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Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis.

ABSTRACT: Statistical analysis to evaluate mechanistic pathways can be limited by non-causal associations as well as co-linearity of high-dimensional data. Here, we present a protocol evaluating statistical associations between multiple exposure variables (sociodemographic and behavioral), immune biomarkers, and HIV acquisition. We describe steps for study setup, combining Least Absolute Shrinkage and Selective Operator with the standard regression approach, and building nested models. This approach can determine to what extent associations between risks for exposure contributes to HIV acquisition with or without associated changes in immune activation. For complete details on the use and execution of this protocol, please refer to Bender Ignacio et al.¹.

SUBMITTER: Dasgupta S

PROVIDER: S-EPMC10568409 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis.

Dasgupta Sayan S Ulrich Angela K AK Duerr Ann A Bender Ignacio Rachel A RA

STAR protocols 20231003 4

Statistical analysis to evaluate mechanistic pathways can be limited by non-causal associations as well as co-linearity of high-dimensional data. Here, we present a protocol evaluating statistical associations between multiple exposure variables (sociodemographic and behavioral), immune biomarkers, and HIV acquisition. We describe steps for study setup, combining Least Absolute Shrinkage and Selective Operator with the standard regression approach, and building nested models. This approach can d ...[more]

PMID: 37792538

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Project description:BackgroundHyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease.MethodsPatients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated.ResultsA total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%.ConclusionsWe developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.

Project description:Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary. Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS). Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen-containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients. Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.

Project description:BackgroundPostoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.Research questionCan a predictive model be developed to accurately identify patients at high risk of PRF?Study design and methodsIn this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.ResultsTwo hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).InterpretationThis single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

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Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis.

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Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis.

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