Hepatic levels of S-adenosylmethionine regulate the adaptive response to fasting

26 p.-6 fig.-1 tab.-1 graph. abst.There has been an intense focus to uncover the molecular mechanisms by which fasting triggers the adaptive cellular responses in the major organs of the body. Here, we show that in mice, hepatic S-adenosylmethionine (SAMe)—the principal methyl donor—acts as a metabolic sensor of nutrition to fine-tune the catabolic-fasting response by modulating phosphatidylethanolamine N-methyltransferase (PEMT) activity, endoplasmic reticulum-mitochondria contacts, β-oxidation, and ATP production in the liver, together with FGF21-mediated lipolysis and thermogenesis in adipose tissues. Notably, we show that glucagon induces the expression of the hepatic SAMe-synthesizing enzyme methionine adenosyltransferase α1 (MAT1A), which translocates to mitochondria-associated membranes. This leads to the production of this metabolite at these sites, which acts as a brake to prevent excessive β-oxidation and mitochondrial ATP synthesis and thereby endoplasmic reticulum stress and liver injury. This work provides important insights into the previously undescribed function of SAMe as a new arm of the metabolic adaptation to fasting.M.V.-R. is supported by Proyecto PID2020-119486RB-100 (funded by MCIN/AEI/10.13039/501100011033), Gilead Sciences International Research Scholars Program in Liver Disease, Acción Estratégica Ciberehd Emergentes 2018 (ISCIII), Fundación BBVA, HORIZON-TMA-MSCA-Doctoral Networks 2021 (101073094), and Redes de Investigación 2022 (RED2022-134485-T). M.L.M.-C. is supported by La CAIXA Foundation (LCF/PR/HP17/52190004), Proyecto PID2020-117116RB-I00 (funded by MCIN/AEI/10.13039/501100011033), Ayudas Fundación BBVA a equipos de investigación científica (Umbrella 2018), and AECC Scientific Foundation (Rare Cancers 2017). A.W. is supported by RTI2018-097503-B-I00 and PID2021-127169OB-I00, (funded by MCIN/AEI/10.13039/501100011033) and by “ERDF A way of making Europe,” Xunta de Galicia (Ayudas PRO-ERC), Fundación Mutua Madrileña, and European Community’s H2020 Framework Programme (ERC Consolidator grant no. 865157 and MSCA Doctoral Networks 2021 no. 101073094). C.M. is supported by CIBERNED. P.A. is supported by Ayudas para apoyar grupos de investigación del sistema Universitario Vasco (IT1476-22), PID2021-124425OB-I00 (funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe,” MCI/UE/ISCiii [PMP21/00080], and UPV/EHU [COLAB20/01]). M.F. and M.G.B. are supported by PID2019-105739GB-I00 and PID2020-115472GB-I00, respectively (funded by MCIN/AEI/10.13039/501100011033). M.G.B. is supported by Xunta de Galicia (ED431C 2019/013). C.A., T.L.-D., and J.B.-V. are recipients of pre-doctoral fellowships from Xunta de Galicia (ED481A-2020/046, ED481A-2018/042, and ED481A 2021/244, respectively). T.C.D. is supported by Fundación Científica AECC. A.T.-R. is a recipient of a pre-doctoral fellowship from Fundación Científica AECC. S.V.A. and C.R. are recipients of Margarita Salas postdoc grants under the “Plan de Recuperación Transformación” program funded by the Spanish Ministry of Universities with European Union’s NextGeneration EU funds (2021/PER/00020 and MU-21-UP2021-03071902373A, respectively). T.C.D., A.S.-R., and M.T.-C. are recipients of Ayuda RYC2020-029316-I, PRE2019/088960, and BES-2016/078493, respectively, supported by MCIN/AEI/10.13039/501100011033 and by El FSE invierte en tu futuro. S.L.-O. is a recipient of a pre-doctoral fellowship from the Departamento de Educación del Gobierno Vasco (PRE_2018_1_0372). P.A.-G. is recipient of a FPU pre-doctoral fellowship from the Ministry of Education (FPU19/02704). CIC bioGUNE is supported by Ayuda CEX2021-001136-S financiada por MCIN/AEI/10.13039/501100011033. A.B.-C. was funded by predoctoral contract PFIS (FI19/00240) from Instituto de Salud Carlos III (ISCIII) co-funded by Fondo Social Europeo (FSE), and A.D.-L. was funded by contract Juan Rodés (JR17/00016) from ISCIII. A.B.-C. is a Miguel Servet researcher (CPII22/00008) from ISCIII.Peer reviewe

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)