Malignant rhabdoid tumour (MRT) is a rare and aggressive childhood malignancy that occurs in the kidneys or central nervous system and is associated with very poor prognosis. Chemoresistance is a major issue in the treatment of this malignancy leading to an urgent need for a greater understanding of its underlying mechanisms in MRT and novel treatment strategies for MRT patients. The balance between oxidative stress mediated by reactive oxygen species (ROS) and the antioxidant system has become a subject of interest in cancer therapy research. Studies have implicated key players of the antioxidant system in chemotherapeutic including the well-known antioxidant glutathione (GSH) and the transcription factor nuclear erythroid-related factor-2 (Nrf2).  This study evaluated the role of these components in the response of MRT cells to treatment with the commonly used chemotherapeutic agent, cisplatin.This study characterised the basal levels of GSH, ROS and Nrf2 in a panel of MRT cell lines and found a correlation between the expression profile of the antioxidant defence system and cisplatin sensitivity. Results showed that treatment with ROS scavenger N-acetylcysteine (NAC) protected cells from cisplatin-induced ROS and apoptosis. Interestingly, depleting GSH levels with the inhibitor buthionine sulphoximine (BSO) enhanced cisplatin-induced ROS and sensitised cells to cisplatin. Lastly, targeting Nrf2 with the small molecule inhibitor ML385 or by siRNA diminished GSH levels, enhanced ROS and sensitised resistant MRT cells to cisplatin.These results suggest that targeting the Nrf2/GSH antioxidant system may present a novel therapeutic strategy to combat chemoresistance in rhabdoid tumours. Malignant rhabdoid tumour (MRT) is a rare and highly metastatic paediatric neoplasm. These tumours typically arise in the kidneys and in the central nervous system where they are referred to malignant rhabdoid tumour of the kidney (MRTK) and as atypical teratoid rhabdoid tumours (ATRT), respectively. However, they can also occur in any other soft tissue sites  and antioxidants. The cellular defence system against oxidative stressors, which are mediated by ROS and xenobiotic toxicants, utilise many antioxidants including vitamins, glutathione (GSH) generating enzymes (e.g. glutamate cysteine ligase) and phase 2 drug metabolising enzymes (e.g. glutathione S-transferases)   was prepared as a 20 mM stock solution in H2O.Cisplatin  was dissolved in sterile 0.9% NaCl. Antimycin A  and ML385  were dissolved in DMSO. N-acetylcysteine  was prepared as a 100 mM stock in dH2O the day of the treatment.Stock solutions were stored at − 20 °C and working solutions were prepared fresh in sterile PBS  or dH2 . The G401 cell line was derived from a 3-month-old male MRTK patient. BT12 and BT16 cells were grown in Roswell Park Memorial Institute (RPMI) medium + GlutaMAX™ supplemented with 10% (v/v) Foetal Bovine Serum (FBS) and 1% (v/v) PenStrep . G401 cells were a kind gift from Prof Maureen O’Sullivan in the National Children’s Research Centre and are available from ATCC. G401 cells were grown in Dulbecco’s Modified Eagles Medium (DMEM) + GlutaMAX™ supplemented with 10% (v/v) FBS and 1% (v/v) PenStrep . Cells were incubated at 37 °C with 5% COBT12, BT16 and G401 cell lines were seeded in 96-well plates and left overnight to adhere. The following day, they treated with a range of concentrations of cisplatin for 24, 48 and 72 h. After the required incubation period, 20 μl of AlamarBlue™  was added to each well and the plate was left to incubate in the dark for 5 h at 37 °C. Fluorescence was measured on a SpectraMax plate reader at an excitation wavelength of 544 nm and an emission wavelength of 590 nm. All alamar blue assays were performed in triplicate. Dose–response curves were obtained and used to determine the IC50 values (GraphPad Prism 5.0).5 cells/mL for BT12 and BT16 and 5 × 104 cells/mL for G401 cells. The following day, the cells were treated with the relevant drugs for the required timepoint. Apoptosis analysis was performed through flow cytometric analysis of annexin V- fluorescein isothiocyanate  and propidium iodide  stained cells as previously described  stained cells as previously described   for 15 min at 37 °C in the dark, and then washed with warm PBS and analysed by flow cytometry. The MFI values of Mitosox (510/580 nm) were obtained with the FL-2 channel using the bandpass filter of 585/340 nm on a BD Accuri C6 flow cytometer.Cells were seeded in six well plates at a density of 1 × 10The ratio of GSH (reduced) and GSSG (oxidised glutathione), an indicator of oxidative stress, was determined using the Promega GSH/GSSG-Glo™ kit . Cells were seeded in a 96 well plate and the following day they were treated with the relevant drugs for the required time. After treatment, media was removed, and the assay was conducted according to the manufacturer’s instructions and luminescence was measured using the Luminoskan™ Microplate Luminometer. The GSH/GSSG ratio was calculated using the formula: GSH/GSSG ratio = In this assay, two moles of GSH are generated from one mole of GSSG, a twofold adjustment was applied to the GSSG values to obtain an accurate measurement of the GSSG levels, as per the recommended guidelines.γ-tubulin  to ensure equal loading.Western blotting of Nrf2 was conducted as previously described  at a final concentration of 50 nM. Western blot analysis was employed to confirm Nrf2 knockdown at 24 h post-transfection.The knockdown of Nrf2 was achieved using on-target plus human Nrf2 siRNA SMART pool , while non-targeting pool served as a negative control. Cells were seeded in six well plates at a density of 1 × 10p < 0.05, **p < 0.01 and ***p < 0.001 were deemed statistically significant. Correlation analysis was performed using GraphPad Prism 5 by plotting percentage cell viability  in response to cisplatin treatment (10 µM) against either relative GSH, relative Nrf2 expression or relative ROS levels. R values and p values were obtained from the correlation analysis.GraphPad Prism 5  was used for statistical analysis of experimental data. Results are displayed as the mean ± standard error of the mean. Statistical analysis was performed using a one-way ANOVA with Tukey’s post hoc test or a paired t test for comparison of two groups. Values of *Three MRT cell lines, BT12, BT16 and G401 were assessed to determine sensitivity to cisplatin, a commonly used chemotherapeutic agent for the treatment of MRT. The cells were incubated with 1 and 10 µM of cisplatin for 48 h, after which cell viability was measured. Previously, BT12 and BT16 cells have been used as a model for studying cisplatin resistance in MRT with BT12 cells designated as cisplatin-sensitive and BT16 cells designated as cisplatin-resistant , and stained with HN-acetylcysteine (NAC) is a ROS scavenger and a synthetic precursor of cysteine and glutathione. It has a protective effect against ROS due to its free radical scavenging function by regulating the thiol redox status and increasing intracellular glutathione levels  which inhibits gamma-glutamylcysteine synthetase, the enzyme required in the first step of glutathione synthesis  domain of Nrf2 and interferes with the binding of Nrf2 to regulatory DNA-binding sequences thereby inhibiting downstream target gene expression. To further confirm the results obtained using pharmacological inhibition, a genetic approach to target Nrf2 using siRNA was also employed.Inhibition of Nrf2 with ML385 resulted in a decrease of GSH and GSH:GSSG ratio in BT16 cells confirming that Nrf2 can regulate GSH production in MRT. Additionally, ML385 also enhanced cisplatin-induced ROS and cisplatin-mediated apoptotic cell death indicating the Nrf2/GSH antioxidant pathway modulates cisplatin resistance in MRT. Genetic inhibition of Nrf2 similarly showed a significant increase in cisplatin-induced ROS and importantly, a significant enhancement in cisplatin-induced apoptosis when comparing cells that were transfected with Nrf2 siRNA and treated with cisplatin to cells that were transfected with NT siRNA and treated with cisplatin.Similar results were recently observed by Sun et al. who showed that ML385 enhanced ROS levels and cisplatin mediated cell death in ovarian cancer cells (Sun et al. In addition to alterations in the antioxidant system, it should be noted that Nrf2 inhibition may result in modulations in signal transduction pathways such as MAPK cascades. Furthermore, expression of multidrug resistance proteins, which exerts cisplatin efflux in cancer cells, has been shown to be regulated by Nrf2 (Bao et al. In conclusion, this study has found a correlation between the expression profile of the antioxidant defence system and sensitivity to cisplatin in a panel of MRT cell lines. MRT cells with higher resistance to cisplatin exhibited higher levels of GSH and Nrf2 expression as well as lower levels of intracellular ROS. In response to cisplatin, ROS levels became significantly elevated in all cell lines examined and oxidative stress was increased in the two more cisplatin-sensitive cell lines. Administration of the ROS scavenger NAC elicited a protective effect from cisplatin-induced ROS and apoptosis indicating that ROS is an important mediator of cisplatin cytotoxicity in MRT. Most importantly, targeting either GSH with the inhibitor BSO or Nrf2 through an siRNA approach or with the small molecule inhibitor ML385 sensitised resistant MRT cells to cisplatin-induced cell death suggesting that targeting the Nrf2/GSH antioxidant defence pathway may present a novel therapeutic avenue in the design of new treatment strategies for MRT and a means of overcoming chemoresistance.


