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Influence of climate and heatwaves on dengue transmission in Sao Paulo and Natal, Brazil

Dengue fever, a mosquito-borne viral disease, poses a significant public health challenge whose transmission dynamics are highly sensitive to climatic conditions. However, the effects of extreme weather events like heatwaves remain poorly understood. This study investigated the influence of climatic factors and heatwaves on dengue incidence in two key Brazilian hotspots: the subtropical megacity of São Paulo (Sao Paulo State) and the tropical coastal city of Natal (Rio Grande do Norte State). We analyzed weekly confirmed dengue cases and meteorological data (temperature, precipitation, heatwaves) from 2014 to 2023. Distributed lag non-linear models and negative binomial regression were used to assess the complex, delayed associations between meteorological variables and dengue infections. Over the study period, 149,468 dengue cases were reported in São Paulo and 80,999 in Natal. Transmission patterns differed significantly, with Natal exhibiting more regular epidemic cycles. Our models revealed that higher minimum temperatures were associated with increased dengue risk in both cities. Conversely, and perhaps counter-intuitively, higher maximum temperatures and total precipitation showed negative associations with dengue cases. The impact of heatwaves was strikingly different between the locations. In São Paulo, the occurrence of a heatwave was associated with a 70% reduction in dengue risk in subsequent weeks (Relative Risk [RR]: 0.30, 95% Confidence Interval [CI]: 0.18–0.49). In contrast, no statistically significant association between heatwaves and dengue was observed in Natal. Our findings demonstrate that the relationship between extreme heat and dengue transmission is not uniform and can be inhibitory, challenging the assumption that warming consistently favors vector proliferation. These location-specific insights are critical for developing more accurate, tailored public health early-warning systems and caution against one-size-fits-all climate adaptation strategies for vector-borne diseases.

Optimizing NPSB fertilizer rates for enhanced yield and yield components of mung bean (Vigna radiata L:) varieties

Ethiopia’s mung bean sector faces profound constraints: persistently degraded soils, critically low adoption of essential NPSB fertilizers, and a severe shortage of improved varieties. These factors collectively cripple the crop’s inherent productivity and national potential. To directly address these barriers, this two-year field study (2022 and 2023) evaluated the synergistic effects of improved mung bean varieties and NPSB fertilizer application on crop performance. The experiment was conducted using a factorial design within a randomized complete block layout, replicated 3 times, to test 3 key varieties: NVL-1, N-26, and Arkebe, at 5 different NPSB fertilizer rates of 0, 25, 50, 75, and 100 kg per hectare. Results decisively demonstrated that optimal growth and yield parameters were consistently achieved at the highest fertilizer levels (75 and 100 kg ha ⁻ ¹). The N-26 variety emerged as the best across critical metrics, including plant height, branching, seed yield, and harvest index throughout both years. A standout performance occurred in 2022, where N-26 combined with 100 kg ha ⁻ ¹ NPSB produced a peak grain yield of 1.94 t/ha. Arkebe’s best yield was 1.78 t/ha at 75 kg ha ⁻ ¹, higher than other varieties. Economic analysis further solidified N-26’s superiority: paired with 100 kg ha ⁻ ¹ NPSB, it delivered the highest net benefits, 47,704.17 ETB per hectare in 2022 and 49,856.85 ETB per hectare in 2023. Therefore, applying NPSB fertilizer at 100 kg ha ⁻ ¹ to the N-26 variety is recommended to maximize mung bean productivity and profitability in the studied context.

Impact of green credit policy on the operation of Vietnamese commercial banks: An empirical study using difference-in-differences model

Green credit is one of the important activities that commercial banks show their responsibility to the environment. In Vietnam, the Government and the State Bank of Vietnam have implemented numerous regulations pertaining to green credit in order to encourage and support environmentally-friendly financial activity. Against a backdrop of diverse and often conflicting perspectives on the economic implications of sustainable development and EGS principles, empirical analysis of green finance policies is critically important. The article examines the effects of the Green Credit Policy on commercial banks’ operations in Vietnam using panel data from financial statements and annual reports of commercial banks from 2012 to 2022. Specifically, the study focuses on changes in profits, cost management, non-performing loan and capital adequacy ratio. The analysis is conducted using difference in differences (DID) model, with the sample divided into groups that implemented the policy and groups that did not. The study’s findings indicate the following impacts: (1) there is no empirical evidence supporting the notion that the green credit policy increases the profitability of commercial banks, (2) there is no empirical evidence suggesting that the green credit policy reduces the cost-to-income ratio, (3) the implementation of the green credit policy does have a negative impact on non-performing loan ratio of banks. The research findings serve as the foundation for the authors to propose several suggestions for commercial banks and the State Bank of Vietnam.

Case-control study of autonomic symptoms in the setting of Long COVID with tilt table testing

Background Autonomic symptoms and orthostatic syndromes have been reported in Long COVID, but few studies have characterized findings using head up tilt table testing. Objective To characterize autonomic responses to positional changes among individuals with Long COVID. Methods We assessed autonomic symptoms using the Composite Autonomic Symptom Scale 31 (COMPASS 31) instrument and performed head up tilt table testing for 30 minutes at 70 degrees among individuals with Long COVID and recovered comparators. Results We included 26 participants (median age 56 years, 50% female median 25 months after first COVID): 16 with Long COVID and 10 recovered comparators. COMPASS 31 scores (0–100, higher is worse) were higher among those with Long COVID (median 30.5 vs 8, p = 0.003). Heart rate was 8 beats per minutes higher throughout tilt among those with Long COVID (95% CI 1.1 to 14.4; p = 0.02); there were no differences in blood pressure. Ten (63%) with Long COVID had symptoms during tilt compared to none among recovered participants (p = 0.003). Three (19%) with Long COVID had clinically abnormal findings: one each with orthostatic hypotension, and delayed orthostatic hypotension, and cardioinhibitory/vasovagal presyncope. Conclusions Among those with chronic autonomic symptoms in the setting of Long COVID, symptoms were common during tilt testing, and heart rate was increased, but most did not meet diagnostic criteria for a clinically abnormal hemodynamic response. Further research into mechanisms of autonomic symptoms in Long COVID is urgently needed.

The pursuit of novel head and neck cancer biomarkers – tissue and blood expression of chloride intracellular channels family

Introduction The chloride intracellular channels (CLICs) engage in cancer pathogenesis and have been considered various cancer biomarkers and therapeutic targets. Preliminary research suggests CLICs may be important players in head and neck squamous cell carcinoma (HNSCC). There is a need for reliable HNSCC biomarkers besides well-known HPV and PD-L1. Aim The study aimed to assess the role of CLICs in HNSCC pathogenesis and as potential disease biomarkers. Methods We compared the CLIC1–CLIC6 genes expression between the HNSCC tumors (n = 99) and the tissue from the free surgical margin (n = 74) at the mRNA level with RT-qPCR and at the protein level with Western Blot. To investigate the role of CLIC1-CLIC6 proteins as potential HNSCC blood biomarkers, we performed the ELISA assays on blood serum from 38 HNSCC patients and eight healthy individuals. Results We found significant differences in the expression of every analyzed CLIC. At the mRNA level, CLIC1 and CLIC4 were overexpressed in oral cancer tissue, CLIC3, CLIC5, and CLIC6 were down-expressed; in laryngeal cancer tissue, CLIC2 and CLIC3 were down-expressed. Tumor staging impacted CLIC1 and CLIC6 tissue expression, and histological grade impacted CLIC6 tissue expression. At the protein level, CLIC3 was down-expressed in oral cancer tissue. Furthermore, CLIC1 and CLIC3 proteins were overexpressed, and CLIC4 and CLIC6 were down-expressed in the oral cancer patients’ blood serum compared to the control group. Conclusion The different expression patterns of CLICs in HNSCC patients’ tissues and blood serum suggest that they may play an essential role in HNSCC pathogenesis and serve as biomarkers for HNSCC detection.

Luteolin alleviates CUMS-induced depressive-like behavioral deficits in mice through blocking the JAK2/STAT3 pathway

This study probed into the potential effects of luteolin (LUT) on depressive-like behavioral deficits caused by chronic unpredictable mild stress (CUMS) in mice, with a focus on its underlying molecular mechanisms. Western blot analysis revealed that CUMS notably activated the JAK2/STAT3 pathway, as indicated by elevated levels of phosphorylated JAK2 (p-JAK2) and p-STAT3. Treatment with LUT notably diminished p-JAK2 and p-STAT3, suggesting that LUT alleviates CUMS-induced depressive-like behavioral deficits by blocking the JAK2/STAT3 pathway. Behavioral assessments, including the forced swim and sucrose preference tests, demonstrated that LUT remarkably improved depressive-like symptoms. Furthermore, LUT treatment diminished the levels of pro-inflammatory cytokines, which were elevated by CUMS, further supporting the involvement of LUT in exerting antidepressant activities via the modulation of inflammatory responses. This study is the first to integrate multidimensional evidence from behavioral tests, neuroinflammation, and the JAK2/STAT3 signaling pathway, systematically demonstrating that luteolin alleviates CUMS-induced depression-anxiety comorbidity through synergistic regulation of an antioxidant‒anti-inflammatory‒neural signaling network.

The impact of analytical cognitive style on business model innovation in new ventures: The moderating role of self-efficacy and environmental uncertainty

In the context of digital-intelligent transformation, the deep integration of data elements has reshaped the cognitive boundaries of entrepreneurial decision-making. New ventures that leverage rational, data-driven analysis to guide strategic choices can transcend the bounded rationality of traditional experiential decision-making, thereby enhancing operational efficiency, market competitiveness, and long-term sustainability. Drawing on a social cognitive perspective, this study empirically examines survey data from 138 start-up firms to investigate the impact of analytical cognitive style on business model innovation in new ventures. Results indicate that analytical cognitive style is positively associated with both efficiency-oriented and novelty-oriented types of BMI. Moreover, entrepreneurial self-efficacy positively moderates the relationship between analytical cognitive style and efficiency-oriented BMI, while negatively moderating the relationship between analytical cognitive style and novelty-oriented BMI. Additionally, environmental uncertainty negatively moderates the link between analytical cognitive style and novelty-oriented BMI. These findings provide meaningful theoretical insights into the cognitive foundations of BMI and offer practical guidance for entrepreneurs seeking to innovate under conditions of uncertainty.

Addressing multicollinearity in general linear model: A novel approach for ridge parameter with performance comparison

The problem of ill-conditioned data or multicollinearity is common in regression modelling. The problem results in imprecise parameter estimation which leads to inability of gauging true impact of explanatory variables on the response. Also, due to strong multicollinearity, standard errors of parameter estimates get inflated leading to wider confidence intervals and hence increased risk of type-II error. To handle the problem, different approaches have been proposed in literature. Primarily, such techniques penalize the coefficient estimates in one way or other. Ridge regression is one of the most applied among such techniques. In ridge regression, a penalty term is added in the objective function of the general linear model. That penalty term introduces a small amount of bias in parameter estimates with an objective to decrease the mean square error. In the current article, some new choices for ridge constant are proposed. The performance of proposed ridge choices are compared through Monte Carlo simulations under different scenarios, using mean square error as measure of performance. The simulation results indicate that the proposed ridge estimator performs better than existing ridge constants, in most cases catering for severity of multicollinearity, number of explanatory variables, sample size and error variance structure. The simulation results were further corroborated by comparing performance of proposed ridge penalties using two real-life applications.

Scale for students’ attitude towards AIGC feedback in english pronunciation learning: Development, validation and application

This study develops and validates the Scale of Students’ Perception of AIGC Feedback for English Pronunciation Learning. The research was conducted at a university in northern China using a convenience sampling method. The exploratory factor analysis (EFA) involved 207 participants, while the confirmatory factor analysis (CFA) included 229 participants. Based on interviews with 10 students who had used AIGC tools for English pronunciation learning, 16 representative items were identified. Expert validation was performed through interviews with 8 experts—four English pronunciation teachers with extensive experience using AIGC in teaching, and four AIGC specialists. Content validity was confirmed, and all items were retained. The EFA results revealed four dimensions: Accuracy, Strictness, Clarity, and Personalisation. The CFA results demonstrated good structural and convergent validity. However, the discriminant validity was slightly problematic. Concurrent validity was confirmed by the high correlation between the scale and perceived English Pronunciation Self-efficacy. The study has several limitations, including its cross-sectional design, limited sample diversity, and reliance on traditional validation methods (EFA and CFA), suggesting the need for test-retest reliability, a more diverse sample, and alternative methods like Item Response Theory (IRT) or Network Analysis in future research. The validated scale offers valuable insights into how students perceive and interact with generative AI tools, and it can serve as a useful instrument for educators and researchers interested in exploring the impact of AI feedback systems on language learning.

MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol

Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to conventional prediction algorithms but have not been thoroughly evaluated in this population. We hypothesized that AI/ML techniques incorporating all available data in a patient’s medical record could achieve similar, if not higher, performance in the prediction of mortality in MT patients as compared to existing models. Patients from the American College of Surgeons Trauma Quality Improvement Project database (TQIP) were retrospectively reviewed. Those receiving ≥ 5 units of red blood cells and/or whole blood within the first four hours of arrival were defined as MT patients. Those receiving ≥10 units were identified as ultramassive transfusion (UMT) patients. ML models were created to predict 6-hour mortality using variables available at different time points, including patient arrival. Of 5,481,046 patients in TQIP from 2017 to 2021, 47,744 received MT and 20,337 of these received UMT. Using only variables available on arrival, MT AUROC was 0.901 [95% CI 0.895–0.910] which increased to 0.943 [95% CI 0.938–0.948] with addition of 4-hour variables. For UMT, arrival AUROC was 0.858 [95% CI 0.846–0.872] and increased to 0.922 [95% CI 0.914–0.931] at 4 hours. ML models reliably predict mortality in both MT and UMT patients. These are the only ML models trained on MT and UMT patients. Future work can focus on prospective implementation of these models with potential direct integration into the electronic medical record. Real-time utilization of comprehensive patient data may enhance clinical decision-making regarding which patients should continue receiving massive transfusion, thus optimizing the allocation of this limited resource.

The physical demands of Major League Soccer match-play with specific reference to high-intensity activity by position, venue and opposition quality

This study examined the running loads of Major League Soccer matches across three seasons. Data was obtained from 1243 individual matches which included 800 players (26 ± 1.1 years) from 28 teams. Data was collected via optical tracking system. All data from players who completed at least 85-minutes of match play were included. Physical performance measures included total distance (m) (TD), high-speed running (19.8–25.2 km ⋅ hr1) (HSR), sprint distance (>25.2 km ⋅ hr1) (SpD), sprint efforts (n) and high-intensity running (>19.8 km ⋅ hr1) (HID). Data was analysed to observe the average match running loads of the measures of physical performance as a whole and within the respective positions, temporal and seasonal. The data was processed using R statistical software. Linear mixed models were used to analyse statistical significance. The average total distance covered was 9950 ± 990m. The average high-speed running 519 ± 171m, whereas the average sprint distance was 166 ± 98. The average sprints (n) were 10 ± 5. CM cover the most total distance (10510 ± 1000m) while full backs and wide midfielders cover the most high-speed running and sprint distance (599 ± 147; 225 ± 98m). Contextual factors such as quality of opposition and venue have an impact on the movement demands of match-play with players covering less TD against higher ranked teams and higher SpD and HID with teams of weaker opposition. However, players performed less TD and SpD when playing away. Furthermore, signifying the importance of understanding a teams’ principles of play and their affect on match running loads.

Correction: Associations of serum keratin 1 with thyroid function and immunity in Graves’ disease

Optimal SoC range determination for battery storage to smooth wind power output and extend battery lifespan

The increase in the consumption of electrical energy in the world has increased the trend towards renewable energy for the production of electricity in small and large scales. One of the major renewable energies that have attracted the attention of experts are wind turbine (WT) resources. Due to their dependence on wind speed, these sources have large fluctuations in output power. For this purpose, it is necessary to use electrical energy storage devices that can reduce the fluctuations of wind turbine output power by proper and fast charging and discharging. The use of such energy storage systems also increases network costs and operational complexity. Also, considering that wind turbine output power fluctuations are high and at a high speed, the charging and discharging of these energy storage devices will also occur with a large number of times and will lead to a reduction in the life of this equipment. If the size of these batteries is chosen in such a way that they can be charged and discharged in smaller intervals, their lifespan will be improved and the use of these equipment will be in more favorable conditions. In this paper, an attempt will be made to choose the appropriate state of charge (SoC) range for energy storage devices along with wind turbine resources. The simulation of wind turbine and battery storage in micro-grid and in on-grid condition has been implemented in MATLAB software.

VAR consultation patterns and their association with fouls and misconduct: An analysis of the top five European football leagues

Delays and controversies surrounding Video Assistant Referee (VAR) consultations have raised concerns in European football, particularly regarding the types of infractions that prompt referee interventions. This study analysed referee data from 6,232 matches across five seasons in the top five European leagues to identify the foul and misconduct behaviours most strongly associated with VAR referrals. Using clustering and logistic regression, we found that a limited set of offences, most notably handball, off-the-ball challenges, professional fouls, and simulation, were consistently linked to higher consultation frequency. While descriptive comparisons suggested some variation between leagues, league affiliation itself was not a significant predictor once foul type was considered. The findings indicate that VAR is predominantly engaged for offences that are both subjective and potentially decisive in match outcomes. These insights have practical implications for referees, coaches, and players by highlighting the need for strategies that minimise unnecessary consultations, improve game flow, and enhance the consistency of officiating in elite football.

On utilizing gaze behavior to predict movement transitions during natural human walking on different terrains

Human gaze behavior is crucial for successful goal-directed locomotion. In this study we explore the potential of gaze information to improve predictions of walk mode transitions in real-world urban environments which has not been investigated in great detail, yet. Using a dataset with IMU motion data and gaze data from the Pupil Labs Invisible eye tracker, twenty participants completed three laps of an urban walking track with three walk modes: level walking, stairs (up, down), and ramps (up, down). In agreement with previous findings, we found that participants directed their gaze more towards the ground during challenging transitions. They adjusted their gaze behavior up to four steps before adjusting their gait behavior. We trained a random forest classifier to predict walk mode transitions using gaze parameters, gait parameters, and both. Results showed that the more complex transitions involving stairs were easier to predict than transitions involving ramps, and combining gaze and gait parameters provided the most reliable results. Gaze parameters had a greater impact on classification accuracy than gait parameters in most scenarios. Although prediction performance, as measured by Matthews’ correlation coefficient (MCC), declined with increasing forecasting horizons (from one to four steps ahead), the model still achieved robust classification performance well above chance level (MCC = 0), with an average MCC of 0.60 when predicting transitions from level walking to stairs (either up or down) four steps in advance. The study suggests that gaze behavior changes in anticipation of walk mode transitions and the expected challenge for balance control, and has the potential to significantly improve the prediction of walk mode transitions in real-world gait behavior.

Gum Arabic containing Allium sativum L. essential oil-based nanoparticles as biofumigant grain protectant against Callosobruchus maculatus F.

