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2,226 results on '"Malik, Rayaz A."'

2. Greater exposure to PM2.5 and PM10 was associated with lower corneal nerve measures: the Maastricht study - a cross-sectional study

Author

Mokhtar, Sara B. A., Viljoen, Jessica, van der Kallen, Carla J. H., Berendschot, Tos T. J. M., Dagnelie, Pieter C., Albers, Jeroen D., Soeterboek, Jens, Scarpa, Fabio, Colonna, Alessia, van der Heide, Frank C. T., van Greevenbroek, Marleen M. J., Bosm, Hans, Kroon, Abraham A., Nuijts, Rudy M. M. A., Gijs, Marlies, Lakerveld, Jeroen, Malik, Rayaz A., Webers, Carroll A. B., Stehouwer, Coen D. A., and Koster, Annemarie

Published
2024
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3. CVOT Summit Report 2023: new cardiovascular, kidney, and metabolic outcomes

Author

Schnell, Oliver, Barnard-Kelly, Katharine, Battelino, Tadej, Ceriello, Antonio, Larsson, Helena Elding, Fernández-Fernández, Beatriz, Forst, Thomas, Frias, Juan-Pablo, Gavin, III, James R., Giorgino, Francesco, Groop, Per-Henrik, Heerspink, Hiddo J. L., Herzig, Stephan, Hummel, Michael, Huntley, George, Ibrahim, Mahmoud, Itzhak, Baruch, Jacob, Stephan, Ji, Linong, Kosiborod, Mikhail, Lalic, Nebosja, Macieira, Sofia, Malik, Rayaz A., Mankovsky, Boris, Marx, Nikolaus, Mathieu, Chantal, Müller, Timo D., Ray, Kausik, Rodbard, Helena W., Rossing, Peter, Rydén, Lars, Schumm-Draeger, Petra-Maria, Schwarz, Peter, Škrha, Jan, Snoek, Frank, Tacke, Frank, Taylor, Bruce, Jeppesen, Britta Tendal, Tesfaye, Solomon, Topsever, Pinar, Vilsbøll, Tina, Yu, Xuefeng, and Standl, Eberhard

Published
2024
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5. Spinal disinhibition: evidence for a hyperpathia phenotype in painful diabetic neuropathy

Author

Marshall, Anne, Kalteniece, Alise, Ferdousi, Maryam, Azmi, Shazli, Jude, Edward B, Adamson, Clare, D’Onofrio, Luca, Dhage, Shaishav, Soran, Handrean, Campbell, Jackie, Lee-Kubli, Corinne A, Hamdy, Shaheen, Malik, Rayaz A, Calcutt, Nigel A, and Marshall, Andrew G

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Neurodegenerative, Peripheral Neuropathy, Clinical Research, Chronic Pain, Mental Health, Diabetes, Pain Research, Aetiology, 2.1 Biological and endogenous factors, Neurological, diabetes, neuropathy, pain, spinal disinhibition, phenotype, Clinical sciences, Biological psychology
Abstract

The dominant sensory phenotype in patients with diabetic polyneuropathy and neuropathic pain is a loss of function. This raises questions as to which mechanisms underlie pain generation in the face of potentially reduced afferent input. One potential mechanism is spinal disinhibition, whereby a loss of spinal inhibition leads to increased ascending nociceptive drive due to amplification of, or a failure to suppress, incoming signals from the periphery. We aimed to explore whether a putative biomarker of spinal disinhibition, impaired rate-dependent depression of the Hoffmann reflex, is associated with a mechanistically appropriate and distinct pain phenotype in patients with painful diabetic neuropathy. In this cross-sectional study, 93 patients with diabetic neuropathy underwent testing of Hoffmann reflex rate-dependent depression and detailed clinical and sensory phenotyping, including quantitative sensory testing. Compared to neuropathic patients without pain, patients with painful diabetic neuropathy had impaired Hoffmann reflex rate-dependent depression at 1, 2 and 3 Hz (P ≤ 0.001). Patients with painful diabetic neuropathy exhibited an overall loss of function profile on quantitative sensory testing. However, within the painful diabetic neuropathy group, cluster analysis showed evidence of greater spinal disinhibition associated with greater mechanical pain sensitivity, relative heat hyperalgesia and higher ratings of spontaneous burning pain. These findings support spinal disinhibition as an important centrally mediated pain amplification mechanism in painful diabetic neuropathy. Furthermore, our analysis indicates an association between spinal disinhibition and a distinct phenotype, arguably akin to hyperpathia, with combined loss and relative gain of function leading to increasing nociceptive drive.

