Risk Factors for Diagnosis of Psoriatic Arthritis, Psoriasis, Rheumatoid Arthritis, and Ankylosing Spondylitis : A Set of Parallel Case-control Studies

Funding Information: This work was supported in part by the National Institutes of Health (NIH), Grant K23 AR063764, to the principal investigator AO, and internal funds from the University of Pennsylvania. MD was supported by the NIH, Grant K23 AR06912701. 1E. Meer, BA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; 2T. Thrastardottir, MPH, T.J. Love, MD, PhD, Department of Medicine/Rheumatology, University of Iceland and Landspitali, Reykjavik, Iceland; 3X. Wang, MD, Y. Chen, PhD, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; 4M. Dubreuil, MD, Department of Medicine/Rheumatology, Boston University, Boston, Massachusetts, USA; 5J.M. Gelfand, MD, MSCE, Department of Biostatistics, Epidemiology and Informatics, and Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; 6A. Ogdie, MD, MSCE, Department of Biostatistics, Epidemiology and Informatics, and Department of Medicine/ Rheumatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. JMG has served as a consultant for BMS, Boehringer Ingelheim, Lilly, Janssen Biologics, Novartis, UCB (DSMB), Neuroderm (DSMB), Dr. Reddy’s Labs, Pfizer, and Sun Pharma, receiving honoraria; receives research grants (to the Trustees of the University of Pennsylvania) from AbbVie, Boehringer Ingelheim, Janssen, Novartis, Celgene, Ortho Dermatologics, and Pfizer; and received payment for continuing medical education work related to psoriasis that was supported indirectly by Lilly, Ortho Dermatologics, and Novartis. JMG is a co-patent holder of resiquimod for treatment of cutaneous T-cell lymphoma, is a Deputy Editor for the Journal of Investigative Dermatology, receiving honoraria from the Society for Investigative Dermatology, and is a member of the Board of Directors for the International Psoriasis Council, receiving no honoraria. TJL has received reimbursement from Celgene for speaking about guidelines for the treatment of psoriatic arthritis. AO has served as a consultant for AbbVie, Amgen, BMS, Celgene, Corrona, Global Health Living Foundation, Janssen, Lilly, Novartis, Pfizer, and Takeda, and has received grants to the University of Pennsylvania from Pfizer and Novartis and to Forward from Amgen; her husband has received royalties from Novartis. EM, TT, MD, XW, and YC declare no conflicts of interest relevant to this article. Address correspondence to Dr. A. Ogdie, University of Pennsylvania, Division of Rheumatology, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA. Email: [email protected]. Accepted for publication July 16, 2021. Publisher Copyright: © 2022 The Journal of Rheumatology.Objective. To compare potential risk factors for the diagnosis of psoriatic arthritis (PsA), psoriasis (PsO), rheumatoid arthritis (RA), and ankylosing spondylitis (AS). Methods. Four parallel case-control studies were conducted within The Health Improvement Network using data between 1994 and 2015. Patients with PsA, PsO, RA, or AS were identified using validated code lists and matched to controls on age, sex, practice, and year. Risk factors were selected in the time prior to diagnosis. Multivariable logistic regression models were constructed for each disease using automated stepwise regression to test potential risk factors. Results. Patients with incident PsA (n = 7594), PsO (n = 111,375), RA (n = 28,341), and AS (n = 3253) were identified and matched to 75,930, 1,113,345, 283,226, and 32,530 controls, respectively. Median diagnosis age was 48 (IQR 38–59), 43 (IQR 28–60), 60 (IQR 48–71), and 41 (IQR 32–54) years, respectively. In multivariable models, there were some shared and some differing risk factors across all 4 diseases: PsA was associated with obesity, pharyngitis, and skin infections; PsA and PsO were associated with obesity and moderate alcohol intake; PsA and AS were associated with uveitis; and PsA and RA were associated with preceding gout. Both RA and AS were associated with current smoking, former moderate drinking, anemia, osteoporosis, and inflammatory bowel disease. All shared former or current smoking as a risk factor; statin use was inversely associated with all 4 diseases. Conclusion. Shared and different risk factors for PsA, PsO, RA, and AS were identified. Statin use was inversely associated with all 4 conditions.Peer reviewe

