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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: Addict Behav. 2019 Oct 31;102:106196. doi: 10.1016/j.addbeh.2019.106196

The Number of Biological Parents with Alcohol Use Disorder Histories and Risk to Offspring through Age 30

Derek B Kosty a,b, Richard F Farmer a, John R Seeley a,b, Kathleen R Merikangas c, Daniel N Klein d, Jeff M Gau a,b, Susan C Duncan a, Peter M Lewinsohn a
PMCID: PMC6934894  NIHMSID: NIHMS1544723  PMID: 31783247

Abstract

Objective

We investigated associations between the number of parents with histories of alcohol use disorder (AUD) and several offspring (proband) variables through age 30: occurrence of AUD and, separately, alcohol dependence; onset age of the initial AUD episode; time to recovery from the first AUD episode; number of distinct AUD episodes; and cumulative duration of AUD across episodes.

Methods

Offspring data were collected during four assessment waves of a longitudinal epidemiological study of psychiatric disorders with a regionally representative sample. The reference sample included 730 offspring with diagnostic data from at least one parent. Offspring were assessed with semi-structured diagnostic interviews between mid-adolescence and young adulthood and parents were assessed when offspring were approximately 24 years of age.

Results

As the number of parents with AUD increased, offspring risk for AUD and alcohol dependence also increased. Latent growth model results indicated that offspring AUD risk trajectories increase in severity as a function of the number of parents with AUD. This pattern of results was not observed for other AUD course-related features in offspring (i.e., number of distinct episodes; months required for recovery from initial episode; cumulative duration across episodes).

Conclusions

The number of parents with a history of AUD is associated with overall offspring risk for AUD and alcohol dependence and elevated AUD risk trajectories through age 30. The number of parents with AUD may be a more relevant risk factor for onset-related characteristics of AUD in offspring than for its longitudinal course.

Keywords: Alcohol use disorders (AUD), parental AUD, number of parents, clinical features, trajectories

1. Introduction

Within the United States, 7.5 million children younger than age 18, or 10.5% of youth within this age group, are estimated to live with at least one parent with an alcohol use disorder (AUD) [1]. In addition, parental drinking behavior is consistently and strongly associated with future drinking behavior in offspring [2], including alcohol abuse and dependence [3–13]. The observation that parental history of alcohol misuse is an important risk factor for AUD in offspring has led investigators to hypothesize that offspring risk may be amplified when both parents have histories of problematic alcohol use or AUD. In a prospectively assessed community-based sample, for example, the number of parents with AUD predicted the progression from occasional to regular alcohol use in offspring but was not associated with an increased risk for offspring AUD by age 17 [9]. In contrast, population-based research has indicated that offspring who lived with a parent during an AUD episode had a higher risk of developing a substance use disorder compared to non-exposed offspring, and that risk was greatest when offspring were directly exposed to two parents with AUD [14].

The limited literature on the number of parents with AUD has focused primarily on offspring risk for hazardous alcohol use in general and not on specific AUD course-related features demonstrated over time. Related research, however, suggests that the number of parents with AUD may also be related to course-related features such as the timing, magnitude, and duration of AUD risk; AUD episode recurrence; recovery status; and the diagnosis of alcohol dependence versus abuse. In representative community-based samples, for example, AUD recurrence and AUD-related impairment in probands were associated with an increased risk for AUD among first-degree family members [15, 16]. Studies with non-representative samples have similarly suggested that longer AUD episode durations and AUD recurrence are associated with greater familial risk [17, 18]. Findings from treatment or clinic-based samples also indicate that alcohol dependence is a stronger predictor of AUD in family members than alcohol abuse [19, 20]. Nonetheless, the extent to which the severity and natural course of problematic alcohol use in offspring is associated with the number of parents with AUD remains unclear.

1.1. The Current Study

Despite the commonality of AUD histories among parents and the documented familial transmission of risk, few studies have examined associations between the number of parents with AUD histories and AUD risk and course-related features among offspring (probands). Knowledge of the additional risk afforded by two versus one parent(s) with AUD histories may help inform theoretical models of AUD development, contribute to an understanding of risk-related mechanisms, and advance preventive efforts to reduce the intergenerational conveyance of hazardous alcohol use by identifying persons who are at greatest risk for AUD-related impairment [21].

