Sickle cell allele HBB-rs334(T) is associated with decreased risk of childhood Burkitt lymphoma in East Africa.

Burkitt lymphoma (BL) is an aggressive B-cell lymphoma that significantly contributes to childhood cancer burden in sub-Saharan Africa. Plasmodium falciparum, which causes malaria, is geographically associated with BL, but the evidence remains insufficient for causal inference. Inference could be strengthened by demonstrating that mendelian genes known to protect against malaria-such as the sickle cell trait variant, HBB-rs334(T)-also protect against BL. We investigated this hypothesis among 800 BL cases and 3845 controls in four East African countries using genome-scan data to detect polymorphisms in 22 genes known to affect malaria risk. We fit generalized linear mixed models to estimate odds ratios (OR) and 95% confidence intervals (95% CI), controlling for age, sex, country, and ancestry. The ORs of the loci with BL and P. falciparum infection among controls were correlated (Spearman's ρ = 0.37, p = .039). HBB-rs334(T) was associated with lower P. falciparum infection risk among controls (OR = 0.752, 95% CI 0.628-0.9; p = .00189) and BL risk (OR = 0.687, 95% CI 0.533-0.885; p = .0037). ABO-rs8176703(T) was associated with decreased risk of BL (OR = 0.591, 95% CI 0.379-0.992; p = .00271), but not of P. falciparum infection. Our results increase support for the etiological correlation between P. falciparum and BL risk.

Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations.

BACKGROUND: Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles. OBJECTIVES: Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles. METHODS: We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts. RESULTS: Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED. CONCLUSIONS: Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

BACKGROUND: Objective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health. OBJECTIVES: To identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification. METHODS: In a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m2] 22 ± 11.6) consumed ad libitum a UPF-DP (80% UPF) and an unprocessed DP (UN-DP; 0% UPF) for 2 wk each. Metabolites were measured using liquid chromatography with tandem mass spectrometry in ethylenediaminetetraacetic acid plasma, collected at week 2 and 24-h, and spot urine, collected at weeks 1 and 2, of each DP. Linear mixed models, adjusted for energy intake, were used to identify metabolites that differed between DPs. RESULTS: After multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP. CONCLUSIONS: Consuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Varying-coefficients for regional quantile via KNN-based LASSO with applications to health outcome study.

Health outcomes, such as body mass index and cholesterol levels, are known to be dependent on age and exhibit varying effects with their associated risk factors. In this paper, we propose a novel framework for dynamic modeling of the associations between health outcomes and risk factors using varying-coefficients (VC) regional quantile regression via K-nearest neighbors (KNN) fused Lasso, which captures the time-varying effects of age. The proposed method has strong theoretical properties, including a tight estimation error bound and the ability to detect exact clustered patterns under certain regularity conditions. To efficiently solve the resulting optimization problem, we develop an alternating direction method of multipliers (ADMM) algorithm. Our empirical results demonstrate the efficacy of the proposed method in capturing the complex age-dependent associations between health outcomes and their risk factors.

Utilization of Medicare's chronic care management services by primary care providers.

BACKGROUND: Medicare billing codes introduced in 2015 reimburses primary care providers for non-face-to-face, chronic care management (CCM) services rendered by clinical staff. PURPOSE: The purpose of this manuscript was to describe provider trends in billed CCM services and identify factors associated with CCM utilization. METHODS: Observational study using Medicare Public Use Files, 2015 to 2018. General, family, geriatric, and internal medicine physicians, nurse practitioners (NPs), and physician assistants (PAs) with billed primary care services were included. Multivariable analyses modeled associations between the CCM services and type of provider, adjusting for year, primary care services, practice, and patient characteristics. FINDINGS: Among 140,465 physicians and 141,118 NPs/PAs, CCM services increased each year, yet remained underutilized: 2% to 7% of physicians and 0.3% to 1.3% of NPs/PAs billed CCM in 2018. Increases in beneficiaries (p < .0001), percentage of dually enrolled (p = .0134), and primary care services (p < .0001) predicted higher CCM utilization. DISCUSSION: CCM utilization reflects practice-based efforts to improve patient access to care by enhancing care delivery.

