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Background and Aims: Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later., Method: We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained ( R 2 )., Result: The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R 2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R 2 (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models., Conclusion: Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Chen, Kho, Guarischi-Sousa, Zhou, Panyard, Azizi, Gupte, Watson, Abbasi and Assimes.)

Background: While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD., Method: Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis., Result: The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD., Conclusion: A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score., Competing Interests: Conflict of Interest None of the authors have conflicts of interest to report

Aims/hypothesis: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits., Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO 2 max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R 2 ) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM., Results: Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R 2 of truncal fat and VO 2 max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO 2 max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM., Conclusions/interpretation: Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM., Competing Interests: Conflict of Interest None of the authors have conflicts of interest to report.

Objective: South Asians (SAs) may possess a unique predisposition to insulin resistance (IR). We explored this possibility by investigating the relationship between 'gold standard' measures of adiposity, fitness, selected proteomic biomarkers, and insulin sensitivity among a cohort of SAs and Europeans (EURs)., Methods: A total of 46 SAs and 41 EURs completed 'conventional' (lifestyle questionnaires, standard physical exam) as well as 'gold standard' (dual energy X-ray absorptiometry scan, cardiopulmonary exercise test, and insulin suppression test) assessments of adiposity, fitness, and insulin sensitivity. In a subset of 28 SAs and 36 EURs, we also measured the blood-levels of eleven IR-related proteins. We conducted Spearman correlation to identify correlates of steady-state plasma glucose (SSPG) derived from the insulin suppression test, followed by multivariable linear regression analyses of SSPG, adjusting for age, sex and ancestral group., Results: Sixteen of 30 measures significantly associated with SSPG, including one conventional and eight gold standard measures of adiposity, one conventional and one gold standard measure of fitness, and five proteins. Multivariable regressions revealed that gold standard measures and plasma proteins attenuated ancestral group differences in IR, suggesting their potential utility in assessing IR, especially among SAs., Conclusion: Ancestral group differences in IR may be explained by accurate measures of adiposity and fitness, with specific proteins possibly serving as useful surrogates for these measures, particularly for SAs., Competing Interests: Conflict of Interest: Authors have no conflicts of interest.

Introduction: Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear., Methods: We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269)., Results: CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10 -5 ) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10 -7 ). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10 -6 ) and CSF p-tau levels (P = 4.38 × 10 -9 )., Discussion: These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels., Highlights: Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia., (© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery., (© 2023. The Author(s).)

Introduction: A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest., Methods: We conducted a CSF proteome-wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation., Results: We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10 -5 ) and 636 significant protein-biomarker associations (P < 6.07 × 10 -6 ). Proteins from glucose and carbon metabolism pathways were enriched among amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers., Discussion: These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD., Highlights: Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing. Glucose/carbon metabolic pathways enriched among amyloid/tau-associated proteins. Key glucose/carbon metabolism protein associations independently replicated. CSF proteome outperformed other omics data in predicting amyloid/tau positivity. CSF metabolomics identified and replicated a succinylcarnitine-phosphorylated tau association., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

Background: Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models., Objective: We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers., Methods: We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD., Results: The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5)., Conclusion: These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Objective: Smoking increases cardiopulmonary and rheumatic disease risk, yet tobacco cessation intervention is rare in rheumatology clinics. This study aimed to implement a rheumatology staff-driven protocol, Quit Connect, to increase the rate of electronic referrals (e-referrals) to free, state-run tobacco quit lines., Methods: We conducted a quasi-experimental cohort study of Quit Connect at 3 rheumatology clinics comparing tobacco quit line referrals from 4 baseline years to referrals during a 6-month intervention period. Nurses and medical assistants were trained to use 2 standardized electronic health record (EHR) prompts to check readiness to quit smoking within 30 days, advise cessation, and connect patients using tobacco quit line e-referral orders. Our objective was to use EHR data to examine the primary outcome of tobacco quit line referrals using pre/post design., Results: Across 54,090 pre- and post-protocol rheumatology clinic visits, 4,601 were with current smokers. We compared outcomes between 4,078 eligible pre-implementation visits and 523 intervention period visits. Post-implementation, the odds of tobacco quit line referral were 26-fold higher compared to our pre-implementation rate (unadjusted odds ratio [OR] 26 [95% confidence interval (95% CI) 6-106]). Adjusted odds of checking readiness to quit in the next 30 days increased over 100-fold compared to pre-implementation (adjusted OR 132 [95% CI 99-177]). Intervention led to e-referrals for 71% of quit-ready patients in <90 seconds; 24% of referred patients reported a quit attempt., Conclusion: Implementing Quit Connect in rheumatology clinics was feasible and improved referrals to a state-run tobacco quit line. Given the importance of smoking cessation to reduce cardiopulmonary and rheumatic disease risk, future studies should investigate disseminating cessation protocols like Quit Connect that leverage tobacco quit lines., (© 2021 American College of Rheumatology.)

