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. Author manuscript; available in PMC: 2015 Feb 2.
Published in final edited form as: Drug Alcohol Depend. 2009 Dec 16;108(0):43–48. doi: 10.1016/j.drugalcdep.2009.11.011

A new definition of early age at onset in alcohol dependence

Yann Le Strat a, Bridget F Grant b, Nicolas Ramoz a, Philip Gorwood a,c
PMCID: PMC4313551  NIHMSID: NIHMS628544  PMID: 20018459

Abstract

Objective

The accurate cut-off of an early onset of alcohol dependence is unknown. The objectives of this analysis are (1) to confirm that ages at onset variability in alcohol dependence is best described as a two sub-groups entity, (2) to define the most appropriate cut-off, and (3) to test the relevancy of such distinction.

Method

Data were drawn the Epidemiologic Survey on Alcohol and Related Conditions (NESARC). This study focused on the 4,782 adults with lifetime alcohol dependence.

Results

The best-fit model distinguished two subgroups of age at onset of alcohol dependence, with a cut-off point at 22 years. Subjects with an earlier onset of alcohol dependence (≤22 years old) reported higher lifetime rates of specific phobia, antisocial behaviors and nearly all addictive disorders.

Conclusions

The early onset of alcohol dependence is best defined as beginning before the age of 22 years.

Keywords: NESARC, alcohol dependence, admixture test, DSM-V, age at onset

1. Introduction

Alcohol dependence and excess drinking are leading causes of morbidity and premature death (Di Castelnuovo et al., 2006), affecting 5.4% of the general population lifetime (Kessler et al., 2005). The age at onset of alcohol dependence varies widely, with a consistent peak at age 18–19 years old and then a rapid decline of onset after 25 years old (Li et al., 2004; Reich et al., 1998). The age at onset is an important characteristic of alcohol dependence in most of the existing typologies. These typologies diverge in the number of types as well as in the clinical variables included in their models, co-morbid addictive disorders or antisocial personality disorder being sometimes excluded. Despite this high variability and the lack of scientific validation of the definition of a specific threshold, age at onset of alcohol dependence continues to be included in most of alcohol dependence typologies. Indeed, early onset of alcohol dependence is associated with a family history of alcohol use disorders (Limosin et al., 2001), and some genetic markers may particularly (Edenberg et al., 2008) or specifically (Dahmen et al., 2005) contribute to an earlier onset of the disease. Furthermore, an early age at onset of alcohol dependence is associated with greater impulsivity, more severe dependence (Hingson et al., 2006b), and more severe and frequent alcohol withdrawal complications (Le Strat et al., 2008a). Finally, antisocial behaviors are the most consistent co-morbidity associated with an early onset of alcohol dependence (Kuperman et al., 2005; McGue et al., 2001). These studies have several limitations. For example, they use an arbitrary cut-off, often defined on the basis of clinical rather than epidemiological sample. They also rely on a single distribution model, which might not be appropriate for age-at-onset data (Mayberg et al., 2005). Moreover, some of these studies are based on the age at onset of usual alcohol consumption whereas the onset of alcohol dependence might be clinically more relevant.

The aim of our study is to discover the appropriate model for age at onset distribution by using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Particularly, we examine whether there is a mixture of overlapping age at onset distribution that defines more homogenous sub-groups of alcohol dependent patients. An additional goal of this study is to examine sociodemographic and mental disorders associated with an early onset of alcohol dependence.

2. Methods

2.1 Participants

Subjects were participants in NESARC, a nationally representative face-to-face survey of 43,093 respondents aged 18 years and older (response rate, 81%), conducted by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) in 2001–2002. The NESARC assessed the civilian non- institutionalized population residing in the United States. African-Americans and Hispanics were oversampled, as were young adults. The research protocol, including informed consent procedures, received full ethical review and approval from the US Census Bureau and the Office of Management and Budget.

2.2 Measures

The NESARC used the National Institute on Alcohol Abuse and Alcoholism's Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV version (AUDADIS-IV), a structured diagnostic interview made for non-clinician interviewers (Gorwood, 2003). Algorithms were designed to produce diagnoses of alcohol dependence consistent with the final DSM-IV criteria. Several studies have documented good to excellent retest reliability (Grant et al., 1995).