Quantile forward regression for high-dimensional survival data.

Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean outcome, targeting average people. Additionally, the direction and magnitude of covariates' effects on the mean outcome may not hold across different quantiles of the outcome distribution. To accommodate the heterogeneous characteristics of covariates and provide a flexible risk model, we propose a quantile forward regression model for high-dimensional survival data. Our method selects variables by maximizing the likelihood of the asymmetric Laplace distribution (ALD) and derives the final model based on the extended Bayesian Information Criterion (EBIC). We demonstrate that the proposed method enjoys a sure screening property and selection consistency. We apply it to the national health survey dataset to show the advantages of a quantile-specific prediction model. Finally, we discuss potential extensions of our approach, including the nonlinear model and the globally concerned quantile regression coefficients model.

The relationship between gut microbiota and short chain fatty acids in the renal calcium oxalate stones disease.

The relationship of gut microbiota and calcium oxalate stone has been limited investigated, especially with no study of gut microbiota and short chain fatty acids (SCFAs) in nephrolithiasis. We provided Sprague Dawley rats of renal calcium oxalate stones with antibiotics and examined the renal crystals deposition. We also performed a case-control study by analyzing 16S rRNA microbial profiling, shotgun metagenomics and SCFAs in 153 fecal samples from non-kidney stone (NS) controls, patients with occasional renal calcium oxalate stones (OS) and patients with recurrent stones (RS). Antibiotics reduced bacterial load in feces and could promote the formation of renal calcium crystals in model rats. In addition, both OS and RS patients exhibited higher fecal microbial diversity than NS controls. Several SCFAs-producing gut bacteria, as well as metabolic pathways associated with SCFAs production, were considerably lower in the gut microbiota among the kidney stone patients compared with the NS controls. Representation of genes involved in oxalate degradation showed no significance difference among groups. However, fecal acetic acid concentration was the highest in RS patients with high level of urinary oxalate, which was positively correlated with genes involvement in oxalate synthesis. Administration of SCFAs reduced renal crystals. These results shed new light on bacteria and SCFAs, which may promote the development of treatment strategy in nephrolithiasis.

Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach.

Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening, as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods may produce false negatives and positives. Moreover, screening approaches typically need to involve tuning parameters and extra modeling steps in order to reach a final model. We propose a sequential conditioning approach by dynamically updating the conditioning set with an iterative selection process. We provide its theoretical properties under the framework of generalized linear models. Powered by an extended Bayesian information criterion as the stopping rule, the method will lead to a final model without the need to choose tuning parameters or threshold parameters. The practical utility of the proposed method is examined via extensive simulations and analysis of a real clinical study on predicting multiple myeloma patients' response to treatment based on their genomic profiles.

The Efficacy of Neoadjuvant Versus Adjuvant Therapy for Resectable Esophageal Cancer Patients: A Systematic Review and Meta-Analysis.

OBJECTIVE: Inconclusive results are available as to whether chemo/radiotherapy should be administered to resectable esophageal cancer patients before surgery (neoadjuvant therapy) or after surgery (adjuvant therapy). The paper, via a meta-analysis of effects of treatment modalities when administering chemo/radiotherapy, aims to systematically evaluate the effect of timing of chemo/radiotherapy and surgery. METHODS: We performed a systematic literature search for clinical trials of neoadjuvant and adjuvant therapy for patients with esophageal cancer. Using meta-analysis, we conducted direct and adjusted indirect comparisons of overall survival, complete resection rate (R0 resection), perioperative mortality, leakage rate and local recurrence in patients with resectable esophageal cancer. RESULTS: A total of 32 studies involving 7985 patients with esophageal cancer were included in the meta-analysis. Twenty-five randomized controlled studies indirectly compared neoadjuvant/adjuvant therapy with surgery alone, while five non-randomized controlled studies and two randomized controlled studies directly compared neoadjuvant with adjuvant therapy. Neoadjuvant therapy followed by surgery, compared with surgery along with adjuvant therapy, showed a significant overall survival advantage in our pooled analysis (HR 0.88; 95% CI 0.79-0.98). Directly compared with adjuvant therapy, neoadjuvant therapy demonstrated a lower local recurrence rate (OR 0.56; 95% CI 0.43-0.74) with low heterogeneity (I2 = 1%). Neoadjuvant therapy, comparing to surgery with or without adjuvant therapy, showed a significantly higher R0 resection rate (OR 2.86; 95% CI 2.02-4.04) with moderate heterogeneity (I2 = 38%) and no significant differences in postoperative anastomotic leakage (P = 0.50). However, neoadjuvant therapy, compared with surgery adjuvant therapy, significantly increased perioperative mortality in both direct and indirect comparisons (P < 0.01). CONCLUSIONS: We found that neoadjuvant therapy was associated with higher overall survival and R0 resection rate without increasing postoperative anastomotic leakage for patients with resectable esophageal cancer, whereas neoadjuvant therapy was associated with higher perioperative mortality after esophagectomy.

