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. Author manuscript; available in PMC: 2026 Feb 18.
Published in final edited form as: Alcohol Clin Exp Res (Hoboken). 2025 Mar 9;49(4):843–853. doi: 10.1111/acer.70021

Effects of at-risk alcohol use on nighttime blood pressure, urinary catecholamines, and sleep quality in midlife adults

Keng-Yu Chang 1,*, Tabitha Haun 1,*, Zhaoli Liu 2, Alfredo Gil 1, Ziba Taherzadeh 1, Paul J Fadel 1, Shane A Phillips 3, Mariann R Piano 4, Chueh-Lung Hwang 1
PMCID: PMC12912107  NIHMSID: NIHMS2140192  PMID: 40059037

Abstract

Background:

The association between alcohol and hypertension has been predominantly based on office blood pressure (BP) measurements. However, little is known about the effect of alcohol use on nighttime BP and the underlying mechanisms. The purpose of this study was to investigate the effects of at-risk alcohol use on nighttime BP, urinary catecholamines, and sleep quality in midlife adults.

Methods:

A total of 32 midlife men and 30 postmenopausal women, free of major clinical diseases and nonsmokers (age: 58 ± 4; mean ± SD), were included. Among all participants, 22 were currently taking antihypertensive medications. At-risk drinkers were defined as those who had a dried blood spot phosphatidylethanol level ≥20 ng/mL. All participants completed 24-h ambulatory BP monitoring and urine collection to determine nighttime (or asleep) BP and nighttime urinary catecholamine levels. Sleep quality was determined by using the Pittsburgh Sleep Quality Index.

Results:

In midlife adults free of antihypertensive medications, at-risk drinkers had a higher nighttime systolic (118 ± 14 vs. 107 ± 14 mmHg, p = 0.02) and diastolic BP (70 ± 9vs. 62 ± 9 mmHg, p = 0.003) than low-risk drinkers with no between-group differences in sleep quality component scores (p ≥ 0.14). In midlife adults taking antihypertensive medications, no difference in nighttime BP was found between at-risk drinkers and low-risk drinkers (p ≥ 0.68), with a higher score for the “use of sleeping medication” component in high-risk drinkers (p = 0.02). Regardless of antihypertensive medication use, no difference between at-risk drinkers and low-risk drinkers was found in nighttime urinary catecholamine levels (p ≥ 0.19).

Conclusions:

Our findings suggest that in midlife adults free of antihypertensive medication use, at-risk alcohol use is associated with an increase in nighttime BP, and the increase in nighttime BP may be mediated by mechanisms other than increased catecholamines and poor sleep quality.

Keywords: alcohol drinking, cardiovascular risks, middle-aged adults

INTRODUCTION

At-risk drinking, often defined as exceeding the 2020-2025 Dietary Guidelines for Americans’ recommended drinking limits (i.e., no more than two drinks per day for men and no more than one drink per day for women), has been increasing in the U.S., especially in adults between 50-64 years of age (Grucza et al., 2018). These midlife adults may face increased risks related to the intersection of alcohol use and hypertension. Recent findings from a meta-analysis of 23 studies suggest a positive association between any alcohol use and the risk of hypertension (Cecchini et al., 2024). This interplay between alcohol and hypertension in midlife adults is an important health consideration, especially considering more than half of midlife adults have hypertension (Tsao et al., 2023). The association between alcohol and hypertension has been predominantly based on office blood pressure (BP) measurements. However, office BP measurements are susceptible to emotional states, such as anxiety from being in the doctor’s office (or laboratory), which can be associated with an increase in BP (i.e., white coat hypertension). Little is known about the effect of alcohol use on BP in daily life (or out-of-office BP), which can be measured from ambulatory BP monitoring (ABPM) throughout a 24-hour period. Compared with office BP measurements, ABPM measurements provide a better risk prediction for cardiovascular disease and all-cause mortality (Conen and Bamberg, 2008, Dolan et al., 2005). In addition, several studies have reported that an increase in nighttime BP, relative to daytime BP, was a strong risk factor for cardiovascular mortality (Ohkubo et al., 2002) and future cardiovascular events (Boggia et al., 2011, Boggia et al., 2007).

Overactivity of the sympathetic nervous system increases the release of catecholamines and is thought to be the mechanism underlying alcohol-induced hypertension. Data from several studies suggest that acute alcohol consumption increases urinary and plasma catecholamine levels (Scanlan et al., 2000, Ireland et al., 1984, Anton, 1965, Ekman et al., 1993), and an increase in plasma catecholamines is linked to an increase in BP following acute alcohol consumption (Ireland et al., 1984). Our laboratory reported that compared with alcohol abstainers, young adults who regularly engaged in binge drinking (five drinks or more per sitting for men and four drinks or more per sitting for women; > 2 years duration) had an increased level of 24-hour urinary catecholamines (Hwang et al., 2020). These catecholamines also play important roles in sleep and arousal (Mendelson, 2001, Kjaerby et al., 2022). Greenlund et al. reported that in young adults, one-time evening binge-like alcohol consumption affected sleep cycles and reduced sleep duration and efficiency (Greenlund et al., 2021), which may contribute to a poor sleep quality with long-term at-risk alcohol use. Independent of alcohol use, poor sleep is associated with the risk of hypertension. Studies have reported that shorter duration of sleep was related to a smaller decline in BP from daytime to nighttime in adolescents (Mezick et al., 2012), as well as an increased risk of developing hypertension in middle-aged adults (33 to 45 years) (Knutson et al., 2009). To the best of our knowledge, no studies have examined the effects of alcohol use on nighttime BP, nighttime urinary catecholamines, and sleep quality in midlife adults.

