Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Loneliness, Mental Health, and Substance Use among US Young Adults during COVID-19

As COVID-19 converges with loneliness and addiction epidemics in the US, both public health and mental health experts forecast dramatic increases in substance use and mental health conditions. This cross-sectional study evaluated relationships of loneliness with depression, anxiety, alcohol use, and drug use during COVID-19, and assessed perceived increases in these symptoms in young adults. Between April 22 and May 11, 2020, 1,008 participants ages 18-35 were recruited through social media to a one-time, online anonymous survey. Symptomatology was assessed using six scales. Perceived changes since COVID-19 were evaluated using 5-point Likert scales. Forty-nine percent of respondents reported loneliness scores above 50; 80% reported significant depressive symptoms; 61% reported moderate to severe anxiety; 30% disclosed harmful levels of drinking. While only 22% of the population reported using drugs, 38% reported severe drug use. Loneliness was associated with higher levels of mental health symptomatology. Participants reported significant increases across mental health and substance use symptoms since COVID-19. While direct impacts of COVID-19 could only be calculated with pre-pandemic assessments of these symptoms, estimates indicate elevated psychosocial symptomatology and suggest that symptoms could have worsened since the pandemic. Findings underscore the importance of prevention and intervention to address these public health problems

Latent class analysis of loneliness and connectedness in US young adults during COVID-19

The coronavirus disease 2019 (COVID-19) pandemic in the United States has exacerbated a number of mental health conditions and problems related to prolonged social isolation. While COVID-19 has led to greater loneliness and a lack of social connectedness, little is known about who are the most affected and how they are impacted. Therefore, we performed a Latent Class Analysis using items from two scales - the UCLA Loneliness Scale and the Social Connectedness Scale - to characterize different experiences of loneliness and connectedness, examine their relationship with mental health and substance use symptoms, including depression, anxiety, drinking, and drug use. Data were drawn from an anonymous one-time online survey examining the mental health of 1008 young adults (18-35 years old) during COVID-19. A latent class analysis (LCA) was conducted to observe and identify classes based on responses to loneliness and connectedness scale items, and to examine the existence of subgroups among this young adult population. We identified a 4-class model of loneliness and connectedness: (1) Lonely and Disconnected - highest probabilities in items of loneliness and disconnectedness, (2) Moderately Lonely and Disconnected - adaptive levels of some isolation and disconnection during COVID-19, (3) Ambivalent Feelings - displaying negative responses in particular to negatively-worded items while simultaneously affirming positively worded items, and (4) Connected and Not Lonely - lowest probabilities in items of loneliness and disconnectedness. Key findings include (1) the delineation of classes by levels of loneliness and connectedness showcasing differential mental health and substance use symptoms, (2) the utility of item-level evaluation with LCA in determining specific classes of people in need of outreach and intervention, and (3) the promise of social connection to bolster resilience in young adults

The Learning Collaboratory: developing and evaluating public health students’ skills while promoting community health

IntroductionComplex and continuous developments in health and healthcare require innovative changes in programs that educate public health scientists and professionals. Public health change agents need critical competencies to confront today and tomorrow’s leading problems including leadership, communication, interprofessional practice, and systems thinking.The context: challenges in public health educationPublic Health training programs teach competencies through their applied field experience and culminating project, typically late in the program, and often implemented in isolation from peers and faculty. Objectives and skills do not always align closely with community-based program needs. Students pursuing a degree in science in public health need to deeply comprehend multi-dimensional and interconnected systemic problems and communicate with diverse stakeholders across disciplines to produce relevant community-engaged research. The University of Miami Public Health Learning Collaboratory (LC) was established to transform the learning experience of public health master’s students by providing opportunities to develop necessary core skills for effective public health practice early in their training, while applying these skills to address real-world public health needs in the community.The Learning Collaboratory: structure, pedagogical approach and programmatic detailsSpanning an average of 3 semesters, the LC promotes student involvement in collaborative and impactful capstone and thesis projects. Practice-based teaching and service learning are central approaches to teaching cross-cutting competencies of leadership, communication, problem solving, collaboration, and systems thinking in public health. Significant to the approach is the engagement of previous cohorts of senior students to teach back to junior students, further integrating concepts learned. Long term alumni feedback recognized strengths of the program, including its structure, teamwork & collaboration, critical thinking & problem solving, guidance, nurture & support, teaching back, and content & curriculum. Community partners agreed the LC prepared students to practice in the field of public health.DiscussionThe LC is a promising model for master’s level public health education and community application, given the opportunities it provides to strengthen and integrate students’ public health skills in a supportive environment, and enhance the transferability and sustainability of student and faculty’s community public health work

A Snapshot of Doctoral Training in Epidemiology: Positioning us for the Future

While epidemiology core competencies are established by the Association of Schools and Programs of Public Health for masters-level trainees, no equivalent currently exists for the doctoral level. Thus, the objective of the Doctoral Education in Epidemiology Survey (2019) was to collect information on doctoral-level competencies in general epidemiology (PhD) degree programs and other pertinent information from accredited programs in the United States and Canada. Participants (doctoral program directors or knowledgeable representatives of the program) from 57 institutions were invited to respond to a 39-item survey (18 core competencies; 9 non-core or emerging topic-related competencies; and 12 program-related items). Participants from 55 institutions (96.5%) responded to the survey, of whom over 85% rated 11 out of 18 core competencies as "very important" or "extremely important." Over 80% of the programs currently emphasize 2 of 9 non-core competencies, i.e., competency to (1) develop and write grant proposals, and (2) assess evidence for causality on the basis of different causal inference concepts. "Big Data" is the most frequently cited topic currently lacking in doctoral curricula. Information gleaned from previous efforts and this survey should prompt a dialog among relevant stakeholders to establish a cohesive set of core competencies for doctoral training in epidemiology

Untreated substance use disorder affects glycemic control: Results in patients with type 2 diabetes served within a network of community-based healthcare centers in Florida

Patients with diabetes and comorbid substance use disorders (SUD) experience poor diabetes management, increased medical complications and mortality. However, research has documented that patients engaged in substance abuse treatment have better management of their comorbid conditions. The current study examines diabetes management among patients with type 2 diabetes, with and without comorbid SUD, receiving care at Florida-based Federally Qualified Health Centers (FQHC) of Health Choice Network (HCN). A retrospective analysis was conducted using deidentified electronic health records of 37,452 patients with type 2 diabetes who received care at a HCN site in Florida between 2016 and 2019. A longitudinal logistic regression analysis examined the impact of SUD diagnosis on achievement of diabetes management [HbA1c < 7.0% (53 mmol/mol)] over time. A secondary analysis evaluated, within those with an SUD diagnosis, the likelihood of HbA1c control between those with and without SUD treatment. The longitudinal assessment of the relationship between SUD status and HbA1c control revealed that those with SUD (N = 6,878, 18.4%) were less likely to control HbA1c over time (OR = 0.56; 95% CI = 0.49-0.63). Among those with SUD, patients engaged in SUD treatment were more likely to control HbA1c (OR = 5.91; 95% CI = 5.05-6.91). Findings highlight that untreated SUD could adversely affect diabetes control and sheds light on the opportunity to enhance care delivery for patients with diabetes and co-occurring SUD

Aiming High: Monitoring Population Level Indicators of Child Wellbeing as a Goal of Community-Academic Partnerships

