Modelled dietary scenarios often fail to reflect true dietary practice and do not account for variation in the environmental burden of food due to sourcing and production methods. Here we link dietary data from a sample of 55,504 vegans, vegetarians, fish-eaters and meat-eaters with food-level data on greenhouse gas emissions, land use, water use, eutrophication risk and potential biodiversity loss from a review of 570 life-cycle assessments covering more than 38,000 farms in 119 countries. Our results include the variation in food production and sourcing that is observed in the review of life-cycle assessments. All environmental indicators showed a positive association with amounts of animal-based food consumed. Dietary impacts of vegans were 25.1%  of high meat-eaters  for greenhouse gas emissions, 25.1% (7.1–44.5%) for land use, 46.4% (21.0–81.0%) for water use, 27.0% (19.4–40.4%) for eutrophication and 34.3% (12.0–65.3%) for biodiversity. At least 30% differences were found between low and high meat-eaters for most indicators. Despite substantial variation due to where and how food is produced, the relationship between environmental impact and animal-based food consumption is clear and should prompt the reduction of the latter. Modelled estimates of the environmental impact of dietary choices often fail to reflect true dietary practice. This study links a dietary dataset from 55,000 UK consumers with food-level data on GHG emissions, land use, water use, eutrophication and biodiversity to compare the environmental burden of different levels of meat consumption. The food system is also responsible for 70% of the world’s freshwater use and 78% of freshwater pollution3. About three quarters of the World’s ice-free land area has been affected by human use, primarily agriculture4, and land-use change (primarily deforestation for agriculture) is a major source of biodiversity loss6.The substantial impact of the global food system on the environment is well established. It is estimated that the food system was responsible for 18 Gt of carbon dioxide equivalent  will not be enough without major dietary change8. The environmental impact of animal-based foods is generally higher than for plant-based foods because of both direct processes related to livestock management  production by ruminants) and indirect processes through the inefficiency of using crops for animal feed rather than directly for human consumption10. For this reason, proposed diets for global sustainable food production require most high-income countries to radically reduce consumption of animal-based foods and converge on levels that are higher than currently consumed in many low-income countries8.To feed a growing global population while remaining within proposed safe environmental boundaries for GHG emissions, land use, water use, water pollution and biodiversity loss, we will need changes in diets12 and that diets with reduced animal-based foods tend to be healthier and have lower environmental impact13. However, modelled dietary scenarios may not reflect true dietary practice, and modelled environmental and health outcomes can be strongly affected by assumptions made by the modellers. Also, previous modelled dietary scenarios have not reflected the considerable variation in environmental indicators due to both region of food production and agricultural production methods3 and therefore will have underestimated the uncertainty associated with their findings. While we continue to use average values of environmental impact for food categories, we cannot know whether the observed differences in environmental impact between dietary groups still exist after accounting for variation in food production systems. We therefore need to link data from dietary surveys of real-life dietary patterns with large datasets of environmental indicators to ascertain whether the relationship between animal-based food consumption and environmental outcomes shown in modelling studies is robust.Systematic reviews of modelled dietary scenarios have shown that vegan and vegetarian diets have substantially lower GHG emissions, land use and water use requirements than meat-containing diets14. These estimates only captured one aspect of the environmental impact of food systems, and the data for GHG emissions were derived from a single source with no information about variation within individual food groups due to sourcing or production15. Also, GHG emissions data were not presented as disaggregated gases, losing climatically important information16. In this paper, we link a validated food frequency questionnaire (FFQ) to estimates from a review of 570 life-cycle assessments3 (LCAs) to estimate the GHG emissions  and carbon dioxide (CO2), in addition to combined CO2e emissions), water use, land use, water pollution and biodiversity impact associated with observed diets of vegans, vegetarians, fish-eaters and meat-eaters in the UK  of the high meat-eaters group. There were also substantial differences in dietary CO2 emissions between groups of meat-eaters. Dietary CO2 emissions of low meat-eaters were 57.2% (37.8–74.9%) of the high meat-eaters. CH4 is a GHG that, in terms of agricultural emissions, is predominantly associated with production of ruminants—it is therefore unsurprising to see wide disparities in CH4 emissions associated with the different diet groups. CH4 emissions from high meat-eaters were 15.3 (10.3–27.1) times higher than from vegan diets. N2O emissions are predominantly associated with fertilizer use, and therefore gradients in N2O emissions by diet group are mostly a result of the inefficiencies associated with raising crops for animal feed. This gradient is shallower than for CH4 but still substantial, with N2O emissions for high meat-eaters 3.6 (2.4–6.0) times higher than for vegans.For GHG emissions, there was a positive association with amount of animal-based food consumption Table . Dietary2O and especially CH4 compared to 100-year Global Warming Potential (GWP100). The ranking of different dietary emission footprints remained the same, but the relative advantage of vegans over diets incorporating animal products decline slightly, with the high meat-eaters responsible for 3.6 (2.4–6.1) times greater GTP100 emissions than vegans, and low meat-eaters 1.8 (1.4–2.6) times greater. For the 20-year Global Warming Potential (GWP20), all footprints were greater, and the relative difference between vegan and other footprints was even more pronounced: high meat-eater diets were 5.1 (3.5–8.4) times greater than vegans.Table Table −1 are shown in Supplementary Section 4) and 38% (water use) of the footprint of high meat-eaters. For low meat-eaters, the impact is between 37% (land use) and 67% (water use) of high meat-eaters.The results of our sensitivity analyses where we did not standardize diets to 2,000 kcal d−1) diets for all indicators, and 95% uncertainty intervals were below 50% for all outcomes except water use and biodiversity. There are also large differences in the environmental impact of diets for groups with lower (but still some) meat consumption. For GHG emissions, eutrophication and land use, the impact for low meat-eaters was at least 30% lower than for high meat-eaters. Large food-level variation in the environmental indicators due to region of origin and method of food production does not obscure differences between diet groups.Diet-related environmental impacts vary substantially by diet groups within this cohort of UK adults which includes a large sample of vegans, vegetarians and fish-eaters. For measures of GHG emissions, land use, water use, eutrophication and biodiversity, the level of impact is strongly associated with the amount of animal-based products that are consumed. Point estimates for vegan diets were associated with less than half of the impact of high-meat-eater  or have been proposed in the UK Childhood Obesity Plan22. The UK Government’s dietary policy is underpinned by its food-based dietary guidelines (FBDGs), known as the Eatwell Guide23. A recent systematic review of national FBDGs found that the large majority are not compatible with the proposed downscaling of ‘planetary boundaries’ for food production—if the UK population consumed the diet recommended by the Eatwell Guide, it would not stay within boundaries for GHG emissions, water use, land use and eutrophication suggested by the paper24. Incorporating environmental sustainability into FBDGs  may be the first step towards implementation of population-level policies that have been shown to support shifts away from animal-based foods26.There are many population-level interventions that could be implemented to support transitions towards lower meat diets. The UK Health Alliance on Climate Change recommends that sustainable diets should be supported by mandatory environmental labelling on foods, regulation of promotions and taxation of high-carbon foods14, land use, water use, water pollution and biodiversity. The paper uses only empirical measures of diet, thereby verifying previous modelling work that has suggested multiple environmental benefits of low-meat diets27. By using self-identification as vegan, vegetarian and fish-eater, we ensure that our methods include all dietary patterns within