Chen-Yang Su

All Publications

• Global, Regional, and National Burden of Heart Failure, 1990-2023: A Systematic Analysis for the Global Burden of Disease Study 2023. JAMA cardiology Johnson, C. O., Abbasi, M., Abbasifard, M., Abbastabar, H., ElHafeez, S. A., Abdel Razeq, N. M., Abdelgalil, A. A., Abdolizadeh, A., Abdoun, M., Abdrabou, M., Abdullahi, A., Abebe, T. B., Abejew, A. A., Abil, O. Z., Abiodun, O., Aboagye, R. G., Abohashem, S., Aboouf, M. A., Abouelmagd, M. E., Abouzid, M., Abramov, D., Abtahi, D., Abuadas, F. H., Abu-Gharbieh, E., Abuhelwa, A. Y., Abushanab, D., Adams, L. C., Addo, I. Y., Adedokun, K. A., Adegbile, O. E., Adegoke, N. A., Adeleke, O. T., Adeniyi, M. A., Adesina, M. A., Adesola, R. O., Adhikari, K., Adisu, M. A., Adoma, P. O., Afoakwah, C., Afrifa-Yamoah, E., Sohail Afzal, M., Aghaali, M., Agide, F. D., Agordoh, P. D., Agostinis Sobrinho, C., Agrawal, P., Agyemang-Duah, W., Ahmad, D., Ahmad, M., Ahmad, S., Ahmad, S., Ahmad, S., Ahmad, W., Ahmadi, A., Ahmadi, S., Ahmadian, S., Ahmadzadeh, K., Ahmed, A., Ahmed, G. S., Ahmed, M. B., Ahmed, M., Ahmed, S., Ahmed, S. A., Ajami, M., Ajayi, A. I., Akhigbe, R. E., Akhtar, M. N., Al Awaidy, S. T., Al Bahhawi, T., Al Hasan, S. M., Al Nawayseh, M. K., Al Thaher, Y., Al Zaabi, O. A., Al Zoubi, M. A., Alahdab, F., Al-Ahmad, M. M., Alalalmeh, S. O., Alam, M. K., Alam, R., Alanezi, F., Alanzi, T., Al-Ashwal, F. Y., Alavi, R., ALBashtawy, M. S., Aldabbour, B., Al-Dalakta, A., Aldawsari, K. A., Al-Dewik, N., Aleidi, S. M., Alfalki, A. M., Algammal, A., Algethami, M., Alhabib, K., Alhalaiqa, F. N., Alhumaydhi, F. A., Ali, I., Ali, M. D., Ali, M. U., Ali, R., Ali, S. S., Ali, S. Y., Alif, S. M., Aligaz, E. M., Alimohammadi, M., Na'uzo, M. A., Al-Jabi, S. W., Aljohani, M. S., Aljunid, S. M., Alkubati, S. A., Alla, F., Allemailem, K. S., Allouh, M. Z., Almagharbeh, W. T., Almahmeed, W. A., Al-Marwani, S., Almazan, J., Alnaeem, M. M., Alniss, H. Y., Alomari, M. A., Alosta, M. R., Al-Qudah, M., Al-Qudimat, A. R., Alrawashdeh, A., Al-Rifai, R. H., Alrimawi, I., Alsaadi, M., Alshahrani, N. Z., Altaf, A., Altobaishat, O., Altwalbeh, D., Alvis-Guzman, N., Al-Worafi, Y. M., Aly, H., Alyahya, M. S., Al-Zalabani, A. H., Alzoubi, A., Alzoubi, K. H., Al-Zubairi, A. S., Al-Zyoud, W. A., Amini, S., Amirtharaj, A. D., Ammirati, E., Amu, H., Amusa, G. A., Anagnostakis, F., Ananda, R., Andrei, C. L., Ang, S. P., Anil, A., Anjanappa, S., Anokye, R., Anuoluwa, B. S., Anuoluwa, I. A., Anvari, S., Anyasodor, A. E., Apostol, G. L., Arabi, H., Arabloo, J., Aravkin, A., Areda, D., Aremu, A., Arias de la Torre, J., Aripov, T., Arjmand, G., Ärnlöv, J., Aryntayeva, N., Asamane, E. A., Ascione, G., Asdaq, S. M., Ashraf, S., Ashraf, T., Asiamah-Asare, B. K., Aslam, M. S., Assariparambil, A. R., Ataç, Ö., Athari, S. S., Atorkey, P., Aujayeb, A., Aung, Z. Z., Aurangzeb, K., Awan, U. A., Awol, K. L., Awotidebe, A. W., Ayed, A., Azad, A. K., Azadnia, A., Azami, H., Azarboo, A., Azarian, M., Aziz, M. Y., Aziz, S. A., Azzolino, D., Babu, A. S., Bacha, R., Badar, M., Bagheri, N., Baghlaf, K., Baig, A. A., Bakhshali, M. A., Balakrishnan, S., Baltatu, O. C., Bandara, R. P., Banik, B., Barqawi, H. J., Basharat, Z., Bashir, S., Bashiri, A., Basiru, A., Bastan, M. M., Batarseh, N., Batchi-Bouyou, A. L., Batool, S., Beeraka, N. M., Behnam, B., Belay, B. M., Bente Kamal Tune, S. N., Ben-Umeh, K. C., Berihun, A. A., Bhagat, D. S., Bhardwaj, N., Bhardwaj, P., Bhaskar, S., Bhattacharjee, P., Bhattacharya, S., Bhatti, G. K., Bhuiyan, M. M., Bimal, T., Bitar, A. N., Bizzozero-Peroni, B., Bohn, L., Borran, M., Carvajal, A. B., Bouaoud, S., Boxe, C., Brant, L. C., Bugiardini, R., Bui, L. P., Bulamu, N. B., Busch, F., Bustanji, Y. K., Campos, L. A., Cao, Y., Cao, Z., Capodici, A., Carvalho, F., Chakraborty, A., Chakraborty, C., Chakraborty, S., Chan, J. S., Chandrasekaran, B., Chattu, V. K., Chau, L. D., Chaudhary, A. A., Cheema, A. A., Chen, A. T., Chen, H., Chen, H., Chen, M. X., Chen, X., Chew, N., Chi, G., Ching, P. R., Chong, B., Chong, K., Chopra, D., Chopra, H., Choudhari, S. G., Chowdhury, E. K., Chowdhury, R., Chua, J. M., Chung, S. C., Chung, S., Cicero, A. F., Corda, M., Cosma, C., Costa, V. M., Cruz-Martins, N., Dadras, O., Dai, J., Dai, X., Dalakoti, M., Damasceno, A., D'Amico, E., D'Anna, L., Darcho, S. D., Davletov, D., Dejenie, T. A., Delgado-Enciso, I., Dergaa, I., Derviševic, E., Desai, H. D., Devanbu, V. G., Dhali, A., Dhane, A. S., Dhungel, B., Di Pumpo, M., Dias da Silva, D., Do, H. P., Do, T. C., Dohare, S., Dorostkar, F., Doshi, O. P., Dourado, P. M., Dumbili, E., Duraes, A. R., Duraisamy, S. B., Dutta, S., Dutta, S., E'mar, A. R., Edeh, C., Edvardsson, D. J., Ejeta, D., Ekholuenetale, M., Adel El Arab, R., El Sayed, I., Eladl, M. A., Elalfy, A., Elgendy, I. Y., Elhadi, M., El-Huneidi, W., Elkhanishy, S., Elmeligy, O. A., Elmonem, M. A., Elmoselhi, A. B., Elnaem, M. H., Eltahawy, A. S., Emeto, T. I., Eva, F. N., Fabin, N., Fadavian, H., Fagbamigbe, A. F., Fakhradiyev, I., Fareed, M., Farhana, A., Al-Juhyiish, W. F., Fazylov, T., Fekadu, G., Fekadu, G., Fernandez-Jimenez, R., Ferrara, P., Ferreira, N., Fischer, F., Flor, L. S., Fogacci, F., Fornari, C., Rodrigues, C. F., Foschi, M., Gad, A. G., Gaipov, A., Ganessane, E., Gangachannaiah, S., Ganguli, A., Gao, D., Garcia, F. B., Garg, C., Garlasco, J., Gaye, B., Gebrehiwot, M., Matin, A. G., Getacher, L., Getahun, G. K., Gete, K. Y., Ghaderzadeh, M., Ghaffari, K., Jolfayi, A. G., Ghafoury, R., Ghamkhar, A., Gharaibeh, L., Ghasemi, M., Ghashghaee, A., Ghazy, R. M., Ghith, N. M., Ghoba, S., Gil, A., Gilani, S. A., Gillum, R. F., Gizachew, S., Göbölös, L., Golechha, M., Goleij, P., Golinelli, D., Golmohammadi, M., Goshu, Y. A., Goulart, A. C., Guadie, H. A., Guha, A., Gunawardane, D. A., Gupta, L., Gupta, M., Gupta, R., Gupta, S., Gutiérrez-Murillo, R. S., Guzmán-Muñoz, E., Hadi, N. R., Haghtalab, A., Halboup, A. M., Halder, P., Halim, S. A., Hamdy, N. M., Hamed, M., Hamidi, H., Hamidi, S., Hamilton, E. B., Hanif, A., Hanifi, N., Hanna, F., Haq, M. A., Haque, O. I., Hasaballah, A. I., Hasan, F., Hasan, M. K., Hasan, S. M., Hasani, H., Hashempur, M. H., Hassan, I. N., Hassan, I. I., Hassan, M. I., Hassan, M., Hassan, N. A., Hassan, S., Havmoeller, R. J., Hay, S. I., Hayatu, A., He, W. Q., Heidari, G., Heidari, M., Hein, Z. M., Hewage, S. A., Hezam, K., Hiraike, Y., Hoang, M., Holla, R., Hossain, A., Hossain, M. S., Hossain, S. B., Hosseinzadeh, M., Hostiuc, M., Huang, J., Hushmandi, K., Hussein, D., Huynh, T. N., Hwang, B. F., Ibitoye, S. E., Ibrahim, F. M., Ibrahim, R. K., Ibrayeva, A., Ikram, A., Ilic, I., Ilic, M., Ilyasu, S., Imoh, L. C., Iqbal, D., Iqhrammullah, M., Irfan, B., Islam, M. R., Ismoldayev, Y., Iso, H., Iwagami, M., Iwu, C. D., Iwu-Jaja, C., Iyalomhe, O. E., Izadidehkordi, S., Izquierdo-Condoy, J. S., Jaafari, J., Jacob, J., Jadidi, A., Jafari-Khounigh, A., Jaganathan, V., Jahrami, H., Jaiswal, V., Yengejeh, R. J., Jamal, A., Jamaluddin, J., Jamil, S., Jawaid, T., Jayasinghe, Y. A., Jeganathan, J., Jeong, S., Ji, Z., Jonas, J. B., Joo, T., Jose, J., Jose, J., Joseph, A. P., Joseph, M. A., Joshi, K. J., Joshua, C. E., Joukar, F., Jozwiak, J. J., Jung, S. H., Juweid, M., Kaambwa, B., Kadir, D. H., Kahe, F., Kakkar, A. K., Kalavani, K., Kalra, S., Kamorudeen, R. T., Kamyshnyi, O., Kang, J., Kankam, S. B., Kanmiki, E. W., Kanmodi, K. K., Kannan, S., Kar, S. K., Karajizadeh, M., Karakasis, P., Behnagh, A. K., Karkhah, S., Kashoo, F. Z., Kashyap, M. K., Kassa, N. I., Kaur, A., Kaur, A., Kayode, G. A., Kayola, K. K., Kazemian, S., Kebede, A. Z., Kebede, Y. T., Khaksar, M. A., Khalil, A. A., Khan, A., Khan, A., Khan, I. H., Khan, M., Khan, M. I., Khan, M. F., Khan, M. A., Khan, M. U., Khan, Y. S., Khan, Z., Khasbage, S. U., Khatatbeh, H., Khatatbeh, M., Kheirallah, K. A., Khokhar, M., Khosla, P., Khosravi, F., Khosravi, S., Khubchandani, J., Kifle, Z. D., Kim, H. J., Kim, J., Kim, K., Kim, M. S., Kimokoti, R. W., Kisa, A., Kondlahalli, S. K., Kolahi, A. A., Kompani, F., Korzh, O., Koulmane