Selective activation of prodrugs in breast cancer using metabolic glycoengineering and the tetrazine ligation bioorthogonal reaction.

Increasing the selectivity of chemotherapies by converting them into prodrugs that can be activated at the tumour site decreases their side effects and allows discrimination between cancerous and non-cancerous cells. Herein, the use of metabolic glycoengineering (MGE) to selectively label MCF-7 breast cancer cells with tetrazine (Tz) activators for subsequent activation of prodrugs containing the trans-cyclooctene (TCO) moiety by a bioorthogonal reaction is demonstrated. Three novel Tz-modified monosaccharides, Ac4ManNTz 7, Ac4GalNTz 8, and Ac4SiaTz 16, were used for expression of the Tz activator within sialic-acid rich breast cancer cells' surface glycans through MGE. Tz expression on breast cancer cells (MCF-7) was evaluated versus the non-cancerous L929 fibroblasts showing a concentration-dependant effect and excellent selectivity with ≥35-fold Tz expression on the MCF-7 cells versus the non-cancerous L929 fibroblasts. Next, a novel TCO-N-mustard prodrug and a TCO-doxorubicin prodrug were analyzed in vitro on the Tz-bioengineered cells to probe our hypothesis that these could be activated via a bioorthogonal reaction. Selective prodrug activation and restoration of cytotoxicity were demonstrated for the MCF-7 breast cancer cells versus the non-cancerous L929 cells. Restoration of the parent drug's cytotoxicity was shown to be dependent on the level of Tz expression where the Ac4ManNTz 7 and Ac4GalNTz 8 derivatives (20 µM) lead to the highest Tz expression and full restoration of the parent drug's cytotoxicity. This work suggests the feasibility of combining MGE and tetrazine ligation for selective prodrug activation in breast cancer.

Safety, Tolerability, and Dose-Limiting Toxicity of Lacosamide in Patients With Painful Chronic Pancreatitis: Protocol for a Phase 1 Clinical Trial to Determine Safety and Identify Side Effects.

BACKGROUND: Chronic abdominal pain is the hallmark symptom of chronic pancreatitis (CP), with 50% to 80% of patients seeking medical attention for pain control. Although several management options are available, outcomes are often disappointing, and opioids remain a mainstay of therapy. Opioid-induced hyperalgesia is a phenomenon resulting in dose escalation, which may occur partly because of the effects of opioids on voltage-gated sodium channels associated with pain. Preclinical observations demonstrate that the combination of an opioid and the antiseizure drug lacosamide diminishes opioid-induced hyperalgesia and improves pain control. OBJECTIVE: In this phase 1 trial, we aim to determine the safety, tolerability, and dose-limiting toxicity of adding lacosamide to opioids for the treatment of painful CP and assess the feasibility of performance of a pilot study of adding lacosamide to opioid therapy in patients with CP. As an exploratory aim, we will assess the efficacy of adding lacosamide to opioid therapy in patients with painful CP. METHODS: Using the Bayesian optimal interval design, we will conduct a dose-escalation trial of adding lacosamide to opioid therapy in patients with painful CP enrolled in cohorts of size 3. The initial dose will be 50 mg taken orally twice a day, followed by incremental increases to a maximum dose of 400 mg/day, with lacosamide administered for 7 days at each dose level. Adverse events will be documented according to Common Terminology Criteria for Adverse Events (version 5.0). RESULTS: As of December 2023, we have currently enrolled 6 participants. The minimum number of participants to be enrolled is 12 with a maximum of 24. We expect to publish the results by March 2025. CONCLUSIONS: This trial will test the feasibility of the study design and provide reassurance regarding the tolerability and safety of opioids in treating painful CP. It is anticipated that lacosamide will prove to be safe and well tolerated, supporting a subsequent phase 2 trial assessing the efficacy of lacosamide+opioid therapy in patients with painful CP, and that lacosamide combined with opiates will lower the opioid dose necessary for pain relief and improve the safety profile of opioid use in treating painful CP. TRIAL REGISTRATION: Clinicaltrials.gov NCT05603702; https://clinicaltrials.gov/study/NCT05603702. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50513.

Proteomics and Machine Learning in the Prediction and Explanation of Low Pectoralis Muscle Area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified 8 biomarkers associated with low pectoralis muscle area (PMA). We built 3 random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified 2 distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Cost effectiveness of nonpharmacological prevention programs for diabetes: A systematic review of trial-based studies.