Data used in this research come from the Oregon Adolescent Depression Project (OADP), a community-based and multi-generational longitudinal study in which psychiatric and substance use disorders in probands and first-degree relatives were assessed with structured interviews referenced to DSM criteria [22–24]. Large-scale community-based studies on the parental transmission of AUD risk are uncommon, and those that do exist usually involve cross-sectional designs [7], judgments of parental AUDs based exclusively on offspring reports [7, 9], or non-standard assessments of alcohol abuse criteria [25]. Furthermore, family studies of AUD risk often involve clinical or high-risk samples that may lead to different conclusions concerning parental transmission of AUD risk, perhaps because of greater disorder severity found within these populations [26]. The present study overcomes these limitations and addresses four questions related to the parental transmission of AUD risk to offspring:

  1. Does overall risk for AUD in offspring through age 30 increase as a function of the number of parents with a lifetime history of AUD?

  2. What is the impact of the number of parents with AUD on offspring AUD risk trajectories through age 30?

  3. Do clinical features of AUD in offspring (i.e., cumulative duration across episodes, number of episodes, months required for recovery from initial episode, lifetime alcohol dependence versus abuse diagnosis) differ as a function of the number of parents with a lifetime history of AUD?

We hypothesized that as the number of parents with AUD increases, overall offspring risk for AUD will likewise increase. We also expected corresponding increases in the intercept and slope of offspring AUD risk trajectories through age 30 and in the severity of AUD course-related features in offspring.

2. Methods

2.1. Participants

Offspring

The offspring sample was initially randomly drawn from 9 high schools in 2 urban and 3 rural communities in western Oregon at approximately age 16, for a total T1 sample of 1,709. Demographic characteristics of the T1 sample were highly similar to corresponding regional census data, and follow-up phone contacts with non-participants revealed no demographic differences with participants in the areas of head of household gender, family size, number of parents in household, parents’ employment status, middle class socioeconomic status, or race [23]. One year following T1, T2 was initiated and 1,507 offspring (88% of the T1 sample) were reassessed. A sampling stratification procedure was implemented at T3 (~ age 24) due to budgeting constraints [27]. To increase the psychiatric variability and strengthen the ethnic diversity of the sample, eligible participants included all persons with a positive history of a psychiatric diagnosis by T2 (n = 644) and a randomly selected subset of never mentally ill participants (n = 457 of 863 persons). Of these 1,101 eligible persons, 941 (85%) completed T3. Of the 941 T3 offspring, 816 (87%) participated in T4 (~ age 30). T4 participants self-identified as predominantly female (59%), Caucasian (89%), and married (53%), and 43% reported earning a bachelor’s degree or higher. Study discontinuation between T1 and T4 was related to male sex, childhood disruptive behavior disorders, and histories of substance use disorder [23, 28, 29].

Parents

During the T3 offspring assessment, lifetime psychiatric histories of parents were evaluated for 730 biological mothers (89%) and 719 biological fathers (88%). At the time of the parental assessments, mean ages for mothers and fathers were 49.3 (SD = 4.9) and 51.3 years (SD = 6.2), respectively. Forty-four percent of the parents reported having earned a bachelor’s degree or higher. In analyses presented below, the sample included families with complete offspring AUD data from childhood to age 30 and lifetime diagnostic data from at least one parent (n = 730 or 89% of families from the T4 offspring panel).

2.2. AUD Diagnostic Procedures

Offspring

During the T1, T2, and T3 assessments, offspring were interviewed with a version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS) that combined features of the Epidemiologic and Present Episode versions [30, 31]. Follow-up assessments of disorders at T2 and T3 also involved the joint administration of the Longitudinal Interval Follow-Up Evaluation (LIFE) [32] that, in conjunction with the K-SADS, provided detailed information about the presence and course of disorders since participation in the previous interview. The T4 assessment included administration of the LIFE and the Structured Clinical Interview for Axis I DSM-IV Disorders–Non-Patient Edition (SCID-NP) [33].