those categories including those that breach some of the definitions of the groups—this means our estimates are likely to reflect real dietary practices as opposed to comparison of idealized diet groups.This paper uses one of the largest datasets available on the diets of vegans and vegetarians to compare the environmental impact of different diet groups over ten environmental measures. The analyses contribute to the literature that shows the benefit of low-meat diets for reduction of GHG emissions3—henceforth, ‘the Poore and Nemecek database’. Doing this shows that although uncertainty for any single food group is large, when this uncertainty is combined over multiple food groups to produce aggregated dietary estimates, we can still observe clear differences between diet groups. Our primary results are based on a Monte Carlo analysis where 1,000 estimates of each food’s environmental impact are produced based on varying measures due to food sourcing and production methods. In our secondary results , for other components we had to assume emissions were entirely CO2. We reiterate calls for studies on GHG emissions, particularly those relating to agriculture and food, to provide disaggregated emissions to enable the most reliable analyses31.An additional contribution of our research was providing disaggregated GHG emissions and exploring multiple CO3. However, the data on dietary consumption were collected in the 1990s, and dietary preferences are likely to have changed since then. This is mitigated somewhat by the fact that the FFQ was linked to the environmental footprint of food and drink on sale in the UK in 2019, but this will not account for category-level changes in consumption since the 1990s. More recent datasets of dietary consumption in the UK are available, including datasets based on a representative sample of the UK population . However, the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford dataset  is the most recent data available in the UK that has a large sample of vegan and vegetarian diets, necessary for these analyses. Data collection is underway on the Feeding the Future study34, which aims to update estimates of food intake in vegans and vegetarians (and meat-eaters) in the UK. Updating our analyses using more timely data will provide evidence of whether trends in new meat and dairy alternatives have affected the environmental impact of plant-based diets.Our analyses are subject to the following further limitations. The data on the environmental footprint of foods are taken from a snapshot of food and drink on sale in the UK in 2019 linked to the most comprehensive publicly available dataset of LCAs of the environmental impact of foods currently available35. Our sensitivity analysis  differing levels of consumption of organic produce could not be assessed. While we included multiple environmental indicators in our analyses, there are other ethical aspects that vary by region and method of agricultural production that are not included here . Finally, as the Poore and Nemecek database is not comprehensive and our uncertainty analyses are not weighted towards more common food production practices, our uncertainty intervals do not fully incorporate all the uncertainty associated with these comparisons between diet groups. As new agricultural practices aimed at reducing the environmental impact of the food system  becomes more widespread and LCA data become more readily available, our analyses should be updated.The FFQ that we used has been validated against food records and biomarkers for estimation of the nutritional quality of the diet, but no such validation has taken place for estimating environmental outcomes. However, a previous validation study compared dietary GHG emissions estimated by a different FFQ with estimates from a 24 h diet recall and showed acceptable levels of agreement between the two33, which estimated that in 2016–2019 the average consumption of all meat  in 19–64 year olds was 99 g d−1, and 77 g d−1 in the 65+ age group. We estimated the prevalence of vegans and vegetarians using data from a recent Ipsos Mori survey37. Using these data to scale our results to the population of the UK, we estimate that the annual dietary environmental footprint of adults in the UK amounts to 120 MT of CO2e, 230,000 km2 of agricultural land, 15 km3 of agricultural water, 690 kT of phosphate equivalents (PO4e) and 0.06 terrestrial vertebrate species destined for extinction. Our estimate of 120 MT of CO2e is similar to the most recent estimate from EDGAR-FOOD 38, which produces globally comparable estimates using Food and Agriculture Organization of the United Nations food balance sheet data and estimates UK food systems emissions in 2015 to be 113 MT of CO2e. Our estimates for water use, eutrophication and biodiversity are similar to results for the UK published by the World Wildlife Fund39 of 19 km3 of agricultural water, 645 kT of PO4e and 0.03 species destined for extinction each year. While our estimate of total GHG emissions is similar to that from EDGAR-FOOD, the proportion of individual gases is different. For our estimates, the contribution to CO2e of N2O is about 7% for all diet groups, and for CH4 the contribution increases from 6% in vegans to 21% in high meat-eaters. Equivalent estimates from EDGAR-FOOD for the UK are 17% for N2O and 35% for CH4. This may be a result of discrepancies in how we derive separate N2O, CH4 and CO2 emissions making inferences from the Poore and Nemecek database, as noted above, and the way separate gases are handled in the Food and Agriculture Organization Statistics Division (FAOSTAT) emissions in EDGAR-FOOD, further highlighting the challenges in obtaining individual gas data.By scaling our results to the national level, we can compare our absolute estimates of environmental impact with other estimates from the literature. To do this, we used data from the UK’s gold standard diet monitoring programme, the National Diet and Nutrition Survey14. The estimates presented here are slightly lower for plant-based diet groups and slightly higher for meat-eating groups. Other studies have compared the environmental impacts of observed diet groups defined by exclusion of meat or dairy42, but they have not included as many environmental measures as here nor incorporated uncertainty in estimates due to region of origin and production method. Dietary GHG emissions for US vegetarians in the Adventist Health Study 2 cohort42, standardized to a 2,000 kcal diet, were 70.8% (70.5–71.2%) of emissions from non-vegetarian diets, similar to the difference between vegetarians and the medium meat-eaters (58.5%) observed in our sample. An analysis of 29,210 French adults in the NutriNet-Sante Study included data on 464 pesco-vegetarians , 406 vegetarians and 297 vegans40. For both GHG emissions and land use, that study40 found the same relationship as shown in our paper, with lowest environmental impact for vegans, similar impact for vegetarians and fish-eaters, and highest impact for meat-eaters. They also found similar relative differences between vegans and meat-eaters, with dietary GHG emissions of vegans being 24.5% (19.2–29.8%) of the meat-eaters and 35.6% (29.9–41.3%) for land use.Previous estimates of dietary GHG emissions for vegans, vegetarians, fish-eaters and meat-eaters in the EPIC-Oxford cohort have been made using a similar method based on GHG emissions data from a single studyThere is a strong relationship between the amount of animal-based foods in a diet and its environmental impact, including GHG emissions, land use, water use, eutrophication and biodiversity. Dietary shifts away from animal-based foods can make a substantial contribution to reduction of the UK environmental footprint. Uncertainty due to region of origin and methods of food production do not obscure these differences between diet groups and should not be a barrier to policy action aimed at reducing animal-based food consumption.See the data availability statement for details of where to access the data for this study.43. Between 1993 and 1999, data were collected on 65,411 adults aged 20 years and over. Individuals were recruited through advertising in vegetarian and health food magazines, through direct mailout from vegetarian and vegan societies and through collaborating general practices. Recruited individuals were then encouraged to recruit acquaintances. All participants were residents in the UK.Data on food consumption comes from the baseline data collection of the EPIC-Oxford prospective cohort study44. The FFQ was used to estimate food group and nutrient intakes, and participants were classified into self-identified dietary groups  by their responses to the following four yes or no questions:Do you eat any meat ? Do you eat any fish? (Vegans and vegetarians respond ‘No’.)Do you eat any eggs (including eggs in cakes or other baked goods)? (Vegans respond ‘No’.)Do you eat any dairy products ? (Vegans respond ‘No’.)Dietary assessment was conducted using a 130-item FFQ that assesses the usual levels of consumption of food items over the previous 12 months. The FFQ has been validated against weighed food records and several recovery and concentration biomarkers−1), medium meat-eaters (≥50 to <100 