Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study

BACKGROUND

Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations.

METHODS

To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data were collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg.

RESULTS

Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions.

LIMITATIONS

The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometric data.

CONCLUSIONS

In summary, our results suggest a strong link between post-COVID depression and fatigue, highlighting the need for integrative treatment approaches.

Spatial Relationships in the Tumor Microenvironment Demonstrate Association with Pathologic Response to Neoadjuvant Chemoimmunotherapy in Muscle-invasive Bladder Cancer

BACKGROUND

Platinum-based neoadjuvant chemotherapy (NAC) is standard for patients with muscle-invasive bladder cancer (MIBC). Pathologic response (complete: ypT0N0 and partial:

OBJECTIVE

Using the NanoString GeoMx platform, we performed proteomic digital spatial profiling (DSP) on transurethral resections of bladder tumors from 18 responders (

DESIGN, SETTING, AND PARTICIPANTS

Pretreatment tumor samples were stained by hematoxylin and eosin and immunofluorescence (panCK and CD45) to select four regions of interest (ROIs): tumor enriched (TE), immune enriched (IE), tumor/immune interface (tumor interface = TX and immune interface = IX).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

DSP was performed with 52 protein markers from immune cell profiling, immunotherapy drug target, immune activation status, immune cell typing, and pan-tumor panels.

RESULTS AND LIMITATIONS

Protein marker expression patterns were analyzed to determine their association with pathologic response, incorporating or agnostic of their ROI designation (TE/IE/TX/IX). Overall, DSP-based marker expression showed high intratumoral heterogeneity; however, response was associated with markers including PD-L1 (ROI agnostic), Ki-67 (ROI agnostic, TE, IE, and TX), HLA-DR (TX), and HER2 (TE). An elastic net model of response with ROI-inclusive markers demonstrated better validation set performance (area under the curve [AUC] = 0.827) than an ROI-agnostic model (AUC = 0.432). A model including DSP, tumor mutational burden, and clinical data performed no better (AUC = 0.821) than the DSP-only model.

CONCLUSIONS

Despite high intratumoral heterogeneity of DSP-based marker expression, we observed associations between pathologic response and specific DSP-based markers in a spatially dependent context. Further exploration of tumor region-specific biomarkers may help predict response to neoadjuvant chemoimmunotherapy in MIBC.

PATIENT SUMMARY

In this study, we used the GeoMx platform to perform proteomic digital spatial profiling on transurethral resections of bladder tumors from 18 responders and 18 nonresponders from two studies of neoadjuvant chemotherapy (gemcitabine and cisplatin) plus immune checkpoint inhibitor therapy (LCCC1520 [pembrolizumab] and BLASST-1 [nivolumab]). We found that assessing protein marker expression in the context of tumor architecture improved response prediction.

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Key Words

• BLASST-1
• Bladder cancer
• Digital spatial profiling
• Elastic net regression
• GeoMx
• LCCC1520
• Neoadjuvant chemoimmunotherapy

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MESH Headings

• Humans
• Cisplatin
• Neoadjuvant Therapy/methods
• Gemcitabine
• Nivolumab/therapeutic use
• Proteomics
• Tumor Microenvironment
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/pathology
• Biomarkers, Tumor
• Muscles/pathology
• Immunotherapy
• Neoplasm Invasiveness
• Cystectomy

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Grants

• P30 CA016086 NCI NIH HHS
• R01 CA241810 NCI NIH HHS
• T32 CA244125 NCI NIH HHS

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Pathology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Bentley Midkiff Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gabriela De la Cruz Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Vladmir Makarov Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Leah Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Petros Grivas Department of Medicine, Division of Medical Oncology, University of Washington, Fred Hutchinson Cancer Center, Seattle, WA, USA
Marc A Bjurlin Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA; Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA; Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Shilpa Gupta Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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Tissue-specific RNA methylation prediction from gene expression data using sparse regression models

N6-methyladenosine (m6A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m6A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

BACKGROUND

Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity.

METHODS

This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes.

RESULTS

Findings indicated that the final modified model fitted the data accurately with [Formula: see text]= 16.09, P < .001, [Formula: see text]/ degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators.

CONCLUSIONS

The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.

