use of platelet function (PFT) or genetic testing in specific clinical scenarios to guide antiplatelet therapy.4,6 Notably, a recent analysis of a large cohort of patients undergoing PFT with light transmission aggregometry (LTA) showed hyper-reactive or hypo-reactive platelet phenotypes, not only in patients with concomitant antiplatelet therapy, but also in controls (CTR) and healthy volunteers (HV).7
Several clinical factors, including advanced age, diabetes mellitus (DM), chronic kidney disease (CKD), and certain ethnicities, have been identified as contributors to variability in platelet reactivity and are well-recognized indicators of thrombotic risk.5,8 However, the influence of sex on platelet reactivity and its prognostic significance remains a subject of debate.9,10 Emerging evidence suggests sex-specific differences in platelet biology and responses to antiplatelet agents, yet no specific recommendations addressing these differences have been provided in international guidelines or consensus statements.11 Moreover, existing studies comparing platelet reactivity between men and women are often limited by small sample sizes and methodological inconsistencies, which are susceptible to intra- and interlaboratory variability.12,13
On this background, we used a large single-centre dataset of patients undergoing platelet reactivity assessment by the gold standard LTA with a standardized and consistent methodology to investigate potential sex differences in platelet reactivity in subjects with or without concomitant antiplatelet therapy.
Materials and methods
This retrospective analysis involved 11 913 individuals who underwent PFT at the Policlinico Umberto I University Hospital, Unit of Advanced Diagnosis Platelet Disorders (Rome, Italy) between 1 January 2004, and 31 December 2022. The complete database, as well as the detailed description of blood sampling and laboratory assessment procedures has been previously reported.7 In brief, LTA using a standardized methodology were assessed at 4 min and reported as percentage of platelet aggregation (PA%), using the following platelet agonists: (i) adenosine diphosphate (ADP) at the concentration of 2μM; collagen at the concentration of 2 μg/mL; epinephrine at the concentrations of 10 μM, arachidonic acid (AA) at the concentration of 0.5 and 0.75 μM.7
The population was categorized into five groups: (i) HV, including individuals without cardiovascular (CV) risk factors or concomitant antiplatelet therapy; (ii) CTR, including individuals with at least one CV risk factor (hypertension, DM, hypercholesterolaemia, previous thrombotic event) but not treated with antiplatelet therapy; (iii) patients on low-dose aspirin 75–150 mg/day (ASA); (iv) patients on clopidogrel 75 mg/day (CLOP); (v) patients on DAPT treated with a combination of ASA (75–150 mg/day) and CLOP (75 mg/day). Antiplatelet therapy, for either primary or secondary prevention, was prescribed at the treating physician’s discretion. In this study, such categories were divided into females (F) and males (M).
Exclusion criteria have been reported in detail in a previous study.7 Briefly, all patients presenting platelet disorders, abnormal bleeding, and every subject affected by diseases or taking therapies influencing PFT and those taking different dosages of antiplatelet therapies nor compliant.
Study endpoints
The primary endpoint of the study was the difference in PA% measured 4-minute after exposure to the various platelet agonists (ADP, collagen, epinephrine, and AA) between females and males within each of the five groups of patients (HV, CTR, ASA, CLOP, and DAPT).
Statistical analysis
Data were reported as median and interquartile ranges (IQRs) of the PA%. Patient categorical variables are represented as frequencies and percentages. Comparisons between categorical variables were conducted using two-tailed Fisher’s exact test or Pearson’s χ² test. Wilcoxon-Mann–Whitney U test was used for comparing continuous variables not normally distributed, as assessed by the Kolmogorov–Smirnov test. To mitigate selection bias across all groups (except HV), the Inverse Probability of Treatment Weighting (IPTW) method with Covariate Balancing Propensity Score (CBPS) was employed.14 In this approach, each patient is assigned a weight based on their propensity score to create a pseudo-population with balanced baseline characteristics. Specifically, CBPS optimizes both propensity score estimation and covariate balance between treatment groups. Following weighting, covariates were well balanced between treated and untreated groups. Importantly, in this weighted pseudo-population generated by IPTW, summary statistics no longer reflect raw patient counts but instead represent the sum of the weights assigned to individuals. Each subject contributes proportionally to their weight, and tables showing descriptive statistics are therefore interpreted in terms of weighted totals. Age, hypertension, hypercholesterolaemia, smoking, diabetes, CAD/myocardial infarction, peripheral artery disease, previous stroke/transient ischaemic attack (TIA), retinal thrombosis, carotid obstruction, beta-blocking agents, calcium channel blocker, angiotensin-converting enzyme-I, angiotensin receptor blockers/angiotensin 2, nitrates, statin, antidiabetic drugs, diuretic, omega-3 fatty acids were used for weights estimation. To address missing data on age, K-nearest neighbours imputation was applied. The absolute standardized mean differences (ASMD) were reported in order to assess balancing across groups; variables with ASMD < 0.1 were considered as balanced; weighted means (SD) and percentages were reported after IPTW, weighted medians and IQR for response to ADP (2 µM), collagen (2 µg/mL), epinephrine (10 µM), AA (0.5 mM), and AA (0.75 mM) were reported. A P-value lower than 0.05 was deemed statistically significant. All analyses were conducted using GraphPad Prism 9, version 9.1.0 and R 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). A pre-specified secondary analysis according to age (<50 years vs. >50 years) was performed within each of the five groups of patients.
