Systolic Blood Pressure Change

Introduction

Systolic blood pressure (SBP) represents the pressure within the arteries when the heart contracts. While SBP values are often assessed at single time points, understanding the change in SBP over time, rather than just a static reading, provides crucial insights into an individual’s cardiovascular health. This dynamic aspect of SBP is influenced by a complex interplay of various factors, including age, sex, ancestry, smoking status, and medication use.^[1] Unlike cross-sectional measurements, SBP change often follows non-linear trajectories, highlighting the importance of longitudinal studies to capture its true variability.^[1]

Biological Basis

The variability in SBP change over time has a significant biological and genetic basis. Genome-wide association studies (GWAS) examine how genetic variations, particularly single nucleotide polymorphisms (SNPs), contribute to traits like SBP change.^[1] Researchers investigate how specific SNPs are associated with different trajectories of SBP change or with its response to interventions, using advanced statistical models such as structural equation modeling (SEM) and latent class growth modeling (LCGM).^[1]For instance, studies have identified SNPs associated with SBP, diastolic blood pressure (DBP), and pulse pressure.^[1] These genetic analyses often involve large datasets of high-quality SNPs, frequently imputed from reference panels, to identify significant associations.^[2] Examples include associations of rs11750990 , rs10499113 , rs35300112 , and rs1862746 with SBP changes or response to drugs.^[3] Additionally, variants in genes like FOXA1 have been linked to DBP response.^[3]

Clinical Relevance

Measuring and understanding SBP change is clinically relevant for predicting disease progression and prognosis, especially for conditions like hypertension.^[1]For example, the nocturnal blood pressure dipping phenomenon, defined by the night-to-day SBP ratio, is a critical indicator; abnormal dipping patterns can be associated with adverse cardiovascular outcomes.^[1] Specific SNPs, such as rs4905794 , have been linked to blood pressure dipping and even to markers of target organ damage like left ventricular mass index (LVMI).^[1] Furthermore, SBP change is a key phenotype in pharmacogenomic studies, where the genetic influence on an individual’s blood pressure response to antihypertensive medications, such as β1-blockers or thiazide diuretics, is investigated.^[2] Identifying genetic biomarkers related to drug response can pave the way for more personalized and effective treatment strategies.

Social Importance

The study of SBP change also carries significant social importance, particularly in addressing health disparities. Research has shown that genetic effects on SBP response can vary across different ancestral populations. For example, certain genome-wide significant SNPs may be associated with a decreased systolic response in one population but not in others, highlighting ancestry as a crucial factor in the effect size of common alleles.^[4]Understanding these population-specific genetic influences is essential for developing equitable and effective public health interventions and precision medicine approaches. By identifying genetic factors that predict SBP trajectories and drug responses, researchers aim to improve the prevention, diagnosis, and treatment of hypertension and related cardiovascular diseases across diverse communities.

Methodological and Statistical Constraints

Research on systolic blood pressure change is often constrained by sample sizes, particularly in specific cohorts or for certain analyses. For instance, some studies include fewer than a thousand individuals, or even hundreds in discovery and replication cohorts, which can limit the power to detect novel genetic associations or reliably replicate existing ones.^[1] While efforts are made to correct for statistical issues like genomic inflation in meta-analyses, inherent statistical limitations mean that identified genomic signals are often considered potential associations requiring further validation rather than definitive conclusions.^[2]This necessitates ongoing research to strengthen confidence in findings and avoid effect-size inflation.

Furthermore, specific methodological choices can introduce constraints. Some analyses have been limited to a subset of the genome, such as only odd-numbered chromosomes, implying that additional relevant loci may remain undiscovered in other chromosomal regions.^[1] The assumption of fixed effects in meta-analyses, while common, might not fully capture the heterogeneity of effects across different studies or populations, potentially obscuring true genetic influences or leading to less precise estimates.^[1]The need for subsequent replication in independent cohorts, even when attempted, highlights the provisional nature of many initial genetic findings, especially when replication cohorts themselves are of modest size.^[5]