For most analyses, we combined alcohol abuse and dependence diagnoses to form a single binary indicator of AUD (presence versus absence). AUD status and AUD course features through age 30 served as the dependent variables in this study, and were evaluated in accordance with DSM-IV criteria [34]. Although DSM-5 combines substance abuse and dependence into a single diagnostic category, the correspondence between DSM-IV and DSM-5 criteria is high [35]. Trajectory based analyses involved modeling binary indicators of the presence versus absence of AUD in offspring within two-year intervals from age 14 to 30 (i.e., 14 to 15.9, 16 to 17.9, … , 28 to 29.9). Offspring AUD-related features examined included (a) the number of months required to recover from the initial AUD episode among those who recovered, (b) a diagnosis of alcohol dependence versus abuse, (c) the number of distinct AUD episodes, and (d) the cumulative duration of AUD, calculated as the total number of months in an episode from childhood through age 30. Interrater agreement for new AUD diagnoses in offspring at each wave since the previous interview was satisfactory (mean κ across waves = .77). When both raters agreed on the occurrence of an AUD episode, interrater agreement on clinical features was acceptable: ICC = .92 for disorder onset age, ICC =.87 for offset age, and κ =.62 for alcohol dependence versus abuse diagnosis.

Definitions of episode recovery and recurrence used in this study are consistent with those used for previous studies with this sample [36] and are informed by earlier descriptions of these concepts [37, 38], LIFE interview naming conventions [32], and DSM-IV guidelines. Remission refers to offset of an AUD episode lasting at least one full month but < 12 months during which the participant did not endorse any diagnostic criteria for AUD. During this remission period, participants may have used alcohol in the absence of alcohol-related problems as defined in AUD diagnostic criteria. The re-emergence of any AUD symptomatology during the remission period was regarded as a continuation of the episode (i.e., a relapse). Episode resolution, defined by a period of uninterrupted remission of at least 12 months, is considered as a recovery from the episode. A recurrence is regarded as the emergence of a new AUD episode after a period of recovery.

Parents

Lifetime diagnostic assessments of parents were performed with the SCID-NP [33]. Informant reports of past psychiatric disorders were assessed with the revised Family Informant Schedule and Criteria, modified for DSM-IV [39]. Whenever possible, direct evaluations for each parent were supplemented with reports from another family member. When direct interviews were not possible, we attempted to interview at least two first-degree relatives about the psychiatric history of the target relative, with final diagnostic decisions based on the best-estimate method [40].

We modeled offspring outcomes as a function of a parental AUD grouping variable defined by the number of parents with a lifetime history of AUD. In instances where data were missing for one parent (< 2% of the sample), the parental AUD group assignment was based on a single parent. Interrater agreement for parental AUD based on the independently derived best-estimate diagnoses was excellent (κ = .96).

2.3. Analysis

We conducted three sets of analyses to address our research questions. In the first set, we examined the association between the parental AUD group (number of parents with a lifetime history of AUD) and offspring AUD status through age 30 (present versus absent) using a contingency table analysis and the Mantel-Haenszel χ2 test for linear trend.

In the second set of analyses, we examined the influence of the parental AUD group on the trajectory of AUD risk in offspring through age 30. Preliminary unconditional latent growth models (LGMs) were estimated to determine the population AUD risk trajectory over time. The first unconditional LGM included an intercept factor (probability of meeting criteria for AUD at age 14) and a linear slope factor (rate of change in probability of meeting criteria for AUD over time) predicting offspring AUD status at each age interval. An alternative unconditional LGM included an intercept factor and linear and quadratic slope factors. We performed a χ2 difference test based on log-likelihood values and scaling correction factors to determine whether a linear or curvilinear functional form for the trajectory of AUD risk resulted in superior model fit. In conditional LGMs, we regressed the intercept and slope factors on the same dummy coded variables used in the first set of analyses.

In a third set of analyses conducted with only offspring with positive AUD histories, we examined whether the number of parents with AUD predicted offspring AUD course-related features. We applied appropriate analysis techniques based on the distribution of the course feature variable (dependent variable). We performed multiple regression to predict cumulative duration and time required for recovery from the initial episode, Poisson regression to predict the count of the number of distinct episodes, and contingency table analyses for the presence of a lifetime dependence versus abuse diagnosis.