Published
2023

8. Contributors

Author

Abbasi, Muhammad Zafar Iqbal, primary, Abdul Rehman, Rabia, additional, Ahmedani, Muhammad Yakoob, additional, Albache, Nizar, additional, Ali-Eltom, Mohamed, additional, Askari, Saima, additional, Azad Khan, Abul Kalam, additional, Basit, Abdul, additional, Basit, Khalid Abdul, additional, Bhowmik, Bishwajit, additional, Boulton, Andrew J.M., additional, Buyukbese, Mehmet Akif, additional, Eichorst, Barbara, additional, Eigenmann, Cecile Alice, additional, El-Sayed, Adel Abdel Aziz, additional, Faheem, Osman, additional, Farooqi, Meena Iqbal, additional, Fawwad, Asher, additional, Ghafoor, Erum, additional, Hakeem, Rubina, additional, Hinchliffe, Robert J., additional, Hussain, Akhtar, additional, Hydrie, Muhammad Zafar Iqbal, additional, Jabbar, Abdul, additional, Jamil, Bushra, additional, Jenkins, Alicia Josephine, additional, Khan, Muhammad Farhatullah, additional, Khan, Shair Zaman, additional, Khan, Uzma Zubair, additional, Khan, Yasmin, additional, Little, Erin Elizabeth, additional, Majumdar, Kalpita, additional, Malik, Rayaz Ahmed, additional, Mehar, Salma, additional, Memon, Riaz Ahmed, additional, Miyan, Zahid, additional, Mohan, Viswanathan, additional, Moyeen, Fauzia, additional, Niazi, Asima Khan, additional, Ogle, Graham David, additional, Pedrosa, Hermelinda Cordeiro, additional, Rashid, Thamina, additional, Riaz, Musarrat, additional, Saif-Ul Haque, M., additional, Salman, Ahmed, additional, Schwartz, Peter Egbert Hermann, additional, Senneville, Eric, additional, Shafique, Kashif, additional, Sheikh, Aisha, additional, Sheikh, Muhammad Adil, additional, Shiraz, Mahwish, additional, Simmons, David, additional, Soomro, Marium Habib, additional, Sultan, Sharjeel, additional, Syed, Furrukh Omair, additional, Tayeb, Khaled Abdullah, additional, Unnikrishnan, Ambika Gopalakrishnan, additional, Venkatesan, Radha, additional, Wild, Sarah, additional, Yousuf, Sanobia, additional, and Zafar, Awn Bin, additional

Published
2024
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9. A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations

Author

Haque, Fahmida, Reaz, Mamun B. I., Chowdhury, Muhammad E. H., Malik, Rayaz, Alhatou, Mohammed, Kobashi, Syoji, Ara, Iffat, Ali, Sawal H. M., Bakar, Ahmad A. A, and Srivastava, Geetika

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for identifying DSPN using the extra tree model. The area under the curve (AUC) of the nomogram for the internal and external datasets were 0.9421 and 0.946, respectively. From the developed nomogram, the probability of having DSPN was predicted and a DSPN severity scoring system for MNSI was developed from the probability score. The model performance was validated on an independent dataset. Patients were stratified into four severity levels: absent, mild, moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN probability less than 50%, 75% to 90%, and above 90%, respectively. Conclusions: This study provides a simple, easy-to-use and reliable algorithm for defining the prognosis and management of patients with DSPN., Comment: 21 pages, 6 Figures, 11 Tables

