We found that participants who tested positive for cocaine, particularly individuals with levels indicative of moderate to heavy use, had higher rates of disengagement from PrEP care. Marcus and colleagues found a similar pattern in their study of PrEP patients in an integrated healthcare system.In that study, patients with a documented history of drug use were nearly twice as likely to discontinue PrEP care as those without a history of drug use. Regular clinical follow-up every three months that includes HIV testing and screening for sexually transmitted infections is recommended by current practice guidelines.Routine STI screening can facilitate early identification and treatment given the high incidence of STIs among PrEP users seen in clinical practice.Extended gaps in followup can also lead to interruptions in PrEP adherence and persistence, which have been associated with sero-conversions.Expanded efforts are needed to assist persons who use cocaine with completing routine follow-up visits that allow for regular monitoring and present an opportunity to support adherence, discuss risk reduction, and address cooccurring stimulant use. Results provided no evidence of increased renal toxicity or adverse events from the concurrent use of cocaine and PrEP. Given the high prevalence of stimulant use among MSM and transgender women, it is important for clinicians to be aware of potential renal complications. In a systematic review of nephrotoxicities related to drugs of abuse, cocaine use was associated with an elevated risk of rhabdomyolysis and ischemic nephropathy.Ongoing monitoring of renal function as recommended by PrEP clinical guidelines is warranted,indoor vertical growing systems but more intensive renal monitoring with concomitant stimulant use does not seem indicated.
The biggest strength of our analysis is the use of objective measures to assess both stimulant use and PrEP adherence. The use of biologic measures can eliminate the social desirability bias associated with self-report. However, our results have some limitations. Studies have found that the incorporation of drug analytes in hair can vary by pigmentation, potentially resulting in artificially higher concentrations in persons with darker hair.Plasma tenofovir concentrations reflect recent adherence and may not accurately reflect long-term adherence patterns. Measurement of renal function among the most severe cocaine users at later time points in iPrEx OLE may be limited as these individuals were more likely to miss follow-up visits. Lastly, we were not sufficiently powered to stratify our analysis by age as glomerular dysfunction may be more apparent among older individuals.Our findings demonstrate that biologically confirmed cocaine use is associated with lower odds of achieving prevention-effective PrEP adherence and that those with the highest levels of cocaine use are at greater risk for disengagement from PrEP care.Findings from this study can inform targeted efforts to simultaneously reduce cocaine use and enhance engagement along the PrEP care continuum to optimize the public health benefits of PrEP. Whereas genetic studies have traditionally ascertained cases for a particular disorder, PBCs may contain individuals who can serve as cases for numerous different disorders. However, several limitations need to be considered. The ascertainment of PBCs, while not focused on a specific diagnosis, is never random and therefore does not represent the general population. For example,andMe and UKB20 research participants are more highly educated and have higher SES than the general population. In addition, similar to traditionally ascertained genetic cohorts, current PBCs are overwhelmingly made up of individuals of European ancestry; although MVP is a notable exception.
Another limitation of PBCs is that certain disorders are underrepresented; for example, in UKB, the frequency of schizophrenia is lower than the general population, perhaps reflecting the lower rate at which schizophrenia patients volunteered to participate in such a rigorous study. The age of subjects in PBCs is another potential limitation. For example, the use of diagnoses for childhood onset disorders like ADHD and autism have changed dramatically over the past few decades, meaning that older subjects will have a lower than expected prevalence of these diagnoses. In addition, the prevalence of environmental exposures , which modulate the prevalence of many traits and diseases, have changed over time, which may confound various genetic studies. Lastly, privacy and intellectual property concerns restrict the sharing of raw data and even the results obtained from some PBCs, these restrictions impede data sharing. Despite these limitations, PBCs are attractive because they are economical, offer the potential to dramatically increase sample size, provide a much greater diversity of phenotypes, and lend themselves to innovative study designs.In some PBCs, clinical diagnoses are not available. However, self-reported clinical diagnoses may be available. For obvious reasons, these self-reported diagnoses must be interpreted with caution; however, the strength of the genetic correlation between gold-standard diagnoses and self-reported diagnoses helps to address this concern. For example, self-reported MDD and clinician assigned MDD showed a robust genetic correlation. In other cases, self-reported diagnoses are unavailable, but screening tools can be used to approximate diagnoses. For example, scores from the Alcohol Use Disorder Identification Test , which is as a screening tool for AUD, were available in research participants from and Me and UKB. Sanchez-Roige et al found that when AUDIT scores were converted into a case control phenotype, they were highly genetically correlated with AUD 25 .