A network-based variable selection approach for identification of modules and biomarker genes associated with end-stage kidney disease.

AIMS: Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach. METHODS: Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset. RESULTS: Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-ß and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively. CONCLUSION: Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.

Mapping Together Kinetic and Kinematic Abilities of the Hand.

Millions of people have reduced hand function; this loss of function can be due to injury, disease, or aging. Loss of hand function is identified as reduced motion abilities in the fingers or a decrease in the ability of the fingers to generate force. Unfortunately, there are limited data available regarding each finger's ability to produce force and how those force characteristics vary with changes in finger posture. To relate motion and force abilities of the fingers, first, an approach to measure and map them together is needed. The goal of this work was to develop and demonstrate a method to quantify the force abilities of the fingers and map these forces to the kinematic space associated with each finger. Using motion capture and multiaxis load cells, finger forces were quantified at different positions over their ranges of motion. These two sets of data were then converted to the same coordinate space and mapped together. Further, the data were normalized for the index finger and mapped as a population space model. The ability to quantify motion and force data for each finger and map them together will provide an improved understanding of the effects of treatments and rehabilitation, identifying functional loss due to injury or disease, and device design.

Consistent Estimation of Generalized Linear Models with High Dimensional Predictors via Stepwise Regression.

Predictive models play a central role in decision making. Penalized regression approaches, such as least absolute shrinkage and selection operator (LASSO), have been widely used to construct predictive models and explain the impacts of the selected predictors, but the estimates are typically biased. Moreover, when data are ultrahigh-dimensional, penalized regression is usable only after applying variable screening methods to downsize variables. We propose a stepwise procedure for fitting generalized linear models with ultrahigh dimensional predictors. Our procedure can provide a final model; control both false negatives and false positives; and yield consistent estimates, which are useful to gauge the actual effect size of risk factors. Simulations and applications to two clinical studies verify the utility of the method.

Lymph Node Station-Based Nodal Staging System for Esophageal Squamous Cell Carcinoma: A Large-Scale Multicenter Study.

BACKGROUND: The American Joint Committee on Cancer (AJCC) nodal staging for esophageal squamous cell carcinoma (ESCC) has been defined by the number of metastatic lymph nodes (N system). However, the precise counting of individual positive lymph nodes is difficult and unreliable in some clinical settings, which calls for a more available and reliable system. This study examined the performance of a newly proposed nodal staging category, termed the S system, based on the number of metastatic lymph node stations. METHODS: Using the Kaplan-Meier method and Cox-regression analysis, this study retrospectively analyzed the overall survival (OS) of 2285 ESCC patients who underwent esophagectomy in three major China hospitals. Predictive models were constructed, and C-indices were computed to evaluate the discriminatory power of the S system, and to compare it with the N system. RESULTS: The categories defined by the S system were more homogeneous in terms of OS than those defined by the N system. Overall, the S system had a slightly better C-index (p = 0.659) than the N system ((p = 0.658). Subgroup analyses also showed that the C-index of the S system was slightly better than that of the N system for each subgroup of sex and age, but the two were comparable for each subgroup defined by the tumor location. CONCLUSION: The S system demonstrated a competing prognostic performance compared with the current AJCC N system. Due to the relatively easy accessibility of the number of metastatic lymph node stations, the S system may offer an easier option for cancer staging without a loss of discriminative power.

Multiclass linear discriminant analysis with ultrahigh-dimensional features.