Mood and anxiety disorders included in the survey were panic disorder, agoraphobia, specific phobia, social phobia, generalized anxiety disorder, major depressive episode and manic episode. All mood and anxiety diagnoses were primary or independent, e.g they excluded disorders that were substance-induced or due to a general medical condition. Lifetime history of suicide attempt, antisocial personality disorder (ASPD) and conduct disorder, as well as lifetime addiction - including pathological gambling, nicotine dependence, cannabis, cocaine, opioid, amphetamine, solvent, tranquilizer, sedative, hallucinogen and heroin abuse or dependence - were all assessed through the AUDADIS-IV and defined by DSM-IV criteria. Sex, age, race/ethnicity, educational level, marital status, income, urbanicity and region were used as covariates in the analyses. Age at interview was categorized into 4 groups: (1) 18–29 years, (2) 30–44 years, (3) 45–64 years and (4) 65 years and older. Race/ethnicity was categorized into 5 groups: (1) white, (2) black, (3) American Indian/Alaska Native, (4) Asian Native Hawaiian/Pacific Islander and (5) Hispanic/Latino. Educational level distinguished 3 groups: (1) less than high school, (2) high school graduate and (3) some college or higher. Marital status was classified in 4 groups (1) married or living common-law, (2) widowed, (3) divorced or separed and (4) never married. Household income was between (1) $0–$19.999, (2) $20.000–$34.999, (3) $35.000–$59.999 and (4) $60.000 or greater.

Information on family history of first and second degree relatives was elicited for each relative separately by asking whether any of the relatives had been an alcoholic or problem drinker, defined as a person who has physical or emotional problems because of drinking, problems at work of school because of drinking, problems with the police because of drinking-like drunk driving, or a person who seems to spend a lot of time drinking or being hungover.

2.3 Data analysis

The age at onset distribution was investigated with the maximum likelihood estimation of the finite normal mixture (Kolenikov). We tested multiple mixture models with increasing number of theoretical components and assessed whether this increase improve the fit by using Akaike’s information criterion (AIC) of parsimony. We also computed the Log likelihood value (LLH) for each model, with AIC=−2*log-likelihood+2*k, where k is the number of estimated parameters.We then defined age at onset cut-off points by adding or subtracting one standard deviation from the estimated mean of the theoretical component distribution, following a method described elsewhere (Lin et al., 2006).

According to these results, we divided the sample into subgroups of homogeneous ages at onset. Distributions of sociodemographic and clinical correlates of the subgroups were compared using chi-square. The odds ratio and confident intervals were estimated through a logistic regression. Sociodemographic covariates in the regressions included age at interview, race, marital status, educational level, household income, level of urbanicity and region. The appropriate statistical weight was employed when mentioned to ensure the data were representative of the population.

3. Results

At some point in their lives, 4782 participants of the NESARC sample met DSM-IV alcohol dependence criteria. Of these, age at onset was available for 4697 (99.6%). Admixture analysis decompounded the observed distribution of age at onset into a mixture of several normal theoretical distributions. The best-fitting model in terms of AIC had two components with a mean (SD) of 19.6 (S.D= 2.7) and 32.9 (S.D= 10.2) years (Table 1).

Table 1.

Fit of mixture models with different components

Model Number of

the component		 Characteristics of the model AIC LLH
Mean S.D S.E
One component 1 24.7 9.3 0.13 3.4*104 −1.714*104
Two components 1 19.6 2.7 0.06 3.1*104 −1.567*104
2 32.9 10.2 0.35
Three components 1 19.0 2.3 0.1 3.1*104 −1.558*104
2 24.8 4.7 0.7
3 36.4 10.7 0.8
Four components 1 18.7 2.1 0.11 3.1*104 −1.555*104
2 22.4 4.0 0.7
3 33.5 8.3 1.0
4 55.3 8.7 5.5
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AIC: Akaike information criteria; LLH, Log likelihood value

Since patients with ASPD have a highly specific pattern of disorder and co-morbidity, and typically have a very young age at onset, antisocial personality disorder could be a confounding factor. Therefore, we performed another admixture analysis excluding subjects with ASPD. No significant difference was detected, either for the number of clusters or the threshold distinguishing them (Table 4).

