But most clients do not quit using; on the contrary, many significantly increase their daily dose, so the intervention reduces their disorder on some criteria while possibly increasing their disorder by other criteria. Figure 1F, reproduced from Cramer et al. , illustrates the kind of elaborate causal network that Borsboom, Kendler, and their colleagues have recently proposed as a more realistic model for many traits. In their framework, latent constructs neither cause observed manifestations nor does an explicit subset of observed variables constitute the latent construct . Rather, the latent construct is an emergent property of the entire network. An implication of the causal structure in Figures 1E,F is even when simple 1- factor models fit the data, the fit may be spurious in that the model assumed by the equations may be very different than the model that validly describes the processes that generated the data. Moreover, combining them in an “any two of the following” recipe will obscure the valuable information contained in that causal structure. Judging from past experience, we might expect the next DSM to surface in about a decade. So in the spirit of constant improvement, I respectfully urge DSM developers to consider pursuing, in parallel, at least three kinds of alternative DSM candidates: a pure reflective model, a pure formative model, and a pure causal network model. One of the three may emerge as superior. But diagnostic systems attempt to serve multiple goals, and it may be advantageous to use different systems for different purposes. These arguments for greater theoretical and psychometric coherence might seem to have a sort of ivory-tower fastidiousness, if not outright neuroticism. After all, the perfect is surely the enemy of the good, indoor grow cannabis and the DSM does a good job much of the time, at least as judged by the utility that clinicians and managed care organizations seem to find in it. But I think there are good practical reasons for improving the coherence of the DSM substance use.
One is that it might provide a better linkage to drug policy. A decade ago, I argued that contemporary thinking about addiction was surprisingly inconsequential for major public policy debates about drug use, or for empirical drug policy analysis . The DSM-5 probably helps to close that gap, as it emphasizes the harmful consequences that citizens care about. On the other hand, the gap between the DSM and drug science may be growing rather than shrinking. For example, a recent review of seven major scientific theories of drug addiction examines whether each theory can account for various “addictive phenomena.” Of the seven theories, four offer an account of withdrawal and three an account of tolerance – two explicit DSM criteria. Six offer accounts of relapse, and four an account of binging – two phenomena that aren’t directly mentioned in the DSM but are closely related to other DSM criteria. But all seven offer accounts of craving, a criterion that only recently entered the DSM checklist. And four address “sensitization” – which is increasingly recognized as a signature feature of the etiology of addiction but receives no mention in the DSM.Judging from past experience, we might expect the next DSM to surface in about a decade. So in the spirit of constant improvement, I respectfully urge DSM developers to consider pursuing, in parallel, at least three kinds of alternative DSM candidates: a pure reflective model, a pure formative model, and a pure causal network model. One of the three may emerge as superior. But diagnostic systems attempt to serve multiple goals, and it may be advantageous to use different systems for different purposes. These arguments for greater theoretical and psychometric coherence might seem to have a sort of ivory-tower fastidiousness, if not outright neuroticism. After all, the perfect is surely the enemy of the good, and the DSM does a good job much of the time, at least as judged by the utility that clinicians and managed care organizations seem to find in it. But I think there are good practical reasons for improving the coherence of the DSM substance use. One is that it might provide a better linkage to drug policy.
A decade ago, I argued that contemporary thinking about addiction was surprisingly inconsequential for major public policy debates about drug use, or for empirical drug policy analysis . The DSM-5 probably helps to close that gap, as it emphasizes the harmful consequences that citizens care about. On the other hand, the gap between the DSM and drug science may be growing rather than shrinking. For example, a recent review of seven major scientific theories of drug addiction examines whether each theory can account for various “addictive phenomena.” Of the seven theories, four offer an account of withdrawal and three an account of tolerance – two explicit DSM criteria. Six offer accounts of relapse, and four an account of binging – two phenomena that aren’t directly mentioned in the DSM but are closely related to other DSM criteria. But all seven offer accounts of craving, a criterion that only recently entered the DSM checklist. And four address “sensitization” – which is increasingly recognized as a signature feature of the etiology of addiction but receives no mention in the DSM.Historically, molecular genetic research on AAB has been limited to the examination of a small number of candidate genes with purported biological relevance; only recently have researchers begun to conduct atheoretical genome-wide scans for this phenotype.In our genome-wide investigation, we found that autosomal SNPs accounted for ~ 25% of the variation in a dimensional measure of AAB. Although this estimate was not statistically significant ,growing cannabis which is likely attributable to our modest sample size, it maps nicely to meta-analytic findings that additive genetic influences account for 32% of the variation in antisocial behavior.Our finding also maps to recent GCTA analyses in a community-based sample, where it was found that common genetic variation accounted for 26% of the variation in a behavioral disinhibition phenotype.No SNP reached genome-wide significance in our GWAS of AAB. Our most associated SNP, rs4728702, was located in ABCB1 on chromosome. In our gene-based tests, ABCB1 was significant at the genome-wide level; however, we did not find an association for this gene in our replication sample. In expression analyses, we also found that ABCB1 is robustly expressed in human brain. This provides some biologically plausible evidence that ABCB1 variation could be associated with behavioral outcomes.