g d−1) and high meat-eaters (≥100 g d−1). These cut-offs were selected as they split the cohort into three similarly sized groups and allow for direct comparison with other published studies.In addition, we split the meat-eaters into three groups based on amount of daily consumption: low meat-eaters .For these analyses, we excluded participants if they were aged 80 years or over, or under the age of 20 years at recruitment, did not complete at least 80% of the FFQ, did not complete the questions required for classification into dietary groups, or produced estimates of daily energy intake that were deemed unfeasible4, N2O and CO2 and estimates of water use, land use, eutrophication  and biodiversity impact on terrestrial vertebrates, were taken from the Poore and Nemecek database—a review of 570 LCAs covering results from over 38,000 farms in 119 countries covering five continents3. Disaggregated GHG estimates were not always available in the Poore and Nemecek database. Where they were not available, CH4 and N2O emissions were assumed to be the sum of emissions from agricultural practices where these GHGs dominate  and CO2 was assumed to be the remaining component. We selected all of the environmental indicators available in the Poore and Nemecek database except acidification (because of gaps in the data) and water scarcity . The life cycle estimates are valid up to the retail setting. The database contains LCAs published between 2000 and 2016 that met inclusion criteria based around a minimum standard of reporting.The environmental data on emissions of CH4 conversion factor = 27, N2O = 273) using conversion factors from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change46. As agricultural emissions contain a substantial non-CO2 component, aggregated CO2e emission footprints can vary depending on the method used to define CO2-equivalence. Following United Nations Environment Programme and Society of Environmental Toxicology and Chemistry (UNEP-SETAC) Life Cycle Initiative guidance30, we explored total dietary footprints using two additional metrics in addition to the de facto standard GWP100. These were the GTP100 , suggested as representing longer-term climate impacts, and the GWP20 , suggested as providing insight into very short-term impacts.We used data on the GHGs to estimate aggregated GWP100 .Agricultural water use, measured in m4e, gPO4e .Eutrophication potential measured in g of PO47. The index is specific to 170 crops in 184 countries48. The measure we use only accounts for the impact of land-based food production on terrestrial vertebrates, and therefore does not account for biodiversity loss of terrestrial plants or invertebrates, or any aspect of marine biodiversity. This measure is not usually used to assess the potential biodiversity impact of diets consumed by a single individual on a single day. Therefore the units of measurement are very small (10−12 species destined for extinction), and the measure is better understood as a comparative measure across diet groups.Biodiversity impact, which is measured as the number of species destined for extinction as a result of agricultural practices. This variable accounts for the impacts of land cover expansion  and ongoing use of agricultural land, and is weighted depending on the location of land useThe exact measures used for our environmental measures are:50. We then linked the 289 food items with the environmental indicators data via an intermediary step involving a database (foodDB) of all food and drink items available for purchase in eight UK online supermarkets28. We linked an extract of 57,000 food and drinks from October 2019 with the environmental dataset using a process that is described in detail elsewhere51. Briefly, each ingredient in each product in the data extract was linked with food categories from the Poore and Nemecek database. Then, for each food product, the percentage composition of each ingredient was estimated in a two-stage process: first, the per cent composition provided in the ingredients list by the manufacturer was used if it was provided; second, for the remaining ingredients, we used an algorithm to estimate the per cent composition of remaining ingredients using composition and nutrition information from similar products and following UK food-labelling regulations, such that the composition of all ingredients in a product sums to 100% and each ingredient accounts for at least as much of the product as the subsequent ingredient. The accuracy of the approach was assessed by applying the algorithm to a subset of 1,550 foods in the database where the percentages of all ingredients were known. In the extreme scenario where it was assumed that no per cent composition of any ingredient was known, the algorithm on average produced estimates of environmental measures that were within 2% of the known environmental measure across all assessed products. While most products and ingredients identified in foodDB do not provide information on the agricultural methods used for their production, where we identified foods or ingredients labelled as ‘organic’ we linked them with data on LCAs for organic production methods in the Poore and Nemecek database.The process for linking EPIC-Oxford data with environmental assessments is summarized in Fig. n = 132) that could be linked directly with data from the Poore and Nemecek database. These foods were either single-ingredient foods  or were commonly consumed staples . These links are shown in the To link the 289 food items from the FFQ with environmental data, we first identified those foods n = 32 that c14.For the remaining 157 foods, we matched on keywords with products from the food and drinks in the foodDB data extract. We matched with 4,015 unique food and drink products. The median number of product matches was 11, ranging from 1 for frozen mousse to 500 for chips. To link with multiple foods, we used the mean of the environmental impact. These links are shown in the We compared age, gender and measures of dietary intake across the diet groups, and differences were assessed by analysis of variance for continuous variables and Pearson’s chi-squared test for categorical variables. To account for different energy intakes across diet groups, environmental measures were standardized to a daily diet of 2,000 kcal by proportionately scaling all consumption of different food and drinks. This allowed us to isolate the differences between the diet groups that are purely a result of the composition (rather than the amount) of food consumed. As kilocalorie intake varies by age and gender, and these variables also vary by diet group, standardizing the kilocalorie intake also protects our results from confounding. In addition, standardizing the kilocalorie intake of diets avoids the potential for artificial differences that could result if the average portion sizes for fruit, vegetables and cereals differ across diet groups. However, standardizing by kilocalorie intake also obscures differences that result from variation in kilocalorie intake by diet group; therefore, as a sensitivity analysis we reproduced all results without standardization to a daily diet of 2,000 kcal.All of the results that compare environmental measures by diet group have been standardized by the age and gender breakdown in the full EPIC-Oxford sample, so that the influence of age and gender are removed from comparisons. Our results are presented for both genders combined. We have also analysed the data separately for men and women and did not find any differences for any environmental indicators in our primary analyses.Our primary results are derived from a two-stage Monte Carlo analysis that accounted for uncertainty due to variation in agricultural production methods and where food is produced. For example, the EPIC-Oxford FFQ collects data on consumption of beef. This FFQ item is linked with two items from the McCance and Widdowson nutrition tables (beefsteak and beef fat). The environmental footprint of beefsteak varies depending on how it is produced  and where it is produced . This variability is captured by the LCAs in the Poore and Nemecek database—there are 24 LCAs of ‘bovine meat (beef herd)’ in the database. Stage 1 of our Monte Carlo analysis produced distributions of environmental indicators for all of the foods that were linked to the EPIC-Oxford FFQ simultaneously. For each food, we randomly drew 1,000 samples from the distributions of each environmental indicator in the Poore and Nemecek database (for multi-ingredient foods, this would involve drawing across multiple Poore and Nemcek categories—see the Our secondary analysis accounts for uncertainty due to variation in individual-level diet choices for the EPIC-Oxford participants. We estimated marginal results from a regression analysis adjusted for age and gender, where environmental indicators are fixed at the median level from the Poore and Nemecek database. The marginal results are equivalent to the age and gender standardized results from the primary analysis but only incorporate uncertainty from sampling variance. The secondary results are shown in Supplementary Tables Supplementary InformationSupplementary Tables 1–13.Supplementary Data 1Supplementary data tables.


