Prevalence and Determinants of Ambulatory Hypertension Phenotypes in a Semi-Urban Nigerian Population

Prevalence and Determinants of Ambulatory Hypertension Phenotypes in a Semi-Urban Nigerian Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Determinants of Ambulatory Hypertension Phenotypes in a Semi-Urban Nigerian Population Ebenezer Adekunle Ajayi, Joseph Olusesan Fadare, Babajide Adewoyin Adeleke, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484932/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Ambulatory blood pressure monitoring (ABPM) provides more accurate diagnostic and prognostic information than office blood pressure (BP), yet it remains underutilised in sub-Saharan Africa. As a result, the actual distribution of hypertension phenotypes identified through ABPM is poorly understood. This study aimed to determine the prevalence and determinants of hypertension phenotypes using 24-hour ABPM in a semi-urban Nigerian population. Methods: This community-based cross-sectional study involved 348 adults in Ido-Ekiti, Nigeria. Sociodemographic and clinical data were collected using the WHO STEPS protocol. Office BP was measured with standardised methods, and 24-hour ABPM (CONTEC ABPM60®) provided daytime, night-time, and 24-hour BP readings. Hypertension phenotypes were defined using standard thresholds. Multivariable logistic regression identified predictors. Results: Sustained hypertension occurred in 31.8%, ambulatory hypertension in 32.4%, nocturnal hypertension in 56.8%, isolated nocturnal hypertension in 26.1%, and morning hypertension in 40.6%. White-coat hypertension was found in 15.3%, masked hypertension in 4.3%, and true normotension in 34.7%. Females had higher rates of morning (46.4% vs 33.3%; p = 0.013), ambulatory (38.3% vs 25.0%; p = 0.008), and masked hypertension (7.7% vs 0%; p = 0.001). Sustained, nocturnal, and morning hypertension increased markedly with age (p < 0.001), while true normotension declined. Higher income (₦301,000–₦500,000) independently predicted sustained (OR 12.12), nocturnal (OR 31.3), and morning hypertension (OR 20.8). Secondary education was protective. True normotension was more likely among adults aged 40–59 years and those with higher incomes. Conclusion: ABPM revealed a high burden of sustained, nocturnal, and morning hypertension undetectable by office BP alone. Strong socioeconomic and age gradients support wider ABPM integration and phenotype-specific management in sub-Saharan Africa. Ambulatory blood pressure monitoring hypertension phenotypes nocturnal hypertension morning hypertension masked hypertension white-coat hypertension Nigeria Figures Figure 1 Figure 2 Figure 3 Background Hypertension remains one of the leading contributors to global cardiovascular morbidity and mortality, and it continues to rise disproportionately in low- and middle-income countries (LMICs) despite declining rates in many high-income settings. 1 – 3 Sub-Saharan Africa, in particular, faces a growing burden driven by demographic ageing, lifestyle transitions, and persistently low levels of awareness, treatment, and control. 1 , 3 Despite this, conventional estimates of hypertension prevalence based solely on office blood pressure (BP) measurements may substantially underestimate the actual burden of abnormal BP regulation in the region, given the growing recognition of masked hypertension, nocturnal hypertension, and other ambulatory BP abnormalities that remain undetected in routine clinical practice. Although office BP measurement remains the most widely used screening tool, it captures only a limited snapshot of an individual’s BP profile and fails to reflect dynamic circadian patterns. 4 Ambulatory blood pressure monitoring (ABPM), by providing multiple measurements over the 24-hour cycle, including daytime activity, sleep periods, and early-morning transitions, offers a more comprehensive assessment of BP behaviour. 2 , 5 ABPM enables the identification of clinically relevant hypertension phenotypes, such as white-coat hypertension, masked hypertension, sustained hypertension, nocturnal hypertension, and morning hypertension, each associated with distinctive cardiovascular risk profiles. 2 , 5 Understanding these ABPM-derived phenotypes is increasingly important for modern hypertension care as this improves diagnostic accuracy, reduces misclassification, and enhances cardiovascular risk stratification by detecting abnormalities that office BP alone cannot reveal. 2 , 5 Sustained, nocturnal, and morning hypertension have been linked to greater cardiovascular mortality than normotension, underscoring the prognostic value of out-of-office assessments. 2 In addition, masked hypertension, present in an estimated 15–30% of individuals with normal clinic BP, often goes undetected without ABPM and is associated with risks similar to sustained hypertension. 6 , 7 White-coat hypertension, conversely, affects around 31% globally but varies widely across populations. 6 Nocturnal hypertension, prevalent in Asian populations due to high salt intake and salt sensitivity, occurs in roughly 23% of the general population in pooled studies. 8 , 9 In African populations, high rates of non-dipping and nocturnal hypertension have been reported, including a 78% non-dipping prevalence in Black Africans with uncontrolled hypertension in the CREOLE trial. 10 Kenyan population-based ABPM data report masked hypertension at 7.6% and white-coat hypertension at 3.8%, highlighting geographical variability. 11 Despite its diagnostic and prognostic advantages, ABPM remains underutilised across LMICs, including Nigeria. Limited availability, cost constraints, and lack of training contribute to its restricted use, resulting in sparse population-level data on ABPM-defined hypertension phenotypes. Consequently, the distribution, determinants, and clinical implications of these phenotypes remain poorly characterised in many African communities. This study sought to address this gap by characterising the spectrum of hypertension phenotypes using 24-hour ABPM in a semi-urban Nigerian population. The study examined the prevalence of ABPM-defined phenotypes and evaluated demographic and socioeconomic determinants of these patterns. Information on the distribution of phenotypes and their associated risk factors in this setting is essential for improving diagnostic precision, guiding targeted interventions, and informing population-level screening strategies within the evolving cardiovascular landscape of sub-Saharan Africa. Methods Study Design and Setting This community-based cross-sectional study was conducted in Ido-Ekiti, Ekiti State, Nigeria, as part of the Ambulatory Blood Pressure Study in Ido-Ekiti. The town represents a semi-urban population undergoing rapid lifestyle and socioeconomic transitions typical of many expanding Nigerian communities. Participants and Eligibility Criteria Adults aged ≥ 18 years who had resided in the community for at least six months and provided informed consent were eligible. Exclusion criteria included pregnancy, known cardiovascular disease, severe physical disability, and inability to tolerate ambulatory blood pressure monitoring (ABPM). Sampling Procedure A multi-stage sampling technique was employed to select a sample that best represented the community. The sample size was calculated to detect a hypertension prevalence of 28.9% with a precision of 5% and a 95% confidence level. Therefore, the minimum number of participants required was 316. Data Collection and Measurements Data were collected using structured WHO STEPS questionnaires and standard anthropometric procedures. Office blood pressure was measured using validated automated and mercury sphygmomanometers, in accordance with international guidelines. 12 The mean of two stable readings taken after a rest period was recorded. Office hypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg. Twenty-four-hour ABPM was performed using the CONTEC ABPM60® device. Measurements were taken every 30 minutes from 7:00 to 22:00 (daytime) and every 60 minutes from 22:00 to 7:00 (nighttime). Participants were instructed to maintain their usual activities and to record their waking and sleep times. Valid recordings required ≥ 20 daytime and ≥ 7 nighttime readings. Mean daytime, nighttime, and 24-hour blood pressure values were computed from all valid readings. Hypertension Phenotype Definitions Phenotypes were defined using office BP and ABPM thresholds as follows: 13 White-coat hypertension : Office BP ≥ 140/90 mmHg with normal 24-hour (< 130/80 mmHg), daytime (< 135/85 mmHg), and nighttime (< 120/70 mmHg) ambulatory BP. Masked hypertension : Normal office BP but elevated ABPM values (24-hour ≥ 130/80 mmHg, daytime ≥ 135/85 mmHg, or nighttime ≥ 120/70 mmHg). Ambulatory hypertension : Mean 24-hour BP ≥ 130/80 mmHg, or daytime BP ≥ 135/85 mmHg, or nighttime BP ≥ 120/70 mmHg. Sustained hypertension : Office BP ≥ 140/90 mmHg plus mean 24-hour BP ≥ 130/80 mmHg. Morning hypertension : Average morning BP ≥ 135/85 mmHg within the first two hours after waking, irrespective of office or other ABPM values. True normotension (or controlled hypertension) : Office BP < 140/90 mmHg and all ABPM values below diagnostic thresholds. Statistical Analysis All analyses were conducted using IBM SPSS version 29. Descriptive statistics summarised sociodemographic and clinical characteristics. The chi-square test was used to assess associations between categorical variables, and one-way ANOVA was used to compare continuous variables across phenotype groups. Statistical significance was set at p < 0.05. Multivariable binary logistic regression models were constructed to identify independent predictors of each hypertension phenotype. Predictor variables included age group, sex, monthly income, level of physical activity, work status, educational level, alcohol use and cigarette smoking in the last twelve months. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Results Distribution of Ambulatory and Office Blood Pressure Phenotypes The distribution of hypertension phenotypes is summarised in Fig. 1 . Sustained hypertension was present in 31.8% of participants, while ambulatory hypertension (based on daytime, nighttime, or 24-hour thresholds) was identified in 32.4%. Nocturnal hypertension was the most prevalent phenotype, affecting 56.8%, and isolated nocturnal hypertension occurred in 26.1%. Morning hypertension was also common (40.6%). White-coat hypertension accounted for 15.3%, whereas masked hypertension, though clinically relevant, was infrequent (4.3%). True normotension was present in 34.7% of participants. Collectively, sustained, nocturnal, and morning hypertension emerged as the dominant phenotypes in this semi-urban community. Sex Differences in Hypertension Phenotypes As shown in Table 1 , several phenotypes exhibited significant sex-related variation. Morning hypertension was more prevalent in females (46.4%) than males (33.3%) (χ² = 6.18, p = 0.013). Ambulatory hypertension followed a similar pattern (females 38.3% vs males 25.0%, χ² = 6.98, p = 0.008). Masked hypertension occurred exclusively in females (7.7%, χ² = 12.00, p = 0.001). True normotension was more common among males (41.0%) than females (29.6%) (χ² = 5.01, p = 0.025). Other phenotypes, including sustained hypertension, nocturnal hypertension, isolated nocturnal hypertension, and white-coat hypertension, showed no statistically significant sex differences. Table 1 Association between Hypertension Phenotypes and gender (N = 348) Hypertension Phenotype Male (%) Female (%) χ² df p-value Office Hypertension 38.0 47.4 3.09 1 0.079 Morning Hypertension 33.3 46.4 6.18 1 0.013 Ambulatory Hypertension 25.0 38.3 6.98 1 0.008 White Coat Hypertension 13.3 16.8 0.8 1 0.37 Masked Hypertension 0.0 7.7 12.0 1 0.001 Nocturnal Hypertension 53.2 59.7 1.49 1 0.222 Isolated Nocturnal Hypertension 29.5 23.5 1.63 1 0.202 Sustained Hypertension 30.7 32.7 0.16 1 0.694 True Normotension 41.0 29.6 5.01 1 0.025 Note. χ² = Chi-square test statistic; df = degrees of freedom. p-values are based on two-tailed tests. Statistical significance was set at p < .05. Age-Related Patterns in Hypertension Phenotypes Marked age gradients were observed across phenotypes (Table 2 , Fig. 2 ). Office hypertension increased from 16.0% among adults aged 18–39 years to 67.3% in those ≥ 60 years (χ² = 56.03, p < .001). Sustained hypertension rose sharply from 6.6% in the youngest group to 52.0% among older adults (χ² = 51.07, p < .001). Nocturnal hypertension showed the steepest age-associated rise, ranging from 31.8% in young adults to 86.7% in participants ≥ 60 years (χ² = 63.28, p < .001). Morning hypertension also increased significantly with age (χ² = 34.95, p < .001). In contrast, true normotension declined from 59.8% in young adults to 8.2% in those ≥ 60 years (χ² = 60.30, p < .001). White-coat hypertension and isolated nocturnal hypertension also varied significantly by age category, although the magnitudes of association were smaller than for the major phenotypes. Table 2 Association Between Blood Pressure Phenotypes and Age Groups (N = 348) Hypertension Phenotype 18–39 yrs (%) 40–59 yrs (%) ≥ 60 yrs (%) χ² Df p-value Office Hypertension 16.0 47.2 67.3 56.0 2 < .001 Morning Hypertension 18.7 44.9 58.2 35.0 2 < .001 Ambulatory Hypertension 9.3 38.8 48.0 39.5 2 < .001 White Coat Hypertension 9.4 14.1 23.5 8.0 2 0.018 Masked Hypertension 1.9 6.3 4.1 2.9 2 0.232 Nocturnal Hypertension 31.8 55.1 86.7 63.3 2 < .001 Isolated Nocturnal Hypertension 22.4 19.0 40.8 15.5 2 < .001 Sustained Hypertension 6.6 36.6 52.0 51.1 2 < .001 True Normotension 59.8 34.0 8.2 51.1 2 < .001 Note. χ² = Chi-square test statistic; df = degrees of freedom. p-values based on two-tailed tests. Statistical significance set at p < .05. Predictors of Hypertension Phenotypes Multivariable logistic regression results (Table 3 , Fig. 3 ) identified income, educational attainment, and age group as significant predictors of several phenotypes as follows: Sustained Hypertension Income ₦301,000–₦500,000 significantly increased odds (OR = 12.12, 95% CI: 1.30–112.89; p = .028). Secondary education was protective (OR = 0.09, 95% CI: 0.02–0.40; p = .001). Nocturnal Hypertension Higher income (₦301,000–₦500,000) was a strong predictor (OR = 31.3, 95% CI: 2.19–447.92; p = .011). Morning Hypertension Increased odds with income ₦301,000–₦500,000 (OR = 20.8, 95% CI: 2.18–198.82; p = .008). Secondary education reduced the likelihood (OR = 0.22, 95% CI: 0.06–0.82; p = .025). True Normotension More likely among adults aged 40–59 years (OR = 13.32, 95% CI: 1.75–101.57; p = .012). Increased among participants within the high-income category (OR = 4.68, 95% CI: 1.07–20.52; p = .041). No significant independent predictors were identified for white-coat or masked hypertension. Table 3 Binary Logistic Regression Analysis of Predictors of Hypertension Phenotypes Hypertension Phenotype Predictor Variable B S.E. Wald p-value Exp(B) 95% CI for Exp(B) Sustained Hypertension Monthly Income (₦301,000–₦500,000) 2.494 1.139 4.798 0.028 12.12 1.30–112.89 Educational Level (Secondary) -2.399 0.751 10.198 0.001 0.09 0.02–0.40 Age Group (≥ 60 years) -0.697 0.682 1.044 0.307 0.5 0.13–1.90 Constant -4.358 70753.865 0.0 1.0 0.01 — Nocturnal Hypertension Age Group (40–59 years) 1.501 0.794 3.571 0.059 4.48 0.95–21.27 Monthly Income (₦301,000–₦500,000) 3.444 1.358 6.433 0.011 31.3 2.19–447.92 Gender (Male) 0.326 0.433 0.568 0.451 1.39 0.59–3.23 Constant -16.653 69454.41 0.0 1.0 — — Morning Hypertension Monthly Income (₦301,000–₦500,000) 3.035 1.152 6.944 0.008 20.8 2.18–198.82 Educational Level (Secondary vs. No Formal) -1.538 0.684 5.053 0.025 0.22 0.06–0.82 Gender (Male) 0.49 0.416 1.389 0.239 1.63 0.72–3.69 Age Group (40–59 years) -0.033 0.659 0.003 0.96 0.97 0.27–3.52 Constant -42.32 70749.307 0.0 1.0 — — Masked Hypertension Gender (Male) 49.8 4159.892 0.0 0.99 4.25×10²¹ — Monthly Income (< ₦70,000) -17.435 2309.511 0.0 0.994 0 — Monthly Income (₦70,000–₦300,000) 48.656 4011.029 0.0 0.99 1.35×10²¹ — Constant 95.956 85967.31 0.0 0.999 — — White Coat Hypertension Monthly Income (₦301,000–₦500,000) -1.702 1.002 2.884 0.089 0.18 0.03–1.30 Gender (Male) 0.534 0.605 0.78 0.377 1.71 0.52–5.58 Age Group (40–59 years) 1.048 0.908 1.331 0.249 2.85 0.48–16.92 Constant 70.681 69504.69 0.0 0.999 — — True Normotension Age Group (40–59 years) 2.589 1.036 6.242 0.012 13.32 1.75–101.57 Monthly Income (₦301,000–₦500,000) 1.543 0.754 4.187 0.041 4.68 1.07–20.52 Gender (Male) 0.906 0.474 3.648 0.056 2.47 0.98–6.27 Constant -35.486 71391.34 0.0 1.0 — — DISCUSSION This community-based ABPM study reveals a substantial burden of out-of-office blood pressure abnormalities in a semi-urban Nigerian population, demonstrating that reliance on office BP alone would miss or misclassify several clinically relevant hypertension phenotypes. Notably, nocturnal hypertension (56.8%), morning hypertension (40.6%), and sustained hypertension (31.8%) emerged as the dominant phenotypes, while masked hypertension was comparatively rare (4.3%). These findings reinforce the need for broader adoption of ABPM in African health systems, where office-based diagnosis continues to dominate clinical practice despite well-known limitations. 4 Comparison With Sub-Saharan African and Global Evidence The prevalence of sustained hypertension in this sample aligns with national estimates from home BP–based studies in Nigeria, such as the REMAH study (38.1%). 14 It also broadly parallels regional data showing sustained hypertension rates of 34% in men and 48% in women across sub-Saharan Africa. 15 The sharp age gradient observed, rising from 6.6% in young adults to 52% in those ≥ 60 years, is consistent with African and global patterns. 10 , 16 – 19 This reflects the accumulation of vascular stiffness, metabolic risk, and reduced baroreflex sensitivity with ageing. 16 The high prevalence of nocturnal hypertension mirrors data from other African cohorts. For example, CREOLE trial participants demonstrated a 78% non-dipping rate, 10 and hospital-based Nigerian samples have reported nocturnal hypertension exceeding 70%. 