Laxminarayana, S. L., Krishan, K., Krishnamoorthy, V., Kua, C. H., Kuanar, A., Kuddus, M., Kulimbet, M., Kumar, A., Kumar, A., Kumar, L., Kumar, N., Kumar, P., Kumar, S., Kundu, S., Kunutsor, S. K., Kurniasari, M. D., Kurpad, K. P., Kurpas, D., Kustanti, C. Y., Kusuma, D., Kuttybayev, A., Kwak, T., Kytö, V., L C, P., Lahariya, C., Lai, H., Lakanova, B., Laksono, T., Larsson, A. O., Le, T., Le, T. H., Ledda, C., Lee, H., Lee, H. L., Lee, H., Lee, R. L., Lee, S. W., Lee, W. C., Leivaditis, V., Leone, P. P., Leong, E., Li, C., Li, J., Li, J., Li, M., Li, M. C., Li, Q., Li, X., Li, Z. G., Lian, Y., Lin, J., Lindholm, D., Linn, S., Liu, H., Liu, X., Liu, X., Liu, X., Liu, Y., Llanaj, E., Lodhi, M. S., Lokunarangoda, N. C., López-Cortés, A., López-Gil, J. F., Lu, K., Lubinda, J., Lucchetti, G., Ludhiadch, A., Luo, P., Lusk, J. B., Lv, L., Lytvyak, E., Madadi, F., Madureira-Carvalho, Á. M., Maffia, P., Mahalingam, S., Mahamed, S. A., Mahmood, N. H., Mahmoud, M. A., Majid, A., Malik, A. A., Malik, B., Malik, T., Malkamu, B., Maniya, M. T., Mannan, F., Mannethodi, K., Mansourian, M., Mantovani, L. G., Manzoor, I., Manzoor, S., Maqbool, T., Marateb, H. R., Marino, M., März, W., Marzo, R. R., Marzouk, S., Masi, S., Mathangasinghe, Y., Mathur, N., Mavrovounis, G., McPhail, S. M., Meftah, E., Mehari, M., Meto, T. M., Melisa, S., Melwani, S., Memon, A. R., Menezes, G. A., Mengistie, E. A., Mensah, G. A., Meo, A. S., Meo, S. A., Mercogliano, M., Merlino, G., Mesfin, R. A., Mestrovic, T., Mettananda, C., Mettananda, S., Metwally, M. M., Miao, K. H., Miazgowski, B., Mikrajab, M. A., Minervini, G., Ming, W. K., Khan Minhas, A. M., Mirshahvalad, S. A., Mishra, V., Mitra, T., Mogessie, Y., Mohamed, A. E., Mohamed, N. S., Ahmed, H. M., Siraj, H. M., Mohammad, T., Mohammadzadeh, Z., Mohammed, M., Mohammed, O., Mohammed, S., Mokdad, A. H., Mondal, H., Montazerinamin, S., Moradi, A., Morgan, A. K., Morovvati, M., Morsy, M. M., Mosaddeghi-Heris, R., Mostafa, M. S., Mousavi, P., Mozafar, M., Yousefi, K. M., Mubarik, S., Muche, E. A., Mudenda, S., Muhammad, J. S., Mukhopadhyay, A., Mulita, F., Munjal, K., Musa, S., Mushtaq, A., Muthu, S., Muvunyi, C. M., Mwita, J. C., Myung, W., Nabipoorashrafi, S., Nagarajan, A. J., Naik, G. R., Nainu, F., Najmuldeen, H. H., Nargus, S., Nascimento, B. R., Nascimento, G. G., Naser, M. I., Nasiri, H., Nasoufidou, A., Nassar, M., Natto, Z. S., Nayak, B. P., Nekliudov, N., Nekouei, O., Nepal, S., Nguyen, D., Nguyen, N. P., Niazi, R. K., Nikoobar, A., Nketia, R., Nkrumah-Boateng, P. A., Noreen, M., Nosseir, N. S., Noubiap, J. J., Nri-Ezedi, C. A., Ntsekhe, M., Nugen, F., Nzoputam, C. I., Nzoputam, O. J., Oancea, B., Oduro, M. S., Ogbo, F. A., Ogunrinde, A., Ojedoyin, O. O., Okeke, S. R., Okesanya, O. J., Oladejo, M. K., Olagunju, A. T., Olalusi, O. V., Moraes Oliveira, G. M., Olorukooba, A. A., Olorunlana, A., Oluwole, O. G., Omar Bali, A., Orscelik, A., Ortiz, A., Ostrominski, J. W., Osuagwu, U. L., Ouyahia, A., Owolabi, M. O., Oyebanji, O. A., Oyelade, T., Ozsahin, I., P A, M., Padron-Monedero, A., Padubidri, J. R., Paija, D. M., Palaniswamy, V., Panda, S. K., Pantazopoulos, I., Stoian, A. P., Papa, M. V., Papadimopoulos, I., Pardhan, S., Roudsari, P. P., Parida, A., Parve, S., Pasovic, M., Passera, R., Patel, B. U., Patel, H. P., Patel, K. N., Patel, M. N., Patel, N. N., Patel, S. V., Patil, N., Patoulias, D., Pattnaik, S., Paudel, U., Pawar, S. D., Toroudi, H. P., Peddireddy, A. M., Peprah, P., Osei, E. P., Pereira, A. C., Perna, S., Petcu, I. R., Petermann-Rocha, F., Pham, T. T., Philip, A. K., Piracha, Z. Z., Pirera, E., Plotnikov, E., Porntaveetus, T., Pourasghary, S., Pourghazi, F., Pruc, M., Pugliese, N. R., Puvvula, J., Qasim, N. H., Qi, X., Qureshi, A., Shahraki, H. R., Rafiei, D., Raghav, P., Raghav, Y. S., Rahim, H. M., Rahimi, S., Rahman, F. S., Rahman, F., Hifz Ur Rahman, M., Rahman, M. M., Rahman, M. R., Rahman, M., Rahman, M. A., Rahmani, A. M., Rahmati, S., Raj, J. P., Raja, A., Raja, H. A., Rajendran, G., Rajendran, J., Rajendran, R., Rajizadeh, M. A., Rajpoot, P. L., Ramadan, M. M., Ramadhan, K., Ramasamy, C., Ramasamy, S. K., Ramírez-Vélez, R., Ramphul, K., Rao, A. G., Rathi, I., Rathish, D., Ravi, R., Rawal, L., Reddy, M. M., Redwan, E. M., Regassa, A. A., Rehman, A., Ur Rehman, N., Ren, J., Rezaei, M., Rezaei, N., Rezaeian, M., Ribeiro, A. L., Rizvi, M. R., Buendia Rodriguez, J. A., Röhr, S., Roever, L., Romadlon, D. S., Rony, M. K., Root, K. T., Ross, A. G., Rotimi, K., Rout, H. S., Roy, A., Roy, S., Rwegerera, G. M., Saad, A. M., Saad, M. O., Saadeddin, A., Saber-Ayad, M. M., Saboor, M., Saddique, M. N., Sadeghi, E., Sadeghi, M., Sadek, B., Sadr, H., Saeb, M. R., Saeed, S., Saeed, U., Saghazadeh, A., Saha, N., Sahban Rafsanjani, A., Saheb Sharif-Askari, N., Sahebkar, A., Sajadi, S. M., Saki, M., Sakshaug, J. W., Salami, B., Saleem, R. S., Saleem, S., Saleh, M. A., Salehi, M., Salem, A., Salihu, A. T., Samodra, Y. L., Samuel, V. P., Samy, A. M., Santos, I. S., Sarate, S., Saravanan, A., Sarmadi, M., Sarode, S., Sarveazad, A., Sassano, M., Satpathy, M., Satyam, S. M., Saurabh, A., Far, M. S., Khai, T. S., Sayeed, A., Schinckus, C., Schlaich, M. P., Schuermans, A., Selvaraj, C., Selvaraj, S., Senthilkumaran, S., Sethi, Y., Setiawan, C. H., Alshohadaei, S. M., Shabalin, S., Shaban-Nejad, A., Shabil, M., Shahab, U., Shaharudin, S., Shahid, I., Shahid, S., Shahid, S. M., Shahid, W., Shahini, E., Shahwan, M. J., Shaikh, M. A., Sham, S., Shamim, M. A., Shams, F., Shams-Beyranvand, M., Shamsutdinova, A., Shan, D., Sharifan, A., Rad, J. S., Sharifianjazi, F., Sharma, A., Sharma, A., Sharma, B., Sharma, K., Sharma, R. K., Sharma, U., Shastry, S., Sheidaei, A., Shenoy, R. R., Shetty, M., Shetty, P., Shetty, P., Shi, J., Shimul, M. M., Shirali, P. A., Shittu, A., Shivarov, V., Shoaib, A., Shool, S., Shorofi, S. A., Shrestha, S., Sidamo, N. B., Siddig, E. E., Silva, J. P., Singh, A. P., Singh, B., Singh, B. P., Singh, H., Singh, J. A., Singh, K., Singh, P. S., Singh, P., Singh, S. K., Singh, S., Sinha, M. K., Skryabin, V. Y., Sokhal, B. S., Solanki, S., Sood, A., Sood, P., Soraneh, S., Sorrentino, M., Spartalis, M., Sriram, S., Srivastava, D. B., Stachteas, P., Starodubova, A., Stubbs, B., Su, C. Y., Subasi, O., Subramaniyan, V., Sullman, M. J., Sun, X., Sundström, J., Swain, C. K., Szarpak, L., Tabaja, C., Tabatabaei, S. M., Tabche, C., Tabibi, R., Tabor, B. B., Tagiisuran, B., Taheri, N., Tajabadi, S., Tam, H. L., Tan, C. S., Tanashat, M., Tazhiyeva, A. Y., Tedla, M. G., Temedie-Asogwa, T., Temsah, M. H., Teramoto, M., Thakar, V., Thangaraju, P., Thangavelu, L., Tharwat, I., Thayakaran, R., Thomas, J., Thornton, C. S., Ticoalu, J. H., Tomo, S., Tonapa, S. I., Touko, A. D., Tovani-Palone, M. R., Tran, Q. T., Tran, T. Q., Tran, T. H., Trico, D., Triplett, T., Tristan, C. D., Truong, Q. X., Tse, G., Tsegaye, G. W., Tully, P. J., Ul Hassan, A., Ullah, A., Ullah, H., Ullah, I., Ullah, R., Umair, M., Umakanthan, S., Umapathi, K. K., Umar, H. O., Umar, M., Unim, B., Unnikrishnan, B., Upadhyay, E., Usman, J. S., Uwishema, O., Ozsahin, D. U., Uzunçibuk, H., Vaithinathan, A. G., Valdez, P. R., Van den Eynde, J., Varghese, J., Vasankari, T. J., Verma, P., Vinh, T. B., Vitiello, A., Vlassov, V., Vos, T., Vu, L. V., Wada, A. S., Wang, L., Wang, W., Wang, Y., Ward, P. R., Wasihun, Y. M., Wei, X., Weintraub, R. G., Wicaksana, A. L., Wickramasinghe, N. D., Wu, Z., Wubie, Y. M., Xiao, H., Xiao, N., Xie, W., Xu, X., Yadegar, A., Yahoo, S., Yahya, G., Yamagishi, K., Yan, L. D., Yang, C., Yang, H., Yano, Y., Yao, H., Yassin, M. A., Yasufuku, Y., Yaya, S., Yesuf, S. A., Yewodiaw, T. K., Yigit, A., Yin, D., Yismaw, Y. E., Yon, D. K., Yonemoto, N., Younis, M. Z., Yu, C., Yu, H., Yu, Y., Yunusa, S., Yunusa, I., Zafar, M., Zafarjafarzadeh, N., Zaghampour, M., Zamora, N., Zare, I., Zarea, K., Zargar, S., Zastrozhin, M., Zawiah, M., Zeariya, M. G., Zeru, E. M., Zhang, B., Zhang, D. X., Zhang, H., Zhang, J. M., Zhang, Y., Zhang, Z., Zhanuzakov, M., Zhong, C. C., Zhou, X. D., Zhu, B., Zhumagaliuly, A., Zia, H., Zoghi, G., Zoromba, M. A., Zyoud, A. H., Zyoud, S. H., Murray, C. J., Roth, G. A. 2026