The medical profession is one of the most highly respected and desired professions among students worldwide, most likely because it provides opportunities for both a financially and socially rewarding career. However, while it has been quite established that factors such as self-interest, family pressure, friend pressure, and socioeconomic status do influence the choice of medicine among students worldwide, the exact reasons for an individual to join a medical school may actually vary worldwide. The aim of this study was to comprehensively explore factors influencing medical students to opt in/out medical careers in Sudan.An institutional based descriptive cross-sectional study was conducted at University of Khartoum in the year 2022 with a random sample of 330 students that was obtained from the medical students at the The University of Khartoum, Faculty of Medicine using stratified random sampling.Self-interest was the most common factor influencing opting in (choosing) medical profession (70.6%) (n = 233), followed by getting a very high score in high-school that qualifies into the faculty (55.5%) (n = 183). Regarding the factors affecting medical students’ choices, parental pressure was the main factor (37.0%) (n = 122), followed by other relatives’ pressure (12.4%) (n = 41), and 4.2% (n = 14) chose Peer pressure. 59.7% (n = 197) of the participants stated that they were not affected by any of these factors. Most of the participants felt that the general perception of the medical profession by society is that it is prestigious and has good career opportunities, only 5.8% (n = 19) believed that it is “Not appreciated at all” by the society. A statistically significant association was found between the type of admission & parent pressure . out of 330 participants, (56.1%) (n = 185) have opted out i.e. lost their interest or regretted their choice of medical career. Academic difficulties was the most common factor causing students to opt-out of the medical career (37%) (n = 122) followed by Multiple suspensions of education (35.2%) (n = 116), Current political & security conflicts in Sudan (29.7%) (n = 98), Poor quality of education (24.8%). The proportion of students having regrets for the medical profession was significantly higher among females. Over one third of the participants reported having depressive symptoms more than half days of the week. No statically significant correlation was established between the academic level and having these depressive symptoms and no statistically significant correlation was established between the decision to opt-out and the academic level (class) of the individuals (P = 0.105).Over half of Sudanese medical students at the University of Khartoum have already lost their interest or regretted their choice of medical career choice. Whether these future doctor chose to drop out or continue their path in the medical career suggests that they are more prone to serious hardships in their future careers. A careful comprehensive approach should further explore and try to offer solutions for problems like “Academic difficulties”, “multiple suspension of education”, and “poor quality of education” for they were the most common factors that caused medical students to opt out of the medical career. The medical profession is one of the most highly respected and desired professions among students worldwide, most likely because it provides opportunities for both a financially and socially rewarding career. However, while it has been quite established that factors such as self-interest, family pressure, friend pressure, and socioeconomic status do influence the choice of medicine among students worldwide  2], the, the2], An institutional based descriptive cross-sectional study was conducted at University of Khartoum in the year 2022.At the Faculty of Medicine of the University of Khartoum, the study was conducted virtually. The faculty of Medicine at the University of Khartoum is Sudan’s oldest medical institution, which was established in 1924. Additionally, it is the first medical school in Sudan that received accreditation by the World Federation of Medical Education.The college has a total of 2335 undergraduate students registered at the time of study during the academic year 2021–2022. In terms of inclusion criteria, all undergraduate students at the University of Khartoum – Faculty of Medicine in Khartoum in the first (Batch 98), second (Batch 97 and Batch 96), third (Batch 95), fourth (Batch 94), fifth (Batch 93), and sixth (Batch 92) academic years were included in the study, and no students were excluded.A random sample of 330 students was obtained from the medical students at the The University of Khartoum, Faculty of Medicine using stratified random sampling. Sample size (n) was calculated according to the formula: n = [z2 * p * (1 - p) / e2] / [1 + (z2 * p * (1 - p) / (e2 * N))] Where: z = 1.96 for a confidence level (α) of 95%, p = proportion , N = population size, e = margin of error.The sample size (with finite population correction) is equal to 330.https://www.randomizer.org.The study population was subdivided into 7 groups according to their academic classes, and a list containing all students in each academic class was obtained. Since the study sample size was 330, a proportionate random sample from each group was selected using the online sample randomizer at The calculated sample was further divided into 46, 44, 49, 51, 47, 45, and 48 for batches 92, 93, 94, 95, 96, 97 and 98 respectively.The data were gathered using a novel online self-administered closed-ended questionnaire that was structured by the authors in accordance with the study’s goals and objectives. Lists of all students who are currently registered at the Faculty of Medicine, University of Khartoum were obtained from the faculty administration; afterwards, each person was contacted independently, directly through their own social media accounts. This method was successful, resulting in a 100% response rate.The questionnaire contained 19 questions divided into 3 sections as follow:Contains nine questions related to the sociodemographic characteristics of the participants including: gender, age, academic level (batch), type of Admission, marital Status, place of residence (before joining the university), place of current residence (during university), weekly expenditure, and main source of income.Contains 5 questions related to factors influencing opting in/out : 3 questions investigating the factors influencing opting in including; “Why did you choose the medical profession?”, and whether their choices were “affected by parental pressure, relatives’ pressure, peer pressure or were not affected by either of these factors, and last, the participants were asked about what they think about the “general perception of the medical profession by the society?”. The other 2 questions for opt-out included directly assessing whether or not the students have lost their interest or regretted their choice of medical profession and what factors might have caused them to lose their interest or regret their choice of medical career.Contains two questions that were meant to screen for depressive symptoms among research participants, the participants were asked how often have you been bothered by “Feeling down, depressed, or hopeless?” and “having Little interest or pleasure in doing things? (Anhedonia)” over the last two weeks. The options for these questions ranged from (Never - A few times per year - Once a month - A few times per month - Once a week - A few times per week - Every day).Data was entered and encoded then analyzed by the computer programs -by using SPSS software. The collected data was analyzed through descriptive analysis and using the chi square and t-tests. P value < 0.05 was considered statistically significant. Data were displayed and presented in the forms of tables and charts.330 students responded to the questionnaire out of 330 with an overall response rate of 100%. The mean age was 23.745 years (SD = 2.1583). Of the 330 participants, 220 (66.7%) were females, while 110 (33.3%) were males. Most participants live with their families or relatives 239 (72.4%), while 81 (24.5%) live in the University dormitory. The remainder of the demographic characteristics are illustrated in Table Self-interest was the most common factor influencing opting in (choosing) medical profession (70.6%) (n = 233), followed by getting a very high score in high-school that qualifies into the faculty (55.5%) n = 183). Better job opportunities in the future was the third factor for (45.5%) (n = 150) for our subjects. While (24.2%) (n = 80) of the participants chose “My adult relatives advised me”, 10.9% (n = 36) chose “My parents are doctors”, 5.8% (n = 19) chose “I do not know”! 3.9% (n = 13) chose “My friends opted-in the same career”. Also 3% (n = 10) of our subjects mentioned other options. Figure 83. BetteRegarding the factors affecting medical students’ choices, parental pressure was the main factor (37.0%) n = 122), followed by other relatives’ pressure (12.4%) (n = 41), and 4.2% (n = 14) chose Peer pressure. 59.7% (n = 197) of the participants stated that they were not affected by any of these factors. Figure 2, followRegarding the general perception of the medical profession by society, most of our participants (65.8%) (n = 217) chose “With good career opportunities”, 62.4% (n = 206) chose “Prestigious”, 55.8% (n = 184) chose “Well-paid”, while 5.8% (n = 19) believed that it is “Not appreciated at all”. Figure A statistically significant association was found between the type of admission & parent pressure: Pearson correlation: 0.153 (2-tailed) .Out of 330 participants, (56.1%) (n = 185) have opted out i.e.lost their interest or regretted their choice of medical career. Regarding the factors causing opting out, Academic difficulties was the most common factor (37%) (n = 122) followed by Multiple suspensions of education (35.2%) (n = 116), Current political & security conflicts in Sudan (29.7%) (n = 98), Poor quality of education “i.e teacher and teaching method, the educational content, the learning environment and university management.” 24.8%) (n = 82), health-related conditions (7.3%) (n = 24), Financial difficulties (6.4%) (n = 21). Also (3.3%) (n = 11) mentioned other options. Figure .8%  p = 0.012) Table  Table 2.In light of the data collected, it is evident that while there is a certain level of variance in the opt-out rates among the participants, no statistically significant correlation was established between the decision to opt-out and the academic level (class) of the individuals (P = 0.105).Among the 330 entries 35.2% (n = 116) reported having “little interest or pleasure in doing things” (anhedonia) at least more than half the days over the last two weeks. 16.4% (n = 54) reported having “little interest or pleasure in doing things” (anhedonia) nearly everyday over the last two weeks. 34.3% (n = 113) reported feeling “down, depressed or hopeless” at least more than half the days over the last two weeks. 