The incidence of diabetes mellitus  is increasing worldwide, and hyperglycemia during pregnancy is associated with adverse pregnancy outcomes. Evidence on the safety and efficacy of metformin during pregnancy has accumulated resulting in an increase in its prescription in many reports.  We aimed to determine the prevalence of antidiabetic drug use (insulins and blood glucose-lowering drugs) before and during pregnancy in Switzerland and the changes therein during pregnancy and over time.  We conducted a descriptive study using Swiss health insurance claims (2012-2019). We established the MAMA cohort by identifying deliveries and estimating the last menstrual period. We identified claims for any antidiabetic medication (ADM), insulins, blood glucose-lowering drugs, and individual substances within each class. We defined three groups of pattern use based on timing of dispensation: (1) dispensation of at least one ADM in the prepregnancy period and in or after trimester 2 (T2) ; (2) dispensation for the first time in or after T2 (GDM); and (3) dispensation in the prepregnancy period and no dispensation in or after T2 (discontinuers). Within the pregestational diabetes group, we further defined continuers (dispensation for the same group of ADM) and switchers (different ADM group dispensed in the prepregnancy period and in or after T2).  MAMA included 104,098 deliveries with a mean maternal age at delivery of 31.7. Antidiabetic dispensations among pregnancies with pregestational and gestational diabetes increased over time. Insulin was the most dispensed medication for both diseases. Between 2017 and 2019, less than 10% of pregnancies treated for pregestational diabetes continued metformin rather than switching to insulin. Metformin was offered to less than 2% of pregnancies to treat gestational diabetes (2017-2019).  Despite its position in the guidelines and the attractive alternative that metformin represents to patients who may encounter barriers with insulin therapy, there was reluctance to prescribe it. Diabetes mellitus during pregnancy may either refer to pregestational diabetes mellitus or gestational diabetes mellitus (GDM). GDM refers to any degree of glucose intolerance that is first diagnosed during pregnancy, whereas pregestational diabetes is defined as diabetes mellitus (DM) type 1 or 2) present before conception  or 2 pre.The incidence of diabetes mellitus during pregnancy  has increased worldwide over the last decades and is expected to keep increasing, considering the rise in maternal age at birth and the prevalence of obesity among women of childbearing age –6. MoreoHistorically, insulin was the first-line treatment of both pregestational and GDM during pregnancy since it is one of the few drugs that do not cross the placenta and is effective for the attainment of adequate glucose control . Over thCSS health insurance  covers approximately 17% of the Swiss population . The CSSWe identified all inpatient and outpatient pregnancies recorded through SwissDRG (Diagnosis Related Groups system) and TARMED  delivery codes. Supplementary Table n = 108 deliveries), unless the initial delivery was determined by a TARMED code followed by a DRG code, in which case the delivery date was moved to the DRG code. Separate delivery dates had to be recorded at least 300 days apart.Multiple delivery codes recorded within 30 days of each other were considered as pertaining to the same pregnancy . The delSince neither gestational age nor the beginning of pregnancy are recorded within CSS data, the beginning of pregnancy was estimated using an algorithm, which has previously been validated in US claims data . This meDrugs used in diabetes (A10). We also captured exposure to the following individual antidiabetic drug classes: insulins and analogues (A10A), blood glucose-lowering drugs (A10B), as well as individual drug substances within each drug group. Blood glucose-lowering drugs included both oral hypoglycemic agents and non-insulin injectable glucose-lowering medications. Exposure was defined as at least one dispensation of an ADM in an outpatient setting during the periods of interest .We captured exposure to any antidiabetic medication (ADM) based on recorded Anatomical Therapeutic Classification (ATC) codes for We defined three main groups of ADM users during pregnancy: (1) with dispensation in the prepregnancy period and in or after T2 referred hereafter to as ADM for pregestational diabetes mellitus; (2) with a dispensation for the first time in or after T2 referred hereafter to as ADM for GDM; and (3) with a dispensation in the prepregnancy period and no dispensation in or after T2 referred to as discontinuers. With regard to ADM use in the pregestational diabetes mellitus group, we subdivided pregnancies in two treatment patterns: continuers and switchers. Continuers were defined as pregnancies with a dispensation for the same group of ADM in the prepregnancy period and in or after T2. Switchers were defined as pregnancies with a dispensation of a specific group of ADM in the prepregnancy period and with a switch to a different ADM group in or after T2.For each pregnancy, demographic information was extracted, including the year of delivery and the mother's age at delivery.We quantified the prevalence of exposure to at least one ADM, insulin product, blood glucose-lowering drug, and individual antidiabetic substances, within the above-described groups . Exposure prevalence was calculated over the entire study period as well as by calendar year. Prevalence of exposure was defined as the proportion of pregnancies during which at least one prescription was filled for the respective active substance, divided by the total number of enrolled pregnancies during the respective period. Results are presented as absolute numbers divided by 10000 with 95% confidence intervals.All analyses were performed using the statistical software R studio (version 1.2.5001).We identified a population of 104,098 deliveries from 80,320 women. The mean maternal age at delivery in our cohort was 31.7 years. In total, 31.9% of all deliveries were caesarean sections see .In total, at least one ADM was dispensed to 36.5/10000 pregnancies during the 9 months preceding pregnancy between 2012 and 2019, including at least one insulin product to 22.0/10000  pregnancies, at least one blood glucose-lowering drug to 13.1/10000  pregnancies, and both an insulin product and a blood glucose-lowering drug to 1.4/10000  pregnancies. Among these pregnancies, 25.9/10000  were also dispensed an ADM in or after T2 defined as ADM for pregestational diabetes mellitus. The prevalence of ADM prescriptions for pregestational diabetes mellitus increased between 2012  and 2019 . Among pregnancies in which an ADM was dispensed for pregestational diabetes mellitus between 2012 and 2019, 22.1/10000  continued the use of the same ADM (continuers) and 3.8/10000  switched to a different ADM (switchers).In total, 10.7/10000  pregnancies in which an ADM was dispensed before pregnancy discontinued their treatment (unexposed in T2 and T3), with an increasing prevalence over time between 2012  and 2019 .Among pregnancies in which at least one ADM was dispensed during the prepregnancy period and in or after T2 between 2012 and 2019 (25.9/10000), 22.1/10000  continued the use of the same ADM. This included 19.5/10000  pregnancies dispensed insulin only, 1.4/10000  dispensed both insulin and a blood glucose-lowering drug in the prepregnancy period (and to both or either one during or after T2), and 1.2/10000  a blood glucose-lowering drug only. The proportion of insulin continuers decreased between 2012  and 2019  while that of blood glucose-lowering drugs continuers increased . This tendency seemed especially relevant when comparing blood glucose-lowering drugs continuers between the late years of observation  to the early years . The most dispensed insulin in continuers during the prepregnancy period between 2012 and 2019 was insulin aspart (42.3%), followed by insulin lispro (20.9%). The most dispensed blood glucose-lowering drug was metformin (66.1%), followed by glucagon-like peptide 1 (GLP-1) receptor agonists (21.0%) and sodium-glucose cotransporter-2 (SGLT2) inhibitors (8.1%). Supplementary Table Among pregnancies in which at least one ADM was dispensed during the prepregnancy 25.9/10000) and switched to a different ADM in or after T2 (3.8/10000) between 2012 and 2019, all were dispensed a blood glucose-lowering drug during the prepregnancy and switched to insulin during pregnancy (no pregnancy was switched from insulin to a blood glucose-lowering drug). Among pregnancies exposed to a blood glucose-lowering drug in the prepregnancy period and exposed to an ADM in or after T2 (5.0/10000) between 2012 and 2019, there was a switch to insulin