Trial-based economic value of prevention programs for diabetes is inexplicit. We aimed to review the cost-effectiveness of nonpharmacological interventions to prevent type-2 diabetes mellitus (T2DM) for high-risk people. Six electronic databases were searched up to March 2022. Studies assessing both the cost and health outcomes of nonpharmacological interventions for people at high-risk of T2DM were included. The quality of the study was assessed by the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist. The primary outcome for synthesis was incremental cost-effectiveness ratios (ICER) for quality-adjusted life years (QALYs), and costs were standardized in 2022 US dollars. Narrative synthesis was performed, considering different types and delivery methods of interventions. Sixteen studies included five based on the US diabetes prevention program (DPP), six on non-DPP-based lifestyle interventions, four on health education, and one on screening plus lifestyle intervention. Compared with usual care, lifestyle interventions showed higher potential of cost-effectiveness than educational interventions. Among lifestyle interventions, DPP-based programs were less cost-effective (median of ICERs: $27,077/QALY) than non-DPP-based programs (median of ICERs: $1395/QALY) from healthcare perspectives, but with larger decreases in diabetes incidence. Besides, the cost-effectiveness of interventions was more possibly realized through the combination of different delivery methods. Different interventions to prevent T2DM in high-risk populations are both cost-effective and feasible in various settings. Nevertheless, economic evidence from low- and middle-income countries is still lacking, and interventions delivered by trained laypersons and combined with peer support sessions or mobile technologies could be potentially a cost-effective solution in such settings with limited resources.

A Review on Endoscopic Management of Acute Cholecystitis: Endoscopic Ultrasound-Guided Gallbladder Drainage and Endoscopic Transpapillary Gallbladder Drainage.

A percutaneous cholecystostomy tube (PCT) is the conventionally favored nonoperative intervention for treating acute cholecystitis. However, PCT is beset by high adverse event rates, need for scheduled reintervention, and inadvertent dislodgement, as well as patient dissatisfaction with a percutaneous drain. Recent advances in endoscopic therapy involve the implementation of endoscopic transpapillary drainage (ETP-GBD) and endoscopic ultrasound-guided gallbladder drainage (EUS-GBD), which are increasingly preferred over PCT due to their favorable technical and clinical success combined with lower complication rates. In this article, we provide a comprehensive review of the literature on EUS-GBD and ETP-GBD, delineating instances when clinicians should opt for endoscopic management and highlighting potential risks associated with each approach.

Age-dependent association of central blood pressure with cardiovascular outcomes: a cohort study involving 34 289 participants using the UK biobank.

BACKGROUND: It remained unclear whether central blood pressures (BP) was more closely associated with cardiovascular disease (CVD) than brachial BP in different age groups. OBJECTIVES: To investigate the age-stratified association of CVD with brachial and central BPs, and to evaluate corresponding improvement in model performance. METHODS: This cohort study included 34 289 adults without baseline CVD from the UK Biobank dataset. Participants were categorized into middle-aged and older aged groups using the cut-off of age 65 years. The primary endpoint was a composite cardiovascular outcome consisting of cardiovascular mortality combined with nonfatal coronary events, heart failure and stroke. Multivariable-adjusted hazard ratios expressed CVD risks associated with BP increments of 10 mmHg. Akaike Information Criteria (AIC) was used for model comparisons. RESULTS: In both groups, CVD events were associated with brachial or central SBP ( P  ≤ 0.002). Model fit was better for central SBP in middle-aged adults (AIC 4427.2 vs. 4429.5), but model fit was better for brachial SBP in older adults (AIC 10 246.7 vs. 10 247.1). Central SBP remained significantly associated to CVD events [hazard ratio = 1.05; 95% confidence interval (CI) 1.0-1.1] and improved model fit (AIC = 4426.6) after adjustment of brachial SBP only in the middle-aged adults. These results were consistent for pulse pressure (PP). CONCLUSION: In middle-aged adults, higher central BPs were associated with greater risks of CVD events, even after adjusting for brachial BP indexes. For older adults, the superiority of central BP was not observed. Additional trials with adequate follow-up time will confirm the role of central BP in estimating CVD risk for middle-aged individuals.

Comparative analysis of symptom profile and risk of death associated with infection by SARS-CoV-2 and its variants in Hong Kong.

