Sex differences in how a low sensitivity to alcohol relates to later heavy drinking.
- Authors
- Schuckit, Marc A; Smith, Tom L; Trim, Ryan S; Kuperman, Samuel; Kramer, John; Hesselbrock, Victor; Bucholz, Kathleen K; Nurnberger, John I; Hesselbrock, Michie; Saunders, Gretchen
- Year
- 2012
- Journal
- Drug and alcohol review
- PMID
- 22708705
- DOI
- 10.1111/j.1465-3362.2012.00469.x
- PMCID
- PMC3459074
INTRODUCTION AND AIMS: A low level of response (LR), or low sensitivity, to alcohol is a genetically influenced characteristic that predicts future heavy drinking and alcohol problems. While previous analyses of how LR relates to heavier drinking reported the process is similar in males and females, some potential sex differences have been identified. This difference is further explored in these analyses. DESIGN AND METHODS: Prospective structural equation models (SEMs) were evaluated for 183 young adult females and 162 males, none of Asian background, from the Collaborative Study on the Genetics of Alcoholism. Invariance analyses and SEM evaluations by sex were used to compare across females and males for these primarily Caucasian (75%), non-Asian young (mean age 19) subjects. RESULTS: The prospective SEM for the full set of 345 subjects had good fit characteristics and explained 37% of the variance. While the initial invariance analyses identified few sex differences, comparisons of correlations and direct evaluations of path coefficients across males and females indicated that only females showed a link between a low LR and future alcohol problems that was partially mediated by more positive alcohol expectancies and drinking to cope. These sex differences were reflected in the different structures of the SEM results for female versus male subjects. DISCUSSION AND CONCLUSIONS: These prospective results indicate that there might be some important sex differences regarding how a lower LR relates to alcohol outcomes that should be considered in protocols focusing on preventing the impact of LR on future drinking problems.
The Structural Equation Model (with the Measurement Model included) for the full set of 345 subjects. Weight-adjusted level of response to alcohol (LR) and sex (SEX) are both manifest variables. Peer drinking (PEER) is a latent variable with indicators derived from the Important People and Activities Scale: peera = perceived peer status (from abstainer to heavy drinker), peerb = perceived peer drinking frequencies, peerc = perceived maximum drinking quantities. Alcohol expectancy (EXPECT) is a latent variable with indicators derived from the adolescent and adult Alcohol Expectancy Scales: aeqg = Global Positive Expectancies, aeqs = Social Behavior Expectancies, aeqx = Sex Enhancement, aeqr = Expected Relaxation feelings. Drinking to cope (COPE) is a latent variable with indicators derived from the 6 items of the Drinking to Cope scale with 2 items placed in each of 3 parcels: dtca, dtcb, dtcc. The outcome variable at Time 2 (ALCOUT) is a latent variable with indicators derived from questions regarding two quantities of drinking and the number of alcohol problems: max = maximum drinks in prior 6 months, most = maximum drinks during the heaviest drinking week in prior 6 months, probs = number of 18 potential alcohol problems during the followup. The significant path coefficients (p < .05) are presented for each path and the R2 is provided for each latent variable including the final model R2 which is given with the Time 2 alcohol outcomes (ALCOUT). Fit indices: CFI = .97, NNFI = .96, RMSEA = .052 (.040 - .064), SRMR = .054.
LLM interpretation
This image is a Structural Equation Model (SEM) diagram illustrating the relationships between manifest variables (SEX, LR) and latent variables (PEER, EXPECT, COPE, ALCOUT). The model shows positive path coefficients from PEER to EXPECT (.27), PEER to COPE (.24), EXPECT to COPE (.60), COPE to ALCOUT (.40), and LR to ALCOUT (.27) and COPE (.09), while a negative path exists from SEX to ALCOUT (-.34). Each latent variable is linked to its respective observed indicators (rectangles), and $R^2$ values are provided for the latent variables, including a final model $R^2$ of .37 for ALCOUT.
The Structural Equation Model with the Measurement Model included for 183 females from Figure 2. See Figure 2 for domain and indicator descriptions. All shown relationships are significant. Fit indices: CFI = .99, NNFI = .99, RMSEA = .027 (.000 - .053), SRMR = .054.
LLM interpretation
This figure is a Structural Equation Model (SEM) diagram illustrating the relationships between latent variables (PEER, EXPECT, COPE, and ALCOUT) and an observed variable (LR) for a sample of 183 females. Directed arrows indicate significant paths between variables, with standardized coefficients provided (e.g., EXPECT to COPE = .62; LR to ALCOUT = .26). The measurement model is shown via paths from latent variables to their respective indicators (e.g., PEER to peera, peerb, and peerc). Fit indices are listed in the legend, including CFI = .99 and RMSEA = .027.
The Structural Equation Model with the Measurement Model included for 163 Males from Figure 2. See Figure 2 for domain and indicator descriptions. All shown relationships are significant. Fit indices: CFI = .96, NNFI = .95, RMSEA = .060 (.038 - .080), SRMR = .059.
LLM interpretation
This figure is a Structural Equation Model (SEM) diagram illustrating the relationships between several latent variables (PEER, EXPECT, COPE, and ALCOUT) and an observed variable (LR). The model shows positive paths from PEER to EXPECT (.26) and COPE (.27), from EXPECT to COPE (.56), from COPE to ALCOUT (.42), and a direct path from LR to ALCOUT (.26). Each latent variable is linked to its respective measurement indicators (e.g., peera-c, aeqg-r, dtca-c, and max-probs) with associated factor loadings.
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