  Abstract

  Heart failure (HF) is a common condition with substantial geographical variation in its prevalence and underlying etiologies. Consistent and comparable data on the burden of HF within populations are needed to inform health system priorities.To estimate the global burden of HF.This cross-sectional study estimates disease burden using Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) methodologies to integrate multiple information sources. Data come from 204 countries and territories from 1990 to 2023 representing the global population with prevalent HF.HF and underlying etiologies.Estimates with 95% uncertainty intervals (UIs) for the prevalence and years lived with disability (YLDs) for HF by age, sex, location, year, and underlying etiology.Globally, there were 56.0 million (95% UI, 49.9-62.2 million) estimated cases of HF in 2023. The estimated prevalence of HF generally increased with age for all locations. While ischemic heart disease and hypertensive heart disease were the 2 most common etiologies globally among older adults, there was substantial variation in etiological pattern by location and age. Congenital heart anomalies and rheumatic heart disease (RHD) were common etiologies in children and young adults, while the proportion of HF due to alcohol use peaked in middle adulthood. Location-specific estimates highlighted regions with higher burden of HF due to specific etiologies, including RHD (south Asia, east Asia, Oceania, southeast Asia, central sub-Saharan Africa, eastern sub-Saharan Africa, southern sub-Saharan Africa, and western sub-Saharan Africa), alcohol use (eastern Europe, Australasia, the Caribbean, and central Europe), and Chagas disease (southern, Andean, tropical, and central Latin America).Heart failure is a significant global public health burden, and the impact is likely to increase as populations age. However, most HF etiologies can be prevented by interventions that reduce exposure to modifiable risk factors, such as elevated blood pressure, excessive alcohol consumption, and tobacco use. Etiology- and location-specific estimates can provide necessary information for public health officials to determine how best to prioritize interventions to reduce HF disease burden.

  View details for DOI 10.1001/jamacardio.2026.4068

  View details for PubMedID 42814432

• Integrative plasma proteomic network analysis identifies physical activity-associated protein modules potentially mediating type 2 diabetes risk CARDIOVASCULAR DIABETOLOGY Wang, Z., Su, C., Lu, J., Hasebe, M., Mejia-Garcia, A., Tsao, H., Hu, J., Lu, T., Zhou, S. 2026; 25 (1)

  Abstract

  Moderate-to-vigorous physical activity (MVPA) lowers the risk of type 2 diabetes (T2D). Identifying protein modules that mediate this association may help dissect these biological underpinnings, but they remain incompletely characterized.We studied 28,842 diabetes-free UK Biobank participants with baseline plasma proteomics. Guideline-adherent MVPA was defined as ≥ 600 metabolic equivalent of task-minutes per week. We first evaluated associations of MVPA with incident T2D and 2910 plasma proteins, followed by mediation analyses of individual proteins. We then identified protein co-expression modules using multiscale embedded gene co-expression network analysis (MEGENA), tested module-level associations with MVPA, and mediation of incident T2D. Protein modules were further evaluated using module-level Mendelian randomization, whole-genome-sequencing genome-wide association studies, human pancreatic islet single-cell RNA sequencing, leave-one-out sensitivity analyses, and pathway enrichment analyses.Over a median 13.60 years (IQR: [12.82, 14.35]), 1225 participants developed incident T2D. Guideline-adherent MVPA was associated with lower T2D risk and with 650 circulating proteins, of which 451 were Bonferroni-significant mediators. MEGENA analysis identified 171 robust modules, with 67 mediating the MVPA-T2D association. Twelve terminal modules, representing the finest-resolution functions, were selected for further analysis based on their significant mediated effects and enrichment in potential individual mediators. Genetic and functional analyses highlighted two modules: CKB-IGFBP2-centric module was positively associated with MVPA (β [95% CI] 0.196 [0.168, 0.224]) and genetically associated with lower T2D risk (OR [95% CI] 0.57 [0.43, 0.77]). Conversely, RIDA-PCBD1-centric module was inversely associated with MVPA (- 0.134 [- 0.162, - 0.108]) and genetically associated with higher T2D risk (1.54 [1.13, 2.09]). Compared to individual protein analyses, these modules captured unique pathways, supporting the CKB-IGFBP2-centered and RIDA-PCBD1-centered modules as antigen-presentation-enriched and complement/apoptic-cell-clearance-enriched modules, respectively. Finally, the constituent genes of both modules were enriched in T2D-related gene changes in pancreatic β-cells, with the antigen-presentation-enriched module showing additional enrichment across α-, δ-, and ductal cells.MVPA may reduce T2D risk through two coordinated plasma protein networks rather than only through individual circulating proteins. Evaluating coordinated protein modules may provide a system-level biological framework for understanding how exercise protects against T2D.

  View details for DOI 10.1186/s12933-026-03363-1

  View details for Web of Science ID 001883474700001

  View details for PubMedID 42778924

  View details for PubMedCentralID PMC13602772

• Combination therapy with mineralocorticoid receptor antagonists and SGLT2 inhibitors in chronic kidney disease: an updated meta-analysis JOURNAL OF NEPHROLOGY Kamido, H., Hasebe, M., Su, C., Sawa, N., Yoshiji, S. 2026

  View details for DOI 10.1093/joneph/aajag136

  View details for Web of Science ID 001851720500001

  View details for PubMedID 42610389

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S., Singh, P., Singh, S., Singh, S. K., Singh, S., Singh, V., Sinha, M. K., Sinha, R., Skryabin, V. Y., Sokhal, B. S., Sood, P., Soraneh, S., Sorensen, R. J., Sorrentino, M., Spartalis, M., Srivastav, P., Stachteas, P., Stanikzai, M. H., Starodubova, A. V., Straube, S., Stubbs, P., Su, C. Y., Subramaniyan, V., Suhag, A., Sulaieva, O., Sulaiman, S. K., Suleiman Odidi, M., Sulistiyorini, D., Sun, J., Sun, Z., Swain, C. K., Szarpak, L., Ty, S. S., Tabaee Damavandi, P., Tabarés-Seisdedos, R., Tabatabaei, S. M., Tabatabaeizadeh, S. A., Tabatabai, S., Tabche, C., Tadele, A., Tagiisuran, B., Taha Osman Ali, E., Taheri Soodejani, M., Taiba, J., Tajabadi, S., Talic, S., Talukder, A., Tamehri Zadeh, S. S., Tamiru Adugna, D., Tampa, M., Tan, J., Tanabayeva, D., Tanashat, M., Tang, J., Tantisattamo, E., Tariku, M. K., Tariq, S., Tavangar, S. M., Tedla, M. G., Temsah, R., Teramoto, M., Tesfamariam, W. B., Tesfu, A. A., Tewari, J., Thakar, V., Thangavelu, L., Tharwat, I., Tharwat, S., Thienemann, F., Thiruvengadam, M., Thomas, N. K., Ticoalu, J. H., Tiruye, T. Y., Tiwari, K., Tleshev, M., Tomo, S., Tonelli, M., Topor-Madry, R., Touvier, M., Tovani-Palone, M. R., Tran, A. T., Tran, T. H., Tran Minh Duc, N., Trico, D., Tristan, C. D., Tse, G., Tseriotis, V. S., Tualeka, A. R., Tye, S. C., Udoakang, A. J., Ullah, A., Ullah, R., Ullah, S., Umair, M., Umar, L., Upadhya, D., Upadhyay, E., Usman, J. S., Uzun Ozsahin, D., Uzunçibuk, H., Vaithinathan, A. G., Vakili, O., Van den Eynde, J., Varghese, J., Vasankari, T. J., Vellingiri, B., Venketasubramanian, N., Verma, A. K., Verma, P., Villalobos-Daniel, V. E., Vlassov, V., Wan, J. Y., Wang, C., Wang, N., Wang, Q., Wang, S., Wang, S., Wang, W., Wang, W., Wang, W., Wang, Y., Wang, Y., Wang, Z., Wani, T. A., Waqar, A. B., Wei, M. Y., Weintraub, R. G., Wibowo, Y. C., Wicaksana, A. L., Wickramasinghe, D. P., Wickramasinghe, N. D., Wilandika, A., Witts, W. K., Wonde, T. E., Wongsin, U., Wu, P., Xiao, G., Xie, W., Xu, S., Xu, S., Xu, W., Xu, X., Yadav, V., Yadegar, A., Yahoo, S., Yamagishi, K., Yang, H., Yano, Y., Yao, H., Yarahmadi, A., Yaribeygi, H., Yesuf, S. A., Yin, D., Yismaw, Y. E., Yon, D. K., Yonemoto, N., Yu, C., Yu, E. A., Yu, H., Yu, Y., Yuan, Q., Yuzbashian, E., Zafar, M., Zaghampour, M., Zamagni, G., Zamora, N., Zanghì, A., Zargar, S., Zawiah, M., Zeariya, M. G., Zeru, E. M., Zhang, B., Zhang, C. J., Zhang, H., Zhang, J. M., Zhang, Y., Zhang, Z., Zhanuzakov, M., Zhao, Z., Zheng, M. H., Zhong, A., Zhong, C. C., Zhou, H., Zhou, J., Zhou, X. D., Zhu, Z., Zhumagaliuly, A., Zitoun, O. A., Zoghi, G., Zoromba, M. A., Zumla, A., Zyoud, A. H., Zyoud, S. H., Zyoud, S. H., Hay, S. I., Mokdad, A. H., Murray, C. J., Roth, G. A. 2026