Within the framework of Fisher's discriminant analysis, we propose a multiclass classification method which embeds variable screening for ultrahigh-dimensional predictors. Leveraging interfeature correlations, we show that the proposed linear classifier recovers informative features with probability tending to one and can asymptotically achieve a zero misclassification rate. We evaluate the finite sample performance of the method via extensive simulations and use this method to classify posttransplantation rejection types based on patients' gene expressions.

Covariance-Insured Screening.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss weak signals. By incorporating the inter-feature dependence, a covariance-insured screening approach is proposed to identify predictors that are jointly informative but marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and a real data study for selecting potential genetic factors related to the onset of multiple myeloma.

Management of tunneled-cuffed catheter-related right atrial thrombosis in hemodialysis patients.

OBJECTIVES: Catheter-related right atrial thrombosis (CRAT) is an underreported but potentially life-threatening complication associated with the use of tunneled-cuffed catheters among hemodialysis (HD) patients. Because little is known about the evidence-based guidelines for the optimal management of CRAT among HD patients, this article reports findings based on 20 patients diagnosed with CRAT after catheter replacement and anticoagulation treatment. METHODS: The article retrospectively reviews the hospital records of 20 HD patients treated in the West China Hospital with diagnosis of CRAT from March 2013 to May 2016. Once CRAT was diagnosed, tunneled-cuffed catheters were exchanged over a guidewire in situ and the locations of the new catheter tips were adjusted to be away from the original sites. Immediately after the insertion of a new tunneled-cuffed catheter and at the end of each HD session, both ports of the catheters were locked with unfractionated heparin solution. Patients younger than 70 years of age were treated with warfarin at a target International Normalized Ratio of 1.5 to 1.9, whereas those older than 70 years were treated with dual antiplatelet therapy. All patients were on regular dialysis without thrombolysis or thrombectomy. RESULTS: During the follow-up, two patients died of gastrointestinal massive hemorrhage and one died of acute myocardial infarction. No fatal pulmonary embolism or other CRAT complication-related deaths were observed. A total of eight patients had complete dissolution of CRAT, and 12 patients had reduction in thrombi size. CONCLUSIONS: Maintenance of HD by replacing catheters and providing oral anticoagulation/antiplatelet therapies may be an effective strategy for treating HD patients with CRAT.

Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes.

Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of identifying predictive biomarkers, understanding biological mechanisms, and making accurate predictions. To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. The proposed new class of statistics, with a flexible weighting scheme, is general and includes the Kolmogorov statistic as a special case. Moreover, the method does not rely on rigid regression model assumptions and can be easily implemented. We show that our method possesses sure screening properties, and confirm the utility of the proposal with extensive simulation studies. We apply the method to analyze a multiple myeloma study on detecting gene signatures for cancer patients' survival.

The Effect of Overhydration on Mortality and Technique Failure Among Peritoneal Dialysis Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Overhydration is common among peritoneal dialysis (PD) patients and can affect PD-related outcomes. This paper aims to systematically investigate whether bioimpedance-assessed overhydration is a predictor for mortality and technique failure in PD patients. METHODS: We conducted a systematic review and meta-analysis of cohort studies on overhydration and prognosis in PD patients, strictly complying with the Preferred Reporting Items for Systematical Reviews and Meta-analyses. RESULTS: Eight articles met the selection criteria and 5 studies were included in the meta-analysis. Meta-analyses-revealed overhydration, defined as a high ratio of extracellular water/total body water (ECW/TBW), was significantly associated with higher risk for all-cause mortality and technique failure. Other higher dichotomized overhydration indicators and continuous hydration variables all indicated overhydration as a significant risk factor for all-cause mortality. CONCLUSION: Overhydration, defined by a higher ratio of ECW/TBW, might be an independent predictor for all-cause mortality and technique failure among PD patients. However, more studies are needed to confirm this conclusion. Video Journal Club 'Cappuccino with Claudio Ronco' at https://www.karger.com/Journal/ArticleNews/223997?​sponsor=52.

Conditional screening for ultra-high dimensional covariates with survival outcomes.

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorily known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a diffuse large B-cell lymphoma dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data.