Therefore, this study aimed to examine the effect of alcohol use on nighttime BP, urinary catecholamines, and sleep quality in midlife adults. We hypothesized that compared with midlife adult low-risk drinkers, midlife adults with at-risk alcohol use (at-risk drinkers) would have a higher level of nighttime BP and urinary catecholamines and a lower sleep quality.

MATERIALS AND METHODS

Study Overview

Midlife men and postmenopausal women (50-64 years) were recruited via flyers, postcard, electronic advertisements, radio advertisements, and ResearchMatch.org. Participants who were interested in participating in a study regarding the effect of alcohol use on BP were asked to complete an online pre-screening survey. Those who met the pre-screening criteria were invited to the laboratory for informed consent and their eligibility was confirmed after completing a medical history questionnaire (including anti-hypertensive medication use) and physical examination (body weight, height, waist circumference, seated BP, blood cholesterol and liver function tests). Eligible participants completed alcohol use assessment, ABPM, urine collection for catecholamine analysis, and questionnaires regarding sleep quality. In addition, to better understand a potential influence of catecholaminergic activation in at-risk drinkers, participants also completed a heart rate variability (HRV) assessment. This study was approved by the Institutional Review Boards of the University of Illinois at Chicago (IRB# 2020-0390) and the University of Texas at Arlington (IRB#2022-0189) and conformed to the Declaration of Helsinki.

Subjects

Participants were excluded if they had (1) a history of diabetes, cardiovascular disease (e.g., stroke, myocardial infarction), liver, or renal disease; (2) office BP ≥160/100 mm Hg; (3) obesity (BMI≥35 kg/m2); (4) severe hyperlipidemia (LDL cholesterol≥190 mg/dl); (5) current use of hormone replacement therapy (i.e., estrogen, progesterone, or testosterone); (6) current or history of smoking and illicit drug use; (7) a history of seizures, cancer, or inflammatory disease (i.e., gout or rheumatoid); (8) active infection (in the past 2 months); (9) unstable body weight (>5% change during the past 6 months); and (10) regular aerobic exercise, a form of physical activity that includes a structured, repeatable, and strategic training plan for improving health or aerobic fitness (U.S. Department of Health and Human Services, 2018)(i.e., at least 30 min of moderate-intensity aerobic exercise, such as cycling for 3 times per week). For women, only those who had a cessation of menses for at least one year were included.

Alcohol Use Assessment

Using previously published methods (Piano et al., 2015), phosphatidylethanol (PEth) was measured via the dried blood spot (DBS) method. Using a single finger prick, 5 blood spots (< 20 µl) were placed onto a DBS card and sent to USDTL (U.S. Drug Testing Lab, Des Plaines, IL) for PEth (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanol [16:0/18:1]) measurement by HPLC LC/MS/MS analysis. The limit of quantification for DBS PEth is 8 ng/mL (limit of detection 2 ng/mL). Based upon the previous assessment of PEth in middle-aged and older adults (Hahn et al., 2023) and recommendations by others (Ulwelling and Smith, 2018), in this study, participants were categorized as low-risk drinkers (i.e., “light to no alcohol consumers” with PEth levels < 20 ng/ml) or at-risk drinkers (i.e., “significant consumers” with PEth levels ≥20 ng/ml). In addition, to assess the risk of alcohol use disorder, participants completed the U.S. Alcohol Use Disorders Identification Test (USAUDIT) (Hwang et al., 2020) and the total USAUDIT and USAUDIT-C (sum of the first three questions) scores were calculated. A higher USAUDIT score indicates a higher risk of alcohol use disorder, and a higher USAUDIT-C score indicates a higher amount of weekly consumption and occasions of binge drinking.

Ambulatory Blood Pressure Monitoring

Participants completed a 24-hour ABPM by wearing a portable device (Mobil-O-Graph; IEM, Stolberg, Germany or Oscar 2; SunTech Medical, Morrisville, NC, USA) on the non-dominant arm. Blood pressure was measured every 30 minutes during the awake period (daytime) and every 60 minutes during the sleep period (nighttime) (Thijs et al., 2007). Only participants with at least 10 daytime BP readings and at least five nighttime BP readings were included in the analysis (Thijs et al., 2007). The readings over the 24-hour period, daytime, and nighttime were averaged. Nighttime BP dipping was calculated as the difference between daytime and nighttime BP, and the dipping ratio was calculated as the difference divided by daytime BP (Burgos-Alonso et al., 2021).