Resting-State Functional Connectivity Explained Psychotic-like Experiences in the General Population and Partially Generalized to Patients and Relatives

Background

Psychotic-like experiences (PLEs) are considered the subclinical portion of the psychosis continuum. Research suggests that there are resting-state functional connectivity (rsFC) substrates of PLEs, yet it is unclear if the same substrates underlie more severe psychosis. Here, to our knowledge, we report the first study to build a cross-validated rsFC model of PLEs in a large community sample and directly test its ability to explain psychosis in an independent sample of patients with psychosis and their relatives.

Methods

Resting-state FC of 855 healthy young adults from the WU-Minn Human Connectome Project (HCP) was used to predict PLEs with elastic net. An rsFC composite score based on the resulting model was correlated with psychotic traits and symptoms in 118 patients with psychosis, 71 nonpsychotic first-degree relatives, and 45 healthy control subjects from the psychosis HCP.

Results

In the HCP, the cross-validated model explained 3.3% of variance in PLEs. Predictive connections spread primarily across the default, frontoparietal, cingulo-opercular, and dorsal attention networks. The model partially generalized to a younger, but not older, subsample in the psychosis HCP, explaining two measures of positive/disorganized psychotic traits (the Structured Interview for Schizotypy: β = 0.25, pone-tailed = .027; the Schizotypy Personality Questionnaire positive factor: β = 0.14, pone-tailed = .041). However, it did not differentiate patients from relatives and control subjects or explain psychotic symptoms in patients.

Conclusions

Some rsFC substrates of PLEs are shared across the psychosis continuum. However, explanatory power was modest, and generalization was partial. It is equally important to understand shared versus distinct rsFC variances across the psychosis continuum.

County-level analysis on occupation and ecological determinants of child abuse and neglect rates employing elastic net regression

BACKGROUND

Occupation is a known determinant of worker physical and behavioral health risk, yet most previous studies have focused on unemployment, underemployment, and job satisfaction to understand child maltreatment risk.

OBJECTIVE

This county-level study (n = 278) investigated the association between occupation and child maltreatment rates and community well-being in California, Colorado, Minnesota, Oregon, and New Mexico.

PARTICIPANTS AND SETTING

States were selected due to having comparable, publicly available county-level data on substantiated child abuse and neglect rates within a five-year span between 2015 and 2020.

METHODS

Using US Census Bureau American Community Survey data, we collected percentages of the employed population among 13 occupations. Five additional community health indicators came from the County Health Rankings and Roadmaps. Elastic net linear regression was used for variable selection and because of explanatory variables' interrelationships. Linear regression was used to model individual industries positively associated with child abuse rates.

RESULTS

The elastic net model selected ten important variables in explaining child maltreatment rates. Important occupational sectors were agriculture, forestry, fishing (AFF), manufacturing, wholesale, retail, finance, and education. Important community indicators included housing, injury deaths, and poor mental health days. Only AFF and retail showed greater child abuse rates with increasing percentages of the workforce in these occupations in unadjusted models (AFF: β = 0.03 SE = 0.01, p = 0.02; Retail: β = 0.09 SE = 0.04, p = 0.02).

CONCLUSIONS

Our findings suggest group-level effects of counties with a larger AFF and retail presence experiencing higher child maltreatment rates. Given that numerous prior studies of county economies note the strong associations of certain employment types with cultural attitudes, educational opportunities, regional biases, and other unmeasured variables, future studies should incorporate individual level data in a multilevel framework.

Correlating metal exposures and dietary habits with hyperuricemia in a large urban elderly cohort by artificial intelligence

