Risk Factors in Optimal Management of Hypertension in Elderly Patients Following 2017 American College of Cardiology

Abstract

Introduction/Objectives:

The 2017 American Heart Association hypertension management guidelines recommended optimal control of blood pressure under 130/80 mmHg. We aimed to study the factors associated with suboptimal and uncontrolled hypertension in the elderly patients.

Methods:

We performed a retrospective review of suburban outpatient records of patients with hypertension, aged 65 years and older, and grouped into optimally controlled (OC; BP <130/80 mmHg), sub-optimally controlled (SOC; BP 130-139/80-89 mmHg), and uncontrolled (UC; BP≥140/90 mmHg) groups; and compared the associations of variables.

Results:

Among 1311 patients, there were 610 (46.5%) patients in OC, 391 (29.9%) in SOC, and 310 (23.6%) in UC groups. Mean ages were comparable (OC = 78 ± 8.1, SOC = 77 ± 7.4, UC = 78 ± 7.3 years; P = .760). In all groups, the majority of patients were White followed by BIPOC (Black-indigenous-and-other-people-of-color; OC = 78.5% vs 21.5%, SOC = 78.3% vs 21.7%, and UC = 71% vs 29%, respectively). There were more BIPOC patients in UC compared to OC group (29.0% vs 21.5%; P = .011). Mean body-mass-index (BMI) of patients in SOC and UC groups were greater than OC group (27.9 ± 6.3 vs 26.9 ± 6.3 kg/m²; P = .047; 28.1 ± 6.3 vs 26.9 ± 6.3 kg/m2; P = .027; respectively). There were significantly higher associations of certain comorbidities in SOC compared to OC group, such as transient ischemic attack (12.3% vs 3.6%; P < .001), hyperlipidemia (72.4% vs 56.2%; P < .001), atrial fibrillation (19.2% vs 11%; P < .001), HFpEF (5.4% vs 1.5%; P < .001), osteoarthritis (38.9% vs 30.5%; P = .006), malignancy (32.2% vs 19.5%; P < .001), and left ventricular hypertrophy (LVH; 27.4% vs 15.9%; P < .001). Logistic regression analysis showed that when compared to BIPOC, White race had lower odds of UC (OR = 0.63, 95% CI = 0.45-0.90). For every unit increase in BMI, there were greater odds of SOC (OR = 1.04, 95% CI = 1.01-1.06) and UC (OR = 1.04, 95% CI = 1.01-1.16). Patients with hyperlipidemia and LVH had greater odds of SOC (OR = 1.72, CI = 95% 1.25-2.37; and OR = 2.13, 95% CI = 1.02-4.43; respectively).

Conclusion:

In patients with sub-optimal and uncontrolled hypertension, there is a significantly higher association of BIPOC race, elevated BMI, hyperlipidemia, and left ventricular hypertrophy.

Keywords

sub-optimal and uncontrolled hypertension hypertension management risk factors for hypertension 2017 American Heart Association hypertension management guidelines risk factors of sub-optimal and uncontrolled hypertension

Introduction

Hypertension is one of the leading causes of disease burden in the United States and worldwide.¹ Hypertension is a risk factor for several morbid and deadly conditions such as coronary heart disease (CAD), cerebrovascular accidents (CVA), both acute and chronic kidney disease (CKD), and congestive heart failure (CHF). It is often associated with other risk factors for cardiovascular disease such as diabetes mellitus (DM) and obesity which then create an additive risk for the consequential diseases. Hypertension is, in fact, the leading preventable cause of cardiovascular disease and is second only to cigarette smoking as the leading preventable cause of death worldwide.¹ Because this is a modifiable risk factor, controlling hypertension reduces these risks, however not all patients with hypertension are adequately controlled.

Hypertension affects cardiac function by causing left ventricular hypertrophy (LVH), CHF, atherosclerotic coronary heart disease (CHD), and arrhythmias, such as atrial fibrillation (AF).

LVH and CHD can be reduced by controlling blood pressure. The SPRINT trial demonstrated that stricter blood pressure control in the older adults was associated with a significant reduction in cardiovascular events, which points to the idea that hypertension has a graded risk factor profile: the tighter the blood pressure control, the fewer associated risks and comorbidities.² CVA is the second most common cause of death worldwide, second to heart disease, and hypertension is the strongest risk factor for CVA. CVA is also associated with significant disability. CVA risk is dynamic and, like heart disease, incrementally associated with the severity of hypertension, and likewise hypertension management reduces risk for CVA.