15.2% (n = 50) reported feeling “down, depressed or hopeless” nearly everyday over the last two weeks. Between different academic level, fourth-year students (Batch 94) were the most common to report reported having “little interest or pleasure in doing things” (anhedonia) with a percentage 46.9% (n = 23) reported having the symptoms at least more than half the days over the last two weeks and 26.5% (n = 13) of students reported having the symptoms nearly everyday over the last two weeks, students of Batch 94 were also the most common students report feeling “down, depressed or hopeless” with a percentage of 42.8% (n = 21) reported having the symptoms at least more than half the days over the last two weeks and 22.8% (n = 11) reported reported having them nearly everyday over the last two weeks. However no statically significant correlation was established between the academic level and having these depressive symptoms (P = 0.1). A significant association was found between Opting out and having “little interest or pleasure in doing things” (anhedonia) over the last two weeks (p = 0.0001). A significant association was found between opting out and feeling “down, depressed or hopeless” over the last two weeks (p = 0.0001). Table This study was to conducted to identify factors influencing Sudanese medical students to opt in/out medical careers. This study included 330 students two third of which were females. Most (88.2%) of the participants were urban residents. The type of admission 269(81.5%) were classified as General admission / and 61 (18.5%) as Private admission, this finding is consistent with the fact that the general admission allocated seats in the faculty represent about 80% of the total number of seats when compared to the seats allocated for private admission.Our study found that the most common factor for opting in a medical profession was self interest (70.6%). Choosing “self interest” as a factor for opting in reflects intrinsic motivation and hence having the vast majority of participants select this choice is quite reassuring. It is fair to say that this finding is relatively consistent with some studies in literature; self interest was the most common factor (86.4%) in a study conducted in Saudi Arabia . A studyThe second most common factors for opting-in found in our study was getting a very high score in high-school that qualifies into the faculty; logically, most of students who opt in a medical career and enrolled in the faculty of medicine actually would have already gotten a very high score that qualifies them into the faculty of medicine, this is why it’s understandable to have the majority of the participants choosing this option as one of the factors that caused them to opt in a medical profession. However, what is really interesting is the fact that we found 4.6% (n = 15) of the participants stating that they opted in the medical profession only because they got a very high score in high-school that qualified them into the faculty”!Why did you choose the medical profession?” under the section “other options”. Upon analyzing these answers, we found that they are markedly diverse, ranging from answers that show substantial interest like :“I love medicine” and “I want to help people and develop the health status in my country” to ambiguous and uncertain answers like :“there are no other choices” and “I did not see any better option” to answers that completely indicate that the student did not even choose to opt in the medical career by themselves like :“I did not choose it, my family did” And “my parents forced me”!Also interestingly 3% (n = 10) of our subjects decided to write their own answers to our question “Unlike other studies investigating factors that influence opting in medical career , 13], , , 18, [1]We think that the percentage of parents’ pressure in our study (37%) is high when compared to other similar studies where we have seen percentages as little as 0.66%, 7.3% and 11.6%  10]. Th. Th10]. When we asked our participants about their general perception of the medical profession by society, the majority regarded it with good career opportunities (65.8%), Prestigious (62.4%), and Well-paid (55.8%). From our point of view, these results reflect and confirm the positive society impression towards them. Despite that we found 5.8% of the population believe that the medical profession is not appreciated at all.Regarding opting out, we believe that the study has uncovered some interesting and quite alarming findings, having over half of the medical students at the University of Khartoum 56.1%) losing their interest or regretting their choice of medical career. Up to our knowledge, this is one of the highest percentages ever to be reported on such a subject, in Telangana, India, for instance, a study mentioned that “It was shocking to see almost 40% of the students were having regret feeling for choosing medical profession.” % losing Overall, we found this level of regret is even higher than the level of career choice regret among healthcare professionals and potential healthcare professionals during the COVID-19 pandemic (40.9%) which indeed was a highly stressful and catastrophic situation.  The pastOverall, this situation is quite similar to a nearby country, Ethiopia, in which 35% of students of medical school at Addis Ababa University, the best in the country, felt the standard of medical education was below their expectation , in anotWe believe that there are some further hidden factors that attribute to the regret of choice among medical students, this is very evident due to the fact that a statistically significant correlation was found between opting out and having “little interest or pleasure in doing things” (anhedonia) over the last two weeks and also between opting out and feeling “down, depressed or hopeless” over the last two weeks, these two findings highly suggest that symptoms of depression can very well be one of these aforementioned hidden factors and it is fair to say you can never tell which one comes first. Self-interest was the main reason for opting in in our research, and it’s only logical that students who have lost their interest in doing things that were once enjoyable lose their interest in their studies and regret their choice.Career choice regret is an established risk factor for burnout  But we aBeing conducted among the medical students at the University of Khartoum the generalizability of this study to the broader Sudanese medical students population is relatively limited and further studies should be conducted to fully inspect the whole Sudanese medical students community. Generalizing the study to the global community of medical students is also limited due to cultural and contextual factors that are found uniquely in Sudan. Another notable limitation was due to the study design itself, as since this was a cross-sectional study assessing changes in factors influencing opting in/out over time was limited and hence further longitudinal studies should be conducted to assess changes in factors over a longer period of time.Over half of Sudanese medical students at the University of Khartoum have already lost their interest or regretted their choice of medical career choice. Whether these future doctor chose to drop out or continue their path in the medical career suggests that they are more prone to serious hardships in their future careers. A careful comprehensive approach should further explore and try to offer solutions for problems like “Academic difficulties”, “multiple suspension of education”, and “poor quality of education” for they were the most common factors that caused medical students to opt out of the medical career.


This implies eliminating CO2 emissions or balancing them with removals while mitigating CH4 emissions to reduce their radiative forcing over time. The global cattle sector  mainly emits CH4 and N2O and will benefit from understanding the extent and speed of CH4 reductions necessary to align its mitigation ambitions with global temperature goals. This study explores the utility of an alternative usage of global warming potentials (GWP*) in combination with the Transient Climate Response to cumulative carbon Emissions (TCRE) to compare retrospective and projected climate impacts of global livestock emission pathways with other sectors (e.g. fossil fuel and land use change). To illustrate this, we estimated the amount and fraction of total warming attributable to direct CH4 livestock emissions from 1750 to 2019 using existing emissions datasets and projected their contributions to future warming under three historical and three future emission scenarios. These historical and projected estimates were transformed into cumulative CO2 equivalent (GWP100) and warming equivalent (GWP*) emissions that were multiplied by a TCRE coefficient to express induced warming as globally averaged surface temperature change. In general, temperature change estimates from this study are comparable to those obtained from other climate models. Sustained annual reductions in CH4 emissions of 0.32% by the global cattle sector would stabilize their future effect on global temperature while greater reductions would reverse historical past contributions to global warming by the sector in a similar fashion to increasing C sinks. The extent and speed with which CH4 mitigation interventions are introduced by the sector will determine the peak temperature achieved in the path to net-zero GHG.Limiting warming by the end of the century to 1.5°C compared to pre-Industrial times requires reaching and sustaining net zero global carbon dioxide (CO Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Using a simple GWP*-TCRE framework together with a set of scenarios/assumptions, the authors presented the estimates for the past and projected contribution of livestock/ruminant CH4 emissions to the global temperature. The GWP*-TCRE framework is a simplified way to better, even not accurate, quantify the global warming potential for short-lived greenhouse gases like CH4. The topic and the results are interesting and useful for informing climate mitigation pathways involving all major GHGs. The manuscript deserve a publication after some revision. My a few concerns are as follows.(1) Warming contribution from CH4 emissions from livestock sector is the main focus of this study. However, the definition of the CH4 emissions used is confusing across the text. “Livestock”, “cattle”, “cattle and buffalo” were all used casually in the current text, though the emission quantity can be quite different. Livestock emissions including ruminant and monogastric ones; cattle (or beef cattle and dairy cows) and buffaloes are ruminants, while sheep and goats are also ruminants that emitted a substantial part of the livestock CH4 emissions; CH4 emissions are also from pig and poultry, especially manure management. In addition, there are enteric CH4 emissions and those from manure management. It is absolutely necessary to clarify in every place of the manuscript what kinds of CH4 emissions  were accounted and used in the calculation of GWP* and their TCRE. It can also be clarified in a paragraph in the Materials and Methods, and used consistently across the manuscript.(2) A lot of scenarios were set in this study to illustrate the effects of CH4 emissions. Readers will probably get lost during the long description between L197 and L261. It is necessary to provide an overview figure or table to illustrate the setups and especially the rational and the purpose. People can come back to the figure or table when read the super long results section.