Phenotypic Heterogeneity and Challenges

Defining and accurately measuring systolic blood pressure change trajectories presents significant challenges, contributing to phenotypic heterogeneity. The common practice of correcting SBP for medication use by adding a fixed constant, such as 15 mm Hg, is a broad approximation that may not accurately reflect the individualized physiological response to various antihypertensive drugs.^[1] Moreover, SBP response can be influenced by numerous unmeasured or unaccounted factors, such as surgical stimulation or volume status in acute settings, which act as residual confounders and can impact the observed associations.^[3]Careful data cleaning, including the exclusion of implausible blood pressure or BMI values, is essential but may also inadvertently reduce sample size or introduce subtle biases.^[6] The analysis of longitudinal SBP data relies on specific assumptions and data handling procedures that can affect interpretation. Studies often require a minimum number of SBP measurements over time and may exclude individuals with extreme changes, potentially biasing the sample towards more stable trajectories and missing genetic influences on rapid or atypical SBP shifts.^[1] The assumption that missing data are completely at random, while simplifying analysis, may not always hold true in real-world longitudinal cohorts, leading to biased estimates if missingness is related to underlying genetic or environmental factors.^[1] While efforts are made to adjust for key covariates like age, sex, and BMI, the decision to avoid over-adjustment in subsequent analyses implies a delicate balance between controlling for known confounders and preserving the independent signal of the trait.^[1]

Generalizability and Ancestry-Specific Limitations

A significant limitation in understanding systolic blood pressure change is the challenge of generalizing findings across diverse ancestral populations. Genetic variants identified in predominantly European, Asian, or African descent populations may not fully capture the genetic architecture or modify longitudinal SBP effects in other groups, such as Mexican Americans.^[1]This lack of generalizability can lead to missed genetic modifiers or an incomplete understanding of disease progression in underrepresented populations, hindering the development of universally applicable biomarkers or treatments.^[1] Despite the benefits of multiethnic studies in observing differences across population groups, the inherent statistical limitations often mean that observed associations require further rigorous validation before being considered definitive across all ancestries.^[3] Furthermore, specific ancestral groups face limitations in data availability and analytical depth. For instance, heritability estimates for SBP change may be restricted to European American populations due to insufficient sample sizes in other groups, such as African Americans, thereby limiting comprehensive understanding of genetic contributions across all major ethnic backgrounds.^[1] Studies focusing on specific family cohorts, while valuable for identifying within-family genetic effects, may not fully represent the broader genetic diversity or environmental exposures of a larger population, potentially introducing cohort-specific biases.^[1] Addressing these disparities requires larger, more inclusive studies with adequate representation and statistical power across all ancestral groups.

Incomplete Genetic Understanding and Environmental Influences

Despite advances in identifying genetic loci associated with blood pressure, a substantial portion of the heritability for systolic blood pressure change remains unexplained, often referred to as “missing heritability.” While heritability estimates suggest a genetic component to SBP change, these values can vary and may be systematically decreased by factors such as antihypertensive medication use, complicating the precise estimation of genetic contributions.^[1] Current genetic studies may not fully account for all possible genetic effects or modifiers influencing longitudinal SBP changes, particularly if they rely on previously identified cross-sectional genetic markers or focus on only a subset of the genome.^[1] This indicates that many genetic determinants, including rare variants or complex gene-gene interactions, are yet to be discovered.

The dynamic nature of systolic blood pressure change is also heavily influenced by a complex interplay of environmental factors and potential gene-environment interactions, many of which are not fully captured or understood. Factors such as age, sex, ancestry, smoking status, and medication use are known to influence SBP trajectories, but their intricate interactions with genetic predispositions are challenging to model comprehensively.^[1] Furthermore, specific environmental or physiological states, such as the degree of surgical stimulation or an individual’s volume status, can act as unmeasured residual confounders, impacting observed blood pressure responses and potentially obscuring underlying genetic effects.^[3] A complete picture requires integrating these diverse environmental influences into genetic models to better predict and understand SBP change.

Variants

Genetic variations play a crucial role in influencing an individual’s systolic blood pressure (SBP) change, encompassing a range of biological pathways from structural integrity to metabolic regulation. Several identified variants are associated with SBP, highlighting the complex genetic architecture underlying this trait.