Table 4.

Fit of mixture models with different components in alcohol dependent subjects without antisocial personality disorder.

Model Number of

the component		 Characteristics of the model AIC LLH
Mean S.D S.E
One component 1 25.2 9.5 0.1 3.0*104 −1.482*104
Two components 1 21.9 5.5 0.1 2.8*104 −1.411*104
2 43.2 5.5 0.3
Three components 1 21.9 5.5 0.2 2.8*104 −1.375*104
2 37.8 4.3 0.3
3 43.2 5.5 0.3
Four components 1 20.2 3.3 0.1 2.8*104 −1.375*104
2 31.9 3.3 0.3
3 42.9 3.3 0.3
4 59.3 3.3 0.5
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AIC, Akaike information criteria; LLH, Log likelihood value

We obtained one cut-off point at 22 years, corresponding to the mean age at onset of the younger group plus one standard deviation as well as to the mean age at onset of the late onset group minus one standard deviation. The early onset group, comprising subjects with age at onset 22 years or less, represents 58% of the sample, whereas the late onset group represents 42% of the sample (Figure 1).

Figure 1.

Figure 1

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Observed single normal distribution (Inline graphic) and theoretical two component mixture distribution (Inline graphic) of age at onset of alcohol dependence in the 4697 alcohol-dependent participants of the NESARC sample.

The sociodemographic correlates of early onset of alcohol dependence are presented in Table 2. Alcohol dependent men exhibit a higher proportion of early onset alcohol dependence. Black ethnic group had decreased odds of early onset of alcohol dependence. Being widowed, divorced or separated was associated with significantly decreased odds of early onset, whereas being never married was associated with significantly increased odds of early onset when compared with married /common-law individuals.

Table 2.

Sociodemographic correlates among people with alcohol dependence

Sociodemographic variables Early onset class
(≤22 years) a 		Late onset class
(>22 years) a 		Odds ratio
(95% CI) b
N % N %
Sex
  Female 1007 32.5 783 35.3 1.00
  Male 1709 67.5 1197 64.7 1.11 (0.98–1.25)*
Race/ethnicity
  White 1920 80.1 1249 75.6 1.00
  Black 257 5.5 358 10.4 0.46 (0.39–0.55)***
  American Indian/Alaska native 81 3.5 51 3.1 1.03 (0.72–1.47)
  Asian/Native Hawaiian/Pacific 59 2.4 28 1.7 1.37 (087–2.16)
  Islander
  Hispanic/Latino 399 8.5 294 9.1 0.88 (0.75–1.04)
Age
  18–29 1126 43.2 202 10.2 1.00
  30–44 1030 36.9 782 39.9 0.24 (0.20–0.28)***
  45–64 485 17.7 813 41.9 0.10 (0.09–0.13)***
  65+ 75 2.1 183 7.9 0.07 (0.05–0.10)***
Educationnal level
  Less than high school 61 2.3 107 4.4 1.00
  High school graduate 962 35.4 794 40.9 2.12 (1.53–2.95)***
  Some college or higher 1693 62.3 1079 54.7 2.75 (1.99–3.80)***
Marital status
  Married /common-law 1221 52.0 921 57.1 1.00
  Widowed 36 0.7 106 4.0 0.26 (0.17–0.38)***
  Divorced or separed 380 10.1 578 22.9 0.50 (0.42–0.58)***
  Never married 1079 37.2 375 16.0 2.17 (1.88–2.51)***
Income
  $0–$19.999 644 19.4 497 20.1 1.00
  $20.000–$34.999 573 19.9 445 21.0 0.99 (0.84–1.18)
  $35.000–$59.999 680 26.3 509 27.0 1.03 (0.87–1.21)
  $60.000 or greater 819 34.4 529 31.9 1.19 (1.02–1.40)**
Urbanicity
  Urban 935 28.5 741 31.0 1.00
  Rural 1781 71.5 1239 69.0 1.14 (1.01–1.28)**
Region
  Northeast 436 16.8 303 16.3 1.00
  Midwest 765 29.8 465 24.7 1.14 (0.95–1.38)
  South 764 28.0 638 30.5 0.83 (0.69–1.00)**
  West 751 25.3 574 28.5 0.91 (0.76–1.10)
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a

Numbers are unweighted values. percentages are weighted values.

b

Reference group is individuals with late onset (>22 years old) alcohol dependence. Boldface type indicates significance.