Hypertension has also been shown to be predictive of later cognitive dysfunction and dementia. The kidneys are also affected by hypertension, potentially leading to CKD and end stage renal disease (ESRD). This relationship is also graded by the degree of hypertension. Renal disease can also worsen hypertension due to changes in the renin-angiotensin aldosterone system (RAAS), which then leads to a positive feedback cycle resulting in worsening hypertension and renal disease. Additionally, the peripheral arteries are affected by hypertension and the vascular effects are seen anywhere the atherosclerotic disease develops secondary to hypertension, mostly in the lower extremity vascular supply, which can cause claudication or limb ischemia due to peripheral artery disease (PAD).²

The 2017 American College of Cardiology—American Heart Association Guidelines for hypertension management³ differed significantly from the previous JNC-8 guidelines⁴ in that the definition of hypertension was lowered to ≥130/80 mm Hg rather than the previous definition of ≥140/90 mm Hg. Given these updated changes and the graded relationship between hypertension and its comorbidities, we aimed to find associations between the demographics and comorbid medical conditions based on the degree or control of patients’ hypertension in our elderly patients aged 65 years and older.

Materials and Methods

Study Design and Setting

This study was a retrospective study, non-matched, that utilized convenience sampling of the existing electronic medical records of an entire cohort of our elderly patient population who were evaluated and treated in our suburban internal medicine primary care office, which is a part of a large urban not-for-profit tertiary healthcare system.

Participants

The inclusion criteria of our study were patients aged 65 years and older who had hypertension and visited our suburban internal medicine office between September 23, 2022 and September 22, 2023. Our exclusion criteria were patients younger than 65 years of age and patients without a diagnosis of hypertension.

Variables

The data for our study were collected from the outpatient records and included the following: demographics (age, sex, race, and ethnicity); objective parameters, such as body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse; comorbidities, such as DM, CVA, transient ischemic attack (TIA), cerebral artery aneurysm (CAA), peripheral arterial disease (PAD), carotid stenosis, hyperlipidemia, atrial fibrillation (A Fib), CAD, heart failure with reduced ejection fraction (HFrEF), heart failure with preserved ejection fraction (HFpEF), left ventricular hypertrophy (LVH), coarctation of the aorta, renal artery stenosis (RAS), osteoarthritis (OA), other rheumatological disorders, mental health disorders, chronic obstructive pulmonary disease (COPD), asthma, obstructive sleep apnea (OSA), CKD, liver diseases, immunodeficiency, malignancy, hypothyroidism, and hyperthyroidism; medication usage, such as antithrombotics, non-steroidal anti-inflammatory drugs (NSAIDS), statins, glucocorticoids, levothyroxine, selective serotonin reuptake inhibitors (SSRI), selective norepinephrine reuptake inhibitors (SNRI), central nervous system (CNS) stimulants, anticoagulants, thiazide diuretic, loop diuretic, potassium-sparing diuretic, calcium channel blockers (CCB), angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), beta blockers, hydralazine, and clonidine; laboratory parameters, such as total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), white blood cell (WBC) count, hemoglobin (Hb), platelet count, glycosylated hemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), thyroid-stimulating hormone (TSH), urine protein; and echocardiogram findings, such as left ventricular ejection fraction (LVEF) and left ventricular hypertrophy (LVH). The recorded data were compiled into a Microsoft Excel (2016, Redmond, Washington, USA) spreadsheet.

Data Source and Access

The Institutional Review Board (IRB 22-218) of our healthcare system reviewed and approved our study. The data collected were utilized exclusively for research purposes in compliance with the Health Insurance Portability and Accountability Act (HIPAA) requirements. Informed consent waivers were approved by the IRB. The data were available in the Epic healthcare software (Epic Systems Corporation, Wisconsin, USA) electronic medical records and were accessible to all the investigators.

Bias

In order to minimize the potential for sub-optimal blood pressure control due to challenges in management interposed by a specific underlying medical condition, we excluded patients who had a preexisting diagnosis of primary hyperaldosteronism, pheochromocytomas, renovascular hypertension, or Cushing’s syndrome. Additionally, we excluded those patients who had a history of transient hypertension due to a specific underlying condition which later resolved, such as pre-eclampsia or eclampsia.

Study Sample Size

Our study included the entire population of 1311 patients who had hypertension and who received care in our office.