20,21 Although the prevalence in this community cohort was comparatively lower, it remained higher than most Western estimates and comparable to African-American findings from the Jackson Heart Study (49–62%). 22 African populations, like Asian populations, typically demonstrate greater nocturnal BP loads, possibly due to salt sensitivity, autonomic dysregulation, sleep quality, and environmental factors. 9 , 23 Morning hypertension, present in two out of five participants, was also more frequent than the 20–35% typically reported in high-income countries. 24 Its higher prevalence in females aligns with evidence of increased morning BP surges in postmenopausal women and women with greater visceral adiposity or sleep-related disturbances. 25 Similar findings have been noted in Ghana and South African cohorts, though estimates vary. 26 , 27 The prevalence of masked and white-coat hypertension varies significantly across different regions. Worldwide, masked hypertension affects roughly 15–30% of individuals and carries a cardiovascular risk similar to that of sustained hypertension, while white-coat hypertension occurs in 31%. 6,7 In this study, the rate of masked hypertension was relatively low at 4.3% compared to global figures, aligning with findings from Kenya (7.6%) but lower than rates reported in Malawi among HIV-positive patients (35.8%). 5,11 The prevalence of white-coat hypertension (15.3%) was also below global estimates, 6,7 comparable to reports from Kenya (3.8%), 11 Nigeria (11.9%), 14 and HIV-positive cohorts in Malawi (12.6%). 5 These differences likely reflect variations in diagnostic criteria, population characteristics, and sample sizes; the small number of masked cases in our study might also magnify proportional variability. Nevertheless, the substantial regional differences highlight the importance of ABPM in accurately identifying hypertension phenotypes across sub-Saharan Africa. The exceptionally high masked hypertension prevalence among HIV-positive populations further emphasises the importance of including ABPM where possible. 5 Sociodemographic Determinants and Contextual Interpretations There was a clear, statistically significant rise in adverse hypertension phenotypes and a corresponding decline in true normotension across increasing age groups in this study. This pattern is consistent with age-related physiological changes, including arterial stiffness, endothelial dysfunction, and reduced baroreflex sensitivity, and mirrors findings from Nigerian and other African studies reporting increased hypertension prevalence with age. 17 , 19 Early Nigerian work similarly documented rising BP with ageing, 18 and global trends show that systolic BP increases steadily with age, while diastolic BP rises until midlife before plateauing or declining; these mechanisms largely explain phenotype shifts in older populations. 16 Morning hypertension, ambulatory hypertension, and masked hypertension were significantly more common among females, with masked hypertension occurring exclusively in women. This suggests a higher vulnerability to certain abnormal BP phenotypes, some of which are linked to elevated cardiovascular risk. An Italian Study also reported a higher prevalence of white-coat hypertension in women. 25 Other phenotypes, such as sustained, nocturnal, and white-coat hypertension, did not differ significantly by sex in this study, and similar inconsistencies in sex-specific patterns have been reported across African studies. 17 Gender did not independently predict any hypertension phenotype in the multivariable models in this study; however, evidence from other African cohorts suggests that female sex may be an independent risk factor for hypertension in older adults. 28 This indicates that while crude prevalence differences exist, other demographic or behavioural determinants may exert a stronger influence when adjusting for multiple factors. Income emerged as a consistent predictor of adverse phenotypes. Participants earning ₦301,000–₦500,000 (about $ 200- $ 350) per month had markedly increased odds of sustained nocturnal and morning hypertension. This contrasts with patterns in high-income countries, where lower socioeconomic status more often predicts hypertension. 29 However, in African settings undergoing rapid socioeconomic transition, higher income may be associated with dietary westernisation, reduced physical activity, greater psychosocial stress, and increased consumption of salt-rich convenience foods. Similar observations have been reported in Mozambique, where wealthier groups had higher hypertension prevalence. 30 This context-specific socioeconomic gradient is increasingly recognised as a hallmark of African hypertension epidemiology. 28 , 31 Secondary education was protective against sustained and morning hypertension. This may reflect improved health literacy, better dietary knowledge, or greater engagement with preventive health services. Yet, the association between education and hypertension remain inconsistent across African countries and may vary with urbanisation level, economic structures, and cultural behaviours. 28 Notably, gender was not an independent predictor of any phenotype in multivariable analysis despite significant crude differences in masked, ambulatory, and morning hypertension. This indicates that underlying sociodemographic or behavioural factors likely drive these apparent disparities rather than biological sex alone. Previous African studies have reported similarly inconsistent sex patterns. 15 , 25 , 28 Clinical and Public Health Implications The predominance of nocturnal and morning hypertension underscores the importance of circadian BP phenotyping in African clinical practice. These phenotypes are strongly associated with left ventricular hypertrophy, stroke, heart failure, and renal deterioration, independent of daytime BP. 32,33 Their detection has direct implications for treatment decisions, such as evaluating sleep disorders, adjusting medication timing (chronotherapy), and prioritising lifestyle measures that influence nighttime BP (salt intake, weight loss, sleep hygiene). Given the high prevalence of unrecognised abnormalities, ABPM should be prioritised for individuals with borderline office BP, discordant symptoms, or suspected resistant hypertension. At a population level, interventions targeting salt reduction, increased physical activity, and stress management could mitigate circadian BP abnormalities. Strengths and Limitations Strengths of this study include its community-based sampling, comprehensive ABPM protocol, and standardised phenotypic classification. Simultaneously evaluating multiple ABPM-defined phenotypes and sociodemographic determinants enhances interpretability. However, several limitations warrant consideration. The cross-sectional design prevents causal inference. Self-reported behavioural data may be influenced by recall and social desirability biases. The sample, drawn from a single semi-urban town, may not be generalisable to rural or metropolitan Nigerian populations. Small numbers in some subgroups (e.g., masked hypertension) limit the stability of regression estimates. Finally, resource constraints inherent to ABPM studies in LMICs prevented the collection of biomarkers or actigraphy data that could further refine sleep–wake classification. Conclusion This study demonstrates a high burden of nocturnal and morning hypertension and significant age and socioeconomic gradients in a semi-urban Nigerian population. These findings highlight the limitations of office BP alone and emphasise the need for broader integration of ABPM in primary care systems, along with phenotype-specific management strategies. Further prospective research is needed to elucidate the prognostic implications of circadian BP abnormalities and to inform cost-effective, context-appropriate interventions in African populations. Declarations Corresponding Author Ebenezer Adekunle Ajayi Ethical Approval Ethical approval was granted by the Research and Ethics Committee of the Federal Teaching Hospital, Ido Ekiti (Protocol No: ERC/2025/05/06/1244A). Written informed consent was secured from all participants. Confidentiality was maintained through anonymised data coding, and all procedures adhered to the Declaration of Helsinki (2013 revision). Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This study received no specific funding from public, commercial, or not-for-profit sources. Author Contribution AEA drafted the proposal for this study, participated in data collection, analysis, and interpretation, and was involved in both the initial and final drafts of the manuscript.FJO participated in data analysis, interpretation, and both the initial and final drafts of the manuscript.ABA participated in study design, data collection and analysis, and the initial draft of the manuscript.AO was involved in the design of the study, data analysis, interpretation of results, and preparation of the final draft of the manuscript. Acknowledgement We acknowledge and thank Dr. Toba Osasona and Mr. Olaoluwa Akinsanoye, of the Federal Teaching Hospital, Ido Ekiti, for their assistance in collecting data for this study. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 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Ogah OS, Okpechi I, Chukwuonye II, Akinyemi JO, Onwubere BJ, Falase AO, et al. Blood pressure, prevalence of hypertension and hypertension related complications in Nigerian Africans: A review. World J Cardiol. 2012;4:327. Olamoyegun M, Oluyombo R, Iwuala SO, Asaolu S. Epidemiology and patterns of hypertension in semi-urban communities, south-western Nigeria. Cardiovascular journal of South Africa/Cardiovascular journal of Southern Africa. 2016;27:356. Available from: https://doi.org/10.5830/cvja-2016-037 Ajayi OE, Ajayi EA, Akintomide OA, Adebayo RA, Ogunyemi SA, Oyedeji AT, Balogun MO. Ambulatory blood pressure profile and left ventricular geometry in Nigerian hypertensives. J Cardiovasc Dis Res. 2011;2(3):164–71. 10.4103/0975-3583.85263 . Amjo I, Adebayo R, Akinyele OA, Olanipekun OA, Adesanya OS, Williams OT et al. Diurnal rhythm of blood pressure among Nigerians with hypertension using 24-hour ambulatory blood pressure monitoring. Pan African Medical Journal. 2020;36:240. Available from: https://doi.org/10.11604/pamj.2020.36.240.24088 Thomas SJ, Booth JN, Bromfield SG, Seals SR, Spruill TM, Ogedegbe G et al. Clinic and ambulatory blood pressure in a population-based sample of African Americans: the Jackson Heart Study. Journal of the American Society of Hypertension. 2017;11:204. Available from: https://doi.org/10.1016/j.jash.2017.02.001 Tang A, Yang E, Ebinger JE. Non-Dipping Blood Pressure or Nocturnal Hypertension: Does One Matter More? Current Hypertension Reports. 2023;26:21. Available from: https://doi.org/10.1007/s11906-023-01273-1 Kario K, Wang J, Chia YC, Wang T, Li Y, Siddique S et al. The HOPE Asia network 2022 up-date consensus statement on morning hypertension management. Journal of Clinical Hypertension. 2022;24:1112. Available from: https://doi.org/10.1111/jch.14555 Omboni S, Khan N, Kunadian V, Olszanecka A, Schutte AE, Mihailidou AS. Sex Differences in Ambulatory Blood Pressure Levels and Subtypes in a Large Italian Community Cohort. Hypertension. 2023;80:1417. Available from: https://doi.org/10.1161/hypertensionaha.122.20589 Pisa PT, Micklesfield LK, Kagura J, Ramsay M, Crowther NJ, Norris SA. Different adiposity indices and their association with blood pressure and hypertension in middle-aged urban black South African men and women: findings from the AWI-GEN South African Soweto Site. BMC Public Health. 2018;18:524. Available from: https://doi.org/10.1186/s12889-018-5443-4 Atibila F, Hoor GA, ten, Donkoh ET, Abdul IW, Kok G. Prevalence of hypertension in Ghanaian society: a systematic review, meta-analysis, and GRADE assessment. Systematic Reviews. BioMed Central; 2021;10:220. Available from: https://doi.org/10.1186/s13643-021-01770-x Bosu WK, Aheto JMK, Zucchelli E, Reilly ST. Determinants of systemic hypertension in older adults in Africa: a systematic review. BMC Cardiovascular Disorders. 2019;19:173. Available from: https://bmccardiovascdisord.biomedcentral.com/articles/ 10.1186/s12872-019-1147-7 Nakagomi A, Yasufuku Y, Ueno T, Kondo K. Social determinants of hypertension in high-income countries: A narrative literature review and future directions. Hypertension Research. 2022;45:1575. Available from: https://www.nature.com/articles/s41440-022-00972-7 Matsuzaki M, Sherr K, Augusto O, Kawakatsu Y, Ásbjörnsdóttir K, Chale F et al. The prevalence of hypertension and its distribution by sociodemographic factors in Central Mozambique: a cross sectional study. BMC Public Health. 2020;20:1843. Available from: https://doi.org/10.1186/s12889-020-09947-0 Cois A, Ehrlich R. Analysing the socioeconomic determinants of hypertension in South Africa: a structural equation modelling approach. BMC Public Health. 2014;14:414. Available from: https://doi.org/10.1186/1471-2458-14-414 Liu J, Li Y, Asayama K, Zhang XY, Cheng H, Park S et al. Asian Expert Consensus on Nocturnal Hypertension Management. Hypertension. 2025;82:945. Available from: https://doi.org/10.1161/hypertensionaha.124.24026 Parati G, Pengo MF, Avolio A, Azizi M, Bothe TL, Burnier M et al. Nocturnal blood pressure: pathophysiology, measurement and clinical implications. Position paper of the European Society of Hypertension. Journal of Hypertension. 2025;43:1296. Available from: https://doi.org/10.1097/hjh.0000000000004053 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8484932","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580135071,"identity":"ed72841d-a50c-49d0-9993-f9983c6224de","order_by":0,"name":"Ebenezer Adekunle Ajayi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACPmbGB1BmApjFw0dICxszswFMC5jFw0ZQCwNCC5sERISQFnagy37U3JPjb08+Vvk1x04GaMjDRzfwO4zZsOdYsbHEmWdpt2W3JQMdxmZsnINXC/8xaQa2hMSGGzlmtyW3MQO18LBJ49fCzP6b4V9C/XyglmLJbfVEaWFjZmxLSDAAamH8uO0wUVqYJXv7Egw3nnmWLM247TgPGzMBv/DzH2b88ONbgrzc8eSDH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAFNaPXsf/EqIAAAAAElFTkSuQmCC","orcid":"","institution":"Federal Teaching Hospital Ido-Ekiti","correspondingAuthor":true,"prefix":"","firstName":"Ebenezer","middleName":"Adekunle","lastName":"Ajayi","suffix":""},{"id":580135073,"identity":"6607c91a-79d4-4eca-8815-482e85e14152","order_by":1,"name":"Joseph Olusesan Fadare","email":"","orcid":"","institution":"Ekiti State University","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"Olusesan","lastName":"Fadare","suffix":""},{"id":580135075,"identity":"e7b08e57-cc60-4615-9bcf-807acf6d06f1","order_by":2,"name":"Babajide Adewoyin Adeleke","email":"","orcid":"","institution":"Federal Teaching Hospital Ido-Ekiti","correspondingAuthor":false,"prefix":"","firstName":"Babajide","middleName":"Adewoyin","lastName":"Adeleke","suffix":""},{"id":580135076,"identity":"3128553c-83ff-4a3a-b270-53027f573b07","order_by":3,"name":"Olusegun Atolani","email":"","orcid":"","institution":"Federal Teaching Hospital Ido-Ekiti","correspondingAuthor":false,"prefix":"","firstName":"Olusegun","middleName":"","lastName":"Atolani","suffix":""}],"badges":[],"createdAt":"2025-12-31 01:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8484932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8484932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101353518,"identity":"202bcc30-646f-4320-9327-2831b33499a5","added_by":"auto","created_at":"2026-01-28 19:31:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63891,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of hypertension phenotypes among participants\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8484932/v1/81291116bbce840ada2b60a5.png"},{"id":101353519,"identity":"5a479278-eba5-4473-9d06-5d8c28416988","added_by":"auto","created_at":"2026-01-28 19:31:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200412,"visible":true,"origin":"","legend":"\u003cp\u003eStacked Phenotype Distribution by Age\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8484932/v1/6da73d695aa5d669cd53df1c.png"},{"id":101353520,"identity":"25c15de2-6201-43bf-bf3c-c7b13b7d90fc","added_by":"auto","created_at":"2026-01-28 19:31:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119812,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing significant predictors of hypertension phenotypes based on binary logistic regression analysis. Odds ratios (Exp(B)) are presented with 95% confidence intervals. The vertical dashed line represents the null effect (OR = 1).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8484932/v1/bf870ef051f01fcb9b366222.png"},{"id":101397880,"identity":"c6a12634-1d7d-4f3e-a417-d0b68968b2df","added_by":"auto","created_at":"2026-01-29 09:37:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704373,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8484932/v1/84525436-716b-403e-9bc3-f92f6073b052.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Determinants of Ambulatory Hypertension Phenotypes in a Semi-Urban Nigerian Population","fulltext":[{"header":"Background","content":"\u003cp\u003eHypertension remains one of the leading contributors to global cardiovascular morbidity and mortality, and it continues to rise disproportionately in low- and middle-income countries (LMICs) despite declining rates in many high-income settings.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Sub-Saharan Africa, in particular, faces a growing burden driven by demographic ageing, lifestyle transitions, and persistently low levels of awareness, treatment, and control.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Despite this, conventional estimates of hypertension prevalence based solely on office blood pressure (BP) measurements may substantially underestimate the actual burden of abnormal BP regulation in the region, given the growing recognition of masked hypertension, nocturnal hypertension, and other ambulatory BP abnormalities that remain undetected in routine clinical practice.