(3) Most of the figures were consisted with a single line or with several subplot presenting similar information. It is recommended to combine some of the figures e.g., Fig. 1 and 3, or improve the quality and the presentation of the figures, e.g., subplots can be combined into one with three bars for each sensitivity period in Fig. 7. Most current figures were not publishable for their current form.(4) The text is unnecessarily long, which reads like a boring report without focus. I would strongly suggest a substantial shortening of the Results and Discussion section, showing only the core findings and information. For example, numbers that do not trigger further discussion or implications could be neglected. Paragraph between L395 and 405 seems redundant  A few important terms may be mis-used or mis-phrased. For example, it is not clear what does the “sustained” mean. Sustainable? Or constant? L389 “Tier” rather than “tear level”.Reviewer #2: Your research is interesting, but there are still some problems that confuse me.1.You mentioned that “These differences are hidden when describing the effects of climate mitigation using annual CO2-e emissions and when targets are based on aggregated annual emission rates” in the research, but I don’t understand “the effects” and the reasons for “the effects”.2.You mentioned that “Sustained annual reductions in CH4 emissions of 0.31% by the global cattle sector would stabilize their future effect on global temperature while greater reductions would reverse historical past contributions to global warming by the sector in a similar fashion to increasing C sinks”. But I don't know why emitting less CH4 emissions (greater reductions of 0.31%) rather than negative CH4 emissions would “reverse historical temperature impacts from previous decades”. Can you provide some level of details about the conclusion?3.The feasibility of the target  and the related measures need to be taken, such as a shift to sustainable diets.4.The blue line  does not appear in the figure 9.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  13 Jun 2023COMMENTS TO THE AUTHORREVIEWER #1: Using a simple GWP*-TCRE framework together with a set of scenarios/assumptions, the authors presented the estimates for the past and projected contribution of livestock/ruminant CH4 emissions to the global temperature. The GWP*-TCRE framework is a simplified way to better, even not accurate, quantify the global warming potential for short-lived greenhouse gases like CH4. The topic and the results are interesting and useful for informing climate mitigation pathways involving all major GHGs. The manuscript deserve a publication after some revision. Thanks for this positive feedback.My a few concerns are as follows:(1) Warming contribution from CH4 emissions from livestock sector is the main focus of this study. However, the definition of the CH4 emissions used is confusing across the text. “Livestock”, “cattle”, “cattle and buffalo” were all used casually in the current text, though the emission quantity can be quite different. Livestock emissions including ruminant and monogastric ones; cattle (or beef cattle and dairy cows) and buffaloes are ruminants, while sheep and goats are also ruminants that emitted a substantial part of the livestock CH4 emissions; CH4 emissions are also from pig and poultry, especially manure management. In addition, there are enteric CH4 emissions and those from manure management. It is absolutely necessary to clarify in every place of the manuscript what kinds of CH4 emissions  were accounted and used in the calculation of GWP* and their TCRE. It can also be clarified in a paragraph in the Materials and Methods, and used consistently across the manuscript.We agree that it may be confusing why we use different databases for the different analysis. We used livestock CH4 emissions data  for the long-term analysis (1750-2019) and global cattle  for shorter analysis (1961-2019). Our main focus would have been “global cattle” if we had had long-term (1750-2019) stats but the long-term stats (1750-2019) are only available for aggregated livestock. Our analysis included both enteric CH4 and manure management CH4. We have tried to clary these points in the text: We have this text in the material and methods section:L104-108. “the retrospective warming added by CH4 emissions from livestock in aggregate and from global cattle  globally between 1750-2019 and 1961-2019, respectively, and (iii) analyse, using these metrics, the implications on future global temperature change of adopting different CH4 mitigation strategies that lead to warming stabilisation by the global cattle industry.”L114-118 “It must be noted that we used total livestock  CH4 emissions for the long-term retrospective analysis (1750-2019) since data disaggregating livestock emissions by species (i.e. cattle) were not available for the years before 1961. For the short-term analysis (1961-2019) we used historical cattle (dairy and beef) and buffalo CH4 emissions .” We also explained that short term analysis data for CH4 referred to cattle (dairy and beef) and buffalo CH4 emissions together and used the term “Global Cattle” for simplicity purposes meaning both cattle and buffalo. L117-L118 “For the short-term analysis (1961-2019) we used historical cattle (dairy and beef) and buffalo CH4 emissions .” L124-L126 “Short-term (1961-2019) historical cattle and buffalo annual enteric and manure CH4 emissions were obtained from the FAOSTAT database [11]. For simplicity purposes, “global cattle” refers to cattle (dairy and beef) and buffalo in this study.” For both livestock CH4  and Global Cattle CH4 , we accounted for both of the main sources of CH4: enteric and that from manure management. This is mentioned in:L119-120. “Long-term (1750-2019) historical livestock annual enteric and manure CH4 emissions data were obtained from…”: L124-L125. “Short-term (1961-2019) historical cattle and buffalo annual enteric and manure CH4 emissions were obtained from the FAOSTAT database [11]. “ L164-L166. In figure 2 caption we also now refer to “Global Cattle” for uniformity of terms: New “Fig 2. Methane emissions from global cattle  (a)…”. (2) A lot of scenarios were set in this study to illustrate the effects of CH4 emissions. Readers will probably get lost during the long description between L197 and L261. It is necessary to provide an overview figure or table to illustrate the setups and especially the rational and the purpose. People can come back to the figure or table when read the super long results section.Thanks for this recommendation. We have elaborated a new table (now Table 1) to clarify the characteristics of each of the scenarios analyses.(3) Most of the figures were consisted with a single line or with several subplot presenting similar information. It is recommended to combine some of the figures e.g., Fig. 1 and 3, or improve the quality and the presentation of the figures, e.g., subplots can be combined into one with three bars for each sensitivity period in Fig. 7. Most current figures were not publishable for their current form.First, we rearranged all figures for best format so now they all have good resolution and 600 dpi. As recommended, we combined Fig 1 and 3 into new Fig 1 and Fig 7 has been combined into one figure . (4) The text is unnecessarily long, which reads like a boring report without focus. I would strongly suggest a substantial shortening of the Results and Discussion section, showing only the core findings and information. For example, numbers that do not trigger further discussion or implications could be neglected. Paragraph between L395 and 405 seems redundant  : “It is recognized that GWP100 shows…”.(5) A few important terms may be mis-used or mis-phrased. For example, it is not clear what does the “sustained” mean. Sustainable? Or constant? L389 “Tier” rather than “tear level”.“Sustained” was meant to be used for reductions in emissions that are constant over time. Now we have amended and clarify this in all text. For all scenarios we have explicitly indicated the % of increase/decrease in annual emissions. For instance, for case study 5:L269-L281.“Analyzing the projected global warming associated with 3 futures global CH4 cattle emission pathways leading to a stabilisation of global temperatures at year 2050 (1990-2050 period) varying in the time and intensity when absolute CH4 emission rates are reduced as follows: (i) fast: large reductions (greater than those required for sustained emission rate reductions: 0.5% decrease per year) in global CH4 cattle emissions are introduced during the first decades (2020-2040) followed by reductions at smaller rates (2040-2050) (0.1% decrease per year).(ii) sustained: reductions in global CH4 cattle emissions in a sustained way starting in 2020   (iii) delayed: increase in absolute emission rates for the first 2 decades (2020-2040) (0.25% increase per year), followed by large reductions (0.84% decrease per year) in global CH4 cattle emissions are introduced during the last decade (2040-2050). “ “Tear” was a typo. Now we amended to Tier: “depending on the level of methodological complexity used ”REVIEWER #2: Your research is interesting, but there are still some problems that confuse me.Thanks for this feedback, we will try to clarify the text to deal successfully with your confusion.1.You mentioned that “These differences are hidden when describing the effects of climate mitigation using annual CO2-e emissions and when targets are based on aggregated annual emission rates” in the research, but I don’t understand “the effects” and the reasons for “the effects”.L76-78. The sentence has been changed for clarification to. “These differences in temperature change are hidden when calculating and using annual CO2-e emissions to describe the impact of mitigation and targets based on aggregated annual emission rates”2.You mentioned that “Sustained annual reductions in CH4 emissions of 0.31% by the global cattle sector would stabilize their future effect on global temperature while greater reductions would reverse historical past contributions to global warming by the sector in a similar fashion to increasing C sinks”. But I don't know why emitting less CH4 emissions (greater reductions of 0.31%) rather than negative CH4 emissions would “reverse historical temperature impacts from previous decades”. Can you provide some level of details about the conclusion?First, we updated the % to 0.32% as 0.31% had been obtained with a previous formulation of the GWP* metrics and used in our first manuscript submissions by mistake . A reduction in annual emission rates will have a different effect on the level of GHG (concentration) in the atmosphere depending on whether a GHG is a short-lived gas (e.g. CH4) or a long-lived gas (e.g. CO2). If it is a long-lived gas, the CO2 level (or concentration) and hence the amount of associated warming to this CO2 concentration in the atmosphere will get to a stable condition  only if we reduce CO2 emissions (net) to 0. So we reach an equilibrium between emissions and sinks. This is what is referred as net-0 CO2. For short-lived gases (e.g. CH4), CH4 is mostly removed from the atmosphere by chemical reaction (reacts with OH- for example) much faster than CO2, persisting for about 12 years (instead of 100s or even 1000s of years as