during pregnancy in 3.8/10000  pregnancies. The relative proportion of blood glucose-lowering drugs switchers decreased over time . Among pregnancies in which a blood glucose-lowering drug was prescribed during the prepregnancy period and then switched to insulin during pregnancy, metformin was dispensed to 32.3% of them in the prepregnancy period, followed by dipeptidyl peptidase 4 (DPP-4) inhibitors (22.6%) and GLP-1 receptor agonists (18.0%) , a blood glucose-lowering drug was dispensed to 8.1/10000  during the prepregnancy period ) and insulin was dispensed to 2.5/10000 ). The relative proportion of discontinuers was stable over time both among blood glucose-lowering drugs-exposed pregnancies  and insulin-exposed pregnancies  and 2019 . Among these pregnancies, 254.5/10000  were prescribed at least one insulin product, 2.8/10000  at least one blood glucose-lowering drug, and 0.5/10000  both a blood glucose-lowering drug and an insulin product. The proportion of insulin-exposed pregnancies in the GDM group has slightly decreased when comparing the early  and late years  of observation, while that of blood glucose-lowering drugs-exposed pregnancies in the GDM group has increased . This study is aimed at determining the prevalence and patterns of exposure to ADMs before and during pregnancy between 2012 and 2019 in Switzerland using the MAMA cohort. We identified 104,087 deliveries with a mean maternal age at delivery of 31.7 years and 31.9% of caesarean sections. These numbers were in line with those reported by the Swiss Federal Statistical Office for the overall population of women giving birth in Switzerland , 30. TheOverall, an ADM was dispensed to 25.9/10000 (0.3%) pregnancies during both the prepregnancy period and in or after T2 between 2012 and 2019, likely reflecting the prevalence of pregestational diabetes mellitus in our cohort. This prevalence increased over the study period . A previous observational European study based on healthcare databases reported a similar prevalence of pregestational diabetes mellitus in Italy, the Netherlands, Denmark, and the UK between 2004 and 2009, ranging from 0.3% to 0.5% . Within In total, an ADM was dispensed to 10.7/10000 (0.1%) pregnancies during the prepregnancy period and discontinued the use of any ADM during pregnancy. This represented one-third of all pregnancies exposed to an ADM in the prepregnancy period, similarly to the proportion reported in the European multinational study 11-35%) . Of thes% . Of thFinally, an ADM was dispensed for the first time during or after trimester in 257.7/10000 (2.6%) pregnancies. These women likely reflect pharmacologically treated GDM. This prevalence more than doubled over the study period. Prevalence of pregnancies receiving their first ADM prescription in the 2nd or 3rd trimester in the multinational European study varied between 0.2% in Denmark to 1.9% in Tuscany  . These lAlmost all pregnancies pharmacologically treated for GDM were prescribed insulin exclusively, mostly insulin detemir and human insulin. This is in line with the Swiss recommendations for clinical practice (RCP) stating that both Neutral Protamine Hagedorn (NPH) insulins  and insulin detemir may be used during pregnancy . During Our group of pregestational diabetes was defined as pregnancies exposed to an ADM both during prepregnancy and in or after T2. The reason for this definition was to exclude women who stopped taking their diabetes medication during pregnancy and were therefore likely taking antidiabetic medication for reasons other than pregestational diabetes, such as polycystic ovary syndrome (PCOS) or infertility (group of discontinuers). Regarding gestational diabetes mellitus (GDM), ideally, we would have liked to distinguish between GDM and overt diabetes during pregnancy. Overt diabetes, defined as a woman who has a glucose level that meets the threshold of diabetes in non-pregnant adults, may be due to preexisting diabetes that was previously unknown or prediabetes that progressed during pregnancy. In Switzerland, some women with overt diabetes may be diagnosed through early screening based on specific criteria . We could have created a group of women with overt diabetes for those with a first prescription after the first trimester. However, some women with overt diabetes may not present any of the above-mentioned risk factors for early screening or may simply not want to participate in it. Thus, we estimated that distinguishing precisely between the two was not possible with the data we had and decided to create only one group. We therefore used the cutoff of a first prescription in or after the second trimester to capture both women with overt diabetes  and women with GDM (who should have a prescription at the end of the second trimester or beginning of the third). One might suggest that we only capture women in the third trimester, as those who received a prescription in the second trimester are likely to continue receiving one in the third. However, the number of times a woman needs to refill her prescription can vary based on factors like medication type, dosage, and packaging. Therefore, if we had set our cutoff after the 24th week, it is possible that some women who had started their medication before this time may have already obtained enough medication to last beyond our cutoff and would not have been included in our analysis.(BfS), we believe our results were not substantially affected by channeling.To our knowledge, this study is the first to evaluate utilisation of ADM before and during pregnancy over time on a population-based level in Switzerland. Our results are based on large administrative database covering 17% of the Swiss population in 2021, the MAMA cohort. However, the lack of information on the sociodemographic and anthropometric characteristics (BMI) of included women prevents us from generalizing our results to the entire population of Swiss pregnant women. Nevertheless, maternal age at delivery has been shown to be a reliable proxy for socioeconomic status , which iSince information on medication dispensations was recorded automatically as a byproduct of routine pharmacy care, our results are not susceptible to maternal volunteer or recall bias. Still, it is not possible to know whether the medications were actually taken by the women and whether they were taken close to the time of dispensation. In addition, gestational age and trimesters were calculated based on an algorithm which has been validated in US claims data but not in Swiss data, and thus, we cannot exclude the possibility of some exposure misclassification. However, given that we focused on rather long periods of interest  instead of specific trimesters, this risk should have been minimized. Our data only included information on ambulatory dispensations, and we did not have information on inpatient medication use. However, we believe that this limitation is not likely to have affected the prevalence of exposure to antidiabetic medications in the study, as these medications are typically prescribed and dispensed in an outpatient setting. Therefore, missing information on inhospital prescriptions is not expected to have a significant impact on the study's findings related to antidiabetic medication use. Finally, we did not include pregnancies resulting in a miscarriage or an elective/medical abortion since the aim of the study was to look at longitudinal patterns of antidiabetic medication use. However, we do not believe that our prevalence of antidiabetic use would have been influenced by the inclusion of these women since neither insulin nor metformin  have been associated with a higher risk of miscarriage .In this study, the increased dispensations of antidiabetic medications among women with pregestational diabetes mellitus and GDM in Switzerland reflect the worldwide rise in the prevalence of diabetes mellitus among women of childbearing age. Insulin was the most frequently dispensed medication to treat both diseases. However, between 2017 and 2019, women with a pregestational blood glucose-lowering drug (mostly metformin) increasingly continued the same treatment throughout pregnancy rather than switching to insulin . Nonetheless, this still accounted for a much smaller proportion than women who switched treatment. Most women (>98%) with GDM were treated with insulin, and very few women seem to have been offered metformin as an alternative in most recent years (2017-2019). The pattern of use of metformin suggests that there is a reluctance to prescribe it, despite its position in the guidelines. However, it offers an attractive alternative for patients for whom the introduction of insulin therapy represents a barrier, and the latest evidence is reassuring regarding the long-term health of children exposed in utero.


Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systematic comparison of two LLMs and two structure-based models (CNNs) and show that the different model types have distinct strengths and weaknesses. The overall prediction accuracies are largely uncorrelated between the sequence- and structure-based models. Overall, the two structure-based models are better at predicting buried aliphatic and hydrophobic residues whereas the two LLMs are better at predicting solvent-exposed polar and charged amino acids. Finally, we find that a combined model that takes the individual model predictions as input can leverage these individual model strengths and results in significantly improved overall prediction accuracy. In fact, several deep neural network models, including large language models (LLMs) and convolutional neural networks (CNNs), have helped with the engineering of proteins with enhanced function/stability10. Interestingly, the deep learning models employed to date differ substantially in their architectures and, more importantly, the type of input data they are trained on. While LLMs are typically trained on large amounts of sequence data, CNNs and other model architectures have also been trained on input consisting of protein structures13, and models trained on both types of input data have produced meaningful insight on protein variants.Machine learning models such as neural networks are increasingly being used to computationally explore protein variants suitable for protein engineering and to predict their effects on protein structure and function15, predicting post-translational modifications and biophysical attributes4, predicting protein structure17, and even predicting entire protein-protein complexes18. At the same time, CNNs trained on structure data have been successful in enhancing enzyme function activity by suggesting activity enhancing protein variants10. Because sequence- and structure-based models learn from different protein representations, it is not clear whether they have inherent differences or instead make substantially equivalent predictions. Additionally, because the different model architectures have typically not been applied head-to-head to the same problem, excellent performance of one architecture in one problem area does not imply that a different architecture might not perform similarly well. Further progress on this question requires a direct comparison of different model architectures on the same problem.Sequence data is significantly more abundant than structure data, so it would be convenient if models could be trained purely on sequence data. And in fact, sequence-based models, and specifically protein large language models (protein LLMs) using transformer architectures, have been successfully employed in a number of contexts, including predicting variant effects and protein fitness4 and ESM1b5, and we compare them to two structure-based 3D CNN models, which we refer to here as CNN19 and RESNET12. We find that all these models generally predict the masked residue with similar accuracy. However, because the models differ in how they “see” protein biochemistry and what they take as input data, they tend to make distinct predictions that reflect different aspects of the underlying protein biochemistry. Further, we show that we can improve model performance by combining the models into a single, joint model. The combined model can compensate for weaknesses of the individual models by learning how to integrate the various model predictions and preferentially relying on the specific predictions that it estimates to be the most accurate for a given site in a given protein structure.Here, we compare two existing sequence-based and two existing structure-based models head-to-head on the task they were originally trained for, the prediction of a masked residue. We use two different sequence-based LLMs, protBERT5 and protBERT4, and two structure-based models, a 3D CNN19 and a Residual Network (RESNET)12. Our first question was whether any one of the models was consistently outperforming the others. We generated predictions for every residue in a test set of 147 protein structures. We found that the average accuracy across the 147 structures was 60.74%, 64.4%, 64.8% and 68.3%, respectively, for the ESM1b, CNN, RESNET and protBERT models , passes them into two intermediate dense layers, and ultimately outputs a set of 20 probabilities, where each node represents the probability of one of 20 amino acids . We traiWe next assessed how well the different models predict specific amino acid classes. We pooled all sites from all 147 test proteins and divided the sites by amino acid class . Therefore, we used it here as an approximation of model accuracy, which cannot be defined for individual sites (a site either is or is not predicted correctly). We found that all models had a tendency to have higher confidence in their predictions for buried residues (RSA near zero) than for exposed residues (RSA of 0.2 or larger)  contributed to differences in predictions between the models. We did this by correlating the Relative Solvent Accessibility (RSA) of each site in our dataset with the model confidence for that site. Model confidence is defined as the probability with which the top scoring amino acid is predicted at a site, and it correlates well with model accuracy ( larger) . HoweverWe have compared the performance of two existing structure- and two existing sequence-based neural network models on the task of predicting masked residues in proteins, the task that all these models were originally trained on. We have found that the two different model types vary widely in their performance on specific proteins, even if their average accuracies are similar. Predictions by structure-based models are not correlated or at most weakly correlated to predictions by sequence-based models. The structure-based models perform best at predicting aliphatic core residues, whereas the sequence-based models provide more accurate predictions for polar, solvent-exposed sites. We have further constructed a combined model that uses the predictions of the individual models as input and turns them into an ensemble prediction. This combined model has achieved significantly higher prediction accuracy than the individual models, and importantly, can leverage the individual models for their specific strengths. The combined model seems to be able to select the individual model predictions that are most suitable for specific sites.22. More recently, the self-supervised technique masked-language modeling (MLM) used to train LLMs has been applied to predict masked residues in protein sequences6. Such LLMs can capture interactions between amino acids that are far apart in the sequence but spatially close in the structure, relying solely on input sequence for inference, without the need for additional features or annotations17. By contrast, 3D CNNs are fundamentally different from LLMs as they take the local protein structure rather than the entire amino-acid sequences as input23. The input consists of a voxelized box built around a focal residue which is removed before training or inference. The advantage of this structural data as model input is that it directly provides information on physical contacts within a protein, which are most critical for accurate residue prediction19.Sequence-based language transformer models and structure-based convolutional models differ widely in their training data, their internal structures, and their strengths and weaknesses. The transformer architecture has grown popular in the fields of natural language processing and image recognitionBecause LLMs do not use any structural information as input, we might expect them to perform consistently worse than 3D CNNs at predicting masked residues. However, we have seen that this is not the case for the four models considered here; both model types predicted residues with comparable overall accuracies. At the same time, there were clear differences between model types. The structure-based models tended to perform better at predicting aliphatic or hydrophobic residues, whereas the LLMs had an edge for polar or charged amino acids. These differences may be due in part to the fact that the latter amino acids are more common on the surface of the protein, where the voxelized input boxes to the structure-based models are partially empty and thus provide less data to base inference on.26. Considering that the distributions of probabilities can differ widely even if the same amino acid is predicted to be the most likely, we expect that these distributions carry meaningful information about the microenvironment surrounding the focal residue in the folded protein. Moreover, we would expect similar microenvironments to have similar distributions27. Consequently, the combined model can use the probability distributions of the four input models to infer the likely microenvironment of the focal residue and then infer which amino acid to place at this location. This is not a simple majority rule among the models but a non-trivial inference task, as can be seen from the fact that the model on occasion makes unique predictions distinct from any of the input models.The consequence of the differing strengths and weaknesses of the two model architectures is that the different models can be combined for improved overall performance. The network output for all four models consists of a distribution of 20 probabilities, one for each of the amino acids. This distribution of probabilities can also be viewed as an embedding for a biochemical environment28, respectively, whereas the structure-based models we used here (both with 12M parameters) were trained with ⁓1.6M and ⁓ 2.3M microenvironments sampled from ⁓ 17K and 23K sequences with a 50% sequence similarity. The large training sets for the language models raise the possibility that these models overfit to their training datasets to the point of memorization. With enough