OP0203 GENE-DIET INTERACTION ON THE RISK OF INCIDENT GOUT AMONG WOMEN – PROSPECTIVE COHORT STUDY OVER 32 YEARS

Background:

Although gout is conventionally known as a male condition, the recent Global Burden of Disease (GBD) Study found disproportionate worsening among women.1 We have found Dietary Approaches to Stop Hypertension (DASH) diet is independently associated with a lower risk of incident gout, while Western diet is associated with increased risk.2 Whether these risks vary according to genetic risk remains unknown.

Objectives:

To investigate the influence of genetic predisposition on the relation between diets (one protective and another hazardous) and gout risk in a large prospective US cohort of women over 32 years.

Methods:

We examined the role of genes on the association between two dietary patterns (DASH and Western) on the risk of incident gout in 18,512 women from the Nurses’ Health Study. Using validated food frequency questionnaires, for each participant we derived: 1) DASH score emphasizing fruits, vegetables, nuts, legumes, whole grains, low-fat dairy, and reduced intake of saturated fat and sugar-sweetened beverages (SSBs) and 2) Western diet score characterized by high intake of red and processed meats, SSBs, desserts, French fries, and refined grains. A genetic risk score (GRS) was derived using 114 serum urate single nucleotide polymorphisms from the latest GWAS consortium.3

Results:

There were 523 incident gout cases meeting ACR survey criteria4 (170 vs. 353 in GRS below and above the mean, respectively) (Table 1). Among women with GRS below and above the mean, the multivariable relative risks (RRs) of gout were 1.0, 1.56. 1.32, 0.89, and 0.61 (0.34 to 1.09) and 1.0, 1.0, 0.85, 0.51, and 0.68 (0.49 to 0.96), for quintiles (Q) 1 through 5 of DASH score, respectively (p for interaction = 0.69) (Table 1). For the Western diet, RRs for Q1 through 5 were 1, 1.34, 1.07, 1.33, and 1.63 (0.91 to 2.93) for those with GRS below the mean and 1.0, 1.17, 0.93, 1.27, and 1.77 (1.19 to 2.61) among those with GRS above the mean, respectively (p for multiplicative interaction = 0.64).

Table 1.

Relative Risk of Gout by Quintiles of DASH and Western Diet Score, Stratified by Mean GRS

DASHBelow MeanAbove MeanQ1Q2Q3Q4Q5Q1Q2Q3Q4Q5P InteractionNo. Cases27495121227589903465Person-Years39208472475722734953587643981545853554013473356521Age-Adjusted RR1.0 (ref)1.43 (0.89, 2.29)1.22 (0.76, 1.96)0.8 (0.45, 1.42)0.5 (0.28, 0.88)1.0 (ref)0.97 (0.72, 1.33)0.79 (0.58, 1.07)0.47 (0.31, 0.70)0.54 (0.39, 0.76)0.73MV-Adjusted* RR1.0 (ref)1.56 (0.97, 2.51)1.32 (0.82, 2.12)0.89 (0.50, 1.59)0.61 (0.34, 1.09)1.0 (ref)1.0 (0.73, 1.37)0.85 (0.63, 1.17)0.51 (0.33, 0.76)0.68 (0.49, 0.96)0.69WesternBelow MeanAbove MeanQ1Q2Q3Q4Q5Q1Q2Q3Q4Q5P InteractionNo. Cases21362839465270567699Person-Years47397493484783747589452834552947913473574644785Age-Adjusted RR1.0 (ref)1.49 (0.86, 2.56)1.26 (0.71, 2.23)1.71 (1.00, 2.93)2.22 (1.31, 3.74)1.0 (ref)1.21 (0.85, 1.74)0.98 (0.67, 1.43)1.35 (0.94, 1.93)1.88 (1.34, 2.65)0.72MV-Adjusted* RR1.0 (ref)1.34 (0.78, 2.32)1.07 (0.60, 1.90)1.33 (0.76, 2.34)1.63 (0.91, 2.93)1.0 (ref)1.17 (0.81, 1.68)0.93 (0.63, 1.38)1.27 (0.87, 1.84)1.77 (1.19, 2.61)0.64

*Adjusted for age (continuous), menopause, use of hormone therapy (never, past or current), history of hypertension, systolic and diastolic blood pressure (continuous), alcohol (continuous), total energy intake (continuous), and intake of meat, seafood, and dairy foods (continuous).

Conclusion:

In this prospective female cohort that ascertained gout with standardized criteria over 32 years, regardless of genetic predisposition, DASH diet was similarly associated with lower risk of incident gout while Western diet was associated with a higher risk. The anticipated absolute impact of diet among genetically predisposed females was larger with greater absolute risk difference. These data agree with the recent GBD Study’s recommendation for intensive dietary and anti-obesity measures for gout prevention, especially in females.1

References:

[1]Xia et al., PMID 31624843

[2]Keller et al., PMID: 28487277

[3]Tin et al., PMID 31578528

[4]Wallace et al., PMID: 856219

Acknowledgements:

The authors thank the participants of the NHS.

CY is supported by the Rheumatology Research Foundation Scientist Development Award and NIH T32 AR007258. HC is supported by NIH P50AR060772 and R01AR065944.

Disclosure of Interests:

None declared

POS1124 IDENTIFYING POTENTIAL CLASSIFICATION CRITERIA FOR CALCIUM PYROPHOSPHATE DEPOSITION DISEASE (CPPD): RESULTS FROM THE INITIAL PHASES

Background:

Classification criteria for calcium pyrophosphate deposition disease (CPPD) will facilitate clinical research on this common crystalline arthritis. ACR/EULAR are jointly sponsoring development of CPPD classification criteria using a multi-phase process.

Objectives:

To report preliminary results from the first two phases of a four-phase process for developing CPPD classification criteria.

Methods:

CPPD classification criteria development is overseen by a 12-member Steering Committee. Item generation (Phase I) included a scoping literature review of five literature databases and contributions from a 35-member Combined Expert Committee and two Patient Research Partners. Item reduction and refinement (Phase II) involved a Combined Expert Committee meeting, discussions among Clinical, Imaging, and Laboratory Advisory Groups, and an item rating exercise to assess the influence of individual items toward classification. The Steering Committee reviewed the modal rating score for each item (range -3 [strongly pushes away from CPPD] to +3 [strongly pushes toward CPPD]) to determine items to retain for future phases of criteria development.

Results:

Item generation yielded 420 items (312 from the literature, 108 from experts/patients). The Advisory Groups eliminated items they agreed were unlikely to distinguish between CPPD and other forms of arthritis, yielding 127 items for the item rating exercise. Fifty-six items, most of which had a modal rating of +/- 2 or 3, were retained for future phases (see Table 1). As numerous imaging items were rated +3, the Steering Committee recommended focusing on imaging of the knee, wrist, and one additional affected joint for calcification suggestive of CPP crystal deposition.