Epidemiological studies using conventional statistical methods have reported an association between individual metal exposure and hyperuricemia (HUA). There is also evidence that diet may influence HUA development, although the available data are inconsistent. We therefore used an elastic net regression (ENR) model to screen the usefulness of various environmental and dietary factors as predictors of HUA in a large sample cohort. This study included 6217 subjects drawn from the Shenzhen Aging Related Disorder Cohort. We obtained information on the subjects' dietary habits via face-to-face interviews and used inductively coupled plasma mass spectrometry (ICP-MS) to measure the urinary concentrations of 24 metals to which elderly persons in large urban areas may be exposed. An elastic net regression (ENR) model was generated to screen the utility of the metals and dietary factors as predictors of HUA, and we demonstrated the superiority of the ENR model by comparing it to a traditional logistic regression model. The identified predictors were used to create a clinically usable nomogram for identifying patients at risk of developing HUA. The area under curve (AUC) value of the final model was 0.692 for the training set and 0.706 for the test set. Important predictors of HUA were Zn, As, V, and Fe as well as consumption of wheat, beans, and rice; the corresponding estimated odds ratios and 95% confidence intervals were 1.091 (0.932,1.251), 1.190 (1.093,1.286), 0.924 (0.793,1.055), 0.704 (0.626,0.781), 0.998 (0.996,1.001), 0.993 (0.989,0.998), and 1.001 (0.998,1.002), respectively. In contrast to previous studies, we found that both urinary metal concentrations and dietary habits are important for predicting HUA risk. Exposure to specific metals and consumption of specific foods were identified as important predictors of HUA, indicating that the incidence of this disease could be reduced by reducing exposure to these metals and promoting improved dietary habits.

Association of urinary heavy metals co-exposure and adult depression: Modification of physical activity

OBJECTIVE

This study aimed to evaluate the association between urinary heavy metal mixture exposure and depression, and the modifying role of physical activity in the effects of heavy metal mixture on depression risk was also considered.

METHODS

Data of this study were derived from the National Health and Nutrition Examination Survey 2011-2016. Depression was measured by the Patient Health Questionnaire. We first selected 6 (cadmium, cobalt, tin, antimony, thallium, and mercury) from 14 heavy metals through elastic net regression for further analysis. Then binomial logistic regression, generalized additive model, environment risk score (ERS), and weighted quantile sum (WQS) regression were adopted to assess the effects of six metals individual and cumulative exposure on depression risk. Finally, we also examined whether physical activity could mitigate the effects of heavy metal co-exposure on depression risk.

RESULTS

Totally, 4212 participants were included and 7.40% of subjects were with depression. We found urinary tin and antimony were separately associated with increased odds of depression (Sb: OR = 1.285, 95% CI: 1.064-1.553; Sn: OR = 1.281, 95% CI: 1.097-1.495), and a linear dose-response relationship between tin and depression was also noticed (P < 0.05). Meanwhile, urinary heavy metals co-exposure was positively related to depression risk (ERS_Q4: OR = 2.691, 95% CI: 1.399-5.174; WQS_positive: OR = 1.465, 95% CI: 1.063-2.021), in which tin, antimony, and cadmium were identified with greater contributions to the overall mixture effect. In both ERS and WQS models, the significant positive association between the metal mixture and depression risk remained only in those who were inactive in physical activity.

CONCLUSION

Our study concluded the detrimental effect of heavy metals in combined exposure on the risk of depression, which might be attenuated by physical activity.

Application of Elastic Net Regression for Modeling COVID-19 Sociodemographic Risk Factors

Objectives

COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19.

Methods

We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period).

Results

Statistically, elastic net improved prediction when compared to multiple regression, as almost every HHS model consistently had a lower root mean square error (RMSE) and satisfactory R2 coefficients. These analyses show that the percentage of minorities, disabled individuals, individuals living in group quarters, and individuals who voted Democratic correlated significantly with COVID-19 attack rate as determined by Variable Importance Plots (VIPs).

Conclusions

The percentage of minorities per county correlated positively with cases in the earlier time period and negatively in the later time period, which complements previous research. In contrast, higher percentages of disabled individuals per county correlated negatively in the earlier time period. Counties with an above average percentage of group quarters experienced a high attack rate early which then diminished in significance after the primary vaccine rollout. Higher Democratic voting consistently correlated negatively with cases, coinciding with previous findings regarding a partisan divide in COVID-19 cases at the county level. Our findings can assist policymakers in distributing resources to more vulnerable counties in future pandemics based on SVI.

Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression

Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook

Objectives

To comprehend the relationship between various indicators of health service equity and patients’ health expenditure poverty in different regions of China, identify areas where equity in health service is lacking and provide ideas for improving patients’ health expenditure poverty.