Results
A total of 5687 patients met study entry criteria: 428 HV (F = 273; M = 155), 1055 CTR (F = 725; M = 330); 3289 patients undergoing ASA (F = 2058; M = 1231), 430 patients undergoing clopidogrel (F = 272; M = 158), and 485 patients undergoing DAPT (F = 166; M = 319). Baseline characteristics and concomitant medications before and after IPTW are reported in Supplementary material online, Table S1 for the HV group and in Table 1 for the CTR, ASA, CLOP, and DAPT groups. No significant differences were observed between males and females in the HV group. However, in other groups, baseline characteristics such as hypertension, smoking, previous myocardial infarction, and prior ischaemic stroke or TIA exhibited some imbalances, which were adjusted after IPTW.
Table 1.
Baseline characteristics and concomitant medications according to sex before and after IPTW in controls (CTR group), patients on aspirin therapy (ASA group), patients on clopidogrel therapy (CLOP group), and patients on aspirin plus clopidogrel therapy (DAPT group)
| CTR | |||||||
|---|---|---|---|---|---|---|---|
| Before IPTW | After IPTW | ||||||
| F | M | P | ASMD | F | M | ASMD | |
| n/sum of weights (IPTW) | 725 | 330 | 1058.00 | 1058.00 | |||
| Age (mean ± SD, years) | 63.98 (12.92) | 63.38 (14.81) | 0.529 | 0.043 | 63.96 (12.58) | 63.96 (14.48) | 0.028 |
| Clinical conditions and risk factors | |||||||
| Hypertension, n (%) | 422 (58.2) | 227 (68.8) | 0.001 | 0.221 | 654.6 (61.9) | 654.6 (61.9) | <0.001 |
| Hypercholesterolaemia, n (%) | 276 (38.1) | 110 (33.3) | 0.148 | 0.099 | 388.5 (36.7) | 388.5 (36.7) | <0.001 |
| Smoking, n (%) | 152 (21.0) | 76 (23.0) | 0.468 | 0.050 | 225.0 (21.3) | 225.0 (21.3) | <0.001 |
| Diabetes, n (%) | 67 (9.2) | 41 (12.4) | 0.125 | 0.103 | 105.5 (10.0) | 105.5 (10.0) | <0.001 |
| CAD/MI, n (%) | 5 (0.7) | 5 (1.5) | 0.301 | 0.079 | 10.8 (1.0) | 10.8 (1.0) | <0.001 |
| PAD, n (%) | 9 (1.2) | 1 (0.3) | 0.186 | 0.107 | 9.4 (0.9) | 9.4 (0.9) | <0.001 |
| Previous stroke/TIA, n (%) | 197 (27.2) | 99 (30.0) | 0.375 | 0.063 | 294.2 (27.8) | 294.2 (27.8) | <0.001 |
| Retinal thrombosis, n (%) | 10 (1.4) | 3 (0.9) | 0.765 | 0.044 | 12.0 (1.1) | 12.0 (1.1) | <0.001 |
| Carotid obstruction, n (%) | 47 (6.5) | 17 (5.2) | 0.487 | 0.057 | 62.2 (5.9) | 62.2 (5.9) | <0.001 |
| Medications | |||||||
| Beta-blocking agents, n (%) | 125 (17.2) | 45 (13.6) | 0.149 | 0.100 | 169.3 (16.0) | 169.3 (16.0) | <0.001 |
| Calcium channel blocker, n (%) | 117 (16.1) | 60 (18.2) | 0.424 | 0.054 | 171.9 (16.2) | 171.9 (16.2) | <0.001 |
| ACE-I, n (%) | 119 (16.4) | 80 (24.2) | 0.003 | 0.195 | 208.3 (19.7) | 208.3 (19.7) | <0.001 |
| ARBs/angiotensin 2, n (%) | 136 (18.8) | 84 (25.5) | 0.014 | 0.162 | 219.9 (20.8) | 219.9 (20.8) | <0.001 |
| Nitrates, n (%) | 9 (1.2) | 6 (1.8) | 0.575 | 0.047 | 14.8 (1.4) | 14.8 (1.4) | <0.001 |
| Statin, n (%) | 157 (21.7) | 55 (16.7) | 0.068 | 0.127 | 207.8 (19.6) | 207.8 (19.6) | <0.001 |
| Antidiabetic drugs, n (%) | 61 (8.4) | 38 (11.5) | 0.112 | 0.104 | 97.5 (9.2) | 97.5 (9.2) | <0.001 |
| Diuretic, n (%) | 43 (5.9) | 15 (4.5) | 0.387 | 0.062 | 55.3 (5.2) | 55.3 (5.2) | <0.001 |