Conclusion:

The ACR/EULAR CPPD classification criteria working group has adopted both data- and expert-driven approaches, leading to 56 candidate items broadly categorized as clinical, imaging, and laboratory features. Remaining steps for criteria development include domain establishment, item weighting through a multi-criteria decision analysis exercise, threshold score determination, and criteria validation.

Table 1.

Categories of items retained for future phases of classification criteria development

Age in decade at symptom onsetAcute inflammatory arthritis (e.g. knee, wrist, 1st MTP joint*)Recurrence and pattern of joint involvement (e.g. 1 self-limited episode, >1 self-limited episode)Physical findings (e.g. palpable subcutaneous tophus*, psoriasis*)Co-morbidities and family history (e.g. Gitelman disease, hemochromatosis, familial CPPD)Osteoarthritis location and features (e.g. 2nd or 3rd MCP joint, wrist)Synovial fluid findings (e.g. CPP crystals present, CPP crystals absent on 1 occasion* or 2 occasions*, monosodium urate crystals present*)Laboratory findings (e.g. hypomagnesemia, hyperparathyroidism, rheumatoid factor*, anti-CCP*)Plain radiograph: calcification in regions of fibro- or hyaline cartilage+Plain radiograph: calcification of the synovial membrane/capsule/tendon+Conventional CT: calcification in regions of fibro- or hyaline cartilage+Conventional CT: calcification of the synovial membrane/capsule/tendon+Ultrasound: CPP crystal deposition in fibro- or hyaline cartilage+Ultrasound: CPP crystal deposition in synovial membrane/capsule/tendons+Dual-energy CT: CPP crystal deposition in fibro- or hyaline cartilage+Dual-energy CT: CPP crystal deposition in synovial membrane/capsule/tendon+

*Potential negative predictor +Assessed in the knee, wrist, and/or 1 additional affected joint

Disclosure of Interests:

Sara Tedeschi Consultant of: NGM Biopharmaceuticals, Tristan Pascart: None declared, Augustin Latourte Consultant of: Novartis, Cattleya Godsave: None declared, Burak Kundaki: None declared, Raymond Naden: None declared, William Taylor: None declared, Nicola Dalbeth Speakers bureau: Abbvie and Janssen, Consultant of: AstraZeneca, Dyve, Selecta, Horizon, Arthrosi, and Cello Health, Tuhina Neogi: None declared, Fernando Perez-Ruiz: None declared, Ann Rosenthal: None declared, Fabio Becce Consultant of: Horizon Therapeutics, Grant/research support from: Siemens Healthineers, Eliseo Pascual: None declared, Mariano Andrés: None declared, Thomas Bardin: None declared, Michael Doherty: None declared, Hang Korng Ea: None declared, Georgios Filippou: None declared, John FitzGerald: None declared, Marwin Gutierrez: None declared, Annamaria Iagnocco: None declared, Tim Jansen Speakers bureau: Abbvie, Amgen, BMS, Grunenthal, Olatec, Sanofi Genzyme, Consultant of: Abbvie, Amgen, BMS, Grunenthal, Olatec, Sanofi Genzyme, Minna Kohler Speakers bureau: Lilly, Consultant of: Novartis, Frederic Lioté: None declared, Mark Matza: None declared, Geraldine McCarthy Consultant of: PK Med, Roberta Ramonda: None declared, Anthony Reginato: None declared, Pascal Richette: None declared, Jasvinder Singh Speakers bureau: Simply Speaking, Consultant of: Crealta/Horizon, Medisys, Fidia, UBM LLC, Trio health, Medscape, WebMD, Adept Field Solutions, Clinical Care options, Clearview healthcare partners, Putnam associates, Focus forward, Navigant consulting, Spherix, Practice Point communications, Francisca Sivera: None declared, Alexander So: None declared, Lisa Stamp: None declared, Janeth Yinh: None declared, Chio Yokose: None declared, Robert Terkeltaub Consultant of: Sobi, Horizon Therapeutics, Astra-Zeneca, Selecta, Grant/research support from: Astra-Zeneca, Hyon Choi: None declared, Abhishek Abhishek Consultant of: NGM Biopharmaceuticals.

POS1174 HYPERINFLAMMATION AND CLINICAL OUTCOMES FOR PATIENTS WITH SYSTEMIC RHEUMATIC DISEASES HOSPITALIZED FOR COVID-19: A COMPARATIVE COHORT STUDY

Background:

COVID-19 can induce a hyperinflammatory state resulting in cytokine storm, which can lead to poor outcomes. Patients with systemic rheumatic diseases may be at increased risk for respiratory failure with COVID-19. Therefore, we investigated the relationship between rheumatic disease, hyperinflammation, and clinical outcomes among hospitalized COVID-19 patients.

Objectives:

To compare laboratory values, hyperinflammation, and clinical outcomes of hospitalized COVID-19 rheumatic patients and matched comparators.

Methods:

We performed a comparative cohort study of patients with polymerase chain reaction (PCR)-confirmed COVID-19 requiring hospitalization between 3/1/20-7/7/20 at a large health care system. We compared each systemic rheumatic disease case to up to 5 matched (by age, sex, and date of +SARS-CoV-2 PCR) comparators without systemic rheumatic disease. We extracted laboratory values from their hospitalization to compare peaks/troughs of individual laboratory results by case status and derived the COVID-19-associated hyperinflammation score (cHIS), a composite of 6 laboratory domains (0-6, ≥2 indicating hyperinflammation), as previously developed1. We used multivariable logistic regression to estimate ORs for COVID-19 outcomes by hyperinflammation and case status.

Results:

We identified 57 hospitalized rheumatic disease cases (mean age 67 years, 67% female) and 232 matched comparators hospitalized with PCR-confirmed COVID-19. Among cases, 26 (46%) had rheumatoid arthritis and 14 (25%) had systemic lupus erythematosus. Most cases (34, 60%) had active rheumatic disease. At baseline, 15 (27%) of cases were treated with biologic DMARDs, and 32 (56%) were using glucocorticoids. We analyzed 39,900 total laboratory results (median 85 per patient). Cases had higher peak neutrophil-to-lymphocyte ratio (9.6 vs 7.8, p=0.02), LDH (421 vs 345 U/L, p=0.04), creatinine (1.2 vs 1.0 mg/dL, p=0.01), and BUN (31 vs 23 mg/dL, p=0.03) than comparators but similar peak CRP (149 vs 116 mg/L, p=0.11, Figure 1). Cases had higher peak median cHIS (3 vs 2, p=0.01). Peak cHIS ≥2 had higher odds of intensive care unit (ICU) admission (OR 3.45, 95%CI 1.98-5.99), mechanical ventilation (OR 66.0, 95%CI 9.0-487.8), and mortality (OR 16.4, 95%CI 4.8-56.4) compared to cHIS <2 (Table 1). Cases had increased risk of ICU admission (OR 2.0, 95%CI 1.1-3.7) and mechanical ventilation (OR 2.7, 95%CI 1.4-5.2) than comparators.

Table 1.