Statistical Methods

Statistical analysis was performed by utilizing the SPSS (Statistical Package for the Social Sciences, version 15.01, IBM, Armonk, New York, USA) software. Study participants were divided into 3 groups based on the degree of blood pressure control. The first group included patients with optimally controlled blood pressure (OC; BP <130/80 mm Hg); the second group included patients with sub-optimally controlled blood pressure (SOC; BP = 130-139/80-89 mm Hg); and the third group included patients with uncontrolled blood pressure (UC; BP ≥ 140/90 mm Hg). The associations of variables were compared between these groups. For the continuous variables, we conducted a skewness test to determine whether the data were normally distributed or nonparametric. Analysis of normally distributed data was performed using 1-way ANOVA, while analysis of non-normally distributed data was conducted using the Kruskal-Wallis test. Pearson Chi-square tests were utilized for the analysis of categorical variables. The statistical significance of this study was defined as P < .05.

Results

A total of 1311 patients participated in this study. Six hundred and ten (46.5%) patients were in the optimally controlled (OC) group, 391 (29.9%) were in the sub-optimally controlled (SOC) group, and 310 (23.6%) patients were in the uncontrolled (UC) group. The mean ages of patients of the OC group (78 ± 8.1 years), the SOC group (77 ± 7.4 years), and the UC group (78 ± 7.3 years) were comparable (Table 1). Although there were more women in all the groups, nevertheless the sex difference was not statistically significant (Table 1). Majority of the patients identified as White followed by BIPOC (Black, Indigenous, and other people of color; OC = 78.5% vs 21.5%, SOC = 78.3% vs 21.7%, and UC = 71% vs 29%, respectively).There were significantly greater proportion of BIPOC patients in the UC group compared to SOC or OC groups (P = .011; Table 1). Only a minor proportion of patients identified as of Hispanic ethnicity, which was comparable between the groups.

Table 1.

Baseline Characteristics of the Optimal and Suboptimal Blood Pressure Control Groups.

