Disease Patterns Research Articles

A comprehensive analysis of the health effects associated with smoking in the largest population using UK Biobank genotypic and phenotypic data

BackgroundSmoking is a widespread behavior, while the relationship between smoking and various diseases remains a topic of debate. ObjectiveWe conducted analysis to further examine the identified associations and assess potential causal relationships. MethodsWe utilized seven single nucleotide polymorphisms (SNPs) known to be linked to smoking extracting genotype data from the UK Biobank, a large-scale biomedical repository encompassing comprehensive health-related and genetic information of European descent. Phenome-wide association study (PheWAS) analysis was conducted to map the association of genetically predicted smoking status with 1,549 phenotypes. The associations identified in the PheWAS were then meticulously examined through two-sample Mendelian randomization (MR) analysis, utilizing data from the UK Biobank (n = 487,365) and the Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) (n = 337,334). This approach allowed us to comprehensively characterize the links between smoking and disease patterns. ResultsThe PheWAS analysis produced 34 phenotypes that demonstrated significant associations with smoking (P = 0.05/1460). Importantly, sickle cell anemia and type 2 diabetes exhibited the most significant SNPs (both 85.71% significant SNPs). Furthermore, the MR analyses provided compelling evidence supporting causal associations between smoking and the risk of following diseases: obstructive chronic bronchitis (IVW: Beta = 0.48, 95% confidence interval (CI) 0.36-0.61, P = 1.62×10−13), cancer of the bronchus (IVW: Beta = 0.92, 95% CI 0.68-1.17, P = 2.02×10−13), peripheral vascular disease (IVW: Beta = 1.09, 95% CI 0.71-1.46, P = 1.63×10−8), emphysema (IVW: Beta = 1.63, 95% CI 0.90-2.36, P = 1.29×10−5), pneumococcal pneumonia (IVW: Beta = 0.30, 95% CI 0.11-0.49, P = 1.60×10−3), chronic airway obstruction (IVW: Beta = 0.83, 95% CI 0.30-1.36, P = 2.00×10−3) and type 2 diabetes (IVW: Beta = 0.53, 95% CI 0.16-0.90, P = 5.08×10−3). ConclusionThis study affirms causal relationships between smoking and obstructive chronic bronchitis, cancer of the bronchus, peripheral vascular disease, emphysema, pneumococcal pneumonia, chronic airway obstruction, type 2 diabetes, in the European population. These findings highlight the broad health impacts of smoking and support smoking cessation efforts.

Association between air pollution, meteorological factors and hand, foot, and mouth disease incidence in Zhejiang Province: A Bayesian spatio-temporal modelling study

Temperature and humidity are known to influence seasonal patterns of hand, foot and mouth disease (HFMD) transmission. However, the effect of air pollution and climate-air pollution interaction on the intensity of HFMD epidemics is poorly understood. This study aims to quantify the exposure-response effects of air pollution on HFMD risk by level of meteorological indicators in Zhejiang Province. A spatiotemporal Bayesian hierarchical model was used to determine the exposure-response association between air pollution, meteorological factors and the relative risk (RR) of HFMD. The model was fitted to monthly HFMD case data for the 92 counties in Zhejiang Province between January 2013, and December 2021. We assessed both linear interaction effects between air pollutants and meteorological factors on HFMD, and the modification role of extreme weather conditions on the nonlinear air pollutant-HFMD association. A 10 μg/m³ increase in PM10 and NO2 concentration raised the HFMD risk by 6.6% (RR = 1.066, 95% CI: 1.042, 1.092) and 20.8% (RR = 1.208, 95% CI: 1.162, 1.256) respectively. HFMD was negatively associated with O3 (RR = 0.980, 95% CI: 0.968, 0.993), SO2 (RR = 0.820, 95% CI: 0.776, 0.868) and CO (RR = 0.770, 95% CI: 0.621, 0.956). Temperature, humidity, and precipitation positively interacted with most air pollutants on HFMD, while wind speed had a negative interaction effect. In higher temperature conditions, the HFMD risk associated with PM10 increased. Elevated humidity levels amplified the risk associated with PM10, as did CO concentrations within a certain range. Heavy rainfall intensified the risk of HFMD associated with PM10, SO2, and CO. Results of this study showed that air pollution was associated with HFMD incidence, with different climate factors playing various modification roles in the associations and primarily leading to increased risk under extreme conditions. These findings provide a theoretical basis for personal protection and government prevention and control measures.