Associations of peak cHIS and systemic rheumatic disease with COVID-19 hospitalization outcomes

Intensive care unit admissionMechanical ventilationDeath%Adjusted OR (95%CI)%Adjusted OR (95%CI)%Adjusted OR (95%CI)Hospitalization outcomes by hyperinflammation on cHIS1cHIS <2 (n=112)21%1.0 (Ref)1%1.0 (Ref)3%1.0 (Ref)cHIS ≥2 (n=177)48%3.5 (2.0-6.0)37%66.2 (9.0-487.8)27%16.4 (4.8-56.4)Hospitalization outcomes by rheumatic disease statusComparators (n=232)30%1.0 (Ref)19%1.0 (Ref)16%1.0 (Ref)Rheumatic cases (n=57)51%1.87 (1.03-3.40)39%2.46 (1.30-4.67)21%1.32 (0.61-2.88)

Matching factors: age, sex, and date of +PCR.

1Adjusted for age, sex, and case status.

2Adjusted for race, smoking, comorbidities, and body mass index.

cHIS, COVID-19-associated hyperinflammation score; CI, confidence interval; OR, odds ratio; PCR, polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Conclusion:

Patients with systemic rheumatic disease hospitalized for COVID-19 had higher risk for hyperinflammation, kidney injury, and mechanical ventilation than non-rheumatic comparators. We validated the cHIS in our cohort, which was strongly associated with poor COVID-19 outcomes. These findings highlight that hospitalized patients with rheumatic diseases may be vulnerable to poor COVID-19 outcomes.

References:

[1]Webb BJ et al. Clinical criteria for COVID-19-associated hyperinflammatory syndrome. Lancet Rheumatol. 2020 Dec;2(12):e754-e763.

Disclosure of Interests:

Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Jiaqi Wang: None declared, Alisa Mueller: None declared, Xiaoqing Fu: None declared, Lauren Prisco: None declared, Lily Martin: None declared, Kathleen Vanni: None declared, Alessandra Zaccardelli: None declared, Claire Cook: None declared, Hyon Choi Consultant of: Dr. Choi reports consultancy fees from Takeda, Selecta, GlaxoSmithKline, and Horizon, Grant/research support from: Dr. Choi reports research support from AstraZeneca., Yuqing Zhang: None declared, Ellen Gravallese: None declared, Zachary Wallace Consultant of: Dr. Wallace reports consulting fees from Viela Bio and MedPace., Grant/research support from: Dr. Wallace reports research support from Bristol-Myers Squibb and Principia., Jeffrey Sparks Consultant of: Dr. Sparks reports consultancy fees from Bristol-Myers Squibb, Gilead, Inova, Janssen, Optum, and Pfizer., Grant/research support from: Dr. Sparks reports research support from Amgen and Bristol-Myers Squibb.

Effects of anti-resorptive drugs on implant survival and peri-implantitis in patients with existing osseointegrated dental implants: a retrospective cohort study

The effect of anti-resorptive drug (ARD) usage among patients with successful dental implant osseointegration is controversial. This study showed an increased risk of implant failure in ARD users. Risk factors included pre-existing marginal bone loss, overdenture, diabetes, and a short interval between implant placement and ARD administration.

INTRODUCTION

This retrospective study aimed to determine whether anti-resorptive drug (ARD) usage increased risk of implant failure among patients with successful implant osseointegration. Additionally, the study investigated risk factors that affected implant survival rate in ARD users.

METHODS

Eighty ARD users with 344 implants who had more than 12 months of follow-up from the initiation of ARD treatment during the period between 2008 and 2017 were included, along with 80 non-ARD users from the same period. The primary outcome was dental implant survival. Kaplan-Meier survival curves and Cox proportional hazard models were used for survival analysis.

RESULTS

Average follow-up was 85.3 months. Implant survival rates were 89.83% in ARD users and 96.03% in non-ARD users. In the univariate Cox proportional hazard model, risk of implant failure was significantly higher in patients with pre-existing marginal bone loss (MBL), diabetes, and concurrent bone augmentation. However, risk of implant failure was significantly lower when the interval between implant placement and ARD administration was < 36 months. Compared with overdenture, single crown and fixed splinted users had lower risk of implant failure. In multivariate analysis, variables including pre-existing MBL, diabetes, < 36-month interval between implant placement and ARD treatment, and usage of fixed splinted prosthesis were significantly associated with increased risk of implant failure.

CONCLUSIONS

ARD administration after implant osseointegration was correlated with a reduced implant survival rate. Pre-existing MBL, diabetes, type of final prosthesis, and the interval between implant placement and initiation of ARD administration influenced risk of implant failure.

Long-Term Exposure to Urban Particulate Matter on the Ocular Surface and the Incidence of Deleterious Changes in the Cornea, Conjunctiva and Retina in Rats

We investigated the time-dependent deleterious ocular changes induced by urban particulate matter (UPM) in vitro and in vivo. UPM treatment decreased human corneal epithelial cell migration and survival. Fluorescein scores were consistently increased by UPM application for 16 weeks. One week of rest at 2 or 4 weeks led to a recovery trend, whereas two weeks of rest at 8 weeks induced no change. UPM treatment decreased the tear film break-up time at 2 weeks, which was thereafter maintained until 16 weeks. No changes were found after periods of rest. UPM-treated eyes exhibited greater corneal epithelium thickness than normal eyes at 2 weeks, which recovered to normal at 4 and 8 weeks and was significantly decreased at 16 weeks. Apoptotic cell number in the epithelium was increased at 2 weeks, which remained constant except at 8 weeks. IL-6 expression in the cornea of the right eye continually increased for 16 weeks, and significant recovery was only observed at 8 weeks after 2 weeks of rest. Ocular pressure was significantly increased in the right eye at 12 and 16 weeks. Topical UPM application to the eye induced deleterious changes to various closely related parts of the eye.

FRI0355 IMPACT OF BIOLOGIC THERAPY ON THE INCIDENCE OF PSA AMONG PATIENTS WITH PSORIASIS

Background:

One of the strongest known risk factors for the development of psoriatic arthritis (PsA) is psoriasis. A key question is whether treatment of psoriasis may prevent or delay onset of PsA.

Objectives:

To compare the incidence of PsA among patients with psoriasis treated with a biologic compared to those treated with a non-biologic therapy for psoriasis

Methods:

We performed a retrospective cohort study in the Optum de-identified Electronic Health Record dataset between 2006-2017. Patients with two or more ICD codes for psoriasis between the ages of 16 and 90, who were initiating an oral medication, a biologic therapy, or phototherapy (defined as no preceding codes for the therapy in the prior 12 months) were identified. Covariates at baseline were determined in the 12 months prior to therapy initiation. The outcome of interest was PsA as defined by one ICD code. The incidence of PsA was described overall and within each therapy group. We analyzed the data in two ways: a) a multivariable Cox model using a time varying exposure (once the patient was exposed to a biologic, they were considered always exposed) derived from automated stepwise regression and b) propensity score matching (greedy matching, caliper 0.1) between biologic-exposed patients and oral/phototherapy exposed patients.

