Case Report: A Pancreatic Ductal Adenocarcinoma Patient With Concurrent Targetable Somatic Novel KANK1-ALK, UPP2-NTRK3 Fusion, and Pathogenetic Germline BRCA Mutation

Pancreatic ductal adenocarcinoma (PDAC) is presently one of the cancers with the worst survival rates. The current treatment options for PDAC are relatively scarce due to insufficient understanding of molecular characteristics and subtypes of PDAC. Based on next-generation sequencing (NGS), we firstly presented a case about a KRAS wild-type pancreatic ductal adenocarcinoma patient harboring a concurrent targetable rare somatic novel KANK1-ALK, UPP2-NTRK3 fusion, and pathogenetic germline BRCA mutation. These two novel fusion statuses were assayed by immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH). Our findings demonstrated that comprehensive and systematic screening of PDAC for actionable genomic alteration may substantially improve the therapeutic prospects for a sizeable fraction of patients with PDAC. To improve the management of PDAC in an era of precision medicine, it is important to identify ALK or NTRK fusion-positive and pathogenic germline mutation subsets of patients who can benefit from targeted therapies.

[Epidemiological characteristics of local outbreak of COVID-19 caused by SARS-CoV-2 Delta variant in Liwan district, Guangzhou]

Objective: To analyze the epidemiological characteristics of a local outbreak of COVID-19 caused by SARS-CoV-2 B.1.617.2(Delta) variant in Liwan district, Guangzhou, and provide evidence for the further prevention and control of the Delta variant of COVID-19. Methods: From May 21 to June 18, 2021, the incidence data of COVID-19 caused by Delta variant were obtained from National Notifiable Disease Report System of Chinese Disease Prevention and Control Information System and Liwan District Center for Disease Control and Prevention of Guangzhou.Frequency analysis (proportions), histograms, and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. The incubation period and time-varying reproduction numbers (R_t) estimations were used for the further analysis. Results: By June 18, 2021, a total of 127 COVID-19 cases caused by Delta variant was reported in Liwan district. The youngest case was aged 2 years and the oldest was aged 85 years. There were 18.9% (24/127) aged <18 years, 43.3% (55/127) aged 18-59 years, and 37.8% (48/127) aged ≥60 years, the male to female ratio of the cases was 1∶1.35 (54∶73). The cases were mainly retired people (32.3%, 41/127), the jobless or unemployed (18.1%, 23/127), and students (16.5%, 21/127). The infections mainly occurred in Baihedong (70.1%, 89/127) and Zhongnan street (23.6%, 30/127) communities in the southern area of Liwan district. The median incubation period of the Delta variant infection was 6 days (range: 1-15 days). The clinical classification were mainly common type (64.6%, 82/127). The basic reproduction number (R₀) was 5.1, R_t which once increased to 7.3. The transmissions mainly occurred in confined spaces, such as home (26.8%), restaurant (29.1%), neighborhood (3.9%), and market (3.1%), the household clustering was predominant. Close contacts tracing (66.1%) and community screening (33.1%) were the main ways to find the infections. Conclusion: The COVID-19 outbreak caused by Delta variant in Liwan district of Guangzhou was highly contagious, with the obvious characteristics of household clustering and high proportions of cases in adults aged 18-59 years and elderly people aged ≥60 years.

Contour Transformer Network for One-Shot Segmentation of Anatomical Structures

Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.

Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.^{1 1}https://github.com/viggin/DeepLesion_manual_test_set.