Variable	Variable	Optimally controlled (n = 610) (A)	Sub-optimally controlled (n = 391) (B)	P (A vs B)	Uncontrolled (n = 310) (C)	P (A vs C)
Age	Years, mean (SD)	78 (8.1)	77 (7.4)	.389	78 (7.3)	.760
Sex	Male, n (%)	262 (43.0)	168 (43.0)	.996	149 (48.1)	.140
Sex	Female, n (%)	348 (57.0)	223 (57.0)		161 (51.9)
Race	White, n (%)	479 (78.5)	306 (78.3)	.921	220 (71.0)	.011
Race	BIPOC, n (%)	131 (21.5)	85 (21.7)		90 (29.0)
Ethnicity	Hispanic, n (%)	10 (1.6)	8 (2.0)	.637	4 (1.3)	.683
Weight	BMI (kg/m²), mean (SD)	26.9 (6.3)	27.9 (6.3)	.047	28.1 (6.3)	.027
Vitals	SBP (mmHg), mean (SD)	119 (9)	131 (7)	<.001	150 (13)	<.001
	DBP (mmHg), mean (SD)	69 (7)	76 (8)	<.001	80 (10)	<.001
	Pulse (BPM), mean (SD)	76 (17)	73 (14)	.002	75 (13)	.472
Comorbidities	DM, n (%)	226 (37.0)	155 (39.6)	.410	955 (30.6)	.054
	CVA, n (%)	54 (8.9)	30 (7.7)	.511	19 (6.1)	.149
	TIA, n (%)	22 (3.6)	48 (12.3)	<.001	10 (3.2)	.766
	CAA, n (%)	1 (0.2)	3 (0.8)	.305	3 (1.0)	.114
	PAD, n (%)	34 (5.6)	20 (5.1)	.754	21 (6.8)	.468
	Carotid stenosis, n (%)	28 (4.6)	13 (3.3)	.324	12 (3.9)	.613
	Hyperlipidemia, n (%)	343 (56.2)	283 (72.4)	<.001	230 (74.2)	<.001
	A Fib, n (%)	67 (11.0)	75 (19.2)	<.001	47 (15.2)	.132
	CAD, n (%)	127 (20.8)	92 (23.5)	.312	50 (16.1)	.088
	HFrEF, n (%)	19 (3.1)	6 (1.5)	.118	9 (2.9)	.860
	HFpEF, n (%)	9 (1.5)	21 (5.4)	<.001	5 (1.6)	.872
	LVH, n (%)	16 (2.6)	43 (11.0)	<.001	15 (4.8)	.078
	Co Aorta, n (%)	3 (0.5)	0 (0.0)	.285	1 (0.3)	1.000
	RAS, n (%)	4 (0.7)	0 (0.0)	.160	1 (0.3)	.516
	OA, n (%)	186 (30.5)	152 (38.9)	.006	75 (24.2)	.045
	Other rheum ds, n (%)	88 (14.4)	7 (1.8)	.141	48 (15.5)	.669
	Mental health ds, n (%)	153 (25.1)	108 (27.6)	.372	76 (24.5)	.581
	COPD, n (%)	54 (8.9)	32 (8.2)	.707	20 (6.5)	.203
	Asthma, n (%)	64 (10.5)	48 (12.3)	.382	39 (12.6)	.342
	OSA, n (%)	71 (11.6)	34 (8.7)	.138	32 (10.3)	.549
	CKD, n (%)	101 (16.6)	84 (21.5)	.050	67 (21.6)	.061
	Liver ds, n (%)	39 (6.4)	15 (3.8)	.091	25 (8.1)	.346
	Immunodeficiency, n (%)	3 (0.5)	2 (0.5)	1.000	5 (1.6)	.128
	Malignancy, n (%)	119 (19.5)	126 (32.2)	<.001	75 (24.2)	.100
	Hypothyroidism, n (%)	145 (23.8)	68 (17.4)	.016	51 (16.5)	.010
	Hyperthyroidism, n (%)	10 (1.6)	7 (1.8)	.857	2 (0.6)	.209
Medications	Antithrombotics, n (%)	196 (32.1)	186 (47.6)	<.001	118 (38.1)	.073
	NSAIDs, n (%)	149 (24.4)	84 (21.5)	.282	51 (16.5)	.006
	Statin, n (%)	487 (79.8)	287 (73.4)	.018	219 (70.6)	.002
	Glucocorticoid, n (%)	22 (3.6)	13 (3.3)	.813	9 (2.9)	.576
	Levothyroxine, n (%)	128 (21.0)	61 (15.6)	.034	38 (12.3)	.001
	SSRI, n (%)	98 (16.1)	47 (12.0)	.076	36 (11.6)	.070
	SNRI, n (%)	41 (6.7)	14 (3.6)	.033	14 (4.5)	.190
	CNS Stimulant, n (%)	3 (0.5)	0 (0.0)	.285	3 (1.0)	.411
	Anticoagulant, n (%)	132 (21.6)	44 (11.3)	<.001	49 (15.8)	.035
	Thiazide diuretic, n (%)	214 (35.1)	84 (21.5)	<.001	81 (26.1)	.006
	Loop diuretic, n (%)	90 (14.8)	54 (13.8)	.678	52 (16.8)	.423
	Pot sparing diuretic, n (%)	39 (6.4)	27 (6.9)	.750	19 (6.1)	.876
	CCB, n (%)	205 (33.6)	106 (27.1)	.003	125 (40.3)	.045
	ACEI, n (%)	141 (23.1)	91 (23.3)	.954	78 (25.2)	.491
	ARB, n (%)	175 (28.7)	145 (37.1)	.005	118 (38.1)	.004
	Beta blocker, n (%)	232 (38.0)	174 (44.5)	.042	130 (41.9)	.252
	Hydralazine, n (%)	11 (1.8)	7 (1.8)	.988	13 (4.2)	.032
	Clonidine, n (%)	2 (0.3)	7 (1.8)	.033	4 (1.3)	.187
Lab parameter	TC (mg/dL), mean (SD)	165.4 (51.2)	169.3 (43.4)	.418	167.7 (41.8)	.762
	LDL-C (mg/dL), mean (SD)	88.9 (43.9)	90.6 (36.6)	.811	92.5 (34.4)	.413
	TG (mg/dL), mean (SD)	109.4 (59.2)	114 (51.9)	.410	113.6 (57.3)	.530
	HDL-C (mg/dL), mean (SD)	56.8 (17.2)	56.4 (15.2)	.894	54.1 (15.2)	.048
	WBC (×10⁹/L), mean (SD)	7.1 (2.3)	6.8 (2.9)	.284	6.8 (2.2)	.295
	Hb (g/dL), mean (SD)	12.7 (1.8)	13.3 (6.8)	.087	13.2 (1.6)	.185
	Platelets (×10³/mcL), mean (SD)	248 (73)	239 (71)	.120	236 (66)	.041
	HbA1c (%), mean (SD)	6.4 (6.3)	6.8 (12.3)	.694	6.2 (1.0)	.909
	eGFR (mL/min/1.73 m²), mean (SD)	61.7 (18.1)	66.1 (19.9)	<.001	63.8 (16.6)	.252
	TSH (mIU/L), mean (SD)	2.37 (1.86)	2.39 (2.32)	.986	2.39 (1.87)	.982
	Urine protein (mg/dL), (median, 25th-75th)	26.8 (7-90.7)	14.9 (7.7-61.6)	.319	21.2 (12.4-58.9)	.673
Echocardiogram	LVEF (%), mean (SD)	61.6 (6.5)	61.4 (6.6)	.083	60.6 (6.8)	.069
Echocardiogram	LVH, n (%)	97 (15.9)	107 (27.4)	<.001	58 (18.7)	.282