  Abstract

  Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C-related burden is critical for guiding prevention and treatment strategies.To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends.This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals.Population-level LDL-C concentrations.Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C-attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals.In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C-attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings.Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

  View details for DOI 10.1001/jama.2026.8628

  View details for PubMedID 42525403

• Cardiovascular Efficacy of GLP-1 Receptor Agonists by Kidney Function: An Updated Meta-Analysis of Randomized Trials Including the SOUL Trial DIABETES OBESITY & METABOLISM Hasebe, M., Su, C., Yabe, D., Yoshiji, S. 2026; 28 (9): 8407-8417

  Abstract

  To evaluate the cardiovascular efficacy and absolute benefit of glucagon-like peptide-1 receptor agonists (GLP-1RAs) by baseline estimated glomerular filtration rate (eGFR).PubMed and EMBASE were searched to 29 April 2026 for randomized placebo-controlled trials of GLP-1RAs in adults with type 2 diabetes or overweight/obesity that reported eGFR-stratified major adverse cardiovascular events (MACE). Hazard ratios (HRs) were extracted for eGFR < 60 and ≥ 60 mL/min/1.73 m2. Random-effects meta-analyses estimated pooled HRs within each eGFR stratum and the pooled ratio of HRs (RHR) comparing eGFR < 60 versus ≥ 60 mL/min/1.73 m2. Exploratory absolute risk reduction (ARR) and number needed to treat (NNT) were derived from placebo-group MACE risk and the corresponding pooled HR.Nine publications from eight trials were included, comprising 70 822 participants; 70 534 had eGFR-stratified MACE data. GLP-1RAs similarly reduced MACE risk among participants with eGFR ≥ 60 and < 60 mL/min/1.73 m2, with pooled HRs of 0.83 (95% CI 0.77-0.90; p < 0.001; I 2 = 35.6%) and 0.83 (95% CI 0.74-0.93; p < 0.001; I 2 = 40.9%), respectively. The pooled RHR showed no evidence of effect modification (1.02, 95% CI 0.85-1.21; p = 0.84). ARR was larger with eGFR < 60 than ≥ 60 mL/min/1.73 m2 (2.6% vs. 1.6%), corresponding to NNTs of 39 (95% CI 26-91) versus 62 (95% CI 46-101).GLP-1RAs reduced MACE risk similarly across eGFR strata, while lower eGFR was associated with a larger estimated absolute cardiovascular benefit. These findings support GLP-1RA therapy in individuals with reduced kidney function.

  View details for DOI 10.1111/dom.71035

  View details for Web of Science ID 001803400000001

  View details for PubMedID 42348164

  View details for PubMedCentralID PMC13449011

• Burden of cancer attributable to occupational asbestos exposure in the Americas, 1990-2023: an analysis using the Global Burden of Disease Study 2023 LANCET REGIONAL HEALTH-AMERICAS Girardi, F., Brauer, M., Force, L. M., Hay, S. I., Spearman, S., Malta, D., Bustamante-Teixeira, M., Nogueira, M., Guerra, M., Adams, L. C., Adedokun, K., Adegbile, O. E., Alfalki, A. M., Alkhawam, M., Alrimawi, I., Areda, D., Azadnajafabad, S., Barbic, F., Berihun, A., Bhatnagar, A., Carvajal, A., Castaneda-Orjuela, C. A., Chattu, V., Chung, S., Dai, X., dos Santos, W., Ebohon, O., El Bayoumy, I., Ezenwankwo, E., Fan, X., Gad, A., Gholamrezanezhad, A., Ilyas, M., Jalloh, M., Jamal, A., Jibat, N. T., Kamireddy, A., Kamorudeen, R. T., Kankam, S., Karaye, I. M., Khatab, K., Khosravi, F., Kisa, A., Manjani, L., Mestrovic, T., Mokdad, A. H., Kiasary, S., Murray, C. J. L., Nassar, M., Negash, A., Negassa, M. T., Olagunju, A. T., Orscelik, A., Paranjkhoo, P., Patel, N., Pawar, S., Pourghazi, F., Puvvula, J., Rashid, M., Rodriguez, J., Roy, S., Sabet, C., Seylani, A., Sherchan, S. P., Singh, J. A., Straube, S., Su, C., Taiba, J., Vieira, A., Burkart, K. 2026; 58: 101463

  Abstract

  Asbestos remains a leading occupational carcinogen, particularly in countries where its use persists despite known health risks. This study provides a systematic analysis of the burden of cancer attributable to occupational asbestos exposure in the Americas from 1990 to 2023, using estimates from the Global Burden of Disease (GBD) Study 2023. Age-standardised mortality and disability-adjusted life-years (DALYs) attributable to asbestos were analysed for mesothelioma, lung, laryngeal, and ovarian cancers, stratified by sex and region.We conducted a descriptive analysis to assess spatiotemporal trends in the burden of cancer attributable to occupational asbestos in the Americas from 1990 to 2023. We analysed trends in age-standardized mortality and DALY rates using segmented joinpoint regression. Age-period-cohort analyses were performed for age-specific mortality and DALY rates. All analyses were stratified by cancer type, sex and GBD regions, with estimated 95% uncertainty intervals (95% UI).In 2023, High-income North America had the highest burden of cancer attributable to occupational asbestos, with 5·1 deaths (95% UI 3·9; 6·4) and 84·9 DALYs (65·6; 108·5) per 100,000 population for both sexes. However, the region also experienced the most pronounced decline, with average annual reductions of 2·0% (-2·0; -1·9) in mortality and 2·5% (-2·5; -2·4) in DALYs. Southern Latin America had the second highest rates for cancer attributable to occupational asbestos, with 2·7 deaths (2·1; 3·5) and 53·1 DALYs (40·4; 69·3) for both sexes in 2023, and showed the strongest increase in women with 2·3% (2·2; 2·4) both in mortality and DALYs annually. Age-period-cohort modelling revealed marked increases in burden of cancer attributable to occupational asbestos among women, with mortality and DALY rate ratios (RR) for lung cancer rising to 1·31 (1·20; 1·44) in Tropical and Southern Latin America, and RR for mesothelioma rising to 1·22 (1·06; 1·40) in Southern Latin America.Our study revealed inequalities in the burden of cancer attributable to occupational asbestos exposure among regions in the Americas, as well as remarkable sex disparities. Although rates were declining in North America, there is growing concern over rising rates of lung cancer and mesothelioma among women in Tropical and Southern Latin America regions, especially in Argentina and Brazil. These disparities likely reflect differences in environmental and industrial regulatory practices, as well as gendered occupational exposure patterns. Also, upward trends in female lung cancer rates may reflect increased smoking among women, while mesothelioma is much more specific to asbestos exposure. Despite regulatory advances, legacy exposures and ongoing asbestos use persist in parts of Latin America, reinforcing the need for stricter occupational health policies and asbestos bans. The findings underscore the shifting epidemiology of asbestos-related cancers and call for targeted prevention efforts, improved surveillance, and gender-responsive occupational protections.This study was partially funded by the Bill & Melinda Gates Foundation and "Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)".

  View details for DOI 10.1016/j.lana.2026.101463

  View details for Web of Science ID 001745759800001

  View details for PubMedID 42005088

  View details for PubMedCentralID PMC13085095

• Global, regional, and national burden of meningitis, its risk factors, and aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. The Lancet. Neurology 2026; 25 (5): 451-468

  Abstract

  Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework.GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions.In 2023, there were 259 000 (95% uncertainty interval 202 000-335 000) global deaths and 2·54 million (2·20-2·93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86 600 [53 300-149 000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98 700 deaths (77 000-127 000) and 594 000 cases (514 000-686 000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality.Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings.Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.

  View details for DOI 10.1016/S1474-4422(26)00101-8

  View details for PubMedID 41911930

• Estimated Glomerular Filtration Rate Change and Heart Failure Events With GLP-1 Receptor Agonists: A Meta-Analysis and Meta-Regression of Randomised Controlled Trials DIABETES OBESITY & METABOLISM Hasebe, M., Kamido, H., Su, C., Keidai, Y., Yabe, D., Yoshiji, S. 2026; 28 (7): 5649-5657

  Abstract

  To examine whether on-treatment changes in kidney function and cardiometabolic markers are associated with the magnitude of heart failure (HF) risk reduction across large-scale trials of glucagon-like peptide-1 receptor agonists (GLP-1RAs).We searched PubMed and Embase from inception to 29 January 2026 for randomised, placebo-controlled GLP-1RA trials enrolling ≥ 1000 adults with type 2 diabetes and/or overweight or obesity and a follow-up of at least one year. The primary outcome was clinical HF events (hospitalisation for HF, with or without urgent HF visits). We pooled hazard ratios (HRs) using random-effects meta-analysis and performed mixed-effects meta-regression to test whether between-group changes in annualised estimated glomerular filtration rate (eGFR) improvement, body weight, systolic blood pressure (SBP), and heart rate were associated with the treatment effect on clinical HF events.Eleven trials were included (90 867 participants; 2863 clinical HF events). GLP-1RAs reduced the risk of clinical HF events (HR 0.86, 95% CI 0.80 to 0.93, p < 0.001, I 2  = 2.1%). In meta-regression, annualised eGFR improvement was significantly associated with greater HF risk reduction (p = 0.009); at the trial level, each 1 mL/min/1.73 m2/year greater improvement in eGFR corresponded to a 21% lower hazard of clinical HF events (95% CI 7% to 32%). In contrast, changes in body weight, SBP, and heart rate were not associated with HF risk reduction.Across large-scale GLP-1RA trials, greater annualised eGFR improvement was associated with larger reductions in clinical HF events. Annualised eGFR change may serve as a useful marker of HF benefit with GLP-1RAs.