Cortical atrophy patterns in myelin oligodendrocyte glycoprotein antibody-associated disease.

Global brain volume changes in patients with myelin oligodendrocyte glycoprotein antibody-associated disease compared with healthy controls (HC) could be revealed by magnetic resonance imaging, but specific atrophy patterns of cortical structures and relation to cognitive impairment are not yet comprehensively known. Thus, we aimed to investigate cortical thickness differences in patients with myelin oligodendrocyte glycoprotein antibody-associated disease compared with HC. 3-Tesla brain magnetic resonance imaging was performed in 23 patients with myelin oligodendrocyte glycoprotein antibody-associated disease and 49 HC for voxel-wise group comparisons and neuropsychological testing in patients. Surface-based morphometry with region of interest-based surface analysis and region of interest-based extraction of cortical thickness was performed in patients compared with HC and in patient subgroups with and without cognitive impairment. Comparing patients with myelin oligodendrocyte glycoprotein antibody-associated disease with HC, exploratory surface-based morphometry demonstrated cortical volume reduction in pericalcarine and lingual cortical regions. Region of interest-based surface analysis specified reduced cortical thickness in the adjacent pericalcarine and orbitofrontal regions in myelin oligodendrocyte glycoprotein antibody-associated disease, as well as reduced temporal cortical thickness in patients with cognitive impairment (n = 10). Patients without cognitive impairment (n = 13) showed only circumscribed cortical brain volume loss compared with HC in the pericalcarine region. In conclusion, cortical atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease was characterized by cortical thickness reduction in the adjacent pericalcarine and orbitofrontal regions, with a tendency of temporal thickness reduction in cognitively impaired patients.

Myeloperoxidase-ANCA IgG induces different forms of small vessel vasculitis based on type of synergistic immune stimuli

Anti-neutrophil cytoplasmic autoantibody (ANCA) vasculitis has diverse patterns of injury including microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). Necrotizing and crescentic glomerulonephritis (NCGN) occurs in all syndromes and as renal limited vasculitis (RLV). Single dose intravenous ANCA IgG-specific for mouse myeloperoxidase (MPO) causes RLV in mice. Although multiple mouse models have elucidated ANCA-IgG induced necrotizing and crescentic glomerulonephritis (NCGN), pathogenesis of ANCA-induced granulomatosis and vasculitis outside the kidney has not been clarified. To investigate this, we used intravenous MPO-ANCA IgG in the same strain of mice to induce different patterns of lung disease mirroring patients with granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). Repeated intravenous MPO-ANCA IgG induced GPA with NCGN, lung capillaritis, arteritis and granulomatosis. Lung leukocyte phenotypes were evaluated by immunohistochemical image analysis and by flow cytometry. ANCA lung capillaritis and microabscesses began within one day and evolved into granulomas in under seven days. Influenza plus single dose MPO-ANCA IgG induced MPA with NCGN, lung capillaritis and arteritis, but no granulomatosis. Allergic airway disease caused by house dust mites or ovalbumin plus single dose intravenous MPO-ANCA IgG induced EGPA with eosinophilic bronchiolitis, NCGN, capillaritis, arteritis, and granulomatosis. Thus, our study shows that the occurrence and pattern of lung lesions are determined by the same ANCA IgG accompanied by different synergistic immune factors.

Imaging of Disease and Normal Variant Patterns in Pediatric Hips.

The pediatric hip undergoes significant changes from infancy through adolescence. Proper maturation is crucial for the development of a stable and functional hip joint. Imaging interpretation of the pediatric hip requires distinguishing normal variants and maturation patterns from pathology. We review femoral ossification centers, variants, and conditions that affect the proximal femur, such as Legg-Calvé-Perthes disease; the acetabulum, such as developmental hip dysplasia; the acetabular labrum, such as femoroacetabular impingement; and synovial pathology in children through adolescence. Understanding the spectrum of hip conditions and using advanced imaging techniques are essential for the accurate diagnosis and effective management of pediatric hip disorders.