| Omega-3, n (%) | 0.03 (0.16) | 0.05 (0.22) | 0.050 | 0.123 | 0.04 (0.18) | 0.04 (0.18) | <0.001 |
| ASA | |||||||
|---|---|---|---|---|---|---|---|
| Before IPTW | After IPTW | ||||||
| F | M | P | ASMD | F | M | ASMD | |
| n/sum of weights (IPTW) | 2058 | 1231 | 3288.50 | 3288.50 | |||
| Age (mean ± SD, years) | 67.95 (12.03) | 69.16 (11.62) | 0.012 | 0.102 | 68.31 (10.91) | 68.31 (11.12) | 0.002 |
| Clinical conditions and risk factors | |||||||
| Hypertension, n (%) | 1179 (57.3) | 759 (61.7) | 0.014 | 0.089 | 1930.2 (58.7) | 1930.2 (58.7) | <0.001 |
| Hypercholesterolaemia, n (%) | 734 (35.7) | 502 (40.8) | 0.004 | 0.105 | 1234.4 (37.5) | 1234.4 (37.5) | <0.001 |
| Smoking, n (%) | 229 (11.1) | 232 (18.8) | <0.001 | 0.218 | 453.1 (13.8) | 453.1 (13.8) | <0.001 |
| Diabetes, n (%) | 175 (8.5) | 139 (11.3) | 0.010 | 0.093 | 313.0 (9.5) | 313.0 (9.5) | <0.001 |
| CAD/MI, n (%) | 30 (1.5) | 27 (2.2) | 0.129 | 0.055 | 55.1 (1.7) | 55.1 (1.7) | <0.001 |
| PAD, n (%) | 21 (1.0) | 12 (1.0) | 1.000 | 0.005 | 32.9 (1.0) | 32.9 (1.0) | <0.001 |
| Previous stroke/TIA, n (%) | 534 (25.9) | 327 (26.6) | 0.712 | 0.014 | 862.8 (26.2) | 862.8 (26.2) | <0.001 |
| Retinal thrombosis, n (%) | 43 (2.1) | 20 (1.6) | 0.430 | 0.034 | 66.1 (2.0) | 66.1 (2.0) | <0.001 |
| Carotid obstruction, n (%) | 117 (5.7) | 125 (10.2) | <0.001 | 0.166 | 249.4 (7.6) | 249.4 (7.6) | <0.001 |
| Medications | |||||||
| Beta-blocking agents, n (%) | 328 (15.9) | 229 (18.6) | 0.049 | 0.071 | 553.8 (16.8) | 553.8 (16.8) | <0.001 |
| Calcium channel blocker, n (%) | 380 (18.5) | 206 (16.7) | 0.221 | 0.045 | 600.6 (18.3) | 600.6 (18.3) | <0.001 |
| ACE-I, n (%) | 397 (19.3) | 343 (27.9) | <0.001 | 0.203 | 743.2 (22.6) | 743.2 (22.6) | <0.001 |
| ARBs/angiotensin 2, n (%) | 459 (22.3) | 250 (20.3) | 0.189 | 0.049 | 702.0 (21.3) | 702.0 (21.3) | <0.001 |
| Nitrates, n (%) | 50 (2.4) | 69 (5.6) | <0.001 | 0.162 | 114.2 (3.5) | 114.2 (3.5) | <0.001 |
| Statin, n (%) | 560 (27.2) | 382 (31.0) | 0.021 | 0.084 | 936.6 (28.5) | 936.6 (28.5) | <0.001 |
| Antidiabetic drugs, n (%) | 154 (7.5) | 108 (8.8) | 0.206 | 0.047 | 262.6 (8.0) | 262.6 (8.0) | <0.001 |
| Diuretic, n (%) | 156 (7.6) | 90 (7.3) | 0.837 | 0.010 | 247.6 (7.5) | 247.6 (7.5) | <0.001 |
| Omega-3, n (%) | 0.03 (0.18) | 0.07 (0.25) | <0.001 | 0.161 | 0.05 (0.21) | 0.05 (0.21) | <0.001 |
| CLOP | |||||||
|---|---|---|---|---|---|---|---|
| Before IPTW | After IPTW | ||||||
| F | M | P | ASMD | F | M | ASMD | |
| n/sum of weights (IPTW) | 272 | 158 | 431.8 | 433.8 | |||
| Age (mean ± SD, y) | 71.32 (9.28) | 73.46 (9.90) | 0.034 | 0.223 | 72.20 (8.72) | 72.12 (10.16) | 0.009 |
| Clinical conditions and risk factors | |||||||
| Hypertension, n (%) | 180 (66.2) | 106 (67.1) | 0.916 | 0.019 | 288.5 (66.8) | 290.5 (67.0) | 0.003 |
| Hypercholesterolaemia, n (%) | 147 (54.0) | 53 (33.5) | <0.001 | 0.422 | 203.7 (47.2) | 205.7 (47.4) | 0.005 |
| Smoking, n (%) | 36 (13.2) | 50 (31.6) | <0.001 | 0.452 | 90.5 (20.9) | 90.5 (20.9) | 0.002 |
| Diabetes, n (%) | 30 (11.0) | 25 (15.8) | 0.178 | 0.141 | 50.1 (11.6) | 52.1 (12.0) | 0.012 |