Results:

Among 215,386 patients with psoriasis without PsA at baseline, 9,848 were excluded for prior biologic exposure, and among the remaining, 60,258 initiated phototherapy, oral or biologic therapy during follow up. Among 22,461 new biologic initiations, 29,121 oral therapy and 8,676 phototherapy initiations, the mean age was lower in the biologics group compared to the non-biologic groups (46.9 vs 50.8), with a similar proportion of females and Caucasians. Observational time was also similar. A total of 1,643, 1,813, and 122 new PsA cases occurred over 60,739, 85,670, and 28,528 person/years (PY) of follow up, respectively (incidence 27.1, 21.2 and 4.2 per 1,000 person years respectively). Using a traditional multivariable adjustment approach with time varying exposure, the age and sex adjusted and fully adjusted HR (95% CI) for biologic users were 1.01 (0.99-1.04) and 0.93 (0.91-0.95), respectively. However, after propensity score matching, the HR (95% CI) was 1.64 (1.51-1.77). Survival curves cross, however, at approximately 8 years (Figure 1) and most of the new diagnoses of PsA occurred shortly after therapy initiation (Figure 2).

Conclusion:

Confounding by indication or protopathic bias may explain the observed association of biologic therapy with the development of PsA among patients with psoriasis. Some patients may be receiving therapy because they have both psoriasis and early symptoms of PsA or their PsA diagnosis is not recorded appropriately. Given the directional discrepancy in the results between traditional modeling and propensity score analysis, further work is needed to understand the nature of this relationship.

Figure

Figure 3.

Directed Acyclic Graphdescribing potential confounders in relationship between therapy prescription and diagnosis of PsA

Disclosure of Interests:

Alexis Ogdie Grant/research support from: Pfizer, Novartis, Consultant of: Abbvie, Amgen, BMS, Celgene, Corrona, Janssen, Lilly, Pfizer, Novartis, Thorvardur Love: None declared, Junko Takeshita: None declared, Joel Gelfand Grant/research support from: grants (to the Trustees of the University of Pennsylvania) from Abbvie, Boehringer Ingelheim, Janssen, Novartis Corp, Celgene, Ortho Dermatologics, and Pfizer Inc., Consultant of: BMS, Boehringer Ingelheim, Janssen Biologics, Novartis Corp, UCB (DSMB), Neuroderm (DSMB), Dr. Reddy’s Labs, Pfizer Inc., and Sun Pharma, Paid instructor for: received payment for continuing medical education work related to psoriasis that was supported indirectly by Lilly, Ortho Dermatologics and Novartis., Jose Scher Consultant of: Novartis, Janssen, UCB, Sanofi., Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood, Robert Fitzsimmons: None declared, Christopher T. Ritchlin Grant/research support from: UCB Pharma, AbbVie, Amgen, Consultant of: UCB Pharma, Amgen, AbbVie, Lilly, Pfizer, Novartis, Gilead, Janssen, Joseph F. Merola Consultant of: Merck, AbbVie, Dermavant, Eli Lilly, Novartis, Janssen, UCB Pharma, Celgene, Sanofi, Regeneron, Arena, Sun Pharma, Biogen, Pfizer, EMD Sorono, Avotres and LEO Pharma

THU0020 NO CAUSAL ASSOCIATION OF SERUM URATE OR GOUT WITH ALZHEIMER’S DISEASE: A MENDELIAN RANDOMIZATION ANALYSIS

Background:

Several epidmiologic studies have found a lower risk of Alzheimer’s disease (AD) among individuals with a history of gout1or high serum urate levels2, which are the precursor to gout. Serum urate may have neuroprotective benefits for AD, however it is possible that reverse causation and residual confounding could explain the observational evidence.

Objectives:

To study the causal associations of serum urate and gout with Alzheimer’s disease using Mendelian Randomization (MR) methods.

Methods:

Two-sample MR was performed to examine the causality of: 1) serum urate on Alzheimer’s disease and 2) gout on Alzheimer’s disease. Single nucleotide polymorphisms (SNP) identified from a genome-wide association study of 457,690 adults described 183 SNPs associated with serum urate and gout, which were used as instrumental variables3. Additional single-SNP analyses were conducted using SNPs from three genes identified as major determinants of urate levels (SLC2A9, SLC22A12, and ABCG2). SNPs for AD came from the International Genomics of Alzheimer’s Project, comprised of 35,274 AD cases and 59,163 cognitively normal elderly controls4. Inverse-variance weighted (IVW) models were the primary method used to examine the associations between each exposure and risk of AD. Additional analyses examined the potential impact of pleiotropy via MR-Egger models. Single-SNP analyses used the Wald ratio. All analyses were performed using R.

Results:

There was no evidence of a causal association between genetically-determined serum urate or gout and risk of AD from IVW analyses (both p>0.1) (Table 1). MR-Egger analyses yielded similar estimates (both p>0.1) and the intercepts of the MR-Egger regressions did not suggest the presence of directional pleiotropy (p=0.64 for serum urate exposure and p=0.98 for gout exposure) (Table 1). Additionally, none of the three individual SNPs were significantly associated with risk of AD (all p>0.05) (Table 2).

Table 1.

Association of combined SNPs for serum urate and gout with Alzheimer’s disease

Number of SNPsOR95% CIp valueMR-Egger intercept (p value)Serum urate exposure (per 1 mg/dL increase)IVW1581.040.98-1.110.187MR-Egger1581.060.96-1.170.228-0.001 (0.645)Gout exposure (gout vs. non-gout)IVW1581.030.99-1.060.161MR-Egger1581.030.98-1.070.2905.477 (0.976)Table 2.

Association of individual SNPs for serum urate and gout with Alzheimer’s disease

GeneSNPOR95% CIp-valueSerum urate exposure (per 1 mg/dL increase)SLC2A9rs37759471.121.00-1.260.059SLC22A12rs5317630.920.71-1.200.545ABDG2rs749049711.220.99-1.500.059Gout exposure (gout vs. non-gout)SLC2A9rs37759471.081.00-1.170.059SLC22A12rs5317630.940.77-1.150.545ABCG2rs749049711.061.00-1.130.059

Conclusion:

Using both serum urate and gout as instrumental variables in MR analysis, these findings suggest that serum urate and gout are not causal determinants for the development of AD. The inverse associations described in observational studies may in part be due to confounding or reverse causality.

References:

[1]Lu N, et al. Gout and the risk of Alzheimer’s disease: a population-based, BMI-matched cohort study.Annals of the Rheumatic Diseases, 2016.doi:10.1136/annrheumdis-2014-206917

[2]Scheepers LEJM, et al. Urate and risk of Alzheimer’s disease and vascular dementia: A population-based study.Alzheimer’s & Dementia, 2019.doi:10.1016/j.jalz.2019.01.014

[3]Tin A, et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.Nature Genetics, 2019.doi:10.1038/s41588-019-0504-x

[4]Kunkle BW, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, Tau, immunity and lipid processing.Nature Genetics, 2019. doi: 10.1038/s41588-019-0358-2

Disclosure of Interests:

Shelby Marozoff: None declared, Natalie McCormick: None declared, Jeewoong Choi: None declared, Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood

OP0015 SEX-SPECIFIC U-SHAPED RELATIONSHIP BETWEEN SERUM URATE AND MORTALITY IN THE UNITED STATES GENERAL POPULATION

Background:

A U-shaped relationship may exist between serum urate (SU) and cardiovascular events, although the relationship between SU and mortality is unclear. The most recent EULAR recommendations for gout advise against maintaining SU <3 mg/dl for prolonged periods.