Construction of multifunctional porcine acellular dermal matrix hydrogel blended with vancomycin for hemorrhage control, antibacterial action, and tissue repair in infected trauma wounds

Prevention of bacterial infection and reduction of hemorrhage, the primary challenges posed by trauma before hospitalization, are essential steps in prolonging the patient's life until they have been transported to a trauma center. Extracellular matrix (ECM) hydrogel is a promising biocompatible material for accelerating wound closure. However, due to the lack of antibacterial properties, this hydrogel is difficult to be applied to acute contaminated wounds. This study formulates an injectable dermal extracellular matrix hydrogel (porcine acellular dermal matrix (ADM)) as a scaffold for skin defect repair. The hydrogel combines vancomycin, an antimicrobial agent for inducing hemostasis, expediting antimicrobial activity, and promoting tissue repair. The hydrogel possesses a porous structure beneficial for the adsorption of vancomycin. The antimicrobial agent can be timely released from the hydrogel within an hour, which is less than the time taken by bacteria to infest an injury, with a cumulative release rate of approximately 80%, and thus enables a relatively fast bactericidal effect. The cytotoxicity investigation demonstrates the biocompatibility of the ADM hydrogel. Dynamic coagulation experiments reveal accelerated blood coagulation by the hydrogel. In vivo antibacterial and hemostatic experiments on a rat model indicate the healing of infected tissue and effective control of hemorrhaging by the hydrogel. Therefore, the vancomycin-loaded ADM hydrogel will be a viable biomaterial for controlling hemorrhage and preventing bacterial infections in trauma patients.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine, etc. Results In the external validation, the lesion-level PPV and sensitivity of the model were 57.9% and 67.0%, respectively. On average, the model detected 2.1 findings per set, and among them, 0.9 were false positives. ULDor worked well for detecting liver lesions, with a PPV of 78.9% and a sensitivity of 92.7%, followed by kidney, with a PPV of 70.0% and a sensitivity of 58.3%. In internal validation with NLST test set, ULDor obtained a PPV of 75.3% and a sensitivity of 52.0% despite the relatively high noise level of soft tissue on images. Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs. With further optimisation and iterative upgrades, ULDor may be well suited for extensive application to external data.

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = -0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.

[Gene-body mass index interaction on coronary heart disease in Chinese adult twins]

Objective: To explore the gene-body mass index (BMI) interaction on coronary heart disease (CHD) in the Chinese adult twins. Methods: A total of 20 340 same-sex twin pairs registered in the Chinese National Twin Registry (CNTR) were enrolled in this study. Classical twin structure equation model was used to estimate the gene-BMI interaction on CHD. Results: After adjusting for age, we found that genetic variance of CHD differed as the function of BMI in male twins, which indicated the presence of a gene-BMI interaction on CHD (P=0.008).The genetic moderating effect (β_a) was -0.14 (95%CI: -0.22--0.04), indicating that for each logarithmic transformation value of BMI increase, genetic path parameters would decrease by 0.14, which would result in the decrease of genetic variance of CHD. And the heritability of CHD was 0.77 (95%CI: 0.65-0.86) among the male twins with lower BMI (<24.0 kg/m²), but 0.56 (95%CI: 0.33-0.74) among the male twins with high BMI (≥24.0 kg/m²). However, there was no evidence suggesting that BMI could moderate genetic variants of CHD in female. Conclusion: We found a significant gene-BMI interaction on CHD in the Chinese male adult twins in China, and the heritability of CHD was higher among the twins whose BMI was <24.0 kg/m².

The Potential Impact and Usability of the Eighth Edition TNM Staging Classification in Oral Cavity Cancer

AIMS

In the current eighth edition head and neck TNM staging, extranodal extension (ENE) is an adverse feature in oral cavity squamous cell cancer (OSCC). The previous seventh edition N1 with ENE is now staged as N2a. Seventh edition N2+ with ENE is staged as N3b in the eighth edition. We evaluated its potential impact on patients treated with surgery and postoperative intensity-modulated radiotherapy (IMRT).

MATERIALS AND METHODS

OSCC patients treated with primary surgery and adjuvant (chemo)radiotherapy between January 2005 and December 2014 were reviewed. Cohorts with pathological node-negative (pN-), pathological node-positive without ENE (pN+_pENE-) and pathological node-positive with ENE (pN+_pENE+) diseases were compared for local control, regional control, distant control and overall survival. The pN+ cohorts were further stratified into seventh edition N-staging subgroups for outcomes comparison.

