Epigenome-wide association study of total nicotine equivalents in multiethnic current smokers from three prospective cohorts.

The impact of tobacco exposure on health varies by race and ethnicity and is closely tied to internal nicotine dose, a marker of carcinogen uptake. DNA methylation is strongly responsive to smoking status and may mediate health effects, but study of associations with internal dose is limited. We performed a blood leukocyte epigenome-wide association study (EWAS) of urinary total nicotine equivalents (TNEs; a measure of nicotine uptake) and DNA methylation measured using the MethylationEPIC v1.0 BeadChip (EPIC) in six racial and ethnic groups across three cohort studies. In the Multiethnic Cohort Study (discovery, n = 1994), TNEs were associated with differential methylation at 408 CpG sites across >250 genomic regions (p < 9 × 10-8). The top significant sites were annotated to AHRR, F2RL3, RARA, GPR15, PRSS23, and 2q37.1, all of which had decreasing methylation with increasing TNEs. We identified 45 novel CpG sites, of which 42 were unique to the EPIC array and eight annotated to genes not previously linked with smoking-related DNA methylation. The most significant signal in a novel gene was cg03748458 in MIR383;SGCZ. Fifty-one of the 408 discovery sites were validated in the Singapore Chinese Health Study (n = 340) and the Southern Community Cohort Study (n = 394) (Bonferroni corrected p < 1.23 × 10-4). Significant heterogeneity by race and ethnicity was detected for CpG sites in MYO1G and CYTH1. Furthermore, TNEs significantly mediated the association between cigarettes per day and DNA methylation at 15 sites (average 22.5%-44.3% proportion mediated). Our multiethnic study highlights the transethnic and ethnic-specific methylation associations with internal nicotine dose, a strong predictor of smoking-related morbidities.

Transcranial Low-Intensity Focused Ultrasound Stimulation of the Visual Thalamus Produces Long-Term Depression of Thalamocortical Synapses in the Adult Visual Cortex.

Transcranial focused ultrasound stimulation (tFUS) is a noninvasive neuromodulation technique, which can penetrate deeper and modulate neural activity with a greater spatial resolution (on the order of millimeters) than currently available noninvasive brain stimulation methods, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). While there are several studies demonstrating the ability of tFUS to modulate neuronal activity, it is unclear whether it can be used for producing long-term plasticity as needed to modify circuit function, especially in adult brain circuits with limited plasticity such as the thalamocortical synapses. Here we demonstrate that transcranial low-intensity focused ultrasound (LIFU) stimulation of the visual thalamus (dorsal lateral geniculate nucleus, dLGN), a deep brain structure, leads to NMDA receptor (NMDAR)-dependent long-term depression of its synaptic transmission onto layer 4 neurons in the primary visual cortex (V1) of adult mice of both sexes. This change is not accompanied by large increases in neuronal activity, as visualized using the cFos Targeted Recombination in Active Populations (cFosTRAP2) mouse line, or activation of microglia, which was assessed with IBA-1 staining. Using a model (SONIC) based on the neuronal intramembrane cavitation excitation (NICE) theory of ultrasound neuromodulation, we find that the predicted activity pattern of dLGN neurons upon sonication is state-dependent with a range of activity that falls within the parameter space conducive for inducing long-term synaptic depression. Our results suggest that noninvasive transcranial LIFU stimulation has a potential for recovering long-term plasticity of thalamocortical synapses in the postcritical period adult brain.

Biological signal processing filters via engineering allosteric transcription factors.

