Case report: Successful treatment of a rare HER2-positive advanced breast squamous cell carcinoma.

Background: Breast squamous cell carcinoma (SCC) is an uncommon and highly aggressive variant of metaplastic breast cancer. Despite its rarity, there is currently no consensus on treatment guidelines for this specific subtype. Previous studies have demonstrated that chemotherapy alone has limited efficacy in treating breast SCC. However, the potential for targeted therapy in combination with chemotherapy holds promise for future treatment options. Case presentation: In this case report, we present a patient with advanced HER2-positive breast SCC, exhibiting a prominent breast mass, localized ulcers, and metastases in the lungs and brain. Our treatment approach involved the administration of HER2-targeted drugs in conjunction with paclitaxel, resulting in a sustained control of tumor growth. Conclusion: This case represents a rare occurrence of HER2-positive breast SCC, with limited available data on the efficacy of previous HER2-targeted drugs in treating such patients. Our study presents the first application of HER2-targeted drugs in this particular case, offering novel therapeutic insights for future considerations. Additionally, it is imperative to conduct further investigations to assess the feasibility of treatment options in a larger cohort of patients.

Hearing Loss and Associated 7-Year Cognitive Outcomes Among Hispanic and Latino Adults.

Importance: Hearing loss appears to have adverse effects on cognition and increases risk for cognitive impairment. These associations have not been thoroughly investigated in the Hispanic and Latino population, which faces hearing health disparities. Objective: To examine associations between hearing loss with 7-year cognitive change and mild cognitive impairment (MCI) prevalence among a diverse cohort of Hispanic/Latino adults. Design, Setting, and Participants: This cohort study used data from a large community health survey of Hispanic Latino adults in 4 major US cities. Eligible participants were aged 50 years or older at their second visit to study field centers. Cognitive data were collected at visit 1 and visit 2, an average of 7 years later. Data were last analyzed between September 2023 and January 2024. Exposure: Hearing loss at visit 1 was defined as a pure-tone average (500, 1000, 2000, and 4000 Hz) greater than 25 dB hearing loss in the better ear. Main outcomes and measures: Cognitive data were collected at visit 1 and visit 2, an average of 7 years later and included measures of episodic learning and memory (the Brief-Spanish English Verbal Learning Test Sum of Trials and Delayed Recall), verbal fluency (word fluency-phonemic fluency), executive functioning (Trails Making Test-Trail B), and processing speed (Digit-Symbol Substitution, Trails Making Test-Trail A). MCI at visit 2 was defined using the National Institute on Aging-Alzheimer Association criteria. Results: A total of 6113 Hispanic Latino adults were included (mean [SD] age, 56.4 [8.1] years; 3919 women [64.1%]). Hearing loss at visit 1 was associated with worse cognitive performance at 7-year follow-up (global cognition: ß = -0.11 [95% CI, -0.18 to -0.05]), equivalent to 4.6 years of aging and greater adverse change (slowing) in processing speed (ß = -0.12 [95% CI, -0.23 to -0.003]) equivalent to 5.4 years of cognitive change due to aging. There were no associations with MCI. Conclusions and relevance: The findings of this cohort study suggest that hearing loss decreases cognitive performance and increases rate of adverse change in processing speed. These findings underscore the need to prevent, assess, and treat hearing loss in the Hispanic and Latino community.

Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk.

Background: Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods: The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results: At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion: PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata.

Proportional rates models for multivariate panel count data.

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.

Improving trial generalizability using observational studies.

Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early-stage non-small-cell lung patients after surgery.

Synthesizing studies for comparing different treatment sequences in clinical trials.

With advances in cancer treatments and improved patient survival, more patients may go through multiple lines of treatment. It is of clinical importance to choose a sequence of effective treatments (eg, lines of treatment) for individual patients with the goal of optimizing their long-term clinical outcome (eg, survival). Several important issues arise in cancer studies. First, cancer clinical trials are usually conducted by each line of treatment. For a treatment sequence, we may have first line and second line treatment data from two different studies. Second, there is typically a treatment initiation period varying from patient to patient between progression of disease and the start of the second line treatment due to administrative reasons. Additionally, the choice of the second line treatment for patients with progression of disease may depend on their characteristics. We address all these issues and develop semiparametric methods under the potential outcome framework for the estimation of the overall survival probability for a treatment sequence and for comparing different treatment sequences. We establish the large sample properties of the proposed inferential procedures. Simulation studies and an application to a colorectal clinical trial are provided.