*

p<0.1.

**

p<0.05.

***

p<0.01

Panic disorder, major depressive episode and pathological gambling were associated with later onset of alcohol dependence when sociodemographic factors were not controlled for (Table 3), but were not significantly involved anymore when corrected for sociodemographic factors. Conversely, ASPD and conduct disorders, cannabis, amphetamine, solvent, tranquilizer, sedative and hallucinogen use disorders were more frequent in subjects with an earlier onset of alcohol dependence, the crude odds-ratio being close to the ones adjusted for socio-demographic factors.

Table 3.

Clinical correlates among people with alcohol dependence

Mental disorder Early onset class
(≤22 years) a 		Late onset class
(>22 years) a 		Crude Odds ratio
(95% CI)		 Adjusted Odds ratio b,c
(95% CI)
N % N %
Lifetime Mental disorders
  Panic disorder 226 8.0 199 10.4 0.81 (0.66–0.99) ** 0.98 (0.78–1.22)
  Agoraphobia 10 0.4 10 0.7 0.73 (0.30–1.75) 1.22 (0.47–3.18)
  Specific phobia 507 18.5 324 16.5 1.17 (1.00–1.36) ** 1.26 (1.06–1.50) **
  Social Phobia 280 10.6 202 11.5 1.01 (0.84–1.22) 1.13 (0.91–1.40)
  Generalized anxiety disorder 238 8.2 204 10.0 0.84 (0.69–1.02) 1.06 (0.86–1.33)
  Major depressive episode 906 30.7 722 35.9 0.87 (0.78–0.98) * 0.95 (0.82–1.10)
  Manic disorder 319 10.4 210 11.5 1.22 (0.93–1.35) 1.03 (0.84–1.28
0.90 (0.70–1.15)
  Suicide attempt 214 13.4 156 14.6 0.93 (0.74–1.12)
Antisocial disorders
  ASPD 466 16.5 192 11.0 1.93 (1.61–2.30) ** 1.86 (1.53–2.27) **
  Conduct disorder without ASPD 58 1.9 20 1.0 2.14 (1.28–3.57) ** 1.89 (1.09–3.29) *
Lifetime addiction
  Nicotine dependence 1324 48.8 924 49.6 1.09 (0.97–1.22) 1.22 (1.07–1.40) **
  Cannabis use disorder 1032 37.9 489 26.2 1.87 (1.64–2.12) ** 1.67 (1.45–1.93) **
  Cocaine use disorder 405 13.5 264 14.2 1.14 (0.96–1.24) 1.26 (1.05–1.51) *
  Opioid use disorder 27 1.2 24 1.0 0.82 (0.47–1.43) 0.84 (0.45–1.56)
  Amphetamine use disorder 316 11.5 162 8.4 1.48 (1.21–1.80) ** 1.72 (1.38–2.14) **
  Solvent use disorder 70 2.2 17 1.1 3.05 (1.78–5.20) ** 3.23 (1.82–5.73) **
  Tranquilizer use disorder 160 5.6 75 4.3 1.59 (1.20–2.15) ** 1.84 (1.35–2.50) **
  Sedative use disorder 174 6.3 75 4.3 1.74 (1.32–2.30) ** 2.11 (1.56–2.85) **
  Hallucinogen use disorder 312 11.5 91 4.8 2.70 (2012–3.43) ** 2.34 (1.80–3.04) **
  Heroin use disorder 45 1.2 31 1.1 1.57 (0.93–2.65) 3.07 (1.73–5.44) **
  Pathological gambling 41 1.1 49 2.4 0.60 (0.40–0.92) * 0.78 (0.49–1.25)
Family history of alcohol problem
  Positive history 2054 75.8 1509 76.6 0.97 (0.84–1.11) 1.20 (1.02–1.40) *
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ASPD, antisocial personality disorder.

*

p<0.05.