Method

Data from China Family Panel Studies (CFPS) in 2018 and the HFGT index formula were used to calculate the health expenditure poverty index of each province. Moreover, Global Moran’s I and Local Moran’s I test are applied to measure whether there is spatial aggregation of health expenditure poverty. Finally, an elastic net regression model is established to analyze the impact of health service equity on health expenditure poverty, with the breadth of health expenditure poverty as the dependent variable and health service equity as the independent variable.

Results

In the developed eastern provinces of China, the breadth of health expenditure poverty is relatively low. There is a significant positive spatial agglomeration. “Primary medical and health institutions per 1,000 population”, “rural doctors and health workers per 1,000 population”, “beds in primary medical institutions per 1,000 population”, “proportion of government health expenditure” and “number of times to participate in medical insurance (be aided) per 1,000 population” have a positive impact on health expenditure poverty. “Number of health examinations per capita” and “total health expenditure per capita” have a negative impact on health expenditure poverty. Both effects passed the significance test.

Conclusion

To enhance the fairness of health resource allocation in China and to alleviate health expenditure poverty, China should rationally plan the allocation of health resources at the grassroots level, strengthen the implementation of hierarchical diagnosis and treatment and encourage the investment in business medical insurance industry. Meanwhile, it is necessary to increase the intensity of medical assistance and enrich financing methods. All medical expenses of the poorest should be covered by the government.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06675-y.

Development and validation of a risk score for predicting postoperative delirium after major abdominal surgery by incorporating preoperative risk factors and surgical Apgar score

STUDY OBJECTIVE

To develop and validate a simple delirium-predicting scoring system in patients undergoing major abdominal surgery by incorporating preoperative risk factors and intraoperative surgical Apgar score (SAS).

DESIGN

Observational retrospective cohort study.

SETTING

A tertiary general hospital in China.

PATIENTS

1055 patients who received major abdominal surgery from January 2015 to December 2019.

MEASUREMENTS

We collected data on preoperative and intraoperative variables, and postoperative delirium. A risk scoring system for postoperative delirium in patients after major open abdominal surgery was developed and validated based on traditional logistic regression model. The elastic net algorithm was further developed and evaluated.

MAIN RESULTS

The incidence of postoperative delirium was 17.8% (188/1055) in these patients. They were randomly divided into the development (n = 713) and validation (n = 342) cohorts. Both the logistic regression model and the elastic net regression model identified that advanced age, arrythmia, hypoalbuminemia, coagulation dysfunction, mental illness or cognitive impairments and low surgical Apgar score are related with increased risk of postoperative delirium. The elastic net algorithm has an area under the receiver operating characteristic curve (AUROC) of 0.842 and 0.822 in the development and validation cohorts, respectively. A prognostic score was calculated using the following formula: Prognostic score = Age classification (0 to 3 points) + arrythmia + 2 * hypoalbuminemia + 2 * coagulation dysfunction + 4 * mental illness or cognitive impairments + (10-surgical Apgar score). The 22-point risk scoring system had good discrimination and calibration with an AUROC of 0.823 and 0.834, and a non-significant Hosmer-Lemeshow test P = 0.317 and P = 0.853 in the development and validation cohorts, respectively. The bootstrapping internal verification method (R = 1000) yielded a C-index of 0.822 (95% CI: 0.759-0.857).

CONCLUSION

The prognostic scoring system, which used both preoperative risk factors and surgical Apgar score, serves as a good first step toward a clinically useful predictive model for postoperative delirium in patients undergoing major open abdominal surgery.

Prediction of type 2 diabetes mellitus based on nutrition data

Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013–14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to build our predictive model and select among 193 food intake variables. After selecting the significant predictor variables, we built a logistic regression model with these variables as predictors and T2DM status as the outcome. The values of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of our predictive model were calculated. Eleven out of the 193 food intake variables were selected for inclusion in our model, which yielded a value of area under the ROC curve of 0⋅79 and a maximum PPV, NPV and accuracy of 0⋅37, 0⋅98 and 0⋅91, respectively. The present results suggest that nutrition data should be implemented in predictive models to predict the risk of T2DM, since they improve their performance and they are easy to assess.