RESULTS

In total, 478 patients were evaluated: 173 pN-; 159 pN+_pENE-; 146 pN+_pENE+. Outcomes at 5 years were: local control was identical (78%) in all cohorts (P = 0.892), whereas regional control was 91%, 80% and 68%, respectively (P < 0.001). Distant control was 97%, 87%, 68% (P < 0.001) and overall survival was 75%, 53% and 39% (P < 0.001), respectively. Overall survival for N1 and N2a subgroups was not significantly different. In the seventh edition N2b subgroup of pENE- (n = 79) and pENE+ (n = 79) cohorts, overall survival was 67% and 37%, respectively. In the seventh edition N2c subgroups, overall survival for pENE- (n = 17) and pENE+ (n = 38) cohorts was 65% and 35% (P = 0.08), respectively. Overall, an additional 128 patients (42% pN+) were upstaged as N3b.

CONCLUSIONS

When eighth edition staging was applied, stage migration across the N2-3 categories resulted in expected larger separations of overall survival by stage. Patients treated with primary radiation without surgical staging should have outcomes carefully monitored. Strategies to predict ENE preoperatively and trials to improve the outcomes of pENE+ patients should be explored.

[Meteorological factors and related lag effects on scrub typhus in southwestern Yunnan]

Objective: To analyze the influence and related lag effects of meteorological factors on scrub typhus (ST) in southwestern Yunnan, to provide a reference for the corresponding prevention and treatment measures. Methods: Data on ST and meteorology in Yunnan province from 2007 to 2018 were collected. A distributed lag nonlinear model was conducted to study the cumulative lag effects of temperature, relative humidity, and rainfall on ST. Results: From 2007-2018, a total of 19 975 ST cases were reported in southwestern Yunnan. Weekly mean temperature, mean relative humidity, and the risk of ST all showed J-shaped curves. The cumulative risk of ST increased with mean temperature >23 ℃, mean relative humidity >80%, and cumulative rainfall between 20 and 60 mm or over 100 mm, weekly. Taking the median value as the reference, higher temperature (22.27 ℃, 23.45 ℃), relative humidity (80.14%, 84.38%) and rainfall (37.17 mm, 74.42 mm) all increased the risk of disease while lower temperature (11.22 ℃,14.83 ℃), relative humidity (53.18%,65.36%) and rainfall (0.00 mm,0.55 mm) showed opposite effects. The temperature-lag effect lasted for 10 and 16 weeks, respectively, with ST's risk the highest during the week of exposure. Humidity-lag effects usually last for 10 and 17 weeks. The lag effect of rainfall lasted for 25 weeks, while the disease's risk was the highest in the 4^th week. Conclusion: Factors as temperature, relative humidity, and precipitation showed nonlinear and lag effects on ST. High temperature, high relative humidity, and an appropriate amount of rainfalls increase the risk of ST. The authorities of public health should implement effective prevention and control measures according to meteorological conditions.

[Modification of physical activity on genetic effects of type 2 diabetes mellitus: a twin pairs cohort study in China]

Objective: To explore the modification effect of physical activity on the genetic effects of type 2 diabetes mellitus (T2DM). Methods: The univariate moderation model was fitted to calculate the modifying effect of physical activity on the genetic effects of T2DM based on the data of 12 107 pairs of same gender twins aged 30 years and older enrolled by the Chinese National Twin Registry in 11 provinces/cities in China. Results: After adjusting for age and gender, the heritability of T2DM was 0.56 (0.31-0.84). Qualified physical activity could attenuate the genetic effects of T2DM. The heritability of T2DM in twin pairs with qualified physical activity was 0.46 (0.06-0.88), which was lower than that in twin pairs without qualified physical activity during the same model [0.68(0.36-0.94)]. Conclusion: T2DM is a moderate genetic disease, physical activity can modify the genetic effects of T2DM.