Gum Arabic nanoparticles (GA NPs) were used to nano-encapsulate Allium sativum or garlic essential oil (GO) using the freeze-drying technique. The fumigant toxicity of GO and GO-GA nanoparticles was evaluated against Callosobruchus maculatus , a major pest of stored products. Adults were exposed to concentrations of 10.0, 5.0, 2.5, and 1.25 µL/L air for 24 hours to evaluate the lethal concentration (LC) values. Gas Chromatography-Mass Spectrometry for GO identified diallyl trisulfide (38.78%), allyl methyl trisulfide (23.93%), and diallyl disulfide (13.66%) as the main compounds. Dynamic light scattering and transmission electron microscope tests verified the stability and uniformity of the produced nanoparticles, which were distinguished by their small particle size (15.10 nm), low PDI value (0.31), and negative zeta potential (−10.20). A high encapsulation efficiency of 84.74 ± 1.74% was achieved for the produced nanoparticles. The linkage and interaction between GO and GA as a polymer were confirmed by Fourier transform infrared spectroscopy. After 24-hour exposure, GO-GA NPs resulted in lower LC 50 values (1.14 µL/L air) than GO (2.08 µL/L air) against C. maculatus adults. The inclusion of GO-GA NPs at LC 40 had a significant post-effect on progeny production of C. maculatus , resulting in a significant reduction in the number of deposited eggs and adult emergence, which led to a significant decrease in the percentage of adult emergence to 15.23 ± 5.46 compared to 61.33 ± 2.94, as observed in the GO treatment. GO-GA NPs enhanced the persistence activity, exhibiting a continued toxic effect for >30 days, with a PT 50 of 22.29 days compared to 12.79 days for GO. This study suggested that nano-formulation could enhance the efficiency of garlic oil as an eco-friendly grain protectant to control C. maculatus adults.

Self-learning adaptive neuro-fuzzy approximation of robust control behavior in electric power steering systems

Data training algorithms based on Artificial Intelligence (AI) often encounter overfitting, underfitting, or bias issues. This article presents the design of a hybrid self-learning algorithm to address the above challenges. The proposed approach is developed by integrating fuzzy logic and neural network structures into an Adaptive Network-Based Fuzzy Inference System (ANFIS), which leverages the strengths of both components. This integration is considered a key contribution of the study. Compared to conventional training algorithms, the proposed ANFIS demonstrates high training accuracy while maintaining strong interpolation and prediction capabilities, even under varying conditions. The model is designed with three inputs and one output, trained using data derived from a high-performance robust controller for Electric Power Steering (EPS) systems. Simulation results show that the training error of the proposed ANFIS remains below 1.7% in well-trained cases and under 6.1% in interpolation scenarios. Moreover, the algorithm maintains a prediction error of less than 9.3% when applied to scenarios outside the training domain. The issue of overfitting is significantly resolved, unlike in the case of the Backpropagation Neural Network (BPNN), which is used as a benchmark for comparison. Overall, the proposed algorithm significantly improves data training accuracy and generalization performance.

Antihypertensive therapy to prevent cardiac death: A study of combined ACE inhibitors and β-blockers—a retrospective cohort study in Tsunan Town, Japan

Antihypertensive treatment is widely known to reduce the risk of cardiovascular mortality; however, its protective effect, specifically against cardiac death, remains unclear. In this study, we examined whether a treatment strategy prioritizing the combined use of angiotensin-converting enzyme inhibitors and β-blockers reduces the risk of cardiac death in outpatient hypertensive patients. This retrospective observational cohort study was conducted at a single facility over a 30-year period, using data obtained between 1987 and 2016. Between 1992 and 2001, a combined treatment approach using angiotensin-converting enzyme inhibitors and β-blockers was preferentially used to suppress neurohumoral factors, with calcium channel blockers and diuretics used as supplementary medications. Standardized mortality ratios for all-cause mortality, cardiac death, and cerebrovascular death during each period were tracked and compared with nationwide data in Japan. Since 1992, the standardized mortality ratios for all-cause mortality and cardiac death in Tsunan Town have significantly decreased and fallen below the national averages. The present study focused on the role of neurohumoral factors, and we observationally evaluated the impact of combined therapy with angiotensin-converting enzyme inhibitors and β-blockers on the prognosis of patients with hypertension. While providing a perspective that has not been sufficiently examined to date, our findings should be regarded as the generation of an important hypothesis that warrants confirmation through future rigorous interventional studies.