Urinary Catecholamines

Urine samples were collected over the same 24-hour period as the ABPM in two polyethylene containers (with 25 ml of 6N hydrochloric acid), one for daytime and another for nighttime (urine generated during sleep and the first morning collection). Participants were asked to keep urine samples refrigerated and deliver the samples to the laboratory within three days after the collection. For safety concerns, some participants received containers without hydrochloric acid, and hydrochloric acid was added to the urine samples as soon as the samples were delivered to the laboratory. Urine samples were sent to either Alverno or LabCorp for analysis of catecholamines (including epinephrine, norepinephrine, and dopamine).

Heart Rate Variability

Participants completed an in-laboratory 10-min heart rate recording in a supine position, after resting for at least 15 min. Participants were asked to fast and abstain from caffeine and exercise for at least 12 hours and abstain from alcohol for at least 20 hours prior to study visit. Using previously published methods (Skow et al., 2022, 1996), a 5-min recording segment with stable breathing and no irregular cardiac cycles was used for HRV analysis in the time and frequency domain. In the time domain, HRV was measured as the root mean square of successive differences between normal heartbeats (RMSSD) and in the frequency domain as high-frequency (HF; 0.15–0.40 Hz) and low-frequency power (LF; 0.04–0.15 Hz) using fast Fourier transformation with a Hanning window (Nevrokard, Izola, Slovenia) in both absolute (ms2) and normalized units (n.u.). An index of cardiac autonomic balance was calculated as LF/HF ratio.

Sleep Quality

Participants completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire to assess sleep quality over the past month (Buysse et al., 1989). The PSQI consists of 19 self-rated items and five items that are rated by a bed partner or roommate, if applicable. The component scores (range from 0 to 3) were calculated for each of the following seven components: “subjective sleep quality”, “sleep latency”, “sleep duration”, “habitual sleep efficiency”, “sleep disturbances”, “use of sleeping medication”, and “daytime dysfunction”. To determine the PSQI global score, the sum of the scores for the seven components was calculated. A higher score is associated with worse sleep quality, with a global PSQI score >5 indicating poor sleep quality.

Potential Confounding Factors

To control for potential confounding factors related to BP and sleep quality, mental health, eating habits, and physical activity assessments were made. Briefly, depression was assessed using the eight-item Patient Health Questionnaire depression scale (Kroenke et al., 2009) with a score of 10 or greater indicating major depression. Anxiety was assessed using the State-Trait Anxiety Inventory with a cutoff of 40 for each subscale indicating clinically significant anxiety symptoms. Eating habits were assessed using the Dietary Habit Survey (Connor et al., 1992). Physical activity, defined as any bodily movement generated by voluntary muscle contraction and causing energy expenditure higher than at rest (U.S. Department of Health and Human Services, 2018), was measured using a triaxial accelerometer (ActiGraph, GT9X, USA) for four days (one weekend and three weekdays; ≥10 hours/day of wearing time) (Hwang et al., 2019). Moderate-to-vigorous physical activities are defined as activities with the Freedson activity counts of >1952 counts/min for at least 10 min.

Statistical Analysis

Statistical analyses were conducted using IBM SPSS Statistics (Essentials, Version 29), with an alpha level of 0.05. Data are presented as mean±SD or n (%). To examine the differences between at-risk and low-risk drinkers, an independent t-test was conducted for continuous variables, and χ2 test was used for categorical variables. For continuous variables that were not normally distributed, the Mann–Whitney U test was used to examine group differences. To examine the correlation between USAUDIT-C, ABPM measurement, and urinary catecholamines, Pearson’s correlation was used, while Spearman’s correlation was used to examine the correlation between USAUDIT-C and sleep quality.

RESULTS

Subject Characteristics and Alcohol Use

A total of 62 participants (52% women and 82% Whites) were included in this study, of which 22 participants (55% women and 91% Whites) were currently taking anti-hypertensive medications (with a duration of at least 2 months) with four participants taking two types of medications (Table 1). Regardless of the use of antihypertensive medication, at-risk drinkers had a higher level of PEth, USAUDIT, and USAUDIT-C than low-risk drinkers (Table 1), with no difference in age, body weight, body mass index, physical activity, eating habits, anxiety, depression, liver function, and blood lipid profiles between groups (Table 1). In participants free of anti-hypertensive medications, at-risk drinkers had higher seated BP than low-risk drinkers, while in participants taking anti-hypertensive medications, no difference in seated BP was found between at-risk drinkers and low-risk drinkers (Table 1).

Table 1.