Prenatal exposure to multiple phenolic compounds, fetal reproductive hormones, and the second to fourth digit ratio of children aged 10 years in a prospective birth cohort

Select phenols are known to possess hormone-disrupting properties, but no previous study has addressed the potential effects of prenatal exposure to phenol mixtures on fetal reproductive hormones and children's second to fourth digit (2D: 4D) ratio, a marker for in utero testosterone (T) exposure. We aimed to explore interrelations of prenatal phenol exposures individually and in mixtures, cord serum reproductive hormones, and 2D: 4D ratio of children aged 10 years. Urinary 11 phenol concentrations were determined from 392 pregnant women participating in a longitudinal birth cohort. We estimated associations of prenatal phenol exposures individually and in mixtures with cord reproductive hormones and children's 2D:4D ratio using three statistical approaches, including generalized linear models (GLMs), elastic net regression (ENR) models and Bayesian kernel machine regression (BKMR) models. In female newborns, the three models showed that maternal triclosan (TCS) concentrations were significantly negatively associated with cord serum T levels [regression coefficient (β) = -0.076, 95% confidence interval (CI): 0.138, -0.013; p = 0.018]. Additionally, maternal urinary bisphenol A (BPA) levels were related to decreases in 2D:4D ratio of the left hand in girls by GLMs (β = -0.003, 95% CI: 0.007, -0.001; p = 0.024) and ENR models, but not BKMR models. We provided evidence that prenatal TCS exposure predicted lower cord serum T levels, and maternal BPA exposure was related to decreased 2D:4D ratio of the left hand in females.

Effect of Different Direct Compaction Grades of Mannitol on the Storage Stability of Tablet Properties Investigated Using a Kohonen Self-Organizing Map and Elastic Net Regression Model

This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25 °C, 75% relative humidity for 1 week. Then, measurement of the tablet properties was conducted on both pre- and post-storage tablets. The tablet properties were tensile strength (TS), friability, and disintegration time (DT). The experimental data were analyzed using a Kohonen self-organizing map (SOM). The SOM analysis successfully classified the test grades into three distinct clusters having different changes in the behavior of the tablet properties accompanying storage. Cluster 1 showed an obvious rise in DT induced by storage, while cluster 3 showed a substantial change in mechanical strength of the tablet including a reduction in the TS and a rise in friability. Furthermore, the data were analyzed using an Elastic net regression technique to investigate the general relationships between the powder properties of mannitol and the change behavior of the tablet properties. Consequently, we succeeded in identifying the crucial powder properties for the storage stability of the resulting tablets. This study provides advanced technical knowledge to characterize the effect of different direct compaction grades of mannitol on the storage stability of tablet properties.

Prenatal exposure to mixture of heavy metals, pesticides and phenols and IQ in children at 7 years of age: The SMBCS study

OBJECTIVE

Prenatal exposure to heavy metals, pesticides and phenols has been suggested to interfere with neurodevelopment, but the neurotoxicity of their mixtures is still unclear. We aimed to elucidate the associations of maternal urinary concentrations of selected chemical mixtures with intelligence quotient (IQ) in children.

METHODS

Maternal urinary concentrations of selected heavy metals, pesticide metabolites, and phenols were quantified in pregnant women who participated in the Sheyang Mini Birth Cohort Study (SMBCS) from June 2009 to January 2010. At age 7 years, child's IQ score was assessed using the Chinese version of Wechsler Intelligence Scale for Children (C-WISC) by trained pediatricians. Generalized linear regression models (GLM), Bayesian kernel machine regression (BKMR) models and elastic net regression (ENR) models were used to assess the associations of urinary concentrations individual chemicals and their mixtures with IQ scores of the 7-year-old children.

RESULTS

Of 326 mother-child pairs, single-chemical models indicated that prenatal urinary concentrations of lead (Pb) and bisphenol A (BPA) were significantly negatively associated with full intelligence quotient (FIQ) among children aged 7 years [β = -2.31, 95% confidence interval (CI): -4.13, -0.48; p = 0.013, sex interaction p-value = 0.076; β = -1.18, 95% CI: -2.21, -0.15; p = 0.025; sex interaction p-value = 0.296, for Pb and BPA, respectively]. Stratified analysis by sex indicated that the associations were only statistically significant in boys. In multi-chemical BKMR and ENR models, statistically significant inverse association was found between prenatal urinary Pb level and boy's FIQ scores at 7 years. Furthermore, BKMR analysis indicated that the overall mixture was associated with decreases in boy's IQ when all the chemicals' concentrations were at their 75th percentiles or higher, compared to at their 50th percentiles. ENR models revealed that maternal urinary Pb levels were statistically significantly associated with lower FIQ scores (β = -2.20, 95% CI: -4.20, -0.20; p = 0.031).