The recurrent multiwave nature of coronavirus disease 2019 (COVID-19) necessitates updating its symptomatology. We characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. With the COVID-19 cases reported up to August 25, 2022 in Hong Kong, an iterative multitier text-matching algorithm was developed to identify symptoms from free text. Multivariate regression was used to measure associations between variants, symptom development, death, and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Overall, 70.9% (54 450/76 762) of cases were symptomatic with 102 symptoms identified. Intrinsically, the wild-type and delta variant caused similar symptoms among unvaccinated symptomatic cases, whereas the wild-type and omicron BA.2 subvariant had heterogeneous patterns, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting, and sore throat) more frequent in the BA.2 cohort. With ≥2 vaccine doses, BA.2 was more likely than delta to cause fever among symptomatic cases. Fever, blocked nose, pneumonia, and shortness of breath remained jointly predictive of death among unvaccinated symptomatic elderly in the wild-type-to-omicron transition. Number of vaccine doses required for reducing occurrence varied by symptoms. We substantiate that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom-based case identification, increased weighing on symptoms predictive of death in outcome prediction, individual-based risk assessment in care homes, and incorporating free-text symptom reporting.

Cognitive dispersion is related to subtle objective daily functioning changes in older adults with and without cognitive impairment.

Early detection of cognitive and functional decline is difficult given that current tools are insensitive to subtle changes. The present study evaluated whether cognitive dispersion on neuropsychological testing improved prediction of objectively assessed daily functioning using unobtrusive monitoring technologies. Hierarchical linear regression was used to evaluate whether cognitive dispersion added incremental information beyond mean neuropsychological performance in the prediction of objectively assessed IADLs (i.e., computer use, pillbox use, driving) in a sample of 104 community-dwelling older adults without dementia (Mage = 74.59, 38.5% Female, 90.4% White). Adjusting for age, sex, education, and mean global cognitive performance, cognitive dispersion improved prediction of average daily computer use duration (R2 Δ = 0.100, F Change, p = 0.005), computer use duration variability (R2 Δ = 0.089, F Change p = 0.009), and average daily duration of nighttime driving (R2 Δ = 0.072, F Change p = 0.013). These results suggest cognitive dispersion may improve prediction of objectively assessed functional changes in older adults without dementia.

Adverse jaw outcomes from immune checkpoint inhibitors for head-and-neck cancer? Case reports.

Radiation treatment plays a mainstream role in the management of head and neck cancers (HNSCC). Adverse effects from radiation therapy include osteoradionecrosis of the jaw, and rarely, pathological fracture. Immune checkpoint inhibitors (ICI) such as pembrolizumab are of growing relevance to the management of metastatic and recurrent HNSCC. Adverse impact on bone secondary to medications such as pembrolizumab and nivolumab have been sporadically documented in the literature. The objective of this manuscript is to raise awareness of possible increase in risk for adverse jaw outcomes in patients with HNSCC exposed to both radiation treatment to the jaws and ICI therapy. This manuscript documents adverse jaw outcomes including osteonecrosis and pathological fracture of the mandible in two patients receiving pembrolizumab for management of HNSCC and had received prior radiation treatment. A potential link between immunotherapy and adverse jaw outcomes is consistent with our growing understanding of osteoimmunology, investigating the closely interrelated processes in bone remodeling and immune system function, in health and disease. It is important to ascertain if pembrolizumab poses an incremental risk for such outcomes, beyond the risk from prior radiation, for patients managed with radiation treatment and ICI therapy for HNSCC. The general dentist may encounter such patients either in the context of facilitating dental clearance prior to initiation of chemotherapy, or rarely, with poorly explained jaw symptoms and must be alert to the possibility of occurrence of such adverse jaw events to facilitate timely diagnosis and optimal patient management.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIA) are early measures of lung injury automatically detected on chest computed tomography (CT) scans. QIA is associated with impaired respiratory health and shares features with advanced lung diseases, but its biological underpinnings are not well understood. Objective: We analyzed high-throughput plasma proteomic panels within two multi-center cohorts to identify novel protein biomarkers of QIA. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (Genetic Epidemiology of COPD, COPDGene) and 2,925 participants in a younger population cohort (Coronary Artery Disease Risk in Young Adults, CARDIA) with the SomaLogic SomaScan assays. We measured QIA using a local density histogram method. We assessed the associations between proteomics levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg False Discovery Rate p-value ≤0.05). Measurements and Main Results: 852 proteins were significantly associated with QIA in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including Biological Processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; Cellular Components in extracellular regions; and Molecular Functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIA in an older, smoking population with higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Five-year follow-up of a phase 2 study of ibrutinib plus fludarabine, cyclophosphamide, and rituximab as initial therapy in CLL.