[A descriptive analysis on body mass index distribution in adult twin pairs in China]

Objective: To describe the differences in body mass index (BMI) distribution in adult twins registered in Chinese National Twin Registry (CNTR), and provide evidence for the risk factor analysis and prevention and control of overweight or obesity. Methods: A total of 32 725 twin pairs aged 18 years and above who completed the questionnaire survey during 2010-2018 and had complete registered information in CNTR and normal body weight and length were included in the analysis on the population and region specific distributions of BMI of twin pairs and the difference in BMI in twin pairs. Results: The twin pairs included in the analysis were aged (34.6±12.4) years, the twin pairs of same gender accounted for 79.7%. The average BMI was 22.5 kg/m². The overall prevalence of obesity and overweight was 4.9% and 23.7%, respectively. Participants who were men, 50-59 years old, married, had lower education level, and lived in northern China had higher overweight rate and obesity rate (P<0.001). The difference in overweight or obesity prevalence between monozygotic (MZ) twin pars and dizygotic (DZ) twin pairs was not significant, but firstborn twin pairs had slightly higher rates of overweight and obesity than later-born twin pairs (P<0.05). The analysis in same gender-twin pairs indicated that the difference in BMI was associated with age (trend test: P<0.001), and the difference was more obvious in DZ twin pair in MZ pair and this difference increased with age. The concordant rate of BMI was higher in MZ twin pairs than DZ twin pairs (P<0.05). Conclusion: The distribution of BMI of twin pairs varied with population and region and BMI varied with age due to its genetic nature.

CT-Based Risk Factors for Mortality of Patients With COVID-19 Pneumonia in Wuhan, China: A Retrospective Study

Purpose: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality have not been directly reported. We aim to determine the CT-based quantitative predictors for COVID-19 mortality. Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume (V) of total pneumonia infection and the average Hounsfield unit (HU) of consolidation areas was automatically quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality. Results: The study included 238 patients (women 136/238, 57%; median age, 65 years, IQR 51-74 years), 126 of whom were survivors. The V-HU score was an independent predictor (hazard ratio [HR] 2.78, 95% confidence interval [CI] 1.50-5.17; p = 0.001) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU score (c-index: 0.695 vs. 0.728; p < 0.001). Older patients (age ≥ 65 years; HR 3.56, 95% CI 1.64-7.71; p < 0.001) and younger patients (age < 65 years; HR 4.60, 95% CI 1.92-10.99; p < 0.001) could be further risk-stratified by the V-HU score. Conclusions: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Biaryl Formation via Base-Promoted Direct Coupling Reactions of Arenes with Aryl Halides

In the absence of ligand, Cs2CO3-promoted cross-coupling reaction of arenes with cyano-/nitro-substituted aryl halides in DMSO affording biaryls is reported. The cyano/nitro group in biaryls is useful and convenient for further transformation. The formation of dibenzofurans resulting from the reactions between arenes and 1-bromo-2-iodobenzene is also reported. On the basis of control experiments and theoretical studies, a radical mechanism is proposed for the formation of biaryls.

OP0005 DIETARY HYPERINSULINEMIC POTENTIAL AND RISK OF INCIDENT GOUT: 3 PROSPECTIVE COHORT STUDIES OF US MEN AND WOMEN

Background:

Gout and the metabolic (insulin resistance) syndrome frequently coexist. Intravenous insulin has been shown to raise serum urate (SU) levels in physiologic studies and a Mendelian Randomization study also showed a causal role of insulin on the risk of gout. However, it is unknown whether habitual hyperinsulinemic dietary intake confers gout risk.

Objectives:

Prospectively examine the relation between two distinct insulin-related dietary indices and risk of incident gout in three large cohorts of US women and men over 30 years.