  View details for DOI 10.1111/dom.70749

  View details for Web of Science ID 001739008200001

  View details for PubMedID 41969171

  View details for PubMedCentralID PMC13243948

• HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases (vol 6, 1113, 2026) COMMUNICATIONS BIOLOGY Butler-Laporte, G., Farjoun, J., Nakanishi, T., Lu, T., Abner, E., Chen, Y., Hultstrom, M., Metspalu, A., Milani, L., Magi, R., Nelis, M., Hudjashov, G., Yoshiji, S., Ilboudo, Y., Liang, K. Y. H., Su, C., Willett, J. D. S., Esko, T., Zhou, S., Forgetta, V., Taliun, D., Richards, J. 2026; 9 (1)

  View details for DOI 10.1038/s42003-026-09877-4

  View details for Web of Science ID 001732587400001

  View details for PubMedID 41927953

  View details for PubMedCentralID PMC13046769

• Global burden of lower respiratory infections and aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023 LANCET INFECTIOUS DISEASES Sirota, S., Bender, R., Dominguez, R., Movo, A., Swetschinski, L. R., Araki, D. T., Han, C., Wool, E. E., He, J., Carter, A., Jabbar, A. A. J., Aalipour, M., Aalruz, H., Dolatabadi, Z., Abbastabar, H., Abd ElHafeez, S., Abdalla, A., Abdalla, M., Abdullah, Abdallah, E. M., Razeq, N., Abdelkader, A., Abd-Elsalam, S., Abdelwahab, S., Abdoun, M., Abdous, A., Abdrabou, M. M., Aziz, J., Abdulah, D., Abdullahi, A., Abdul-Rahman, T., Abdykerimova, K., Getahun, H., Abedi, A., Abejew, A., Abidi, S., Abil, O., Abiodun, O., Aboagye, R., Abolhassani, H., Abonie, U., Aborode, A., Abourashed, N. M., Abtahi, D., Abu, Z., Abu Farha, R., Abuadas, F. A., Abubakar, A., Abubakar, U., Abu-Elala, N., Abu-Gharbieh, E., Abuhelwa, A. Y., Abukhadijah, H. J., Abushanab, D., Acharya, A., Acharya, K., Acharya, S., Achore, M., Adal, O., Adams, L. C., Addo, I., Adebisi, T., Adedia, D., Adedokun, K., Adegoke, N. A., Adekanmbi, V., Adeleke, O., Adesina, M., Adesola, R., Adetunji, C., Adetunji, J., Adewumi, I., Adhana, M., Adhikary, R., Adiba, A., Adiga, U., Adnan, M., Adnani, Q., Affinito, G., Afzal, S., Agafari, G., Aggarwal, N., Agordoh, P., Agrawal, A., Agyemang-Duah, W., Ahadi, M., Ahinkorah, B., Ahmad, A., Ahmad, A., Ahmad, D., Ahmad, F., Ahmad, I., Ahmad, K., Ahmad, M. M., Ahmad, R., Ahmad, S., Ahmad, T., Ahmadi, A., Ahmadi, M., Ahmed, A., Ahmed, A., Ahmed, G., Ahmed, H., Ahmed, M., Ahmed, M., Ahmed, M., Ahmed, N., Ahmed, S., Ahmed, S., El-Imam, I., Akeju, O., Akhigbe, R., Akhtar, M., Akinosoglou, K., Akram, H., Al Awaidy, S., Al Hasan, S., Al Zoubi, M., Al-Abbadi, M., Alajlani, M. M., Al-Aly, Z., Alam, K., Alam, M., Alanezi, F., Alanzi, T. M., Alao, J., Alarifi, A., Al-Ashwal, F. Y., Alavi, S., Albashtawy, M., AlBataineh, M. T., Al-Dewik, N., Aldhaleei, W. A., Aleidi, S. M., Alemnew, F., Alfalki, A. M., Algammal, A. M., Alhumaidi, A., Alhumaydhi, F. A., Ali, H., Ali, K., Ali, M., Ali, M., Ali, M., Ali, R., Ali, S., Ali, S., Al-Iede, M., Alif, S., Alimohammadi, M., Rokny, H., Alipour, M., Al-Jabi, S. W., Aljofan, M., Aljunid, S., Allemailem, K. S., Al-Marwani, S., Almazan, J., Al-Mekhlafi, H. M., Almobayed, A., Alnaeem, M., Alnakshabandie, W., Alniss, H., Alomari, M. A., Alqahtani, J. S., Al-Qudimat, A., Al-Raddadi, R. M., Alrimawi, I., Alrousan, S., Alsabri, M. A., Alshahrani, N. Z., Alshetaiwi, H., Alsowaida, Y., Altaany, Z., Altaf, A., Al-Tammemi, A. B., Al-Tawfiq, J. A., Althobiani, M. A., Altirkawi, K. A., Alvis-Guzman, N., Alvis-Zakzuk, N. J., Alwafi, H., Al-Wardat, M., Al-Worafi, Y., Aly, H., Alyahya, M., Alzoubi, K. H., Al-Zubairi, A. S., Al-Zubayer, M., Al-Zyoud, W. A., Amafah, E., Mohammadi, M., Amhare, A., Amini, S., Aminu, K., Aminu, N., Aminzare, M., Amiri, S., Amirtharaj, A., Amugsi, D. A., Amusa, G., Anagnostakis, F., Anbesu, E., Ancuceanu, R., Anderlini, D., Anil, A., Anjana, P., Anjorin, A., Annadurai, K., Anuoluwa, B., Anuoluwa, I., Anvari, S., Anwer, R., Anyasodor, A., Apostol, G., Appati, W., Arabloo, J., Arockiaraj, J., Arooj, M., Artamonov, A. A., Arumuganainar, D., Arystan, N., Asdaq, S., Asebot, M., Asefa, S., Asemahagn, M. A., Ashraf, S., Ashraf, T., Ashrafi, M., Aslam, M., Asri, Y., Assefa, D., Assembekov, B., Atac, O., Athari, S., Atre, S. R., Atta, J., Atwan, Z. A., Augello, M., Aujayeb, A., Aurangzeb, K., Awan, S., Awoke, A., Awosile, B. B., Ayele, H., Ayipo, Y., Ayyoubzadeh, S., Azad, A. M., Azadi, D., Azami, H., Aziz, S., Azizan, A., Babatope, A., Babu, G., Bacha, I., Badar, M., Badran, A., Baghizadeh, F., Baghlaf, K. 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M., Zhang, H., Zhang, J., Zhang, P., Zhang, X., Zhang, Z., Zhanuzakov, M., Zhao, C., Zhao, L., Zhu, B., Zhu, Z., Zhumagaliuly, A., Zielinska, M., Zumla, A., Zyoud, A. H., Zyoud, S. H., Zyoud, S. H., Hay, S. I., Naghavi, M., Murray, C. J. L., Kyu, H., GBD 2023 Lower Resp Infect Antimicrobial Resistance Collaborators 2026; 26 (4): 343-361

  Abstract

  Lower respiratory infections (LRIs) remain the world's leading infectious cause of death. This analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides global, regional, and national estimates of LRI incidence, mortality, and disability-adjusted life-years (DALYs), with attribution to 26 pathogens, including 11 newly modelled pathogens, across 204 countries and territories from 1990 to 2023. With new data and revised modelling techniques, these estimates serve as an update and expansion to GBD 2021. Through these estimates, we also aimed to assess progress towards the 2025 Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) target for pneumonia mortality in children younger than 5 years.Mortality from LRIs, defined as physician-diagnosed pneumonia or bronchiolitis, was estimated using the Cause of Death Ensemble model with data from vital registration, verbal autopsy, surveillance, and minimally invasive tissue sampling. The Bayesian meta-regression tool DisMod-MR 2.1 was used to model overall morbidity due to LRIs. DALYs were calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for all locations, years, age groups, and sexes. We modelled pathogen-specific case-fatality ratios (CFRs) for each age group and location using splined binomial regression to create internally consistent estimates of incidence and mortality proportions attributable to viral, fungal, parasitic, and bacterial pathogens. Progress was assessed towards the GAPPD target of less than three deaths from pneumonia per 1000 livebirths, which is roughly equivalent to a mortality rate of less than 60 deaths per 100 000 children younger than 5 years.In 2023, LRIs were responsible for 2·50 million (95% uncertainty interval [UI] 2·24-2·81) deaths and 98·7 million (87·7-112) DALYs, with children younger than 5 years and adults aged 70 years and older carrying the highest burden. LRI mortality in children younger than 5 years fell by 33·4% (10·4-47·4) since 2010, with a global mortality rate of 94·8 (75·6-116·4) per 100 000 person-years in 2023. Among adults aged 70 years and older, the burden remained substantial with only marginal declines since 2010. A mortality rate of less than 60 deaths per 100 000 for children younger than 5 years was met by 129 of the 204 modelled countries in 2023. At a super-regional level, sub-Saharan Africa had an aggregate mortality rate in children younger than 5 years (hereafter referred to as under-5 mortality rate) furthest from the GAPPD target. Streptococcus pneumoniae continued to account for the largest number of LRI deaths globally (634 000 [95% UI 565 000-721 000] deaths or 25·3% [24·5-26·1] of all LRI deaths), followed by Staphylococcus aureus (271 000 [243 000-298 000] deaths or 10·9% [10·3-11·3]), and Klebsiella pneumoniae (228 000 [204 000-261 000] deaths or 9·1% [8·8-9·5]). Among pathogens newly modelled in this study, non-tuberculous mycobacteria (responsible for 177 000 [95% UI 155 000-201 000] deaths) and Aspergillus spp (responsible for 67 800 [59 900-75 900] deaths) emerged as important contributors. Altogether, the 11 newly modelled pathogens accounted for approximately 22% of LRI deaths.This comprehensive analysis underscores both the gains achieved through vaccination and the challenges that remain in controlling the LRI burden globally. Furthermore, it demonstrates persistent disparities in disease burden, with the highest mortality rates concentrated in countries in sub-Saharan Africa. Globally, as well as in these high-burden locations, the under-5 LRI mortality rate remains well above the GAPPD target. Progress towards this target requires equitable access to vaccines and preventive therapies-including newer interventions such as respiratory syncytial virus monoclonal antibodies-and health systems capable of early diagnosis and treatment. Expanding surveillance of emerging pathogens, strengthening adult immunisation programmes, and combating vaccine hesitancy are also crucial. As the global population ages, the dual challenge of sustaining gains in child survival while addressing the rising vulnerability in older adults will shape future pneumonia control strategies.Gates Foundation.

