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Published in final edited form as: Alcohol. 2022 Sep 22;107:144–152. doi: 10.1016/j.alcohol.2022.08.010

Influence of Age and Sex on Alcohol Pharmacokinetics and Subjective Pharmacodynamic Responses following Intravenous Alcohol Exposure in Humans

Vatsalya Vatsalya 1,2, Nia D Byrd 1, Bethany L Stangl 1, Reza Momenan 3, Vijay A Ramchandani 1,*
PMCID: PMC10023287  NIHMSID: NIHMS1838974  PMID: 36152778

Abstract

There is substantial inter-individual variation in alcohol metabolism and response that are likely due to sex and age; however, these are not well understood. We investigated age and sex influences on alcohol elimination rate (AER) and subjective responses following intravenous (IV) administration in nondependent drinkers. Participants underwent a 2-session study where they received IV alcohol (target level: 0.05 g%) and placebo in counter-balanced order. AER was higher in males than in females across age groups. These differences were partly explained by sex differences in lean body mass and liver volume. Alcohol significantly increased peak feelings of high, intoxication, drug-effects, liking-effects, and wanting-more, with no major sex differences. There were no age-related differences in feelings of high and intoxication; however, the older group reported significantly lower peak liking-effects and stimulation responses than the younger group. These findings highlight the significant impact of sex and age as sources of variability in the clinical pharmacology of alcohol.

Keywords: Age, Alcohol, Sex, Alcohol Elimination Rate, Liver Volume, Subjective Response

INTRODUCTION

Alcohol is among the most widely studied drugs in the world, not just because it is widely used and misused, but also because of its unique and complex pharmacology. The substantial variability in alcohol pharmacokinetics (PK) and pharmacodynamics (PD) have been extensively documented, however a comprehensive understanding of the sources of variation in its PK and PD remains to be elucidated. This is particularly relevant to understanding how these sources of variation may contribute to the risk of an individual developing alcohol use disorder.

Several studies have demonstrated significant sex differences in the PK of alcohol (V. Ramchandani et al., 2001; Thomasson, 2002). In general, females have a lower proportion of body water than men of similar body weight and therefore they achieve higher blood alcohol concentrations after drinking equivalent amounts of alcohol (Mumenthaler et al., 1999). One study reported that females also showed higher alcohol disappearance rates (in mg%/min) following oral alcohol administration of alcohol compared to males (Thomasson et al., 1995), which could be attributed to sex differences specifically associated with stomach and liver alcohol dehydrogenase activity. Our previous work using intravenous (IV) alcohol demonstrated lower alcohol elimination rates (in grams per hour) in females compared with males, and this difference was attributed to smaller lean body mass and liver volumes in females compared with men (Kwo et al., 1998).

Age is another potentially major source of variability in alcohol PK; however, very few studies have investigated age-related differences in alcohol PK, and most did not examine sex differences in the effect of age on alcohol metabolism. It is well known that there are age-related changes in body composition and organ sizes (Clasey et al., 1999; Gallagher et al., 1997). The proportion of lean body mass and skeletal muscle mass decreases with increasing age, and total body water decreases while percent body fat increases with age. These changes in composition would be expected to alter the distribution of alcohol, which is a small polar molecule that distributes primarily into the body water. Indeed, studies have shown that older participants do have a smaller volume of distribution of alcohol, and achieve higher alcohol levels than younger individuals, even when doses normalized to body weight are administered (Jones & Neri, 1985; Oneta et al., 2001; Pozzato et al., 1995; Vestal et al., 1977). These differences are consistent with age-related changes in lean body mass and total body water; however, it remains unclear if the systemic metabolism of alcohol is altered with age. Studies have also shown sex differences in the influence of age on body composition; however, it is not known how the combination of sex and age influences on body composition would influence the PK of alcohol in young and older women.

Evidence for sex- and age-related differences in the PD to alcohol is present in the literature; although, it is not clear if these differences are due to differences or alterations in pharmacodynamic sensitivity to alcohol or are a consequence of differences in exposure due to alterations in the PK of alcohol between groups. One approach to understanding sources of individual variation in the PD of alcohol is to compare responses at standardized levels of exposure across individuals, which can be achieved using the alcohol clamp method (O’Connor et al., 2000). This method employs IV alcohol infused to achieve and maintain a prescribed target breath alcohol concentration (BrAC) across individuals. This is achieved via individualized infusion profiles derived from a physiologically-based pharmacokinetic (PBPK) model-based algorithm. The alcohol clamp provides tightly controlled exposures which enables the characterization of PD responses and its determinants across individuals. The IV route, while not naturalistic, has been demonstrated to result in responses that do not differ from that following oral alcohol administration, providing a degree of ecological validity of the approach (Plawecki et al., 2019). Importantly, the alcohol clamp also provides a precise estimate of the alcohol elimination rate, enabling the evaluation of the metabolism of alcohol and its determinants across individuals as well.