These examples demonstrate that, even when clinical diagnoses are not available, there is still significant value in using self-reported information from PBCs for genetic studies of psychiatric disorders.In general, there is a trade off between phenotyping depth and sample size . The quest for larger sample sizes has led to the adoption of “minimal phenotyping” where a complex disease or trait may be reduced to a single yes or no question. Minimal phenotyping is sometimes criticized because it implicitly assumes that minimal phenotypes are merely noisy measurements of a true underlying phenotype. Cai et al sought to empirically examine this question by considering both self-reported diagnosis of MDD and clinician measurements of the cardinal symptoms of MDD and found that minimal phenotyping yielded a qualitatively different trait. Another empirical examination of minimal phenotyping used a multivariate framework to evaluate several psychiatric disorders and self-report measures of their cardinal symptoms. That study identified large genetic correlations between some disorders and symptom pairs , but very modest genetic correlations between other pairs . Despite these limitations,robust genetic signals — of something — can be obtained using minimal phenotyping; how useful these signals will be for understanding the pathophysiology of psychiatric disorders is a matter of ongoing debate, but when large, minimally phenotyped datasets exist, it seems natural that they should be analyzed. Regardless of whether diagnoses are made by an expert clinician, a structured interview, or self-report, there is a broader question about whether or not the current diagnostic categories are optimal for genetic research, given that the DSM was never intended to be a research tool. A recent review summarized this issue with the memorable phrase “our genes don’t seem to have read the DSM”. Initiatives such as the National Institute of Mental Health Research Domain Criteria and Hierarchical Taxonomy of Psychopathology provide new ways of classifying psychiatric disorders based on dimensions of observable behavioral and neurobiological measures, rather than diagnostic categories. These approaches have not been universally accepted. Even before RDoC,vertical cannabis grow there was widespread enthusiasm for genetic studies of endophenotypes ; however, studies of endophenotypes flourished in the era of candidate genes, when the necessity of large sample sizes was not generally understood. This may have fostered undue skepticism about the utility of endophenotypes for genetic research. There are several recent examples of adequately powered genome-wide association studies of endophenotypes. For example, impulsivity, which has been defined as “actions which are poorly conceived, prematurely expressed, unduly risky or inappropriate to the situation, and that often result is undesirable consequences”appears to meet the criteria for an endophenotype for multiple psychiatric disorders, including attentiond eficit/hyperactivity disorder and several substance use disorders . Numerous genetic studies have now shown that various measures of impulsivity34–36 and sensation seeking39 are heritable and that they are genetically correlated with both ADHD and various substance use related traits. In addition, risk tolerance , which has also been proposed as an endophenotype for both ADHD and substance use disorders, was recently measured in over one million individuals . Although risk tolerance was measured using a minimal phenotype , risk tolerance was clearly heritable and the large sample size allowed identification of 124 genome-wide significant loci. Some of these loci have also been implicated in clinically defined traits. Furthermore, risk tolerance was positively genetically correlated with numerous clinically relevant traits . This study illustrates the power of minimal phenotyping to capture an endophenotype that informs complex disorders and also conforms to the RDoC framework. In a third example, Ibrahim-Verbaas et al performed a GWAS for executive function, which can be considered an endophenotype for multiple psychiatric traits. Intriguingly, GWAS of sensation seeking, risk tolerance18 and executive function all identified a locus that included the gene CAMD2, which was subsequently associated with AUD9 .
Whether all of these associations are due to a single locus or multiple loci is far from clear, but the index SNPs for these studies are typically co-inherited , consistent with a single causal locus. Another example of an intriguing endophenotype is self-reported loneliness , which is a strong predictor of mortality and life satisfaction and appears to precede the onset of MDD. Several recent GWAS of loneliness have identified several significant loci and shown that a genetic predisposition to loneliness is genetically correlated with psychiatric, cardiovascular, and metabolic disorders. By assigning polygenic risk scores to individuals for whom electronic medical records were also available, Dennis et al showed that genetic liability for loneliness increased the risk to develop coronary artery disease more robustly than MDD. Thus, loneliness is an endophenotype that is relevant to both MDD and a variety of somatic disorders. While some endophenotypes may be amenable to minimal phenotyping, others represent extremely deep and rich data types. For example, by passively collecting data from wearable devices and smartphones, certain endophenotypes relevant to psychiatric disorders can be measured. In a recent GWAS of circadian rhythm, wearable devices were used to gather objective measures of sleep timing, duration and quality. More recently, structural connectivity from fMRI was proposed as endophenotype for IQ. Elliott et al used 3,144 functional and structural brain imaging phenotypes from UKB to conduct GWAS that identified novel associations that included genes relevant to brain development, pathway signaling and plasticity.Despite compelling examples like these, there has not been a coordinated effort to define and explore the endophenotype space. Whereas psychiatric disorders require ascertainment of cases and controls, endophenotypes are continuous and could therefore be measured at scale in PBCs . The Psychiatric Genomics Consortium has subdivided psychiatric genetic studies into working groups for each major diagnostic category; in contrast, while individual groups have been formed around specific projects , there is no coordinated effort to establish a similar set of working groups focused on GWAS of endophenotypes or RDoC traits; however, we feel such an effort is overdue. The approach we are proposing will be orthogonal to the efforts of the PGC because RDoC traits and endophenotypes “split” diagnostic categories into discrete units of analysis. The SUD field provides a good example of how a complex disorder can be split into smaller, more biologically meaningful units. SUD develop in accordance with an obligate longitudinal pattern: drug experimentation → regular use → harmful use → transition to compulsive use → quit attempts → relapse . Approaching SUD with a case control framework merges the genetic liability for each of these stages into a single phenotype, obscuring the distinct biological factors relevant at each stage. In contrast, several recent projects have focused on individual stages of SUD, which can help to address this limitation. For example, GSCAN used data from almost 1 million individuals to examine a number of SUD-related traits, including smoking initiation. In another example, the genetic relationship between alcohol consumption and AUD was explored using the AUDIT, a 10- item questionnaire that measures alcohol use and misuse. By dissecting the genetic contribution for alcohol consumption vs problematic use , Sanchez-Roige et al and Kranzler et al9 showed a surprisingly low correlation between alcohol consumption and AUD ; however, the correlation between problematic alcohol use and AUD was stronger.