Participant Characteristics in Midlife Adult Low-Risk Drinkers and At-Risk Drinkers

Free of Antihypertensive Medications
 With Antihypertensive Medications
Low-Risk Drinkers (n=23) At-Risk Drinkers (n=17) P Value Low-Risk Drinkers (n=11) At-Risk Drinkers (n=11) P Value
Age (yrs.) 57.4±4.4 56.3±.4.4 0.44 59.9±4.2 60.8±3.7 0.40a
Sex (M/F) 10/13 10/7 0.34 3/8 7/4 0.09
Anti-hypertensive med (n)
  CCBs 0 0 - 1 3 0.59
  ACEIs 0 0 - 1 4 0.31
  ARBs 0 0 - 6 2 0.18
  ARAs 0 0 - 1 0 >0.99
  Diuretics 0 0 - 2 0 0.48
  Beta-Blockers 0 0 - 2 3 >0.99
Body weight (kg) 79.7±16.8 79.6±14.5 0.98 75.6±9.4 82.1±16.6 0.27
Body mass index (kg/m²) 28.1±4.9 26.8±3.2 0.32 27.0±2.8 27.4±4.5 0.77
Seated SBP (mmHg) 114±14 126±14 0.015 128±9 131±13 0.47
Seated DBP (mmHg) 72±8 80±9 0.006 79±8 79±6 0.98
Seated Heart Rate (bpm) 67±8 62±15 0.27 62±6 64±11 0.60
USAUDIT 4.6±4.6 11.1±5.3 <0.001 a 3.6±4.9 14.5±7.4 <0.001 a
USAUDIT-C 4.0±3.0 8.6±3.2 <0.001 2.5±2.1 10.1±3.2 <0.001
DBS PEth (ng/mL) 2±4 131±178 <0.001 a 1±2 55±28 <0.001 a
MVPA (min/day) 15±26 15±23 0.90a 13±17 8±13 0.44
Daily Steps (min/day) 7494±4154 8964±4345 0.49a 6690±2666 6213±2733 0.68
Diet Habit Survey Score 160±42 153±29 0.73a 151±22 149±21 0.80a
PHQ-8≥10 (n) 3 0 0.12 0 0 -
STAI-S≥40 (n) 2 0 0.21 0 2 0.14
STAI-T≥40 (n) 6 1 0.10 0 0 -
AST, SGOT (IU/L) 21.1±6.1 22.0±8.1 0.94a 21.8±5.4 28.4±18.3 0.37a
ALT, SGPT (IU/L) 22.8±11.7 23.1±10.8 0.94a 18.7±4.4 23.4±7.9 0.10
Bilirubin (mg/dL) 0.7±0.6 0.7±0.3 0.71a 0.4±0.2 0.7±.5 0.06
Creatinine (mg/dL) 0.8±0.2 0.9±0.2 0.60 0.8±0.2 0.9±0.2 0.44
Albumin (g/dL) 4.4±0.3 4.5±.3 0.32 4.4±0.3 4.5±0.4 0.29
Total cholesterol (mg/dL) 197±32 204±44 0.56 189±24 186±8 0.77a
Triglycerides (mg/dL) 105±54 113±49 0.63a 94±29 123±67 0.20
HDL (mg/dL) 59±17 69±26 0.16a 61±12 58±14 0.71
LDL (mg/dL) 119±30 114±37 0.68 111±21 105±22 0.55
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Data expressed as n or mean ± SD or n. P values are from the Pearson Chi-Square tests for categorical variables and from the independent t tests or from Independent-Samples Mann-Whitney U Test if data are non-normal distributed (labeled with a). P < 0.05 indicates statistical significance (highlighted in bold).

ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; CCBs, calcium channel blockers; ARAs, aldosterone receptor antagonists; SBP, systolic blood pressure; DBP, diastolic blood pressure; USAUDIT, US Alcohol Use Disorders Identification Test; DBS PEth, dried blood spot phosphatidylethanol; MVPA, moderate to vigorous physical activity; PHQ-8, eight-item Personal Health Questionnaire Depression Scale; STAI-S, The State-Trait Anxiety Inventory for state anxiety; and STAI-T, The State-Trait Anxiety Inventory for trail anxiety.

Nighttime BP, Urinary Catecholamines, HRV, and Sleep Quality

In participants free of anti-hypertensive medications, at-risk drinkers had a higher level of nighttime BP as well as BP dipping ratio, while in participants taking anti-hypertensive medications, no difference in ABPM measurements was found between at-risk drinkers and low-risk drinkers (Table 2). Regardless of the use of anti-hypertensive medications, no difference between at-risk drinkers and low-risk drinkers was found in urinary catecholamines, HRV, and PSQI global and component scores, except “daytime urinary norepinephrine levels” (Table 2) and “medication needs to sleep” (Table 3). In participants taking anti-hypertensive medications, at-risk drinkers had a higher level of daytime urinary norepinephrine and a higher “use of sleeping medication” component score than low-risk drinkers (Table 3).

Table 2.