Given the substantial inter- and intra-individual variability in alcohol PK and PD, it is critical to systematically assess the impact of determinants such as age and sex on the clinical pharmacology of alcohol, particularly as it relates to the risk of adverse consequences of alcohol in these groups. Indeed, the population is aging, and by 2030 the number of adults over 65 is expected to reach 1 in 5 (United States Census Bureau, 2010). Therefore, the consequences of alcohol use, including the risk for problematic drinking patterns such as binge drinking, risk for falls and drug interactions, as well as comorbid psychiatric disorders, may lead to greater burdens on our healthcare system (Keyes et al., 2019; Joshi et al., 2021; Fenollal-Maldonado et al., 2022).

The present study was thus designed to examine the effects of age and sex on alcohol elimination rates and subje0ctive responses to alcohol in healthy male and female social (non-alcohol-dependent) drinkers that were stratified into two age groups: younger (21–25 year-old) and older (55–65 year-old). An additional aim of the study was to examine how variation in alcohol PK may be explained by sex- and age-related differences in lean body mass and liver volume. This was a placebo-controlled, two-session, crossover study in which participants received an alcohol clamp exposure at a target BrAC level of 50 mg% and saline in counter-balanced order.

METHODS

This was a two-part, randomized, placebo-controlled study of alcohol metabolism in healthy, nondependent adults. Male and female participants between 21–25 years or 55–65 years of age who were in good health (as determined by history and physical, lab tests, and EKG) and current non-smokers were recruited in the study. Participants were social drinkers with no current or past history of alcohol dependence or abuse. Additional exclusion criteria included: (1) significantly elevated serum liver function measures (AST, ALT, bilirubin, albumin); (2) positive hepatitis or HIV test; (3) positive history of flushing reactions to alcohol, (4) positive breath alcohol test or urine drug screen at screening or at the start of infusion sessions, (5) positive urine pregnancy test for females.

A total of 48 participants (12 young females, 12 young males, 12 older females, and 12 older males) completed the 2-session investigation. Young females had normal menstrual cycles and were tested during the follicular phase of their cycle (within 7 days of offset of menses) and have had a negative urine pregnancy (hCG) test at the start of each study session. Both study sessions were scheduled within the same cycle (within the 7-day window) for the young females, however, in some cases sessions were separated by 28–30 days to allow both sessions to be conducted in the same phase of consecutive menstrual cycles. Older females were post-menopausal, with cessation of menses for at least 12 months prior to enrollment into the study, and a serum follicle stimulating hormone (FSH) level >40 IU/L. The study protocol was approved by the Combined Neuroscience Institutional Review Board at the National Institutes of Health, Bethesda MD, and participants were enrolled following written informed consent.

Procedures

Participants arrived at the clinic having fasted (for ~7 hrs) when they received a metabolic diet (standardized ~300 cal) and completed a brief medical and drinking history questionnaire. With the completion of safety assessments, baseline measures and clinical procedures, participants received infusions of either 6%v/v alcohol or saline for a total of 180 min, administered in counter-balanced order between sessions. The target exposure profile for the alcohol clamp was a 15-min ramp to a BrAC of 50 mg% which was then maintained at that level for 165 min. This alcohol clamp exposure was based on the infusion-rate profile derived from a physiologically-based pharmacokinetic model for alcohol and individualized for each participant based on their height, weight, age and sex (Ramchandani, Bolane, et al., 1999). A detailed explanation of the methodology used for the current study has been previously published (Vatsalya et al., 2012). Serial breathalyzer measurements were recorded using the Alcotest 7410 handheld breathalyzer (Drager Safety Inc., CO) to ensure that the BrACs were within 5 mg% of the target and to enable minor adjustments to the infusion rates to overcome errors in parameter estimation and experimental variability (O’Connor et al., 1998; O’Connor et al., 2000; Ramchandani, Bolane, et al., 1999). The BrAC readings were obtained every 15–20 min until BrAC fell below 20 mg%. To minimize the effect of food on alcohol metabolism (V. A. Ramchandani et al., 2001), participants remained fasted till the end of alcohol clamping procedure was completed and their BrAC level had fallen below 20 mg%.