Abbreviations: A Fib, Atrial fibrillation; ACEI, Angiotensin converting enzyme inhibitor; ARB, Angiotensin receptor blocker; BIPOC, Black, Indigenous, and people of color; BMI, Body mass index; BPM, Beats per minute; CAA, Cerebral artery aneurysm; CAD, Coronary artery disease; CCB, Calcium channel blocker; CHF, Congestive heart failure; CKD, Chronic kidney disease; CNS stimulants, Central nervous system stimulants; Co Aorta, Coarctation of aorta; COPD, Chronic obstructive pulmonary disease; CVA, Cerebrovascular accident; DBP, Diastolic blood pressure; DM, Diabetes mellitus; eGFRu, Estimated glomerular filtration rate; Hb, Hemoglobin; HbA1c, Glycosylated hemoglobin; HDL-C, High density lipoprotein cholesterol; HFpEF, Heart failure with preserved ejection fraction; HFrEF, Heart failure with reduced ejection fraction; LDL-C, Low density lipoprotein cholesterol; Liver ds, Liver disease; LVEF, Left ventricular ejection fraction; LVH, Left ventricular hypertrophy; LVH, Left ventricular hypertrophy; Mental health ds, Mental health disorders; n, Number of patients; NSAIDs, Nonsteroidal anti-inflammatory drugs; OA, Osteoarthritis; OSA, Obstructive sleep apnea; Other Rheum Ds, Other rheumatological disorders; PAD, Peripheral arterial disease; Pot sparing diuretic, Potassium sparing diuretics; RAS, Renal artery stenosis; SBP, Systolic blood pressure; SD, Standard deviation; SNRI, Serotonin–norepinephrine reuptake inhibitor; SSRI, Selective serotonin reuptake inhibitor; TC, Total cholesterol; TG, Triglyceride; TIA, Transient ischemic attack; TSH, Thyroid stimulating hormone; WBC, White blood cell count.

The mean BMI of the patients in the SOC and UC groups were significantly greater than the OC group (27.9 ± 6.3 vs 26.9 ± 6.3 kg/m2; P = .047; and 28.1 ± 6.3 vs 26.9 ± 6.3 kg/m2; P = .027, respectively; Table 1). The mean SBP in the SOC group and UC group were significantly higher than that the OC group (131 ± 7 vs 119 ± 9 mm Hg; P < .001; and 150 ± 13 vs 119 ± 9 mm Hg; P < .001; respectively). Similarly, the mean DBP in the SOC group and UC group were significantly higher than that the OC group (76 ± 8 vs 69 ± 7 mm Hg; P < .001; and 80 ± 10 vs 69 ± 7 mm Hg; P < .001; respectively; Table 1). The mean pulse rates were within the normal range in all the groups and there was no significant differences between the groups (Table 1).

Compared to the OC group, SOC group had a significantly higher frequencies of associations of comorbid conditions, such as TIA (3.6% vs 12.3%; P < .001), hyperlipidemia (56.2% vs 72.4%; P < .001), A Fib (11.0% vs 19.2; P < .001), HFpEF (1.5% vs 5.4; P < .001), LVH (2.6% vs 11.0%; P < .001), OA (30.5% vs 38.9%; P = .006), and malignancy (19.5% vs 32.2%; P < .001; Table 1). The frequency of patients with hypothyroidism were significantly greater in the OC group compared to the SOC group (23.8% vs 17.4%; P = .016). Compared to the OC group, the UC group had a significantly higher frequency of association of hyperlipidemia (56.2% vs 74.2%; P < .001), and significantly lower frequencies of associations of OA (30.5% vs 24.2%; P = .045), and hypothyroidism (23.8% vs 16.5%; P = .010; Table 1). Other comorbidities, such as DM, CVA, CAA, PAD, carotid stenosis, CAD, HFrEF, coarctation of aorta, RAS, other rheumatological disorders, mental health disorders, COPD, asthma, OSA, CKD, liver diseases, immunodeficiency, and hyperthyroidism did not show significant differences in their frequencies between the 3 groups (Table 1).