CO2). Hence, small reductions in annual CH4 rates will already lead to a stabilisation of the CH4 concentration in the atmosphere, leading to heat trapping capacities that could have been found decades before that time . This is briefly mentioned in the Introduction section: L 71-L76 “Whereas methane (CH4)’s impacts on temperature varies strongly with time after emissions occur due to its short atmospheric life, CO2’s impact on temperature remains relatively constant for hundreds of years after the emission occurs [7]. Also, CO2, once emitted, leads to increasing global temperature until net-zero CO2 emissions are reached. By contrast, reductions in CH4 emissions lead to reversing warming within a few decades.” This is very well explained in [7]  in relation with how GWP* metrics can help mimic these GHG different dynamics for short and long-lived GHG. The concept is well known as indicated in Cain et al 2019 and expressed in the text. L78-80. “In fact, GWP* was not developed to capture this behaviour, which was already well known, but to demonstrate that it could be quantified relatively simply while continuing to use the already familiar GWP concept” This is also well explained in recent articles like Cain et al et al. 2021 [6] Paragraph from this article (but not in our article): “CO2 emissions continue increasing global mean temperature until net-zero emissions are reached, with potential for lowering temperatures with net-negative emissions. By contrast, reducing CH4 emissions starts to reverse CH4-induced warming within a few decades. Coming from this Cain et al. 2021 [6] we added this paragraph in the M&M section:L196-L199. Emissions are used from years t  and from t-20 (20 years prior). This allows GWP* to represent the impact of new emissions , stable emissions  and reducing emissions (which reverses some past warming) [6].The concept is very much shown as results and discussion in new Fig 7, where whereas reduction in CO2 emissions by 1% would still lead to temperatures until 2100 . The same reduction (1%) for CH4 rates would lead to temperatures from 0.15 oC in 2020 to 0.12 oC in 2020, thus meaning a certain temperature impact reverse to the level of warming that CH4 had caused until the 1970s. 3.The feasibility of the target  and the related measures need to be taken, such as a shift to sustainable diets.Even though assessing the feasibility of GHG reductions per se is beyond the scope of this article, there is some reference to the type of strategies required and where these strategies may or may not apply: L502-l513. “Reisinger et al. [19] advocated for strategies to reduce CH4 emissions through more efficient production, technological advances and demand side changes, and importantly always carefully considering their interactions with land-based C sequestration. The challenge is that projected growth in livestock production systems and current CH4 emissions from ruminant livestock are greatest in low-and middle-income countries where the nutrient supply to the population could be severely compromised if animal-derived food demand is dramatically shortened. In addition, most production-side mitigation strategies that were designed for developed countries don’t apply to the production systems used in low- and middle-income countries [31] [32]. These socioeconomic and environmental aspects of mitigation were ignored in some studies [33] that explored the climate impact of implementing even more drastic scenarios, such as eliminating animal agriculture.”Additionally, we added a reference in L589 pointing at to some recent study [37] that has evaluated (towards the 1.5-2 oC temperatures objectives of the Paris Agreement) the scope of adopting mitigation measures to reduce CH4 emissions from livestock.4.The blue line  does not appear in the figure 9.There was a mistake in the final stage of the figure formatting. Now the figure  shows a blue line.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 26 Jun 2023Retrospective and projected warming-equivalent emissions from global livestock and cattle calculated with an alternative climate metric denoted GWP*PONE-D-23-08709R1Dear Dr. del Prado,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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Despite the evidence supporting the efficacy of the ketogenic diet (KD) on weight and type 2 diabetes (T2D) management, adherence to the KD is challenging. Additionally, no studies have reported changes in PA among individuals with overweight/obesity and T2D who have followed KD. We mapped out the methods used to assess adherence to the KD and level of physical activity (PA) in lifestyle interventions for weight and T2D management in individuals with overweight/obesity and T2D and compared levels of KD adherence and PA in these interventions.Articles published between January 2005 and March 2022 were searched in MEDLINE, CINAHL, and Scopus. Studies that included KD in lifestyle interventions for adults with T2D and overweight/obesity and measured ketone levels were included.The eleven included studies comprised eight randomized controlled trials. They mainly used self-reported measures to evaluate adherence to the KD and level of PA. We found studies reported higher carbohydrate intake and lower fat intake than the KD regimen. Great inconsistencies were found among studies on the measurement and reporting of ketone and PA levels.Our results demonstrated the need to develop intervention strategies to improve adherence to the KD, as well as the necessity of developing standardized diet and PA assessment tools to establish a stronger evidence base for including KD in lifestyle interventions for weight and T2D management among adults with overweight/obesity and T2D. Diabetes is a significant public health concern. The projected prevalence of type 2 diabetes (T2D) is expected to rise from 6.28% in 2017 to 10.9% by 2045 . For exaLifestyle interventions including diet, physical activity (PA), and weight management components have been recommended by American Diabetes Association (ADA) as the frontline treatment strategy for people living with T2D . Diet isThe ketogenic diet (KD) is a very low-carbohydrate diet that restricts daily carbohydrate intake to ~20–50 g/d or >10% of total daily caloric intake , with 70Recent meta-analyses of randomized controlled trials consistently demonstrated the efficacy of KD for individuals with overweight/obesity and T2D compared to usual diet or other recommended diets for diabetes management –16. For The efficacy of KD for adults with overweight/obesity and T2D can be limited by the declined adherence to the diet. Studies have found that participants who had higher levels of adherence to the KD achieved better weight loss outcomes compared to those with lower adherence , 17. HowThis scoping review was conducted following the modified Arksey & O’Malley’s 5-step framework and reported following the PRISMA-ScR guidelines , 23.This review was guided by the following questions: What are the assessment methods employed in previous lifestyle interventions to evaluate adherence to the KD and PA among persons living with T2D and overweight/obesity? What are the levels of adherence to the KD and PA among these individuals during the intervention? Scoping reviews aim to “map the literature on a particular topic” . Given tThe following procedures were performed to identify relevant studies: 1) An initial search in the MEDLINE database was performed using the terms “Ketogenic Diet” and “Type 2 Diabetes” to identify keywords and form a comprehensive search strategy (Appendix  An initi2) (6) included persons living with T2D. A KD was defined as having <10% of daily total caloric intake from carbohydrates, or a daily carbohydrate intake of 20–50 grams (g) [The inclusion criteria for this study were as follows: (1) interventional studies including randomized and non-randomized controlled trials, quasi-experimental designs, and before-and-after studies; (2) published between January 2005 and March 2022; (3) written in English; (4) examining the effect of self-prepared KD with free food choices as part of a lifestyle intervention; (5) included adults with overweight/obesity  . Lifestyrams (g) . StudiesThe search results were imported into the Endnote  to remove duplicate citations, and screened for title and abstracts through Rayyan , an online literature review software. Two reviewers independently assessed the titles and abstracts in Rayyan using the aforementioned criteria. All studies meeting the eligibility criteria were retrieved for full-text screening. Any discrepancies in the inclusion or exclusion of studies were resolved through discussion with a third reviewer.Data was extracted from eligible studies using a data extraction form Table  that wasThe findings of this scoping review were summarized and reported in two ways, in line with our two research questions. We provided a narrative summary of the assessment methods used to evaluate adherence to KD and levels of physical activity levels. We also provided a descriptive numerical summary of the levels of adherence to the KD and physical activity.The total number of articles retrieved through the initial electronic search were 7491. After removing duplicates and screening abstracts, 74 articles remained to be read in full. Based on selection criteria, a total of 14 articles representing eleven different trials were included in this review  or a clinical diagnosis. Inclusion criteria regarding T2D medication varied, with some studies excluding individuals using insulin or other T2D medication, while others did not specify T2D medication requirements. The total number of participants following a KD was 689, with the mean age ranging from 38.3 ± 26 [2, and the mean baseline A1c ranged from 6.3 ± 1.1 [Table 8.3 ± 26  to 64.8 8.3 ± 26  years. O8.3 ± 26  to 40.4 8.3 ± 26  kg/m2, a.3 ± 1.1  to 8.9 ±.3 ± 1.1 , 38, wit.3 ± 1.1  to 100% .3 ± 1.1 , 37–40, .3 ± 1.1 , 38–40, .3 ± 1.1 , 41, and.3 ± 1.1 , 35, 36..3 ± 1.1 , 34, 39..3 ± 1.1 , 35, 36,Table n = 9) [n = 10) [n = 1) [n = 5) [n = 2) [n = 2) [n = 1) [n = 9) and encouraging participants to better adhere to KD as a behavior change strategy (n = 5). However, studies varied on measurement target and timeframes, with some studies measuring urine ketone levels (n = 5), and others blood beta-hydroxybutyrate (BHB) levels (n = 6) at varying frequencies ranging from daily to yearly [As shown in Table n = 9) –37, 41 a[n = 10) –41 and 2 [n = 1) . Formats [n = 5) –31, 41,  [n = 2) , 36, 24- [n = 2) , 33, 37,o yearly , 40. Of o yearly , 34–36, o yearly , and 1 oo yearly .Two studies limited daily caloric intake in the KD group, while daily caloric intake goals varied in the control group across studies , 36  [n = 2) [n = 1). Two studies did not provide information on how adherence was defined based on ketone levels [Four studies used the presence of ketones to define KD adherence , 36, 40,n = 2) , 41, pla [n = 2) , 39, ande levels .Depending on the criteria used to determine adherence and how it was reported, the percentage of participants who adhered to the KD varied across studies, ranging from 9.5% at 16 weeks  to 98% aTwo studies measured changes in plasma ketone concentrations in participants following the KD and observed an increase in plasma ketone levels during the intervention compared