Ambulatory Blood Pressure, Urine Catecholamines, and Heart Rate Variability in Midlife Adult Low-Risk Drinkers and At-Risk Drinkers

Free of Antihypertensive Medications
 With Antihypertensive Medications
Low-Risk Drinkers (n=23) At-Risk Drinkers (n=17) P Value Low-Risk Drinkers (n=11) At-Risk Drinkers (n=11) P Value
Ambulatory Blood Pressure
 SBP, 24-hour (mmHg) 119±12 125±13 0.11 126±9 128±11 0.74
 SBP, Awake (mmHg) 122±12 128±13 0.19 129±9 131±12 0.60
 SBP, Asleep (mmHg) 107±14 118±14 0.02 117±12 116±13 0.90
 SBP, Dipping Ratio (%) 12.3±5.9 7.7±4.6 0.01 9.4±5.9 11.3±6.3 0.47
 DBP, 24-hour (mmHg) 71±9 77±7 0.02 76±9 76±7 0.96
 DBP, Awake (mmHg) 74±9 79±7 0.08 78±9 78±9 0.90
 DBP, Asleep (mmHg) 62±9 70±9 0.003 68±8 66±9 0.68
 DBP, Dipping Ratio (%) 16.2±7.5 10.6±6.4 0.009 13.4±5.3 15.5±10.4 0.55
Urine Catecholamines
 Epinephrine, 24-hour (µg) 5.2±2.4 5.0±2.4 0.85 4.9±2.8 6.1±3.6 0.39
 Epinephrine, Awake (µg) 4.2±1.8 3.7±2.1 0.38 3.0±1.5 4.7±2.7 0.09
 Epinephrine, Asleep (µg) 1.0±1.1 1.4±1.2 0.20a 1.9±2.4 1.5±1.4 0.57
 Norepinephrine, 24 hours (µg) 46±18 41±20 0.14a 38±8 55±26 0.06
 Norepinephrine, Awake (µg) 33±15 27±12 0.11a 26±10 38±17 0.047
 Norepinephrine, Asleep (µg) 12±6 14±13 0.57a 13±9 17±12 0.19a
 Dopamine, 24-hour (µg) 248±109 231±94 0.81a 208±68 262±107 0.17
 Dopamine, Awake (µg) 151±71 120±44 0.24a 112±48 150±64 0.14
 Dopamine, Asleep (µg) 97±53 111±75 0.81a 96±56 112±55 0.50
Urine Volume, 24-hour (mL) 2100±950 2350±800 0.37 2050±800 2250±1150 0.59
Urine Volume, Awake (mL) 1350±850 1500±600 0.23a 1350±600 1300±700 0.85
Urine Volume, Asleep (mL) 750±400 850±300 0.11a 700±350 950±500 0.15
Heart Rate Variability 1
  RMSSD (ms) 41.3±18.3 53.2±31.4 0.20 43.9±25.1 35.7±14.1 0.52
  LF (ms2) 1375±1596 1719±1602 0.27a 1074±1136 1181±1318 >0.99a
  LF (n.u.) 58.3±20.6 56.7±18.5 0.82 59.5±20.5 52.5±17.2 0.53
  HF (ms2) 620±634 1217±1433 0.26a 653±606 646±466 0.98
  HF (n.u.) 34.0±16.3 37.5±8.0 0.55 33.5±17.4 43.9±17.2 0.30
  LF/HF 2.82±3.31 2.79±4.03 0.68a 2.90±2.81 1.51±1.10 0.52a
  Heart rate (bpm) 61±7 58±11 0.37 62±8 58±8 0.39
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Data expressed as n or mean ± SD. P values are from the independent t tests or from Independent-Samples Mann-Whitney U Test if data are non-normal distributed (labeled with a). P < 0.05 indicates statistical significance (highlighted in bold).

SBP, systolic blood pressure; DBP, diastolic blood pressure; RMSSD, the root mean square of successive differences between normal heartbeats; LF, low-frequency power; and HF, high-frequency power.

1

n=19/16/9/5

Table 3.

Sleep Quality in Midlife Adult Low-Risk Drinkers and At-Risk Drinkers

Free of Antihypertensive Medications
 With Antihypertensive Medications
Low-Risk Drinkers (n=23) At-Risk Drinkers (n=17) P Value Low-Risk Drinkers (n=11) At-Risk Drinkers (n=11) P Value
Sleep Duration (hr/day) 6.9±0.9 7.2±0.7 0.21 7.7±1.3 7.1±0.8 0.19
PSQI-Score
  Subjective sleep quality 1.0±0.7 0.8±0.7 0.37a 1.1±0.8 0.9±0.3 0.80a
  Sleep latency 0.8±0.7 1.0±0.9 0.63a 0.8±0.6 0.7±0.5 0.80a
  Sleep duration 0.4±0.6 0.2±0.4 0.34a 0.4±0.7 0.4±0.5 0.85a
  Habitual sleep efficiency 0.5±0.8 0.4±0.6 0.98a 0.4±0.5 0.3±0.5 0.75a
  Sleep disturbances 1.0±0.3 1.2±0.4 0.23a 7.0±0 0.9±0.3 0.75a
  Use of sleeping medication 0.6±0.8 0.6±1.2 0.79a 0.4±0.9 1.8±1.3 0.02 a
  Daytime dysfunction 0.7±0.8 0.3±0.5 0.14a 0.5±0.7 0.8±1.0 0.61a
  Global score 5.0±2.5 4.5±2.6 0.53a 4.5±2.4 5.8±2.4 0.08a
Poor sleep quality (n) 8 6 0.97 2 7 0.03
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Data expressed as n or mean ± SD. P values are from the Pearson Chi-Square tests for categorical variables and from the independent t tests or from Independent-Samples Mann-Whitney U Test if data are non-normal distributed (labeled with a). P < 0.05 indicates statistical significance (highlighted in bold).