IMAGE PROCESSING-DRIVEN DEEP LEARNING MODEL FOR PLANT DISEASE DETECTION TO ENHANCE IRRIGATION EFFICIENCY IN SMART AGRICULTURE

In the era of smart agriculture, efficient irrigation management is crucial for optimizing crop yield and resource use. Traditional methods of plant disease detection often rely on manual inspection, which is time-consuming and prone to errors. Smart agriculture leverages technology to improve agricultural practices. Accurate and timely plant disease detection is vital for effective irrigation management and overall crop health. Current methods are limited by their manual nature and inability to process large volumes of data quickly. Manual plant disease detection is labor-intensive and may not provide timely information, leading to inefficient irrigation practices. This inefficiency can result in reduced crop yield and wasted resources. LeNet integrates advanced image processing techniques with a deep learning architecture tailored for plant disease detection. The model utilizes convolutional neural networks (CNNs) to analyze plant leaf images, identifying disease patterns with high precision. LeNet incorporates preprocessing steps such as image normalization and augmentation to enhance model robustness. The network is trained on a comprehensive dataset of plant disease images, employing transfer learning to leverage pre-trained weights for improved accuracy. Evaluation of LeNet on a test dataset comprising 10,000 images demonstrated an impressive accuracy of 92.5%, with a precision of 90.3% and recall of 94.1%. The model significantly outperforms traditional methods, reducing disease detection time by 60% and enhancing irrigation efficiency by 30%. The reduction in water usage and increased crop yield were observed in practical trials.

Cardiovascular Disease Patterns, Mortality, and Hospitalization Trends in Adults Over 18: Insights From the Behavioral Risk Factor Surveillance System Database.

Background Cardiovascular diseases (CVDs), including coronary artery disease, heart attacks, strokes, and hypertension, are the leading cause of global morbidity and mortality. Despite advancements in diagnostic techniques, treatment protocols, and public health initiatives, the prevalence of CVD continues to rise. Hence, understanding trends of predisposing factors for CVD and current treatment modalities such as medication use and frequency of hospitalization is essential for developing effective interventions and improving public health strategies. This study leverages Behavioral Risk Factor Surveillance System (BRFSS) data to analyze these trends among adults older than 18 years. Methods Data were sourced from the BRFSSdatabase, analyzing CVD patterns from 2019 to 2021. The study included adults with high cholesterol or blood pressure, coronary heart disease, stroke, and heart failure. Data analysis utilized age-adjusted prevalence, mortality, and hospitalization rates. Results The analysis of the BRFSS data revealed several key trends in CVD patterns from 2019 to 2021. There was a statistically significant increase (p<0.05) in the age-adjusted prevalence of adults taking medication for high cholesterol, rising from 28.9% to 31%, and for controlling high blood pressure, increasing from 57.7% to 60.4%. From 2019 to 2021, coronary heart disease mortality increased from 360,900 to 382,820, while stroke deaths rose from 150,005 to 162,890. Trends show rising mortality for both conditions despite missing data for some years. Mortality rates for coronary heart disease and stroke also rose and were statistically significant (p<0.05), with coronary heart disease mortality increasing from 88 to 92.8 cases per 100,000, and stroke mortality from 37 to 41.1 cases per 100,000.Hospitalization rates for heart failure among Medicare beneficiaries aged 65 and older initially decreased in 2020, likely due to the COVID-19 pandemic impacting hospital admissions, but rose again in 2021 as healthcare-seeking behaviors normalized.Significant gender and racial disparities were observed, with higher mortality rates among males (127.4 per 100,000) and Black, non-Hispanic individuals (110.5 per 100,000). Conclusions This study highlights the increasing medication use for cholesterol and blood pressure among adults older than 18 years, yet mortality rates for coronary heart disease and stroke persist. Significant gender and racial disparities in medication use and mortality rates were observed. These findings underscore the need for targeted public health intervention towards improving medication adherence and addressing social determinants of health, to reduce CVD burden and enhance health equity across diverse populations.