to baseline –40. HallAll included studies provided lifestyle education on PA or encouraged PA during the intervention, with one study including supervised exercise sessions  Table 33. Five sA low level of adherence to the KD prevents valid assessment of the efficacy of the diet and may limit its effect on weight and T2D management . HoweverThe methods for assessing adherence to the KD varied greatly across the included studies. Self-reported methods such as 24-hour dietary recall and dietary records are commonly used for assessing diet adherence in patients with T2D and can be extended to diverse eating patterns . SubjectOn the other hand, ketone levels represent an objective biomarker for determining KD adherence. The KD stimulates the synthesis of ketone bodies in the liver as an energy source, resulting in elevated levels of circulating ketones in the blood and urine . Using kOur findings are consistent with previous research indicating that adherence to the self-prepared KD is low . DespiteInterestingly, our analysis revealed that the caloric intake was comparable between the KD group and the control group, as well as other lifestyle interventions with varying macronutrient compositions . MeanwhiThe greater improvement in weight loss and diabetes management observed in the KD group could also be attributed to the changes PA levels. Previous research has found that KD could improve physical function, reduce fatigue, and increase perceived energy levels in women with ovarian or endometrial cancer after 12 weeks . AdditioSurprisingly, none of the included studies specified the ketogenic ratio for the KD group. The ketogenic ratio is defined as the grams of fat to the grams of carbohydrate plus protein . A minimThere are several key strengths of our review. First, we examined KD adherence based on both objective and subjective methods, providing a thorough understanding of low adherence within the population. Second, by excluding feeding-controlled trials and diet-only trials, we focused on lifestyle interventions that integrate KD for managing obesity and T2D. Considering the increased adoption of KD as part of lifestyle interventions along with PA and/or behavior change strategies, our review offers valuable insights into the real-world level of adherence to KD. Third, we are the first study to describe and compare changes in PA levels following KD interventions in this population. Despite existing evidence emphasizing the importance of PA as a key component of lifestyle interventions and its independent effect on weight management and T2D outcomes, our findings shed light on the necessity of assessing changes in PA in future KD interventions for this population. Lastly, our use of a consistent definition of KD with clear macronutrient cutpoints enhanced the comparability between studies and ensured the reliability of our findings.This scoping review has several limitations that should be considered. First, most of the included studies were conducted in the United States, which could limit the generalization of the findings to other countries with different eating habits. Second, although all studies included “adults” as participants, the sample was predominantly middle-aged adults, which limited the generalization of intervention findings to adults of other age groups. Third, the included studies varied in study design, intervention delivery mode, and definition for KD. This inherent lack of homogeneity across the included studies made it difficult to perform a meaningful comparison across studies. Fourth, our assessment of the studies was limited to comparing dietary and physical activity levels at “baseline” and “final follow-up”, providing restricted insight into how these levels changed during the intervention. Finally, we did not undertake a quality appraisal of the included studies due to the nature of the scoping review.In conclusion, this scoping review aimed to identify the methods used to evaluate KD adherence and PA levels in adults with overweight/obesity and T2D in lifestyle interventions and to examine their levels of macronutrient intake to KD. We observed considerable variability in the methods for assessing diet and PA and found that adherence to the KD was low, particularly due to excessive carbohydrate intake and inadequate fat intake. To improve future interventions, we recommend using the ketogenic ratio to prescribe the KD and adopting machine learning techniques to generate personalized nutrition recommendations that meet the KD requirements. Additionally, standardized approaches and transparent reporting of diet and PA assessments are essential to facilitate their translation into healthcare practice and policy.Appendix 1


Recent technical innovations enable the identification of an image-derived input function (IDIF) from vascular structures, but are frequently still encumbered by the need for interrupted sequences or prolonged recordings that are seldom available outside of a research setting. In this study, we tested simplified methods for quantitation of FDG-Ki by linear graphic analysis relative to the descending aorta IDIF in oncology patients examined using a Biograph Vision 600 PET/CT with continuous bed motion (Aarhus) or using a recently installed Biograph Vision Quadra long-axial field-of-view (FOV) scanner (Bern).Until recently, quantitation of the net influx of 2-[(52–67 min)), and the terminal image-derived arterial FDG concentration (Ca(52–67 min)). These relationships enabled estimation of the missing AUC from late recordings of the IDIF, from which we then calculated FDG-Ki in brain by two-point linear graphic analysis using a population mean ordinate intercept and the single late frame. Furthermore, certain aspects of the IDIF data from Aarhus showed a marked age-dependence, which was not hitherto reported for the case of FDG pharmacokinetics.Correlation analysis of the coefficients of a tri-exponential decomposition of the IDIFs measured during 67 min revealed strong relationships among the total area under the curve (AUC), the terminal normalized arterial integral (thetaKi in brain using a single averaged frame from the interval 52–67 min post-injection, with minimal error relative to calculation from the complete dynamic sequences.The observed interrelationships between pharmacokinetic parameters in the IDIF measured during the PET recording support quantitation of FDG- Serial arterial sampling was a feature of the first phase of PET studies with the glucose analog 2-8Ffluorodse (FDG) . While tse (FDG) , or by sse (FDG) . Alternase (FDG) . In yet se (FDG) .Ki; ml g−1 min−1) quantitation of FDG uptake in brain and other organs, which might call into question the need for absolute quantitation of FDG-PET in most applications. However, we note that global normalization procedures in brain studies can lead to misinterpretation of findings in neurodegenerative diseases [Ki can be more relevant for the assessment of brain metabolism in health and disease conditions. Image-derived arterial input functions (IDIF) are well-established for quantitation of Ki/CMRglc in experimental animals, based on observations of the blood pool in the heart ventricle , wi, wiKi; me  combined with a population-based continuous IDIF (0–70 min) obtained through bed motion in the Biograph Vision 600 PET/CT  scanner [Ki using an attenuated scanning protocol [In human PET recordings with limited field of view, repeated bed motion to record in alternation from the heart and head can enable noninvasive quantitation of FDG-Ki . For exaKi . In Aarh scanner . As an a scanner ) is of t scanner . In Bern scanner . We and protocol –24.52–67 min), with a single PET frame obtained by averaging the frames recorded in the interval 52–67 min post FDG injection. We constrained our two point linear graphical analyses  by the extrapolated VD at time zero, as described below.These diverse approaches address the general need for simplified and reliable methods for quantitation of FDG PET, ideally without any need for interrupted or continuous PET recordings. In the present study, we undertook to develop abbreviated methods for the absolute quantitation of cerebral FDG uptake, relative to IDIFs obtained from the descending aorta as imaged by multiple passes of bed motion , or with single bed whole body imaging . We first undertook linear graphic analysis of FDG uptake in whole gray matter (GM) and whole white matter (WM) using the entire 67 min dynamic recordings in groups of oncology patients examined in Aarhus or Bern. Due to imperfect harmonization of the imaging protocols at the two sites, we did not pool the data in our analysis, but rather compared the platform stability of our approach. Next, we undertook a pharmacokinetic analysis of the individual IDIFs to identify features that could be used to capture the plasma integral  knowing either the late phase of the IDIF (35–67 min post injection) or only the terminal blood FDG concentration (Ca(n = 11), lymphoma (n = 14), lung cancer (n = 7), and various other oncological indications (n = 30) [The Aarhus study was a retrospective evaluation of prospectively collected clinical FDG PET/CT data, approved by the local ethics committee of the Central Denmark Region (1-10-72-188-19). Inclusion into the dynamic whole-body (D-WB) protocol was solely based on whether patients were deemed fit to lie still for nearly 70 min while in the PET/CT scanner; in practice, this excluded patients with cognitive impairments. The study population of 52  patients had been referred for FDG-PET due to infection/inflammation ((n = 30) .3, 2-mm Gaussian filter and relative scatter correction). SUV images were normalized to body weight and injected dose.Aarhus participants were scanned using a fully automated multiparametric PET acquisition protocol  on a Siemens Biograph Vision 600 PET/CT scanner  with 26.2 cm axial field of view. First, we performed a low-dose whole body CT , and then initiated PET recording at the start of a standardized injection of FDG (4 MBq/kg) using an Intego PET Infusion System . The 70-min PET protocol consisted of an initial 6-min dynamic scan of the chest region, including the aorta, heart and liver, followed by a 64-min dynamic WB-PET, which consisted of 16 continuous bed motion passes: 7 × 2-min WB passes followed by 9 × 5-min WB passes. PET images were reconstructed using software version VG76A and list-mode data extending to approximately 67 min post-injection . Although the tool “MRI Probability/Inhomogeneity” was developed for MRI images, it performs reasonably well for FDG PET data in conjunction with carefully chosen cut-off values. Individual VOIs were generated using a cut-off of > 99.5% for GM probability and > 90% for WM, based on pilot evaluation of the first few subjects, with quality control according to visual inspection of the overlay maps.We used the SUV head image for GM and WM matter VOI definition in Aarhus. Each subject's skull and brain was manually isolated using a box-shaped VOI, with rigid matching of the isolated head to an FDG PET template included within PMOD. Tissue probability maps of GM, WM, and cerebrospinal fluid of each subject were then generated using PMOD's adaption of the unified segmentation method from the SPM8 or SPM12 toolbox (n = 9), breast cancer (n = 6), lung cancer (n = 4) and various