Published
2022

13. A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images

Author

Khandakar, Amith, Chowdhury, Muhammad E. H., Reaz, Mamun Bin Ibne, Ali, Sawal Hamid Md, Hasan, Md Anwarul, Kiranyaz, Serkan, Rahman, Tawsifur, Alfkey, Rashad, Bakar, Ahmad Ashrif A., and Malik, Rayaz A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ~95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97 %. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting., Comment: 23 pages, 8 Figures

Published
2021

16. Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Author

Rashid, Asrar, Al-Obeidat, Feras, Kanthimathinathan, Hari Krishnan, Benakatti, Govind, Hafez, Wael, Ramaiah, Raghu, Brierley, Joe, Hanisch, Benjamin, Khilnani, Praveen, Koutentis, Christos, Brusletto, Berit S., Toufiq, Mohammed, Hussain, Zain, Vyas, Harish, Malik, Zainab A, Schumacher, Maike, Malik, Rayaz A, Deshpande, Shriprasad, Quraishi, Nasir, Kadwa, Raziya, Sarpal, Amrita, Shaikh, M. Guftar, Sharief, Javed, Zaki, Syed Ahmed, Phatak, Rajesh, Deep, Akash, Al-Dubai, Ahmed, and Hussain, Amir

Published
2024
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19. A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations

Author

Zhang, Ning, Francis, Susan, Malik, Rayaz, and Chen, Xin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Semantic image segmentation is one of the most important tasks in medical image analysis. Most state-of-the-art deep learning methods require a large number of accurately annotated examples for model training. However, accurate annotation is difficult to obtain especially in medical applications. In this paper, we propose a spatially constrained deep convolutional neural network (DCNN) to achieve smooth and robust image segmentation using inaccurately annotated labels for training. In our proposed method, image segmentation is formulated as a graph optimization problem that is solved by a DCNN model learning process. The cost function to be optimized consists of a unary term that is calculated by cross entropy measurement and a pairwise term that is based on enforcing a local label consistency. The proposed method has been evaluated based on corneal confocal microscopic (CCM) images for nerve fiber segmentation, where accurate annotations are extremely difficult to be obtained. Based on both the quantitative result of a synthetic dataset and qualitative assessment of a real dataset, the proposed method has achieved superior performance in producing high quality segmentation results even with inaccurate labels for training., Comment: 4 pages, accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 2020

Published
2020

20. Tau associated peripheral and central neurodegeneration: Identification of an early imaging marker for tauopathy

Author

Marquez, Alexandra, Guernsey, Lucie S, Frizzi, Katie E, Cundiff, Morgan, Constantino, Isabel, Muttalib, Nabeel, Arenas, Fernanda, Zhou, Xiajun, Lim, Sze Hway, Ferdousi, Maryam, Ponirakis, Georgios, Silverdale, Monty, Kobylecki, Christopher, Jones, Matthew, Marshall, Andrew, Malik, Rayaz A, and Jolivalt, Corinne G

Subjects
Biomedical and Clinical Sciences, Ophthalmology and Optometry, Aging, Neurosciences, Brain Disorders, Alzheimer's Disease, Acquired Cognitive Impairment, Peripheral Neuropathy, Alzheimer's Disease Related Dementias (ADRD), Rare Diseases, Eye Disease and Disorders of Vision, Physical Injury - Accidents and Adverse Effects, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Dementia, Frontotemporal Dementia (FTD), Aetiology, 2.1 Biological and endogenous factors, Neurological, Alzheimer Disease, Animals, Cornea, Female, Frontotemporal Dementia, Humans, Memory Disorders, Mice, Mice, Transgenic, Microscopy, Confocal, Middle Aged, Nerve Degeneration, Peripheral Nervous System Diseases, Tauopathies, tau Proteins, Corneal confocal microscopy, Memory deficits, Neurodegeneration, Peripheral neuropathy, Tau, Clinical Sciences, Neurology & Neurosurgery, Biochemistry and cell biology
Abstract