| CAD/MI, n (%) | 9 (3.3) | 4 (2.5) | 0.776 | 0.046 | 13.5 (3.1) | 13.5 (3.1) | 0.001 |
| PAD, n (%) | 0 (0.0) | 2 (1.3) | 0.134 | 0.160 | 0.0 (0.0) | 2.0 (0.5) | 0.058 |
| Previous stroke/TIA, n (%) | 157 (57.7) | 67 (42.4) | 0.003 | 0.310 | 225.8 (52.3) | 225.8 (52.1) | 0.005 |
| Retinal thrombosis, n (%) | 7 (2.6) | 1 (0.6) | 0.268 | 0.155 | 8.9 (2.1) | 8.9 (2.1) | 0.001 |
| Carotid obstruction, n (%) | 26 (9.6) | 10 (6.3) | 0.282 | 0.120 | 37.3 (8.6) | 37.3 (8.6) | 0.002 |
| Medications | |||||||
| Beta-blocking agents, n (%) | 62 (22.8) | 28 (17.7) | 0.222 | 0.126 | 91.7 (21.2) | 91.7 (21.1) | 0.003 |
| Calcium channel blocker, n (%) | 50 (18.4) | 34 (21.5) | 0.450 | 0.079 | 81.8 (18.9) | 81.8 (18.9) | 0.002 |
| ACE-I, n (%) | 42 (15.4) | 29 (18.4) | 0.501 | 0.078 | 69.1 (16.0) | 69.1 (15.9) | 0.002 |
| ARBs/angiotensin 2, n (%) | 77 (28.3) | 49 (31.0) | 0.583 | 0.059 | 128.9 (29.9) | 128.9 (29.7) | 0.003 |
| Nitrates, n (%) | 12 (4.4) | 5 (3.2) | 0.615 | 0.065 | 19.7 (4.6) | 19.7 (4.5) | 0.001 |
| Statin, n (%) | 116 (42.6) | 44 (27.8) | 0.003 | 0.314 | 165.9 (38.4) | 167.9 (38.7) | 0.006 |
| Antidiabetic drugs, n (%) | 29 (10.7) | 24 (15.2) | 0.174 | 0.135 | 48.9 (11.3) | 50.9 (11.7) | 0.012 |
| Diuretic, n (%) | 28 (10.3) | 4 (2.5) | 0.002 | 0.321 | 29.8 (6.9) | 29.8 (6.9) | 0.001 |
| Omega-3, n (%) | 0.08 (0.27) | 0.10 (0.30) | 0.392 | 0.084 | 0.09 (0.29) | 0.09 (0.29) | 0.001 |
| DAPT | |||||||
|---|---|---|---|---|---|---|---|
| Before IPTW | After IPTW | ||||||
| F | M | P | ASMD | F | M | ASMD | |
| n/sum of weights (IPTW) | 165 | 319 | 501.1 | 503.1 | |||
| Age (mean ± SD, years) | 69.94 (11.73) | 61.97 (13.32) | <0.001 | 0.635 | 62.87 (13.54) | 62.91 (10.72) | 0.004 |
| Clinical conditions and risk factors | |||||||
| Hypertension, n (%) | 88 (53.3) | 174 (54.5) | 0.848 | 0.024 | 274.2 (54.7) | 275.2 (54.7) | <0.001 |
| Hypercholesterolaemia, n (%) | 59 (35.8) | 165 (51.7) | 0.001 | 0.326 | 234.5 (46.8) | 235.5 (46.8) | <0.001 |
| Smoking, n (%) | 10 (6.1) | 20 (6.3) | 1.000 | 0.009 | 38.8 (7.7) | 39.8 (7.9) | 0.007 |
| Diabetes, n (%) | 14 (8.5) | 23 (7.2) | 0.594 | 0.047 | 51.7 (10.3) | 52.7 (10.5) | 0.006 |
| CAD/MI, n (%) | 43 (26.1) | 23 (7.2) | <0.001 | 0.523 | 66.4 (13.3) | 66.4 (13.2) | 0.002 |
| PAD, n (%) | 0 (0.0) | 1 (0.3) | 1.000 | 0.079 | 0.0 (0.0) | 1.0 (0.2) | 0.050 |
| Previous stroke/TIA, n (%) | 29 (17.6) | 18 (5.6) | <0.001 | 0.379 | 44.8 (8.9) | 44.8 (8.9) | 0.001 |
| Retinal thrombosis, n (%) | 0 (0.0) | 1 (0.3) | 1.000 | 0.079 | 0.0 (0.0) | 1.0 (0.2) | 0.050 |
| Carotid obstruction, n (%) | 7 (4.2) | 12 (3.8) | 0.808 | 0.025 | 32.0 (6.4) | 33.0 (6.6) | 0.009 |
| Medications | |||||||
| Beta-blocking agents, n (%) | 25 (15.2) | 105 (32.9) | <0.001 | 0.425 | 128.0 (25.5) | 129.0 (25.6) | 0.002 |
| Calcium channel blocker, n (%) | 33 (20.0) | 18 (5.6) | <0.001 | 0.440 | 60.4 (12.1) | 61.4 (12.2) | 0.005 |
| ACE-I, n (%) | 27 (16.4) | 84 (26.3) | 0.016 | 0.245 | 125.8 (25.1) | 125.8 (25.0) | 0.002 |
| ARBs/angiotensin 2, n (%) | 33 (20.0) | 30 (9.4) | 0.002 | 0.303 | 47.9 (9.6) | 47.9 (9.5) | 0.001 |
| Nitrates, n (%) | 11 (6.7) | 31 (9.7) | 0.308 | 0.111 | 39.1 (7.8) | 39.1 (7.8) | 0.001 |