other tumor types (n = 5). Details of the analysis of data from these patients are reported elsewhere [3, with smoothing using a post-reconstruction Gaussian filter (FWHM 2 mm). Images were reconstructed with the high sensitivity mode, which employs a maximum ring difference of 85. The PET data were corrected for randoms, scatter, attenuation, and radioactive decay, following the methods used as clinical standard. Standard uptake value (SUV) images were generated by normalizing the late frame images to body weight and injected dose. Attenuation correction of PET emission data was by means of co-registered low-dose CT scans .The Bern data consisted of FDG-PET scans from 24 patients  under investigation for lymphoma , where C is a constant and λ3 is the fractional rate constant for the renal clearance of plasma FDG. Next, the extrapolate magnitude of Ce−λ3(t) was subtracted from the total measured IDIF concentration series, and the intermediate phase calculated as Be−λ2(t) . Thus, the entire IDIF was described using a function similar to the exponential form used previously by Feng et al. [In Aarhus and Bern we noted the time to first sign of blood radioactivity and the time to peak radioactivity in each case. We then plotted the natural logarithm of the blood radioactivity as a function of circulation time. Linear regression of the late phase semi-log plots gave the plasma radioactivity concentration as Ce(t) Fig. A, followg et al. , namely,(52–67 min)), the measured area under the curve (AUC) or plasma integral to the end of the PET recording, and the corresponding AUCs estimated from one, two, and three exponential terms. The measured area under the curve (AUC) to 67 min (AUC(0–67 min)) was calculated for each case, along with the corresponding AUCs calculated for the mono-, bi, and tri-exponential functions, which were then calculated as percentage recovery of the measured AUC, with evaluation of the exponential components beginning at the time (circa 0.5 min) of the first appearance of radioactivity in the measured IDIF. The final arterial FDG concentration was calculated as the mean of three measurements during the last three frames (52–67 min), and the corresponding normalized arterial input  was the ratio of the AUC(0–67 min)) to the measured Ca(52–67 min).For each individual, we calculated the mean plasma FDG concentration measured during the final 15 min of the PET recording (Can = 52) and Bern (n = 24) data. An initial exploration of the correlations revealed a number of relationships useful for modeling of the IDIF and calculating the complete AUC from various late phase recordings, including a single frame PET recording corresponding to 52–67 min post injection. The magnitude of the standard net blood–brain clearance  in WM and GM was calculated by linear graphic analysis of PET frames in the interval 12–67 min post injection relative to the complete measured IDIFs, using the mean ordinate intercept for GM  as one point in the voxelwise two-point Patlak analysis of the magnitude of FDG-Ki, separately for whole GM and whole WM.The relationships between the various plasma kinetic parameters and age of the individual were calculated as a correlation matrix, separately for Aarhus (−1), the fractional rate constant for the elimination of FDG from arterial blood. We then obtained a population mean correction factor for the AUC of the Ce−λ3(t) term for each individual to estimate the AUC of their complete IDIF, and thereby calculated for each individual the terminal theta(52–67 min) relative to the terminal blood concentration (Ca(52–67 min). We then estimated Ki as a two point Patlak-Gjedde plot defined by the ordinate intercept in the IDIF evaluation for GM  and the tracer distribution volume calculated from the mean radioactivity concentrations measured in GM, WM, and blood during the final three frames of the PET recording. Method 2: From the site-specific empirical relationships between AUC(0–67) as a function of Ca(52–67 min), we similarly calculated Ki from Ca(55–67 min) as a two point Patlak plot. Method 3: We similarly used the site-specific empirical relationships between theta(52–67 min) and λ3 measured during the interval 35–67 min post injection. Method 4: We similarly used the site-specific empirical relationships between theta(52–67 min) and subject age in the two populations.We performed four kinds of single-point Patlak analysis, using pharmacokinetic relationships that emerged from the IDIF analyses, as described below. In Method 1, we defined the limiting slope of the semi-log transformation of the FDG blood concentration during the interval 35–67 min, and calculated λ3 (minThere were 52 cases from Aarhus of mean (SD) age 56 (14) years. The mean (SD) time to first plasma radioactivity was 0.24 (0.05) min and the mean (SD) time to peak radioactivity was 0.56 (0.09) min. There were 24 cases from Bern of mean (SD) age 60 (16) years. The mean (SD) time to first plasma radioactivity was 0.51 (0.11) min and the mean (SD) time to peak radioactivity was 0.80 (0.11) min.(0–67 min), as well as with the corresponding late phase plasma FDG concentration (Ca(52–67 min). The magnitude of the late phase elimination rate constant λ3 correlated negatively with the magnitude of Ca(52–67 min) in Aarhus data, positively with theta(52–67 min) in both data sets, and inversely with age in the Aarhus data set.A semi-logarithmic plot of the FDG-IDIF as a function of time shows the intermediate (λ2) and slow (λ3) phases of the elimination of FDG in a representative individual, with omission of the decomposition of the fast λ1) phase for the sake of clarity  were identical in the two data sets (Table The population mean pharmacokinetic results for Aarhus and Bern are presented in Table 2−λ3(t)) showed excellent agreement between Aarhus (85%) and Bern (84%) (Table (52–67 min). At both research sites, Method 1 gave good agreement with the corresponding complete IDIF estimates of FDG-Ki for GM and WM.The percentages of the total AUC recovered from the late phase component of the IDIF (Ce%) Table ; these r(0–67) as a function of Ca(52–67 min) for the Aarhus data set  is estimated from the individual Ca(52–67 min). Method 2 estimates of FDG-Ki using the site-specific empirical relationships were in good agreement with IDIF results  (min) versus individual λ3 (min−1) for the Aarhus data set  is estimated from the individual λ3, which was calculated from the semi-logarithmic plot of the plasma FDG concentration as a function of time during the interval 30–67 min post injection. Method 3 estimates of FDG-Ki using the site-specific empirical relationship were in good agreement with IDIF results  (min) versus age from the Aarhus data set  is estimated from the individual subject’s age, thus affording estimation of the FDG-Ki from a single PET frame measured during 52–67 min post injection. Method 4 estimates of FDG-Ki from both sites were in good agreement with IDIF results  and CMRglc. Recent developments in IDIF-based methods have given new impetus to quantitative FDG-PET, which may avoid certain interpretative pitfalls related to global normalization or other semi-quantitative approaches. Such approaches include interrupted PET recordings consisting of early and late phase sequences [Ki image [Ki evaluation in tumors from the present group of oncology patients examined with the Biograph Vision Quadra [Ki from relatively brief dynamic or static late-phase recordings, which are based on simple pharmacokinetic principles applied to an IDIF from the descending aorta, as acquired through bed motion (Aarhus), or from simultaneous recording of vascular and cerebral structures (Bern). While complete methodological harmonization between sites was impossible due to the differing instrumentation, the scanners at Bern and Aarhus have similar spatial resolution and TOF resolution. However, the ten-fold higher sensitivity of the LAFOV scanner in Bern [Ki.Our motivation in conducting these studies derives from the logistic difficulty and expense of conducting dynamic PET recordings for the quantitation of FDG net influx  as well as net tracer influx (Ki) [Ki [1,k2,k3) for FDG by formulating the TAC in a target region as a function of a “source region,” thus making superfluous the measurement of blood concentration [There are a number of advanced approaches for noninvasive (blood-free) quantitation of FDG kinetics, or quantitation from abbreviated scanning protocols. IDIFs from the internal carotid as defined in MRI gave good agreement with blood measurements during 60 min dynamic PET recordings . Recent lux (Ki) , 32. In (Ki) [Ki . We and (Ki) [Ki , 24. Altntration ; that ap−λ1(t)), partitioning of plasma FDG into bodily tissues (Be−λ2(t)), and renal elimination of FDG from the body (Ce−λ3(t)). In both data sets, the correlations among the various parameters of the tri-exponential function  extrapolated to the time of injection accounted for 85% (Aarhus) or 84% (Bern) of the total AUC from time of injection to the end of the 67 min recording. In Method 1, using these recoveries as correction factors, ln-transformation of a dynamic data recorded during the interval 35–67 min post injection gives a robust estimation of the total AUC (Table ), with relatively little error due to uncertainty in the contributions of the early pharmacokinetic phases, namely Ae−λ1(t) + Be−λ2(t). Thus, Method 1 gave good estimates of GM Ki in both data sets, with a tendency for 10% underestimation in the evaluation of WM Ki. As such, the initial 35 min of the recording seems hardly necessary for quantitation of net brain uptake in whole GM, so long as the late phase data are sufficient for a good estimation of Ce−λ3(t) in the blood compartment. We note that this application of a two-point linear graphic analysis is constrained by knowledge of the ordinate intercept in the GM linear graphic analysis (0.55 ml g−1), which corresponds well to the ratio K1/k2 (VD) from compartmental analysis of human FDG-PET data [Returning to the importance of the late phase governed by λ3 in determining the total AUC, we see that CeUC Table , with re(0–67 min) and the mean arterial concentration measured during the final three frames of the PET recording (Ca(52–67 min))  enables the estimation of the complete AUC(0–67 min). Therefore, Method 2 gives FDG-Ki estimates that are in good agreement with the corresponding IDIF estimates (Table (52–67 min) and λ3 gave equally good FDG-Ki results relative to the full dynamic IDIF method (Table (52–67 min) and subject age also performed similarly well  and λ3 in the Bern data, especially considering the good accord between sites for other relationships presented in Table In both data sets, we found strong relationships between AUCn)) Fig. D. Put anes Table . Similarding (Ca5–67 min) ding (Ca5–67 min) In our earlier analysis of the Bern data, a dynamic recording covering the first 10–15 min post-injection, followed by a brief scan an hour later, gave excellent quantitation of FDG trapping in tumor lesions, again relative to an IDIF captured by the long axial FOV Biograph Vision Quadra tomograph ; we are Ki) in brain, relative to linear graphic analysis of the entire dynamic sequence. This reflects certain canonical relationships between the mean FDG concentrations measured in the descending aorta at one hour after administration and the total AUC of the IDIF to the end of the recording; in effect, the various simplified methods presented herein are functionally equivalent. Notably, the generalizability of present methods remains uncertain for patients with diabetes [We show that single static frames recorded at approximately one hour after FDG administration suffice for accurate quantitation of the net tracer influx (diabetes , renal f