Objectives:

To examine the relationship between SU and all-cause and cause-specific mortality in large cohorts in the United States (US).

Methods:

We examined National Health and Nutrition Examination Survey (NHANES) data from 1988-1994 (NHANES III) and 1999-2007 including subjects aged ≥18 with an enrollment SU measurement. We used Cox proportional hazards regression models to estimate sex-specific mortality risk relative to a referent SU 5-6 mg/dL, adjusting for NHANES cycle, age, race, body mass index (BMI), education, alcohol use, smoking, hypertension, total cholesterol, estimated glomerular filtration rate (GFR), and competing risks, using age as a time scale for survival analysis.

Results:

Among 19,954 men and 21,853 women, there were 5,714 male deaths and 4,901 female deaths (median follow-up 14.2 ± 6.9 years). Among men, there was a 33% increased all-cause mortality risk at SU <4 mg/dL (HR 1.33, 95% CI 1.17-1.51) and 52% increased all-cause mortality risk at SU >8 mg/dL (HR 1.52, 95% CI 1.37-1.69) compared to subjects with SU 5-6 mg/dL, driven by cause-specific mortality from diabetes at low SU and chronic lower respiratory diseases and cardiovascular disease at high SU (Table). In women, there was no increased mortality risk at low SU and a 45% increased all-cause mortality risk at SU >7 mg/dL (HR 1.45, CI 1.31-1.61) compared to subjects with SU 5-6 mg/dL, driven by cause-specific mortality from diabetes. Mortality from Alzheimer’s disease was lower at high SU among men (HR 0.23, 95% CI 0.05-0.99) and women (HR 0.54, 95% CI 0.25-1.15).

Table.

Multivariable hazard ratios for all-cause and cause-specific mortality in NHANES III and 1999-2007.

Conclusion:

In large cohorts representative of the US population, there was a U-shaped relationship between SU and all-cause mortality in men but not women. In men with low SU, mortality was driven primarily by diabetes, which may be explained by the uricosuric effect of uncontrolled hyperglycemia in diabetes patients. The lower mortality from Alzheimer’s disease at high SU agrees with previously shown inverse associations between gout and Alzheimer’s disease. Further studies are needed to determine the presence of causality underlying these associations.

Disclosure of Interests:

Kristin D’Silva: None declared, Chio Yokose: None declared, Leo Lu: None declared, Yuqing Zhang: None declared, Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood

SAT0604 FAST FOOD HABITS AND SERUM URATE CHANGE IN YOUNG ADULTS: 15-YEAR PROSPECTIVE ANALYSIS

Background:

Fast food consumption has strong positive associations with weight gain and insulin resistance.1Obesity and insulin resistance are in turn strongly associated with elevated serum urate (SU) levels, largely mediated by insulin’s anti-uricosuric ability.2

Objectives:

To investigate the relation between fast food consumption and changes in SU over a 15-year period among young black and white adults in the United States.

Methods:

Participants for the CARDIA study included 3,122 young (age 18-30 years in 1985-86) black and white adults in the United States who were followed up with repeated dietary and clinical assessments and had both baseline and year 15 SU measurement available. Frequency of fast food consumption (fast food frequency, FFF) was quantified on a semicontinuous scale and classified as <1, 1-2, or >2 times per week. We used multivariable linear regression models to investigate the association of FFF at baseline as well as change in FFF with 15-year changes in SU.

Results:

Our analysis included data from 3,122 subjects who had SU data available both at baseline and year 15 (Table 1). After adjustment for age, sex, education, baseline height and weight, and baseline SU, baseline FFF (defined as 3 times per week year 0 differences between participants) was independently associated with increases in SU among both black (beta=0.11, p=0.04) and white (beta=0.11, p=0.01) individuals (Table 2). Change in FFF (defined as 3 times a week 15-year change within participants) was also independently associated with increases in SU among white (beta=0.09, p=0.01) individuals but not blacks (beta=0.03, p=0.93) (Table 2). There was a significant correlation between weight change and SU change (correlation coefficient 0.34, p<0.001).Figure 1depicts the joint associations of year 0 FFF and 15-year changes in FFF with change in weight. Compared to the average 15-year SU change among participants with baseline FFF <1 time per week and 15-year FFF change <0 time per week, those with high FFF at both baseline and follow-up had an extra 0.21 mg/dL increase (i.e., 75% of overall population SU increase over 15 years [0.28 mg/dL]) in SU during that time. After adjusting for covariates in model 2, change in weight (beta=0.03, p<0.001) and homeostasis model for insulin resistance (HOMA) (beta=0.05, p<0.001) remained significantly associated with SU change.

Table 1.

Participant Characteristics

CharacteristicBlacks (n=1468)Whites (n=1654)Age, years (year 0)24.4 (3.8)25.6 (3.3)Male (%)4448Weight, kg (year 0)72.8 (16.7)70.0 (14.0)Weight, kg (year 15)87.9 (20.9)80.7 (18.6)Serum urate, mg/dL (year 0)5.1 (1.4)5.4 (1.4)Serum urate, mg/dL (year 15)5.6 (1.4)5.5 (1.4)

All values reported as mean (SD) unless otherwise noted.

Table 2.

Mean Adjusted Change in Serum Urate by Baseline and Change in Fast Food Frequency

Fast Food VariableBlacksWhitesBeta (SE)pBeta (SE)pModel 1Baseline0.11 (0.04)0.010.11 (0.04)0.01Change0.003 (0.033)0.930.09 (0.04)0.01Model 2Baseline0.12 (0.04)0.010.09 (0.04)0.02Change0.004 (0.03)0.880.08 (0.04)0.03

Model 1: age, sex, education, baseline height and weight, baseline SU

Model 2: model 1 + alcohol, physical activity, and smoking (both baseline and year 15 change)

Conclusion:

Fast-food consumption has strong positive associations with SU, suggesting that fast food increases the risk of hyperuricemia and gout. The observed association is likely mediated by weight gain and resultant changes in insulin resistance.

References:

[1]Pereira MA, Kartashov AI, Ebbeling CB, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet 2005;365:36-42.

[2]Mount DB MT, Mandal A. Insulin: Genetic and Physiological Influences on Human Uric Acid Homeostasis [abstract]. Arthritis Rheumatol 2018; 70 (suppl 10).