Statistical estimation

LGMs were conducted using Mplus statistical software version 8 [41], and model parameters were estimated using full information maximum likelihood estimation with robust standard errors. The probit link function was used to accommodate the dichotomous nature of AUD status. By transforming dichotomous data, the probit link function imposes a threshold model that assumes an underlying normally distributed liability, or risk, for having the dichotomous trait. Variance in the liability is modeled in similar ways as the corresponding variance in continuous variables. Because growth models assume measurement invariance over time, thresholds were held constant across age intervals [42, 43]. SPSS was used for all other analyses.

Weighting procedure

Caucasians without a psychiatric diagnosis by T2 were under sampled at T3 by way of the stratified sampling procedure noted earlier. To adjust for the stratified sampling procedure, all analyses used sampling weights whereby Caucasian participants without a lifetime diagnosis by T2 were assigned a weight of 2.05, a value that reflects the probability of being sampled at T3. In contrast, all participants with a psychiatric diagnosis and all non-Caucasian participants were assigned a weight of 1.0.

3. Results

3.1. Descriptive Results

Descriptive data pertaining to all study variables are presented in Table 1. The weighted lifetime prevalence rate for AUD through age 30 among the 730 offspring with parent data was 34% (42% for males, 28% for females).

Table 1.

Weighted Descriptive Statistics for Study Variables

Variable n (%) or M (SD)
Prevalence of AUD in offspring (probands) within each age interval, n (%)
 Age 14–15.9 21 (3%)
 Age 16–17.9 50 (7%)
 Age 18–19.9 96 (13%)
 Age 20–21.9 115 (16%)
 Age 22–23.9 100 (14%)
 Age 24–25.9 89 (12%)
 Age 26–27.9 95 (13%)
 Age 28–29.9 85 (12%)
 Lifetime through age 30 249 (34%)
AUD course-related feature in offspring
 Cumulative duration in months, M (SD) 41.2 (37.4)
 Number of episodes, M (SD) 1.3 (0.6)
 Months required for recovery from initial episode, M (SD) 17.3 (5.6)
 Lifetime dependence (versus abuse) diagnosis, n (%) 144 (58%)
Parental lifetime AUD history group, n (%)
 Zero parents 385 (53%)
 One parent 288 (40%)
 Two parents 56 (8%)
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Note. AUD = alcohol use disorder. M = mean, SD = standard deviation. AUD course-related feature data are based on offspring with a history of AUD. Months required for recovery from initial episode was coded as missing for offspring who did not recover by T4. Weighted results account for the stratified sampling procedure implemented at T3.

3.2. Offspring Risk for AUD in Relation to Parental AUD Histories

To clarify offspring risk for AUD when zero, one, or two parents had lifetime AUD histories, we examined contingency tables for offspring AUD status by an ordinal variable defined as the number of parents with AUD. Results indicated that as the number of parents with AUD histories increased, the percentage of offspring with AUD likewise increased (29% of offspring with no parental AUD history, 39% of offspring with one parental AUD history, and 46% of offspring with two parental AUD histories; Mantel-Haenszel χ2 = 10.56, p = .001).

3.3. Influence of Parental AUD on the Trajectory of Offspring Risk through Age 30

Our second research question examined whether the trajectory of AUD risk is distinct for offspring with zero, one, or two parents with lifetime AUD. Preliminary unconditional LGMs were used to estimate the overall trajectory of offspring AUD risk across eight two-year age intervals spanning age 14 to 30. A visual inspection of the data and a χ2 difference test indicated that the inclusion of linear and quadratic growth factors resulted in significantly better fit when compared to a linear-only model (χ2 difference = 313.31, p < .001). The linear and quadratic slope coefficients were statistically significant (linear slope estimate = 2.28, t = 5.61, p < .001; quadratic slope estimate = −0.39, t = −6.11, p < .001), and the threshold estimate was 5.34 (t = 7.57, p < .001). Figure 1 illustrates the observed and model-estimated probabilities of AUD from ages 14 to 30.

Figure 1.

Figure 1.

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Overall risk probability of alcohol use disorder (AUD) by age interval.

In conditional LGMs, we regressed the intercept and the linear and quadratic slope factors on dummy coded variables representing the parental AUD history group. When compared to offspring with no parental history of AUD, those with one parent with an AUD history had a significantly larger intercept (p = .001), smaller linear slope (p = .009), and larger quadratic slope (p = .011). Those with two parents with an AUD history had a significantly larger quadratic slope factor (p = .017) when compared to offspring with no parental AUD history. Non-significant trends suggest larger intercept (p = .058) and smaller linear slope factors (p = .086) among those with two parents with an AUD history compared to offspring with no parental AUD history. We detected no statistically significant differences in slope or intercept factors between offspring with one and two parents with an AUD history (ps > .440). The parental AUD history groups explained 6%, 5%, and 4% of the variance in the latent intercept, linear slope, and quadratic slope factors, respectively. Figure 2 illustrates the model-implied probabilities of offspring AUD by parental AUD status.

Figure 2.

Figure 2.

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Model-implied risk probability of alcohol use disorder (AUD) by age interval and number of parents with AUD histories.

3.4. Parental AUD Histories and AUD Course-Related Features in Offspring

For our third research question we examined AUD course-related features in offspring by parental lifetime AUD history group membership. Descriptive data for these comparisons are provided in Table 2. Multiple and Poisson regression analysis results revealed that parental AUD group membership was not significantly related to offspring’s cumulative duration of AUD (ps > .253), time required for recovery from initial AUD episode among those who recovered from the initial episode (ps > .427), or the number of AUD episodes (ps > .177).

Table 2.

AUD Course-Related Features in Offspring by Parental Lifetime AUD History Group

Parental AUD History Group
AUD-Related Feature in Offspring Zero Parents (n = 115) One Parent (n = 122) Two Parents (n = 31)
Cumulative duration in months, M (SD) 38.5 (35.0) 41.4 (36.9) 52.3 (47.8)
Number of episodes, M (SD) 1.2 (0.5) 1.4 (0.6) 1.5 (0.8)
Months required for recovery from initial episode, M (SD) 17.9 (6.1) 16.8 (4.9) 17.4 (6.5)
Lifetime dependence versus abuse diagnosis, n (%) 55 (49%) 71 (64%) 18 (69%)
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Note. AUD = alcohol use disorder. M = mean, SD = standard deviation. Months required for recovery from initial episode was coded as missing for offspring who did not recover by T4. Risk of alcohol dependence was linearly association with the number of parents with a lifetime history of AUD (Mantel-Haenszel χ2 = 6.12, p = .013). No other contrasts between parental AUD history groups were statistically significant (ps > .177).

To clarify offspring risk for alcohol dependence versus abuse when zero, one, or two parents had lifetime AUD histories, we examined contingency tables for offspring alcohol dependence status by the parental AUD grouping variable. Results revealed that rates of alcohol dependence in offspring displayed a significant positive linear association with the number of parents with a lifetime history of AUD (49% of offspring with no parental AUD history, 64% of offspring with one parental AUD history, and 69% of offspring with two parental AUD histories; Mantel-Haenszel χ2 = 6.12, p = .013).

4. Discussion

The purpose of this study was to examine associations between the number of parents with AUD and AUD-related outcomes in offspring. We found that as the number of parents with AUD increased, offspring risk for AUD and alcohol dependence also increased. Latent growth model results indicated that offspring AUD risk trajectories may increase in severity as a function of the number of parents with AUD. The probability of meeting criteria for AUD during the initial age interval of 14 to 15.9 was approximately 2% if zero parents had an AUD history and 6% if one or two parents had an AUD history. Offspring without parental histories of AUD and those with one parent with an AUD history experienced similar slopes in risk trajectories that peaked during the age interval of 20 to 21.9 years, with risk probabilities of 14% and 17%, respectively. Although slopes did not statistically significantly differ across parent AUD groups in the conditional growth models, descriptive results suggested that offspring with two parents with AUD histories may have experienced a more sustained risk trajectory that peaked during the age interval of 24 to 25.9 years, with a risk probability of 25%. Overall, findings are consistent with other research that suggests the number of parents with AUD predicts elevated risk for offspring alcohol use or AUD [9, 14].