At baseline, during and after the infusion, the following PD measures were obtained from subjective questionnaires: Drug Effects Questionnaire (DEQ) (Holdstock & de Wit, 2001) and the Biphasic Alcohol Effects Scale (BAES) (Martin et al., 1993). These perceptions of alcohol effects including high, intoxication, stimulation and sedation were measured using paper-and-pencil questionnaires that were completed by the participants at various times during the study (Ramchandani, O’Connor, et al., 1999).

Liver volume (LV) was determined using magnetic resonance imaging (MRI) performed on a 1.5 T General Electric Signa Scanner (Milwaukee, Wisconsin). The MRI images were acquired using a standard protocol, with breath-hold gradient echo 3D sequences, which image the entire liver in a single breath-hold. Typical scan parameters were: orientation – axial, TR – 6 ms, TE – minimum, flip angle – 15 degrees, FOV – 35 cm, .75 phase FOV, matrix – 256 × 160, NEX −0.5, slice thickness 5 mm, slices per volume 32, but was optimized for each patient. The entire procedure takes about 30 minutes.

Data Analysis

Estimation of Lean Body Mass (LBM) and Liver Volume (LV):

From the DEXA scan, lean body mass (LBM) as well as total bone mineral content, total body fat and regional fat-free soft tissue was calculated using standard methods. Liver volume was estimated based on the method described by McNeal et al. (McNeal et al., 1988). Briefly, from the MRI scanned images, the area of the liver was manually traced on each slice using a workstation, and the volume was calculated by summing the areas, and multiplying by the center to center slice distance of 5 mm. For each participant, the sequence was repeated 3 times, and the average of the 3 calculated volumes was determined as the liver volume (LV).

Estimation of Alcohol Elimination Rate (AER) and Descending Limb Slope (DLS):

AER was computed by multiplying the steady state infusion pump rate (ml/hr) by the concentration of alcohol in the infusate (0.06 g alcohol/ml), which was assayed by gas chromatography. This parameter was used as the primary measure for analysis (Ramchandani et al., 2006). DLS was estimated using linear regression of the BrAC vs. time measurements obtained following the end of the infusion.

Statistical Analysis:

Statistical analyses were performed to examine the effects of age and sex on AER, lean body mass (LBM) and liver volume (LV), as well as the effects of age and sex on subjective responses. The questions of interest related to AER include: (a) whether AER, AER per unit LBM, and AER per unit LV differ between young and older males and females, and (b) whether LV or LV per unit LBM differ between young and older males and females. Variables (AER, AER/LBM, AER/LV, LV and LV/LBM) were compared using analysis of variance (ANOVA) to examine the main and interactive effect of age and sex. Additionally, multiple regression analysis was performed to examine the relationships between AER and LBM as well as between AER and LV, within and across each group, to estimate the proportion of variance in AER that was attributable to these predictors.

Repeated Measures ANOVA was used to evaluate subjective responses. Age-and Sex-groups were used as factors to evaluate the significance of independent and interaction-based associations. Visual analog scale of high, and intoxication (derived from the Subjective High Assessment Scale, SHAS) were evaluated. DEQ questionnaire was evaluated for five different measures; “do you feel drug effects?” “do you like drug effect?” “do you feel high?”, and “would you like more”. Biphasic Alcohol Effects Scale was used to evaluate “sedation” and “stimulation”. These questionnaires have been used widely in studies of the subjective effect of alcohol on various domains of sensations and perceptions (Ramchandani, Bolane, et al., 1999). For all measures, the peak response and change from baseline to peak response were analyzed.

All statistical analyses were conducted using SPSS version 25 (IBM Corporation, New York). The α-level for significance was set at 0.05.

RESULTS

Participants

The study included 48 healthy non-smoking social drinkers, stratified by sex and age as shown in Table 1. Body weight and total body water (TBW) were higher, as expected, in the males compared to the females across age groups. TBW normalized for body weight (L/kg) showed a significant interaction effect between age and sex, with post-hoc analysis revealing significant increases in the following order: older females = younger females < older males < younger males. Lean body mass (LBM) and liver volume (LV) were significantly lower in females compared to males across both age groups; however, liver volume normalized for lean body mass (L/kg) was significantly higher in females compared to males.

Table. 1:

Morphometric and Recent Drinking History distribution of the study population.

Young Females (n=12) Young Males (n=12) Older Females (n=12) Older Males (n=12)
Age [years] 23.1 ± 1.0 23.1 ± 1.1 59.3 ± 3.8 59.3 ± 2.3
Height [cm] 165.5 ± 6.9 177.3 ± 9.6 162.5 ± 6.6 176.6 ± 6.7
Weight [kg]a 62.7 ± 11.8 78.0 ± 10.6 67.5 ± 9.2 86.4 ± 16.0
Total Body Water (TBW) [L]a 31.1 ± 3.6 45.9 ± 4.3 32.4 ± 4.4 44.8 ± 5.3
TBW per unit Weight [L/kg]c 0.50 ± 0.04 0.59 ± 0.03 0.48 ± 0.02 0.53 ± 0.04
Lean Body Mass (LBM) [kg]a 42.9 ± 7.1 62.7 ± 9.0 41.4 ± 4.0 61.2 ± 6.5
Liver Volume (LV) [L]a 1.23 ± 0.23 1.60 ± 0.28 1.38 ± 0.38 1.57 ± 0.18
LV per unit LBM [L/kg]a 0.029 ± 0.005 0.026 ± 0.003 0.033 ± 0.009 0.025 ± 0.003
Recent Drinking History (90-day Timeline Followback)
Total Drinks 37.2 ± 26.7 57.5 ± 100.9 51.5 ± 35.6N 51.2 ± 42.3
Drinking Days b 18.8 ± 10.6 15.8 ± 15.5 25.0 ± 30.8N 36.2 ± 27.5
Drinks per Drinking Dayb 1.8 ± 0.6 2.6 ± 2.0 1.3 ± 1.0N 1.3 ± 0.7
Heavy Drinking Days 1.3 ± 2.3 4.0 ± 10.7 1.3 ± 2.5N 0.08 ± 0.29
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a:

Significant sex difference (p<0.05).

b:

Significant age difference (p<0.05).

c:

Significant age × sex interaction (p<0.05).

N:

data only available for four subjects in this sub-group.

Alcohol Elimination Rates (AER)

Effects of Age and Sex

The alcohol elimination rate or AER (g/hr) was estimated from the steady-state infusion rate as described in the Methods section. Two-way analysis of variance of AER by sex and age showed a significant main effect of sex (F (1, 46) = 22.619, p ≤ 0.001, η2 = 0.340), with no main or interaction effects of age. As shown in figure 1a, females (means, M ± standard error = 6.30 ± 0.24), had approximately 27% lower AER than males (8.51 ± 0.41) across age groups. AER normalized for LBM (AER/LBM in g/hr/kg) showed no significant effects of sex or age (figure 1b). AER per unit LV (g/hr/L) also did not show a significant effect of sex or age (figure 1c). These results indicate that the sex differences in AER may be related to lean body mass or more specifically to liver volume differences between males and females. The descending limb slope or DLS (mg%/hr) was estimated from the post-infusion BrAC-time data as described in the Methods section. Two-way analysis of variance of DLS by sex and age showed no significant main or interaction effects of sex and age (figure 1d).

Figure 1:

Figure 1:

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Alcohol Elimination Rate (AER), AER per unit Lean Body Mass (LBM), AER per unit Liver Volume (LV), and Descending Slope Limb (DSL) among the four age × sex groups. Data presented as Mean ± Standard Deviation. Males showing significantly higher AER compared to females across age groups (p < 0.011). There was no sex difference in AER per unit LBM or AER per unit LV. DLS showed no sex or age differences.

Influence of Lean Body Mass and Liver Volume on AER

A multiple regression was performed on AER using lean body mass (LBM), sex, and age. LBM was found to be a significant predictor of AER (R2=0.404, p≤0.001). Age and sex were not significant predictors of the model (Fig. 2a). Another multiple regression was performed using liver volume (LV), sex, and age. LV and sex were significant predictors of the model (Fig. 2b) explaining 35.8% of the variance when combined (p=0.041). Males had higher values of LV as well as higher AER.

Figure 2:

Figure 2:

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Linear relationship between AER and Lean Body Mass (top panel) and Liver Volume (bottom panel). There were significant associations between AER and LBM and between AER and LV across groups.

Alcohol Subjective Responses: Effects of Age and Sex

Subjective response measures were analyzed from two self-report scales: Biphasic Alcohol Effects Scale (BAES) and Drug Effects Questionnaire (DEQ) measures.

Peak Scores

There was a significant main effect of treatment on all the peak subjective measures (all p values <= 0.01), with higher scores following alcohol compared to the placebo session (fig. 3). Peak score for the DEQ liking-effects item showed a significant treatment X age interaction (F (1, 44) = 8.148, p = 0.007, η2 = 0.156) with the younger group showing a greater effect following alcohol compared to the older group. Peak score for the BAES stimulation subscale showed a significant treatment X age X sex interaction (F (1, 44) = 4.226, p = 0.046, η2 = 0.088). Further, there was a significant treatment X age interaction (F (1, 44) = 5.339, p = 0.019, η2 = 0.119), with the younger group showing greater alcohol-induced stimulation compared to the older group.