**

p<0.01

a

Numbers are unweighted values. percentages are weighted values.

b

Reference group is individuals with late onset (>22 years old) alcohol dependence. Boldface type indicates significance.

c

Adjusted for sociodemographic variables (sex, race/ethnicity, age, educational level, marital status, income, urbanicity, region).

Specific phobia was the only mental disorder still associated with an early onset of alcohol dependence among alcohol dependent subjects, when controlled for sociodemographic factors (Table 3). Opioid use disorder and pathological gambling were the sole addictions that were not associated with an early onset of alcohol dependence when adjusting for sociodemographics factors. A positive history of alcohol related problems was also associated with an early onset of alcohol dependence.

4. Discussion

This study ascertains the validity of a two solution factor of age at onset in alcohol dependence. Particularly, the age at onset of alcohol dependence can be decomposed by admixture analysis into two normal distributions, with a cut-off at 22 years. Our finding is in line with previous researches, which suggest that the cut-off between early and late onset groups should range between 19 and 25 years (Buydens-Branchey et al., 1989; Hingson et al., 2006a). This is the first study to examine whether the age at onset distribution in alcohol dependence is a mixture of several normal distributions. This approach was previously used successfully in bipolar type I affective disorder, in schizophrenia (Schurhoff et al., 2004), as well as in obsessive compulsive disorder (Delorme et al., 2005) and suicide attempt (Slama et al., 2009). The size of the epidemiological sample studied herein is large, and even more important, the NESARC is a national representative sample using a well validated interview, whereas previous studies using admixture test to examine the distribution of age at onset relied on relatively small and clinical samples.

The relationships between socioeconomic status, educational levels and alcohol consumption are complex. Particularly, while lower socioeconomic status is associated with a higher risk of alcohol dependence, our findings suggest that, among alcohol dependent subjects, individuals with the highest income have a younger age at onset of dependence. Others found no difference in socioeconomic status between subjects with early versus late onset substance use disorder (Clark et al., 1998). Considering the educational level, the literature raised contradictory findings, since a 10 years prospective follow-up study found that subjects with the highest educational level have the lowest risk of alcohol dependence (Harford et al., 2006), while another 5 years prospective work, men with the highest educational level exhibit the greatest increase in at-risk drinking from grade 12 to adulthood (Bingham et al., 2005). In the NESARC, we found that educational level is associated with an early age at onset among alcohol dependent subjects. Taken together, these findings suggest that socioeconomic and educational achievement may have protective effects against the development of alcohol dependence in general and in particular against the late onset form.

The main clinical correlates of the early onset class of alcohol dependence were specific phobia, antisocial behaviors and most of substance use disorders. The finding that specific phobia is the only mental disorder associated with an early onset of dependence among alcohol dependent subjects is unexpected. Nevertheless, specific phobia was associated with an increased risk of alcohol lifetime alcohol dependence (adjusted O.R 2,7,CI: 2.41–3.02) (Stinson et al., 2007), this risk being highly comparable with the one associated with social phobia (adjusted OR: 2.7, CI:2.36–3.20) (Stinson et al., 2007) or generalized anxiety disorder (adjusted OR: 2.8, CI:2.46–3.24) (Grant et al., 2005). The association of specific phobia with an early onset of alcohol dependence may also be related to the early age at onset of specific phobia in the NESARC (mean 9,1 years) (Stinson et al., 2007). Indeed, the age at onset of the comorbid disorder is a plausible confounding factor, since individuals with an early onset of mental disorders exhibit early alcohol consumption and usually have an early age at interview. Therefore, the fact that social phobia is the sole mental disorder associated with an early alcohol dependence could be related to the observation that specific phobia is the mental disorder with the earliest age at onset. Furthermore, the temporal sequencing of specific phobia and alcohol dependence is unusual among anxiety and mood disorders, since specific phobia, like social phobia, often precedes alcohol dependence whereas alcohol dependence is temporally primary to all other disorders, including panic disorder, general anxiety disorder, dysthymia, major depression, and mania/hypomania. Other axis I disorders, including mood and anxiety disorders were not associated with an early age at onset of alcohol dependence. This finding is consistent with the fact that most historical and contemporary classifications of alcohol dependence do not include psychiatric disorders.