Anxiety is a prevalent condition with a substantial associated burden of morbidity. Previous literature investigating effects of anxiety on mortality rates has found conflicting results. This is in part due to inadequate consideration of comorbid depression as a confounder and analysing sub-types of anxiety together. The objective of this study was to compare mortality risks in people diagnosed with anxiety.We undertook a retrospective cohort study using the ‘The Health Improvement Network’ database (a UK primary care dataset) between 1st January 2005 to 1st January 2018. 345 903 patients with anxiety (exposed group) were matched to 691 449 unexposed patients. Cox regression analyses were used to adjusted hazard ratios (HRs) for mortality risk.During the study period 18 962 patients (5·5%) died in the exposed group compared to 32 288 (4·7%) in the unexposed group. This translated into a crude HR for all of 1·14 (95% CI 1·12 − 1·16), which remained significant after adjustment for key co-variates (including depression) giving a final HR of 1·05 (95% CI 1·03 − 1·07). When broken down by sub-type of anxiety  had phobias, 82·7%  has ‘other’ types, and 7·0%  had stress related anxiety) there were markedly different effect sizes. The adjusted model for the stress-related anxiety sub-type demonstrated a HR of 0·88 (95% CI 0·80 − 0·97). Conversely, the HR was increased in ‘other’ sub-types to 1·07 (95% CI 1·05 − 1·09) and non-significant in phobia types of anxiety.A complex relationship is found between anxiety and mortality. The presence of anxiety slightly increased the risk of death, but this risk varies depending on the type of anxiety diagnosed.The online version contains supplementary material available at 10.1186/s12888-023-04877-8. Anxiety encompasses a heterogeneous group of mental illnesses, often managed in primary care. A 2013 UK survey found the weekly prevalence of anxiety was 6.6% and a 2020 study using UK Biobank data found the lifetime prevalence of anxiety was 7% .The mortality and morbidity effects of anxiety are profound and it has been described that the population attributable risk for excess mortality due to anxiety is 4.3%, higher than even that of psychosis 0.63%) . Whereas3% . WherA systematic review found the pooled relative risk for mortality attributable to anxiety to be 1·43 (95% CI 1·24 − 1·64), however, this was limited as it lacked control for important confounders such as comorbid mental ill health . One UK To date, to the authors knowledge there has not been a UK-based retrospective cohort study based in primary care settings investigating the mortality risks associated with an anxiety diagnosis, whilst considering the effect of both comorbid depression and sub-types of anxiety. This is important to clarify as the largest burden of anxiety occurs in a primary care setting, and the risks associated with the disease are important to guide physical health support that may be required.A retrospective open cohort study, utilising quantitative data from ‘The Health Improvement Network’ (THIN) database, was conducted comparing patients with coded anxiety diagnoses (exposed group) to unexposed patients. Patients who were over the age of 18 years were eligible for cohort entry, and the study period was set between 1st January 2005 to 1st January 2018.THIN database contains anonymised electronic records taken from over 700 general practices using Vision software . PatientTo mitigate the risk of under-recording of the outcome of interest and ensure data quality, GPs were only included from the later of the following dates: 1 year after installation of Vision software systems or acceptable mortality recording date . Read codes that may indicate anxiety diagnoses were assessed for their suitability and sub-typed according to the ICD-10  criteriaRead code lists can be found from Additional file Exposed patients were matched by age (± one year) and gender to two unexposed participants from the same general practice (GP) who did not have a recorded diagnosis of anxiety. This selection formed the primary cohort.The index date (when follow-up measurement began) was defined as a year after the anxiety disorder was diagnosed in the exposed group. This was done to exclude end-of-life anxiety diagnoses where there may be issues with reverse causality i.e. known expected deaths causing anxiety in the patient. For the unexposed group the corresponding exposure patient’s index date was assigned to avoid immortality time bias . The patThe following co-variates at baseline were considering in our modelling due to their independent association with mortality: age, gender, ethnicity, alcohol use, smoking status, Charlson Comorbidity Index (CCI) , Townsenp < 0.05 (two-sided).STATA version SE 15 (Statacorp 2017) was used for the analysis, with statistical significance set at Cox proportional hazard models (assumptions were tested and no violations noted) were used to calculate crude HRs and adjusted HRs. These models were constructed in a sequence of pre-defined steps, which had been selected based on their known importance from previous work and as such deemed both clinically important and explanatory.The steps in adjustment were: (1) matched demographic factors  (2) other demographic factors  behavioural factors  (4) comorbid mental illness , and (5) physical comorbidity (as measured by the CCI). Sensitivity analyses were performed for each sub-type of anxiety with their respective controls in the same steps.Individual covariates effects were considered in the reported model, which did not include ethnicity as over half of the data were missing for this variable. The pairwise analyses for the anxiety sub-types were limited to those whose numbers accounted for greater than 1% of the database.A total of 345 903 patients had a coded incident anxiety diagnoses during the study period who were matched by age and gender to 691 449 unexposed patients. The total study time for all the participants was 5 528 153 person years. A greater proportion of the exposed group were more deprived, current smokers, heavy drinker and had a high proportion of comorbid mental and physical illness. More details can be seen in Table There were 32 288 deaths in the exposed group compared to 18 962 in the exposed group. Taking into consideration person years follow, this related to a crude mortality rate in the unexposed group was 8·9 per 1 000 person years compared to 10·1 per 1 000 person years in the exposed group. This translated into a crude HR of 1·14 (95% CI 1·12 − 1·16) depicted in Fig. Dependent on the adjustment model used, the analysis showed an excess mortality of between 5 and 20%. The largest drop in risk occurred after depression and SMI were included in the model. See Table The raised mortality associated with anxiety may not be as substantial as that associated with some other mental ill health conditions, due to the prevalence of anxiety, this still translates into a substantial preventable public health burden. Further research should aim to examine bio-psycho-social pathways responsible for the inflated mortality rates to identify any suitable targets for interventions which may include more stringent monitoring of physical health and lifestyle choices made by patients diagnosed with anxiety.When examining subtypes of anxiety, it appeared phobic anxieties showed no significant association with mortality, whereas stress-related conditions appeared to reduce mortality risk only after full adjustment with a HR of 0·89 (95% CI 0·80 − 0·98). ‘Other’ F41 diagnoses significantly raised the adjusted HR to 1·07 (95% CI 1·05 − 1·10) and remained significant throughout all of the models. See Table The clinically important model model 5), excluding ethnicity, and its effects on all the covariates for each of the three anxiety sub-types is shown in Table , excludiTo the authors knowledge this was the first study investigating the mortality risks associated with an anxiety diagnosis, whilst considering the effect of both comorbid depression and sub-types in a UK primary care database. Our results showed a crude 14% increase in the risk of mortality when diagnosed with anxiety, dropping to a smaller but still significant increase of 5% after adjustment. For the sub-types, only the ‘other’ types of anxiety increased mortality risks, with a 7% increase following adjustment. There was evidence for stress-related conditions leading to a 12% reduction in risk at this level of adjustment. The substantial drop in risk when accounting for important covariates demonstrates that co-associated conditions play an important role in the relationship between anxiety and mortality.The findings of this study should be considered in light of its limitations. Due to the retrospective nature of the study, the study is limited by the accuracy of contemporaneous recording. Regarding the sub-types, GAD and panic disorder (which would account for most of the “other” diagnoses) are thought to account for around 50% of all anxiety diagnoses, with phobias accounting for around 10%, and OCD accounting for around 1% . Our datPrior to this study there have been several conflicting results in this area, with studies derived from secondary patients having had higher HRs than found (may be due to greater severity of symptomology or greater burden of SMI) or other cohorts showing insignificant association although may not have considered sub-types of anxiety –14.Considering the effect of individual covariates in our modelling, the variables with the largest effects when considered on their own were increased severity of physical comorbidities, smoking status, severe mental illness, heavy alcohol use and being socioeconomically deprived. Comorbid depression and age also increased the mortality risks, though to a lesser extent. One study did find that HR increased for males only, which are reflected in our findings . HoweverThe Danish registry study examining subtypes of anxiety found that phobias increase the mortality risk by 30–50%, “other” types by 30–60%, and stress-related conditions by 50–70% [In conclusion, anxiety disorders as a group have been found to slightly increase the risk of death compared to those without anxiety after adjustment for clinically relevant covariates. Some sub-types even appear to have protective effects, which is reassuring. However, even though the raised mortality associated with anxiety may not be as substantial as that associated with some other mental ill health conditions, due to the prevalence of anxiety, this still translates into a substantial preventable public health burden. Further research should aim to examine bio-psycho-social pathways responsible for the inflated mortality rates to identify any suitable targets for interventions which may include more stringent monitoring of physical health and lifestyle choices made by patients diagnosed with anxiety.Additional file 1. Read codes.