CACNA1C, SCN10A, MYH11 and MYH6, that are enriched in biological processes related to cardiac muscle development and muscle contractility. In a Mendelian Randomisation framework, we demonstrate that higher genetically predicted fitness is causally associated with lower risk of type 2 diabetes independent of adiposity. Integration with proteomic data identified N-terminal pro B-type natriuretic peptide, hepatocyte growth factor-like protein and sex hormone-binding globulin as potential mediators of this relationship. Collectively, our findings provide insights into the biological mechanisms underpinning cardiorespiratory fitness and highlight the importance of improving fitness for diabetes prevention.Higher cardiorespiratory fitness is associated with lower risk of type 2 diabetes. However, the causality of this relationship and the biological mechanisms that underlie it are unclear. Here, we examine genetic determinants of cardiorespiratory fitness in 450k European-ancestry individuals in UK Biobank, by leveraging the genetic overlap between fitness measured by an exercise test and resting heart rate. We identified 160 fitness-associated loci which we validated in an independent cohort, the Fenland study. Gene-based analyses prioritised candidate genes, such as  Being fit has been linked to a lower risk of type 2 diabetes, but it is unclear whether this relationship is causal. Using large scale studies with genetic data and measurements of cardiorespiratory fitness, the authors show evidence that higher genetically predicted fitness is causally associated with lower risk of type 2 diabetes independent of adiposity. Fitness is a multifactorial trait2. Twin and family studies suggest that fitness has a strong genetic component, with an estimated heritability of 40–70%6. Fitness can be improved through exercise training8; however, it is important to recognise that fitness is a complex trait, distinct from physical activity behaviour, representing a dimension of physical health that independently predicts various health outcomes13.Cardiorespiratory fitness, which we refer to in this paper as fitness, is the ability of the circulatory and respiratory systems to supply oxygen to working muscles during prolonged exercise15. A recent meta-analysis of 22 observational studies incorporating 1.6 million individuals  reported that the relative risk of type 2 diabetes was 8% lower per 1 metabolic equivalent of task (MET) increment in fitness, after adjusting for adiposity16. However, improving fitness has not been an explicit intervention target in any of the large diabetes prevention trials that have been conducted to date; thus, it remains unclear whether fitness is causally linked to type 2 diabetes risk. Using a genetic risk score for fitness as an instrumental variable in Mendelian randomisation analyses offers a way of evaluating whether fitness is causally linked to type 2 diabetes as this approach reduces the risk of confounding and reverse causality which can affect observational studies17.Higher fitness is associated with higher insulin sensitivity independently of the effect of physical activity, which in turn, could reduce the risk of cardiometabolic diseases including type 2 diabetes18. However, this study was undertaken in a relatively small cohort with limited statistical power and moderate coverage of the genome. More recently, an analysis of UK Biobank data identified genetic loci associated with fitness which are enriched among genes associated with various cardiometabolic diseases19. However, interpretation of these results is problematic, because fitness estimates from the risk-stratified submaximal bike test were derived using a method recently shown to be biased for UK Biobank bike test data13. No studies have employed validated genetic instruments for fitness to investigate causality of the association with disease outcomes.The HERITAGE Family Study identified several loci that may be associated with fitnessHere, we conduct a genome-wide association study of validated fitness estimates from the subsample with bike test data in the UK Biobank cohort and triangulate the results with a genome-wide association study of resting heart rate in the full cohort to develop an optimised genetic instrument for fitness. We then validate our instrumental variable in an independent cohort Fig. . We appl2max, expressed in ml O2 per min per kg fat-free mass (FFM) in this study) estimated from a submaximal ramped cycle ergometer test20 in the UK Biobank study. The validation of these fitness estimates and a description of the baseline characteristics of participants are reported elsewhere13. In general, fitter people were younger, more likely to be male, more physically active, had higher education level, better lung function, lower body mass index (BMI) and lower resting heart rate. The mean (±standard deviation) fitness level for the 34,179 men included in this analysis was 43.1 (±6.4) ml O2·min−1·kg−1 FFM, and it was 39.8 (±7.1) ml O2·min−1·kg−1 FFM for the 39,395 women : 2-4%) lower risk of developing type 2 diabetes after adjustment for age, sex, adiposity and other potential confounders  score regression intercept was 1.02 (s.e. = 0.01), which suggests that the slight inflation observed was primarily attributed to the polygenic nature of the fitness trait. A genome-wide single nucleotide polymorphism (SNP)-based heritability of 12.7% (s.e. = 1.0%) was estimated using LD score regression21.After excluding individuals of non-European genetic ancestry and those without available genotype data, we performed a genome-wide association study (GWAS) on fitness in 69,416 participants of European Ancestry in the UK Biobank study. We identified 14 genome-wide significant fitness-associated SNPs  (see Methods). We found a strong inverse genetic correlation between fitness and resting heart rate  using LD score regression21. In secondary analyses, we found that the genetic correlations between fitness and other physiologically relevant traits  were not as strong as the correlation between fitness and resting heart rate   and proteomics analyses.To optimise the genetic instrument for fitness, we constructed four instruments for fitness using various criteria and evaluated the validity and strength of these instruments with treadmill-measured fitness in an independent cohort, the Fenland study (see Methods). The genetic instruments were calculated as the sum of the number of fitness-increasing