Desai, Rupak

Background: Stroke is a major health burden and the second leading cause of death globally, only after ischemic heart disease. While observational studies have elaborated on the effect of physical activity (PA) and sedentary behavior (SB) on stroke risk, their findings remain inconsistent and are often influenced by confounding factors and reverse causation. Mendelian randomization (MR) can help overcome these shortcomings and assess potential causal relationships using genetic variants as proxies for exposures. Methods: We conducted a systematic review and meta-analysis of MR studies investigating the effects of PA and SB on stroke risk. Following PRISMA guidelines, we searched PubMed and Google Scholar through May 2025. Eligible studies used two-sample MR designs to estimate the impact of moderate-to-vigorous physical activity (MVPA) and sedentary leisure behavior (SLB) on stroke outcomes. MVPA exposures included self-reported activity and accelerometer-derived measures (e.g., leisure time MVPA ≥425 mg), while SLB included television, screen time, sitting, etc. Key outcomes of interest were any stroke and ischemic stroke events. Random-effects models were used for meta-analysis and heterogeneity was assessed with the I2 statistic. Leave-one-out sensitivity analyses were performed to ensure robustness of the results. Results: Six studies met inclusion criteria. Genetically predicted MVPA was associated with a lower risk of stroke (pooled OR = 0.73; 95% CI: 0.53–1.01; p = 0.06), though the result was not statistically significant (I2= 79.5%). In contrast, SLB showed a significant association with increased stroke risk (pooled OR = 1.15; 95% CI: 1.01–1.31; p = 0.03; I2 = 89.9%). Sensitivity analyses confirmed that findings were not influenced by any single study. Conclusion: These findings support a causal association between SLB and increased stroke risk, with suggestive but non-significant evidence for MVPA’s protective role. Despite significant association, high heterogeneity warrants conscious interpretation of findings. Further MR studies using standardized exposure definitions and updated genetic data are needed to strengthen the results.

Background: Cardiac rehabilitation (CR) is emerging as an essential aspect of secondary prevention in patients recovering from acute myocardial infarction (AMI) or coronary artery disease (CAD). Exercise-based rehabilitation involves high-intensity interval training (HIIT), moderate-intensity continuous training (MICT), and maximum intensity interval training (MIIT). These are integral to improving cardiopulmonary fitness, cardiovascular outcomes, and affect health-related quality of life (HRQoL). HIIT is presumed to show better outcomes, though comparative evidence across exercise types remains inconsistent. Clarifying the differential impact of HIIT, MIIT, and MICT on physical and psychosocial outcomes is vital for optimizing CR. Methods: 5 studies involving 650 post-AMI adults enrolled in CR were included. Most studies compared HIIT and MICT; one study also included maximal-intensity interval training (MIIT), and all others used usual care as a control. Outcomes included cardiorespiratory fitness (VO2 max), functional capacity (6-minute walk test [6MWT]), HRQoL (EQ-5D-5L, MacNew), mental health (PHQ, vitality), sleep quality (PSQI), fatigue (FSS), and physiological measures (blood pressure (BP), lipid profile, stroke volume, VE_peak). A qualitative synthesis of findings was performed due to heterogeneity across studies. Results: All 5 studies indicated that structured exercise improved outcomes. HIIT led to greater gains in VO2max compared to MICT. MIIT, assessed in one study, showed the largest VO2max improvements. HRQoL improved in all modalities across EQ-5D-5L, MacNew, PHQ, and visual analog scales. Functional metrics (6MWT, PPO) and cardiovascular parameters (HDL-C, HR recovery) improved more with HIIT. BP, BMI, and HbA1c improved similarly across groups. In several studies, HIIT had a stronger effect on physical functioning and emotional health. Only HIIT led to a significant drop in depression scores. HDL cholesterol improved more with HIIT. Conclusion: HIIT provides superior gains in cardiorespiratory fitness and physical function compared to MICT or MIIT in CR participants, without compromising safety/quality of life. Mental health, HRQoL, and physiological parameters improve comparably across exercise modalities. These findings support that HIIT is an effective, time-efficient option in contemporary CR planning. Future standardized, large-scale, and long-term studies are needed to assess sustained clinical impact and cardiovascular event reduction.