\u003c/p\u003e \u003cp\u003eAlthough office BP measurement remains the most widely used screening tool, it captures only a limited snapshot of an individual\u0026rsquo;s BP profile and fails to reflect dynamic circadian patterns.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Ambulatory blood pressure monitoring (ABPM), by providing multiple measurements over the 24-hour cycle, including daytime activity, sleep periods, and early-morning transitions, offers a more comprehensive assessment of BP behaviour.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e ABPM enables the identification of clinically relevant hypertension phenotypes, such as white-coat hypertension, masked hypertension, sustained hypertension, nocturnal hypertension, and morning hypertension, each associated with distinctive cardiovascular risk profiles.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUnderstanding these ABPM-derived phenotypes is increasingly important for modern hypertension care as this improves diagnostic accuracy, reduces misclassification, and enhances cardiovascular risk stratification by detecting abnormalities that office BP alone cannot reveal.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Sustained, nocturnal, and morning hypertension have been linked to greater cardiovascular mortality than normotension, underscoring the prognostic value of out-of-office assessments.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In addition, masked hypertension, present in an estimated 15\u0026ndash;30% of individuals with normal clinic BP, often goes undetected without ABPM and is associated with risks similar to sustained hypertension.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e White-coat hypertension, conversely, affects around 31% globally but varies widely across populations.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Nocturnal hypertension, prevalent in Asian populations due to high salt intake and salt sensitivity, occurs in roughly 23% of the general population in pooled studies.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e In African populations, high rates of non-dipping and nocturnal hypertension have been reported, including a 78% non-dipping prevalence in Black Africans with uncontrolled hypertension in the CREOLE trial.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Kenyan population-based ABPM data report masked hypertension at 7.6% and white-coat hypertension at 3.8%, highlighting geographical variability.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite its diagnostic and prognostic advantages, ABPM remains underutilised across LMICs, including Nigeria. Limited availability, cost constraints, and lack of training contribute to its restricted use, resulting in sparse population-level data on ABPM-defined hypertension phenotypes. Consequently, the distribution, determinants, and clinical implications of these phenotypes remain poorly characterised in many African communities.\u003c/p\u003e \u003cp\u003eThis study sought to address this gap by characterising the spectrum of hypertension phenotypes using 24-hour ABPM in a semi-urban Nigerian population. The study examined the prevalence of ABPM-defined phenotypes and evaluated demographic and socioeconomic determinants of these patterns. Information on the distribution of phenotypes and their associated risk factors in this setting is essential for improving diagnostic precision, guiding targeted interventions, and informing population-level screening strategies within the evolving cardiovascular landscape of sub-Saharan Africa.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis community-based cross-sectional study was conducted in Ido-Ekiti, Ekiti State, Nigeria, as part of the Ambulatory Blood Pressure Study in Ido-Ekiti. The town represents a semi-urban population undergoing rapid lifestyle and socioeconomic transitions typical of many expanding Nigerian communities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who had resided in the community for at least six months and provided informed consent were eligible. Exclusion criteria included pregnancy, known cardiovascular disease, severe physical disability, and inability to tolerate ambulatory blood pressure monitoring (ABPM).\u003c/p\u003e\n\u003ch3\u003eSampling Procedure\u003c/h3\u003e\n\u003cp\u003eA multi-stage sampling technique was employed to select a sample that best represented the community. The sample size was calculated to detect a hypertension prevalence of 28.9% with a precision of 5% and a 95% confidence level. Therefore, the minimum number of participants required was 316.\u003c/p\u003e\n\u003ch3\u003eData Collection and Measurements\u003c/h3\u003e\n\u003cp\u003eData were collected using structured WHO STEPS questionnaires and standard anthropometric procedures. Office blood pressure was measured using validated automated and mercury sphygmomanometers, in accordance with international guidelines.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The mean of two stable readings taken after a rest period was recorded. Office hypertension was defined as systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg.\u003c/p\u003e \u003cp\u003eTwenty-four-hour ABPM was performed using the CONTEC ABPM60\u0026reg; device. Measurements were taken every 30 minutes from 7:00 to 22:00 (daytime) and every 60 minutes from 22:00 to 7:00 (nighttime). Participants were instructed to maintain their usual activities and to record their waking and sleep times. Valid recordings required\u0026thinsp;\u0026ge;\u0026thinsp;20 daytime and \u0026ge;\u0026thinsp;7 nighttime readings. Mean daytime, nighttime, and 24-hour blood pressure values were computed from all valid readings.\u003c/p\u003e\n\u003ch3\u003eHypertension Phenotype Definitions\u003c/h3\u003e\n\u003cp\u003ePhenotypes were defined using office BP and ABPM thresholds as follows:\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWhite-coat hypertension\u003c/b\u003e: Office BP\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg with normal 24-hour (\u0026lt;\u0026thinsp;130/80 mmHg), daytime (\u0026lt;\u0026thinsp;135/85 mmHg), and nighttime (\u0026lt;\u0026thinsp;120/70 mmHg) ambulatory BP.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMasked hypertension\u003c/b\u003e: Normal office BP but elevated ABPM values (24-hour\u0026thinsp;\u0026ge;\u0026thinsp;130/80 mmHg, daytime\u0026thinsp;\u0026ge;\u0026thinsp;135/85 mmHg, or nighttime\u0026thinsp;\u0026ge;\u0026thinsp;120/70 mmHg).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAmbulatory hypertension\u003c/b\u003e: Mean 24-hour BP\u0026thinsp;\u0026ge;\u0026thinsp;130/80 mmHg, or daytime BP\u0026thinsp;\u0026ge;\u0026thinsp;135/85 mmHg, or nighttime BP\u0026thinsp;\u0026ge;\u0026thinsp;120/70 mmHg.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSustained hypertension\u003c/b\u003e: Office BP\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg plus mean 24-hour BP\u0026thinsp;\u0026ge;\u0026thinsp;130/80 mmHg.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMorning hypertension\u003c/b\u003e: Average morning BP\u0026thinsp;\u0026ge;\u0026thinsp;135/85 mmHg within the first two hours after waking, irrespective of office or other ABPM values.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrue normotension (or controlled hypertension)\u003c/b\u003e: Office BP\u0026thinsp;\u0026lt;\u0026thinsp;140/90 mmHg and all ABPM values below diagnostic thresholds.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using IBM SPSS version 29. Descriptive statistics summarised sociodemographic and clinical characteristics. The chi-square test was used to assess associations between categorical variables, and one-way ANOVA was used to compare continuous variables across phenotype groups. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMultivariable binary logistic regression models were constructed to identify independent predictors of each hypertension phenotype. Predictor variables included age group, sex, monthly income, level of physical activity, work status, educational level, alcohol use and cigarette smoking in the last twelve months. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of Ambulatory and Office Blood Pressure Phenotypes\u003c/h2\u003e \u003cp\u003eThe distribution of hypertension phenotypes is summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Sustained hypertension was present in 31.8% of participants, while ambulatory hypertension (based on daytime, nighttime, or 24-hour thresholds) was identified in 32.4%. Nocturnal hypertension was the most prevalent phenotype, affecting 56.8%, and isolated nocturnal hypertension occurred in 26.1%. Morning hypertension was also common (40.6%). White-coat hypertension accounted for 15.3%, whereas masked hypertension, though clinically relevant, was infrequent (4.3%). True normotension was present in 34.7% of participants. Collectively, sustained, nocturnal, and morning hypertension emerged as the dominant phenotypes in this semi-urban community.