Signal processing is critical to a myriad of biological phenomena (natural and engineered) that involve gene regulation. Biological signal processing can be achieved by way of allosteric transcription factors. In canonical regulatory systems (e.g., the lactose repressor), an INPUT signal results in the induction of a given transcription factor and objectively switches gene expression from an OFF state to an ON state. In such biological systems, to revert the gene expression back to the OFF state requires the aggressive dilution of the input signal, which can take 1 or more d to achieve in a typical biotic system. In this study, we present a class of engineered allosteric transcription factors capable of processing two-signal INPUTS, such that a sequence of INPUTS can rapidly transition gene expression between alternating OFF and ON states. Here, we present two fundamental biological signal processing filters, BANDPASS and BANDSTOP, that are regulated by D-fucose and isopropyl-ß-D-1-thiogalactopyranoside. BANDPASS signal processing filters facilitate OFF-ON-OFF gene regulation. Whereas, BANDSTOP filters facilitate the antithetical gene regulation, ON-OFF-ON. Engineered signal processing filters can be directed to seven orthogonal promoters via adaptive modular DNA binding design. This collection of signal processing filters can be used in collaboration with our established transcriptional programming structure. Kinetic studies show that our collection of signal processing filters can switch between states of gene expression within a few minutes with minimal metabolic burden-representing a paradigm shift in general gene regulation.

Ambient Air Pollutant Exposures and COVID-19 Severity and Mortality in a Cohort of Patients with COVID-19 in Southern California.

Rationale: Ecological studies have shown air pollution associations with coronavirus disease (COVID-19) outcomes. However, few cohort studies have been conducted. Objectives: To conduct a cohort study investigating the association between air pollution and COVID-19 severity using individual-level data from the electronic medical record. Methods: This cohort included all individuals who received diagnoses of COVID-19 from Kaiser Permanente Southern California between March 1 and August 31, 2020. One-year and 1-month averaged ambient air pollutant (particulate matter ⩽2.5 µm in aerodynamic diameter [PM2.5], NO2, and O3) exposures before COVID-19 diagnosis were estimated on the basis of residential address history. Outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and ICU admissions within 30 days and mortality within 60 days after COVID-19 diagnosis. Covariates included socioeconomic characteristics and comorbidities. Measurements and Main Results: Among 74,915 individuals (mean age, 42.5 years; 54% women; 66% Hispanic), rates of hospitalization, IRS, ICU admission, and mortality were 6.3%, 2.4%, 1.5%, and 1.5%, respectively. Using multipollutant models adjusted for covariates, 1-year PM2.5 and 1-month NO2 average exposures were associated with COVID-19 severity. The odds ratios associated with a 1-SD increase in 1-year PM2.5 (SD, 1.5 µg/m3) were 1.24 (95% confidence interval [CI], 1.16-1.32) for COVID-19-related hospitalization, 1.33 (95% CI, 1.20-1.47) for IRS, and 1.32 (95% CI, 1.16-1.51) for ICU admission; the corresponding odds ratios associated with 1-month NO2 (SD, 3.3 ppb) were 1.12 (95% CI, 1.06-1.17) for hospitalization, 1.18 (95% CI, 1.10-1.27) for IRS, and 1.21 (95% CI, 1.11-1.33) for ICU admission. The hazard ratios for mortality were 1.14 (95% CI, 1.02-1.27) for 1-year PM2.5 and 1.07 (95% CI, 0.98-1.16) for 1-month NO2. No significant interactions with age, sex or ethnicity were observed. Conclusions: Ambient PM2.5 and NO2 exposures may affect COVID-19 severity and mortality.

Healthcare Utilization Among Patients Diagnosed with COVID-19 in a Large Integrated Health System.