Purpose: Obesity is a risk factor for sepsis complications in older adults. We assessed the impact of metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) on outcomes in septic shock. Methods: We conducted a retrospective analysis using the National Inpatient Sample (2016-2020) to identify a cohort of 1,737,075 patients aged 65 years and older who were hospitalized with septic shock, as defined by ICD-10 diagnosis codes. Obese patients without hypertension, diabetes, or hyperlipidemia were classified as MHO; those with ≥1 risk factor as MUHO. Baseline demographics and hospital characteristics were compared using χ² and Mann–Whitney U tests. Multivariable logistic regression adjusted for age, sex, race, comorbidity index, and hospital factors estimated odds ratios (ORs) for respiratory failure, mechanical ventilation, in-hospital mortality, and cardiac arrest. Secondary endpoints included length of stay (LOS) and hospitalization cost. Results: Among 268 050 obese patients, 23 915 (1.4%) were classified as MHO and 244 135 (14.1%) as MUHO. Both phenotypes were more prevalent in females and least common among Asian patients. MHO prevalence was highest among Native Americans, while MUHO was most common among Hispanics in 2020. From 2016 to 2019, the prevalence of both phenotypes remained stable, with modest increases observed during the 2020 sepsis surge. Respiratory failure occurred most frequently in the MHO group (∼70%), followed by MUHO (∼65%) and non-obese patients (∼60%). After adjusting for confounders, both MHO and MUHO groups had higher odds of respiratory failure and mechanical ventilation compared to non-obese patients. In-hospital mortality increased across all groups in 2020; however, MUHO patients had lower adjusted mortality risk. Cardiac arrest rates remained unchanged, while length of stay and hospitalization costs were highest among MHO patients. Conclusions: MHO and MUHO both confer higher respiratory failure and ventilation risk; lower mortality in MUHO warrants further study. Clinical Implications Phenotypic obesity classification may improve sepsis risk stratification and inform metabolic-specific management.

Background With the increase in popularity of cannabis and its use and the lack of large‐scale data on cannabis use and venous thromboembolism and pulmonary embolism (PE), we used a nationally representative cohort of young adults (aged 18–44 years) to compare the odds of admissions and in‐hospital mortality of PE with and without cannabis use disorder (CUD). Methods and Results Identified patients with PE using the National Inpatient Sample (2018) were compared for baseline, comorbidities, and outcomes. Multivariable regression analysis, adjusted for covariates, was used to compare the odds of PE in young patients with CUD (CUD+) versus those without (CUD−) and those with prior venous thromboembolism. Propensity score–matched analysis (1:6) was also performed to assess in‐hospital outcomes. A total of 61 965 (0.7%) of 8 438 858 young adult admissions in 2018 were PE related, of which 1705 (0.6%) had CUD+. On both unadjusted (odds ratio, 0.80 [95% CI, 0.71–0.90]; P <0.001) and adjusted regression analyses, the CUD+ cohort had a lower risk of PE admission. The CUD+ cohort had fewer routine discharges (58.3% versus 68.3%) and higher transfers to short‐term (7.9% versus 4.8%) and nursing/intermediate care (12.6% versus 9.5%) ( P <0.001). The PE‐CUD+ cohort of in‐hospital mortality did not differ from the CUD− cohort. Propensity score–matched (1:6) analysis revealed comparable mortality odds with higher median hospital stay and cost in the CUD+ cohort. Conclusions Young adults with CUD demonstrated lower odds of PE hospitalizations without any association with subsequent in‐hospital mortality. The median hospital stay of the CUD+ cohort was longer, they were often transferred to other facilities, and they had a higher cost.

Prediabetes is a risk factor for ischemic stroke in atrial fibrillation (AF) patients, yet, its impact on recurrent stroke in AF patients remains understudied. Using the 2018 National Inpatient Sample, we investigated the link between Prediabetes and recurrent stroke in AF patients with prior stroke or transient ischemic attack (TIA). Among 18,905 non-diabetic AF patients, 480 (2.5%) had prediabetes. The prediabetic group, with a median age of 78, exhibited a two-fold higher risk of recurrent stroke compared to the non-prediabetic cohort (median age 82), as evidenced by both unadjusted (OR 2.14, 95% CI 1.72–2.66) and adjusted (adjusted for socio-demographics/comorbidities, OR 2.09, 95% CI 1.65–2.64, p < 0.001). The prediabetes cohort, comprising more male and Black patients, demonstrated associations with higher Medicaid enrollment, admissions from certain regions, and higher rates of hyperlipidemia, smoking, peripheral vascular disease, obesity, and chronic obstructive pulmonary disease (all p < 0.05). Despite higher rates of home health care and increased hospital costs in the prediabetes group, the adjusted odds of all-cause mortality were not statistically significant (OR 0.55, 95% CI 0.19–1.56, p = 0.260). The findings of this study suggest that clinicians should be vigilant in managing prediabetes in AF patients, and strategies to prevent recurrent stroke in this high-risk population should be considered.