Pathological hyperphosphorylated tau is a key feature of Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Using transgenic mice overexpressing human non-mutated tau (htau mice), we assessed the contribution of tau to peripheral and central neurodegeneration. Indices of peripheral small and large fiber neuropathy and learning and memory performances were assessed at 3 and 6 months of age. Overexpression of human tau is associated with peripheral neuropathy at 6 months of age. Our study also provides evidence that non-mutated tau hyperphosphorylation plays a critical role in memory deficits. In addition, htau mice had reduced stromal corneal nerve length with preservation of sub-basal corneal nerves, consistent with a somatofugal degeneration. Corneal nerve degeneration occurred prior to any cognitive deficits and peripheral neuropathy. Stromal corneal nerve loss was observed in patients with FTD but not AD. Corneal confocal microscopy may be used to identify early neurodegeneration and differentiate FTD from AD.

Published
2021

21. ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION–DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

Author

Rashid, Asrar, Al-Obeidat, Feras, Hafez, Wael, Benakatti, Govind, Malik, Rayaz A., Koutentis, Christos, Sharief, Javed, Brierley, Joe, Quraishi, Nasir, Malik, Zainab A., Anwary, Arif, Alkhzaimi, Hoda, Zaki, Syed Ahmed, Khilnani, Praveen, Kadwa, Raziya, Phatak, Rajesh, Schumacher, Maike, Shaikh, M. Guftar, Al-Dubai, Ahmed, and Hussain, Amir

Published
2024
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23. Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Author

Rashid, Asrar, Al-Obeida, Feras, Hafez, Wael, Benakatti, Govind, Malik, Rayaz A, Koutentis, Christos, Sharief, Javed, Brierley, Joe, Quraishi, Nasir, Malik, Zainab A, Anwary, Arif, Alkhzaimi, Hoda, Zaki, Syed Ahmed, Khilnani, Praveen, Kadwa, Raziya, Phatak, Rajesh, Schumacher, Maike, Shaikh, Guftar, Al-Dubai, Ahmed, and Hussain, Amir

Published
2023
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24. Optimal Utility of H-Reflex RDD as a Biomarker of Spinal Disinhibition in Painful and Painless Diabetic Neuropathy

Author

Worthington, Anne, Kalteniece, Alise, Ferdousi, Maryam, D’Onofrio, Luca, Dhage, Shaishav, Azmi, Shazli, Adamson, Clare, Hamdy, Shaheen, Malik, Rayaz A, Calcutt, Nigel A, and Marshall, Andrew G

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Peripheral Neuropathy, Clinical Research, Pain Research, Diabetes, Neurodegenerative, Chronic Pain, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, diabetic neuropathy, pain, spinal disinhibition, H-reflex, Clinical sciences
Abstract

Impaired rate-dependent depression of the Hoffman reflex (HRDD) is a potential biomarker of impaired spinal inhibition in patients with painful diabetic neuropathy. However, the optimum stimulus-response parameters that identify patients with spinal disinhibition are currently unknown. We systematically compared HRDD, performed using trains of 10 stimuli at five stimulation frequencies (0.3, 0.5, 1, 2 and 3 Hz), in 42 subjects with painful and 62 subjects with painless diabetic neuropathy with comparable neuropathy severity, and 34 healthy controls. HRDD was calculated using individual and mean responses compared to the initial response. At stimulation frequencies of 1, 2 and 3 Hz, HRDD was significantly impaired in patients with painful diabetic neuropathy compared to patients with painless diabetic neuropathy for all parameters and for most parameters when compared to healthy controls. HRDD was significantly enhanced in patients with painless diabetic neuropathy compared to controls for responses towards the end of the 1 Hz stimulation train. Receiver operating characteristic curve analysis in patients with and without pain showed that the area under the curve was greatest for response averages of stimuli 2-4 and 2-5 at 1 Hz, AUC = 0.84 (95%CI 0.76-0.92). Trains of 5 stimuli delivered at 1 Hz can segregate patients with painful diabetic neuropathy and spinal disinhibition, whereas longer stimulus trains are required to segregate patients with painless diabetic neuropathy and enhanced spinal inhibition.