BACKGROUND: The demands for healthcare resources following a COVID-19 diagnosis are substantial, but not currently quantified. OBJECTIVE: To describe trends in healthcare utilization within 180 days for patients diagnosed with COVID-19 and identify patient factors associated with increased healthcare use. DESIGN: Observational cohort study. PATIENTS: A total of 64,011 patients with a test-confirmed COVID-19 diagnosis from March to September 2020 in a large integrated healthcare system in Southern California. MAIN MEASURES: Overall healthcare utilization during the 180 days following COVID-19 diagnosis, as well as encounter types and reasons for visits during the first 30 days. Poisson regression was used to identify patient factors associated with higher utilization. Analyses were performed separately for patients who were and were not hospitalized for COVID-19. KEY RESULTS: Healthcare utilization was about twice as high for hospitalized patients compared to non-hospitalized patients in all time periods. The average number of visits was highest in the first 30 days (hospitalized: 12.3 visits/30 person-days; non-hospitalized: 6.6) and gradually decreased over time. In the first 30 days, the majority of healthcare visits were telehealth encounters (hospitalized: 9.0 visits; non-hospitalized: 5.6 visits), and the most prevalent reasons for visits were COVID-related diagnoses, COVID-related symptoms, and respiratory-related conditions. For hospitalized patients, older age (≥65: RR 1.27, 95% CI 1.15-1.41), female gender (RR 1.07, 95% CI 1.05-1.09), and higher BMI (≥40: RR 1.07, 95% CI 1.03-1.10) were associated with higher total utilization. For non-hospitalized patients, older age, female gender, higher BMI, non-white race/ethnicity, former smoking, and greater number of pre-existing comorbidities were all associated with increased utilization. CONCLUSIONS: Patients with COVID-19 seek healthcare frequently within 30 days of diagnosis, placing high demands on health systems. Identifying ways to support patients diagnosed with COVID-19 while adequately providing the usual recommended care to our communities will be important as we recover from the pandemic.

Ambient air pollution and COVID-19 incidence during four 2020-2021 case surges.

BACKGROUND: Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure. OBJECTIVE: To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021. METHODS: The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM2.5, NO2, and O3) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020-5/31/2020, 6/1/202-9/30/2020, 10/1/2020-12/31/2020, and 1/1/2021-2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation. RESULTS: The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM2.5, NO2, and O3 were 1.11 (1.04, 1.18) per 2.3 µg/m3,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 µg/m3 for PM2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO2 and 0.96 (0.85, 1.08) per 12.0 ppb for O3. CONCLUSION: Long-term PM2.5 and NO2 exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM2.5 and NO2 exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data.

Rationale: Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives: To develop a model to predict a future diagnosis of lung cancer on the basis of routine clinical and laboratory data by using machine learning. Methods: We assembled data from 6,505 case patients with non-small cell lung cancer (NSCLC) and 189,597 contemporaneous control subjects and compared the accuracy of a novel machine learning model with a modified version of the well-validated 2012 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial risk model (mPLCOm2012), by using the area under the receiver operating characteristic curve (AUC), sensitivity, and diagnostic odds ratio (OR) as measures of model performance. Measurements and Main Results: Among ever-smokers in the test set, a machine learning model was more accurate than the mPLCOm2012 for identifying NSCLC 9-12 months before clinical diagnosis (P < 0.00001) and demonstrated an AUC of 0.86, a diagnostic OR of 12.3, and a sensitivity of 40.1% at a predefined specificity of 95%. In comparison, the mPLCOm2012 demonstrated an AUC of 0.79, an OR of 7.4, and a sensitivity of 27.9% at the same specificity. The machine learning model was more accurate than standard eligibility criteria for lung cancer screening and more accurate than the mPLCOm2012 when applied to a screening-eligible population. Influential model variables included known risk factors and novel predictors such as white blood cell and platelet counts. Conclusions: A machine learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the mPLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.

Lung Cancer: Diagnosis, Treatment Principles, and Screening.

Lung cancer is the second most common cancer in men and women in the United States; however, it remains the leading cause of cancer-related death in the United States and worldwide. The most common but nonspecific symptom of lung cancer is cough. Associated symptoms, including hemoptysis or shortness of breath, or systemic symptoms, including anorexia or weight loss, greatly increase the likelihood of having lung cancer. Referral to a multidisciplinary lung cancer team, imaging, and confirmation through sputum cytology, thoracentesis, fine-needle aspiration, or mediastinoscopy are recommended. If lung cancer is confirmed, treatment options vary based on staging, histology, immunotherapy biomarker testing, and patient health status. Treatments include surgical resection, immunotherapy, chemotherapy, and/or radiotherapy. Family physicians should focus on primary prevention of lung cancer by encouraging tobacco cessation and early recognition by screening at-risk individuals and following guidelines for pulmonary nodules. As of 2021, the U.S. Preventive Services Task Force recommends annual lung cancer screening using low-dose computed tomography starting at 50 years of age in patients with a 20 pack-year history.

