Conceived and designed the experiments: PdT JD PS DG. Performed the experiments: JD AN PC DG AB SL PFL. Analyzed the data: PdT JD DG SL PFL AN PC AB ND PS. Contributed reagents/materials/analysis tools: PdT DG PFL AN PC AB ND PS. Wrote the paper: PdT ND JD PC PS PFL AN ND SL AB.
The authors have declared that no competing interests exist.
Most physiological studies interested in alcohol-dependence examined ethanol as a pharmacological agent rather than a nutrient. We conducted two studies, which assessed the metabolic and endocrine factors involved in the regulation of alcohol and nutrient intake in alcohol-dependent (AD) subjects. We also examined the potential role of a disruption in energy balance in alcohol-dependence.
In
For individuals consuming below12.5 kcal/kg/day of alcohol, alcohol intake is compensated for by a decrease in non-alcoholic nutrient intakes, probably due to changes in metabolic and satiety factors. For individuals consuming above 12.5 kcal/kg/day of alcohol, alcohol accelerates metabolism and decreases fat mass and leptin levels, and the total caloric intake largely exceeds norms. A dual model for regulation of energy intake in AD subjects is proposed.
“Where the brewer passes, the baker does not.” This popular Belgian quotation alludes to the fact that alcohol-dependent (AD) subjects, or at least those who drink beer, often replace food consumption with substantial alcohol consumption. Previous epidemiological studies have indeed shown that alcohol may account for 10% of total energy consumption in alcohol consumers and for more than 50% of dietary intake in AD subjects
Recently, the peptides that influence body and fat mass and present anorexigenic or orexigenic properties have been studied in relation to alcohol consumption. With regard to leptin, Mantzoros et al.
Finally, several researchers have observed a positive correlation between alcohol craving and plasma concentrations of the anorexigenic peptides leptin
Thus, the aim of the present work was to analyse, quantitatively, energy metabolism in AD subjects who vary in levels of alcohol consumption. Indeed, unlike prior research which only selected AD subjects who consumed very high quantities of ethanol, we decided to select all populations of AD subjects. We also excluded all subjects that presented additional illnesses, which may also affect appetite and energy regulation. For this project, we conducted two studies.
The first consisted of careful diet recall interviews with 97 AD inpatients to assess: 1- the importance of alcohol-related or unrelated energy supplies and how both are interrelated; 2- the relation between alcohol and non-alcoholic nutrients intake and Body Mass Index (BMI) and Fat Mass (FM).
A second metabolic study was performed on 24 AD subjects matched for age and gender with 20 control subjects, in order to examine the regulatory mechanisms of energy balance during alcohol-withdrawal. Therefore, at the onset, during, and at the end of the withdrawal, we measured alcoholic and non-alcoholic intake, basal metabolism and morphometric parameters including BMI and FM. We also assessed various anorexigenic and orexigenic peptides, which are potent regulators of the energy balance (leptin, insulin, cortisol) and of the meal duration (ghrelin, peptide YY (PYY), glucagon-like peptide 1 (GLP-1))
Finally, we designed a new comprehensive model to explain the regulation of food and alcohol intakes in AD subjects.
The study protocol was approved by the ethical committee of the hospital (Commission d’éthique biomédicale hospitalo-facultaire, UCL) and all subjects signed an informed consent form prior to the investigation.
A total of 97 inpatients admitted for alcohol withdrawal and rehabilitation, were enrolled in
Patients were interviewed by expert dieticians concerning periods of active alcohol drinking during the 7 days that preceded hospital admission. This dietary recall interview
Between 8 and 9 AM on days 2 (T1), 5 (T2) and 16 (T3) of withdrawal, patients underwent fasting blood samples to determine their plasma glucose, insulin, cortisol, leptin, ghrelin, Peptide YY (PYY) and Glucagon Like Peptide-1 (GLP-1) levels. Aspartate aminotranferase (AST), Alanine aminotransferase (ALT), γ-Glutamyl transpeptidase (γ-GT), total bilirubin, total blood protein (TBP), albumin and mean corpuscular volume (MCV) were measured on day 2. On days 2 and 16, before blood sampling, the patients were also subjected to indirect calorimetry, a thorough food interview regarding the previous 7 days, and a bioelectric impedance measurement of fat mass (FM). The methodology is detailed in
Due to the large sample size (n = 97), we assumed statistical normality for each variable. Independent student’s t tests were used to compare female AD vs. male AD, “low alcohol” drinking AD vs. “high alcohol” drinking AD, and AD patients vs. controls.