Community-academic partnerships (CAPs) aim to improve neighborhood population health. Though measuring the impact of partnership activities at a population level can be difficult, evaluating indicators of wellbeing may increase understanding of how communities benefit from CAPs. This study examined child health indicators over time in two low-income, predominantly Black/African American and Hispanic communities where partnerships between an academic child development center and community coalitions were formed with the intention of improving child well-being. Trends in three child wellbeing indicators (graduation rates, kindergarten readiness, and proportion of youth in school and/or employed) were compared between two CAP communities and several neighboring comparison communities. Data between 2011 and 2017 were analyzed to calculate percent change from baseline and mapped using ArcGIS to visualize trends by zip code. Proportions of youth meeting benchmarks were also determined. Kindergarten readiness and high-school graduation rates improved in CAP communities but not in geographically proximal and socioeconomically similar comparison communities. No improvements were found in the proportion of youth in school or employed. This study revealed population-level indicators improved over time in CAP communities. Because community-level child health and wellbeing are influenced by many factors, this correlation is not proof of a causal relationship. Assessing population level indicators can nonetheless provide insight into the benefit of CAPs, and the commitment to monitoring such outcomes can itself advance how academic and community partners plan activities and set long-term goals

Suicidality as a Predictor of Overdose among Patients with Substance Use Disorders

Increasing rates of overdose and overdose deaths are a significant public health problem. Research has examined co-occurring mental health conditions, including suicidality, as a risk factor for intentional and unintentional overdose among individuals with substance use disorder (SUD). However, this research has been limited to single site studies of self-reported outcomes. The current research evaluated suicidality as a predictor of overdose events in 2541 participants who use substances enrolled across eight multi-site clinical trials completed within the National Drug Abuse Treatment Clinical Trials Network between 2012 to 2021. The trials assessed baseline suicidality with the Concise Health Risk Tracking Self-Report (CHRT-SR). Overdose events were determined by reports of adverse events, cause of death, or hospitalization due to substance overdose, and verified through a rigorous adjudication process. Multivariate logistic regression was performed to assess continuous CHRT-SR score as a predictor of overdose, controlling for covariates. CHRT-SR score was associated with overdose events (p = 0.03) during the trial; the likelihood of overdose increased as continuous CHRT score increased (OR 1.02). Participants with lifetime heroin use were more likely to overdose (OR 3.08). Response to the marked rise in overdose deaths should integrate suicide risk reduction as part of prevention strategies

High suicidality predicts overdose events among people with substance use disorder: A latent class analysis

IntroductionSuicide is the tenth leading cause of death in the United States and continues to be a major public health concern. Suicide risk is highly prevalent among individuals with co-occurring substance use disorders (SUD) and mental health disorders, making them more prone to adverse substance use related outcomes including overdose. Identifying individuals with SUD who are suicidal, and therefore potentially most at risk of overdose, is an important step to address the synergistic epidemics of suicides and overdose fatalities in the United States. The current study assesses whether patterns of suicidality endorsement can indicate risk for substance use and overdose.MethodsLatent class analysis (LCA) was used to assess patterns of item level responses to the Concise Health Risk Tracking Self-Report (CHRT-SR), which measures thoughts and feelings associated with suicidal propensity. We used data from 2,541 participants with SUD who were enrolled across 8 randomized clinical trials in the National Drug Abuse Treatment Clinical Trials Network from 2012 to 2021. Characteristics of individuals in each class were assessed, and multivariable logistic regression was performed to examine class membership as a predictor of overdose. LCA was also used to analyze predictors of substance use days.ResultsThree classes were identified and discussed: Class (1) Minimal Suicidality, with low probabilities of endorsing each CHRT-SR construct; Class (2) Moderate Suicidality, with high probabilities of endorsing pessimism, helplessness, and lack of social support, but minimal endorsement of despair or suicidal thoughts; and Class (3) High Suicidality with high probabilities of endorsing all constructs. Individuals in the High Suicidality class comprise the highest proportions of males, Black/African American individuals, and those with a psychiatric history and baseline depression, as compared with the other two classes. Regression analysis revealed that those in the High Suicidality class are more likely to overdose as compared to those in the Minimal Suicidality class (p = 0.04).ConclusionSuicidality is an essential factor to consider when building strategies to screen, identify, and address individuals at risk for overdose. The integration of detailed suicide assessment and suicide risk reduction is a potential solution to help prevent suicide and overdose among people with SUD