Disclosure of Interests:

Chio Yokose: None declared, Leo Lu: None declared, Natalie McCormick: None declared, Jeewoong Choi: None declared, Yuqing Zhang: None declared, Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood

OP0171 MENDELIAN RANDOMIZATION SHOWS NO CAUSAL ASSOCIATION BETWEEN SERUM URATE OR GOUT AND TYPE-2 DIABETES

Background:

Positive associations between gout1,2or serum urate (SU)3and risk of type-2 diabetes (T2D) have been reported in population-based observational studies, but may be due to residual confounding. As such, causal roles of SU and gout on development of T2D are unclear.

Objectives:

Use two-sample mendelian randomization to estimate the causal effects of SU and gout on T2D and glycemic traits.

Methods:

Aggregate data from three large genome-wide association studies were used to identify genetic variants (SNPs) associated with the exposures and outcomes. Exposure SNPs were sourced from Global Urate Genetics Consortium (> 140,000 individuals); outcome SNPs sourced from DIAbetes Genetics Replication And Meta-analysis consortium (DIAGRAM; > 34,000 T2D cases and > 114,000 controls) and Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; > 46,000 non-diabetics).

We analysed SNPs associated with SU levels (n=28) and gout (n=6) for associations with T2D and three glycemic traits (insulin resistance, fasting insulin levels, and HbA1c) using inverse variance weighted meta-analysis methods. We also specifically examined two SNPs mapping to theSLC2A9gene, which encodes the GLUT9 transporter (for glucose and urate), estimating Wald ratios for these individual SNPs. Analyses were performed with TwoSampleMR package in R and mRnd power calculator.

Results:

Estimated effects of genetically-determined gout on each of the four outcomes (T2D, insulin resistance, fasting insulin levels, and HbA1c) were small and non-significant (p ≥ 0.18), as were the effects of changes in genetically-determined SU levels (Table).

Although the two SNPs in theSLC2A9gene were strongly associated with SU (rs12498742: R2=2.7%, beta=0.37 per mg/dL, p < 10-700) and gout (rs4475146: odds ratio=0.63, p=4.1x10-26), neither was associated with T2D nor any of the glycemic traits (Table).

Applying R2values ≥ 1.9%, we had ≥ 90% power to detect the increased odds of T2D (OR ≥1.151,3) from observational studies.

All Risk SNPs (meta-analysis)OUTCOMEn SNPsGout (vs. non-gout)Serum urate (per 1 mg/dL increase)Effect size (95% CI)pEffect size (95% CI)pHbA1c (%)450.0046 (-0.0087 to 0.0179)0.50-0.0046 (-0.0275 to 0.0183)0.70Insulin resistance (HOMA-IR: log units)450.0108 (-0.0049 to 0.0265)0.180.0016 (-0.0240 to 0.0272)0.90Fasting insulin levels (log pmol/L)180.0046 (-0.0037 to 0.0129)0.28-0.0221 (-0.1035 to 0.0593)0.59Type 2 Diabetes: odds ratio430.98 (0.90 to 1.07)0.721.01 (0.88 to 1.16)0.84SNPs inSLC29AGene (single-SNP analysis)OUTCOMErs4475146Gout (vs. non-gout)rs12498742Serum urate (per 1 mg/dL increase)Effect size (95% CI)pEffect size (95% CI)pHbA1c (%)0.0032 (-0.0139 to 0.0203)0.710.0005 (-0.0205 to 0.0216)0.96Insulin resistance (HOMA-IR: log units)0.0128 (-0.0073 to 0.0328)0.210.0126 (-0.0121 to 0.0373)0.32Fasting insulin levels (log pmol/L)0.0038 (-0.0070 to 0.0147)0.490.0048 (-0.0088 to 0.0185)0.49Type 2 Diabetes: odds ratio0.98 (0.87 to 1.10)0.700.98 (0.85 to 1.13)0.75

HOMA-IR=homeostasis model assessment of insulin resistance

Conclusion:

Evidence from this instrumental variable analysis suggests gout and SU are signals for future T2D, but neither SU or gout itself are causally associated with the development of this condition. As such, interventions targeting SU levels alone are unlikely to lower the risk of T2D.

References:

[1]Rho YH, Lu N, Peloquin CE, et al. Independent impact of gout on the risk of diabetes mellitus among women and men: a population-based, BMI-matched cohort study.Ann Rheum Dis. 2016;75(1):91-95.

[2]Choi HK, De Vera MA, Krishnan E. Gout and the risk of type 2 diabetes among men with a high cardiovascular risk profile.Rheumatology. 2008;47(10):1567-1570.

[3]Bhole V, Choi JWJ, Woo Kim S, De Vera M, Choi H. Serum Uric Acid Levels and the Risk of Type 2 Diabetes: A Prospective Study.Am J Med. 2010;123(10):957-961.

Disclosure of Interests:

Natalie McCormick: None declared, Jeewoong Choi: None declared, Shelby Marozoff: None declared, Hyon Choi Grant/research support from: HC reports research support from Ironwood and Horizon, Consultant of: HC reports consulting fees from Ironwood, Selecta, Horizon, Takeda, Kowa, and Vaxart.

OP0247 PERSISTENT PREMATURE MORTALITY GAP IN IDIOPATHIC INFLAMMATORY MYOPATHY: A GENERAL POPULATION-BASED COHORT STUDY

Background:

Idiopathic inflammatory myopathy (IIM) is associated with significant premature mortality; however, whether the mortality gap has improved over recent years is unknown.

Objectives:

To determine trends in premature mortality in patients with IIM in a large cohort, representative of the United Kingdom (UK) general population.

Methods:

Using The Health Improvement Network (THIN), an electronic medical record database representative of the UK general population, we identified patients with incident IIM between 18 and 89 years of age (defined by at least one Read diagnosis code for dermatomyositis, polymyositis, or interstitial myositis with at least one year of continuous enrollment in THIN prior to the cohort entry date) and up to 10 controls without IIM matched on age, sex, birth year, and database entry year. The cohort was divided in two based on the year of IIM diagnosis: the early cohort (1999-2006) and the late cohort (2007-2014). We calculated adjusted hazard ratios for death using a multivariable Cox-proportional hazards model and adjusted rate differences using an additive hazard model.

Results:

The early cohort consisted of 355 patients with IIM and 3182 matched controls, while the late cohort consisted of 396 IIM patients and 3551 matched controls. In both cohorts, IIM patients had excess mortality compared to matched controls [57.4 vs. 15.2 deaths/1000 person-years (PY) in the early cohort and 43.2 vs. 14.1 deaths/1000 PY in the late cohort] (Table). The corresponding multivariate mortality hazard ratios were 2.73 (95% CI, 1.85 to 4.03) vs. 2.61 (95% CI, 1.75 to 3.89) in the early and late cohorts, respectively (p-value for interaction = 0.63) (Figure). The absolute multivariate mortality differences were 36.6 (95% CI, 20.4 to 52.8) and 25.8 (95% CI, 13.7 to 37.9) deaths/1000 PY, in the early and late cohorts, respectively (p-value for interaction = 0.24).

Conclusion:

In this general population-based cohort study, patients with IIM had over 2.5 times the risk of death compared to matched controls, even after adjusting for comorbidities and medications. Unlike trends seen in rheumatoid arthritis and granulomatosis with polyangiitis, there appears to be no improvement in mortality in IIM in recent years. This highlights the need for improved strategies for the management of patients with IIM and its comorbidities.