Background: Obstructive sleep apnea (OSA) increases the risk of stroke and cardiovascular diseases. However, its impact on geriatric patients with a prior history of stroke/transient ischemic attack (TIA) has not been adequately studied. Methods: We utilized the 2019 National Inpatient Sample in the US to identify geriatric patients with OSA (G-OSA) who had a prior history of stroke/TIA. We then compared subsequent stroke (SS) rates among sex and race subgroups. We also compared the demographics and comorbidities of SS+ and SS− groups and utilized logistic regression models to assess outcomes. Results: Out of 133,545 G-OSA patients admitted with a prior history of stroke/TIA, 4.9% (6520) had SS. Males had a higher prevalence of SS, while Asian-Pacific Islanders and Native Americans had the highest prevalence of SS, followed by Whites, Blacks, and Hispanics. The SS+ group had higher all-cause in-hospital mortality rates, with Hispanics showing the highest rate compared to Whites and Blacks (10.6% vs. 4.9% vs. 4.4%, p < 0.001), respectively. Adjusted analysis for covariates showed that complicated and uncomplicated hypertension (aOR 2.17 [95% CI 1.78–2.64]; 3.18 [95% CI 2.58–3.92]), diabetes with chronic complications (aOR 1.28 [95% CI 1.08–1.51]), hyperlipidemia (aOR 1.24 [95% CI 1.08–1.43]), and thyroid disorders (aOR 1.69 [95% CI 1.14–2.49]) were independent predictors of SS. The SS+ group had fewer routine discharges and higher healthcare costs. Conclusions: Our study shows that about 5% of G-OSA patients with a prior history of stroke/TIA are at risk of hospitalization due to SS, which is associated with higher mortality and healthcare utilization. Complicated and uncomplicated hypertension, diabetes with chronic complications, hyperlipidemia, thyroid disorders, and admission to rural hospitals predict subsequent stroke.

Introduction: Atrial Fibrillation (AF) has been associated with various behavioral risk factors such as tobacco, alcohol, and/or substance abuse. Hypothesis: The main objective is to describe the national trends and burden of tobacco and substance abuse in AF hospitalizations. Methods: The National Inpatient Sample database from 2007 to 2015 was utilized and the hospitalizations with AF were identified using the international classification of disease, Ninth Revision, Clinical Modification (ICD-9-CM) code 427.31 in the primary diagnosis column. They were stratified into without abuse, tobacco use disorder (TUD), substance use disorder (SUD), alcohol use disorder (AUD), and drug use disorder (DUD). We used the Jonckheere-Terpstra trend test to analyze the trend in this study. Results: Out of 3,631,507 AF hospitalizations, 852,110 (23.46%) had TUD, 1851,170 (5.1%) had SUD, 155,681 (4.29%) had AUD and 42,667 (1.17%) had DUD. The trends of all increased substantially during the study period (Figure) . The prevalence of TUD, SUD, AUD, and DUD was substantially increased across all age groups, races, and gender during the study period. Female sex was associated with lower odds TUD (0.57 (0.56-0.57), P-value <0.001), SUD (0.27 (0.26-0.27), P-value <0.001), AUD (0.22 (0.22-0.22), P-value <0.001), and DUD (0.51 (0.5-0.52), P-value <0.001). Among AF hospitalizations, the black race was associated with higher odds of SUD (1.12 (1.1-1.14), P-value <0.001), and DUD(1.42 (1.38-1.47), P-value <0.001). The younger age group (18-35 years), male, Medicare/Medicaid as primary insurance, and lower socioeconomic status were associated with increased risk of both TUD and SUDs. Conclusions: Tobacco and substance use disorder among hospitalized AF patients in the US mainly affects males, younger individuals, white more than black, and those of lower socioeconomic status which demands the development of preventive strategies to address multilevel influences.

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.