Figure 3:

Figure 3:

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Peak subjective responses on DEQ and BAES measures during alcohol and placebo sessions among the four age × sex groups. Placebo: grey bars; Alcohol: black bars. There was a significantly higher response following alcohol compared to placebo for all measures. DEQ liking-effects and BAES Stimulation peak scores showed significant treatment × age interactions with the younger group showing higher responses for both measures following alcohol compared to placebo.

Difference of Peak Scores from Baseline (Delta)

Additional analyses to examine changes in peak subjective response from baseline showed consistent findings as those reported above (p <= 0.02 for main effect of treatment across measures) (fig. 4). Additionally, the peak change from baseline in the DEQ wanting-more item showed a significant treatment X sex interaction (F (1, 44) = 4.795, p = 0.034, η2 = 0.098), with females showing greater change scores compared to males across age groups.

Figure 4:

Figure 4:

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Peak change from baseline subjective responses on DEQ and BAES measures during alcohol and placebo sessions among the four age × sex groups. Placebo: grey bars; Alcohol: black bars. There was a significantly higher peak change in response following alcohol compared to placebo for all measures. DEQ liking-effects and BAES Stimulation peak scores showed significant treatment × age interactions with the younger group showing higher peak change in responses for both measures following alcohol compared to placebo.

DISCUSSION

This investigation estimated the influence of age and sex on AER with acute IV administration of alcohol. The results of our study demonstrated that AER was significantly lower in females than males, but was not influenced by age. This may be partly due to sex differences in lean body mass. Liver volume showed significant association with AER, which appeared to be related to sex differences, indicating that the liver volume differences between females and males could have a limiting as well as contributing role in the sustained level of alcohol in the body. This study also provided indication that approximately 40% of the variance in alcohol elimination rates was attributable to lean body mass, and that ~35% of the variance in alcohol elimination rates was attributable to liver volume (along with sex in the regression models). This underscores the critical role of body mass, and relatedly total body water, and liver volumetrics as critical determinants of the pharmacokinetics of alcohol. Our previous studies, using the alcohol clamp, have shown that females have significantly higher elimination rates of alcohol per unit LBM, in Caucasian as well as African American and Asian participants (Kwo et al., 1998; Li et al., 2000; Ramchandani, O’Connor, et al., 1999; Sato et al., 2001). Other studies have reported that differences in peak concentrations following equivalent low oral doses of alcohol administered to men and women were due to differences in first-pass metabolism of alcohol in the gastrointestinal tract, which was significantly correlated with gastric ADH activity and has been shown to have lower levels in females (Baraona et al., 2001; Frezza et al., 1990; Seitz et al., 1993).

Subjective responses showed that there was a clear (and predictable) difference between alcohol and placebo sessions in peak measures. There were no sex differences in subjective effects of alcohol at comparable BrAC levels. Older subjects reported similar peak feelings of intoxication as younger subjects; however, they did not like the effects of alcohol as much. Age differentiated the variability in the peak value for DEQ liking-effects and BAES stimulation subscale measures, specifically within the older population. The difference in peak (Delta) values from the baseline further supported these significantly different responses when alcohol influence is not present. Again, age (and in the older population) was found to have a significant association with change in DEQ liking-effects and BAES stimulation subscale values, confirming the influence on the rewarding and hedonic effects of alcohol within the older population.

The association of age and sex with the measures evaluated in this study substantiated their significance and role in the pharmacokinetics and pharmacodynamics of alcohol. The older individuals are thought to be more sensitive to alcohol and show greater impairment than younger groups; however, it has not previously been determined if these differences are due to pharmacokinetic or pharmacodynamic factors (Kalant et al., 1998). Pharmacokinetic changes, including a decrease in volume of distribution can result in increased alcohol levels, and therefore increased impairment in older participants following standard doses of alcohol. Pharmacodynamic factors may include a decrease in sensitivity to the initial impairing effects or a decreased ability to develop tolerance to the effects of alcohol (Kalant et al., 1998; Lucey et al., 1999; Tupler et al., 1995).