  View details for DOI 10.1016/S1473-3099(25)00689-9

  View details for Web of Science ID 001732492900001

  View details for PubMedID 41412141

• Phenome-Wide Mendelian Randomization Identifying Circulating Proteins for Cardiovascular Traits in Populations of African Ancestry CIRCULATION-GENOMIC AND PRECISION MEDICINE Selber-Hnatiw, S., Trajanoska, K., Pelletier, J., Su, C., McClelland, P., Taliun, D., Yoshiji, S., Mooser, V., Bherer, C., Zhou, S. 2026; 19 (2): e005159

  Abstract

  Circulating proteins represent robust drug targets with therapeutic potential. Many discoveries have focused on European-ancestry populations, disregarding minuscule yet substantial proteomic differences that may contribute to disease and alter drug generalizability in other ancestry groups.Using 2-sample Mendelian randomization and colocalization, we analyzed the effects of 1562 circulating proteins on 145 cardiometabolic-centric outcomes to identify robust protein-phenotype associations in African-ancestry populations and reveal African-ancestry associations with heterogeneous effects. We further replicated these findings using the proteomic data available from the UK Biobank Pharma Proteomics Project and tested the effect of protein quantity in association with select phenotypes. Population branch statistics were also constructed to examine whether protein-genetic instruments under natural selection could lead to significant protein-outcome associations specific to the African ancestry.We identified 115 robust protein target-outcome associations in African-ancestry populations. Among these, 51 demonstrated heterogeneous effects between African- and European-ancestry populations. We further replicated 4 cross-platform African-ancestry associations in the UK Biobank Pharma Proteomics Project and also revealed 4 significant, direct associations between protein levels and phenotypes. Ultimately, based on our prioritization criteria, we found that CD36 (glycoprotein IIIb), APOC1 (apolipoprotein C1), GSTA1 (glutathione S-transferase alpha 1), and FOLH1 (folate hydrolase 1) were shown to influence lipids and heart diseases, and were uniquely represented in African-ancestry populations. In addition, using population branch statistics, we showed that 47.5% of the 115 significant protein-outcome associations were possibly driven by cis-acting protein quantitative trait loci under natural selection.Multiple lines of evidence were used to interrogate proteomic determinants of cardiometabolic diseases and traits in African-ancestry populations. We highlighted actionable circulating protein targets that could represent potential drug targets for cardiovascular diseases specific to populations with African ancestry.

  View details for DOI 10.1161/CIRCGEN.125.005159

  View details for Web of Science ID 001745200900001

  View details for PubMedID 41657222

• Comment on Sun et al. SGLT2i Versus Metformin for Delirium Prevention in Type 2 Diabetes: A Real-World, Head-to-Head Comparative Study. Diabetes Care 2025;48:1361-1369 DIABETES CARE Hasebe, M., Su, C. 2026; 49 (3): e66-e67

  View details for DOI 10.2337/dc25-1463

  View details for Web of Science ID 001701350800031

  View details for PubMedID 41719461

• Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues NATURE METABOLISM Bocher, O., Arruda, A., Yoshiji, S., Zhao, C., Huerta-Chagoya, A., Su, C., Yin, X., Cammann, D., Taylor, H. J., Chen, J., Suzuki, K., Mandla, R., Yang, T., Matsuda, F., Mercader, J. M., Flannick, J., Meigs, J. B., Wood, A. C., Vujkovic, M., Voight, B. F., Spracklen, C. N., Rotter, J. I., Morris, A. P., Zeggini, E. 2026; 8 (2): 506-520

  Abstract

  Type 2 diabetes (T2D) is a prevalent disease arising from complex molecular mechanisms. Here we leverage T2D genetic associations to identify causal molecular mechanisms in an ancestry-aware and tissue-aware manner. Using two-sample Mendelian randomization corroborated by colocalization across four global ancestries, we analyse 20,307 gene and 1,630 protein expression levels using blood-derived cis-quantitative trait loci (QTLs). We detect causal effects of genetically predicted levels of 335 genes and 46 proteins on T2D risk, with 16.4% and 50% replication in independent cohorts, respectively. Using gene expression cis-QTLs derived from seven T2D-relevant tissues, we identify causal links between the expression of 676 genes and T2D risk, refining known associations such as BAK1 and describing additional ones like CPXM1. Causal effects are mostly shared across ancestries but are highly heterogeneous across tissues. Our findings provide insights into cross-ancestry and tissue-informed multi-omics causal inference approaches and demonstrate their power in uncovering molecular processes driving T2D.

  View details for DOI 10.1038/s42255-025-01444-1

  View details for Web of Science ID 001671695500001

  View details for PubMedID 41593238

  View details for PubMedCentralID PMC12945685

• GLP-1 receptor agonists and cardiovascular outcomes in Asian, Black or African American, and White populations: An updated meta-analysis including the SOUL trial DIABETES OBESITY & METABOLISM Hasebe, M., Su, C., Kamido, H., Yabe, D., Yoshiji, S. 2026; 28 (4): 2806-2814

  Abstract

  To evaluate the cardiovascular efficacy of glucagon-like peptide-1 receptor agonists (GLP-1RAs) in Asian, Black or African American, and White populations, and to assess whether the magnitude of cardiovascular risk reduction differs across these populations.PubMed and EMBASE were searched to 11 November 2025 for randomized placebo-controlled GLP-1RA trials in adults with type 2 diabetes or overweight/obesity that reported race-stratified major adverse cardiovascular events (MACE; cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke). Hazard ratios (HRs) for MACE were extracted for Asian, Black or African American, and White populations. Random-effects meta-analyses were used to obtain pooled HRs and ratios of HRs (RHRs) comparing treatment effects between populations.Nine trials, including the recent SOUL trial, were included, comprising 8164 Asian, 4036 Black or African American, and 62 503 White participants. GLP-1RAs reduced MACE risk in Asian (HR 0.73; 95% CI 0.63-0.85; p < 0.001) and White populations (HR 0.86; 95% CI 0.81-0.91; p < 0.001). In Black or African American populations, the effect was similar to that in White populations (HR 0.88; 95% CI 0.67-1.15; p = 0.34) but did not reach statistical significance. The pooled RHR for Asian versus White populations was 0.84 (95% CI 0.71-0.98; p = 0.027), indicating a significantly greater risk reduction in Asian populations. The RHR for Asian versus Black or African American populations was 0.81 (95% CI 0.57-1.16; p = 0.25), with point estimates favouring Asian populations.GLP-1RAs reduced MACE risk across populations, with greater relative risk reduction in Asian populations and broadly similar benefits in Black or African American and White populations.

  View details for DOI 10.1111/dom.70458

  View details for Web of Science ID 001667009400001

  View details for PubMedID 41565576

  View details for PubMedCentralID PMC12992182

• The Association Between Hemoglobin A_1c and Complications Among Individuals With Diabetes and Severe Chronic Kidney Disease. Diabetes Care 2025;48:1400-1409 DIABETES CARE Hasebe, M., Su, C. 2025; 48 (11): e156-e157

  View details for DOI 10.2337/dc25-1358

  View details for Web of Science ID 001630324000001

  View details for PubMedID 41115223

• Predicting coronary artery disease using machine learning with optimized feature selection INTERNATIONAL JOURNAL OF CARDIOLOGY Su, C., Hasebe, M. 2025; 438: 133584

  View details for DOI 10.1016/j.ijcard.2025.133584

  View details for Web of Science ID 001534030600001

  View details for PubMedID 40623634

• Comment on Gonzalez et al. Diabetes-Related Distress and Glycemic Dysregulation in Everyday Life With Type 1 Diabetes: Which Comes First? Diabetes Care 2025;48:1453-1460 DIABETES CARE Su, C., Hasebe, M. 2025; 48 (11): e160-e161

  View details for DOI 10.2337/dc25-1525

  View details for Web of Science ID 001630324000026

  View details for PubMedID 41115225

• Leveraging Proteomics and Proteogenomics for Understanding Osteoporosis and Other Musculoskeletal Diseases CURRENT OSTEOPOROSIS REPORTS Hasebe, M., Su, C., Kiel, D. P., Yoshiji, S. 2025; 23 (1): 43

  Abstract

  Osteoporosis and musculoskeletal diseases, including osteoarthritis and sarcopenia, contribute substantially to global morbidity and healthcare costs. This review explores how proteogenomics integrates genomic and proteomic data to refine disease classification, identify causal pathways, and accelerate biomarker and drug target discovery.Large-scale proteomic studies, including UK Biobank-based research, have identified circulating proteins associated with musculoskeletal disease risk and progression. Proteomic risk models outperform conventional clinical metrics in predicting outcomes. Genomics, proteomics, and proteogenomics have facilitated the identification of causal markers through methods such as genome-wide association studies, effector gene mapping, and proteome-wide Mendelian randomization. Pathways implicated in disease mechanisms include extracellular matrix remodeling (e.g., COL6A3, COL9A1), metabolic regulation (e.g., IGFBP2, GDF15), inflammatory processes (e.g., TNF family ligands, CXCL17), and sex-hormone-related signaling (e.g., FSHB, SHBG). While these biological processes contribute across osteoporosis, osteoarthritis, and sarcopenia, distinct proteins have also been linked to disease-specific pathophysiology, offering potential therapeutic targets. Genomics, proteomics, and proteogenomics refine our understanding of musculoskeletal conditions and hold strong potential for improving early diagnosis, enhancing risk stratification, and advancing precision treatments.