Background: Sphingomyelin (SM) levels have been associated with Alzheimer's disease (AD), but the association direction has been inconsistent and research on cerebrospinal fluid (CSF) SMs has been limited by sample size, breadth of SMs examined, and diversity of biomarkers available., Objective: Here, we seek to build on our understanding of the role of SM metabolites in AD by studying a broad range of CSF SMs and biomarkers of AD, neurodegeneration, and neuroinflammation., Methods: Leveraging two longitudinal AD cohorts with metabolome-wide CSF metabolomics data (n = 502), we analyzed the relationship between the levels of 12 CSF SMs, and AD diagnosis and biomarkers of pathology, neurodegeneration, and neuroinflammation using logistic, linear, and linear mixed effects models., Results: No SMs were significantly associated with AD diagnosis, mild cognitive impairment, or amyloid biomarkers. Phosphorylated tau, neurofilament light, α-synuclein, neurogranin, soluble triggering receptor expressed on myeloid cells 2, and chitinase-3-like-protein 1 were each significantly, positively associated with at least 5 of the SMs., Conclusion: The associations between SMs and biomarkers of neurodegeneration and neuroinflammation, but not biomarkers of amyloid or diagnosis of AD, point to SMs as potential biomarkers for neurodegeneration and neuroinflammation that may not be AD-specific.

Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study., (© 2021 Wiley-VCH GmbH.)

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

Objective: Recognizing high blood pressure (BP) as the most prevalent cardiovascular risk factor in patients with rheumatic diseases and all adults, experts recommend clinic protocols to improve BP control. The aim of this study was to adapt and implement a specialty clinic protocol, "BP Connect," to improve timely primary care follow-up after high BP measurements in rheumatology clinics., Methods: We examined BP Connect in a 6-month preimplementation and postimplementation quasi-experimental design with 24-month follow-up in 3 academic rheumatology clinics. Medical assistants and nurses were trained to 1) check (re-measuring BPs ≥140/90 mm Hg), 2) advise (linking rheumatic and cardiovascular diseases), and 3) connect (timely [<4 weeks] primary care follow-up using protocoled electronic health record [EHR] orders). We used EHR data and multivariable logistic regression analysis to examine the primary outcome of timely primary care follow-up for patients with in-network primary care. Staff surveys were used to assess perceptions. Interrupted time series analysis was performed to examine sustainability and BP trends in the clinic populations., Results: Across both 4,683 preimplementation and 689 postimplementation rheumatology visits by patients with high BP, 2,789 (57%) encounters were eligible for in-network primary care follow-up. Postimplementation, the odds of timely primary care BP measurement follow-up doubled (odds ratio 2.0, 95% confidence interval 1.4-2.9). Median time to follow-up decreased from 71 days to 38 days. Moreover, rheumatology visits by patients with high BP decreased from 17% to 8% over 24 months, suggesting significant population-level declines (P < 0.01)., Conclusion: Implementing the BP Connect specialty clinic protocol in rheumatology clinics improved timely follow-up and demonstrated reduced population-level rates of high BP. These findings highlight a timely strategy to improve BP follow-up amid new guidelines and quality measures., (© 2018, American College of Rheumatology.)