Methods:

We studied 164,090 women from Nurses Health Study I (1986-2016) and II (1989-2017) and 40,598 men from Health Professionals Follow-up Study (1986-2016), who were free of gout at baseline. Dietary intake and covariates were assessed by validated questionnaires every 4 years. Insulinemic potential of diet was evaluated using 1) food-based empirical dietary index for hyperinsulinemia (EDIH) score that was pre-defined based on circulating C-peptide levels1 and reflects insulin resistance;2 and 2) dietary insulin index (DII), which reflects transient, postprandial insulin secretion.2 We assigned EDIH and DII scores for each participant, adjusted for total energy intake, and prospectively examined the association between scores and incident gout (using ACR survey criteria for gout3), adjusting for potential confounders.

Results:

We ascertained 2,874 incident gout cases over 5,124,490 person-years of follow-up. In pooled multivariable-adjusted analyses, those in the highest EDIH quintile had 1.76-times (95% CI: 1.56 to 1.99) higher gout risk, compared with the lowest (Table 1). This attenuated with further adjustment for BMI (a likely causal intermediate) but remained positive (RR 1.30, 1.15 to 1.48). DII scores were inversely associated with gout risk (RR 0.66, 0.58 to 0.74) (Table 1).

Table 1.

Risk Ratio (95% CI) of Gout According to Quintiles of Insulin-Related Dietary Index

EDIH (measure of insulin resistance)Q1:lowest circulating insulin levelsQ2Q3Q4Q5:highest circulating insulin levelsP for trendN cases430482598631733Person-years1,025,1291,025,2851,025,5741,025,3011,023,651Age-adjusted RR1.00 (Ref)1.13 (1.00-1.29)1.43 (1.26-1.61)1.53 (1.36-1.73)1.85 (1.64-2.09)<.0001MV-Adjusted*RR1.00 (Ref)1.11 (0.98-1.27)1.39 (1.22-1.57)1.47 (1.30-1.67)1.76 (1.56-1.99)<.0001MV-Adjusted**RR (+ BMI)1.00 (Ref)1.03 (0.90-1.17)1.21 (1.06-1.37)1.21 (1.07-1.37)1.30 (1.15-1.48)<.0001Dietary Insulin Index (measure of transient, post-prandial secretion and sensitivity)Q1:lowest insulin sensitivityQ2Q3Q4Q5:greatest insulin sensitivityP for trendN cases783611527498455Person-years1,024,7631,025,7301,025,0751,025,5381,023,834Age-adjusted RR1.00 (Ref)0.79 (0.71-0.88)0.69 (0.62-0.77)0.65 (0.58-0.73)0.59 (0.53-0.66)<.0001MV-Adjusted*RR1.00 (Ref)0.79 (0.71-0.88)0.69 (0.62-0.77)0.66 (0.59-0.74)0.60 (0.53-0.67)<.0001MV-Adjusted**RR (+ BMI)1.00 (Ref)0.78 (0.70-0.87)0.69 (0.62-0.77)0.67 (0.60-0.75)0.66 (0.58-0.74)<.0001

*Multivariable (MV) models adjusted for age (month), White race, smoking, menopause (women only), hormone use (women only), physical activity, history of hypertension, and diuretic use **MV + BMI models further adjusted for BMI (a likely causal intermediate)

Conclusion:

EDIH scores, reflecting chronic hyperinsulinemia (i.e., greater insulin resistance with reduced clearance), were positively associated with the risk of incident gout, even beyond the pathway through adiposity. Conversely, higher DII scores, which reflect short-term, postprandial elevations in insulin levels (and also greater insulin clearance and sensitivity) conferred a lower risk. This corroborates human physiologic experiments and Mendelian Randomization studies showing insulin resistance can increase SU levels by decreasing renal excretion of urate, and supports lowering insulinemic potential of diet as a strategy to reduce gout risk.

References:

[1]Tabung et al. PMID 27821188

[2]Lee et al. PMID 32618519

[3]Wallace et al. PMID 856219

Disclosure of Interests:

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