Difference by age in alcohol metabolism could not be substantiated in this study and certainly a larger study might identify intricate parameters that could not be addressed in our paradigm in older populations, which tend to have an onset of geriatric conditions. It is possible that age-related differences in elimination and/or subjective responses may be more apparent in a more elderly cohort. To our knowledge, there are no published studies of alcohol PK and PD in individuals above 65 years. As the population ages and life expectancies get extended, this may be a relevant question for addressing in the future. Progression of hazardous consequences of alcohol drinking is very likely variable and sex-based differences as described in this study shows the manifestation could initiate even with acute exposure. While the IV administration of alcohol in this study enabled the examination of age and sex effects on alcohol elimination rates and subjective responses, it limits the ability to examine sex and age-related differences in absorption and first-pass metabolism of alcohol. These differences, which have been previously reported would result in differential BrAC peak levels as well as ascending and ascending BrAC-time slopes, which, in turn, could influence the subjective response to alcohol and impact risk for alcohol-related outcomes across the age and sex groups. The design of the current study precludes the examination of this potential source of variation. Additionally, it is possible that differences in alcohol expectancies may contribute to some of the observed variation in responses by age and sex. We did not conduct a check of blinding efficacy of the alcohol vs. saline sessions, and it is likely that most participants may have been able to discern the difference between alcohol and saline despite the non-oral route of administration. Future studies should consider examining expectancies and evaluate blinding efficacy in human lab studies examining differences in subjective responses to alcohol, regardless of route of administration.

Understanding age and sex as sources of variability in alcohol metabolism and response is clinically relevant as both are important predictors of alcohol consumption in older populations. Given the aging US population, the significance of examining the predictors and consequences of problematic alcohol use cannot be overstated (Listabarth et al., 2020). Additionally, changing patterns in alcohol use in women stresses the importance of understanding the role of sex on alcohol metabolism (Tebeka et al., 2020). This study provides clinically significant findings for evaluating individuals considering their sex as an important factor in pathophysiological changes happening at acute stages of alcohol administration.

Supplementary Material

1

HIGHLIGHTS.

Vatsalya et al., Influence of Age and Sex on Alcohol Pharmacokinetics and Subjective Pharmacodynamic Responses following Intravenous Alcohol Exposure in Humans

  • Alcohol elimination rates and acute subjective responses were assessed during acute intravenous alcohol infusion in men and women in 2 age groups – younger (21–25 years) and older (55–65 years).

  • Systemic alcohol elimination rates were higher in males than in females across both age groups; these differences were explained by sex differences in lean body mass and liver volume.

  • Subjective perceptions of high, intoxication, drug liking and wanting more alcohol did not differ by sex.

  • The older group reported similar feeling of high and intoxication but lower liking effects and stimulation compared to the younger groups.

  • These findings highlight the significant impact of sex and age as sources of variability in the clinical pharmacology of alcohol.

Acknowledgments

FUNDING AND ACKNOWLEDGEMENTS:

The study was supported by the NIAAA Division of Intramural Clinical and Biological Research (DICBR) (Z1A AA 000466). Development and use of the computer-assisted alcohol infusion software (CAIS) was supported by Sean O’Connor and Martin Plawecki, Indiana University Alcohol Research Center (P60 AA 07611). The authors gratefully acknowledge the NIH Clinical Center 1-SE Clinic and Day Hospital staff for clinical support, laboratory research staff (Elizabeth Edwards, Mike Hoefer, Nina Saxena, Shilpa Kumar, Seth Eappen) for data collection support, and the study volunteers for their participation in the study. We are eternally grateful for clinical and research support from the late Daniel Hommer, M.D. for this study.

Footnotes

Conflicts of Interest: None of the authors listed have any conflict of interest or disclosure towards the conduct and publication of this study.