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSex Differences in Hypertension Phenotypes\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, several phenotypes exhibited significant sex-related variation. Morning hypertension was more prevalent in females (46.4%) than males (33.3%) (χ\u0026sup2; = 6.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Ambulatory hypertension followed a similar pattern (females 38.3% vs males 25.0%, χ\u0026sup2; = 6.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Masked hypertension occurred exclusively in females (7.7%, χ\u0026sup2; = 12.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). True normotension was more common among males (41.0%) than females (29.6%) (χ\u0026sup2; = 5.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e \u003cp\u003eOther phenotypes, including sustained hypertension, nocturnal hypertension, isolated nocturnal hypertension, and white-coat hypertension, showed no statistically significant sex differences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAssociation between Hypertension Phenotypes and gender (N\u0026thinsp;=\u0026thinsp;348)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOffice Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorning Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmbulatory Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite Coat Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMasked Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNocturnal Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIsolated Nocturnal Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSustained Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrue Normotension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. χ\u0026sup2; = Chi-square test statistic; df\u0026thinsp;=\u0026thinsp;degrees of freedom. p-values are based on two-tailed tests. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAge-Related Patterns in Hypertension Phenotypes\u003c/h2\u003e \u003cp\u003eMarked age gradients were observed across phenotypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Office hypertension increased from 16.0% among adults aged 18\u0026ndash;39 years to 67.3% in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years (χ\u0026sup2; = 56.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Sustained hypertension rose sharply from 6.6% in the youngest group to 52.0% among older adults (χ\u0026sup2; = 51.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eNocturnal hypertension showed the steepest age-associated rise, ranging from 31.8% in young adults to 86.7% in participants\u0026thinsp;\u0026ge;\u0026thinsp;60 years (χ\u0026sup2; = 63.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Morning hypertension also increased significantly with age (χ\u0026sup2; = 34.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In contrast, true normotension declined from 59.8% in young adults to 8.2% in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years (χ\u0026sup2; = 60.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). White-coat hypertension and isolated nocturnal hypertension also varied significantly by age category, although the magnitudes of association were smaller than for the major phenotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAssociation Between Blood Pressure Phenotypes and Age Groups (N\u0026thinsp;=\u0026thinsp;348)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;39 yrs (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026ndash;59 yrs (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 yrs (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOffice Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorning Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmbulatory Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite Coat Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMasked Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNocturnal Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIsolated Nocturnal Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSustained Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrue Normotension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. χ\u0026sup2; = Chi-square test statistic; df\u0026thinsp;=\u0026thinsp;degrees of freedom. p-values based on two-tailed tests. Statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of Hypertension Phenotypes\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) identified income, educational attainment, and age group as significant predictors of several phenotypes as follows:\u003c/p\u003e \u003cp\u003eSustained Hypertension\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIncome ₦301,000\u0026ndash;₦500,000 significantly increased odds (OR\u0026thinsp;=\u0026thinsp;12.12, 95% CI: 1.30\u0026ndash;112.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecondary education was protective (OR\u0026thinsp;=\u0026thinsp;0.09, 95% CI: 0.02\u0026ndash;0.40; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eNocturnal Hypertension\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHigher income (₦301,000\u0026ndash;₦500,000) was a strong predictor (OR\u0026thinsp;=\u0026thinsp;31.3, 95% CI: 2.19\u0026ndash;447.92; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eMorning Hypertension\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIncreased odds with income ₦301,000\u0026ndash;₦500,000 (OR\u0026thinsp;=\u0026thinsp;20.8, 95% CI: 2.18\u0026ndash;198.82; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecondary education reduced the likelihood (OR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.06\u0026ndash;0.82; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.025).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTrue Normotension\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMore likely among adults aged 40\u0026ndash;59 years (OR\u0026thinsp;=\u0026thinsp;13.32, 95% CI: 1.75\u0026ndash;101.57; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.012).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIncreased among participants within the high-income category (OR\u0026thinsp;=\u0026thinsp;4.68, 95% CI: 1.07\u0026ndash;20.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.041).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eNo significant independent predictors were identified for white-coat or masked hypertension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBinary Logistic Regression Analysis of Predictors of Hypertension Phenotypes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Phenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI for Exp(B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSustained Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦301,000\u0026ndash;₦500,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.30\u0026ndash;112.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational Level (Secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u0026ndash;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Group (\u0026ge;\u0026thinsp;60 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u0026ndash;1.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70753.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNocturnal Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Group (40\u0026ndash;59 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u0026ndash;21.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦301,000\u0026ndash;₦500,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.19\u0026ndash;447.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u0026ndash;3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-16.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69454.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMorning Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦301,000\u0026ndash;₦500,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.18\u0026ndash;198.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational Level (Secondary vs. No Formal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u0026ndash;3.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Group (40\u0026ndash;59 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u0026ndash;3.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-42.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70749.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMasked Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4159.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u0026times;10\u0026sup2;\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (\u0026lt; ₦70,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-17.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2309.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦70,000\u0026ndash;₦300,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4011.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.35\u0026times;10\u0026sup2;\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85967.