Analysis of medication usage indicated that compared to the OC group, more patients in the SOC group were treated with antithrombotics (32.1% vs 47.6%; P < .001), ARB (28.7% vs 37.1%; P = .005), beta blockers (38.0% vs 44.5%; P = .042), and clonidine (0.3% vs 1.8%; P = .033; Table 1). The analysis also showed lower usage of the following medications in the SOC group compared to the OC group: statins (73.4% vs 79.8%; P = .018), levothyroxine (15.6% vs 21.0%; P = .034), SNRI (3.6% vs 6.7%; P = .033), anticoagulants (11.3% vs 21.6%; P < .001), thiazides (21.5% vs 35.1%; P < .001), and CCB (27.1% vs 33.6%; P = .003; Table 1). Compared to the OC group, more patients in the UC group were treated with CCB (33.6% vs 40.3%; P = .045), ARB (28.7% vs 38.1%; P = .004), and hydralazine (1.8% vs 4.2%; P = .032; Table 1). In the OC group, compared to the UC group, there were higher frequencies of use of NSAIDs (24.4% vs 16.5%; P = .006), statins (79.8% vs 70.6%; P = .002), levothyroxine (21.0% vs 12.3%; P = .001), anticoagulants (21.6% vs 15.8%; P = .035), and thiazides (35.1% vs 26.1%; P = .006; Table 1). Medications that did not exhibit any significant differences between the groups include glucocorticoids, SSRI, CNS stimulants, loop diuretics, potassium sparing diuretics, and ACEI (Table 1).

Analysis of various laboratory parameters showed that the eGFR was significantly greater in the SOC group than in the OC group (19.9% vs 18.1%; P < .001), and the HDL-C was significantly lower in the UC group than in the OC group (15.2% vs 17.2%; P = .048; Table 1). Other laboratory parameters, such as total cholesterol, LDL-C, TG, WBC count, hemoglobin, platelet count, HbA1c, TSH, and urine protein did not yield any significant differences between the groups. Analysis of echocardiogram findings showed that there was a higher frequency of association of LVH in the SOC group compared to OC group (27.4% vs 15.9%; P < .001), while LVEF was comparable among the OC, SOC, and the UC groups (Table 1).

Logistic regression analysis showed that compared to BIPOC, patients of White race had lower odds of having UC (OR = 0.63; 95% CI = 0.45-0.90; P = .010; Table 3). For each unit increase in BMI, there were greater odds of being SOC (OR = 1.04; 95% CI = 1.01-1.06; P = .002; Table 2) or UC (OR = 1.04; 95% CI = 1.01-1.16; P = .002; Table 3). Patients with hyperlipidemia had greater odds of being SOC (OR = 1.72; 95% CI = 1.25-2.37; P = .001; Table 2) or UC (OR = 2.42; 95% CI = 1.72-3.41; P < .001; Table 3). Patients with LVH had greater odds of being SOC (OR = 2.13; 95% CI = 1.02-4.43; P = .044; Table 2). Use of thiazides showed lower odds of being SOC (OR = 0.54; 95% CI = 0.38-0.75; P < 0.001; Table 2), while use of ARBs showed greater odds of being UC (OR = 1.71; 95% CI = 1.20-2.43; P = .003; Table 3).

Table 2.

Logistic Regression: Outcome Sub-optimal Blood Pressure Control.

Variable	B	P	Exp(B)	95% CI for Exp(B)
Variable	B	P	Exp(B)	Lower	Upper
Age	0.002	.856	1.002	0.982	1.022
Sex	0.018	.905	1.018	0.761	1.362
Race	0.228	.203	0.796	0.561	1.130
Body mass index	0.036	.002	1.036	1.013	1.060
Hyperlipidemia	0.542	.001	1.719	1.247	2.370
Hypothyroidism	−0.184	.319	0.832	0.579	1.195
Atrial fibrillation	−0.262	.335	0.770	0.452	1.310
LVH	0.754	.044	2.126	1.021	4.429
Cancer	0.262	.119	1.299	0.935	1.806
Thiazide	−0.625	<.001	0.535	0.383	0.748
CCB	−0.289	.063	0.749	0.552	1.016
ACEI	0.058	.751	1.060	0.741	1.514
ARB	0.301	.078	1.352	0.967	1.890
Beta blocker	0.125	.446	1.133	0.822	1.563

Abbreviations: ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; LVH, left ventricular hypertrophy.

Table 3.