Mixture survival trees for cancer risk classification.

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma.

Background: Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. Methods: The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. Key Content and Findings: ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance. Conclusions: Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.

High-Dimensional Precision Medicine From Patient-Derived Xenografts.

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

Mendelian randomization analysis of arsenic metabolism and pulmonary function within the Hispanic Community Health Study/Study of Latinos.

Arsenic exposure has been linked to poor pulmonary function, and inefficient arsenic metabolizers may be at increased risk. Dietary rice has recently been identified as a possible substantial route of exposure to arsenic, and it remains unknown whether it can provide a sufficient level of exposure to affect pulmonary function in inefficient metabolizers. Within 12,609 participants of HCHS/SOL, asthma diagnoses and spirometry-based measures of pulmonary function were assessed, and rice consumption was inferred from grain intake via a food frequency questionnaire. After stratifying by smoking history, the relationship between arsenic metabolism efficiency [percentages of inorganic arsenic (%iAs), monomethylarsenate (%MMA), and dimethylarsinate (%DMA) species in urine] and the measures of pulmonary function were estimated in a two-sample Mendelian randomization approach (genotype information from an Illumina HumanOmni2.5-8v1-1 array), focusing on participants with high inferred rice consumption. Among never-smoking high inferred consumers of rice (n = 1395), inefficient metabolism was associated with past asthma diagnosis and forced vital capacity below the lower limit of normal (LLN) (OR 1.40, p = 0.0212 and OR 1.42, p = 0.0072, respectively, for each percentage-point increase in %iAs; OR 1.26, p = 0.0240 and OR 1.24, p = 0.0193 for %MMA; OR 0.87, p = 0.0209 and OR 0.87, p = 0.0123 for the marker of efficient metabolism, %DMA). Among ever-smoking high inferred consumers of rice (n = 1127), inefficient metabolism was associated with peak expiratory flow below LLN (OR 1.54, p = 0.0108/percentage-point increase in %iAs, OR 1.37, p = 0.0097 for %MMA, and OR 0.83, p = 0.0093 for %DMA). Less efficient arsenic metabolism was associated with indicators of pulmonary dysfunction among those with high inferred rice consumption, suggesting that reductions in dietary arsenic could improve respiratory health.

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

Hearing Sensitivity, Cardiovascular Risk, and Neurocognitive Function: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Importance: Both cardiovascular disease risk and hearing impairment are associated with cognitive dysfunction. However, the combined influence of the 2 risk factors on cognition is not well characterized. Objective: To examine associations between hearing impairment, cardiovascular disease risk, and cognitive function. Design, Setting, and Participants: This population-based, prospective cohort, multisite cross-sectional analysis of baseline data collected between 2008 and 2011 as part of the Hispanic Community Health Study/Study of Latinos included 9623 Hispanic or Latino adults aged 45 to 74 years in New York, Chicago, Miami, and San Diego. Exposures: Hearing impairment of at least mild severity was defined as the pure tone average of 500, 1000, 2000, and 4000 Hz greater than 25 dB hearing level (dB HL) in the better ear. Our measure of cardiovascular disease risk was a latent class variable derived from body mass index, ankle-brachial index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, fasting blood glucose, and the Framingham Cardiovascular Risk score. Main Outcomes and Measures: Results on Brief-Spanish English Verbal Learning Test (episodic learning and memory), and Word Fluency (verbal fluency), and Digit Symbol Subtest (processing speed/executive functioning), and a cognitive composite of the mentioned tests (overall cognition). Results: Participants (N = 9180) were 54.4% female and age 56.5 years on average. Hearing impairment was associated with poorer performance on all cognitive measures (global cognition: unstandardized ß, -0.11; 95% CI, -0.16 to 0.07). Cardiovascular grouping (healthy, typical, high cardiovascular disease risk, and hyperglycemia) did not attenuate the associations between hearing impairment and cognition (global cognition: unstandardized ß, -0.11; 95% CI, -0.15 to -0.06). However, cardiovascular grouping interacted with hearing impairment such that hyperglycemia in the context of hearing impairment exacerbated poor performance on learning and memory tasks (F3 = 3.70 and F3 = 2.92, respectively). Conclusions and Relevance: The findings of this cohort study suggest that hearing impairment increases the likelihood that individuals with excessively high glucose perform poorly on learning and memory tasks. Further research is needed to specify the mechanisms by which cardiovascular disease risk and hearing impairment are collectively associated with cognition.