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWhite Coat Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦301,000\u0026ndash;₦500,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u0026ndash;1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u0026ndash;5.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Group (40\u0026ndash;59 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u0026ndash;16.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69504.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTrue Normotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge Group (40\u0026ndash;59 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.75\u0026ndash;101.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly Income (₦301,000\u0026ndash;₦500,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u0026ndash;20.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u0026ndash;6.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-35.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71391.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis community-based ABPM study reveals a substantial burden of out-of-office blood pressure abnormalities in a semi-urban Nigerian population, demonstrating that reliance on office BP alone would miss or misclassify several clinically relevant hypertension phenotypes. Notably, nocturnal hypertension (56.8%), morning hypertension (40.6%), and sustained hypertension (31.8%) emerged as the dominant phenotypes, while masked hypertension was comparatively rare (4.3%). These findings reinforce the need for broader adoption of ABPM in African health systems, where office-based diagnosis continues to dominate clinical practice despite well-known limitations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison With Sub-Saharan African and Global Evidence\u003c/h2\u003e \u003cp\u003eThe prevalence of sustained hypertension in this sample aligns with national estimates from home BP\u0026ndash;based studies in Nigeria, such as the REMAH study (38.1%).\u003csup\u003e14\u003c/sup\u003e It also broadly parallels regional data showing sustained hypertension rates of 34% in men and 48% in women across sub-Saharan Africa.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The sharp age gradient observed, rising from 6.6% in young adults to 52% in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years, is consistent with African and global patterns.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e This reflects the accumulation of vascular stiffness, metabolic risk, and reduced baroreflex sensitivity with ageing.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe high prevalence of nocturnal hypertension mirrors data from other African cohorts. For example, CREOLE trial participants demonstrated a 78% non-dipping rate,\u003csup\u003e10\u003c/sup\u003e and hospital-based Nigerian samples have reported nocturnal hypertension exceeding 70%.\u003csup\u003e20,21\u003c/sup\u003e Although the prevalence in this community cohort was comparatively lower, it remained higher than most Western estimates and comparable to African-American findings from the Jackson Heart Study (49\u0026ndash;62%).\u003csup\u003e22\u003c/sup\u003e African populations, like Asian populations, typically demonstrate greater nocturnal BP loads, possibly due to salt sensitivity, autonomic dysregulation, sleep quality, and environmental factors.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMorning hypertension, present in two out of five participants, was also more frequent than the 20\u0026ndash;35% typically reported in high-income countries.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Its higher prevalence in females aligns with evidence of increased morning BP surges in postmenopausal women and women with greater visceral adiposity or sleep-related disturbances.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Similar findings have been noted in Ghana and South African cohorts, though estimates vary.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe prevalence of masked and white-coat hypertension varies significantly across different regions. Worldwide, masked hypertension affects roughly 15\u0026ndash;30% of individuals and carries a cardiovascular risk similar to that of sustained hypertension, while white-coat hypertension occurs in 31%.\u003csup\u003e6,7\u003c/sup\u003e In this study, the rate of masked hypertension was relatively low at 4.3% compared to global figures, aligning with findings from Kenya (7.6%) but lower than rates reported in Malawi among HIV-positive patients (35.8%).\u003csup\u003e5,11\u003c/sup\u003e The prevalence of white-coat hypertension (15.3%) was also below global estimates,\u003csup\u003e6,7\u003c/sup\u003e comparable to reports from Kenya (3.8%),\u003csup\u003e11\u003c/sup\u003e Nigeria (11.9%),\u003csup\u003e14\u003c/sup\u003e and HIV-positive cohorts in Malawi (12.6%).\u003csup\u003e5\u003c/sup\u003e These differences likely reflect variations in diagnostic criteria, population characteristics, and sample sizes; the small number of masked cases in our study might also magnify proportional variability. Nevertheless, the substantial regional differences highlight the importance of ABPM in accurately identifying hypertension phenotypes across sub-Saharan Africa. The exceptionally high masked hypertension prevalence among HIV-positive populations further emphasises the importance of including ABPM where possible.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Determinants and Contextual Interpretations\u003c/h2\u003e \u003cp\u003eThere was a clear, statistically significant rise in adverse hypertension phenotypes and a corresponding decline in true normotension across increasing age groups in this study. This pattern is consistent with age-related physiological changes, including arterial stiffness, endothelial dysfunction, and reduced baroreflex sensitivity, and mirrors findings from Nigerian and other African studies reporting increased hypertension prevalence with age.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Early Nigerian work similarly documented rising BP with ageing,\u003csup\u003e18\u003c/sup\u003e and global trends show that systolic BP increases steadily with age, while diastolic BP rises until midlife before plateauing or declining; these mechanisms largely explain phenotype shifts in older populations.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Morning hypertension, ambulatory hypertension, and masked hypertension were significantly more common among females, with masked hypertension occurring exclusively in women. This suggests a higher vulnerability to certain abnormal BP phenotypes, some of which are linked to elevated cardiovascular risk. An Italian Study also reported a higher prevalence of white-coat hypertension in women.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Other phenotypes, such as sustained, nocturnal, and white-coat hypertension, did not differ significantly by sex in this study, and similar inconsistencies in sex-specific patterns have been reported across African studies.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGender did not independently predict any hypertension phenotype in the multivariable models in this study; however, evidence from other African cohorts suggests that female sex may be an independent risk factor for hypertension in older adults.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e This indicates that while crude prevalence differences exist, other demographic or behavioural determinants may exert a stronger influence when adjusting for multiple factors.\u003c/p\u003e \u003cp\u003eIncome emerged as a consistent predictor of adverse phenotypes. Participants earning ₦301,000\u0026ndash;₦500,000 (about \u003cspan\u003e$\u003c/span\u003e200-\u003cspan\u003e$\u003c/span\u003e350) per month had markedly increased odds of sustained nocturnal and morning hypertension. This contrasts with patterns in high-income countries, where lower socioeconomic status more often predicts hypertension.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e However, in African settings undergoing rapid socioeconomic transition, higher income may be associated with dietary westernisation, reduced physical activity, greater psychosocial stress, and increased consumption of salt-rich convenience foods. Similar observations have been reported in Mozambique, where wealthier groups had higher hypertension prevalence.\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e This context-specific socioeconomic gradient is increasingly recognised as a hallmark of African hypertension epidemiology.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSecondary education was protective against sustained and morning hypertension. This may reflect improved health literacy, better dietary knowledge, or greater engagement with preventive health services. Yet, the association between education and hypertension remain inconsistent across African countries and may vary with urbanisation level, economic structures, and cultural behaviours.