| Statin, n (%) | 48 (29.1) | 140 (43.9) | 0.002 | 0.311 | 205.1 (40.9) | 206.1 (41.0) | 0.001 |
| Antidiabetic drugs, n (%) | 10 (6.1) | 14 (4.4) | 0.508 | 0.075 | 38.5 (7.7) | 39.5 (7.8) | 0.008 |
| Diuretic, n (%) | 12 (7.3) | 26 (8.2) | 0.859 | 0.033 | 42.4 (8.5) | 42.4 (8.4) | 0.001 |
| Omega-3, n (%) | 0.09 (0.29) | 0.18 (0.39) | 0.008 | 0.267 | 0.13 (0.34) | 0.13 (0.34) | 0.002 |
ACE, angiotensin-converting enzyme; ARBs, angiotensin receptor blockers; CAD, coronary artery disease; F, females; M, males; MI, myocardial infarction; PAD, peripheral artery disease; SD, standard deviation; TIA, transient ischaemic attack; IPTW, inverse probability of treatment weighting; ASMD, absolute standardized mean differences.
Healthy volunteers
Median PA% was significantly increased in HV in response to ADP (85 [45–93] vs. 70 [0–91]; P = 0.004) in females compared to males. There were no differences between females and males in response to collagen (93 [89–95] vs. 93 [89–95]; P = 0.903), epinephrine (90 [78–95] vs. 90 [80–95]; P = 0.446), and AA (93 [89–95] vs. 93 [89–97]; P = 0.442) (Figure 1 and see Supplementary material online, Table S2).
Figure 1.
Scatter dot plot of platelet reactivity assessed by light transmission aggregometry following adenosine diphosphate 2 µM, collagen 2 µg/mL, epinephrine 10 µM, and arachidonic acid 0.5 or 0.75 µM in healthy volunteers stratified by sex. Lines represent median with IQR range. N refers to the number of patients analysed. HV, healthy volunteers; IQR, interquartile range; F, females; M, males; LTA, light transmission aggregometry; ADP, adenosine diphosphate; COLL, collagen; EPI, epinephrine; AA, arachidonic acid; NS, non-significant; PA, platelet aggregation.
At the analysis according to age, females <50 years showed lower PA% in response to epinephrine (88 [75–93] vs. 90 [86–95]; P = 0.005), compared with females >50 years (Table 2). There were no differences in PA% between females <50 or >50 years old in response to other stimuli (Table 2). There were no differences in PA% according to age in response to any stimuli in males (Table 2).
Table 2.
Summary of statistical parameters for healthy volunteers based on age (<50 years old vs. > 50 years old.) in response to ADP (2 µM), collagen (2 µg/mL), epinephrine (10 µM), and arachidonic acid (0.5 mM)
| Agonist (concentration) N° | Median (IQR) | P-value | |
|---|---|---|---|
| female > 50 y.o. | female < 50 y.o. | ||
| ADP (2 µM) 81/146 | 88 (49–93) | 82 (40–92) | 0.190 |
| Epinephrine (10 µM) 81/146 | 90 (86–95) | 88 (75–93) | 0.005 |
| Collagen (2 µg/mL) 81/146 | 94 (90–96) | 93 (89–95) | 0.355 |
| AA (0.5 mM) 76/128 | 94 (90–95) | 93 (88–95) | 0.097 |
| males > 50 y.o. | male < 50 y.o. | ||
| ADP (2 µM) 49/74 | 70 (0–91) | 75 (20–91) | 0.632 |
| Epinephrine (10 µM) 49/74 | 89 (55–93) | 90 (80–94) | 0.160 |
| Collagen (2 µg/mL) 48/74 | 92 (88–95) | 93 (90–95) | 0.107 |
| AA (0.5 mM) 44/59 | 92 (88–95) | 94 (90–99) | 0.348 |
Values are reported as platelet aggregation percentage at 4 min through mean ± standard deviation (SD), median (25–75% interquartile ranges).