PSQI, The Pittsburgh Sleep Quality Index.

Correlations of Alcohol Use to ABPM, Urinary Catecholamines, and Sleep Quality

In participants free of anti-hypertensive medications, there was a positive correlation between USAUDIT-C and nighttime BP (Figure 1). In addition, there was a positive correlation between USAUDIT-C and 24-hour BP (systolic: r=0.37, P=0.02 and diastolic: r=0.56, P<0.001, respectively; Supplement Figure 1) and daytime BP (systolic: r=0.35, P=0.03 and diastolic: r=0.53, P<0.001, respectively; Supplement Figure 2). No association was found between USAUDIT-C and dipping ratio (systolic: r=−0.24, P=0.13 and diastolic: r=−0.19, P=0.24, respectively) and nighttime urinary catecholamines (P≥0.40; Figure 2), and PSQI global and component scores (P≥0.10).

Figure 1.

Figure 1.

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Pearson’s correlation between USAUDIT-C and nighttime systolic and diastolic blood pressure (SBP and DBP) in midlife adults free of anti-hypertensive medications (n=40) and those taking anti-hypertensive medications (n=22). Filled triangles (▲) indicate men at-risk drinkers, open triangles (Δ) indicate men low-risk drinkers, filled circles (●) indicate women at-risk drinkers, and open circles (○) indicate women low-risk drinkers. USAUDIT-C is the sum of scores from the first three questions of the U.S. Alcohol Use Disorders Identification Test (USAUDIT).

Figure 2.

Figure 2.

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Pearson’s correlation between USAUDIT-C and nighttime urinary catecholamines in midlife adults free of anti-hypertensive medications (n=40) and those taking anti-hypertensive medications (n=22). Filled triangles (▲) indicate men at-risk drinkers, open triangles (Δ) indicate men low-risk drinkers, filled circles (●) indicate women at-risk drinkers, and open circles (○) indicate women low-risk drinkers. USAUDIT-C is the sum of scores from the first three questions of the U.S. Alcohol Use Disorders Identification Test (USAUDIT).

In contrast, in participants taking anti-hypertensive medications, there was no association between USAUDIT-C and ABPM measurements (Figure 1, Supplement Figure 1, and Supplement Figure 2), while a higher USAUDIT-C score was associated with a higher level of nighttime urinary norepinephrine levels (Figure 2) as well as 24-hour urinary norepinephrine levels (r=0.53, P=0.01; Supplement Figure 3). A higher USAUDIT-C score was associated with a higher “use of sleeping medication” component score (Figure 3).

Figure 3.

Figure 3.

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Spearman’s correlation between USAUDIT-C and “use of sleeping medication” in midlife adults free of anti-hypertensive medications (n=40) and those taking anti-hypertensive medications (n=22). Filled triangles (▲) indicate men at-risk drinkers, open triangles (Δ) indicate men low-risk drinkers, filled circles (●) indicate women at-risk drinkers, and open circles (○) indicate women low-risk drinkers. USAUDIT-C is the sum of scores from the first three questions of the U.S. Alcohol Use Disorders Identification Test (USAUDIT). The component score of “use of sleeping medication” is calculated by using the Pittsburgh Sleep Quality Index questionnaire.

DISCUSSION

This is the first study to examine the effect of at-risk alcohol use on nighttime BP, urinary catecholamines, and sleep quality in midlife men and postmenopausal women, without a history of tobacco use and several cardiovascular comorbidities. Our main finding was that in midlife adults free of anti-hypertensive medications, at-risk drinkers had higher nighttime BP than low-risk drinkers. In addition, a higher level of nighttime BP was associated with a higher USAUDIT-C score. In contrast, in midlife adults taking anti-hypertensive medications, no difference in ABPM measurements was found between at-risk drinkers and low-risk drinkers and there was no association between USAUDIT-C and ABPM measurements. Interestingly, regardless of anti-hypertensive medication use, no difference between at-risk drinkers and low-risk drinkers was found in nighttime urinary catecholamines and overall sleep quality, suggesting that other mechanisms contribute to the increase in nighttime BP in midlife adult at-risk drinkers.

Our findings suggest that in midlife adults, at-risk alcohol use is associated with increased nighttime BP. This is clinically important because an increase in nighttime BP is a strong risk factor for cardiovascular mortality (Ohkubo et al., 2002) and future cardiovascular events (Boggia et al., 2011, Boggia et al., 2007). Such increases in nighttime BP can contribute to less nighttime BP dipping, as our findings demonstrate that nighttime BP dipping was lower in at-risk drinkers than in low-risk drinkers. Normal night-time BP dipping refers to the physiological decrease in BP that occurs during sleep which typically drops by at least 10-20% during the night compared to daytime levels in healthy individuals (Huart et al., 2023). A lower nighttime BP dipping ratio is associated with an increased risk of cardiovascular disease (de la Sierra et al., 2009). In the present study, we also report an increase in 24-hour and daytime DBP in at-risk drinkers as well as positive correlations between USAUDIT-C and ABPM measurements in midlife adults free of anti-hypertensive medications, suggesting that a higher amount of weekly alcohol consumption and/or possible more occasions of binge drinking were associated with a higher level of ABPM profiles. Along with findings from a recent meta-analysis suggesting that even one standard drink, such as one regular can of beer (5% ethanol), a day can increase the risk of hypertension (Cecchini et al., 2024), our study findings suggest that at-risk drinkers free of taking anti-hypertensive medications have elevated nighttime BP further increasing their risk for hypertension.