training, any model will eventually memorize the training set and be unable to generalize to unseen data30. We saw evidence of potential overtraining when we used more recent models of protBERT and ESM1-b, as well as ESM1-v6. For these models, we obtained accuracies averaging at ⁓95% across proteins  and ProTrans (450M parameters) were trained with ⁓ 27M and > 80M protein sequences with 50% and 100% sequence similarity from UniRefproteins  and S7. In summary, our results show that two different neural network types, due to differences in how they learn protein features, can make unique contributions to the same residue prediction task. At the same time, predictions that match between a sequence-based and a structure-based model are most likely to be correct, implying that although model predictions are not strongly correlated they do show some overlap. It remains an open question to what extent our results here generalize to other models with similar or differing architectures, and whether LLMs can in principle be trained to the point where they perform as well as or better than a structure-based model. One conceptual difficulty with this question is that LLMs use such large training sets and so many parameters that they in essence memorize the entire known protein universe. To what extent such models actually generalize protein biochemistry is difficult to assess.5 and protBERT4, and two structure-based models, CNN19 and RESNET12. The two structure-based models have been trained on the same dataset, while the LLMs have been trained on separate and distinct training datasets. For all models, we defined model performance as the accuracy with which each model could predict masked amino acids in an array of different proteins.We used four pre-existing self-supervised deep learning models trained to predict masked residues: two sequence-based transformer models, ESM1-b19. Our test set is derived from the PSICOV dataset31, which consists of 150 well studied protein structures commonly used for covariation analyses. From this dataset, we removed three proteins for technical reasons, as described19. We verified that none of the remaining 147 proteins were in UniRef50, the training set of the ESM1-b model. Similarly, the CNN and RESNET training set had been chosen to not contain proteins with a sequence similarity of greater than 50% to any of the proteins in the test dataset19. Because protBERT has been trained on nearly every sequence in the UniRef RCSB databank28, we could not guarantee that our test sequences were not in the training data for that model.We first assessed each model separately, using a test dataset we had previously used in studying the 3D CNN modelberteome library (version 0.1.6) available at: https://github.com/tijeco/berteome. The berteome library requires prior installation of the transformers package available at: https://pypi.org/project/transformers/. We used version 4.10.0 of the transformers package. The berteome library generates ESM1-b predictions from the pre-trained esm1b_t33_650M_UR50S model5 and protBERT predictions from the pre-trained Rostlab/prot_bert model4. CNN model output was generated using the network with input box size of 20Å3 as described19. The CNN model is available at https://github.com/akulikova64/CNN_protein_landscape and the RESNET model is available at https://github.com/danny305/MutComputeX.LLM output was generated using the 32.After assessing each individual model, we used the outputs of all four neural networks as training data for a combined model. The combined neural network model consisted of four layers: an input of 80 nodes (20 per model), two intermediate layers of 120 nodes and 60 nodes (“relu” activation function), respectively, and a final output layer of 20 nodes (“softmax” activation function). Each node in the output layer represents the probability of one of the 20 amino acids. All layers are fully connected. The model was implemented in python (v3.8.9) with the Tensorflow library (v2.9.2) using the keras API (v2.9.0)https://cdn.rcsb.org/resources/sequence/clusters/clusters-by-entity-80.txt. To avoid reusing any proteins previously used to train the individual models, we removed any proteins that were part of the training sets of the 3D CNN (training set as described19), RESNET and ESM1-b (UniRef50), as well as the test set (PSICOV) and their homologs of 80% or greater similarity. We did not filter out proteins used to train the protBERT model as it was pre-trained on the UniRef100 dataset, containing most of the proteins in the RCSB database. Proteins longer than 1024 residues were also removed due to length restrictions of the ESM1-b model. Finally, we removed protein structures where we could not add hydrogen atoms or partial charges with PDB2PQR (v3.1.0)33. The resulting training set contained a total of 3209 protein structures.To compile a training set for the combined model we first downloaded all PDB ID’s of proteins clustered into 80% homology groups from the RCSB website using the following link: To produce training data for the combined model, we generated CNN and RESNET predictions for each residue in our 3209 protein structure files. Similarly, the sequences corresponding to the 3209 protein structures were used to generate predictions from the protBERT and ESM-1b models. The combined network was trained on each protein/sequence position in this dataset for 150 epochs, on the goal of being able to predict the residue at each position in a protein. We used a fixed learning rate of 0.0001 using the Adam optimizer and the “categorical crossentropy” loss function. Finally, the fully trained combined network was tested against the same set of 147 proteins we had used initially to test individual models.34 and applying the normalization constants as described35.Relative solvent accessibility values were calculated using freeSASA softwareSupplement 1


The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings.A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a ‘heart age’ tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002).This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour.The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.The online version contains supplementary material available at 10.1186/s13063-023-07345-6. It has been well established that sustained glycaemic control reduces the risk of microvascular complications  and is sIn line with these findings, achieving sustained control of HbA1c levels has become the cornerstone of T2DM management. While maintaining HbA1c below 7.0% (53 mmol/mol) is considered a reasonable goal for most adults with T2DM, international guidelines recommend adjusting target values on an individual basis . AccordiIn practice, however, many patients encounter difficulties in meeting their glycaemic target. In the US, it is estimated that 43% of insured people who were prescribed with oral hypoglycaemic agents in 2012 had suboptimal glycaemic control ; while, Over the past two decades, the search for strategies to improve behavioural management has attracted considerable attention , 17. NumWhile purely educational support  has only produced modest effects, if any, on behavioural or clinical outcomes , findingintention-behaviour gap, whereby some patients fail to initiate and maintain behavioural management despite intending to ) at 6 months in each group will be compared.The change in participants’ CV risk score (5-year CHD probability) will be evaluated at 3 and 6 months. Personalised CV risk scores will be calculated by the JJ risk engine based on participants’ personal characteristics and risk factors, including age, sex, body mass index, duration of T2DM, smoking status, HbA1c level (%), systolic blood pressure (mm Hg), HDL cholesterol (mg/dL), total cholesterol (mg/dL), and urine albumin creatinine ratio (mg/gCre). Systolic blood pressure will be measured at the GP practice by the standard method recommended by the Japanese Society of Hypertension .The change in HbA1c levels will also be evaluated at 3 months.Changes in participants’ behavioural management will be measured using the Japanese-language version of the validated Summary of Diabetes Self-Care Activities Measure (SDSCA) instrument , 54 at eChanges in prescribed medications in terms of drug and/or dosage  will also be recorded at each visit.Doctor-patient communication will be evaluated using the dedicated 8-item subscale of a validated Japanese-language version of the General Practice Assessment Questionnaire (GPAQ) , 56 and Participatory decision-making will be measured using a 7-item subscale of the validated Japanese-language version of the Patient Assessment of Chronic Illness Care (PACIC) scale, at baseline and at 6 months . ParticiPsychosocial self-efficacy will be measured at baseline, 3 and 6 months, using a validated Japanese-language version of the Appraisal of Diabetes Scale (ADS) , 61, a qnd visit.Recall of the CV risk score (effective heart age or 5-year CHD probability) will be assessed as correct or incorrect based on open numerical responses (i.e. depending on whether or not the participant gives the exact right answer) immediately after the baseline visit, and at 3 months, immediately before the 2Emotional response to the CV risk score will be assessed by a 3-item, 7-point Likert scale, ranging from ‘not afraid/anxious/worried at all about the risk score’ to ‘very afraid/anxious/worried about the risk score’, immediately after the baseline visit .Perceived credibility of the CV risk score will be assessed by a 4-item , 7-point Likert scale (from ‘completely disagree’ to ‘completely agree’) adapted from previous similar research, immediately after the baseline visit .Intentions to change behaviour according to the Theory of Planned Behaviour  will be Table Eligible patients who agree to participate in the study following the face-to-face screening appointment will be notified of the start of the trial by the practice staff . They will be informed that their next