ADP, adenosine diphosphate; IQR, interquartile range. y.o., years old.
Controls
Weighted median PA% was significantly increased in CTR in response to ADP (90 [70–94] vs. 86 [37–93]; P < 0.0001) in females compared with males (Figure 2A and see Supplementary material online, Table S3). There was a statistically significant but numerically minimal difference in median PA% between females and males in response to collagen (92 [89–95] vs. 93 [90–95]; P = 0.009) (Figure 2B and see Supplementary material online, Table S3). There were no differences in mean PA% between females and males in response to epinephrine (90 [84–94] vs. 90 [83–95]; P = 0.732) (Figure 2C and see Supplementary material online, Table S3), and AA (92 [90–95] vs. 93 [90–95]; P = 0.157) (Figure 3A and see Supplementary material online, Table S3).
Figure 2.
Scatter dot plot of platelet reactivity assessed by light transmission aggregometry following ADP 2 µM (A), collagen 2 µg/mL (B), and epinephrine 10 µM (C) in the control group, aspirin group, clopidogrel group, dual antiplatelet therapy group populations stratified by sex. Lines represent median with interquartile range range. N refers to the number of patients analysed. CTR, control group; ASA, aspirin group; CLOP, clopidogrel group; DAPT, dual antiplatelet therapy group; IQR, interquartile range; F, females; M, males; LTA, light transmission aggregometry; ADP, adenosine diphosphate; COLL, collagen; EPI, epinephrine; AA, arachidonic acid; NS, non-significant; PA, platelet aggregation.
Figure 3.
Scatter dot plot of platelet reactivity assessed by light transmission aggregometry following AA 0.5 mM in the control group and in the clopidogrel group populations (A) or AA 0.75 mM for the aspirin group and the dual antiplatelet therapy group populations (B), stratified by sex. Lines represent median with interquartile range range. N refers to the number of patients analysed. Abbreviations: CTR, control group; ASA, aspirin group; CLOP, clopidogrel group; DAPT, dual antiplatelet therapy group; IQR, interquartile range; F, females; M, males; LTA, light transmission aggregometry; ADP, adenosine diphosphate; COLL, collagen; EPI, epinephrine; AA, arachidonic acid; NS, non-significant; PA, platelet aggregation.
Aspirin-treated patients
Weighted median PA% in ASA patients was significantly increased in females compared to males in response to ADP (49 [20–68] vs. 40 [0–60]; P < 0.0001) (Figure 2A and see Supplementary material online, Table S3) and collagen (42 [24–68] vs. 35 [20–57]; P < 0.001) (Figure 2B and see Supplementary material online, Table S3). There was a statistically significant but numerically minimal difference in median PA% between females and males in response to AA (15 [0–25] vs. 14 [0–22]; P = 0.009) (Figure 3B and see Supplementary material online, Table S3). There were no differences in median PA% between females and males in response to epinephrine (40 [0–56] vs. 39 [0–55]; P = 0.18) (Figure 2C and see Supplementary material online, Table S3).
Clopidogrel-treated patients
Weighted median PA% in CLOP patients was significantly increased in females compared to males in response to ADP (0 [0–65] vs. 0 [0–40; P = 0.018) (Figure 2A and see Supplementary material online, Table S3), but not to collagen (88 [82–90] vs. 88 [82–91]; P = 0.568) (Figure 2B and see Supplementary material online, Table S3), epinephrine (90 [80–93] vs. 88 [72–92]; P = 0.134) (Figure 2C and see Supplementary material online, Table S3), and AA (90 [86–93] vs. 90 [85–93]; P = 0.476) (Figure 3A and see Supplementary material online, Table S3).
Dual antiplatelet therapy-treated patients
There were no differences in weighted median PA% between females and males in DAPT patients in response to ADP (0 [0–30] vs. 0 [0–10]; P = 0.169) (Figure 2A an see Supplementary material online, Table S3), collagen (18 [0–40] vs. 17 [0–28]; P = 0.242) (Figure 2B and see Supplementary material online, Table S3), epinephrine (0 [0–55] vs. 0 [0–46]; P = 0.556) (Figure 2C and see Supplementary material online, Table S3), and AA (15 [12–50] vs. 0 [0–20]; P = 0.091) (Figure 3B and see Supplementary material online, Table S3).