Logistic Regression: Outcome Uncontrolled Blood Pressure.

Variable	B	P	Exp(B)	95% CI for Exp(B)
Variable	B	P	Exp(B)	Lower	Upper
Age	0.000	.997	1.000	0.979	1.021
Sex	0.305	.054	1.357	0.995	1.851
Race	−0.456	.010	0.634	0.448	0.897
Body mass index	0.037	.002	1.037	1.013	1.162
Hyperlipidemia	0.883	<.001	2.417	1.715	3.406
Hypothyroidism	−0.267	.182	0.765	0.517	1.133
Atrial fibrillation	−0.141	.614	0.869	0.503	1.501
LVH	0.448	.274	1.566	0.701	3.497
Cancer	0.254	.163	1.289	0.903	1.840
Thiazide	−0.262	.132	0.770	0.548	1.082
CCB	0.273	.086	1.314	0.962	1.795
ACEI	0.263	.167	1.300	0.896	1.888
ARB	0.535	.003	1.707	1.203	2.423
Beta blocker	0.237	.167	1.267	0.906	1.772

Abbreviations: ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; LVH, left ventricular hypertrophy.

Discussion

We found that BIPOC race, BMI, hyperlipidemia, and left ventricular hypertrophy were major contributory factors towards SOC and UC hypertension among the elderly. Compared to the White race, BIPOC race has been associated with 1.3 times higher rate of non-fatal stroke, 1.8 times higher rates of fatal stroke, 1.5 times higher rates of heart diseases, and 4.2 times higher rates of end-stage kidney disease due to UC hypertension which is believed to be secondary to genetic and environmental factors.⁵ Another study reported that BIPOC race had 1.99 times higher odds (95% CI = 1.68-2.36) of hypertension than non-Hispanic White individuals, and Mexican Americans had 0.69 times lower odds (95% CI = 0.52-0.92) of hypertension than persons of non-Hispanic White race, which suggests that racial and ethnic differences play a significant role in the optimum control of hypertension, largely linked with higher social support leading to lower blood pressure levels and optimal blood pressure control.⁶ Additionally, racial difference in the blood pressure has been attributed to a variable extent of nocturnal blood pressure dipping in the form of elevated resting diastolic blood pressure in individuals of BIPOC race compared to other racial groups.⁷ Responsiveness to certain anti-hypertensive medications may play a role in the disparities of hypertension control among the racial groups. Patients of BIPOC race have less responsiveness to ACEI, beta blockers, and ARB compared to diuretics and calcium channel blockers.⁵ This is believed to be secondary to relatively suppressed activity of renin angiotensin aldosterone system (RAAS) in the hypertensive patients of BIPOC race, making this population more sensitive to a low-salt diet and less responsive to ACEI, ARB, and beta blockers.⁸ Additionally, challenges in the social determinants of health and equity, such as housing, improper living conditions, and disproportionate incarceration contribute to racial differences in lack of optimal blood pressure control in the BIPOC population which results into increased risk of SOC and UC hypertension and associated end organ damage.^9,10 Our finding of BIPOC race as a factor associated with SOC and UC hypertension, especially in the elderly population, further adds to the body of current literature.

Our finding of association of elevated BMI with SOC and UC hypertension is supported by a study in which the average systolic blood pressure and the average diastolic blood pressure were noted to have increased significantly and linearly across increasing BMI levels in the form of average systolic blood pressure increase by >10 mm Hg and average diastolic blood pressure increase by >5 mm Hg from a normal BMI to a BMI >40 kg/m2 in both men and women.¹¹ Another study investigated the influence of BMI and waist circumference (WC) on effectiveness of antihypertensive drugs. The investigators reported that in men, BP control deteriorated with increase in BMI (OR = 8.58; 95% CI = 5.74-12.83) and WC (OR = 5.09; 95% CI = 3.84-6.74), while in women, this influence was relatively blunted (BMI: OR = 3.63, 95% CI = 2.78-4.74; WC: OR = 1.93, 95% CI = 1.59-2.35). The study concluded that increasing BMI, especially visceral obesity as measured by the WC, limited the effectiveness of antihypertension therapy and influenced optimal blood pressure control.¹² Another study found that higher than normal BMI positively correlated with UC blood pressure (OR = 2.451, 95% CI = 1.145–5.247).¹³ However, a study evaluated patients with UC hypertension and found that the systolic and diastolic blood pressure were similar in obese and non-obese groups, nevertheless the nocturnal dip in blood pressure was lesser in obese group.¹⁴ The mean BMI has been reported as significantly higher in the UC hypertension group than in the OC hypertension group (25.11 vs 23.81 kg/m2; P = .003), while the waist hip ratio, which is a measure of visceral obesity, did not differ in the 2 groups.¹⁵ These findings and our finding of greater odds being SOC and UC with increase in BMI are further supported from the data collected from the Tunisian National Registry which also observed obesity as an independent predictor of UC blood pressure (OR = 1.14; 95% CI = 1.07-1.21).¹⁶ Higher sensitivity of adipocytes to lipolysis and their aptitude to produce pro-inflammatory cytokines have been implicated in elevated blood pressure and subsequent end organ damage in obese patients.¹¹ The renal hemodynamic changes and structural changes in the glomeruli induced by obesity might lead to activation of the renin-angiotensin aldosterone system, thus altering the efficacy of antihypertensive drugs and affecting hypertension control.¹⁴