The comparison of means at T1, T2 and T3 was tested with one-way repeated-measures ANOVA. Data that exhibited a non-normal distribution were log-transformed to obtain normality. The assumption of sphericity, assessed with the Mauchly’s test, was met for each variable (
The characteristics of the population of study 1 are reported in
Males (n = 63) | Females (n = 34) | Males + Females (n = 97) | |
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Age (y) | 49±12 | 49±10 | 49±11 |
Weight (kg) | 77.7±15.1 | 66.4±13.8 |
73.7±15.6 |
BMI (kg/m2) | 24.6±4.3 | 24.6±5.0 | 24.7±4.5 |
FM (%) (M; n = 31/F; n = 14) | 25.3±8.4 | 33.9±6.9 |
28.0±8.9 |
BM theoretical (kcal) | 1743.9±193.3 | 1402.4±162.0 |
1624.2±244.9 |
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Total | 42.7±21.0 | 32.2±12.7 |
39.0±19.1 |
Proteins | 3.6±1.7 | 3.5±1.4 | 3.5±1.5 |
Lipids | 7.6±4.3 | 6.7±3.6 | 7.3±4.1 |
Carbohydrates | 14.1±9.6 | 10.6±5.5 |
12.9±8.5 |
Non-alcohol | 25.3±14.8 | 20.2±8.7 | 23.5±16.0 |
Alcohol | 17.4±11.5 | 12.0±8.1 |
15.5±10.7 |
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Proteins | 9.4±3.4 | 10.7±4.0 |
9.8±3.7 |
Lipids | 19.0±8.1 | 20.3±8.8 | 19.5±8.3 |
Carbohydrates | 32.0±9.7 | 30.7±9.8 | 31.6±9.7 |
Non-Alcohol | 60.1±13.8 | 64.7±12.8 | 61.7±13.6 |
Alcohol | 39.2±14.1 | 38.3±16.6 | 38.9±14.9 |
Values are means ± SD.
p<0.05,
p<0.01,
p<0.001 (Males vs Females).
BMI: body mass index; FM: fat mass; BM: basal metabolism.
From the dietary interviews, we calculated the number of meals taken per 7 days for each participant. It was significantly lower for breakfast (2.72±3.06) than for lunch (4.31±2.79) or for dinner (6.09±2.02) and these proportions were not significantly different across males and females. The nutrient and alcohol (ethanol) intakes are presented in
We then split our 97 subjects population into two (“low alcohol” vs. “high alcohol” drinking AD subjects) groups based on the median value of alcohol intake (12.5 kcal/kg/day). The “low alcohol” group evidenced a higher body weight, a higher BMI and a higher proportion of FM than the “high alcohol” group (
“Low alcohol” (n = 48) | “High alcohol” (n = 49) | |
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Age (y) | 51±10 | 47±12 |
Gender (male/female) | 24/24 | 39/10 |
Weight (kg) | 76.6±15.7 | 71.0±14.9 |
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BMI (kg/m2) | 26.1±4.6 | 23.2±4.0 |
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FM (%) (low; n = 21/high; n = 24) | 32.5±7.8 | 24.0±7.9 |
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Total | 28.1±9.0 | 47.5±17.3 |
Proteins | 3.2±1.3 | 3.5±1.4 |
Lipids | 7.0±3.3 | 6.7±3.4 |
Carbohydrates | 9.6±4.6 | 14.5±8.0 |
Non-alcohol | 19.8±7.8 | 24.7±10.4 |
Alcohol | 8.3±2.7 | 22.8±10.8 |
Values are means ± SD.
p<0.05,
p<0.01,
p<0.001 (“low alcohol” vs “high alcohol”).
The total population of alcoholics was split into two subpopulations depending on whether their consumption was lower or higher than the median value of 12.5 kcal/kg/day of alcohol and described as “low alcohol” or “high alcohol” drinking alcoholics. BMI: body mass index; FM: fat mass.