Thyroid Disease Classification Using Machine Learning Algorithms

Abstract: The thyroid is very important because it helps our body function properly. It does this by secreting special hormones into our bloodstream that control how our body uses energy. Sometimes there may be problems with the thyroid gland secreting too much or too little hormone. This is because not having enough iodine, swelling of the tumor, or the body's immune system attacking the tumor. Doctors can diagnose these problems with blood tests, but sometimes the results may be confusing. Using special technology, doctors can make information easier to understand and detect how a person may have thyroid problems. This analysis includes analysis of the distribution pattern of thyroid disease and data based on the UCI Machine Learning Repository. Detection of thyroid diseases involves machine learning, which plays an vital role in detecting thyroid disease. Using this technique,doctors can better understand and treat thyroid problems.

VISUAL DIALOGUE BETWEEN SCULPTURE AND MEDICINE: THE INTERSECTION OF AESTHETICS AND SCIENCE

The intersection of medicine and art, often explored in numerous articles, delves into detailed depictions of diseases or concepts within the medical field. These depictions aim to speculate on disease effects and patterns of interaction, sometimes reaching scientific conclusions. However, artistic representations may pose scientific, conceptual, and methodological challenges, defining the complex boundary between medicine and art. This article broadens the perspective by examining the relationship between medicine and sculpture, focusing on how these fields can be classified in relation to each other. Rather than concentrating on individual works, the study categorizes the language, methods, and approaches used in works resulting from the intersection of art and medicine. The historical continuum is explored to understand the scope and connections between sculpture and medicine from the past to the present. Using search terms like "art," "sculpture," and "medicine" on platforms such as PubMed, Scopus, and Taylor &amp; Francis Online, the research aims to convey how medicine is reflected in artistic practices. The article highlights the potential for collaboration and mutual enrichment between medicine and art, emphasizing the diverse use of art, particularly sculpture, in various contexts related to medical themes.

Canadian Association of Radiologists Statement on Environmental Sustainability in Medical Imaging.

Immediate and strategic action is needed to improve environmental sustainability and reduce the detrimental effects of climate change. Climate change is already adversely affecting the health of Canadians related to worsening air pollution and wildfire smoke, increasing frequency and intensity of extreme weather events, and expansion of vector-borne and infectious illnesses. On one hand, radiology contributes to the climate crisis by generating greenhouse gas emissions and waste during the production, manufacture, transportation, and use of medical imaging equipment and supplies. On the other hand, radiology departments are also susceptible to equipment and infrastructure damage from flooding, extreme temperatures, and power failures, as well as workforce shortages due to injury and illness, potentially disrupting radiology services and increasing costs. The Canadian Association of Radiologists' (CAR) advocacy for environmentally sustainable radiology in Canada encompasses both minimizing the detrimental effects that delivery of radiology services has on the environment and optimizing the resilience of radiology departments to increasing health needs and changing patterns of disease on imaging related to climate change. This statement provides specific recommendations and pathways to help guide radiologists, medical imaging leadership teams, industry partners, governments, and other key stakeholders to transition to environmentally sustainable, net-zero, and climate-resilient radiology organizations. Specific consideration is given to unique aspects of medical imaging in Canada. Finally, environmentally sustainable radiology programs, policies, and achievements in Canada are highlighted.

Impact of disease activity patterns on health-related quality of life (HRQoL) in patients with systemic lupus erythematosus (SLE)

ObjectiveTo assess the impact of different disease activity patterns—long quiescent (LQ), chronically active (CA) and relapsing-remitting (RR)—on health-related quality of life (HRQoL) in a cohort of patients with systemic lupus...

Alzheimer's Disease and Vascular Dementia, Connecting and Differentiating Features.

Alzheimer's disease (AD) and vascular dementia (VD) are the leading causes of dementia, presenting a significant challenge in differential diagnosis. While their clinical presentations can overlap, their underlying pathologies are distinct. AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles, leading to progressive neurodegeneration. VD, on the other hand, arises from cerebrovascular insults that disrupt blood flow to the brain, causing neuronal injury and cognitive decline. Despite distinct etiologies, AD and VD share common risk factors such as hypertension, diabetes, and hyperlipidemia. Recent research suggests a potential role for oral microbiota in both diseases, warranting further investigation. The diagnostic dilemma lies in the significant overlap of symptoms including memory loss, executive dysfunction, and personality changes. The absence of definitive biomarkers and limitations of current neuroimaging techniques necessitate a multi-modal approach integrating clinical history, cognitive assessment, and neuroimaging findings. Promising avenues for improved diagnosis include the exploration of novel biomarkers like inflammatory markers, MMPs, and circulating microRNAs. Additionally, advanced neuroimaging techniques hold promise in differentiating AD and VD by revealing characteristic cerebrovascular disease patterns and brain atrophy specific to each condition. By elucidating the complexities underlying AD and VD, we can refine diagnostic accuracy and optimize treatment strategies for this ever-growing patient population. Future research efforts should focus on identifying disease-specific biomarkers and developing more effective neuroimaging methods to achieve a definitive diagnosis and guide the development of targeted therapies.

