Repositório Científico do Instituto Nacional de Saúde
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Association of mortality and combined oxidative capacity of ozone and nitrogen dioxide
Publication . Niu, Yue; Chen, Renjie; Sera, Francesco; Urban, Aleš; Vicedo-Cabrera, Ana Maria; Honda, Yasushi; Huber, Veronika; Guo, Yuming; Tong, Shilu; Coelho, Micheline de Sousa Zanotti Staglior; Saldiva, Paulo Hilario Nascimento; Lavigne, Eric; Correa, Patricia Matus; Ortega, Nicolás Valdés; Osorio, Samuel; Achilleos, Souzana; Roye, Dominic; Jaakkola, Jouni J. K.; Ryti, Niilo; Pascal, Mathilde; Schneider, Alexandra; Breitner-Busch, Susanne; Entezari, Alireza; Mayvaneh, Fatemeh; Raz, Raanan; Sheng Ng, Chris Fook; Hashizume, Masahiro; Carrasco, Gabriel; das Neves Pereira da Silva, Susana; Madureira, Joana; Holobaca, Iulian-Horia; Kim, Ho; Lee, Whanhee; Tobias, Aurelio; Íñiguez, Carmen; Guo, Yue Leon; Pan, Shih-Chun; Li, Shanshan; Masselot, Pierre; Bell, Michelle L.; Zanobetti, Antonella; Schwartz, Joel; Gasparrini, Antonio; Kan, Haidong
Ozone (O3) and nitrogen dioxide (NO2) are two common gaseous pollutants that both possess oxidizing properties with consequences for human health and have an inextricable chemical relationship that could have distinct public health impacts when considered in combination. We examined the short-term associations of the combined oxidative capacity of O3 and NO2 (represented by Oxwt, the average of O3 and NO2 concentrations weighted by their standard electrode potential) with total, cardiovascular and respiratory mortality in 380 cities across 23 countries or regions between 1985 and 2020. Over 2 days (LAG01), a 10-ppb increase in Oxwt concentration was associated with an increase of 0.82% (95% confidence interval (CI): 0.55%, 1.10%) in total mortality, 1.09% (95% CI: 0.83%, 1.35%) in cardiovascular mortality and 0.88% (95% CI: 0.31%, 1.45%) in respiratory mortality. We also observed variations in this association by geographic region and study period. More deaths were attributable to Oxwt than to either O3 or NO2 but fewer than the sum of the two. Thus, Oxwt might be a valuable indicator for use in public health efforts to capture the combined effects of O3 and NO2.
Assessing global factors associated with tropical cyclone-related mortality: A population-based longitudinal study
Publication . Huang, Wenzhong; Yang, Zhengyu; Otto, Christian; Mengel, Matthias; Hales, Simon; Zhang, Yiwen; Xu, Rongbin; Bell, Michelle L.; Gasparrini, Antonio; Kan, Haidong; Sera, Francesco; Schwartz, Joel; Lavigne, Eric; Hundessa, Samuel; Yu, Wenhua; Carlos Chua, Paul Lester; Seposo, Xerxes; Goodman, Patrick; Zeka, Ariana; Hashizume, Masahiro; Z S Coelho, Micheline S.; Xu, Zhihu; Ye, Tingting; Yu, Pei; Wu, Yao; Wen, Bo; Liu, Yanming; Li, Shanshan; Guo, Yuming; MCC Collaborators
Background: The underlying factors associated with the substantial global tropical cyclone (TC)-related mortality burden, characterized by its highly variable spatiotemporal patterns, remain unclear. We aimed to identify and assess the key factors associated with TC-related mortality on a global scale.
Methods: We collected mortality records from 2034 locations in 68 countries/territories across five continents (2000-2019) to identify and assess the key factors associated with TC-related mortality on a global scale. Bayesian ensemble models were applied to estimate the associated mortality for each TC event in each location. A random forest regression (RFR) model was employed to assess the relative statistical importance of TC and location characteristics, as well as their interactions, regarding the TC-related mortality.