When examining the relation between BMI and alcohol intake we, surprisingly, observed a negative correlation for both male and female subjects (
A group of 24 AD subjects who had drank alcohol until the day of admission was compared to a group of 20 controls matched for age and gender
AD-T1 (n = 24) | AD-T3 (n = 24) | Controls (n = 20) | |
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Age | 47±10 | 47±10 | 44±9 |
Gender (Male/Female) | 15/9 | 15/9 | 13/7 |
Weight (kg) | 72.8±12.8 | 73.2±13.2 | 75.2±14.1 |
BMI (kg/m2) | 24.6±3.9 | 24.7±4.0 | 24.2±3.0 |
FM (%) | 27.6±9.1 |
25.9±9.4## | 25.3±7.5 |
Waist size (cm) | 93.0±11.4 | 92.0±11.1 | 91.0±11.7 |
BM (theoretical) (kcal) | 1617.0±224.3 | 1621.7±230.7 | 1667.8±267.6 |
BM (calorimetry) (kcal) | 1699.1±285.5 | 1549.1±274.4### | 1674.3±299.0 |
Respiratory Quotient | 0.791±0.057 |
0.843±0.051## | 0.838±0.054 |
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Breakfast | 3.0±3.3 |
4.9±3.2# |
7.0±0.0 |
Lunch | 5.1±2.4 |
6.3±1.4# | 6.5±1.0 |
Dinner | 6.8±0.7 | 6.8±0.7 | 7.0±0.0 |
Values are means ± SD.
p<0.05,
p<0.01,
p<0.001 (AD vs Controls).
p<0.05,
p<0.01,
p<0.001 (AD-T1 vs AD-T3).
Theoretical basal metabolism was calculated according to the Schofield equations. Respiratory Quotient was calculated as CO2 consumed/O2 output. BMI: body mass index; FM: fat mass; BM: basal metabolism.
At the onset of withdrawal, the AD subjects evidenced a similar weight, BMI and waist size and theoretical basal metabolism compared to controls. The FM calculated by impedancemetry was higher in AD subjects than in controls and decreased significantly during withdrawal to values similar to the control values. Basal metabolism, calculated by calorimetry, was similar to that of controls at onset of withdrawal in AD subjects, but it decreased significantly during withdrawal. In AD subjects, at T1, the respiratory quotient (RQ) was significantly lower than that of controls and it increased significantly during withdrawal (
We observed a large decrease in meal frequency at breakfast and lunch in AD subjects at T1 compared with the controls (
AD-T1 (n = 24) | AD-T3 (n = 24) | Controls (n = 20) | |
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Total | 2859.0±1118.3 | 1913.0±456.2 |
2404.4±761.4 |
Proteins | 282.2±63.4 | 282.8±63.8 | 367.9±88.5 |
Lipids | 599.0±196.9 | 707.3±210.2 |
849.7±313.5 |
Carbohydrates | 893.7±434.1 | 906.0±302.6 | 1084.9±362.3 |
Non-alcohol | 1773.5±586.7 | 1891.5±462.2 | 2304.6±716.1 |
Alcohol | 1085.5±704.1 | 21.5±51.0 |
103.7±96.8 |
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Total | 39.9±15.9 | 27.1±9.3 |
31.8±7.2 |
Proteins | 4.0±1.1 | 4.0±1.2 | 4.9±0.8 |
Lipids | 8.5±3.2 | 10.1±4.0 |
11.1±2.7 |
Carbohydrates | 12.3±6.1 | 12.7±5.4 | 14.5±4.3 |
Non-alcohol | 24.9±8.6 | 26.8±9.4 |
30.5±7.0 |
Alcohol | 15.0±10.0 | 0.3±0.6 |
1.4±1.2 |
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Proteins | 10.7±3.1 | 15.0±2.8 |
15.6±2.3 |
Lipids | 22.5±8.4 | 37.2±8.4 |
34.9±4.1 |
Carbohydrates | 30.6±8.5 | 46.7±10.2 |
45.2±4.4 |
Alcohol | 36.2±11.5 | 1.2±2.8 |
4.3±3.4 |
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0.99±0.27 | 1.00±0.31 | 1.22±0.20 |
Values are means ± SD.
p<0.05.
p<0.01.
p<0.001 (vs AD-T1).
p<0.05.
p<0.01 (vs AD-T3).