The Smoking Paradox in the Development of Psoriatic Arthritis among Psoriasis Patients – A Population-Based Study

Objectives: Smoking is strongly associated with an increased risk of psoriatic arthritis (PsA) in the general population, but not among psoriasis patients. We sought to clarify the possible methodologic mechanisms behind this paradox. Methods: Using 1995-2015 data from The Health Improvement Network, we performed survival analysis to examine the association between smoking and incident PsA in the general population and among psoriasis patients. We clarified the paradox using mediation analysis and conducted bias sensitivity analyses to evaluate the potential impact of index event bias and quantify its magnitude from uncontrolled/unmeasured confounders. Results: Of 6.65 million subjects without PsA at baseline, 225,213 participants had psoriasis and 7,057 developed incident PsA. Smoking was associated with an increased risk of PsA in the general population (RR, 1.27; 95% CI, 1.19-1.36), but with a decreased risk among psoriasis patients (RR 0.91; 95% CI, 0.85-0.99). Mediation analysis showed that the effect of smoking on the risk of PsA was mediated almost entirely through its effect on psoriasis. Bias sensitivity analyses indicated that even when the relation of uncontrolled confounders to either smoking or PsA was modest (both RRs = ~1.50), it could reverse the biased estimate of effect of smoking among psoriasis patients (RR=0.9). Conclusions: In this large cohort representative of the UK general population, smoking was positively associated with PsA risk in the general population, but negatively associated among psoriasis patients. Conditioning on a causal intermediate variable (psoriasis) can reverse the association between smoking and PsA, explaining the smoking paradox for the risk of PsA among psoriasis patients

Sensorimotor computation underlying phototaxis in zebrafish

Animals continuously gather sensory cues to move towards favourable environments. Efficient goal-directed navigation requires sensory perception and motor commands to be intertwined in a feedback loop, yet the neural substrate underlying this sensorimotor task in the vertebrate brain remains elusive. Here, we combine virtual-reality behavioural assays, volumetric calcium imaging, optogenetic stimulation and circuit modelling to reveal the neural mechanisms through which a zebrafish performs phototaxis, i.e. actively orients towards a light source. Key to this process is a self-oscillating hindbrain population (HBO) that acts as a pacemaker for ocular saccades and controls the orientation of successive swim-bouts. It further integrates visual stimuli in a state-dependent manner, i.e. its response to visual inputs varies with the motor context, a mechanism that manifests itself in the phase-locked entrainment of the HBO by periodic stimuli. A rate model is developed that reproduces our observations and demonstrates how this sensorimotor processing eventually biases the animal trajectory towards bright regions

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Memory Capacity of Networks with Stochastic Binary Synapses

In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level&nbsp;, in the large&nbsp;&nbsp;and sparse coding limits (). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex.</p

Memory capacity of networks with stochastic binary synapses.

In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level [Formula: see text], in the large [Formula: see text] and sparse coding limits ([Formula: see text]). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex

Capacity at finite .

<p>a,b. as a function of for the SP model and Parameters are chosen to optimize capacity under the binomial approximation. Shown are the result of the gaussian approximation without covariance (cyan) and with covariance (magenta) for these parameters. c. Optimized as a function of for the SP model at . The blue curve is for patterns with fluctuations in the number of selective neurons. The red curve is for the same number of selective neurons in all patterns. The black curve is the number of patterns that would be stored if the network were storing the same amount of information as in the case . d. Same for the MP model, where parameters have been optimized, but the depression-potentiation ratio is fixed at .</p

Storage capacity optimized with inhibition in the SP model.

<p>Blue is for a fixed threshold and fluctuations in the number of selective neurons per pattern. Green, the fluctuations are minimized using inhibition. Red, without fluctuations in the number of selective neurons per pattern. a. Number of stored patterns as a function of the coding level . b. Stored information in bits per synapse, as a function of .</p

Optimized information capacity for the SP model in the limit .

<p>a. as a function of , b. as a function of , the ratio between the number of depressing events and potentiating events at pattern presentation, c. as a function of , d. as a function of the LTP transition probability .</p

Optimized information capacity of the Willshaw model in the limit .

<p>Information is optimized by saturating (19) () and (20): a. as a function of , b. as a function of .</p