Among the variants identified, rs150432347 in the COL6A1 gene and rs138594727 in the CRYBA2 gene have shown significant associations with blood pressure in individuals of African ancestry.^[6] The COL6A1 gene encodes a subunit of collagen VI, a vital component of the extracellular matrix that provides structural support to tissues, including blood vessels. Variations in this gene could influence vascular stiffness and integrity, thereby affecting blood pressure regulation. Similarly, CRYBA2 encodes a beta-crystallin protein, primarily known for its structural role in the eye lens, but crystallins can also be involved in cellular stress responses in various tissues. Additionally, the variant rs11230796 in the MYRF (myelin regulatory factor) gene is associated with increased SBP.^[7] While MYRF is primarily known for its role in myelination, polymorphisms in this gene have also been linked to fatty acid, phospholipid, and blood metabolite levels.^[7]suggesting an indirect influence on cardiovascular health through metabolic pathways.

Another significant variant, rs4905794 , is located in an intergenic region on chromosome 14, where it is thought to harbor regulatory features that affect the expression of the nearby BCL11B gene.^[5] This variant has been associated with nocturnal blood pressure dipping, a physiological phenomenon where blood pressure decreases during sleep.^[5] Expression quantitative trait locus (eQTL) analyses have linked rs4905794 to BCL11B expression in brain regions such as the hippocampus and putamen.^[5] The BCL11Bgene encodes a transcription factor critical for T-cell development and neuronal differentiation, implying that its altered expression could impact neural pathways involved in cardiovascular regulation and potentially influence blood lipoprotein(a) levels, a known cardiovascular risk factor.^[5]Other variants, though lacking specific contextual details regarding their direct impact on systolic blood pressure change, are associated with genes involved in fundamental cellular processes that could indirectly affect cardiovascular function. For instance,rs147110080 is associated with PFKFB2 and YOD1; PFKFB2is a key enzyme in glycolysis, influencing glucose metabolism and cellular energy, whileYOD1 is involved in endoplasmic reticulum (ER)-associated degradation. The variant rs536397959 is linked to KRBA1, a gene encoding a protein often implicated in transcriptional regulation, which can broadly affect gene expression networks relevant to cardiovascular health. Similarly,rs139989095 in SEL1L3 is involved in ER-associated degradation, crucial for protein quality control and cellular stress responses, and rs11568416 in SLC28A3is a solute carrier involved in nucleoside transport, impacting cellular energy and signaling in vascular cells. Further,rs148474705 is associated with TMEM147-AS1 and GAPDHS, with GAPDHS contributing to glycolysis and TMEM147-AS1 being an antisense RNA that may regulate gene expression. Lastly, rs79944011 , located in a region involving SLC35B4 and LINC03060, points to SLC35B4’s role in nucleotide sugar transport, essential for protein glycosylation affecting cell surface interactions, andLINC03060 as a long non-coding RNA with potential regulatory functions. These variants collectively highlight the diverse genetic contributions to blood pressure regulation, often identified through large-scale genomic studies.^[8]

Key Variants

RS ID	Gene	Related Traits
rs147110080	PFKFB2, YOD1	systolic blood pressure change
rs536397959	KRBA1	systolic blood pressure change
rs139989095	SEL1L3	systolic blood pressure change
rs11568416	SLC28A3	systolic blood pressure change
rs150432347	COL6A1	systolic blood pressure change
rs138594727	CRYBA2	systolic blood pressure change
rs148474705	TMEM147-AS1, GAPDHS	systolic blood pressure change diastolic blood pressure change
rs11230796	MYRF	systolic blood pressure change level of phosphatidylethanolamine level of phosphatidylcholine polyunsaturated fatty acids to monounsaturated fatty acids ratio polyunsaturated fatty acids to total fatty acids percentage
rs79944011	SLC35B4 - LINC03060	systolic blood pressure change
rs4905794	RPL3P4 - BCL11B	systolic blood pressure change diastolic blood pressure change

Defining Systolic Blood Pressure Change and its Conceptual Frameworks

Systolic blood pressure (SBP) is a fundamental cardiopulmonary trait representing the pressure in arteries during the heart’s contraction phase.^[9]The concept of “systolic blood pressure change” moves beyond a single static , focusing instead on the dynamic alterations in SBP over time or in response to specific interventions.^[1] This includes the study of “longitudinal blood pressure traits,” which capture variations in SBP across multiple time points within an individual.^[1]Researchers often analyze “trajectories of systolic blood pressure change” to understand distinct patterns of SBP evolution within populations, which can provide insights into disease progression and prognosis.^[1] Furthermore, the trait encompasses “blood pressure responses to interventions,” such as pharmacological treatments, allowing for the assessment of individual variability in how SBP reacts to external factors.^[1]