Published
2021

25. Cardiovascular autonomic neuropathy is associated with increased glycemic variability driven by hyperglycemia rather than hypoglycemia in patients with diabetes

Author

Gad, Hoda, Elgassim, Einas, Mohammed, Ibrahim, Alhaddad, Ahmad Yaser, Aly, Hussein Ahmed Hussein Zaky, Cabibihan, John-John, Al-Ali, Abdulaziz, Sadasivuni, Kishor Kumar, Petropoulos, Ioannis N., Ponirakis, Georgios, Abuhelaiqa, Wajeeha, Jayyousi, Amin, AlMohanadi, Dabia, Baagar, Khaled, and Malik, Rayaz A.

Published
2023
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30. A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

Author

Rashid, Asrar, Brusletto, Berit S., Al-Obeidat, Feras, Toufiq, Mohammed, Benakatti, Govind, Brierley, Joe, Malik, Zainab A., Hussain, Zain, Alkhazaimi, Hoda, Sharief, Javed, Kadwa, Raziya, Sarpal, Amrita, Chaussabel, Damien, Malik, Rayaz A., Quraishi, Nasir, Khilnani, Praveen, Zaki, Syed A., Nadeem, Rashid, Shaikh, Guftar, Al-Dubai, Ahmed, Hafez, Wael, and Hussain, Amir

Published
2023
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41. Abnormal corneal nerve morphology and brain volume in patients with schizophrenia

Author

Ponirakis, Georgios, Ghandi, Reem, Ahmed, Amani, Gad, Hoda, Petropoulos, Ioannis N., Khan, Adnan, Elsotouhy, Ahmed, Vattoth, Surjith, Alshawwaf, Mahmoud K. M., Khoodoruth, Mohamed Adil Shah, Ramadan, Marwan, Bhagat, Anjushri, Currie, James, Mahfoud, Ziyad, Al Hamad, Hanadi, Own, Ahmed, M. Haddad, Peter, Alabdulla, Majid, Malik, Rayaz A., and Woodruff, Peter W.

Published
2022
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42. Corneal Langerhans cells in children with celiac disease

Author

Gad, Hoda, Mohammed, Ibrahim, Saraswathi, Saras, Al-Jarrah, Bara, Ferdousi, Maryam, Petropoulos, Ioannis N., Ponirakis, Georgios, Khan, Adnan, Singh, Parul, Al Khodor, Souhaila, Elawad, Mamoun, Almasri, Wesam, Abdelrahman, Hatim, Hussain, Khalid, Hendaus, Mohamed A., Al-Mudahka, Fatma, Abouhazima, Khaled, Akobeng, Anthony K., and Malik, Rayaz A.

Published
2022
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48. Diabetes and Ramadan: Practical guidelines 2021

Author

Hassanein, Mohamed, Afandi, Bachar, Yakoob Ahmedani, Muhammad, Mohammad Alamoudi, Reem, Alawadi, Fatheya, Bajaj, Harpreet S., Basit, Abdul, Bennakhi, Abdullah, El Sayed, Adel A., Hamdy, Osama, Hanif, Wasim, Jabbar, Abdul, Kleinebreil, Line, Lessan, Nader, Shaltout, Inass, Mohamad Wan Bebakar, Wan, Abdelgadir, Elamin, Abdo, Sarah, Al Ozairi, Ebaa, Al Saleh, Yousef, Alarouj, Monira, Ali, Tomader, Ali Almadani, Abdulrazzaq, Helmy Assaad-Khalil, Samir, Bashier, Alaaeldin Mohammed K., Arifi Beshyah, Salem, Buyukbese, Mehmet Akif, Ahmad Chowdhury, Tahseen, Norou Diop, Said, Samir Elbarbary, Nancy, Elhadd, Tarik A., Eliana, Fatimah, Ezzat Faris, MoezAlIslam, Hafidh, Khadija, Hussein, Zanariah, Iraqi, Hinde, Kaplan, Walid, Khan, Tayyab S., Khunti, Kamlesh, Maher, Salma, Malek, Rachid, Malik, Rayaz A., Mohamed, Mafauzy, Sayed Kamel Mohamed, Mesbah, Ahmed Mohamed, Nazeer, Pathan, Sameer, Rashid, Fauzia, Sahay, Rakesh K., Taha Salih, Bashir, Sandid, Mohamad A., Shaikh, Shehla, Slim, Ines, Tayeb, Khaled, Mohd Yusof, Barakatun-Nisak, and Binte Zainudin, Sueziani