routine diabetes visit will be the baseline visit of the study.On the day of the baseline visit, before seeing the GP, demographic information , diabetes-related characteristics  and baseline measures will be collected using standardised forms or extracted from practice records by the practice staff. Baseline measures collected before the consultation include objective numeracy level assessed using the Japanese-language version of the Lipkus scale , 67 and The same procedure will apply at follow-up visits. The member of the practice staff in charge of data collection will make sure that the questionnaires are properly filled out and that no information is missing. Questionnaires will be linked to individual participants using practice and participant ID numbers that will preserve anonymity.p≤0.10) are observed, the relevant variables will be considered as potential confounders in further analysis.Participants will be described in terms of their baseline characteristics. Practice size  will also be reported. Continuous variables will be denoted using mean and standard deviation  or median and interquartile range ; while categorical variables will be denoted using count and percentage. Between-group differences in terms of participants’ characteristics and baseline measures will be tested using Student t tests and chi-square tests. In case significant between-group differences  principle. Between-group comparisons will be analysed using multilevel analysis to account for the dependency of observations. Between-group differences in the mean changes in primary and secondary outcomes will be estimated using linear mixed-effects models for continuous outcomes and logistic mixed-effects models for binary outcomes. Random effects will account for within-practice clustering and within-patient correlation. Fixed effects will be treatment group indicator (with the control group as the reference category), practice size (stratification variable)  and, wheSensitivity analyses will be carried out to evaluate the robustness of the estimated intervention effect on the primary outcome based on differing missing data assumptions. While our primary analytic approach can provide unbiased estimates under the assumption that data are missing at random, we will examine the sensitivity of statistical inferences to data being missing not at random, i.e. to cases where missingness depends on the unobserved values even after controlling for predictive variables. We will do so using a pattern-mixture model (PMM) approach, which assumes that participants who drop out have a mean outcome that deviates from that of participants who do not drop out by a given offset, and then explores the effect on the findings of various clinically realistic values of this offset (best case and worst-case scenarios) in the two study groups , 70.In addition, the ITT model for the primary outcome will be fitted for the following subgroups of interest: (1) including only patients with HbA1c > 8.0% at baseline to examine the effects of the FEEDBACK intervention on poorly controlled patients; (2) by duration of diabetes; (3) by age and sex; (4) by objective numeracy level; (5) by level of intention to change behaviour at baseline.Assuming individual randomisation, recruitment of at least 76 participants per arm would be required to achieve 80% power with type I error α=0.05. These calculations have assumed the detection of a clinically meaningful difference of 0.5% (5.5 mmol/mol) in change in HbA1c between the two arms  and stanA systematic search of ICCs used in the design of cluster RCTs in T2DM with HbA1c as primary (continuous) outcome found that the median value used in published trials was 0.047 . HoweverBased on these calculations, we aim to enrol 20 practices in the study. After 10 practices are enrolled , each pA standardised training protocol will be developed to minimise variability and maintain intervention protocol fidelity across all practices. GPs participating in the study will receive training consisting of a video featuring a mock consultation, delivered by a member of the research team to ensure that each step of the FEEDBACK intervention (or its control counterpart) is understood and appropriately followed. The members of the practice staff in charge of data collection will also receive instructions on how to properly fill out the standardised forms. Before the start of the trial, the intervention will be piloted in each practice allocated to the intervention group. We will assess the acceptability of the intervention and the feasibility of the data collection process. Should they be required, adjustments to the study procedures will be made.In order to ensure data anonymity, practice and participant ID numbers will be used to combine datasets (baseline and follow-up measures) and to identify participants who withdraw from the study. The research team will not have access to the files enabling to match individual participants to their ID numbers. Participant data  will be stored in a locked cabinet in a researcher’s office. All data will be destroyed by the research team within 5 years after publication.Trial Steering Committee (TSC)The study has two oversight committees:Trial Management Team (TMT)This group is composed of the Principal Investigator, Co-Principal Investigator, study statistician, sponsor’s representative, and two members who are completely independent of the study. The TSC plays a critical role in providing strategic oversight and executive decision-making power in relation to the conduct of the study.The day-to-day running of the trial will be overseen by a group chaired by the Principal Investigator and comprising of the data collection team and the partners of participating GP practices. This group will convene every two weeks to review the trial's progress, discuss any issues that have arisen, and ensure the smooth operation of the trial.Due to the low-risk nature of the intervention, the short duration of the trial, and the fact that interim analyses may not provide necessarily informative results given the nature of the study, there will be no Data Monitoring Committee. The research team will comply with data collection and management procedures approved by the research ethics committee of Kashima Hospital, Fukushima, Japan, and abide by the rules of medical confidentiality.There is no anticipated harm resulting from participation in the trial, and participants will not receive compensation for their involvement. Nevertheless, some participants may express higher emotional response to CV risk communication than others. As emotional response to CV risk score is one of the secondary outcome measures, we will be able identify such participants and inform GPs to make sure that no unnecessary anxiety is triggered as a result of study participation. Should the study provide evidence of the effectiveness of the FEEDBACK intervention, we will provide access to it to study participants in the control group, to ensure they have access to the same benefits as those in the intervention group.Given the short duration of the trial and the minimal risks associated with participation, no particular stopping rules have been set. In particular, we did not consider any stopping rule based on interim analyses. Although we will evaluate changes in HbA1c at the 3-month mark, we do not believe the lack of detectable effect at this stage to be an appropriate criterion for stopping the trial for futility reasons due to the fact that behavioural changes may take a longer period of time to be reflected in HbA1c levels. Any decision to stop the study will be made by the TSC based on recruitment rate or adverse events.There will be no independent audit of the trial. The research team and participating GPs will manage security and quality according to their standard operating procedure.This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at engaging adults with T2DM in behavioural management more effectively. The intervention has been developed to be easily embedded into routine primary care consultation delivered in an outpatient clinic setting. Following Peyrot and Rubin’s 5C framework , practicThe intervention uses several BCTs that have shown promise in promoting behavioural management of T2DM, such as ‘goal setting’, ‘problem-solving’, and ‘action planning’. It also operates two BCTs that have, to our knowledge, not yet been tested for this purpose: communicating CV risks using the risk metric ‘effective heart age’  and ‘contracting for behaviour change’. The development of ‘effective age’ tools to communicate health risks more intuitively has recently attracted increasing attention , includiImportantly, the effectiveness of BCTs in promoting behavioural management of T2DM may vary across populations. Most studies having been conducted in Western countries so far (e.g. 9 out of the 13 RCTs included in a recent systematic review ), there Changes in HbA1c levels between the intervention and control groups will be compared at the individual level, following three visits during which the intervention (or its control counterpart) will be delivered. If proven effective in improving glycaemic control, the FEEDBACK intervention will be relevant to policymakers and clinicians concerned with reducing the burden of T2DM while also minimising programme costs by building on available resources and existing infrastructures. A major strength of this study lies in a careful study design, with recruitment of individual participants being carried out before randomisation of practices to reduce the risk of selection bias. Although this is recommended by international guidelines , recruitProtocol date: 18/01/2023. Protocol version: 1.0. Recruitment of participants started in December 2022 and is expected to be completed in April 2023. Data collection is scheduled to be completed by March 2024, after which the final data analysis will be performed and the findings presented in a separate publication.Additional file 1.