Causes of death and patterns of metastatic disease at the end of life for patients with advanced melanoma in the immunotherapy era.

Despite remarkable advances in immunotherapy, melanoma remains a significant cause of cancer mortality. Many factors concerning melanoma mortality are poorly understood, posing an obstacle to optimal care. We conducted a retrospective observational cohort study of 183 patients with metastatic melanoma who died following immunotherapy treatment to investigate sites of metastases at death, settings of death, and mechanisms of death. The median time from metastatic diagnosis to death was 16.1 months (range 0.3-135.1 months). Most patients experienced hospitalization within 3 months before death (80.3%), with 31.7% dying while hospitalized, 31.2% while in inpatient hospice, and 29.4% while in home hospice. The most common sites of metastases at death were distant lymph nodes (62.8%), lung (57.9%), liver (50.8%), brain (38.8%), and bone (37.7%). The most common causes of death were progressive failure to thrive (57.5%), respiratory failure (22.4%), and infection (21.8%); the vast majority (87.9%) of patients died from melanoma-specific causes. Overall, 10.9% of patients in our cohort had survival >5 years after metastatic diagnosis, and 76.2% of long-term survivors died due to melanoma. This study describes factors associated with melanoma mortality, highlighting an ongoing need for therapeutic advancements.

Incidence of exclusive extrapelvic skeletal metastasis in prostate carcinoma on bone scintigraphy.

Bone is one of the common sites of metastasis from prostate carcinoma. Bone scintigraphy (BS) is one of the most sensitive imaging modalities currently used for bone metastatic work-up. Skeletal metastasis in prostate carcinoma commonly involves pelvic bones but rarely involves extrapelvic-extraspinal sites. To retrospectively analyze the BS data to determine the pattern of skeletal metastases in the prostate carcinoma. This retrospective observational study involves patients with biopsy-proven prostate carcinoma referred for BS for staging assessment. Patients with abnormal BS were evaluated for the pattern of skeletal involvement and data were presented in descriptive format in the form of percentages. A total of 150 patients with biopsy-proven prostate cancer who were referred for staging were included in the study. Thirteen of 150 patients (8.67%) had no abnormal uptake on planar images, ruling out metastatic disease. Twenty-four patients (16%) had heterogeneous uptake in the spine with distribution characteristic of degenerative disease and no scan pattern of metastatic disease. Thirty patients (20%) had multifocal uptake involving both pelvic and extra pelvic bones on planar images typical for skeletal metastasis and were considered metastatic. Eighty-three out of 150 patients (55.3%) had increased tracer uptake, which was indeterminate, thus, single photon emission computed tomography-computed tomography (SPECT-CT) was acquired, which showed 51 with metastatic disease, 31 benign lesions, and one indeterminate finding. Seven of 150 patients had exclusive pelvic bone uptake, which was found to be metastatic in 4/7 patients in SPECT-CT. Fifty six out of 150 patients showed exclusive extrapelvic tracer uptake, of which only 3 had vertebral metastatic disease. None of the patients with increased uptake exclusively in the extrapelvic-extraspinal location was metastatic. The incidence of exclusive extrapelvic skeletal metastatic disease in prostate carcinoma is 2% (excluding one patient with indeterminate findings). Further, none of the patients in the current study had exclusive extrapelvic-extraspinal metastasis. Thus, exclusive extrapelvic-extraspinal focal abnormality on planar BS carries a very low probability of metastatic disease and hence, further imaging or SPECT-CT can be safely avoided in such cases.

The Proteomic Analysis of Chronic Migraine Exosomes Reveals Disease Patterns and Potential Biomarkers.

