Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults

Background: Despite declining opioid prescribing rates in the United States, the annual prevalence of prescription opioid use in adults ≥50 years old is estimated to be 40%, higher than that of younger adults (ages 18-29 years, 36%). As the American population ages, understanding factors that contribute to overall opioid use is a necessary first step in the determination and mitigation of inappropriate prescribing and opioid-related harms. Objective: Assess predictors of prescription opioid use in an adult population with a high prevalence of chronic pain. Methods: Data were from a community-based cohort of White and African American adults aged 50-90 years residing in predominantly rural Johnston County, North Carolina. Univariable and multivariable logistic regression models were used to evaluate sociodemographic and clinical factors in non-opioid users (n=795) at baseline (2006-2010) as predictors of opioid use at follow-up (2013-2015). Variables included age, sex, race, obesity (BMI≥30kg/m2), polypharmacy (5+ medications), educational attainment (<12, ≥12 years), employment (unemployed, employed/retired), insurance (uninsured, public, private), Census block group household poverty rate (<12%, 12–24%, ≥25%), depressive symptoms (Center for Epidemiologic Studies Depression Scale ≥16 or depression diagnosis), perceived social support (moderate/poor [<19], strong [≥19]; Strong Ties Measure of Social Support, range 0-20), pain sensitivity (sensitive [<4kg], normal [≥4kg] pressure pain threshold), and pain catastrophizing (high [≥15], moderate/low [<15]; Pain Catastrophizing Helplessness Subscale, range 0-25). Results: At follow-up, 13% (n=100) of participants were using prescription opioids. In univariable models, younger age, female sex, obesity, polypharmacy, unemployment, public (vs. private) health insurance, higher poverty rate, depressive symptoms, poorer perceived social support, pain catastrophizing, and elevated pain sensitivity were independently associated (p<0.05) with opioid use. In the multivariable model, younger age (60 vs. 70 years; adjusted odds ratio, 95% confidence interval=2.52, 1.08−5.88), polypharmacy (2.16, 1.24−3.77), high pain catastrophizing (2.17, 1.33−3.56), and depressive symptoms (2.00, 1.17−3.43) remained significant independent predictors. Conclusion: The simultaneous assessment of a breadth of clinical and sociodemographic factors identified polypharmacy, pain catastrophizing, and depressive symptoms as modifiable predictors of prescription opioid use. These findings support the incorporation of pharmacological review and behavioral approaches into chronic pain management strategies. Further research is warranted to track changes in these factors as prescription opioid use declines nationwide

Sociodemographic and clinical predictors of prescription opioid use in a longitudinal community-based cohort study of middle-aged and older adults

Chronic pain prevalence in the United States is likely to increase with an aging population. While opioids have commonly been prescribed to manage pain, their use may be more likely in certain patients. The objective of the study is to assess predictors of prescription opioid use in an adult population with a high prevalence of chronic pain. The simultaneous assessment of a breadth of clinical and sociodemographic factors identified polypharmacy, pain catastrophizing, and depressive symptoms as modifiable predictors of prescription opioid use. These findings support the incorporation of pharmacological review and behavioral approaches into chronic pain management strategies

Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults

Background: Despite declining opioid prescribing rates in the United States, the annual prevalence of prescription opioid use in adults ≥50 years old is estimated to be 40%, higher than that of younger adults (ages 18-29 years, 36%). As the American population ages, understanding factors that contribute to overall opioid use is a necessary first step in the determination and mitigation of inappropriate prescribing and opioid-related harms. Objective: Assess predictors of prescription opioid use in an adult population with a high prevalence of chronic pain. Methods: Data were from a community-based cohort of White and African American adults aged 50-90 years residing in predominantly rural Johnston County, North Carolina. Univariable and multivariable logistic regression models were used to evaluate sociodemographic and clinical factors in non-opioid users (n=795) at baseline (2006-2010) as predictors of opioid use at follow-up (2013-2015). Variables included age, sex, race, obesity (BMI≥30kg/m2), polypharmacy (5+ medications), educational attainment (<12, ≥12 years), employment (unemployed, employed/retired), insurance (uninsured, public, private), Census block group household poverty rate (<12%, 12–24%, ≥25%), depressive symptoms (Center for Epidemiologic Studies Depression Scale ≥16 or depression diagnosis), perceived social support (moderate/poor [<19], strong [≥19]; Strong Ties Measure of Social Support, range 0-20), pain sensitivity (sensitive [<4kg], normal [≥4kg] pressure pain threshold), and pain catastrophizing (high [≥15], moderate/low [<15]; Pain Catastrophizing Helplessness Subscale, range 0-25). Results: At follow-up, 13% (n=100) of participants were using prescription opioids. In univariable models, younger age, female sex, obesity, polypharmacy, unemployment, public (vs. private) health insurance, higher poverty rate, depressive symptoms, poorer perceived social support, pain catastrophizing, and elevated pain sensitivity were independently associated (p<0.05) with opioid use. In the multivariable model, younger age (60 vs. 70 years; adjusted odds ratio, 95% confidence interval=2.52, 1.08−5.88), polypharmacy (2.16, 1.24−3.77), high pain catastrophizing (2.17, 1.33−3.56), and depressive symptoms (2.00, 1.17−3.43) remained significant independent predictors. Conclusion: The simultaneous assessment of a breadth of clinical and sociodemographic factors identified polypharmacy, pain catastrophizing, and depressive symptoms as modifiable predictors of prescription opioid use. These findings support the incorporation of pharmacological review and behavioral approaches into chronic pain management strategies. Further research is warranted to track changes in these factors as prescription opioid use declines nationwide

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Association of Traumatic Knee Injury With Radiographic Evidence of Knee Osteoarthritis in Military Officers

Objective: The association between knee injury and knee osteoarthritis (OA) is understudied relative to its importance, particularly in younger populations. This study was undertaken to examine the association of knee injury with radiographic features of knee OA in military officers, who have a physically demanding profession and high rates of knee injury. Methods: Participants were recruited in 2015–2017 from an existing program that enrolled 6,452 military officers during 2004–2009. Officers with a history of knee ligament or meniscal injuries (n = 117 via medical record review) were compared to officers with no history of knee injury (n = 143). Bilateral posteroanterior knee radiographs were obtained using a standardized fixed-flexion positioning frame. All images were read for Kellgren/Lawrence (K/L) grade, osteophyte (OST), and joint space narrowing (JSN) scores. Data were analyzed using linear-risk regression models with generalized estimating equations. Results: Injured and noninjured participants were similar (mean age 28 years, mean body mass index 25 kg/m2, ~40% female). The mean time from first knee injury to imaging among injured participants was 9.2 years. Compared with noninjured knees, greater prevalence of radiographic OA (K/L grade ≥ 2), OST (grade ≥ 1), and JSN (grade ≥ 1) was observed among injured knees, with prevalence differences of +16% (95% confidence interval [95% CI] 10%, 22%), +29% (95% CI 20%, 38%), and + 17% (95% CI 10%, 24%), respectively. Approximately 1 in 6 officers with prior knee injury progressed to radiographic OA by age 30 years. Conclusion: At the midpoint of a projected 20-year military career, officers with a history of traumatic knee injury have a markedly increased prevalence of knee radiographic OA compared to officers without injury

The power of genetic diversity in genome-wide association studies of lipids

Abstract Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4‐23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice