Immanuel Babu Henry Samuel’s research while affiliated with San Francisco VA Medical Center and other places

Objective The ImPACT Post-Concussion Symptom Scale (PCSS) is widely used to document post-concussion symptoms. This study investigated the structural patterns of PCSS using spectral co-clustering on data from U.S. diplomats and their families who served in Cuba and China, who reported experiencing Anomalous Health Incidents (AHIs). Method Clinicians at the University of Pennsylvania evaluated 65 individuals with potential AHI exposure (Median age-group = 35–45 years; 73.84% in Cuba; 49.23% females). PCSS feature scores were discretized into 4-level symptom severity expressions: none, mild, moderate, and severe. The preprocessed data were fit with spectral co-clustering model to extract optimal number of biclusters, by measuring scree plot with principal component analysis and Calinski-Harabasz (CH) score. Final model was also evaluated by CH score and a random label t-test to compare the significance of the clustering results. Results Overall, spectral co-clustering yielded two biclusters with CH score of 20.29 and random label t-test of p < 0.001, suggesting reasonable clustering quality. Biclustering identified two sets of interpretable PCSS symptom characteristics. Cluster centroids X and Y (IDs retracted) were estimated using Manhattan distance, summarizing symptom characteristics of each cluster. More precisely, centroid X represented mild to moderate symptom profiles (e.g., headache, dizziness, drowsiness, and nervousness), while centroidY represented a relative absence of symptoms, except for mild nervousness and emotionality. Conclusion The spectral co-clustering of PCSS reveals patterns of elevated symptoms in one cluster and minimal symptom burden in the other, similar to outcomes observed after mild TBI. These patterns can guide future research directions and ultimately improve patient outcomes in various medical contexts.

Objective The mood-congruency hypothesis suggests that an individual’s attentional resources align with their current emotional state. We investigated whether attention to emotion-eliciting stimuli, as measured by eye-tracking, could be associated with psychological outcomes in Vietnam Veterans. Method We enrolled 117 Vietnam-theater Veterans (MeanAge = 69.5 years; SD = 3.4 years) and measured time to first fixation (TFF) and total fixation duration (TFD) while participants viewed emotional human faces (HAPPY, SAD, ANGRY) paired with neutral expressions (NEUTRAL). Participants completed 60 trials for different emotion-neutral pairs and completed self-report measures of posttraumatic stress disorder (PTSD) using the PTSD Checklist-5 (PCL-5) and posttraumatic growth (PTG) using the PTG Inventory. Linear fixed-effects models (MATLAB, R2023a) were employed to examine self-report measures, focusing on attentional bias toward emotional faces in Model-1, and considering both emotional and neutral faces in Model-2. Results In Model-1, PTG was negatively related to TFF on HAPPY (p < 0.05), but positively related to TFD on SAD (p < 0.001). These findings align with PTG theory, suggesting that struggling with negative emotions may stimulate PTG. Moreover, PTSD symptom severity was negatively associated with TFF on HAPPY (p < 0.05) and positively associated with TFF on NEUTRAL (p < 0.01). In Model-2, greater PTG was associated with longer TFD on both HAPPY (p < 0.05) and NEUTRAL (p < 0.001). Interestingly, greater PTSD symptom severity was associated with longer TFD on HAPPY (p < 0.05) and NEUTRAL (p < 0.001) stimuli. Conclusions Our findings highlight the complex relationship between attentional allocation, emotional stimuli, and psychological outcomes in Vietnam Veterans. Future research will explore these dynamics and the clinical utility of eye tracking.

Introduction Military exposures may present a cumulative load and increased individual susceptibility to negative health outcomes. Currently, there are no comprehensive and validated environmental exposure assessment tools covering the full spectrum of occupational and environmental exposures for Veterans. The Veterans Affairs (VA) War Related Illness and Injury Study Center in Washington, DC, developed the Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT) to establish a structured, comprehensive self-report tool that captures military and non-military occupational and environmental exposures. The VMOAT is clinically insightful, modular, and flexible for adding novel exposures, meeting the needs of modern evolving threats and exposures in both clinical and research settings. This manuscript reviews the ongoing development and validation plans for the VMOAT. Materials and Methods The VMOAT is a self-reported structured questionnaire, and VMOAT 1.0 was developed to cover an individual’s 3 life phases (pre, during, post-military service); 5 exposure domains (chemical, physical, biological, injuries including ergonomic, and psychological stress exposures, plus military preventive health measures); and 64 specific exposures nested within exposure categories. VMOAT 1.0 addresses exposure dose (frequency, duration, proximity, route), and can be administered online via VA approved Qualtrics survey software. VMOAT 1.0 to 2.0 updates began in December 2022 with changes focused on readability, streamlining the exposure history, refining the exposure metrics, and improving the skip logic embedded within the survey design. Results The initial VMOAT 1.0 development included face and construct validation with expert internal and external academic and military collaborators, undergoing an iterative 5-cycle review as well as sample testing among a small group of Veterans. The VMOAT 1.0 was used in Institutional Review Board (IRB)-approved longitudinal study, which has been examined preliminarily to compare the VMOAT 1.0 with other exposure assessments and to compare responses of Explosive Ordnance Disposal Veterans, a high occupational exposure cohort, to non-Explosive Ordnance Disposal Veterans. Ongoing VMOAT 2.0 updates will include integration of experiences from piloting the VMOAT 1.0 as well as additional face and content validation and survey cognitive testing with Veterans. VMOAT 2.0 data will improve the development of exposure-informed models using composite survey data to create scored- and scale-based exposure metrics for specific exposures and exposure domains. These data will highlight the effectiveness of the VMOAT as a structured comprehensive occupational and environmental exposure assessment instrument. Conclusions VMOAT development supports the 2022 Promise to Address Comprehensive Toxics Act and fits into the existing VA exposure assessment approach as a standardized, comprehensive self-reported exposure assessment tool. It can be utilized as a stand-alone instrument or supplemented by clinician interviews in research or specialty evaluation programs. The collected VMOAT self-report information on military occupational and environmental exposures will allow direct evaluation with objective measures of exposure and health outcomes. These data outcomes have a high potential to guide the DoD and VA environmental exposure risk mitigation and risk communication efforts.

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

Introduction Heart failure (HF) with preserved ejection fraction (HFpEF) is closely associated with traditional cardiac risk factors including hypertension. Strategies to prevent the incidence of HFpEF include lifestyle modifications. However, the association between cardiorespiratory fitness (CRF) assessed objectively by a standardized exercise treadmill test (ETT) and HFpEF incidence in hypertensive patients has not been evaluated. Purpose Evaluated the association between CRF and HFpEF incidence in hypertensive patients. Method We assessed CRF in 390,880 hypertensive patients (mean age 62.7 ± 8.6 years by a standardized exercise treadmill test (ETT) performed across US Veterans Affairs Medical Centers. All had no evidence of HF or myocardial infarction prior to completion of the ETT. We established five age-and-gender-specific CRF categories (quintiles) based on peak metabolic equivalents (METs) achieved during the ETT. Q1 (n=80,569; METs=4.6±1.2); Q2 (n=101,358; METs=7.0±1.1): Q3 (n=77,649; METs=8.4±1.2); Q4 (n=97,624; METs=10.3±0.9); Q5 (n=33,680; METs=13.2±1.5) Results During 5,471,090 person-years of follow-up (median 14.0 years), there were 16,493 HFpEF events with an average annual rate of 2.4 events per 1,000 person-years. The adjusted risk of HFpEF decreased progressively across CRF categories as CRF increased, independent of comorbidities. Compared to Q1 (Referent; 4.6±1.2 METs), the risk was 27% lower (hazard ratio [HR] 0.73, 95% CI 0.69-0.75) for individuals within Q2 (7.0 ±1.1 METs) and 61% lower (HR 0.39, 95% CI 0.36-0.43) for those in Q5. Being unfit carried the highest risk (HR, 2.22; 95% CI, 2.33-2.80) of any other comorbidity. Similar findings were observed for those <65.0 and ≥65 years. Conclusions Higher CRF levels were independently associated with lower HRpEF incidence in a dose-response manner in hypertensive patients. The lower risk was observed at relatively low CRF levels (7.0 METs) achievable by most middle-aged and older individuals by engaging in approximately 25-30 minutes of brisk walk daily.

BACKGROUND Hypertension and physical inactivity are risk factors for stroke. The effect of cardiorespiratory fitness (CRF) on stroke risk in patients with hypertension has not been assessed. We evaluated stroke incidence in patients with hypertension according to CRF and changes in CRF. METHODS We included 483 379 patients with hypertension (mean age±SD; 59.4±9.0 years) and no evidence of unstable cardiovascular disease as indicated by a standardized exercise treadmill test. Patients were assigned to 5 age- and sex-specific CRF categories based on peak metabolic equivalents achieved at the initial exercise treadmill test and in 4 categories based on metabolic equivalent changes over time (n=110 576). Multivariable Cox models, adjusted for age, and comorbidities were used to estimate hazard ratios and 95% CIs for stroke risk. RESULTS During a median follow-up of 10.6 (interquartile range, 6.6–14.6) years, 15 925 patients developed stroke with an average yearly rate of 3.1 events/1000 person-years. Stroke risk declined progressively with higher CRF and was 55% lower for the High-fit individuals (hazard ratio, 0.45 [95% CI, 0.42–0.48]) compared with the Least-fit. Similar associations were observed across the race, sex, and age spectra. Poor CRF was the strongest predictor of stroke risk of all comorbidities studied (hazard ratio, 2.24 [95% CI, 2.10–2.40]). Changes in CRF reflected inverse and proportional changes in stroke risk. CONCLUSIONS Poor CRF carried a greater risk than any of the cardiac risk factors in patients with hypertension, regardless of age, race, or sex. The lower stroke risk associated with improved CRF suggests that increasing physical activity, even later in life, may reduce stroke risk.

Objective Chronic hypertension (HTN) is considered a strong risk factor for developing chronic kidney disease (CKD). Increased cardiorespiratory fitness (CRF) is associated with lower CKD risk. However, the CRF-CKD association in patients with HTN has not been assessed. Thus, we examined the the association between CRF and the risk of developing CKD in hypertensive patients. Design and method Hypertensive individuals (n=471,579; age 63.0+8.8 years) with normal kidney function prior to HTN and no evidence of ischemia indicated by a maximal standardized exercise treadmill test (ETT). The cohort was part of the ETHOS study (n=750,302). We established five CRF categories based on age-and-gender-adjusted peak metabolic equivalents (METs) achieved: Least-Fit (4.5±1.2 METs; n=106,955); Low-Fit Fit (6.9±1.1 METs; n=122,612); Moderate-Fit (8.3±1.2 METs; n=91,995); Fit (10.3±1.2 METs; n=111,472); and High-Fit (13.2±1.6 METs; n=38,545). CKD risk across CRF categories was assessed by multivariable Cox Regression analysis, adjusted for age, blood pressure, body mass index (BMI), alcohol abuse, traditional risk factors, and medications. Results During 6,146,773.0 person-years of follow-up (median 12.9 years), 58,701 individuals developed CKD (9.6 events/1,000 person-years of observation). The CRF-CKD association was inverse, independent of comorbidities and graded. The risk of developing CKD was 9% lower for each 1-MET increase (Hazard Ratio [HR] 0.91; 95% confidence interval [CI] 0.90-0.93). When CKD risk was assessed across CRF categories using the Least-fit category as the referent, the risk was 20% lower (HR 0.80; CI 0.78-0.81, p<0.001) for those in the Low-fit category (6.9±1.1 METs). The risk declined progressively with increased CRF and was 49% lower for those in the highest CRF category (HR 0.49; CI 0.47-0.51, p<0.001). Similar findings were observed in the subgroup of hypertensive patients with diabetes mellitus (n=192,966). Conclusions We noted an inverse and dose-response association between CRF and CKD incidence. The risk was attenuated significantly beyond a MET level of 6.9±1.1 METs, suggesting that moderate increases in exercise capacity lowers the risk of developing CKD in hypertensive patients.

Objective The recent ESH Guidelines include elevated resting heart rate (RHR) as an independent risk factor among the established and suggested novel risk factors that influence cardiovascular risk in patients with hypertension, indicating sympathetic overdrive. Increased fitness affects both mortality and RHR. However, the association between RHR and mortality risk, adjusted for cardiorespiratory fitness, age, comorbidities and medication has not been fully explored. Design and method We evaluated the association between RHR and mortality in 422,702 hypertensive patients (mean age 62.5 ± 8.6 years) with no evidence of atrial fibrillation during the entire follow-up. All completed a standardized exercise treadmill test (ETT), with no evidence of overt heart disease prior to and at the time of the ETT. To assess the risk in a wide and clinically relevant spectrum, we established 6 RHR categories per 10 heartbeat intervals ranging from <=60 to >100 beats. We used multivariable Cox regression to assess the RHR-mortality association. We adjusted the models for age, body mass index, cardiac risk factors, exercise capacity, and medications (including b-blockers). Results During the median follow-up of 10.6 years, providing 4,551,327 person-years, there were 107,863 deaths with an average annual rate of 23.7 events per 1,000 person-years. We noted approximately 7% increase in risk for each 10 heart beats. Mortality risk was significantly elevated at a RHR of >80 beats/min (HR; 1.15, CI; 1.12 - 1.17; p <0.006) and increased progressively to 41% (HR; 1.41, CI; 1.36 - 1.45; p <0.001) for those with a RHR of >100 beats/min. Similar trends were noted for subjects aged <60; 60-69; and >=70 years and those treated with b blockers. In all assessments, mortality risk was consistently overestimated when fitness was not considered. Conclusions We noted a progressive increase in mortality risk with increased RHR at >80 bpm in hypertensive patients with no AF. The association was independent of comorbidities, and medications.

Understanding the health outcomes of military exposures is a critical effort for Veterans, their health care team, and national leaders. Veterans Affairs providers receive reports of military exposure related concerns from 43% of Veterans. Understanding the causal influences of environmental exposures on health is a complex task advancement in exposure science and may require interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service-related exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposure from different exposure datatypes and databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.