CONCLUSIONS

Prenatal exposure to selected chemical mixtures may affect intellectual performance at 7 years of age, particularly in boys. Pb and BPA were suspected as primary chemicals associated with child neurodevelopment.

Predicting risk of trace element pollution from municipal roads using site-specific soil samples and remotely sensed data

Studies of environmental processes exhibit spatial variation within data sets. The ability to derive predictions of risk from field data is a critical path forward in understanding the data and applying the information to land and resource management. Thanks to recent advances in predictive modeling, open source software, and computing, the power to do this is within grasp. This article provides an example of how we predicted relative trace element pollution risk from roads across a region by combining site specific trace element data in soils with regional land cover and planning information in a predictive model framework. In the Kenai Peninsula of Alaska, we sampled 36 sites (191 soil samples) adjacent to roads for trace elements. We then combined this site specific data with freely-available land cover and urban planning data to derive a predictive model of landscape scale environmental risk. We used six different model algorithms to analyze the dataset, comparing these in terms of their predictive abilities and the variables identified as important. Based on comparable predictive abilities (mean R² from 30 to 35% and mean root mean square error from 65 to 68%), we averaged all six model outputs to predict relative levels of trace element deposition in soils-given the road surface, traffic volume, sample distance from the road, land cover category, and impervious surface percentage. Mapped predictions of environmental risk from toxic trace element pollution can show land managers and transportation planners where to prioritize road renewal or maintenance by each road segment's relative environmental and human health risk.

Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining

Individuals with a history of non-suicidal self-injury (NSSI) are at alarmingly high risk for suicidal ideation (SI), planning (SP), and attempts (SA). Given these findings, research has begun to evaluate the features of this multi-faceted behavior that may be most important to assess when quantifying risk for SI, SP, and SA. However, no studies have examined the wide range of NSSI characteristics simultaneously when determining which NSSI features are most salient to suicide risk. The current study utilized three exploratory data mining techniques (elastic net regression, decision trees, random forests) to address these gaps in the literature. Undergraduates with a history of NSSI (N = 359) were administered measures assessing demographic variables, depression, and 58 NSSI characteristics (e.g., methods, frequency, functions, locations, scarring) as well as current SI, current SP, and SA history. Results suggested that depressive symptoms and the anti-suicide function of NSSI were the most important features for predicting SI and SP. The most important features in predicting SA were the anti-suicide function of NSSI, NSSI-related medical treatment, and NSSI scarring. Overall, results suggest that NSSI functions, scarring, and medical lethality may be more important to assess than commonly regarded NSSI severity indices when ascertaining suicide risk.

Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity

Approximately 20 drugs have been approved by the FDA for breast cancer treatment, yet predictive biomarkers are known for only a few of these. The identification of additional biomarkers would be useful both for drugs currently approved for breast cancer treatment and for new drug development. Using glycoprotein expression data collected via mass spectrometry, in conjunction with statistical models constructed by elastic net or lasso regression, we modeled quantitatively the responses of breast cancer cell lines to ~90 drugs. Lasso and elastic net regression identified HER2 as a predictor protein for lapatinib, afatinib, gefitinib and erlotinib, which target HER2 or the EGF receptor, thus providing an internal control for the approach. Two additional protein datasets and two RNA datasets were also tested as sources of predictor proteins for modeling drug sensitivity. Protein expression measured by mass spectrometry gave models with higher coefficients of determination than did reverse phase protein array (RPPA) predictor data. Further, cross validation of the elastic net models shows that, for many drugs, the prediction error is lower when the predictor data is from proteins, rather than mRNA expression measured on microarrays. Drugs that could be modeled effectively include PI3K inhibitors, Akt inhibitors, paclitaxel and docetaxel, rapamycin, everolimus and temsirolimus, gemcitabine and vinorelbine. Strikingly, this modeling approach with protein predictors often succeeds for drugs that are targeted agents, even when the nominal target is not in the dataset.