Red meat consumption, cooking mutagens, NAT1/2 genotypes and pancreatic cancer risk in two ethnically diverse prospective cohorts.

There is limited evidence on the association between red meat consumption and pancreatic cancer among ethnic minorities. We assessed this relationship in two large prospective cohorts: the Multiethnic Cohort Study (MEC) and the Southern Community Cohort Study (SCCS). Demographic, dietary and other risk factor data were collected at cohort entry. Red meat intake was assessed using cohort-specific validated food frequency questionnaires. Incident pancreatic cancer cases were identified via linkages to state cancer registries. Cox regression was used to calculate relative risks (RRs) and 95% confidence intervals (CIs) for the association of red meat intake with pancreatic cancer risk in each cohort. We performed additional analyses to evaluate cooking methods, mutagens and effect modification by NAT1/2 genotypes. From a total of 184 542 (MEC) and 66 793 (SCCS) at-risk participants, we identified 1618 (MEC) and 266 (SCCS) incident pancreatic cancer cases. Red meat consumption was associated with pancreatic cancer risk in the MEC (RRQ4vsQ1 1.18, 95% CI 1.02-1.37) and with borderline statistical significance in the SCCS (RRQ4vsQ1 1.31, 95% CI 0.93-1.86). This association was significant in African Americans (RRQ4vsQ1 1.49, 95% CI 1.06-2.11) and Latinos (RRQ4vsQ1 1.44, 95% CI 1.02-2.04) in the MEC, and among African Americans (RRQ4vsQ1 1.55, 95% CI 1.03-2.33) in the SCCS. NAT2 genotypes appeared to modify the relationship between red meat and pancreatic cancer in the MEC (pinteraction = 0.03). Our findings suggest that the associations for red meat may be strongest in African Americans and Latinos. The mechanisms underlying the increased risk for these populations should be further investigated.

Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score.

OBJECTIVE: Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19. METHODS: A retrospective cohort study of adult visits for suspected COVID-19 between March 1 - April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses. RESULTS: 26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880-0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874-0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia (n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759-0.801) and 0.832 (validation, 95% CI 0.794-0.870), while for COVID-19 diagnoses (n = 2059) the AUCs were 0.867 (95% CI 0.843-0.892) and 0.837 (95% CI 0.774-0.899) respectively. CONCLUSION: Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

The Fragility Index of Hip Arthroscopy Randomized Controlled Trials: A Systematic Survey.

PURPOSE: To characterize the fragility index (FI) of statistically significant results from randomized controlled trials (RCTs) in hip arthroscopy. METHODS: The PubMed-MEDLINE, Embase, and Cochrane databases were queried for hip arthroscopy RCTs published between January 2010 and July 2020. RCTs were included if they contained only 2 treatment arms, randomized patients to a 1:1 allocation to each arm, and reported at least 1 statistically significant dichotomous outcome. The fragility quotient was calculated for each RCT by dividing the FI by the sample size. Smaller FIs indicated more fragile results. Risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials. RESULTS: We identified 8 hip arthroscopy RCTs that met all inclusion and exclusion criteria. Most of the studies were assessed to have an overall low risk of bias. In the 2 studies with a moderate risk of bias and 1 study with a high risk of bias, concerns were raised about high rates of crossover and loss to follow-up. The median FI was 4, with FIs ranging from 0 to 14, but half of the studies had an FI of 2 or less. In 4 of the 8 studies, the number of patients lost to follow-up was greater than the FI. CONCLUSIONS: A systematic survey of hip arthroscopy RCTs resulted in a low FI, indicating that the findings tended to be fragile. A low FI was consistent with findings reported in other orthopaedic and medical literature. Given these results, there is a possibility for findings to be altered by factors such as loss to follow-up, measurement subjectivity, crossover, and biased study design. Results on the fragility of hip arthroscopy RCTs were similar to those reported in general or orthopaedic-specific literature. LEVEL OF EVIDENCE: Level II, systematic review of Level I and II studies.