Findings: For TC physical characteristics, the TC-associated cumulative rainfall consistently exhibited stronger influence on mortality than TC-induced surge-driven flood depth or maximum sustained windspeed. However, sociodemographic factors, including population traits (e.g., population density, proportion of the population aged ≤ 9 years, percent of the population aged ≥ 65 years) and socioeconomic and infrastructure development (e.g., built-up ratio), showed greater relative importance than the TC physical attributes and accounted for the majority of the explained variations in TC-related mortality. Furthermore, cumulative rainfall interacted strongly with local sociodemographic conditions and was potentially the most important associated factor for disparities in mortality arising from factor interactions.
Interpretation: The findings highlight the critical role of sociodemographic factors in explaining the global spatiotemporal variability of TC-related mortality, surpassing the relative importance of TC intensity. TC-related rainfall could be a key associated physical factor of the global mortality burden.
Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniques
Publication . Hauns, Sven; Pinto, Frederico G.; Khyriem, Costerwell; Singh, Ankita; Al-Sadi, Azzat; Yazeedi, Talal Al; Mohammad, Rasheed; Cisse, Babacar; Garrett, Timothy J.; Uddin, Mohammed; Soares, Nelson C.; Backofen, Rolf; Alkhnbashi, Omer S.
Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of artificial intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest-based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and BC patients. The datasets were acquired using paper spray ionization mass spectrometry and flow injection-traveling-wave ion mobility-mass spectrometry of individuals diagnosed with PC and BC. When leveraging metabolomic profiling data from two distinct sources, PC urine and serum samples, the proposed model achieved an accuracy up to 98.75% in distinguishing diseased from healthy conditions. Additionally, we employed a BC dataset containing metabolic and lipidomic signatures acquired from core needle biopsies using a miniature MS platform coupled with PSI to assess the fidelity of our implementation across distinct cancer types. Our results on a well-characterized targeted dataset show that we can effectively reduce high-dimensional data into latent feature representations. At the same time, TabPFN captures tumor progression-related changes and feature interaction, thereby enhancing the possibility that the model will be a highly potent and effective tool for stage-specific diagnostic precision. Most existing machine learning approaches for disease diagnosis primarily rely on imaging, genomics, or clinical parameters, often overlooking the critical role of metabolites in identifying disease-specific biochemical signatures. By integrating metabolite-specific data with a robust deep-learning approach, this study demonstrates the transformative potential of AI in metabolomics-based diagnostics. The proposed model offers scalability and versatility, with applications extending beyond oncology to a much broader disease profiling aspect. These findings emphasize the value of combining multi-source metabolomic data with deep learning to advance personalized medicine and enhance diagnostic efficiency in clinical practice.
Urinary multi-mycotoxin exposure in the Portuguese population and associated sociodemographic determinants
Publication . Maris, Elias; Namorado, Sónia; Chen, An; Pero-Gascon, Roger; De Boevre, Marthe; De Saeger, Sarah; Silva, Maria João; Alvito, Paula
European human biomonitoring (HBM) studies have increasingly adopted multi-mycotoxin approaches to capture complex exposure scenarios encountered in real-life diets. This nation-wide study provides the characterization, for the first time, of the mycotoxin profile of 295 urine samples collected from the Portuguese population in the continent and islands (archipelagos of Madeira and Azores), using an optimized and validated LC-MS/MS method for the characterization of 39 mycotoxins/metabolites. Nearly all study participants (99.3%) were exposed to at least one mycotoxin, with around half (54.2%) being co-exposed to only 2 detectable mycotoxins. Free-deoxynivalenol (free-DON, 84.7%) and tenuazonic acid (TeA, 95.6%) were the most prevalent mycotoxins. Citrinin (CIT, 11.5%), α-zearalenone (α-ZEL, 10.5%) and ochratoxin A (OTA, 9.5%) showed detection rates near 10%. Median concentrations (P50) for the two predominant mycotoxins were 0.79 μg/L (0.72 μg/g creatinine) for free-DON and 2.45 μg/L (2.08 μg/g creatinine) for TeA, with maximum values of 11.77 μg/L (10.06 μg/g creatinine) and 90.91 μg/L (114.15 μg/g creatinine), respectively. Sociodemographic patterns showed lower exposure in the Centre region and higher free-DON exposure in southern Portugal and the archipelagos. TeA exposure differed significantly by region and was lowest in the Lisbon Metropolitan Area and Algarve compared to the Center reference region. Low educational levels and spring sampling correlated with higher exposure for both toxins, but only education levels showed statistical significance in the linear regression analyses. For both TeA and free-DON, no differences in exposure were observed by sex, occupational status, or degree of urbanization. Overall, these findings underscore the importance of continuous surveillance, particularly under the current climate change scenario, and highlight the relevance of integrating human biomonitoring into national food safety and public health strategies.