Operationalizing and Diagnostic Criteria for SBP Change

The precise and operational definition of SBP change are crucial for its study. Standardized protocols are employed for SBP readings, including office measurements where participants rest for five minutes in a seated position, followed by three readings taken at two-minute intervals, with the first often discarded to mitigate effects like “white coat syndrome”.^[7] Ambulatory blood pressure monitoring (ABPM) provides a more comprehensive assessment, with devices like the Microlife WatchBP O3 monitor recording SBP at regular intervals throughout the day and night.^[1] In ABPM, night-time BP is typically defined as the mean of values during the actual sleeping period, while daytime BP is the mean of all other values.^[1] Home blood pressure measurements, often taken as the average of readings before and after going to bed over several days, also contribute to understanding SBP in natural settings.^[2] The rate of SBP change per year can be quantified using mixed-effects models, where the predicted random effect representing individual-specific change is used as a phenotype in genetic analyses.^[1]These measurements are rigorously adjusted for known confounders such as age, sex, body mass index (BMI), and population structure, often incorporating principal components to account for genetic ancestry.^[6]

Classification of SBP Change Patterns and Related Terminology

The classification of SBP and its changes is essential for clinical diagnosis and research. Hypertension, a primary clinical condition related to SBP, is typically defined as SBP at least 140 mmHg, diastolic blood pressure (DBP) at least 90 mmHg, or the use of antihypertensive medication.^[1] Specific patterns of SBP change, such as “nocturnal blood pressure dipping,” categorize individuals based on the nocturnal fall in BP, which has significant clinical implications.^[1] The analysis of SBP change often employs advanced statistical methods like structural equation modeling (SEM) to identify covariates associated with change trajectories, thereby enhancing the precision and accuracy of genetic association studies.^[1] Semiparametric latent class growth modeling (LCGM) further allows for the identification of distinct groups of SBP change trajectories within a population.^[1]Key terminology in this field includes SBP (systolic blood pressure), DBP (diastolic blood pressure), BP (blood pressure), BMI (body mass index), and GWAS (genome-wide association study), all crucial for standardized communication in research.^[5]

Early Recognition and Evolving Methodologies

Historical understanding of blood pressure initially focused on single, cross-sectional observations, with early research examining chronic disease risk based on various characteristics.^[10]However, scientific understanding has evolved to recognize the dynamic nature of systolic blood pressure (SBP), acknowledging its significant interindividual variation influenced by factors like age, sex, ancestry, smoking status, and medication use.^[2]This shift led to a greater emphasis on longitudinal studies, which capture SBP changes over time rather than static snapshots, providing a more comprehensive view of cardiovascular health.^[2] The progression in methodology includes the adoption of linear mixed models for analyzing longitudinally measured blood pressure data.^[11] and the application of structural equation modeling (SEM) to identify covariates associated with SBP change trajectories.^[2]These advanced statistical approaches allow researchers to dissect the complex, often non-linear, patterns of SBP change, moving beyond simple averages to explore subphenotypes that may reveal novel genetic susceptibilities and improve understanding of disease progression, such as hypertension.^[2] Landmark studies, like those using data from the Framingham Heart Study, have contributed to identifying genetic influences on blood pressure, further underscoring the importance of sophisticated analytical frameworks.^[2]

Global Burden and Demographic Patterns of Systolic Blood Pressure Change

Hypertension, defined by SBP at least 140 mmHg or diastolic blood pressure (DBP) at least 90 mmHg or being on antihypertensive medication, represents a significant global health burden.^[8]While specific global prevalence and incidence rates for SBP change are not uniformly presented in the provided studies, the widespread impact of hypertension implies a substantial population experiencing adverse SBP changes over their lifespan. Studies have examined life course trajectories of SBP in various cohorts, highlighting the dynamic nature of these patterns across different populations.^[2] Demographic factors profoundly influence SBP change. Age is a primary determinant, with SBP naturally varying and often increasing with advancing age.^[2] Sex also plays a role, alongside ancestry, which is a critical factor leading to interindividual variation in SBP.^[2] Research has specifically investigated SBP patterns in diverse groups, including individuals of African ancestry.^[2] European Americans.^[2] Mexican American families.^[2] Korean cohorts.^[2] and Han Chinese populations.^[2]often adjusting for confounders such as age, sex, body mass index (BMI), and socioeconomic status.^[6] These demographic specificities underscore the need for trans-ethnic meta-analyses to comprehensively understand genetic and environmental influences on SBP change.^[2]

Longitudinal Trajectories and Genetic Insights

Understanding the patterns of systolic blood pressure change over time, known as trajectories, has become a crucial area of epidemiological investigation, as SBP does not necessarily change in a linear fashion.^[2]Studies focusing on blood pressure trajectories, including those examining patterns in the years preceding death, provide valuable insights into long-term cardiovascular health.^[2] This longitudinal perspective offers a more nuanced view than cross-sectional measurements, allowing for the identification of distinct groups with unique SBP change patterns within populations, leveraging methods like group-based trajectory analysis.^[2] Genetic research has increasingly leveraged these longitudinal approaches to uncover the genetic architecture of SBP change. While strong genetic effects on cross-sectional SBP are known, heritability estimates are even higher for longitudinal SBP change.^[2] Genome-wide association studies (GWAS) are now being conducted on SBP change trajectories and average SBP values over time, moving beyond baseline measurements to identify novel genetic loci associated with these dynamic phenotypes.^[8]This innovative approach, including trans-ethnic meta-analyses, aims to minimize trait heterogeneity and identify clinically relevant genetic biomarkers for disease progression and prognosis, such as incident hypertension, by considering the full genetic panel and imputed genotypes.^[6]

Systemic Regulation of Blood Pressure and Vascular Function

The maintenance of blood pressure (BP) is a complex physiological process involving intricate interactions between the heart, blood vessels, kidneys, and the nervous system. Systolic blood pressure (SBP) specifically represents the pressure within arteries during the heart’s contraction phase, and dynamic changes in SBP are critical indicators of cardiovascular health.^[3] Hormones and neurotransmitters, particularly those of the adrenergic system, play a central role in modulating vascular tone and cardiac output. For instance, alpha-1 adrenergic agents like phenylephrine can induce a rapid increase in BP by promoting vasoconstriction.^[3] The inner lining of blood vessels, the endothelium, produces crucial biomolecules such as nitric oxide (NO), a potent vasodilator that helps regulate vascular resistance.^[12] Dysregulation of neuronal nitric oxide synthase (NOS1) or its regulatory protein NOS1APcan impair vascular function and contribute to hypertension.^[7] These systemic and local factors collectively ensure that SBP is maintained within a narrow physiological range, with any disruptions potentially leading to significant health consequences.^[13]

Genetic and Molecular Determinants of Blood Pressure Variability

Systolic blood pressure exhibits substantial interindividual variation, influenced by both environmental factors and significant genetic contributions, with heritability estimates for longitudinal SBP change reaching up to 57%.^[1]Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with SBP, pointing to genes involved in diverse molecular and cellular pathways.^[1] These genetic variations can affect regulatory elements, alter gene expression patterns (eQTLs), and influence the function of critical proteins, enzymes, and receptors that are integral to BP control.^[1] Specific genes implicated in BP regulation include ATP2B1, CSK, ARSG, CSMD1, IGF1, SLC4A4, WWOX, and SFMBT1, which have been linked to blood pressure and/or hypertension susceptibility.^{[14], [15]} Rare coding variants in various genes have also been found to be associated with blood pressure variation, particularly in populations of African ancestry.^[6] Furthermore, polymorphisms in genes such as ATP1B1, RGS5, and SELEhave shown associations with hypertension and blood pressure levels, illustrating the complex genetic architecture underlying SBP changes.^[16]

Circadian Rhythms and Blood Pressure Dipping

Blood pressure naturally follows a circadian rhythm, characterized by a nocturnal dip where SBP and diastolic blood pressure (DBP) typically decrease during sleep.^{[1], [17]}Disruptions in this rhythmic pattern, known as non-dipping, represent a pathophysiological process associated with an increased risk of cardiovascular events. This circadian variation is orchestrated by an internal biological clock, with key clock genes influencing systemic physiological processes.^[18] At the cellular level, components of the circadian clock, such as BMAL1, are expressed in smooth muscle cells and play a crucial role in regulating the blood pressure circadian rhythm.^[18] This molecular mechanism ensures coordinated changes in vascular tone and cardiac activity throughout the 24-hour cycle. Genetic factors can influence the extent of nocturnal dipping, contributing to individual differences in BP patterns and their associated health outcomes.^[1]