ABSTRACT: We previously reported high rates of undetectable minimal residual disease <10-4 (uMRD4) with ibrutinib plus fludarabine, cyclophosphamide, and rituximab (iFCR) followed by 2-year ibrutinib maintenance (I-M) in treatment-naïve chronic lymphocytic leukemia (CLL). Here, we report updated data from this phase 2 study with a median follow-up of 63 months. Of 85 patients enrolled, including 5 (6%) with deletion 17p or TP53 mutation, 91% completed iFCR and 2-year I-M. Five-year progression-free survival (PFS) and overall survival were 94% (95% confidence interval [CI], 89%-100%) and 99% (95% CI, 96%-100%), respectively. No additional deaths have occurred with this extended follow-up. No difference in PFS was observed by immunoglobulin heavy-chain variable region gene status or duration of I-M. High rates of peripheral blood (PB) uMRD4 were maintained (72% at the end of iFCR, 66% at the end of 2-year I-M, and 44% at 4.5 years from treatment initiation). Thirteen patients developed MRD conversion without clinical progression, mostly (77%) after stopping ibrutinib. None had Bruton tyrosine kinase (BTK) mutations. One patient had PLCG2 mutation. Six of these patients underwent ibrutinib retreatment per protocol. Median time on ibrutinib retreatment was 34 months. The cumulative incidence of atrial fibrillation was 8%. Second malignancy or nonmalignant hematologic disease occurred in 13%, mostly nonmelanoma skin cancer. Overall, iFCR with 2-year I-M achieved durably deep responses in patients with diverse CLL genetic markers. Re-emergent clones lacked BTK mutation and retained sensitivity to ibrutinib upon retreatment. This trial is registered at www.clinicaltrials.gov as #NCT02251548.

Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction.

Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.

The Effect of Concussion Mechanism of Injury on Sleep Problems in Active Duty Service Members Following Deployment.

INTRODUCTION: Sleep disruption is pervasive in the military and is generally exacerbated during deployment, partially due to increases in operational tempo and exposure to stressors and/or trauma. In particular, sleep disruption is a commonly reported symptom following deployment-related traumatic brain injury (TBI), though less is known about the prevalence of sleep disturbance as a function of whether the TBI was induced by high-level blast (HLB) or direct impact to the head. TBI assessment, treatment, and prognosis are further complicated by comorbidity with posttraumatic stress disorder (PTSD), depression, and alcohol misuse. Here, we examine whether concussion mechanism of injury is associated with differences in the prevalence of self-reported sleep disturbance following deployment in a large sample of U.S. Marines while accounting for probable PTSD, depression, and alcohol misuse. MATERIALS AND METHODS: This was a retrospective cohort study of active duty enlisted Marines with a probable concussion (N = 5757) who completed the Post-Deployment Health Assessment between 2008 and 2012. Probable concussion was defined as endorsement of a potentially concussive event with corresponding loss or alteration of consciousness. The presence of concussion-related sleep problems was assessed with a dichotomous item. Probable PTSD, depression, and alcohol misuse were assessed using the Primary Care PTSD Screen, the Patient Health Questionnaire-2, and the Alcohol Use Identification Test-Concise, respectively. Logistic regression models investigated the effects of mechanism of injury (HLB vs. impact), PTSD, depression, and alcohol misuse on the presence of sleep problems, adjusting for sex and pay grade. The study was approved by the Naval Health Research Center Institutional Review Board. RESULTS: Approximately 41% of individuals with a probable deployment-related concussion reported sleep problems following the event; 79% of concussed individuals reporting both HLB and probable PTSD reported sleep problems. All main effects were significantly associated with sleep disturbance in adjusted models. PTSD showed the strongest association with sleep disturbance (adjusted odds ratio [AOR] = 2.84), followed by depression (AOR = 2.43), HLB exposure (AOR = 2.00), female sex (AOR = 1.63), alcohol misuse (AOR = 1.14), and pay grade (AOR = 1.10). A significant HLB × PTSD interaction emerged (AOR = 1.58), which suggests that sleep disturbance was elevated among those with both HLB-induced (vs. impact-induced) concussions and presence (vs. absence) of PTSD. No other significant interactions emerged. CONCLUSION: To our knowledge, this is the first study to examine the prevalence of concussion-related sleep complaints following deployment as a function of the mechanism of injury in individuals with and without probable PTSD and depression. Individuals with HLB-induced concussion were twice as likely to report sleep problems as those with an impact-induced concussion. Future work should examine these effects longitudinally with validated measures that assess greater precision of exposure and outcome assessment (e.g., blast intensity and type of sleep disturbance).

Leptin-dependent differential remodeling of visceral and pericardial adipose tissue following chronic exercise and psychosocial stress.