Discussion
To the best of our knowledge, this is the largest study to evaluate sex differences in platelet reactivity using the gold standard LTA with multiple agonists, following a standardized and consistent methodology, both in the presence and absence of antiplatelet therapy. The main findings of the study can be summarized as follows: (i) there was significant interindividual variability in platelet reactivity in response to all agonists in both females and males; (ii) among CTR and HV, females showed increased platelet reactivity as assessed by LTA following ADP stimulus, compared to males; (iii) among patients treated with clopidogrel, females showed higher platelet reactivity assessed by LTA following ADP, compared with males; (iv) among patients treated with aspirin, females showed higher platelet reactivity assessed by LTA following ADP, collagen, and AA stimulus, compared with males; (v) platelet reactivity in response to all stimuli was similar between females and males in the DAPT group; (vi) females in the HV group older than 50 exhibited increased platelet reactivity in response to epinephrine compared to those younger than 50.
Platelet inhibition is the cornerstone for the treatment and prevention of thrombotic complications in ASCVD.1 In this context, two main classes of oral antiplatelet drugs are currently recommended: aspirin and P2Y12 receptor antagonists (i.e. clopidogrel, prasugrel, or ticagrelor).6 Aspirin exerts its antiplatelet effect by irreversibly inhibiting Cyclooxygenase-1 (COX-1) in platelets, leading to a reduction in thromboxane A2 (TXA2) production, a potent promoter of platelet activation.15 P2Y12 inhibitors block ADP-mediated platelet activation.15 Activation of the P2Y12 receptor triggers a signalling cascade essential for platelet activation and fibrinogen binding, ultimately facilitating PA and thrombus formation.
A substantial body of evidence has demonstrated that oral antiplatelet agents, particularly the P2Y12 inhibitor clopidogrel, exhibit significant variability in individual response.3,16,17 This variability can lead to either inadequate (HPR) or excessive platelet inhibition (LPR), both of which have important clinical implications.3,16,17 Notably, growing evidence suggests that a significant proportion of individuals not receiving antiplatelet therapy exhibit hyper- or hypo-reactive platelet phenotypes, which may influence their clinical history as well as the safety and efficacy of antiplatelet treatment.7,18 Several clinical factors, including advanced age, DM, CKD, and certain ethnicities, have been identified as contributors to heightened platelet reactivity and are well-recognized indicators of thrombotic risk.5,8 However, the influence of sex on platelet reactivity and its prognostic significance remains an area of ongoing investigation.10 While international guidelines recommend specific antiplatelet strategies for certain high-risk populations, no tailored recommendations exist according to sex.1,4 These recommendations stem from limited clinical evidence, largely due to the under-representation of females in RCTs, as well as the conflicting results of pharmacodynamic (PD) studies.13,19–24 However, current PD studies are limited in sample size and are often hindered by methodological inconsistencies in platelet reactivity assessment, making comparisons challenging.13,19–24
Recently, there has been significant interest in precision medicine, aimed at optimizing the risk-benefit profile of antiplatelet therapies, given the inherent risk of bleeding associated with these treatments.5 To this end, integrating ischaemic and bleeding risk scoring systems with tools allowing for the appraisal or estimation of the response to antiplatelet therapy (i.e. PFT or genetic testing) has been found to improve outcomes and is recommended by a recent international consensus document.4 With regards to PFT, several bedside or laboratory tests have been proposed for guiding antiplatelet selection in patients undergoing percutaneous coronary interventions.25 Among these, LTA is a laboratory test considered the historical gold standard, but has limitations, including complex sample preparation requiring expert personnel and the inter- and intra-laboratory variability.4 Platelet aggregation assessment by LTA may be induced by several agonists, each reflecting specific pathways involved in thrombus formation. Light transmission aggregometry induced by ADP serves as a marker of P2Y12 receptor activity, making it a valuable test for assessing individual responses to P2Y12 inhibitors.7 Conversely, LTA stimulated by collagen, AA, and epinephrine allow to unravel activation of the TXA2 pathway and more suitable for monitoring aspirin therapy, although AA has been shown to be less reliable for this purpose.7
In our analysis, we confirmed that the large interindividual variability in platelet reactivity observed in both patients receiving and not receiving antiplatelet therapy was similar between females and males.7 Furthermore, we observed that HV and CTR females exhibited higher platelet reactivity in response to ADP compared to males. This difference persisted in patients receiving clopidogrel but was abolished in those undergoing DAPT. Moreover, females treated with ASA showed higher platelet reactivity in response to collagen compared to males, suggesting a sex difference in aspirin responsiveness. These findings align with and expand on those reported by other groups.24,26 Compared to previous studies, our work benefits from a larger sample size, standardized methodology using gold standard LTA, and inclusion of both patients on antiplatelet therapy and HV or CTR. Additionally, we employed a comprehensive panel of agonists (ADP, collagen, epinephrine, and AA) to assess PFT across multiple activation pathways. The observation that females exhibit baseline hyperreactivity of the P2Y12 pathway but a reduced response to aspirin compared to males may have important clinical implications, suggesting that P2Y12 inhibitors may be particularly beneficial in women. These PD findings may have important clinical implications, and are consistent with evidence from an individual participant data meta-analysis including 24 096 patients from six RCTs, which indicates that P2Y12 inhibitor monotherapy is especially effective in women compared to men.27 Additionally, the observation that females treated with clopidogrel monotherapy exhibit heightened platelet reactivity in response to ADP suggests that women may derive greater benefit than men from a guided selection of P2Y12 inhibitors using PFT or genetic testing or from the use of more potent P2Y12 inhibitor, such as prasugrel or ticagrelor. While these experimental findings hold the potential to significantly influence clinical practice, they remain hypothesis-generating and support the need for rigorous clinical trials that could ultimately influence guideline recommendations and clinical decision-making.