Exosomes have been identified as optimal biomarkers to screen for multiple diseases. However, few studies focus on the abundant exosome population isolated from plasma of migraine. This study investigated whether proteins in abundant exosomes can aid in the diagnosis of chronic migraine (CM). Plasma exosomes were collected by centrifugation, from which protein samples were extracted. A pilot study (CM, 18; episodic migraine (EM), 26) followed by a second dataset (CM, 26; EM, 16; tension-type headache (TTH), 20; control, 22) was applied to establish a diagnostic model of CM. We employed proteomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to search for potential candidate biomarkers in plasma exosomes from CM patients. In total, 530 proteins in plasma exosomes were co-detected. Among them, 13 proteins were found significantly dysregulated between the plasma exosomes of CM patients and other groups. The receiver operating characteristic curve analysis revealed a combination of six proteins (upregulated: RAP2B, AK1, BID, DAG1, PICALM, PSMB2) could distinguish CM patients with high accuracy. Linear correlation analysis showed that the combination was significantly correlated with Headache Impact Test (HIT-6) scores (assessing the negative impact of headaches on normal daily activity). The RT-qPCR results showed the same trends in CM models with nitroglycerin as the exosomal protein sequencing results. These data revealed dysregulated proteins in plasma exosomes of CM, and the combination of plasma exosomal proteins RAP2B, AK1, BID, DAG1, PICALM, and PSMB2 could serve as a novel candidate biomarker for CM diagnosis.

Pattern of occupational lung disease among industrial workers attending a medical college of Eastern India.

Occupational lung diseases (OLDs) contribute a significant proportion to the global burden of pulmonary morbidities but are grossly misdiagnosed due to the relative lack of attribution given to occupational exposures. Obstructive lung diseases are known to be associated with long-lasting disability and loss of earning capacity (LOEC) among workers in industrial setups, thus reducing nationwide productivity. In this context, the study aimed to find out the pattern of OLD and factors associated with the severity of it among patients in a tertiary care hospital. The study was a record-based secondary data analysis conducted in the Medical Records Department of a Medical College in Kolkata. A computerized database of patients attending Special Medical Board (SMB) examinations from the Department of Medical Records was utilized for data collection. A data abstraction format was constructed to collect information on pulmonary morbidity, occupational exposure, and sociodemographic and behavioral variables. Extracted data were analyzed in Microsoft Excel and Statistical Package for Social Sciences (SPSS) software. After a review of records, it was shown that 62.3% (66 out of 106 people) of the study subjects had an obstructive type of OLD, the most common being Jute Byssinosis. A negative correlation (Spearman's ρ = -0.136) was found between pulmonary function (FEV1/FVC) and LOEC (%) in the study subjects. In the multivariable logistic regression, exposure to organic dust was found to be significantly associated with worsened lung function {adjusted-Odd's Ratio (95% Confidence Interval) =3.11 (1.1-8.8), P value = 0.03}. OLD is an understated health issue, especially in an industrial diaspora of developing countries, like India. Healthcare facilities should utilize their resources properly for the advancement of medical surveillance in industries where organic dust is produced. Health education of the stakeholders regarding the consequences of OLDs and the benefits of preventive primary approaches will go a long way in alleviating the burden of disease.

Spirometry findings of chronic lung disease in high-altitude residents of Ladakh (&gt;11000 feet above sea level)

Ladakh is a hilly Himalayan dry desert, situated at an altitude of &gt;11000 feet. Studies have demonstrated that the spirometric values of high-altitude residents are significantly higher than those of low-landers. This is a retrospective observational study that analyzes the spirometry pattern in chronic lung diseases among people from Ladakh. Enrolled subjects were clinic-radiologically diagnosed and had at least one spirometry report. The spirometric parameters were analyzed for normal and abnormal patterns of lung function. The abnormal patterns were further classified into types of ventilator defects and their severity. A total of 122 cases were included, with 67 (55%) men. The mean age was 52.2±15.4 years. The most common diseases were chronic obstructive pulmonary disease (COPD) in 51 cases (41%), and asthma in 41 (33%). The median predicted percentage of forced vital capacity (FVC) was 116% (63-179%) with &gt;100% in 105 (85%) patients. The median predicted percentage of the forced expiratory volume in the 1st second (FEV1) was 113% (99-175%) with &gt;100% in 90 (74%) patients. FVC was reduced in 9 (7%) cases, normal in 62 (51%), and more than normal in 49 (42%), with 11 (9%) cases having &gt;150% of the predicted percentage. FEV1 was reduced in 9 (8%) cases, normal in 67 (55%), and more than normal in 46 (37%) cases, with &gt;150% predicted seen in 10 (8%) cases. Similarly, overall, the predicted percentages of both FVC and FEV1 were &gt;100% in all obstructive airway diseases as well as in the separate COPD and asthma subgroups. FVC and FEV1 amongst chronic lung disease patients from Ladakh were more than normal in the majority. These higher values of spirometry led to incorrect disease severity classifications and disease patterns. We propose that studies should be done to devise local reference equations for spirometry for Himalayan high-altitude residents of India.