AD-T1 (n = 24) | AD-T2 (n = 24) | AD-T3 (n = 24) | Controls (n = 20) | |
Leptin (µg/L) | 7,7±5,3 | 8,6±6.0 | 7.0±5,5 |
5,1±3.7 |
Leptin/BMI | 0.30±0,17 | 0.33±0.20 | 0.27±0.18 |
0.21±0.15 |
Ghrelin (pg/ml) | 102.0±34.4 | 99.3±35.5 | 104.6±39.8 | 130.5±48.9# |
PYY (pg/ml) | 110.9±37.3 | 110.1±31.5 | 113.8±38.7 | 91.5±21.4# |
GLP-1(pm/ml) | 2.48±1.57 | 2.24±1.56 | 2.23±1.56 | 3.2±1.2 |
Insulin (µU/ml) | 3.8±1.6 | 5.2±2.8## | 4.5±2.5 | 3.3±1.4 |
Glycemia (mg/dl) | 86.8±10.7 | 79.1±10.2# | 83.5±13.2 | 84.6±8.1 |
HOMA %S | 187.4±70.8 | 151.9±67.3# | 178.4±82.0 | 200.7±83.9 |
HOMA %B | 73.4±21.3 | 113.6±57.5# | 85.6±25.3# |
70.9±15.6 |
Cortisol (ng/ml) | 447.5±154.4 | 419.7±131.4 | 350.2±139.4# |
234.3±57.6### |
Values are means ± SD.
p<0.05,
p<0.01, p<0.001 (compared to AD-T1).
p<0.05,
p<0.01,
p<0.001 (compared to AD-T2).
p<0.05,
p<0.01 (compared to AD-T3).
We next calculated correlations between nutrient intakes and the different orexigenic and anorexigenic factors. All the correlations were non-significant except for a negative correlation between alcoholic, non-alcoholic or total energy intakes and leptin, with Spearman r coefficients of -0.61 (p<0.005), -0.44 (p<0.05) and -0.59 (p<0.005), respectively (
This article focused on energy balance in alcoholic patients before and after alcohol withdrawal. All the patients diagnosed with alcohol-dependence according to the DSM-IV criteria were included in the study and the quantity of ethanol consumed daily did not serve as an exclusion criteria. This permitted the observation of subjects with large ranges of alcohol consumption in their daily diet.
Our methodology was based on a dietary recall interview
From a phenomenological standpoint, we observed a specific modification of the eating habits in AD subjects, with a large decrease in frequency of having breakfast or lunch but a normal frequency of having dinner. Changes in eating habits of AD subjects have previously been correlated with disorganisation of living and social habits
Previous research
We also observed that, in AD subjects, basal metabolism was larger than the theoretical values according to the Schofield equations. It decreased significantly after withdrawal. At T1, basal metabolism strongly correlated with the energy contained in alcoholic beverages. Our data are in keeping with those of Addolorato and colleagues that suggested that the high resting energy expenditure and the preferential utilization of lipids as energy substrates explained the low BMI and FM observed in heavy drinking AD subjects
The paradoxically negative relation between alcohol intake and BMI or FM may be due to ethanol-induced basal metabolism activation, which can be attributed to sympathic activation
Finally, we examined the evolution of various orexigenic and anorexigenic peptides between the onset and the end of withdrawal. Leptin is an adipocyte-derived hormone that is essential for the regulation of food intake and body weight
Notably, however, we observed a negative relation between serum leptin level and oxidative metabolism (
More studies are necessary to determine the level of alcohol intake where BMI and fat mass start to shift towards a decrease in alcoholic patients. Additionally, experimental examining the role of leptin in the context of alcohol dependence would be highly valuable to better ascertain the role of leptin in the management of alcohol consumption, i.e. in ob/ob or db/db mice subjected to high and low level alcohol.
In line with our observations, we propose a model to explain the complex relation between alcohol intake and BMI or FM regulation. This model ascribes a central role to plasma leptin in the regulation of energy intake (
This model provides an explanation to the dual relation that we observed between alcohol intakes and FM, depending on the quantities of alcohol intake. It also suggests that, in AD subjects, the loss of control of intake is not to be attributed solely to an effect of alcohol on the brain reward circuit
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We would like to thank the patients and controls for their involvement and the nurses from Unité Intégrée d’Hépatologie for their technical help in our study.