New-Onset Diabetes, Longitudinal Trends in Metabolic Markers, and Risk of Pancreatic Cancer in a Heterogeneous Population.

BACKGROUND & AIMS: Observational studies of predominantly white populations have found new-onset diabetes to be associated with increased risk of pancreatic cancer. We sought to determine whether this relationship applies to other races or ethnicities and to identify metabolic profiles associated with increased risk of pancreatic cancer. METHODS: We conducted a population-based cohort study of Asian, black, Hispanic and white patients from Kaiser Permanente Southern California from 2006 through 2016 (n = 1,499,627). Patients with diabetes were identified based on glucose and hemoglobin A1c (HbA1c) measurements. We used Cox regression to assess the relationship between diabetes status and duration and pancreatic cancer. For patients with recent diagnoses of diabetes (1 year or less) we compared longitudinal changes in glucose, HbA1c, and weight, from time of diabetes diagnosis through 3 years prior to the diagnosis, in patients with vs without pancreatic cancer. RESULTS: We identified 2,002 incident cases of pancreatic cancer from nearly 7.5 million person-years of follow-up. Compared to patients without diabetes, individuals who received a recent diagnosis of diabetes had an almost 7-fold increase in risk of pancreatic cancer (relative risk, 6.91; 95% CI, 5.76-8.30). Among patients with a recent diagnosis of diabetes, those who developed pancreatic cancer had more rapid increases in levels of glucose (Δslope: cases, 37.47 mg/dL vs non-cases, 27.68 mg/dL) and HbA1c (Δslope: cases, 1.39% vs non-cases, 0.86%) in the month preceding the diagnosis of diabetes, and subtle weight loss in the prior years (slope: cases -0.18 kg/interval vs non-cases 0.33 kg/interval). These longitudinal changes in markers of metabolism were stronger for specific race and ethnic groups. CONCLUSIONS: In a study of a large ethnically diverse population, we found risk of pancreatic cancer to be increased among patients with a diagnosis of diabetes in the past year among different races and ethnicities. Weight loss and rapid development of poor glycemic control were associated with increased risk of pancreatic cancer in multiple races.

Antitumor efficacy of BAFF-R targeting CAR T cells manufactured under clinic-ready conditions.

B-cell malignancies can potentially be cured by CD19 chimeric antigen receptor (CAR) T-cell therapy. Although clinical response rates can be up to 93% in acute lymphoblastic leukemia, treatment-related antigen loss and lack of therapeutic persistence contribute to disease relapse. These shortcomings of current CAR T-cell therapy indicate the need for biologically relevant target selection and for improving the efficacy and persistence of the CAR T cells, which we have addressed by developing a novel B-cell activating factor receptor (BAFF-R) CAR T-cell therapy with improved therapeutic persistence. BAFF-R is a B-cell survival receptor and highly expressed in B-cell malignancies. We developed a prototype CAR T cell that efficiently and specifically eliminated BAFF-R expressing human B-cell tumors in several xenogeneic mouse models, including models of CD19 antigen loss. We proceeded with translational development and validation of BAFF-R CAR T cells produced under current good manufacturing practices (cGMP). cGMP-grade BAFF-R CAR T cells underwent in vitro and in vivo validation in established models to confirm that the potency and efficacy of our original research modeling was replicated. Food and Drug Administration required release testing was performed to ensure our BAFF-R CAR T cells meet specifications for new drug products. Completing and exceeding these requirements, the data fully support the initiation of a first-in-human Phase 1 trial for BAFF-R-positive relapsed/refractory (r/r) B-ALL.