Others have also investigated the effect of alcohol use on ABPM measurements in middle-aged and older adults, with inconsistent findings (Abramson et al., 2010, Jaubert et al., 2014, Nakashita et al., 2009, Ohira et al., 2009). In elderly adults (mean age 71 years), Jaubert et al. reported that those who consumed more than one drink per day exhibited higher nighttime and daytime DBP but similar SBP nighttime and daytime profiles compared to non-drinkers (<1 drink/month) (Jaubert et al., 2014). In adults (30-60 years) without hypertension and cardiovascular disease, Abramson et al. reported that compared with non-drinkers, those who consumed three or more drinks per week had a higher level of daytime BP but similar nighttime BP (Abramson et al., 2010). In two Japanese cohort studies, one study of men aged 35-65 reported that men who consumed more than 3-4 drinks/day had a higher level of daytime BP but a lower level of nighttime BP compared with non-drinkers (Ohira et al., 2009), while another study reported that compared with non-drinkers (mean age 69 years), men who consumed more than 1-2 drinks per day (mean age 65 years) had a higher level of daytime SBP, but similar nighttime BP (Nakashita et al., 2009). Several factors may explain the discrepancies between our results and those of others. First, some studies used a fixed timeframe to define daytime/nighttime BP (Abramson et al., 2010; Ohira et al., 2009) rather than using actual asleep and awake times, which may cause an inaccurate measurement of daytime/nighttime BP. Secondly, previous studies used self-report questionnaires to define drinking categories (Abramson et al., 2010, Jaubert et al., 2014, Nakashita et al., 2009, Ohira et al., 2009), which may be confounded by social desirability bias, recall bias, and lack of knowledge in quantifying alcohol consumption levels. In this study, we used both self-report questionnaires as well as the alcohol use biomarker, PEth. As noted above a PEth cutoff of > 20 ng/mL indicates significant alcohol use in middle-aged and older adults (Hahn et al., 2023). Thirdly, two of the aforementioned studies were conducted on Japanese men (Ohira et al., 2009, Nakashita et al., 2009), who may have different drinking habits from the U.S. population and consume different types of alcoholic beverages. In addition, Asian individuals may have a variant genetic polymorphism of aldehyde dehydrogenase (Isomura et al., 2015), which may contribute to the different effects of alcohol on BP. Finally, two of the aforementioned studies did not account for the use of anti-hypertensive medications in their analysis (Nakashita et al., 2009, Jaubert et al., 2014). Based on our data, the use of anti-hypertensive medications could confound the interpretation of alcohol’s pressor effects.

We found no difference in ABPM measurements between at-risk drinkers and low-risk drinkers taking antihypertensive medications. The majority of our participants taking antihypertensive medications were taking either an angiotensin-converting enzyme inhibitor (ACEI) or an angiotensin receptor blocker (ARB). Therefore, our results suggest a potential mechanistic role for activation of the renin-angiotensin-aldosterone system. In a pre-clinical study of adult rats, da Silva et al. reported that during 4 weeks of ethanol ingestion, plasma catecholamines initially increased but returned to baseline levels at the end, while plasma levels of vasopressin and angiotensin increased throughout the 4 weeks and remained elevated (Da Silva et al., 2013). It is possible that sympathetic activation may contribute to the initial pressor effect of alcohol use, but the activation of the renin-angiotensin-aldosterone system may be a mechanism responsible for the long-term pressor effect of alcohol use (Da Silva et al., 2013). Angiotensin II can increase norepinephrine release through stimulation of angiotensin I receptors on presynaptic sympathetic nerve terminals, resulting in increased plasma levels of norepinephrine. However, it has been shown that in hypertensive humans (without heart failure), there is not a profound antiadrenergic effect with blockade of the AT1 receptors on presynaptic nerve terminals by ARBs or by reducing Angiotensin II levels with ACEIs – meaning that treatment with ARBs or ACEIs in the setting of hypertension does not necessarily result in a significant decrease in circulating NE levels (Krum et al., 2006). Finally, we only measured urinary catecholamines at one-time point and without consideration to the last time a participant consumed alcohol, which may be important to consider in future studies. The etiology of alcohol-induced hypertension is probably multifaceted and may involve more than one mechanism (e.g., endothelial dysfunction and oxidative stress).