  View details for DOI 10.1007/s11914-025-00941-2

  View details for Web of Science ID 001590735500001

  View details for PubMedID 41073837

  View details for PubMedCentralID 10960072

• Letter to the Editor From Hasebe and Su: "Increased Risk of Cardiovascular Diseases in Patients With Chronic Hypoparathyroidism in Sweden" JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM Hasebe, M., Su, C. 2025; 110 (12): e4248-e4249

  View details for DOI 10.1210/clinem/dgaf474

  View details for Web of Science ID 001568651400001

  View details for PubMedID 40853228

• Cardiovascular risk reduction with glucagon-like peptide-1 receptor agonists is proportional to HbA1c lowering in type 2 diabetes: An updated meta-regression analysis incorporating FLOW and SOUL trials DIABETES OBESITY & METABOLISM Hasebe, M., Su, C., Keidai, Y., Minamino, H., Yabe, D., Inagaki, N., Yoshiji, S. 2025; 27 (12): 7210-7220

  Abstract

  To evaluate relationships of cardiovascular and kidney outcomes with glycemic or bodyweight reductions in randomised placebo-controlled trials of glucagon-like peptide-1 receptor agonists (GLP-1RAs), incorporating data from FLOW and SOUL trials.PubMed and EMBASE were searched up to 22 August 2025 for placebo-controlled randomized trials of oral or bolus-type, subcutaneous GLP-1RAs reporting major adverse cardiovascular events (MACE; a composite of cardiovascular death, myocardial infarction, and stroke) in adults with type 2 diabetes. The primary outcome was MACE; secondary outcomes included heart failure (HF) and kidney outcomes. Random-effects meta-analyses were followed by meta-regression evaluating associations with HbA1c and bodyweight reduction.A total of 73 263 individuals were included from 10 trials (ELIXA, LEADER, SUSTAIN-6, EXSCEL, Harmony Outcomes, PIONEER 6, REWIND, AMPLITUDE-O, FLOW, and SOUL). GLP-1RAs reduced MACE by 14% (hazard ratio: 0.86; 95% CI: 0.82 to 0.91; p <0.001), as well as hospitalisation for HF and the composite kidney outcome (both p <0.001). Meta-regression showed that every 1% extra reduction in HbA1c corresponded to a 27% lower HR for MACE (p = 0.015; R2 = 0.61). While HbA1c reduction was not significantly associated with secondary outcomes, the directionality was consistent with MACE. Bodyweight change was not associated with any of the analysed endpoints, including MACE (p = 0.13; R2 = 0.21).HbA1c reduction, not bodyweight change, was significantly and proportionally associated with MACE risk reduction. HbA1c lowering may serve as a useful surrogate for the cardiovascular improvements associated with GLP-1RAs in type 2 diabetes.

  View details for DOI 10.1111/dom.70121

  View details for Web of Science ID 001568832500001

  View details for PubMedID 40926380

  View details for PubMedCentralID PMC12587236

• Interpreting stroke risk after adverse pregnancy outcomes: methodological considerations for sibling analyses and long-term follow-up EUROPEAN HEART JOURNAL Hasebe, M., Su, C. 2025

  View details for DOI 10.1093/eurheartj/ehaf625

  View details for Web of Science ID 001562519800001

  View details for PubMedID 40900097

• In Utero Exposure to Maternal Hyperglycemia and Offspring Type 2 Diabetes Genetic Risk Score Are Independently Associated With Risk of Impaired Glucose Tolerance in Youth. Diabetes Care 2025;48:1356-1360 DIABETES CARE Hasebe, M., Su, C. 2025; 48 (9): e115-e116

  View details for DOI 10.2337/dc25-1167

  View details for Web of Science ID 001555525600025

  View details for PubMedID 40834248

• Diabetes Progression and Its Impact on Kidney Cancer Risk: Insights From a Longitudinal Korean Cohort Study JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM Hasebe, M., Su, C. 2025; 110 (12): e4253-e4254

  View details for DOI 10.1210/clinem/dgaf438

  View details for Web of Science ID 001559197100001

  View details for PubMedID 40747848

• Letter to the Editor From Hasebe and Su: "Plasma Proteins Predict Kidney Function Trajectories in Type 2 Diabetes" JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM Hasebe, M., Su, C. 2025; 110 (11): e3914-e3915

  View details for DOI 10.1210/clinem/dgaf405

  View details for Web of Science ID 001536321700001

  View details for PubMedID 40658788

• Letter to the Editor from Su and Hasebe: "Genomic Correlations, Shared Loci, and Drug Targets Between Polycystic Ovary Syndrome and Asthma: Insights From Genome-Wide Association Analysis" JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM Su, C., Hasebe, M. 2025; 110 (11): e3916-e3917

  View details for DOI 10.1210/clinem/dgaf388

  View details for Web of Science ID 001531708300001

  View details for PubMedID 40629254

• Letter to the Editor From Hasebe and Su: "Adrenalectomy Reduces the Risk of Vertebral Fractures in Patients With Mild Autonomous Cortisol Secretion" JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM Hasebe, M., Su, C. 2025; 110 (11): e3918-e3919

  View details for DOI 10.1210/clinem/dgaf384

  View details for Web of Science ID 001531710000001

  View details for PubMedID 40601821

• Refining the synthesis of age-related biomarkers in chronic kidney disease QJM-AN INTERNATIONAL JOURNAL OF MEDICINE Hasebe, M., Su, C. 2025; 118 (11): 869

  View details for DOI 10.1093/qjmed/hcaf152

  View details for Web of Science ID 001527656700001

  View details for PubMedID 40613729

• Clarifying Assumptions and Interpretation in a Mendelian Randomisation Study of SHBG and Membranous Nephropathy NEPHROLOGY Hasebe, M., Su, C. 2025; 30 (7): e70099

  View details for DOI 10.1111/nep.70099

  View details for Web of Science ID 001547113600003

  View details for PubMedID 40701824

• Machine Learning Models for Predicting Acute and Chronic Kidney Diseases During the Post-Covid-19 Pandemic NEPHROLOGY Su, C., Hasebe, M. 2025; 30 (7): e70097

  View details for DOI 10.1111/nep.70097

  View details for Web of Science ID 001547113600018

  View details for PubMedID 40665552

• Cardiovascular comorbidities and risk of in-hospital mortality in acute pancreatitis INTERNATIONAL JOURNAL OF CARDIOLOGY Hasebe, M., Su, C. 2025; 437: 133508

  View details for DOI 10.1016/j.ijcard.2025.133508

  View details for Web of Science ID 001519092900003

  View details for PubMedID 40532790

• Machine-learning models to predict iron recovery after blood donation: a model development and external validation study LANCET HAEMATOLOGY Lip, W., Sup, C., Meulenbeld, A., Jagirdar, H., Janssen, M. P., Swanevelder, R., Bruhn, R., Kaidarova, Z., Bravo, M., Cao, S., Custer, B., van den Berg, K., Russell, W. 2025; 12 (6): e431-e441

  Abstract

  Machine-learning models directly predicting iron biomarkers after blood donation could help to manage donation-associated iron deficiency and avoid low haemoglobin deferrals. No such models have been externally validated internationally. Our aim was to develop and externally validate machine-learning models predicting returning blood donors' haemoglobin and ferritin.We developed machine-learning models using retrospective blood donation data. The training cohort included 2425 repeat donors (2007-09 US-based RISE study); external validation used 2014-23 cohorts from the USA, South Africa, and the Netherlands. Models predicted donors' ferritin and haemoglobin at return donations by use of variables that are commonly measured by blood collectors (time until donors return, donation history, demographics, and baseline iron biomarkers). Models were selected via cross-validation and externally validated in donors aged at least 15 years in two contexts: those with baseline ferritin and haemoglobin measured (haemoglobin and ferritin) and those with only baseline haemoglobin measured (haemoglobin only). Model performance was assessed by use of root-mean-square percentage error (RMSPE).When predicting return haemoglobin in the RISE cohort, model performance was similar in the haemoglobin and ferritin dataset (n=2625 donation visits, RMSPE=6·78) and haemoglobin only dataset (n=3488 donation visits, RMSPE=6·78). In the external datasets, containing 11 000 to 514 000 donations, RMSPE never increased more than 8%. When predicting return ferritin in RISE, performance was better in the haemoglobin and ferritin dataset (RMSPE=14·9) than in the haemoglobin only dataset (RMSPE=27·4). In external validation, RMSPE never increased more than 0·4% and 28% in the haemoglobin-only datasets and the haemoglobin and ferritin datasets, respectively.Machine-learning models predicting haemoglobin and ferritin at return donations generalised well across diverse settings and could enable individualised approaches to manage iron deficiency while maintaining a sufficient blood supply.The Association for the Advancement of Blood and Biotherapies.For the Dutch translation of the abstract see Supplementary Materials section.

  View details for DOI 10.1016/S2352-3026(25)00068-7

  View details for Web of Science ID 001503595800008

  View details for PubMedID 40447352

• Integrative proteogenomic analysis identifies COL6A3-derived endotrophin as a mediator of the effect of obesity on coronary artery disease NATURE GENETICS Yoshiji, S., Lu, T., Butler-Laporte, G., Carrasco-Zanini-Sanchez, J., Su, C., Chen, Y., Liang, K., Willett, J., Wang, S., Adra, D., Ilboudo, Y., Sasako, T., Koyama, S., Nakao, T., Forgetta, V., Farjoun, Y., Zeberg, H., Zhou, S., Marks-Hultstrom, M., Machiela, M. J., Kaalia, R., Dashti, H., Claussnitzer, M., Flannick, J., Wareham, N. J., Mooser, V., Timpson, N. J., Langenberg, C., Richards, J. 2025; 57 (2): 345-357

  Abstract

  Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity strongly influences circulating protein levels, we investigated proteins mediating the effects of obesity on coronary artery disease, stroke and type 2 diabetes. By integrating two-step proteome-wide Mendelian randomization, colocalization, epigenomics and single-cell RNA sequencing, we identified five mediators and prioritized collagen type VI α3 (COL6A3). COL6A3 levels were strongly increased by body mass index and increased coronary artery disease risk. Notably, the carboxyl terminus product of COL6A3, endotrophin, drove this effect. COL6A3 was highly expressed in disease-relevant cell types and tissues. Finally, we found that body fat reduction could reduce plasma levels of COL6A3-derived endotrophin, indicating a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effects of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.

  View details for DOI 10.1038/s41588-024-02052-7

  View details for Web of Science ID 001405047900001

  View details for PubMedID 39856218

  View details for PubMedCentralID PMC11821532

• MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures BIOINFORMATICS Zhang, W., Su, C., Yoshiji, S., Lu, T. 2024; 40 (11)

  Abstract

  Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships.An open-sourced R package is available at https://github.com/zhwm/MRCorge.

  View details for DOI 10.1093/bioinformatics/btae666

  View details for Web of Science ID 001360546500001

  View details for PubMedID 39513749

  View details for PubMedCentralID PMC11578597

• Multi-Trait Inference of Full Genome-Wide Associations of Type 2 Diabetes Subtypes Uncovers Distinctive Biology and Putatively Causal Genes Yoshiji, S., Ruebenacker, O., Smadbeck, P., Trang Nguyen, Sayici, A., Su, C., Mejia, E., Florez, J., Udler, M., Flannick, J. WILEY. 2024: 394-395

  View details for Web of Science ID 001365421100126

• HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases COMMUNICATIONS BIOLOGY Butler-Laporte, G., Farjoun, J., Nakanishi, T., Lu, T., Abner, E., Chen, Y., Hultstrom, M., Metspalu, A., Milani, L., Magi, R., Nelis, M., Hudjashov, G., Yoshiji, S., Ilboudo, Y., Liang, K. Y. H., Su, C., Willet, J. D. S., Esko, T., Zhou, S., Forgetta, V., Taliun, D., Richards, J. 2023; 6 (1): 1113

  Abstract

  The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease.