Impaired glycemic control as a risk factor for reduced lung function in the Indian diabetic population

Diabetes mellitus (DM) is a metabolic syndrome associated with chronic hyperglycemia, which results in various acute and chronic complications. DM leads to a state of chronic low-grade inflammation, which can have adverse effects on pulmonary functions. There have been contradictory studies related to the relationship between defects in lung functions in diabetic individuals and their correlation with glycemic control and systemic inflammatory markers. The present study aims to compare pulmonary function in controlled and uncontrolled diabetes in the Indian population while exploring the link between inflammatory markers and lung functions in diabetic patients. This observational, case-control study was conducted in the Department of Biochemistry at Sri Guru Ram Das Institute of Medical Sciences and Research in Amritsar, Punjab, on 116 subjects suffering from DM in the age group of 30-65 years. 58 diabetic patients with poor glycemic control [glycated hemoglobin (HbA1c)&gt;7%] and 58 diabetic patients with good glycemic control served as controls (HbA1c≤7%). The duration of the study was two years. Blood samples of each patient were investigated for glycemic control, high-sensitivity C-reactive protein (hsCRP), and serum fibrinogen. Spirometry as a pulmonary function test was undertaken for all participants. The statistical analysis of good and poor glycemic control diabetics showed that the average duration of disease (in years) was 8±5 and 10.2±5.4, respectively. A significant positive correlation was found between inflammatory markers (hsCRP and fibrinogen) and HbA1c and fasting blood glucose. A substantial decline in forced vital capacity and normal values of forced expiratory volume in the first second was observed in poor glycemic control diabetics, depicting a restrictive pattern of lung disease. Lung damage is seen to be more prevalent in patients with a longer duration of disease and increased levels of inflammatory markers. Chronic inflammation due to DM can lead to fibrosis and destruction of lung tissue, resulting in the development of diabetic lung disease, which includes a decline in lung function, an increased risk of infection, and an increased risk of respiratory failure. Therefore, it is essential for individuals with DM to have regular pulmonary function tests and to manage their diabetes to minimize the impact on their lung health.

Major adverse cardiovascular outcomes within distinct pulmonary hypertension patterns in advanced chronic kidney disease: A retrospective cohort analysis

Major adverse cardiovascular outcomes within distinct pulmonary hypertension patterns in advanced chronic kidney disease: A retrospective cohort analysis

Paris 2024 Olympic Games: A risk enhancer for autochthonous arboviral diseases epidemics?

ObjectivesThe anticipated increase in international tourist flows and the first locally acquired dengue cases in the Paris region in October 2023 have raised concerns about potential arbovirus outbreaks during the 2024 Olympics. Unlike previous mass sporting events at risk of arbovirus outbreaks, Paris is a nonendemic arbovirus area, requiring a unique investigation. MethodsTherefore, we analyzed factors conducive to possible arbovirus epidemics in temperate regions: vector distribution in the Paris area, seasonal global arboviral disease patterns, projected visitor demographics, and international flight bookings. Results and conclusionOur results suggest that the expected visitors’ profile for the summer of 2024 should not increase the risk of arbovirus importation into the Paris region compared to a typical year. Conversely, the primary risk of arbovirus outbreaks is likely to come from within France, particularly from the French West Indies, where a notable, albeit declining, dengue outbreak is underway. Vigilant surveillance by French health authorities will ensure that this trend continues.