In the present study, we report no differences between at-risk drinkers and low-risk drinkers in 24-hour, daytime, and nighttime urinary catecholamines in midlife adults free of anti-hypertensive medications. On the other hand, in participants taking anti-hypertensive medications, despite no difference in ABPM measurements between at-risk drinkers and low-risk drinkers, there was a positive association between the USAUDIT-C and urinary norepinephrine levels. Also, urinary norepinephrine levels tended to be higher, although within the reported normal range for urinary norepinephrine levels (15-80 μg/24 hours), in at-risk drinkers taking antihypertensive medications. Whether this indicates a higher adrenergic drive remains unknown, but these findings suggest that other physiological mechanisms, such as activation of the renin-angiotensin may be involved.

The lack of increased urinary catecholamines in at-risk drinkers was unexpected given that we previously found that in young adults, those who regularly engaged in binge drinking had an increased level of 24-hour urinary norepinephrine compared with alcohol abstainers (Hwang et al., 2020). Age and notably the drinking pattern may contribute to the different findings in catecholamines between young and midlife adults. During aging, the responsiveness of the adrenal gland to release catecholamines may decrease (Elhamdani et al., 2002), which contributes to a lesser increase in catecholamines in response to the sympathetic activation induced by at-risk alcohol use in midlife adults. Compared with midlife adults, young adults are more likely to engage in binge drinking, and this drinking pattern may augment the sympathetic activation as well as the release and excretion of catecholamines. We also report no difference in measures of HRV between at-risk drinkers and low-risk drinkers, suggesting that cardiac parasympathetic tone at rest is unaltered by at-risk drinking. In addition, we show no effect of at-risk drinking on LF/HF ratio, suggesting that the balance between resting cardiac parasympathetic and sympathetic tone is not impacted by at-risk drinking. Future studies need to examine the effect of alcohol on HRV during sleep and how it links to an increase in nighttime BP in mid-life adult at-risk drinkers.

Our study reported no differences in overall sleep quality and sleep duration between midlife adult at-risk drinkers and low-risk drinkers, suggesting that at-risk alcohol use is not a major determinant of overall sleep quality in midlife adults. In adults aged 33 to 45 years, Knutson et al. reported that shorter sleep duration was associated with an increased risk of developing hypertension (Knutson et al., 2009). In contrast to this previous finding, our study findings suggest that poor sleep does not contribute to an increase in nighttime BP in at-risk drinkers. Interestingly, in participants on anti-hypertensive medications, at-risk drinkers reported use of sleeping medications (prescribed or over the counter) more frequently compared with low-risk drinkers. In addition, at-risk drinkers on anti-hypertensive medications were more likely to have poor sleep quality. Future studies could investigate the effect of sleeping medications on nighttime BP and its interaction with alcohol use.

This study has several strengths and limitations. First, the sample size was small because of the comprehensive screening process and relatively strict participant inclusion/exclusion criteria. However, we included a relatively homogenous study sample of midlife adults, who were free of major clinical diseases and current tobacco use. We also included only postmenopausal women to eliminate the confounding effects of hormonal changes during the perimenopausal phase. In addition, none of our participants had regular aerobic exercise training and we also found no difference between groups in several potential confounding factors related to mental health, physical activity, and eating habits. Our sample size was not powered to detect sex differences. Although alcohol consumption exceeding the recommended drinking limits is associated with an increased risk of hypertension in men and women (Cecchini et al., 2024), future studies are needed to determine sex-specific mechanisms underlying alcohol’s effects on BP. Our study used PEth as an objective measure for detecting at-risk drinking and as an unbiased way to categorize drinking groups. However, PEth is influenced by several factors, including the number of days since the last drinking episode and individual alcohol metabolism. In addition, PEth cannot distinguish drinking patterns, such as binge drinking, from repeated high daily consumption for alcohol. Future studies should consider evaluating the pattern of drinking, especially binge drinking which is highly prevalent in young adults and correlated to an increase in BP and risks for hypertension (Wellman et al., 2016). Our BP findings in participants taking anti-hypertensive medications, suggest a role for the renin-angiotensin system in mediating the pressor effects of alcohol. However, we did not obtain information regarding the duration of anti-hypertensive therapy, and there was a small number of participants receiving other types of therapy disallowing a comparison and deeper analysis for the potential effects of other anti-hypertensive medication such as calcium channel blockers. There is a dearth of research in the area of alcohol use and anti-hypertensive medications. This is an important topic for future research since people taking anti-hypertensive medications might believe they are protected from the pressor effects of alcohol.

In conclusion, our findings suggest that in midlife adults, at-risk alcohol use is associated with an increase in nighttime BP. This pressor effect of alcohol may be masked by certain types of anti-hypertensive medications. The increase in nighttime BP in at-risk drinkers may be mediated by mechanisms other than increased catecholamines and poor sleep quality.

Supplementary Material

Supplement Figures

ACKNOWLEDGEMENTS

We would like to thank all the study participants for their participation. We would also like to thank Yuliya Drab, Kelly Phan, Jonathan Pham, and Preston Troung for their assistance with data collection and entry. Lately, we would like to thank Dr. Yungfei Kao for providing statistical consultation. This study was funded by the National Institute of Alcohol Abuse and Alcoholism (AA028537).

Funding source:

This work was supported by the National Institute of Alcohol Abuse and Alcoholism (AA028537).

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

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