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REFERENCES

  1. Baraona E, Abittan CS, Dohmen K, Moretti M, Pozzato G, Chayes ZW, Schaefer C, Lieber CS. (2001). Gender differences in pharmacokinetics of alcohol. Alcohol Clin Exp Res. 25(4):502–507. [PubMed] [Google Scholar]
  2. Clasey J, Kanaley J, Wideman L, Heymsfield S, Teates C, Gutgesell M, Thorner M, Hartman M, & Weltman A (1999). Validity of methods of body composition assessment in young and older men and women. Journal of applied physiology, 86(5), 1728–1738. [DOI] [PubMed] [Google Scholar]
  3. Fenollal-Maldonado G, Brown D, Hoffman H, Kahlon C, Grossberg G. (2022) Alcohol use disorder in older adults. Clin Geriatr Med, 38(1):1–22. [DOI] [PubMed] [Google Scholar]
  4. Frezza M, di Padova C, Pozzato G, Terpin M, Baraona E, & Lieber CS (1990). High blood alcohol levels in women: the role of decreased gastric alcohol dehydrogenase activity and first-pass metabolism. New England Journal of Medicine, 322(2), 95–99. [DOI] [PubMed] [Google Scholar]
  5. Gallagher D, Visser M, De Meersman RE, Sepúlveda D, Baumgartner RN, Pierson RN, Harris T, & Heymsfield SB (1997). Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. Journal of applied physiology, 83(1), 229–239. [DOI] [PubMed] [Google Scholar]
  6. Holdstock L, & de Wit H (2001). Individual differences in responses to ethanol and d-amphetamine: a within-subject study. Alcoholism: Clinical and Experimental Research, 25(4), 540–548. [PubMed] [Google Scholar]
  7. Jones A, & Neri A (1985). Age-related differences in blood ethanol parameters and subjective feelings of intoxication in healthy men. Alcohol and Alcoholism, 20(1), 45–52. [PubMed] [Google Scholar]
  8. Joshi P, Duong KT, Trevisan LA, Wilkins KM. (2021) Evaluation and management of alcohol use disorder among older adults. Curr Geriatr Rep, 10(3):82–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Kalant H, Gomberg E, Hegedius A, & Zucker R (1998). Pharmacological interactions of aging and alcohol. Alcohol problems and aging. NIAAA Research Monograph (33), 99–116. [Google Scholar]
  10. Keyes KM, Allel K, Staudinger UM, Ornstein KA, Calvo E. (2019). Alcohol consumption predicts incidence of depressive episodes across 10 years among older adults in 19 countries. Int Rev Neurobiol, 148:1–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Kwo PY, Ramchandani VA, O’Connor S, Amann D, Carr LG, Sandrasegaran K, Kopecky KK, & Li T-K (1998). Gender differences in alcohol metabolism: relationship to liver volume and effect of adjusting for body mass. Gastroenterology, 115(6), 1552–1557. [DOI] [PubMed] [Google Scholar]
  12. Li TK, Beard JD, Orr WE, Kwo PY, Ramchandani VA, & Thomasson HR (2000). Variation in ethanol pharmacokinetics and perceived gender and ethnic differences in alcohol elimination. Alcoholism: Clinical and Experimental Research, 24(4), 415–416. [PubMed] [Google Scholar]
  13. Listabarth S, Vyssoki B, Waldhoer T, Gmeiner A, Vyssoki S, Wippel A, Blüml V, Gruber M, König D. (2020). Hazardous alcohol consumption among older adults: A comprehensive and multi-national analysis of predictive factors in 13,351 individuals. Eur Psychiatry, 64(1):e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Lucey MR, Hill EM, Young JP, Demo-Dananberg L, & Beresford T (1999). The influences of age and gender on blood ethanol concentrations in healthy humans. Journal of studies on alcohol, 60(1), 103–110. [DOI] [PubMed] [Google Scholar]
  15. Maisto SA, Connors GJ, Tucker JA, McCollam JB, & Adesso VJ (1980). Validation of the Sensation Scale, a measure of subjective physiological responses to alcohol. Behaviour research and therapy, 18(1), 37–43. [DOI] [PubMed] [Google Scholar]
  16. Martin CS, Earleywine M, Musty RE, Perrine M, & Swift RM (1993). Development and validation of the biphasic alcohol effects scale. Alcoholism: Clinical and Experimental Research, 17(1), 140–146. [DOI] [PubMed] [Google Scholar]
  17. McNeal GR, Maynard W, Branch R, Powers T, Arns P, Gunter K, Fitzpatrick J, & Partain C (1988). Liver volume measurements and three-dimensional display from MR images. Radiology, 169(3), 851–854. [DOI] [PubMed] [Google Scholar]
  18. Mumenthaler MS, Taylor JL, O’Hara R, & Yesavage JA (1999). Gender differences in moderate drinking effects. Alcohol research and health, 23(1), 55–64. [PMC free article] [PubMed] [Google Scholar]