Consistent with the existing literature, early onset of alcohol dependence was associated with antisocial disorders. Schuckit and Irwin indeed proposed that one type of alcohol dependence (Cloninger’s type II) is characterized mainly by antisocial behaviours more than by alcohol related symptoms (Schuckit and Irwin, 1989). Alcohol dependent patients with an early onset of dependence are more likely to have impaired decision making, higher impulsivity, even when controlled for comorbid drug abuse and age at interview (Dom et al., 2006). These characteristics are partially underlied by genetic factors (for a more complete review, see (Kreek et al., 2005)). The fact that the bimodal distribution of ages at onset of alcohol dependence is maintained, with a non significantly different cut-off, when excluding all patients with antisocial personality disorder, is pleading against the hypothesis that Cloninger’s type II is mainly explained by antisocial personality disorder. Furthermore, the direct evidence (e.g. without correcting for confounding factors) of early versus late onset of alcohol dependence reinforce these two classifications regarding co-morbidity, with more anxious and mood disorders in the later onset group, and more ASPD, familial history of alcohol dependence and addictive disorder’s co-morbidity in the younger age at onset group. Our study has several limitations.

First of all, the method used to choose a cut-off is somehow arbitrary. We followed a methodology described by Lin et al., using points of rarity in the observed age at-onset distribution (Lin et al., 2006), but the use of another methodology could have led to another result. An important point is that our cut-off at 22 years separates the sample into two groups of identical distribution, and correctly classify two third of each group, thus reducing the risk of misclassification in each group. Moreover, the exclusion of subjects with ASPD did not modify our results, thus arguing that the two groups do not differ solely on the presence or absence of an ASPD.

A second restriction is the use of a retrospective measure to assess age at onset of alcohol dependence, which raises concern about the accuracy of these data. In that view, a replication of our findings in a prospective sample is required. Noteworthy, we also used the age at interview as a confounding variable in the logistic regression analyses, thus controlling for its effects.

A third limitation is that AUD assessed family history with information elicited from the respondent, rather than by direct interview. However, methodological research has shown that alcohol dependence can be as validly measured from information from family members as from the relatives directly (Andreasen et al., 1986).

Several studies used age at onset of alcohol dependence as a putative biological marker (Apprey et al., 2005; Le Strat et al., 2008a; Tayo et al., 2005; Zhong and Zhang, 2005). Particularly, age at onset could be genetically mediated, since genetic factors account for 49% of the variation in the age at onset of alcohol dependence (Liu et al., 2004). Moreover, the relationships between alcohol dependence and other addictive disorders are complex and involve several biological and clinical interactions (Funk et al., 2006; Ribeiro-Carvalho et al., 2008). Addictive disorder probably have in common neurobiological pathway (Ribeiro-Carvalho et al., 2008), and share part of the involved genetic and environmental influences to initiation (Xian et al., 2008), the development of a dependence (Kendler et al., 2008; Le Strat et al., 2008b) as well as the cessation of the disorders (McKee et al., 2008). Accordingly, the consideration of a more genetically homogenous subgroup of patients defined by the age at onset of alcohol dependence earlier than 22 years old, and characterized by a higher rate of addictive comorbidities could be meaningful.

The clinical relevance of this cut-off must be assessed. In absence of prospective study, we may only hypothesize that patients with an early onset of alcohol dependence (earlier than 22 years old) will have a worst prognosis. In the literature, as mentioned, retrospective studies found that an early age at onset of alcohol dependence is associated with greater impulsivity, a more severe dependence (Hingson et al., 2006b), and more severe and frequent alcohol withdrawal complications (Le Strat et al., 2008a). This prognosis is even worsened by more frequent addictive comorbidities. Clinicians usually perform screening tests for other addictions when treating a patient with alcohol dependence. This assessment may be particularly relevant if the patient has an early onset disorder.

In summary, we found that the distribution of age at onset in alcohol dependence revealed two subgroups of patients. The optimal cut-off of age at onset of alcohol dependence in this sample is 22 years, which correctly classified two thirds of each group. Future typologies, including DSM-V, could take this cut-off into account in order to improve their accuracy, when efficacy and specificity rather than sensitivity is the priority, for examples in order to matched treatment or in the analysis of complex markers.

Footnotes

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