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNotably, gender was not an independent predictor of any phenotype in multivariable analysis despite significant crude differences in masked, ambulatory, and morning hypertension. This indicates that underlying sociodemographic or behavioural factors likely drive these apparent disparities rather than biological sex alone. Previous African studies have reported similarly inconsistent sex patterns.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Public Health Implications\u003c/h2\u003e \u003cp\u003eThe predominance of nocturnal and morning hypertension underscores the importance of circadian BP phenotyping in African clinical practice. These phenotypes are strongly associated with left ventricular hypertrophy, stroke, heart failure, and renal deterioration, independent of daytime BP.\u003csup\u003e32,33\u003c/sup\u003e Their detection has direct implications for treatment decisions, such as evaluating sleep disorders, adjusting medication timing (chronotherapy), and prioritising lifestyle measures that influence nighttime BP (salt intake, weight loss, sleep hygiene).\u003c/p\u003e \u003cp\u003eGiven the high prevalence of unrecognised abnormalities, ABPM should be prioritised for individuals with borderline office BP, discordant symptoms, or suspected resistant hypertension. At a population level, interventions targeting salt reduction, increased physical activity, and stress management could mitigate circadian BP abnormalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eStrengths of this study include its community-based sampling, comprehensive ABPM protocol, and standardised phenotypic classification. Simultaneously evaluating multiple ABPM-defined phenotypes and sociodemographic determinants enhances interpretability.\u003c/p\u003e \u003cp\u003eHowever, several limitations warrant consideration. The cross-sectional design prevents causal inference. Self-reported behavioural data may be influenced by recall and social desirability biases. The sample, drawn from a single semi-urban town, may not be generalisable to rural or metropolitan Nigerian populations. Small numbers in some subgroups (e.g., masked hypertension) limit the stability of regression estimates. Finally, resource constraints inherent to ABPM studies in LMICs prevented the collection of biomarkers or actigraphy data that could further refine sleep\u0026ndash;wake classification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates a high burden of nocturnal and morning hypertension and significant age and socioeconomic gradients in a semi-urban Nigerian population. These findings highlight the limitations of office BP alone and emphasise the need for broader integration of ABPM in primary care systems, along with phenotype-specific management strategies. Further prospective research is needed to elucidate the prognostic implications of circadian BP abnormalities and to inform cost-effective, context-appropriate interventions in African populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCorresponding Author\u003c/h2\u003e \u003cp\u003eEbenezer Adekunle Ajayi\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e Ethical approval was granted by the Research and Ethics Committee of the Federal Teaching Hospital, Ido Ekiti (Protocol No: ERC/2025/05/06/1244A). Written informed consent was secured from all participants. Confidentiality was maintained through anonymised data coding, and all procedures adhered to the Declaration of Helsinki (2013 revision).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no specific funding from public, commercial, or not-for-profit sources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAEA drafted the proposal for this study, participated in data collection, analysis, and interpretation, and was involved in both the initial and final drafts of the manuscript.FJO participated in data analysis, interpretation, and both the initial and final drafts of the manuscript.ABA participated in study design, data collection and analysis, and the initial draft of the manuscript.AO was involved in the design of the study, data analysis, interpretation of results, and preparation of the final draft of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge and thank Dr. Toba Osasona and Mr. Olaoluwa Akinsanoye, of the Federal Teaching Hospital, Ido Ekiti, for their assistance in collecting data for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeloye D, Owolabi EO, Ojji DB, Auta A, Dewan MT, Olanrewaju TO et al. Prevalence, awareness, treatment, and control of hypertension in Nigeria in 1995 and 2020: A systematic analysis of current evidence. The Journal of Clinical Hypertension. 2021;23:963. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.14220\u003c/span\u003e\u003cspan address=\"10.1111/jch.14220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTewari J, Qidwai KA, Roy S, Saxena M, Rana A, Tewari A et al. Different phenotypes of hypertension and associated cardiovascular and all-cause mortality: a systematic review and meta-analysis. The Egyptian Heart Journal. 2024;76:162. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/hjh.0000000000004053\u003c/span\u003e\u003cspan address=\"10.1097/hjh.0000000000004053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ambulatory blood pressure monitoring, hypertension phenotypes, nocturnal hypertension, morning hypertension, masked hypertension, white-coat hypertension, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-8484932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8484932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAmbulatory blood pressure monitoring (ABPM) provides more accurate diagnostic and prognostic information than office blood pressure (BP), yet it remains underutilised in sub-Saharan Africa. As a result, the actual distribution of hypertension phenotypes identified through ABPM is poorly understood. This study aimed to determine the prevalence and determinants of hypertension phenotypes using 24-hour ABPM in a semi-urban Nigerian population.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis community-based cross-sectional study involved 348 adults in Ido-Ekiti, Nigeria. Sociodemographic and clinical data were collected using the WHO STEPS protocol. Office BP was measured with standardised methods, and 24-hour ABPM (CONTEC ABPM60\u0026reg;) provided daytime, night-time, and 24-hour BP readings. Hypertension phenotypes were defined using standard thresholds. Multivariable logistic regression identified predictors.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eSustained hypertension occurred in 31.8%, ambulatory hypertension in 32.4%, nocturnal hypertension in 56.8%, isolated nocturnal hypertension in 26.1%, and morning hypertension in 40.6%. White-coat hypertension was found in 15.3%, masked hypertension in 4.3%, and true normotension in 34.7%. Females had higher rates of morning (46.4% vs 33.3%; p\u0026thinsp;=\u0026thinsp;0.013), ambulatory (38.3% vs 25.0%; p\u0026thinsp;=\u0026thinsp;0.008), and masked hypertension (7.7% vs 0%; p\u0026thinsp;=\u0026thinsp;0.001). Sustained, nocturnal, and morning hypertension increased markedly with age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while true normotension declined. Higher income (₦301,000\u0026ndash;₦500,000) independently predicted sustained (OR 12.12), nocturnal (OR 31.3), and morning hypertension (OR 20.8). Secondary education was protective. True normotension was more likely among adults aged 40\u0026ndash;59 years and those with higher incomes.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eABPM revealed a high burden of sustained, nocturnal, and morning hypertension undetectable by office BP alone. Strong socioeconomic and age gradients support wider ABPM integration and phenotype-specific management in sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Prevalence and Determinants of Ambulatory Hypertension Phenotypes in a Semi-Urban Nigerian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 19:31:28","doi":"10.21203/rs.3.rs-8484932/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-04T17:52:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58942588574171111460324501903445072798","date":"2026-02-01T10:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T06:38:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T18:52:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-03T02:34:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-03T02:33:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-12-31T01:40:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc4a4d0a-199d-437a-8b16-f645372969e7","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T19:31:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 19:31:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8484932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8484932","identity":"rs-8484932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}