A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy.

Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.

Microvascular Ultrasonic Imaging of Angiogenesis Identifies Tumors in a Murine Spontaneous Breast Cancer Model.

The purpose of this study is to determine if microvascular tortuosity can be used as an imaging biomarker for the presence of tumor-associated angiogenesis and if imaging this biomarker can be used as a specific and sensitive method of locating solid tumors. Acoustic angiography, an ultrasound-based microvascular imaging technology, was used to visualize angiogenesis development of a spontaneous mouse model of breast cancer (n = 48). A reader study was used to assess visual discrimination between image types, and quantitative methods utilized metrics of tortuosity and spatial clustering for tumor detection. The reader study resulted in an area under the curve of 0.8, while the clustering approach resulted in the best classification with an area under the curve of 0.95. Both the qualitative and quantitative methods produced a correlation between sensitivity and tumor diameter. Imaging of vascular geometry with acoustic angiography provides a robust method for discriminating between tumor and healthy tissue in a mouse model of breast cancer. Multiple methods of analysis have been presented for a wide range of tumor sizes. Application of these techniques to clinical imaging could improve breast cancer diagnosis, as well as improve specificity in assessing cancer in other tissues. The clustering approach may be beneficial for other types of morphological analysis beyond vascular ultrasound images.

Volunteerism and Cardiovascular Health: The HCHS/SOL Sociocultural Ancillary Study.

OBJECTIVES: The objective of this study was to examine the association between volunteerism and favorable cardiovascular health (CVH) among Hispanics/Latinos living in the US. METHODS: Data from the Hispanic Community Health Study/Study of Latinos (2008-2011) Sociocultural Ancillary Study were used (N = 4,926; ages 18-74 years). Favorable CVH was defined as positive profiles of all major CVD risk factors: low total serum cholesterol, blood pressure, and body mass index; not having diabetes; and not smoking. Survey-weighted logistic regression models were adjusted for sociodemographic, lifestyle, and psychological factors. In secondary analyses, we tested whether the volunteerism-CVH association was modified by sex, age, or years lived in the US (<10 vs. ≥10 years; a proxy acculturation measure). RESULTS: Prevalence of volunteerism was 14.5%. Compared to non-volunteers, volunteers had 1.67 higher odds of favorable CVH in the fully-adjusted model (Odds Ratio [OR] = 1.67, 95% Confidence Interval [CI] = 1.11, 2.52). There was evidence of effect modification by acculturation; only volunteers who had lived in the US ≥10 years had 2.41 higher odds of favorable CVH (OR = 2.41, 95% CI=1.53, 3.80). There was no evidence of effect modification by sex or age. CONCLUSIONS: Volunteerism was associated with favorable CVH among US Hispanics/Latinos.

Intralesional Triamcinolone May Not Be Beneficial for Treating Acute Hidradenitis Suppurativa Lesions: A Double-Blind, Randomized, Placebo-Controlled Trial.

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, inflammatory condition characterized by recurrent nodules, sinus tracts, comedones, and scarring. Hidradenitis suppurativa is often associated with pain and decreased quality of life. Limited clinical trial data exist regarding the management of acute HS lesions, but clinical experience and a prospective case series suggest that intralesional triamcinolone may be useful. OBJECTIVE: To compare the efficacy of intralesional triamcinolone to placebo for the treatment of HS inflammatory lesions. MATERIALS AND METHODS: This is a double-blind, randomized, placebo-controlled trial comparing intralesional triamcinolone 10 mg/mL, triamcinolone 40 mg/mL, and normal saline (NS). Thirty-two subjects at University of North Carolina Dermatology and Skin Cancer Centers were enrolled for a total of 67 lesions. Subjects reported pain scores, days to resolution, and satisfaction on a standardized survey over a 14-day period. RESULTS: When intralesional injections of triamcinolone 10 mg/mL, triamcinolone 40 mg/mL, and NS were compared, no significant difference was found for days to HS inflammatory lesion clearance, pain reduction at Day 5, or patient satisfaction. CONCLUSION: No statistically significant difference was found between varying concentrations of triamcinolone and NS for the treatment of HS lesions. Steroid injections may be less effective for the management of acute HS than typically presumed.