Table.

Association between idiopathic inflammatory myopathy (IIM) and all-cause mortality according to time period.

1999-20062007-2014IIM cohort (n=355)Non-IIM cohort (n=3182)IIM cohort (n=396)Non-IIM cohort (n=3551)p-value for interactionFollow-up time, years (mean ± SD)2.6 ± 2.12.9 ± 2.13.2 ± 2.43.5 ± 2.4Number of deaths5314055177Death rate/1000 PY (95% CI)57.4 (43.0, 75.1)15.2 (12.7, 17.9)43.2 (32.5, 56.2)14.1 (12.1, 16.3)Age-, sex-, and entry year-matched hazard ratio (95% CI)4.02 (2.89, 5.59)1.00 (ref)3.43 (2.49, 4.73)1.00 (ref)0.50Multivariable-adjusted hazard ratio (95% CI)*2.73 (1.85, 4.03)1.00 (ref)2.61 (1.75, 3.89)1.00 (ref)0.63Age-, sex-, and entry year-matched rate difference/1000 PY (95% CI)42.2 (26.6, 57.9)0.0 (ref)29.1 (17.5, 40.7)0.0 (ref)0.24Multivariable-adjusted rate difference/1000 PY (95% CI)36.6 (20.4, 52.8)0.0 (ref)25.8 (13.7, 37.9)0.0 (ref)0.24

* Multivariable models were adjusted for age, sex, entry year, number of GP visits, BMI, smoking status (i.e., non-smokers, ex-smokers, current smokers), alcohol consumption (i.e., non-drinkers, ex-drinkers, current drinkers), comorbidities, and medication use.

PY, person-year; BMI, body mass index; GP, general practitioner

Figure.

Cumulative mortality of patients with idiopathic inflammatory myopathy and matched controls without IIM in early versus late cohorts (1999-2006 versus 2007-2014).

Disclosure of Interests:

Kristin D’Silva: None declared, Leo Lu: None declared, Alexis Ogdie Grant/research support from: Pfizer to Penn, Novartis to Penn, Amgen to Forward/NDB, Consultant of: Abbvie, Amgen, Bristol-Myers Squibb, Celgene, Corrona, Janssen, Eli Lilly, Novartis, Pfizer, Antonio Aviña: None declared, Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood

FRI0532 THE IMPACT OF GENETICALLY DETERMINED SERUM URATE LEVELS ON THE DEVELOPMENT OF CARDIOVASCULAR DISEASES: A SYSTEMATIC REVIEW AND META-ANALYSIS OF MENDELIAN RANDOMIZATION STUDIES

Background:

Conventional observational studies have identified serum urate (SU) as an independent risk factor for cardiovascular diseases (CVDs),1,2but the causal relationships remain unsettled, with potential confounding and reverse causality. When applied correctly, Mendelian randomization (MR) employing genetic variants as instrumental variables can eliminate these biases and allow for causal inference.

Objectives:

To conduct a systematic review and meta-analysis of English-language, peer-reviewed MR studies on the causal effects of SU on CVDs and assess validation of key MR assumptions.

Methods:

A research librarian conducted a search (inception to January 2020) of four databases (Medline, Embase, Cochrane Library, and Web of Science), which was supplemented by hand-search. Titles and abstracts were screened by two independent reviewers, who subsequently evaluated and extracted data from full-text of selected articles. Pooled meta-analysis was performed using random-effects weighting.

Results:

Of 1014 articles identified, 40 were selected for full-text review and 13 studies reporting on CVDs were included in the systematic review (Figure 1). The first was published in 2009 and five were published in 2018 or 2019 alone. The included studies were of varying quality in regards to satisfying the assumptions for MR design.

Fig. 1

: PRISMA flow diagram.

Overall, there was little evidence for a causal association between SU and risk of CVDs (Table 1). Random-effects meta-analysis revealed that SU was not significantly associated with risk of CVDs (OR=1.04; 95% CI=0.99-1.09) (Figure 2). 11 of the 13 studies reported null estimates for the effects of genetically-determined SU levels on CVDs. Two studies with small numbers of cases (N=125 and 222) reported significant associations, but these pertained to highly-specific subgroups.

Table 1.

Summary of included studies. CAD: Coronary Artery Disease; CHD: Coronary Heart Disease; IHD: Ischaemic heart disease; MI: Myocardial Infarction; MR: Mendelian Randomization; PVD: Peripheral Vascular Disease; SNP: Single Nucleotide Polymorphism

First author; yearNumber of SNPs analyzedOutcome(n cases)PowerMR criteria validated:1. relevance2. pleiotropy3. confoundingConclusionStark; 200910 separatelyCAD (1,473)30%-66%1, 3NullYang; 20108 combinedCHD (3,050)<80%1, 2, 3NullPalmer; 20131 (rs7442295)IHD (3,742)N/A1, 2*, 3NullKleber; 20158 combinedCAD (2,418)PVD (295)N/A1, 2, 3NullHan; 20152 separatelyCHD (1,146)80%1, 2*, 3NullTesta; 20151 (rs734553)CVD events (CVD death, stroke, MI) (222)N/A1, 2*Significant for CV eventsWhite; 201631 combinedCAD (65,877)83%1, 2, 3NullKeenan; 201614 combinedCHD (54,501)Stroke (14,779)>80%1, 2, 3NullLi; 201831 combinedIHD (9,467)MI (3,625)70%1, 2, 3NullLi; 201931 combinedCHD (60,801)MI (43,676)Stroke (10,307)N/A1, 2, 3NullMacias-Kauffer; 20192 separatelyCAD (704)N/A1, 2*, 3NullEfstathiadou; 201928 separatelyCHD (184,305)MI (54,162) Stroke (514,791)>80%1, 2, 3NullChiang; 20198 combinedCHD (125)Stroke (57)N/A1, 2, 3Significant for CHD

* Risk of pleiotropy (assumption 2) is low when utilizing few well-established SNPs

Fig. 2

: Pooled meta-analysis results.

Conclusion:

Evidence from this systematic review does not support a causal role for SU levels and CVDs. As such, interventions targeting SU levels alone are unlikely to lower the risk of CVDs.

References:

[1] Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007-2008. Am J Med 2012;125:679-87 e1.

[2] Choi HK, Curhan G. Independent impact of gout on mortality and risk for coronary heart disease. Circulation 2007;116:894-900

Disclosure of Interests:

Jeewoong Choi: None declared, Natalie McCormick: None declared, Shelby Marozoff: None declared, Mary De Vera: None declared, Hyon Choi Grant/research support from: Ironwood, Horizon, Consultant of: Takeda, Selecta, Horizon, Kowa, Vaxart, Ironwood

0800 Posttraumatic Stress Disorder and REM Sleep Without Atonia in Veterans

Introduction

In veterans, the prevalence of rapid eye movement (REM) sleep behavior disorder(RBD) is higher than the general population, and there is some evidence that this is related to posttraumatic stress disorder(PTSD). In addition, trauma related nightmares (TRNs) interfere with REM sleep and are often accompanied by motor activity. (rem sleep without atonia; RSWA). The purpose of this study is to determine whether the frequency of dream enactment behavior(DEB) and RSWA is different according to the presence of PTSD or trauma.