While hyperlipidemia and obesity are strongly linked to each other, hyperlipidemia has been implicated as an independent risk factor for hypertension control. A cross-sectional study found a higher mean total cholesterol, lower mean HDL-C and lower mean triglyceride levels in the UC hypertension group compared to the OC group.¹⁵ It is also been reported that higher level of total cholesterol was present in male patients with UC hypertension compared to OC male hypertensives, while higher levels of triglycerides were noted in females with UC hypertension. However, the LDL-C was elevated in UC hypertension groups of both sexes.¹⁶ Other studies suggest an association between hyperlipidemia and UC hypertension, with or without concomitant obesity.^15,16 A multivariate logistic regression analysis of 1986 subjects in the Korean Ambulatory Blood Pressure Monitoring Registry identified hyperlipidemia as an independent predictor of masked UC hypertension.¹⁷ Although the mechanism of hyperlipidemia in control of hypertension remains relatively underexplored, nevertheless it has been suggested that hyperlipidemia may affect the volume and viscosity of blood which can play a role in regulation of blood pressure.¹⁶

Our finding of association of LVH with SOC hypertension is supported by several studies.^18
-23 Analysis of various studies shows that the relationship between LVH and SOC hypertension is bidirectional. A longitudinal study followed 2380 hypertensive subjects and found that LVH regression was an independent predictor of blood pressure control and they suggested that LVH regression can be used to assess the efficacy of antihypertensive treatment.¹⁹ In another study, LVH regression was associated with significantly larger reduction in systolic and diastolic blood pressures.²⁰ Other studies have reported that LVH promotes progression of hypertension.^24,25 The mechanism involving LVH progression and SOC hypertension includes an interplay of the impact of cytokines and other necrohormones in response to myocardial mechanical stress, activation of renin angiotensin system, hypertrophy of vascular smooth muscle cells and myocytes, and increased myocardial demand.^26-28

We had some limitations in our study. Although we collected the data on the comorbid conditions based on the documentation of the date of onset in the medical records; for some variables, such as LVH, we could verify the echocardiographic documentation that preceded the diagnosis of hypertension in the majority of the patients, except for a few where we had to rely solely on the documentation of LVH preceding the diagnosis of hypertension as per the progress notes. The study patients represented a suburban population of patients, hence our results cannot be generalized. Our study had the major strength of a large database of patients who had hypertension and who followed a small group of physicians for greater than 2 decades, which allowed the care team in documenting each comorbidity and other variables in a chronological manner.

Conclusion

We conclude that in patients with sub-optimal and uncontrolled hypertension, there is a significantly higher association of BIPOC race, elevated BMI, hyperlipidemia, and left ventricular hypertrophy. Additional studies in diverse settings are necessary to substantiate such association with sub-optimal and uncontrolled hypertension.

Footnotes

Acknowledgements

The authors thank Christine Rickette, RN (study coordinator) for her contribution to this study.

Author Contributions

ZW and SR made substantial contributions to the study design, drafting, data acquisition, data analysis, and manuscript writing. All authors except SP contributed in data collection and manuscript writing. SP contributed in manuscript writing. KH analyzed the data. SR contributed in revising the manuscript critically for improved intellectual content, and final approval for the version to be published.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed Consent

Not applicable. Being a retrospective chart review study the Institutional Review Board waived the need for informed consent.

ORCID iDs

Satwiki Prasad

Krystal Hunter

Satyajeet Roy

Data Availability

The authors declare that data supporting the findings of this study are available within the article.

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Risk Factors in Optimal Management of Hypertension in Elderly Patients Following 2017 American College of Cardiology—American Heart Association Guidelines