Published
2022
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49. Greater exposure to PM2.5 and PM10 was associated with lower corneal nerve measures: the Maastricht study - a cross-sectional study.

Author

Mokhtar, Sara B. A., Viljoen, Jessica, van der Kallen, Carla J. H., Berendschot, Tos T. J. M., Dagnelie, Pieter C., Albers, Jeroen D., Soeterboek, Jens, Scarpa, Fabio, Colonna, Alessia, van der Heide, Frank C. T., van Greevenbroek, Marleen M. J., Bosm, Hans, Kroon, Abraham A., Nuijts, Rudy M. M. A., Gijs, Marlies, Lakerveld, Jeroen, Malik, Rayaz A., Webers, Carroll A. B., Stehouwer, Coen D. A., and Koster, Annemarie

Subjects
AIR pollutants, TYPE 2 diabetes, PARTICULATE matter, FRACTAL dimensions, AIR pollution
Abstract

Background: Epidemiological and toxicological studies indicate that increased exposure to air pollutants can lead to neurodegenerative diseases. To further confirm this relationship, we evaluated the association between exposure to ambient air pollutants and corneal nerve measures as a surrogate for neurodegeneration, using corneal confocal microscopy. Methods: We used population-based observational cross-sectional data from The Maastricht Study including N = 3635 participants (mean age 59.3 years, 51.6% were women, and 19.9% had type 2 diabetes) living in the Maastricht area. Using the Geoscience and hEalth Cohort COnsortium (GECCO) data we linked the yearly average exposure levels of ambient air pollutants at home address-level [particulate matter with diameters of ≤ 2.5 µm (PM2.5), and ≤ 10.0 µm (PM10), nitrogen dioxide (NO2), and elemental carbon (EC)]. We used linear regression analysis to study the associations between Z-score for ambient air pollutants concentrations (PM2.5, PM10, NO2, and EC) and Z-score for individual corneal nerve measures (corneal nerve bifurcation density, corneal nerve density, corneal nerve length, and fractal dimension). Results: After adjustment for potential confounders (age, sex, level of education, glucose metabolism status, corneal confocal microscopy lag time, inclusion year of participants, smoking status, and alcohol consumption), higher Z-scores for PM2.5 and PM10 were associated with lower Z-scores for corneal nerve bifurcation density, nerve density, nerve length, and nerve fractal dimension [stβ (95% CI): PM2.5 -0.10 (-0.14; -0.05), -0.04 (-0.09; 0.01), -0.11 (-0.16; -0.06), -0.20 (-0.24; -0.15); and PM10 -0.08 (-0.13; -0.03), -0.04 (-0.09; 0.01), -0.08 (-0.13; -0.04), -0.17 (-0.21; -0.12)], respectively. No associations were found between NO2 and EC and corneal nerve measures. Conclusions: Our population-based study demonstrated that exposure to higher levels of PM2.5 and PM10 are associated with higher levels of corneal neurodegeneration, estimated from lower corneal nerve measures. Our results suggest that air pollution may be a determinant for neurodegeneration assessed in the cornea and may impact the ocular surface health as well. [ABSTRACT FROM AUTHOR]

Published
2024
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