Finally, in a secondary analysis stratified by age, we found that, among HV, females—but not males—exhibited greater platelet reactivity with advancing age. This finding aligns with previous studies and supports clinical observations that CV risk increases significantly in women after the postmenopausal transition.28 This increase is likely influenced by factors such as hormonal changes, genetic variations affecting COX-1 inhibition, and other sex-specific differences that may contribute to variability in response to antiplatelet therapy.29
Overall, this large PD study, which assessed platelet reactivity using the gold standard LTA with a standardized methodology, highlights significant sex differences in platelet reactivity both with and without antiplatelet therapy. Sex-related disparities in clot formation have also been highlighted using other point-of-care assays, including thromboelastography, a global clotting assay, further supporting the existence of biologically driven differences in thrombus formation between males and females.30,31 These findings provide a rationale for the growing clinical evidence supporting sex-specific responses to different antiplatelet agents and the potential to for considering sex in a precision medicine approach. For this purpose, PFT and genetics may offer advantages in modern setting.4,25
Limitations
As an observational, PD, study, this analysis has several limitations, including the inability to establish causal relationships (only associations), potential confounding due to uncontrolled variables, and the possibility of recall bias affecting the results. Additionally, we were unable to account for treatment duration among patients undergoing SAPT or DAPT. However, patients who did not adhere to the prescribed antiplatelet therapy in the seven days preceding blood withdrawal were excluded. Differences in antiplatelet therapy duration beyond seven days are unlikely to significantly affect the comparison of platelet reactivity between females and males.
Our analysis included only clopidogrel as the P2Y12 inhibitor. Given the significant PD and clinical differences between clopidogrel and more potent and predictable P2Y12 inhibitors, our findings apply specifically to clopidogrel and cannot be generalized to the entire class of P2Y12 inhibitors. Further research is needed to explore sex-related differences in platelet reactivity with other P2Y12 inhibitors, such as prasugrel or ticagrelor.32 Lastly, we did not stratify results by genetic testing (CYP2C19) in the clopidogrel and DAPT groups.
Conclusions
This large PD study highlights significant sex differences in platelet reactivity, both in the presence and absence of antiplatelet therapy. The higher baseline platelet reactivity linked to the P2Y12 receptor pathway and the lower response to aspirin in females compared to males suggest that P2Y12 inhibitors may be the preferred antiplatelet therapy for women. These observations may explain findings from clinical studies suggesting that P2Y12 inhibitor monotherapy might be especially effective in women compared to men.
Supplementary Material
Contributor Information
Mattia Galli, Cardiovascular Department, Maria Cecilia Hospital, GVM Care & Research, Cotignola 48033, Italy; Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina 04100, Italy.
Sergio Terracina, Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy.
Eleonora Schiera, Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy.
Simone De Corci, Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy.
Diego Sangiorgi, Cardiovascular Department, Maria Cecilia Hospital, GVM Care & Research, Cotignola 48033, Italy.
Massimo Mancone, Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy.
Luigi Frati, IRCCS NeuroMed, Pozzilli, Italy.
Sebastiano Sciarretta, Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina 04100, Italy; IRCCS NeuroMed, Pozzilli, Italy.
Dominick J Angiolillo, Division of Cardiology, University of Florida College of Medicine, Jacksonville, FL 32209, USA.
Fabio M Pulcinelli, Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena 324, Rome 00161, Italy.
Supplementary material
Supplementary material is available at European Heart Journal—Cardiovascular Pharmacotherapy online.
Author contributions
M.G., S.T., E.S., S.D.C.: design of the study, acquisition of data, interpretation of data, drafting the article or revising it critically for important intellectual content; D.S. performed statistical analysis with the interpretation of the data of the work; M.M., S.S., L.F. interpretation of data, revising it critically for important intellectual content, final approval of the version to be submitted; D.J.A. interpretation of data, drafting the article or revising it critically for important intellectual content, and final approval of the version to be submitted. F.M.P. conception and design of the study, acquisition of data, analysis and interpretation of data, drafting the article, revising it critically for important intellectual content, final approval of the version to be submitted.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article will be shared on reasonable request to the corresponding author.