Intracellular production of hydrogels and synthetic RNA granules by multivalent molecular interactions.

Some protein components of intracellular non-membrane-bound entities, such as RNA granules, are known to form hydrogels in vitro. The physico-chemical properties and functional role of these intracellular hydrogels are difficult to study, primarily due to technical challenges in probing these materials in situ. Here, we present iPOLYMER, a strategy for a rapid induction of protein-based hydrogels inside living cells that explores the chemically inducible dimerization paradigm. Biochemical and biophysical characterizations aided by computational modelling show that the polymer network formed in the cytosol resembles a physiological hydrogel-like entity that acts as a size-dependent molecular sieve. We functionalize these polymers with RNA-binding motifs that sequester polyadenine-containing nucleotides to synthetically mimic RNA granules. These results show that iPOLYMER can be used to synthetically reconstitute the nucleation of biologically functional entities, including RNA granules in intact cells.

Mule determines the apoptotic response to HDAC inhibitors by targeted ubiquitination and destruction of HDAC2.

Histone deacetylases (HDACs) are major epigenetic modulators involved in a broad spectrum of human diseases including cancers. Administration of HDAC inhibitors (HDACis) leads to growth inhibition, differentiation, and apoptosis of cancer cells. Understanding the regulatory mechanism of HDACs is imperative to harness the therapeutic potentials of HDACis. Here we show that HDACi- and DNA damage-induced apoptosis are severely compromised in mouse embryonic fibroblasts lacking a HECT domain ubiquitin ligase, Mule (Mcl-1 ubiquitin ligase E3). Mule specifically targets HDAC2 for ubiquitination and degradation. Accumulation of HDAC2 in Mule-deficient cells leads to compromised p53 acetylation as well as crippled p53 transcriptional activation, accumulation, and apoptotic response upon DNA damage and Nutlin-3 treatments. These defects in Mule-null cells can be partially reversed by HDACis and fully rescued by lowering the elevated HDAC2 in Mule-null cells to the normal levels as in wild-type cells. Taken together, our results reveal a critical regulatory mechanism of HDAC2 by Mule and suggest this pathway determines the cellular response to HDACis and DNA damage.

Atopic allergic conditions and pancreatic cancer risk: Results from the Multiethnic Cohort Study.

Previous case-control studies have suggested that atopic allergic conditions (AACs) are inversely associated with pancreatic cancer, but this relationship has not been supported in many prospective settings. In this study, we investigated the influence of AACs (asthma, hay fever, or allergy) and the treatment of these conditions on pancreatic cancer risk among participants of the Multiethnic Cohort Study (MEC). AACs and antihistamine use were assessed via a baseline questionnaire when participants joined the MEC in 1993-1996. Risk ratios (RRs) and 95% confidence intervals (CIs) for pancreatic cancer incidence by AACs and antihistamines were calculated using Cox regression, adjusting for age, sex, ethnicity, education, smoking status, family history of pancreatic cancer, body mass index, diabetes, and alcohol intake. We further evaluated associations among subgroups defined by age, sex, ethnicity, follow-up time, and known pancreatic cancer risk factors. During an average 16-year follow-up, 1,455 incident cases of pancreatic cancer were identified among 187,226 white, African American, Latino, Japanese American, and Native Hawaiian men and women. AACs (RR 1.00, 95% CI 0.88-1.12) and antihistamines (RR 0.92, 95% CI 0.78-1.07) were not clearly associated with pancreatic cancer incidence. While these associations were also null for most subgroups, we did observe protective associations of AACs (RR 0.74, 95% CI 0.56-0.98) and antihistamines (RR 0.66, 95% CI 0.45-0.96) among the oldest participants (70+). Our results, in agreement with past prospective studies, suggest that AACs are not associated with pancreatic cancer in general, but the observed protective associations among the oldest age group may warrant future investigation.