ABCB1 codes for a member of the adenosine triphosphate-binding cassette transporters, ABCB1 or P-glycoprotein, which transportmolecules across cellular membranes and also across the blood– brain barrier. ABCB1 is considered a pharmacogenetic candidate gene in view of ABCB1 transporters’ ability to change drug pharmacokinetics. Variation in ABCB1 has been previously associated with a number of psychiatric phenotypes, including opioid and cannabis dependence, as well as with treatment outcomes for depression and addiction.The related rodent gene, Abcb1a, is differentially expressed in three brain regions of alcohol preferring animals compared with non-preferring animals.Furthermore, ethanol exposure changes ABCB1 expression. An in vitro study of human intestinal cells found that ethanol exposure increased messenger RNA ABCB1 expression level, and that these increases were maintained even after a week of ethanol withdrawal.Similarly, ABCB1 expression was increased in lymphoblastoid cell lines following ethanol exposure,and in rodents, Abcb1a expression was increased in the nucleus accumbens of alcohol-preferring rats following alcohol exposure.Taken as a whole, this pattern suggests that ABCB1 has pleiotropic effects across a number of externalizing spectrum behaviors/disorders, and that its expression is affected by ethanol exposure. The former is consistent with findings from the twin and molecular genetics literature, demonstrating that common externalizing disorders and behaviors share genetic influences,and that this shared genetic factor is highly heritable .Supplementary analyses in our own sample were consistent with this hypothesis, and we found evidence that ABCB1 variation was associated with alcohol and cocaine dependence criterion counts. However, we did not find associations between ABCB1 and marijuana or opioid dependence criterion counts. We also found evidence for enrichment across multiple canonical pathways and gene ontologies including cytokine activity, Jak-STAT signaling pathway, toll-like receptor signaling pathway, antigen processing and presentation, cytokine receptor binding and natural killer cell-mediated cytotoxicity. Although the immediate biological relevance of these categories to AAB is not clear, these enrichment findings include many immune-related pathways and may be best interpreted in light of the associations among AAB and alcohol, cannabis, cocaine and opioid dependence criterion counts in the sample. Immune and inflammatory pathways have been hypothesized to be associated with psychiatric disorders across the internalizing and externalizing spectra.For example, it is known that alcohol alters cytokine activity,induces changes in neuroimmune signaling in the brain and that alcohol dependence is associated with low-grade systemic inflammation.
Likewise, the monocytes of individuals who are cocaine dependent show decreased expression of tumor necrosis factor-α and interleukin-6 proinflammatory cytokines in response to a bacterial ligand relative to controls.Four of the top genes to emerge in our analysis are genes for type I interferon , which reside in a cluster on chromosome 9p. Previous studies demonstrate that interferon A treatment of hepatitis C patients can induce multiple psychiatric symptoms including depression51 and impulsivity.Although we did not find significant enrichment for these pathways in our replication sample, these results add preliminary evidence to a growing literature that variation in genes in immune-relevant pathways may predispose individuals to AAB and closely related behaviors. The present study expands upon the initial AAB GWAS by Tielbeek et al. as well as more recently published GWAS of a behavioral disinhibition phenotype,in two important ways. First, we used a case–control sample where the cases met criteria for alcohol dependence. By virtue of the association between alcohol dependence and AAB, and the relatively high rates of individuals meeting clinical cutoffs for criterion A for ASPD in the present sample compared with American population-based prevalence estimates, it is likely that the sample was enriched for genetic variants predisposing individuals toward externalizing spectrum behaviors such as AAB. Previous work indicates that the genetic influences on AAB completely overlap with the genetic influences on alcohol dependence, other drug abuse/dependence and conduct disorder—that is, AAB does not have unique genetic influences above and beyond those shared with these other externalizing disorders.In view of this, gene identification efforts for AAB are likely to be more successful in more severely affected samples or in samples where participants high in AAB also tend to have comorbid alcohol or substance-use disorders, such as the COGA sample. In contrast, for example, only 6% of the participants in the Tielbeek et al. community-based sample met their nondiagnostic AAB case criteria. This sample may also have had low rates of comorbid alcohol and other drug diagnoses, limiting the ability to find genome-wide significant effects. Second, we used a dimensional measure of AAB, which is more powerful than a binary diagnostic variable, and better represents the underlying dimensional structure of AAB.These differences may explain, in part, why we were able to detect a significant genetic association in the present sample.First, our sample size was relatively small. Second, because the COGA case–control alcohol dependence sample is highly affected by AAB, the findings emerging from our study may not generalize to lower-risk populations or other types of high-risk populations. Our null replication attempt may be attributable, in part, to the replication sample being relatively less affected than the discovery sample. There are other instances where genetic associations for externalizing behaviors have replicated within highly affected samples, but not less-affected samples. For example, GABRA2 is associated with alcohol dependence in samples where alcohol-dependent cases came from clinically recruited samples and families densely affected by alcoholism,but not community-based samples.A sample recruited for this purpose is likely to be enriched for genetic variation that predisposes individuals to a range of externalizing behavior problems, including AAB;however, whether our findings generalize to other populations at high risk for AAB is unknown. Third, because we limited the current analyses to European-Americans, our results may not generalize to other racial and ethnic groups. Fourth, similar to all psychiatric outcomes, antisocial behavior has a developmental component, and evidence from the twin literature suggests that there are genetic influences on adolescent and adult antisocial behavior that are distinct from genetic influences on child antisocial behavior.