We also examined whether course features associated with offspring AUD differed as a function of the number of parents with a lifetime history. We found that offspring risk for alcohol dependence versus abuse increased 15% if one parent had a history of AUD and 20% if two parents had a history of AUD relative to offspring with no parental AUDs. This finding is consistent with those of other studies in which alcohol dependence was associated with greater familial risk for AUD [19, 20]. Other course indicators of offspring AUD severity (i.e., cumulative duration of AUD, time required for recovery from initial AUD episode, and the number of distinct AUD episodes) were not significantly associated with the number of parents with a lifetime history of AUD. Contrary to these findings, previous research suggests AUD recurrence and duration of episodes are associated with greater familial risk for AUD [16–18]. Differences in results may be partly explained by methodological features. This study utilized a top-down design emphasizing AUD risk among offspring in relation to parental AUD histories. Previous research involved first-degree relatives in addition to parents [17, 18] and separate alcohol abuse and dependence diagnostic categories [16].

Analyses revealed that parental histories of AUD, as well as the number of parents with such histories, are associated with some AUD course features and severity indicators. Findings generally indicated that any history of parental AUD was associated with greater risk for AUD and alcohol dependence through age 30 and a more severe risk trajectory. Although the number of parents with AUD histories was associated with onset-related characteristics of AUD in offspring, it had little relevance in accounting for statistically significant variation in course-related features.

This study has several strengths including the use of a large regionally representative community-based multigenerational sample, prospective evaluation of proband psychopathology, and semi-structured diagnostic interviews for diagnostic assessments of both parents and probands. Study limitations included sample characteristics and design features. Participants were relatively homogenous with respect to race and geographic location. The generalizability of our findings to more diverse groups of individuals or locations is unclear. Parents were assessed at only one time point (~T3), increasing the potential for retrospective recall bias in the measurement of parental AUD [44]. Potential mechanisms of the transmission of AUD risk from parents to offspring include both genetic [45] and environmental factors [46], but the current study was not designed to test the specific source of influence. Parents may or may not have met criteria for AUD while parenting, thus limiting inferences concerning effects of direct exposure (e.g., alcohol-specific behavior modeling). Only biological parents were included in this study, which ignores the possible role of step-parents with alcohol problems on proband AUD risk. Statistical power was limited for tests of differences in offspring AUD course-related features by parent AUD group, resulting in limited power to detect potential clinically meaningful differences in AUD course-related features between groups. Descriptive results, for example, indicated that the cumulative duration of AUD was 10.9 months greater for offspring with two versus one parent with AUD; however, the difference in means was not statistically significant. These and other findings related to AUD course-related features in offspring should therefore be interpreted with caution. Finally, the role of potential biases associated with proband attrition on study findings are unknown. Although discontinuation of study participation between waves T1 and T2 was greater for males and probands with a history of substance use disorder at T1 [23], it was not greater for those with AUD specifically [36]. Discontinuation after T3 was, however, more common among those with an AUD history by T3 [36]. Because there were few other differences between study discontinuers and completers [28, 47], we are unable to draw informed conclusions about the degree to which attrition biased results.

4.1. Conclusion

Few studies have examined associations between the number of parents with AUD and AUD-related outcomes in offspring. In this study, we found that the number of parents with a lifetime history of AUD is associated with overall offspring risk for AUD, elevated AUD risk trajectories through age 30, and greater risk for alcohol dependence. These results may be translated into clinical settings where parental histories of AUD are used to identify candidates for targeted intervention and prevention programs. Additional research based on more diverse samples is indicated, as are well-controlled tests of hypotheses concerning the mechanisms through which parental AUD increases offspring risk.

Highlights.

  • Prospective evaluation of alcohol use disorder in a representative community sample

  • Semi-structured interviews for diagnostic assessments of probands and parents

  • Number of parents with AUD increased offspring risk for AUD and alcohol dependence

  • Parent AUD histories predict increased severity of offspring AUD risk trajectories

  • Offspring sex did not moderate associations between parental AUD and offspring risk

Footnotes

Conflict of Interest

There are no conflicts of interest to report for any author.

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