Reflector Localization of Breast Lesions and Parameters Associated with Positive Surgical Margins in Women Undergoing Breast Conservation Surgery.

OBJECTIVE: To evaluate our experience with reflector localization of breast lesions and parameters influencing surgical margins in patients with a malignant diagnosis. METHODS: A retrospective institution review board-approved review of our institutional database was performed for breast lesions preoperatively localized from September 1, 2016, through December 31, 2017. Wire localizations were excluded. From electronic medical records and imaging, the following data was recorded: breast density, lesion type and size, reflector placement modality and number placed, reflector distance from lesion and skin, excision of lesion and reflector, tissue volume, margin status, and final pathology. Statistical analysis was performed with a Fisher's exact test, Mann-Whitney test, and logistic regression. P < 0.05 was significant. RESULTS: A total of 111 reflectors were deployed in the breasts of 103 women with 109 breast lesions. Ninety (81.1%) reflectors were placed under mammographic guidance and 21 (18.9%) under US. The lesions consisted of 68 (62.4%) masses, 17 (15.6%) calcifications, 2 (1.8%) architectural distortions, and 22 (20.2%) biopsy markers. Fourteen (21.2%) of 66 cases with a preoperative malignant diagnosis had a positive surgical margin. Final pathology, including 6 lesions upgraded to malignancy on excision, demonstrated 72 (66.0%) malignant, 22 (20.2%) high-risk, and 15 (13.8%) benign lesions. Univariate and multivariate analysis revealed no statistically significant parameters (lesion type or size, placement modality, reflector distance to skin or lesion, specimen radiography or pathology) were associated with a positive surgical margin. CONCLUSION: Reflector localization is an alternative to wire localization of breast lesions. There were no lesion-specific or technical parameters affecting positive surgical margins.

Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout.

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow-up time and the subject-specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood-based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite-dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.

Mendelian randomization of inorganic arsenic metabolism as a risk factor for hypertension- and diabetes-related traits among adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort.

BACKGROUND: Hypertension and diabetes have been associated with inefficient arsenic metabolism, primarily through studies undertaken in populations exposed through drinking water. Recently, rice has been recognized as a source of arsenic exposure, but it remains unclear whether populations with high rice consumption but no known water exposure are at risk for the health problems associated with inefficient arsenic metabolism. METHODS: The relationships between arsenic metabolism efficiency (% inorganic arsenic, % monomethylarsenate and % dimethylarsinate in urine) and three hypertension- and seven diabetes-related traits were estimated among 12 609 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). A two-sample Mendelian randomization approach incorporated genotype-arsenic metabolism relationships from literature, and genotype-trait relationships from HCHS/SOL, with a mixed-effect linear model. Analyses were stratified by rice consumption and smoking. RESULTS: Among never smokers with high rice consumption, each percentage point increase in was associated with increases of 1.96 mmHg systolic blood pressure (P = 0.034) and 1.85 mmHg inorganic arsenic diastolic blood pressure (P = 0.003). Monomethylarsenate was associated with increased systolic (1.64 mmHg/percentage point increase; P = 0.021) and diastolic (1.33 mmHg/percentage point increase; P = 0.005) blood pressure. Dimethylarsinate, a marker of efficient metabolism, was associated with lower systolic (-0.92 mmHg/percentage point increase; P = 0.025) and diastolic (-0.79 mmHg/percentage point increase; P = 0.004) blood pressure. Among low rice consumers and ever smokers, the results were consistent with no association. Evidence for a relationship with diabetes was equivocal. CONCLUSIONS: Less efficient arsenic metabolism was associated with increased blood pressure among never smokers with high rice consumption, suggesting that arsenic exposure through rice may contribute to high blood pressure in the Hispanic/Latino community.

I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms.

We propose a statistical boosting method, termed I-Boost, to integrate multiple types of high-dimensional genomics data with clinical data for predicting survival time. I-Boost provides substantially higher prediction accuracy than existing methods. By applying I-Boost to The Cancer Genome Atlas, we show that the integration of multiple genomics platforms with clinical variables improves the prediction of survival time over the use of clinical variables alone; gene expression values are typically more prognostic of survival time than other genomics data types; and gene modules/signatures are at least as prognostic as the collection of individual gene expression data.