The lymphocyte toxicity assay (LTA) is a proposed surrogate marker of sulfonamide antibiotic hypersensitivity. In the LTA, peripheral blood mononuclear cells (PBMCs) undergo apoptosis more readily in hypersensitive versus tolerant patients when exposed to drug-hydroxylamine metabolites in vitro. The purpose of this study was to identify key gene transcripts associated with increased cytotoxicity from sulfamethoxazole-hydroxylamine in human PBMCs in the LTA. The LTA was performed on PBMCs of 10 patients hypersensitive to trimethoprim-sulfamethoxazole (HS) and 10 drug-tolerant controls (TOL), using two cytotoxicity assays: YO-PRO (n = 20) and MTT (n = 12). mRNA expression profiles of PBMCs, enriched for CD8 + T cells, were compared between HS and TOL patients. Transcript expression was interrogated for correlation with % cytotoxicity from YO-PRO and MTT assays. Correlated transcripts of interest were validated by qPCR. LTA results were not significantly different between HS and TOL patients, and no transcripts were found to be differentially expressed between the two groups. 96 transcripts were correlated with cytotoxicity by YO-PRO ( r  = ±.63-.75, FDR 0.188). Transcripts were selected for validation based on mechanistic plausibility and three were significantly over-expressed by qPCR in high cytotoxicity patients: multi-specific organic anion transporter C ( ABCC5 ), mitoferrin-1 ( SLC25A37 ), and Porimin ( TMEM123 ). These data identify novel transcripts that could contribute to sulfonamide-hydroxylamine induced cytotoxicity. These include SLC25A37 , encoding a mitochondrial iron transporter, ABCC5 , encoding an arylamine drug transporter, and TMEM123 , encoding a transmembrane protein that mediates cell death.

Purpose: We present a case report detailing a challenge in health information technology (HIT) project implementations we term "stakeholder creep": not thoroughly identifying which stakeholders need to be involved and why before starting a project, consequently not understanding the true effort, skill sets, social capital, and time required to complete the project., Methods: A root cause analysis was performed post-implementation to understand what led to stakeholder creep. HIT project stakeholders were given a questionnaire to comment on these misconceptions and a proposed implementation tool to help mitigate stakeholder creep., Findings: Stakeholder creep contributed to an unexpected increase in time (3-month delayed go-live) and effort (68% over expected HIT work hours). Four main clinician/researcher misconceptions were identified that contributed to the development of stakeholder creep: 1) that EHR IT is a single group; 2) that all EHR IT members know the entire EHR functionality; 3) that changes to an EHR need the input of just a single EHR IT member; and 4) that the technological complexity of a project mirrors the clinical complexity. HIT project stakeholders similarly perceived clinicians/researchers to hold these misconceptions. The proposed stakeholder planning tool was perceived to be feasible and helpful., Conclusions: Stakeholder creep can negatively affect HIT project implementations. Projects may be susceptible to stakeholder creep when clinicians/researchers hold misconceptions related to HIT organization and processes. Implementation tools, such as the proposed stakeholder checklist, could be helpful in preempting and mitigating the effect of stakeholder creep., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Among patients with rheumatoid arthritis (RA), smoking increases risk of severe RA and pulmonary and cardiovascular disease. Despite this, little is known about smoking cessation counseling by rheumatologists., Objectives: We examined predictors of tobacco counseling in RA patients who smoke including the effect of perceived RA control. We hypothesized that patients with controlled RA would receive more counseling according to the competing demands model, which explains that preventive care gaps occur as a result of competing provider, patient, and clinic factors., Methods: This secondary data analysis involved RA patients with an additional cardiovascular disease risk factor identified in an academic medical center 2004-2011. Trained abstractors assessed documented smoking counseling and rheumatologists' impression of RA control in clinic notes. We used multivariable logistic regression to predict having received smoking cessation counseling, including sociodemographics and comorbidity in models., Results: We abstracted 3396 RA visits, including 360 visits (10%) with active smokers. Perceived controlled RA was present in 31% of visits involving smokers (39% in nonsmokers). Beyond nurse documentation, providers documented smoking status in 39% of visit notes with smokers and smoking cessation counseling in 10%. Visits with controlled versus active RA were less likely to include counseling (odds ratio, 0.3; confidence interval, 0.1-0.97). Counseling was more likely in visits with prevalent cardiovascular, pulmonary, and psychiatric disease, but decreased with obesity., Conclusions: Smoking cessation counseling was documented in 10% of visits and was less likely when RA was controlled. Given smoking's impact on RA and long-term outcomes, systematic cessation counseling efforts are needed.