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Levi Phillips
Levi Phillips

Crack Cocaine Dependence Dsm Code

The National Survey on Drug Use and Health reported that 913,000 people in the nation met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5)* criteria for dependence or abuse of cocaine in 2014. The Drug Abuse Warning Network report showed that cocaine was included in 505,224 of the nearly 1.3 million visits to the emergency room for drug misuse or abuse. This means that more than one in three or 40 percent of the drug misuse or abuse-related visits to the emergency room involved cocaine. ( -reports/cocaine/what-scope-cocaine-use-in-united-states)

Crack Cocaine Dependence Dsm Code


  • Diagnosis Index entries containing back-references to P04.41: Absorption drug NEC - see Table of Drugs and Chemicals addictive through placenta (newborn) P04.40 - see also Newborn, affected by, maternal, use ofICD-10-CM Diagnosis Code P04.40Newborn affected by maternal use of unspecified drugs of addiction2019 - New Code 2020 2021 2022 2023 Billable/Specific Code Code on Newborn Record cocaine P04.41 Crack baby P04.41 Newborn (infant) (liveborn) (singleton) Z38.2ICD-10-CM Diagnosis Code Z38.2Single liveborn infant, unspecified as to place of birth2016 2017 2018 2019 2020 2021 2022 2023 Billable/Specific Code Newborn/Neonate Dx (0 years) POA Exempt Applicable ToSingle liveborn infant NOS

affected by drugs of addiction P04.40ICD-10-CM Diagnosis Code P04.40Newborn affected by maternal use of unspecified drugs of addiction2019 - New Code 2020 2021 2022 2023 Billable/Specific Code Code on Newborn Record cocaine P04.41 cocaine P04.41 (crack)

In the U.S.A., cocaine is the second most abused illicit drug. Variants within the CHRNB3-A6 gene cluster have been associated with cigarette consumption in several GWAS. These receptors represent intriguing candidates for the study of cocaine dependence because nicotinic receptors are thought to be involved in generalized addiction pathways. Using genotypic data from a GWAS of the Study of Addiction: Genetics and Environment (SAGE) dataset, we tested for association of CHRNB3-A6 SNPs with DSM-5 cocaine use disorder. Multiple SNPs in the region were significantly associated with increased risk of cocaine use disorder. Inclusion of the most significant SNP as a covariate in a linear regression model provided evidence for an additional independent signal within this locus for cocaine use disorder. These results suggest that the CHRNB3-A6 locus contains multiple variants affecting risk for vulnerability to cocaine and nicotine dependence as well as bipolar disorder, suggesting that they have pleiotropic effects.

Drug addiction is a pervasive problem across cultures and is both an economic and psychological burden for the individuals and families involved. Illicit drugs have particularly devastating consequences: Cocaine users have six times the mortality rate of their age-matched peers1 and one study found that crack cocaine was the third most harmful drug overall2. Consequently, a greater understanding of the molecular mechanisms underlying the risk of developing cocaine dependence is urgently needed to aid in targeting prevention and treatment strategies.

In addition to the observed co-occurrence of cocaine dependence with other forms of substance disorders and psychopathology, numerous twin studies indicate a high degree of overlap among genetic factors influencing the liability to a variety of substance use disorders11,12. Genomic studies have also suggested that there are genetic loci that have substance-specific effects but also that loci exist that affect risk for the development of dependence on multiple substances (see4 for review). Loci that have been largely implicated to specifically influence a single substance use disorder include those that exert metabolic influence on the substance of abuse. For instance, SNPs in cytochrome P450 2A6 (CYP2A6), the gene encoding the major nicotine-metabolizing enzyme, affect cigarette consumption13,14 and a SNP in the Alcohol Dehydrogenase 1B (ADH1B) gene affects levels of alcohol consumption15 and risk for alcohol dependence4 via regulation of conversion of alcohol to acetaldehyde.

On the other hand, there are numerous examples of receptor encoding loci whose effects extend across multiple substance dependence phenotypes. One such example is the SNP rs16969968 (D398N) in the cholinergic nicotinic receptor subunit α5 (CHRNA5) that both increases nicotine dependence risk and decreases cocaine dependence risk5,16. The minor allele of this SNP is the most significant and widely replicated variant associated with cigarette consumption and is also associated with protection against cocaine dependence5,14,17. The protective effect of rs16969968 with CD has been replicated in both European and African-Americans16. The same study also found that another SNP in CHRNA5 (rs684513) is associated with risk for cocaine dependence in African-Americans (OR = 1.43, P = 0.0004).

In addition, a cluster of nicotinic receptors on chromosome 8 including CHRNB3-A6 was also previously shown to reduce risk for nicotine-related phenotypes in several GWAS of nicotine dependence and cigarettes smoked per day13,14,18,19,20,21,22,23. An overlapping set of SNPs in the CHRNB3-CHRNA6 region were also reported by Hartz et al. (2011) to be associated with risk of bipolar disorder24. Despite the positive correlation between nicotine dependence and bipolar disorder the associations are in opposite directions, i.e. SNPs in this locus are associated with reduced risk for nicotine dependence but increased risk for bipolar disorder. This suggests that the SNPs affect bipolar disorder independently of their role in nicotine dependence. The role of these specific SNPs in the etiology of CD remains unexplored but several rare variants in CHRNB3 have been associated with increased risk for both cocaine and alcohol dependence25. Together, these results suggest that nicotinic receptors are good candidate genes for susceptibility to nicotine and cocaine dependence vulnerability and that investigation of the role of common and low frequency variants within the CHRNB3-A6 locus in cocaine dependence is warranted.

Values and gray-scale represent r-squared. Importantly, rs9298626, the top SNP associated with DSM-5 cocaine use disorder and the shared SNP that becomes significant when putting this SNP into the respective model as a covariate (rs892413) are circled in blue and SNPs representing the previously discovered association with nicotine dependence (rs1451240, rs6474413 and rs4952) are circled in red.

Because SAGE is composed of individuals from three independent studies, each ascertained for a different substance dependence, we performed stratified analyses both by study and by nicotine dependence and alcohol dependence to determine if there existed a subset of subjects in which the association was most pronounced. The top SNP associated with DSM-5 cocaine use disorder (rs9298626) in the whole SAGE dataset was significantly associated with DSM5 cocaine use disorder in the COGA subset and showed a trend in the same direction in the FSCD and COGEND subsets. Furthermore, when individuals from the whole dataset were stratified by DSM-5 alcohol use disorder, or FTND nicotine dependence there was evidence of association between rs9298626 and cocaine use disorder in both groups (Table 3). This suggests that the observed associations are not an artifact of ascertainment. This analysis suggests that this SNP is associated with DSM-5 cocaine use disorder and that the CHRNB3-A6 locus is robustly associated with DSM-5 cocaine use disorder, regardless of comorbidity or ascertainment.

To further examine the relationship between nicotine dependence and DSM-5 cocaine use disorder associations in this region, we performed additional conditional analyses. In a linear regression model using age, sex, study, DSM-5 alcohol symptom count, total FTND score and rs1451240 genotype as covariates, the association with DSM-5 cocaine use disorder remained significant (Table 5). Lastly, the DSM-5 cocaine use disorder signal remains significant when conditioning on the two rare variants (rs35327613 and rs149775276) recently identified by our group to be associated with DSM-IV alcohol and cocaine dependence symptom count26. This is not surprising given that these rare missense variants are present on the haplotypes containing the major allele of rs9298626, whereas the association reported here is with the minor allele. The fact that the association with cocaine use disorder remains when conditioning on the genome-wide significant signal with nicotine dependence in the region, suggests that the association is independent and not acting through nicotine dependence.

LD bins tagged by rs9298626 and rs892413 each show association with DSM-5 cocaine use disorder in joint SNP analysis. Analyses conditioning on rs9298626 reveal that rs892413 is independently associated with DSM-5 cocaine use disorder. rs892413 is also associated with DSM-5 cocaine use disorder independent of the previously identified genome-wide significant association in the region with nicotine dependence (represented by rs1451240), providing support for a direct effect of this SNP on higher DSM-5 cocaine use disorder risk, as opposed to acting through nicotine dependence risk (Tables 3 and 5).

Our group has previously reported that rare missense variants in CHRNB3 increase risk for cocaine dependence26. The results reported here demonstrate that low frequency and common alleles within the CHRNB3 locus are also associated with increased risk of DSM-5 cocaine use disorder. Cocaine dependence has now been associated with SNPs in two different nicotinic receptor gene clusters, on chromosomes 8 and 15 5,26. It is interesting, however, that the variant on chromosome 15, within CHRNA5 is associated with decreased risk for cocaine dependence, while rs9298626 and other variants in the CHRNB3-A6 region are associated with higher (OR = 2.62) risk for DSM-5 cocaine use disorder.


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