Obesity is driven by an imbalance between caloric intake and energy expenditure, causing excessive storage of triglycerides in adipose tissue at different sites around the body. Increased visceral adipose tissue (VAT) is associated with diabetes, while pericardial adipose tissue (PAT) is associated with cardiac pathology. Adipose tissue can expand either through cellular hypertrophy or hyperplasia, with the former correlating with decreased metabolic health in obesity. The aim of this study was to determine how VAT and PAT remodel in response to obesity, stress, and exercise. Here we have used the male obese Zucker rats, which carries two recessive fa alleles that result in the development of hyperphagia with reduced energy expenditure, resulting in morbid obesity and leptin resistance. At 9 weeks of age, a group of lean (Fa/Fa or Fa/fa) Zucker rats (LZR) and obese (fa/fa) Zucker rats (OZR) were treated with unpredictable chronic mild stress or exercise for 8 weeks. To determine the phenotype for PAT and VAT, tissue cellularity and gene expression were analyzed. Finally, leptin signaling was investigated further using cultured 3T3-derived adipocytes. Tissue cellularity was determined following hematoxylin and eosin (H&E) staining, while qPCR was used to examine gene expression. PAT adipocytes were significantly smaller than those from VAT and had a more beige-like appearance in both LZR and OZR. In the OZR group, VAT adipocyte cell size increased significantly compared with LZR, while PAT showed no difference. Exercise and stress resulted in a significant reduction in VAT cellularity in OZR, while PAT showed no change. This suggests that PAT cellularity does not remodel significantly compared with VAT. These data indicate that the extracellular matrix of PAT is able to remodel more readily than in VAT. In the LZR group, exercise increased insulin receptor substrate 1 (IRS1) in PAT but was decreased in the OZR group. In VAT, exercise decreased IRS1 in LZR, while increasing it in OZR. This suggests that in obesity, VAT is more responsive to exercise and subsequently becomes less insulin resistant compared with PAT. Stress increased PPAR-γ expression in the VAT but decreased it in the PAT in the OZR group. This suggests that in obesity, stress increases adipogenesis more significantly in the VAT compared with PAT. To understand the role of leptin signaling in adipose tissue remodeling mechanistically, JAK2 autophosphorylation was inhibited using 5 µM 1,2,3,4,5,6-hexabromocyclohexane (Hex) in cultured 3T3-derived adipocytes. Palmitate treatment was used to induce cellular hypertrophy. Hex blocked adipocyte hypertrophy in response to palmitate treatment but not the increase in lipid droplet size. These data suggest that leptin signaling is necessary for adipocyte cell remodeling, and its absence induces whitening. Taken together, our data suggest that leptin signaling is necessary for adipocyte remodeling in response to obesity, exercise, and psychosocial stress.

Rheumatoid arthritis, quantitative parenchymal lung features, and mortality among smokers.

OBJECTIVES: There have been limited investigations of the prevalence and mortality impact of quantitative computed tomography (QCT) parenchymal lung features in rheumatoid arthritis (RA). We examined the cross-sectional prevalence and mortality associations of QCT features, comparing RA and non-RA participants. METHODS: We identified participants with and without RA in COPDGene, a multicentre cohort study of current or former smokers. Using a k-nearest neighbor quantifier, high resolution CT chest scans were scored for percentage of normal lung, interstitial changes, and emphysema. We examined associations between QCT features and RA using multivariable linear regression. After dichotomizing participants at the 75th percentile for each QCT feature among non-RA participants, we investigated mortality associations by RA/non-RA status and quartile 4 vs quartiles 1-3 of QCT features using Cox regression. We assessed for statistical interactions between RA and QCT features. RESULTS: We identified 82 RA cases and 8820 non-RA comparators. In multivariable linear regression, RA was associated with higher percentage of interstitial changes (ß = 1.7 ± 0.5, p= 0.0008) but not emphysema (ß = 1.3 ± 1.7, p= 0.44). Participants with RA and >75th percentile of emphysema had significantly higher mortality than non-RA participants (HR 5.86, 95%CI 3.75-9.13) as well as RA participants (HR 5.56, 95%CI 2.71-11.38) with ≤75th percentile of emphysema. There were statistical interactions between RA and emphysema for mortality (multiplicative p= 0.014; attributable proportion 0.53, 95%CI 0.30-0.70). CONCLUSIONS: Using machine learning-derived QCT data in a cohort of smokers, RA was associated with higher percentage of interstitial changes. The combination of RA and emphysema conferred >5-fold higher mortality.

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020.

Importance: The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018. Developing methods to model the increase of obesity over-time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency. Methods: A cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort. Results: Of the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30). Conclusion: United States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long-term weight status.

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.

IMPORTANCE: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. METHODS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. RESULTS: Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. CONCLUSIONS: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population.

BACKGROUND: Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. OBJECTIVE: The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. METHODS: A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. RESULTS: 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). CONCLUSION: Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.