Pharmacogenomics of Antihypertensive Response

The effectiveness of antihypertensive medications, such as beta-blockers and hydrochlorothiazide, varies considerably among individuals, a phenomenon that pharmacogenomics seeks to understand.^{[2], [3]} Genetic variations can influence drug metabolism, the binding affinity of drugs to their target receptors, and downstream signaling pathways, thereby impacting a patient’s blood pressure response to treatment. For example, specific genetic markers may predict an individual’s response to beta-blockers like bisoprolol or atenolol.^[2] Polymorphisms in genes encoding drug targets or metabolic enzymes can modulate the efficacy and side effects of these medications. A polymorphism in exon 4 of the human 3 beta-hydroxysteroid dehydrogenase type I gene (HSD3B1) has been linked to blood pressure regulation and may influence responses to certain antihypertensive agents.^{[3], [19]}Understanding these genetic influences allows for a more personalized approach to hypertension management, potentially improving treatment outcomes by tailoring medication choices based on an individual’s genetic profile.

Neurohormonal and Vascular Regulation

The regulation of systolic blood pressure change involves intricate neurohormonal signaling pathways that finely tune vascular tone and cardiac output. Alpha-1 adrenergic agents like phenylephrine exert their effects by activating specific receptors, leading to intracellular signaling cascades that mediate rapid vascular responses.^[3]The renin-angiotensin system plays a crucial role, with the angiotensin I-converting enzyme (ACE) influencing vascular reactivity; variations in its genotype, such as deletion genotypes, are associated with altered vascular responses.^[20] This system’s involvement in the pressure-flow relationship is fundamental to maintaining circulatory homeostasis.^[21] Nitric oxide (NO) also significantly contributes to vascular regulation, with nitric oxide synthase (NOS) genes linked to enhanced vascular responsiveness.^[3] Endothelial and neuronal nitric oxide synthases, including NOS1AP, are critical for modulating cardiac repolarization and overall vascular function.^{[12], [13], [22]} Furthermore, calcium homeostasis is a key intracellular mechanism, influenced by proteins like BST1 (CD157), which impacts the phosphorylation of focal adhesion kinase and is implicated in hypertension-vascular signaling cascades.^[2] Neuronal calcium handling and cardiac sympathetic neurotransmission are modulated by proteins like CAPONduring conditions such as dysautonomia in hypertension.^[23]

Genetic and Epigenetic Influences

Genetic variations significantly impact systolic blood pressure change by influencing gene expression and protein function through regulatory mechanisms. For instance, the intergenic single nucleotide polymorphism (SNP)rs1230361 has been identified as an expression quantitative trait locus (eQTL) for the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene, indicating a direct link between genotype and gene expression levels.^[1] Other specific genetic variations in genes such as ATP2B1, CSK, ARSG, and CSMD1are known to be related to blood pressure levels and hypertension susceptibility.^[14]Beyond individual variants, genome-wide association studies have pinpointed several genes as hypertension susceptibility loci, includingIGF1, SLC4A4, WWOX, and SFMBT1.^[15] Polymorphisms in genes like the 3-beta-hydroxysteroid dehydrogenase type I gene (HSD3B1) and those in ATP1B1, RGS5, and SELEhave also been associated with blood pressure traits and hypertension.^{[16], [19]} These genetic differences can lead to altered protein modification, post-translational regulation, or allosteric control, ultimately affecting the efficiency and responsiveness of blood pressure regulatory pathways.

Circadian Rhythms and Systems-Level Integration

Systolic blood pressure change exhibits a distinct circadian rhythm, a process largely governed by core clock genes. The proteinBMAL1, for example, is a key component of the molecular clock and plays a direct role in regulating the circadian rhythm of blood pressure.^[18] Similarly, BCL11B affects blood pressure regulation and potential target organ damage by interacting with histone deacetylase 1 (HDAC1) and the nucleosome remodeling and histone deacetylase (NuRD) complex, which are involved in the rhythmic expression of mammalian circadian clock PER genes.^[1] At a systems level, various pathways exhibit crosstalk and network interactions that contribute to emergent properties of blood pressure regulation. For instance, BST1 (CD157) influences multiple signaling cascades, including those related to immune and inflammation responses, thereby indirectly affecting vascular function and hypertension pathophysiology.^[2] Its homolog, CD38, further exemplifies this integration, being involved in diverse processes such as vascular contraction, apoptosis, neural signaling via calcium regulation, renal regulation, and energy metabolism, highlighting its broad impact on cardiovascular and metabolic health.^[2]