  View details for DOI 10.1038/s42003-023-05496-5

  View details for Web of Science ID 001679322300001

  View details for PubMedID 37923823

  View details for PubMedCentralID PMC10624861

• Change in Hemoglobin as an Indicator of Iron Deficiency in Repeat Blood Donors Meulenbeld, A., Turkulainen, E., Su, C., Russell, W., Arvas, M., Janssen, M. WILEY. 2023: 25A-26A

  View details for DOI 10.1111/trf.18_17554

  View details for Web of Science ID 001089460200019

• Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection. Communications medicine Paranjpe, I., Jayaraman, P., Su, C. Y., Zhou, S., Chen, S., Thompson, R., Del Valle, D. M., Kenigsberg, E., Zhao, S., Jaladanki, S., Chaudhary, K., Ascolillo, S., Vaid, A., Gonzalez-Kozlova, E., Kauffman, J., Kumar, A., Paranjpe, M., Hagan, R. O., Kamat, S., Gulamali, F. F., Xie, H., Harris, J., Patel, M., Argueta, K., Batchelor, C., Nie, K., Dellepiane, S., Scott, L., Levin, M. A., He, J. C., Suarez-Farinas, M., Coca, S. G., Chan, L., Azeloglu, E. U., Schadt, E., Beckmann, N., Gnjatic, S., Merad, M., Kim-Schulze, S., Richards, B., Glicksberg, B. S., Charney, A. W., Nadkarni, G. N. 2023; 3 (1): 81

  Abstract

  Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms.Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261).We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury.Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

  View details for DOI 10.1038/s43856-023-00307-8

  View details for PubMedID 37308534

  View details for PubMedCentralID 7279721

• Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity NATURE METABOLISM Yoshiji, S., Butler-Laporte, G., Lu, T., Willett, J., Su, C., Nakanishi, T., Morrison, D. R., Chen, Y., Liang, K., Hultstrom, M., Ilboudo, Y., Afrasiabi, Z., Lan, S., Duggan, N., DeLuca, C., Vaezi, M., Tselios, C., Xue, X., Bouab, M., Shi, F., Laurent, L., Munter, H., Afilalo, M., Afilalo, J., Mooser, V., Timpson, N. J., Zeberg, H., Zhou, S., Forgetta, V., Farjoun, Y., Richards, J. 2023; 5 (2): 248-+

  Abstract

  Obesity is a major risk factor for Coronavirus disease (COVID-19) severity; however, the mechanisms underlying this relationship are not fully understood. As obesity influences the plasma proteome, we sought to identify circulating proteins mediating the effects of obesity on COVID-19 severity in humans. Here, we screened 4,907 plasma proteins to identify proteins influenced by body mass index using Mendelian randomization. This yielded 1,216 proteins, whose effect on COVID-19 severity was assessed, again using Mendelian randomization. We found that an s.d. increase in nephronectin (NPNT) was associated with increased odds of critically ill COVID-19 (OR = 1.71, P = 1.63 × 10-10). The effect was driven by an NPNT splice isoform. Mediation analyses supported NPNT as a mediator. In single-cell RNA-sequencing, NPNT was expressed in alveolar cells and fibroblasts of the lung in individuals who died of COVID-19. Finally, decreasing body fat mass and increasing fat-free mass were found to lower NPNT levels. These findings provide actionable insights into how obesity influences COVID-19 severity.

  View details for DOI 10.1038/s42255-023-00742-w

  View details for Web of Science ID 000935789000002

  View details for PubMedID 36805566

  View details for PubMedCentralID PMC9940690

• Increasing serum iron levels and their role in the risk of infectious diseases: a Mendelian randomization approach INTERNATIONAL JOURNAL OF EPIDEMIOLOGY Butler-Laporte, G., Farjoun, Y., Chen, Y., Hultstrom, M., Liang, K. Y. H., Nakanishi, T., Su, C., Yoshiji, S., Forgetta, V., Richards, J. 2023; 52 (4): 1163-1174

  Abstract

  Increased iron stores have been associated with elevated risks of different infectious diseases, suggesting that iron supplementation may increase the risk of infections. However, these associations may be biased by confounding or reverse causation. This is important, since up to 19% of the population takes iron supplementation. We used Mendelian randomization (MR) to bypass these biases and estimate the causal effect of iron on infections.As instrumental variables, we used genetic variants associated with iron biomarkers in two genome-wide association studies (GWASs) of European ancestry participants. For outcomes, we used GWAS results from the UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative or 23andMe, for seven infection phenotypes: 'any infections', combined, COVID-19 hospitalization, candidiasis, pneumonia, sepsis, skin and soft tissue infection (SSTI) and urinary tract infection (UTI).Most of our analyses showed increasing iron (measured by its biomarkers) was associated with only modest changes in the odds of infectious outcomes, with all 95% odds ratios confidence intervals within the 0.88 to 1.26 range. However, for the three predominantly bacterial infections (sepsis, SSTI, UTI), at least one analysis showed a nominally elevated risk with increased iron stores (P <0.05).Using MR, we did not observe an increase in risk of most infectious diseases with increases in iron stores. However for bacterial infections, higher iron stores may increase odds of infections. Hence, using genetic variation in iron pathways as a proxy for iron supplementation, iron supplements are likely safe on a population level, but we should continue the current practice of conservative iron supplementation during bacterial infections or in those at high risk of developing them.

  View details for DOI 10.1093/ije/dyad010

  View details for Web of Science ID 000932711700001

  View details for PubMedID 36773317

  View details for PubMedCentralID PMC10396421

• Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases NATURE GENETICS Chen, Y., Lu, T., Pettersson-Kymmer, U., Stewart, I. D., Butler-Laporte, G., Nakanishi, T., Cerani, A., Liang, K. Y. H., Yoshiji, S., Willett, J., Su, C., Raina, P., Greenwood, C. M. T., Farjoun, Y., Forgetta, V., Langenberg, C., Zhou, S., Ohlsson, C., Richards, J. 2023; 55 (1): 44-+

  Abstract

  Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

  View details for DOI 10.1038/s41588-022-01270-1

  View details for Web of Science ID 000914418600002

  View details for PubMedID 36635386

  View details for PubMedCentralID PMC7614162

• The dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals CLINICAL PROTEOMICS Butler-Laporte, G., Gonzalez-Kozlova, E., Su, C., Zhou, S., Nakanishi, T., Brunet-Ratnasingham, E., Morrison, D., Laurent, L., Afilalo, J., Afilalo, M., Henry, D., Chen, Y., Carrasco-Zanini, J., Farjoun, Y., Pietzner, M., Kimchi, N., Afrasiabi, Z., Rezk, N., Bouab, M., Petitjean, L., Guzman, C., Xue, X., Tselios, C., Vulesevic, B., Adeleye, O., Abdullah, T., Almamlouk, N., Moussa, Y., DeLuca, C., Duggan, N., Schurr, E., Brassard, N., Durand, M., Del Valle, D., Thompson, R., Cedillo, M. A., Schadt, E., Nie, K., Simons, N. W., Mouskas, K., Zaki, N., Patel, M., Xie, H., Harris, J., Marvin, R., Cheng, E., Tuballes, K., Argueta, K., Scott, I., Greenwood, C. M. T., Paterson, C., Hinterberg, M., Langenberg, C., Forgetta, V., Mooser, V., Marron, T., Beckmann, N., Kenigsberg, E., Charney, A. W., Kim-Schulze, S., Merad, M., Kaufmann, D. E., Gnjatic, S., Richards, J., Mt Sinai COVID-19 Biobank Team 2022; 19 (1): 34

  Abstract

  Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions.We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support.580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10-4). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex.Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection.

  View details for DOI 10.1186/s12014-022-09371-z

  View details for Web of Science ID 000861465600001

  View details for PubMedID 36171541

  View details for PubMedCentralID PMC9516500

• The dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals (vol 19, 34, 2022) CLINICAL PROTEOMICS Butler-Laporte, G., Gonzalez-Kozlova, E., Su, C., Zhou, S., Nakanishi, T., Brunet-Ratnasingham, E., Morrison, D., Laurent, L., Aflalo, J., Aflalo, M., Henry, D., Chen, Y., Carrasco-Zanini, J., Farjoun, Y., Pietzner, M., Kimchi, N., Afrasiabi, Z., Rezk, N., Bouab, M., Petitjean, L., Guzman, C., Xue, X., Tselios, C., Vulesevic, B., Adeleye, O., Abdullah, T., Almamlouk, N., Moussa, Y., DeLuca, C., Duggan, N., Schurr, E., Brassard, N., Durand, M., Del Valle, D., Thompson, R., Cedillo, M. A., Schadt, E., Nie, K., Simons, N. W., Mouskas, K., Zaki, N., Patel, M., Xie, H., Harris, J., Marvin, R., Cheng, E., Tuballes, K., Argueta, K., Scott, I., Greenwood, C. M. T., Paterson, C., Hinterberg, M., Langenberg, C., Forgetta, V., Mooser, V., Marron, T., Beckmann, N. D., Kenigsberg, E., Charney, A. W., Kim-schulze, S., Merad, M., Kaufmann, D. E., Gnjatic, S., Richards, J., Mt Sinai Covid-19 Biobank Team 2022; 19 (1)

  View details for DOI 10.1186/s12014-022-09378-6

  View details for Web of Science ID 000883442300002

• Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection. medRxiv : the preprint server for health sciences Paranjpe, I., Jayaraman, P., Su, C. Y., Zhou, S., Chen, S., Thompson, R., Del Valle, D. M., Kenigsberg, E., Zhao, S., Jaladanki, S., Chaudhary, K., Ascolillo, S., Vaid, A., Kumar, A., Kozlova, E., Paranjpe, M., O'Hagan, R., Kamat, S., Gulamali, F. F., Kauffman, J., Xie, H., Harris, J., Patel, M., Argueta, K., Batchelor, C., Nie, K., Dellepiane, S., Scott, L., Levin, M. A., He, J. C., Suarez-Farinas, M., Coca, S. G., Chan, L., Azeloglu, E. U., Schadt, E., Beckmann, N., Gnjatic, S., Merad, M., Kim-Schulze, S., Richards, B., Glicksberg, B. S., Charney, A. W., Nadkarni, G. N. 2022

  Abstract

  Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

  View details for DOI 10.1101/2021.12.09.21267548

  View details for PubMedID 36093350

  View details for PubMedCentralID PMC9460972