Methods

The patients (n = 2262) who underwent video assisted polysomnography (PSG) and sleep-related questionnaire surveys at Veteran Health Service Medical Center in Republic of Korea were reviewed retrospectively and cross-sectionally. Based on patients diagnosed with PTSD (N = 20; 100% male; 67.9 ± 8.5 years of age), those exposed to trauma but not diagnosed with PTSD (N = 23; 100% male; age 64.0 ± 13.4) and trauma unexposed controls (N = 21; 100% male; age 59.86 ± 10.9) were matched.

Results

In the PTSD group, patients who reported self-reported DEB tended to be more than the traumatic exposure group and the control group (P = 0.022). In-lab video assisted PSG showed no differences in DEB between the three groups, but RSWA. (p = 0.026) After adjusting for age, hypnotics, apnea hypopnea index (AHI), Beck depression inventory (BDI), and periodic limb movement (PLM) arousal factors, RSWA was significantly higher in the PTSD group than in the traumatic exposure group. (p = 0.006)

Conclusion

The result that RSWA was significantly higher in the PTSD group than in the traumatic exposure group suggests that there may be an associated pathophysiology between PTSD and RBD. Longitudinal studies are needed to establish the link between RBD with PTSD and neurodegenerative diseases associated with synucleinopathy.

Support

This study was supported by a VHS Medical Center Research Grant, Republic of Korea. (grant number: VHSMC 19033)

0505 Insomnia: Entrapment of Binaural Auditory Beats on Subjects with Insomnia Symptoms

Introduction

It has been reported that binaural beat stimulation, which has two different frequencies on both ears, is effective in increasing alertness, memory, reducing anxiety, and controlling mood. This study aims to clarify the brain wave entrainment effect of binaural beat and to identify the mechanism of action of the binaural beat.

Methods

Subjects with subclinical insomnia symptoms between 20 and 59 years of age were recruited from the community. Quantitative electroencephalography (EEG) was measured two times before and after the 2 weeks of binaural beat intervention period. An audio apparatus without the distortion of a sound source is set with theta (6 Hz) binaural beat. Participants used the apparatus for 30 minutes before going to bed for 2 weeks.

Results

When the music with binaural beat was played, the relative power of theta frequency increased (occipital, P=0.009). When the music only was played in the laboratory, the relative power of delta (temporal, P=0.004; parietal, P=0.005; occipital, P=0.006) and theta frequency (temporal, P=0.004; central, P=0.001; parietal, P=0.001; occipital, P=0.003) increased and the relative power of alpha decreased (frontal, P=0.008; temporal, P=0.012; central, P=0.008; parietal, P=0,004; occipital, P=0.005). After listening to music with binaural beat for two weeks, the difference of beta power before and after listening to music first in the laboratory was lower than the difference after using music-only devices (P=0.008).

Conclusion

When the binaural beat was played, the entrapment of theta wave appeared. And the music was presumed to have a nonspecific relaxation effect. After exposure to music with binaural beat for 2 weeks, beta power decreased compared to exposure to music alone. Continuous music with binaural beat exposure for 2 weeks is likely to reduce hyper-arousal state and contribute to sleep induction.

Support

None

Unripe Rubus coreanus Miquel Extract Containing Ellagic Acid Regulates AMPK, SREBP-2, HMGCR, and INSIG-1 Signaling and Cholesterol Metabolism In Vitro and In Vivo

Our previous study demonstrated that a 5% ethanol extract of unripe Rubus coreanus (5-uRCK) has hypo-cholesterolemic and anti-obesity activity. However, the molecular mechanisms of its effects are poorly characterized. We hypothesized that 5-uRCK and one of its major bioactive compounds, ellagic acid, decrease cellular and plasma cholesterol levels. Thus, we investigated the hypocholesterolemic activity and mechanism of 5-uRCK in both hepatocytes and a high-cholesterol diet (HCD)-induced rat model. Cholesterol in the liver and serum was significantly reduced by 5-uRCK and ellagic acid. The hepatic activities of HMG-CoA and CETP were reduced, and the hepatic activity of LCAT was increased by both 5-uRCK extract and ellagic acid, which also caused histological improvements. The MDA content in the aorta and serum was significantly decreased after oral administration of 5-uRCK or ellagic acid. Further immunoblotting analysis showed that AMPK phosphorylation in the liver was induced by 5-uRCK and ellagic acid, which activated AMPK, inhibiting the activity of HMGCR by inhibitory phosphorylation. In contrast, 5-uRCK and ellagic acid suppressed the nuclear translocation and activation of SREBP-2, which is a key transcription factor in cholesterol biosynthesis. In conclusion, our results suggest that 5-uRCK and its bioactive compound, ellagic acid, are useful alternative therapeutic agents to regulate blood cholesterol.

Analysis of the relationship between changes in the auditory brainstem response and prognosis in patients with sudden hearing loss

OBJECTIVE

To analyse how the auditory brainstem response changes in patients with sudden sensorineural hearing loss.

METHOD

Data were collected via retrospective medical chart review.

RESULTS

Forty-three patients were included in this study. The mean latency of auditory brainstem response wave 1 was significantly longer for the affected side than for the unaffected side (p = 0.003). The mean latency of auditory brainstem response wave 1 was significantly shorter, and the mean amplitude of auditory brainstem response wave 1 was significantly larger, in the good response group compared to the poor response group. In forward conditional logistic regression analysis, auditory brainstem response wave 1 latency was an independent predictor of a good response (odds ratio = 34.37, 95 per cent confidence interval = 1.56-757.15, p = 0.025).

CONCLUSION

In patients with sudden sensorineural hearing loss, the latency of wave 1 of the auditory brainstem response was significantly increased and was related to prognosis.

Chicken PRDX3 is required for proliferation of chicken embryo fibroblast cells

1. This experiment investigated the influence of chicken PRDX3 on cell proliferation in chick embryo fibroblast cells using PRDX3 knockdown technology.2. A methyl thiazolyl tetrazolium (MTT) assay was performed to assess the effect of chPRDX3 knockdown on fibroblast proliferation. The antioxidant effect was investigated to determine if it directly mediated fibroblast cell proliferation.3. To determine the role of chPRDX3 on cell proliferation, an siRNA mediated knockdown was performed in chick fibroblast cells using an in vitro assay. The proliferation of fibroblast cells transfected with siPRDX3 #3 and siPRDX3 Mix was significantly decreased after 48 h (P < 0.01). In addition, the knockdown of chicken PRDX3 suppressed cell proliferation through an increase in oxidative stress.4. The results demonstrated that chPRDX3 is required for cell proliferation in chicken fibroblast cells. Such findings have important implications for the maintenance of chicken fibroblast cells.