GLP-1 receptor agonists for obesity: eligibility across 99 countries
Publication . Yoo, Sang Gune K.; Teufel, Felix; Theilmann, Michaela; Si, Yajuan; Toure, Elhadji A.; Aryal, Krishna; Bärnighausen, Till; Bait, Abdul; Barreto, Marta; Bovet, Pascal; Brant, Luisa C.C.; Cuschieri, Sarah; Damasceno, Albertino; Farzadfar, Farshad; Fawwad, Asher; Geldsetzer, Pascal; Hambleton, Ian R.; Houehanou, Corine; Howitt, Christina; J Rgensen, Jutta; González-Rivas, Juan P.; Labadarios, Demetre; Marcus, Maja; Martins, João; Mwalim, Omar; Nieto-Martínez, Ramfis; Odili, Augustine N.; Orazumbekova, Binur; Perman, Gastón; Quesnel-Crooks, Sarah; Moghaddam, Sahar Saeedi; Sewpal, Ronel; Sousa-Uva, Mafalda; Sulola, Mubarak A.; Venkataraman, Kavita; Vollmer, Sebastian; Xueling, Sim; Atun, Rifat; Banegas, José R.; Franco, Juan V. A.; Arnott, Clare; Chandiwana, Nomathemba; Huffman, Mark D.; Davies, Justine; Ali, Mohammed K.; Flood, David; Manne-Goehler, Jennifer
We analysed pooled, individual participant data from nationally representative, cross-sectional household health surveys conducted in 99 countries between 2008 and 2021 (appendix pp 2–67). Our sample comprised non-pregnant individuals aged 25–64 years with an available diabetes biomarker, blood pressure measurement, and BMI measurement, as well as complete data on hypertension and diabetes diagnoses (appendix pp 69–75). Eligibility for GLP-1 receptor agonists was determined by applying the inclusion criteria from key randomised clinical trials.3,4 Individuals were defined as eligible for GLP-1 receptor agonists for weight management if they had a BMI of 30 kg/m2 or more or a BMI of 27 kg/m2 or more with hypertension or diabetes, or both—two common weight-related comorbidities. Hypertension was defined based on measured blood pressure and diabetes was defined based on glycaemic markers (HbA1c and plasma glucose). We did not consider other obesity-related comorbidities, such as obstructive sleep apnoea or cardiovascular disease, because data were not consistently available across surveys. For countries in the south, east, and southeast Asian region, we adjusted the eligibility to a BMI of 28 kg/m2 or more or a BMI of 24 kg/m2 or more with hypertension or diabetes, or both, in line with regionally based clinical trials.6 All statistical analyses included sampling weights that we rescaled in proportion to the countries’ population sizes. This study was deemed exempt from regulation by the institutional review board at the University of Michigan (Ann Arbor, MI, USA; HUM00201307).