Feasibility study of a sensor-to-segment calibration method to enhance upper limb motion analysis using an IMU-based system for clinical and home environments

Inertial Measurement Units (IMUs) represent a valid alternative to standard clinical assessment methods, such as clinical scales, for evaluating upper limb kinematics. A key aspect of utilizing IMUs effectively is ensuring precise sensor-to-segment calibration, which accounts for the relative orientation between the sensor and the attached body segment. This calibration is crucial to obtain accurate results. Although reliable calibration methods are available, their application in clinical and home environments remains challenging due to their complexity. This study aimed to validate a picture-based calibration method feasible for a clinical context and compare it against other standard methods. Ten healthy subjects performed daily activity tasks while upper limb kinematics was recorded using an optoelectronic motion capture system and an IMU-based system. Four calibration methods were compared using error metrics, including root mean square deviation (RMSD) and cross-correlation (XCORR). The results demonstrate that the proposed picture-based method provides highly accurate measurements for the first and second Euler rotation angles of the shoulder, with RMSD < 15 ° and XCORR > 0.75 across most of the tasks. For the elbow joint, all calibration methods consistently yielded precise results for the first rotation (RMSD < 15 ° and XCORR > 0.95) across the majority of tasks. The proposed sensor-to-segment calibration method improves the accuracy of upper limb motion data recorded with an IMU-based system compared to traditional methods. Moreover, the calibration approach is easy to use, making it suitable for clinical and home environments.

Chronic companions: An updated national cross-sectional study of metabolic syndrome comorbidities in outpatient visits for hidradenitis suppurativa

Hidradenitis suppurativa (HS) is a painful, chronic inflammatory skin disease associated with significant physical and psychosocial burden. Increasing evidence suggests HS is linked to systemic metabolic dysfunction, including components of metabolic syndrome such as obesity, hypertension, and hyperlipidemia. This study aimed to assess the prevalence of metabolic comorbidities in patients with HS using data from the National Ambulatory Medical Care Survey (NAMCS), a nationally representative dataset of U.S. outpatient visits from 2014 to 2019. We conducted a cross-sectional analysis comparing HS-related visits to age- and sex-matched non-HS visits, using multivariate logistic regression adjusted for demographic and clinical covariates. Among 1.8 million weighted HS-related visits, the most prevalent metabolic comorbidities were hypertension (15.7%), obesity (8.6%), and hyperlipidemia (7.4%). Compared to non-HS controls, HS visits had significantly higher odds of hypertension (adjusted odds ratio [aOR] 2.90; 95% confidence interval [CI]: 2.88–2.92), obesity (aOR 3.12; 3.10–3.15), and hyperlipidemia (aOR 1.76, 1.74–1.77). No significant association was found between HS and type 2 diabetes mellitus (T2DM) or cerebrovascular disease. Mechanistically, chronic systemic inflammation in HS, driven by elevated cytokines such as TNF-α, IL-6, and IL-17, may contribute to endothelial dysfunction and metabolic dysregulation. Obesity, which is commonly associated with HS, exacerbates the inflammatory state and promotes follicular occlusion, while hyperlipidemia may amplify inflammation through oxidative stress and impaired immune resolution. These findings underscore the importance of recognizing metabolic risk factors in patients with HS, particularly within the context of outpatient settings where early intervention is feasible. Early identification and management of these comorbidities may improve long-term health outcomes. Further longitudinal studies are warranted to clarify causal relationships and support the development of multidisciplinary screening and care strategies for this high-risk population.