Metabolic and Disease-Relevant Mechanisms

Metabolic pathways are intrinsically linked to systolic blood pressure change, with energy metabolism being a fundamental aspect. Dysregulation in metabolic processes can contribute to the pathophysiology of hypertension, as seen withCD38’s involvement in energy metabolism, which can influence weight gain and obesity.^[2]These metabolic and signaling pathways often become dysregulated in disease states, leading to compensatory mechanisms that attempt to restore homeostasis but can also contribute to disease progression.

Understanding these disease-relevant mechanisms is crucial for identifying therapeutic targets. For example, the genetic basis of response to antihypertensive drugs like beta-blockers or hydrochlorothiazide is an active area of research, with studies identifying specific genetic loci that influence how individuals respond to these treatments.^{[2], [3]}Such pharmacogenomic insights allow for a more personalized approach to managing systolic blood pressure change by targeting specific pathways and mechanisms that are most relevant to an individual’s genetic makeup and disease presentation.

Prognostic Value and Risk Stratification

Systolic blood pressure (SBP) change, particularly when analyzed through longitudinal trajectories, offers significant prognostic utility that extends beyond single cross-sectional measurements.^[1]These trajectories can identify distinct patterns of SBP evolution over time, providing a more comprehensive indication of future health outcomes and disease progression.^[1]For instance, analyzing the rate of SBP change per year can predict incident hypertension over a decade, enabling earlier identification of individuals at elevated risk.^[8]This approach minimizes trait heterogeneity, facilitating the discovery of clinically relevant genetic biomarkers associated with the progression of conditions like hypertension.^[1] Longitudinal SBP data also proves superior in genetic risk stratification, as studies have shown that using longitudinal SBP outcomes in genome-wide association studies (GWAS) can uncover novel genetic loci not identifiable with baseline cross-sectional data alone.^[1] The high heritability of longitudinal SBP change, estimated at 0.57, further underscores the genetic underpinnings that can be leveraged for personalized medicine approaches.^[1]By utilizing these genetic insights, healthcare providers can better stratify individuals based on their predisposition to specific SBP trajectories, potentially guiding tailored prevention strategies and early interventions before overt disease manifests.^[1]

Clinical Applications in Diagnosis, Treatment, and Monitoring

The of SBP change holds substantial diagnostic and monitoring utility, extending beyond the assessment of static blood pressure values. For example, evaluating nocturnal SBP dipping, defined as the percentage change between mean nighttime and daytime SBP, serves as a crucial diagnostic indicator in hypertensive patients.^[1]Abnormal dipping patterns have been linked to target organ damage, such as increased Left Ventricular Mass Index (LVMI) and specific ECG voltage parameters, even when absolute blood pressure levels are not directly included as covariates.^[1] This highlights the independent clinical significance of SBP change in identifying subtle yet impactful physiological alterations.

Furthermore, SBP change measurements are vital for guiding treatment selection and monitoring therapeutic responses. Rapid SBP changes in response to pharmacological agents, such as alpha-1 adrenergic agents like phenylephrine in the perioperative setting, provide immediate insights into drug efficacy and patient physiology.^[3] Genetic factors and ancestry can influence these rapid responses, suggesting a role for pharmacogenomics in personalizing medication choices and dosages.^[3] Similarly, monitoring SBP response to various antihypertensive drugs, including hydrochlorothiazide or beta1-blockers, through longitudinal or acute change measurements, helps optimize treatment regimens and predict individual patient outcomes.^[3]

Associations with Comorbidities and Physiological Responses

Systolic blood pressure change is intimately associated with the development and progression of various comorbidities and complications. As noted, atypical nocturnal SBP dipping is a significant risk factor for target organ damage, including left ventricular hypertrophy, a critical complication of hypertension.^[1]This specific SBP change pattern, rather than just the absolute SBP, provides a more granular understanding of cardiovascular risk and can assist in identifying individuals who may benefit from more aggressive management or specific chronotherapeutic interventions.^[1] Beyond chronic conditions, SBP change measurements are also critical in understanding acute physiological responses and their genetic underpinnings. The rapid change in SBP following a phenylephrine bolus, for instance, reveals how individual genetic variations and ancestral backgrounds influence acute hemodynamic stability in surgical contexts.^[3] Such rapid responses are complex, being influenced by factors like surgical stimulation and volume status, and identifying their genetic determinants can illuminate overlapping physiological phenotypes and improve perioperative patient management.^[3] The ability to observe significant differences in drug response across diverse population groups underscores the importance of considering ancestry in interpreting these physiological SBP changes.^[3]

Frequently Asked Questions About Systolic Blood Pressure Change

These questions address the most important and specific aspects of systolic blood pressure change based on current genetic research.

