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Carotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome‐wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health records at eight eMERGE sites. We performed GWAS with a model adjusting for age, sex, study site, and genetic principal components of ancestry. In eMERGE we found 1793 CAAD cases and 17,958 controls. Two loci reached genome‐wide significance, on chr6 in LPA (rs10455872, odds ratio [OR] (95% confidence interval [CI]) = 1.50 (1.30–1.73), p = 2.1 × 10−8) and on chr7, an intergenic single nucleotide variant (SNV; rs6952610, OR (95% CI) = 1.25 (1.16–1.36), p = 4.3 × 10−8). The chr7 association remained significant in the presence of the LPA SNV as a covariate. The LPA SNV was also associated with coronary heart disease (CHD; 4199 cases and 11,679 controls) in this study (OR (95% CI) = 1.27 (1.13–1.43), p = 5 × 10−5) but the chr7 SNV was not (OR (95% CI) = 1.03 (0.97–1.09), p = .37). Both variants replicated in UK Biobank. Elevated lipoprotein(a) concentrations ([Lp(a)]) and LPA variants associated with elevated [Lp(a)] have previously been associated with CAAD and CHD, including rs10455872. With electronic health record phenotypes in eMERGE and UKB, we replicated a previously known association and identified a novel locus associated with CAAD.

Purpose Impulse buying has been the focus of attention in the marketing. With the rise of online shopping, online impulse buying phenomenon becomes increasingly serious. Whereas, the impulse buying behavior in an online environment is rarely discussed in relevant literature. The purpose of this paper is to explore the impact of the type of product and time pressure on consumer online impulse buying intention; this is a relatively new issue of marketing academia in China. Design/methodology/approach In this paper, the experimental methodology was adopted to explore the impact of consumer online impulse buying tendencies, the departure from the type of product and the time pressure. Findings Results show that low-involvement feeling products stimulate consumer online impulse buying tendencies. Simultaneously, there is an interaction effect between time pressure and product type, which is, under the influence of time pressure, the enhancement of low-involvement feeling products on consumer online impulse buying tendency is more significant. Originality/value This study discusses the interaction between time pressure and product type on consumers’ online impulse buying tendency, which has not been studied before. While discussing the impact of product types on consumers’ impulse buying tendency on the internet, this paper considers the impact of time pressure on consumers’ impulsive buying tendency, and applies the term of time pressure, a psychological research term, to the field of marketing research, so as to make the cross-links between disciplines closer.

Up to 25% of patients with nonalcoholic fatty liver disease (NAFLD) are not obese but may have a fat or muscle composition that predisposes them to NAFLD. Our aim was to determine whether body composition parameters associate with NAFLD and to identify genetic contributors to this association. This study included two cohorts. The first included 2,249 participants from the Framingham Heart Study who underwent a computed tomography scan to evaluate hepatic steatosis, dual‐energy x‐ray absorptiometry testing to assess body composition, and clinical examination. Body composition parameters were normalized to total body weight. A subset of participants underwent genotyping with an Affymetrix 550K single‐nucleotide polymorphism array. The second cohort, Michigan Genomics Initiative, included 19,239 individuals with genotyping on the Illumina HumanCoreExome v.12.1 array and full electronic health record data. Using sex‐stratified multivariable linear regression, greater central body fat associated with increased hepatic steatosis while greater lower extremity body fat associated with decreased hepatic steatosis. Greater appendicular lean mass was associated with decreased hepatic steatosis in men but not in women. A polygenic risk score for lipodystrophy (regional or global loss of adipose tissue) was associated with increased hepatic steatosis, increased liver fibrosis, and decreased lower extremity fat mass. Conclusion: Greater central body fat associated with increased hepatic steatosis, while greater lower extremity body fat and, in men, greater appendicular lean mass were associated with decreased hepatic steatosis. A genetic risk score for lipodystrophy was associated with NAFLD and liver fibrosis. Our results suggest that buffering of excess energy by peripheral fat and muscle may protect against NAFLD and liver fibrosis in the general population.

Context Glycogen storage diseases are rare. Increased glycogen in the liver results in increased attenuation. Objective Investigate the association and function of a noncoding region associated with liver attenuation but not histologic nonalcoholic fatty liver disease. Design Genetics of Obesity-associated Liver Disease Consortium. Setting Population-based. Main Outcome Computed tomography measured liver attenuation. Results Carriers of rs4841132-A (frequency 2%-19%) do not show increased hepatic steatosis; they have increased liver attenuation indicative of increased glycogen deposition. rs4841132 falls in a noncoding RNA LOC157273 ~190 kb upstream of PPP1R3B. We demonstrate that rs4841132-A increases PPP1R3B through a cis genetic effect. Using CRISPR/Cas9 we engineered a 105-bp deletion including rs4841132-A in human hepatocarcinoma cells that increases PPP1R3B, decreases LOC157273, and increases glycogen perfectly mirroring the human disease. Overexpression of PPP1R3B or knockdown of LOC157273 increased glycogen but did not result in decreased LOC157273 or increased PPP1R3B, respectively, suggesting that the effects may not all occur via affecting RNA levels. Based on electronic health record (EHR) data, rs4841132-A associates with all components of the metabolic syndrome (MetS). However, rs4841132-A associated with decreased low-density lipoprotein (LDL) cholesterol and risk for myocardial infarction (MI). A metabolic signature for rs4841132-A includes increased glycine, lactate, triglycerides, and decreased acetoacetate and beta-hydroxybutyrate. Conclusions These results show that rs4841132-A promotes a hepatic glycogen storage disease by increasing PPP1R3B and decreasing LOC157273. rs4841132-A promotes glycogen accumulation and development of MetS but lowers LDL cholesterol and risk for MI. These results suggest that elevated hepatic glycogen is one cause of MetS that does not invariably promote MI.

The ever-increasing volume of consumer data provide unprecedented opportunities for firms to predict consumer behavior, target customers, and provide customized service. Recent trends of more restrictive privacy regulations worldwide, however, present great challenges for firms whose business activities rely on consumer data. We address these challenges by applying the recently developed federated learning approach --- a privacy-preserving machine learning approach that uses a parallelized learning algorithm to train a model locally on each individual user's device. We apply this approach to data from an online retailer and train a Gated Recurrent Unit recurrent neural network to predict each consumer's click-stream. We show the firm can predict each consumer's activities with a high level of accuracy without the need to store, access, or analyze consumer data in a centralized location, thereby protecting their sensitive information.

Personalized recommendation systems are becoming increasingly popular in e-commerce. One of the core recommendation algorithms is item-based recommendation algorithm, which recommend by finding products that are similar to other products using consumers' purchase data. However, purchase data contains too little information to reveal the real preference of consumers. The purpose of this paper is to extend the item-based top-k recommendation algorithm by incorporating consumers' browse data, which contains more useful information in revealing the preference of consumers. First, we extend the cosine-based method of modeling the similarity matrix between two products and construct an extended model subjected to varying conditions in terms of browse and purchase combination. Then we test our extended algorithm on a simulated data set and apply it to a real world data set provided by an online B2C company. Both the results of the simulation data and the real data show that our extended algorithm performs significantly better than the traditional item-based algorithm, in terms of hit rate (HR) and average reciprocal hit rank (ARHR). Specifically, our extended algorithm generates 113, 089 (4349.58%) more sales than random algorithms and 25, 335 (28.86%) more sales than traditional cosine-based algorithms. We discuss the theoretical and managerial implications in the conclusion part.