Our analysis revealed that HCT, HB, Cr, Urea, Cys, age, sex, SBP, DBP, PP, and HR are important and sensitive attributes (Table 2). For any attribute to be considered important or sensitive, its absolute value difference between the successful and unsuccessful groups should be higher than that of an unimportant attribute. The higher the absolute value difference, the greater is the significance. The data presented in Table 3 further support our results of data visualisation and analysis.

Interestingly, we found that the unsuccessful group was strongly associated with the lower HCT and HB levels, indicating that HCT and HB are sensitive attributes for metoprolol treatment. A study involving 5616 individuals over 10 years has shown that both higher HCT levels (even those within the normal range at baseline) and an increase in HCT level over time are associated with the incidence of hypertension, independent of other related risk factors [26]. The authors concluded that the HCT level has an important role in the development of hypertension. Therefore, we inferred that the antihypertensive mechanism of metoprolol involves the reduction in the level of HCT, i.e., if a patient has a high HCT level, reduction in that level by metoprolol, and vice versa, can be beneficial. Metoprolol also has a higher efficiency in patients with a high HB level than that in patients with a low HB level; this might be because (1) the HCT level is positively correlated with hypertension, (2) blood viscosity varies directly with the HCT level, and (3) there is a strong association between blood viscosity and arterial pressure [27]. As HB is an iron-containing metalloprotein in the red blood cells, it is understandable that the HCT and HB levels have similar predictive trends. However, the HCT and HB levels are often neglected by clinicians when prescribing antihypertensive agents.

Our results showed that the successful group is significantly associated with lower levels of Cr, urea, and Cys. However, the levels of TC, LDL, HDL, and FPG are not included in our drug-related attributes spectrum. The Cr, urea, and Cys levels are commonly used to determine renal function [28]. Viazzi et al. have shown that the kidneys play an important role in the pathogenesis of hypertension [29]. These studies suggest that the deterioration of renal function is an indicator of the development of hypertension. However, our study shows that metoprolol monotherapy might not be sufficient for controlling hypertension in patients with renal dysfunction. The TC, LDL, HDL, and FPG levels may not be accurate indicators of hypertension deterioration; furthermore, our data show that they are not essential factors for indicating the effectiveness of metoprolol.

In a hypertension treatment study, the Hypertension Care Computing Project of the Department of Health and Social Security showed improved survival in hypertensive men treated with beta-blockers; however, this effect was not observed in women [30]. Consistent with these findings, our results reveal sex as a sensitive attribute.

Quarterman et al. reported that the effect of age on the pharmacokinetics of metoprolol and its metabolites is less pronounced than that observed for other drugs [31]. Natale et al. indicated that metoprolol is more likely to benefit older patients [32]. Our data showed that older patients with hypertension exhibit a favourable response to metoprolol. However, more studies are needed to corroborate this conclusion.

The inclusion of SBP, DBP, PP, and HR in the metoprolol-related attribute spectrum is not unexpected and does not need comprehensive discussion. However, it indicates that metoprolol monotherapy may not be sufficient if a patient has multiple deteriorated attributes.

Our results revealed an obvious difference between metoprolol-related attributes and risk factors that clinicians use extensively to diagnose hypertension. Therefore, we suggest that diagnosis must be based on a logic different from that used for drug selection. By matching metoprolol-related attributes, clinicians can focus on the sensitive attributes to improve metoprolol personalisation. Different antihypertensive agents have different pharmacodynamics; therefore, they could have diverse drug-related attributes spectrum. We strongly suggest that drug-related attributes be established to aid in the personalisation of therapeutic drugs. The need for personalisation of hypertension therapy has been documented in the Joint National Committee (JNC) Reports [33, 34]. As antihypertensive drug responses may be influenced by various genetic and environmental factors and their interactions, complete realisation of personalised medicine will undoubtedly require more precise and comprehensive characterisation of individual environmental exposures and measurements from multiple levels across biological hierarchy [6]. To the best of our knowledge, this is the first time a novel, meaningful personalisation method based on drug-related, specific, testable attributes of patients has been proposed to establish drug-related attributes sensitive spectrum using data visualisation. Our metoprolol-related attributes provide a good understanding of this proposal.

In conclusion, our study suggests that HCT, HB, Cr, Urea, Cys, age, sex, SBP, DBP, and PP can be considered metoprolol-related attributes when clinicians personalise antihypertensive agents.

In Table 3, all ‘Yes’ items, except HR, had a higher absolute value of the difference (> 0.05), and the other items had a lower absolute value of the difference (< 0.05). These results supported the data visualisation results and analysis.

First, although our study was limited by the population studied, which involved inpatients only, and had limited applicability for the use of metoprolol specifically as an antihypertensive agent, our HIS had data of 148,735 patients with hypertension. Among these patients, 2134 satisfied our requirement of having all 19 attributes available. Second, considering that our focus was on the practical application of the personalisation method, some important attributes, such as biomarkers, genetic polymorphism, and phenotypes, which are not extensively used, were not analysed in this study. The metoprolol-related attributes spectrum needs further expansion and improvement by incorporating these important attributes. Nevertheless, metoprolol-related attributes identified in this study could be a good reference for clinicians to personalise medicine. The use of artificial intelligence models based on our drug-related attributes spectrum could further improve this process.

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