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1. Will my family’s blood pressure history affect my kids?

Yes, blood pressure changes and the risk of related conditions have a significant genetic basis. Specific genetic variations can be passed down, influencing how your children’s blood pressure might change over time or their response to treatments. Understanding these family patterns helps predict potential risks.

2. Why do my blood pressure meds work differently than others’?

Your genes play a big role in how your body responds to medications. Research shows that specific genetic variations can influence whether drugs like β1-blockers or thiazide diuretics are more or less effective for you. This is why personalized medicine, considering your unique genetic makeup, is so important.

3. Is it bad if my blood pressure stays high at night?

Yes, it can be a concern. If your blood pressure doesn’t “dip” lower at night, it’s called an abnormal nocturnal dipping pattern, and it’s linked to worse cardiovascular outcomes. Some genetic variations, such asrs4905794 , are even associated with this dipping pattern and organ damage.

4. Does my ancestry affect how my blood pressure changes?

Absolutely. Genetic effects on blood pressure response can vary significantly across different ancestral populations. For example, certain genetic markers might lead to a decreased blood pressure response in one population but not another, making ancestry a crucial factor in understanding your risk and treatment.

5. Could a DNA test help choose my best blood pressure medicine?

Potentially, yes. Pharmacogenomic studies investigate how your genes influence your response to antihypertensive medications. Identifying genetic biomarkers could help doctors select the most effective drug for you, paving the way for more personalized and effective treatment strategies.

6. Does my blood pressure change predictably as I get older?

Not always predictably. While age is a factor, blood pressure change often follows non-linear patterns, meaning it doesn’t just go up or down in a straight line. This dynamic variability is influenced by a complex interplay of genetic and environmental factors.

7. Can I beat my family’s blood pressure history with lifestyle?

While genetics have a significant influence on blood pressure changes and risk, lifestyle factors are also crucial. Understanding your genetic predispositions can empower you to make more informed choices about diet, exercise, and other habits to potentially mitigate inherited risks.

8. Does my past smoking still affect my current blood pressure changes?

Yes, your smoking status, even in the past, is one of the factors known to influence the dynamic changes in systolic blood pressure over time. It’s part of the complex interplay of factors that shape your cardiovascular health trajectory.

9. Is how doctors adjust for my blood pressure meds truly accurate?

The common practice of correcting SBP for medication, often by adding a fixed constant like 15 mm Hg, is a broad approximation. It might not perfectly reflect your individual physiological response to different drugs, as that can be highly variable and influenced by many factors.

10. Do men and women’s blood pressure change differently over time?

Yes, sex is recognized as one of the factors that can influence the dynamic changes in systolic blood pressure over time. This highlights that biological differences between men and women contribute to varying cardiovascular health trajectories.

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This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

[1] Justice AE. “Genome-wide association of trajectories of systolic blood pressure change.”BMC Proceedings, vol. 10, no. Suppl 7, 2016, p. 56.

[2] Singh S, et al. “Genome-Wide Meta-Analysis of Blood Pressure Response to β1-Blockers: Results From ICAPS (International Consortium of Antihypertensive Pharmacogenomics Studies).” J Am Heart Assoc, vol. 8, no. 16, 2019, e011822.

[3] Salvi E, et al. “Genome-Wide and Gene-Based Meta-Analyses Identify Novel Loci Influencing Blood Pressure Response to Hydrochlorothiazide.” Hypertension, vol. 68, no. 6, 2016, pp. 1346-1355.

[4] Wenric S, et al. “Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry.” Pharmacogenomics J, vol. 21, no. 1, 2021, pp. 101–10.

[5] Rimpela JM, et al. “Genome-wide association study of nocturnal blood pressure dipping in hypertensive patients.” BMC Med Genet, vol. 19, no. 1, 2018, p. 95.

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