  19. O’Connor S, Morzorati S, Christian J, & Li TK (1998). Clamping breath alcohol concentration reduces experimental variance: application to the study of acute tolerance to alcohol and alcohol elimination rate. Alcoholism: Clinical and Experimental Research, 22(1), 202–210. [PubMed] [Google Scholar]
  20. O’Connor S, Ramchandani V, & Li TK (2000). PBPK modeling as a basis for achieving a steady BrAC of 60±5 mg% within ten minutes. Alcoholism: Clinical and Experimental Research, 24(4), 426–427. [PubMed] [Google Scholar]
  21. Oneta C, Pedrosa M, Rüttimann S, Russell R, & Seitz H (2001). Age and bioavailability of alcohol. Zeitschrift für Gastroenterologie, 39(09), 783–788. [DOI] [PubMed] [Google Scholar]
  22. Plawecki MH, Durrani AM, Boes J, Wetherill L, Kosobud A, O’Connor S, Ramchandani VA (2019). Comparison of Subjective Responses to Oral and Intravenous Alcohol Administration Under Similar Systemic Exposures. Alcohol Clin Exp Res. 43(4), 597–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Pozzato G, Moretti M, Franzin F, Crocè LS, Lacchin T, Benedetti G, Sablich R, Stebel M, & Campanacci L (1995). Ethanol metabolism and aging: the role of “first pass metabolism” and gastric alcohol dehydrogenase activity. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 50(3), B135–B141. [DOI] [PubMed] [Google Scholar]
  24. Ramchandani V, Bosron W, & Li T (2001). Research advances in ethanol metabolism. Pathologie Biologie, 49(9), 676–682. [DOI] [PubMed] [Google Scholar]
  25. Ramchandani VA, Bolane J, Li TK, & O’Connor S (1999). A physiologically-based pharmacokinetic (PBPK) model for alcohol facilitates rapid BrAC clamping. Alcoholism: Clinical and Experimental Research, 23(4), 617–623. [PubMed] [Google Scholar]
  26. Ramchandani VA, Kwo PY, & Li TK (2001). Effect of food and food composition on alcohol elimination rates in healthy men and women. The Journal of Clinical Pharmacology, 41(12), 1345–1350. [DOI] [PubMed] [Google Scholar]
  27. Ramchandani VA, O’Connor S, Blekher T, Kareken D, Morzorati S, Nurnberger J Jr, & Li TK (1999). A preliminary study of acute responses to clamped alcohol concentration and family history of alcoholism. Alcoholism: Clinical and Experimental Research, 23(8), 1320–1320. [PubMed] [Google Scholar]
  28. Ramchandani VA, O’Connor S, Neumark Y, Zimmermann US, Morzorati SL, & De Wit H (2006). The alcohol clamp: applications, challenges, and new directions–an RSA 2004 symposium summary. Alcoholism: Clinical and Experimental Research, 30(1), 155–164. [DOI] [PubMed] [Google Scholar]
  29. Sato N, Lindros KO, Baraona E, Ikejima K, Mezey E, Järveläinen HA, & Ramchandani VA (2001). Sex difference in alcohol-related organ injury. Alcoholism: Clinical and Experimental Research, 25, 40S–45S. [DOI] [PubMed] [Google Scholar]
  30. Seitz H, Egerer G, Simanowski U, Waldherr R, Eckey R, Agarwal D, Goedde H, & Von Wartburg J (1993). Human gastric alcohol dehydrogenase activity: effect of age, sex, and alcoholism. Gut, 34(10), 1433–1437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Tebeka S, De Premorel Higgons A, Dubertret C, Le Strat Y. (2020). Changes in alcohol use and heavy episodic drinking in U.S. Women of childbearing-age and peripartum between 2001–2002 and 2012–2013. Addict Behav, 107:106389. [DOI] [PubMed] [Google Scholar]
  32. Thomasson HR (2002). Gender differences in alcohol metabolism. In Recent developments in alcoholism (pp. 163–179). Springer. [DOI] [PubMed] [Google Scholar]
  33. Thomasson HR, Beard JD, & Li TK (1995). ADH2 gene polymorphisms are determinants of alcohol pharmacokinetics. Alcoholism: Clinical and Experimental Research, 19(6), 1494–1499. [DOI] [PubMed] [Google Scholar]
  34. Tupler L, Hege S, & Ellinwood EH (1995). Alcohol pharmacodynamics in young-elderly adults contrasted with young and middle-aged subjects. Psychopharmacology, 118(4), 460–470. [DOI] [PubMed] [Google Scholar]
  35. Vatsalya V, Issa JE, Hommer DW, & Ramchandani VA (2012). Pharmacodynamic effects of intravenous alcohol on hepatic and gonadal hormones: influence of age and sex. Alcoholism: Clinical and Experimental Research, 36(2), 207–213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Vestal RE, McGuire EA, Tobin JD, Andres R, Norris AH, & Mezey E (1977). Aging and ethanol metabolism. Clinical Pharmacology & Therapeutics, 21(3), 343–354. [DOI] [PubMed] [Google Scholar]

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NIHMS1838974-supplement-1.pdf (219KB, pdf)

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