alleles at each locus carried by each individual, weighted by the corresponding effect size from the associations with fitness in UK Biobank. The radial-filtering approach (see Methods) identified 148 resting heart rate variants which showed a consistent effect with fitness. We combined the 14 variants from the exercise test-based GWAS with 146 of these 148 variants, representing additional loci, resulting in a fourth instrument of 160 variants.2·min−1·kg−1 FFM ; we found a positive correlation for both overall physical activity volume (p = 1.1 × 10−4) and time spent in at least moderate intensity activity (p = 3.7 × 10−4).Each of the four instruments was significantly associated with treadmill-measured fitness in the Fenland study ; however, there was evidence of significant heterogeneity , potentially indicating horizontal pleiotropy (Supplementary Table 27 (see Methods) resulting in a filtered score containing 126 variants, for which we observed a significant and directionally consistent causal association between fitness and type 2 diabetes; 1-SD higher genetically predicted fitness was associated with an 11%  lower risk of developing type 2 diabetes . A series of sensitivity analyses were conducted such as MR Egger28 and weighted median models29. The result was consistent using the MR-PRESSO method30 and also supported by one-sample MR analysis using the optimised genetic instrument as a proxy for fitness in an analysis of association with prevalent type 2 diabetes in UK Biobank , we performed a two-sample inverse variance weighted (IVW) MR analysistes Fig. , with nop = 0.001), but not with other traits (p > 0.01).  and HbA1c) or adiposity (BMI). Similar analytical strategies were applied to each of the intermediate traits as for the MR analyses with type 2 diabetes. We observed a significant association of genetically predicted fitness with fasting insulin after Bonferroni correction for multiple testing (p < 0.05) after controlling for the effects of intermediate traits, individually and collectively  and lower hepatocyte growth factor-like protein  after Bonferroni correction for the number of aptamers tested  . Studies have shown that higher levels of sex hormone-binding globulin reduce the risk of type 2 diabetes31, suggesting a potential mechanism mediating the lower risk of type 2 diabetes with increased levels of fitness. Further MR analysis in the UK Biobank study provided supporting evidence that genetically predicted higher fitness is associated with higher levels of sex hormone-binding globulin (p = 9.6 × 10−11)  in 10,707 individuals from the Fenland study. We observed significant associations between genetically predicted fitness and higher N-terminal pro B-type natriuretic peptide     Fig. . In addiIn this large cohort study, we observed a strong linear inverse association between exercise-measured cardiorespiratory fitness and the risk of developing type 2 diabetes. We used the availability of fitness and resting heart rate data at scale to develop an enhanced 160-SNP genetic risk score for fitness, which we validated in an independent study. Using this genetic risk score in a Mendelian randomisation analysis, we observed an association between fitness and type 2 diabetes risk that was compatible with an underlying causal relationship. Analyses with intermediate traits showed a probable causal relationship with fasting insulin, a measure of insulin resistance known to be linked to fitness. Novel potential intermediate pathways between fitness and type 2 diabetes were also highlighted by the association between genetically predicted fitness and N-terminal pro B-type natriuretic peptide, hepatocyte growth factor-like protein and sex hormone binding globulin.2max, the maximum oxygen uptake during high-intensity exercise, which is considered the best indicator of cardiorespiratory capacity. There are safety concerns to measuring fitness using a maximal exercise test in large-scale epidemiological studies. Although some studies have used extensive pre-testing selection criteria to exclude individuals in whom the test might be risky, this results in tests that are undertaken only in a population of relatively healthy fit individuals, with obvious selection biases and lack of generalisability. Therefore, submaximal tests have been developed, like the bike ergometer test deployed in the UK Biobank study which allows safe measurement in the greatest number of individuals, thus reducing selection bias whilst preserving validity, as indicated by the strong relationship with the gold standard measurement in validation studies13. Additionally, we used the Fenland study to validate and prioritise the genetic instruments constructed in the UK Biobank. There are differences in the tests used to measure fitness between the two studies34, principally that a treadmill test was used in the Fenland study which is a weight-bearing exercise modality. Nonetheless, the successful validation of the derived fitness instrument using data from a different study design may also be a strength since correlation of errors and biases intrinsic to one test modality is less likely to explain strong validation results.Cardiorespiratory fitness is usually defined by VOIn this study, we observed a strong inverse genetic correlation between resting heart rate and fitness in UK Biobank, and given the large sample size available for resting heart rate, we used the overlapping genetic basis of resting heart rate and fitness to create a more robust genetic instrument for fitness than would have been possible using only exercise-based fitness estimates alone. We applied the Radial method to prioritise resting heart rate-associated variants that were associated with fitness, which were then combined with the top fitness-associated variants to create an enhanced genetic instrument for fitness. The proportion of variance explained by this enhanced instrument in the phenotypic fitness level in the independent dataset was double that of the instrument made from only the 14 genome-wide significant variants identified in the analysis of fitness.2·min−1·kg−1 body mass or 1 metabolic equivalent (MET) higher fitness was associated with an 8% (95% CI: 6%-10%) lower relative risk of type 2 diabetes16. In the UK Biobank study, we observed 3% lower risk of diabetes per 1 ml O2·min−1·kg−1 fat-free mass, equivalent to 19% lower risk per standard deviation, using the fat-free mass scaled fitness measure and adjusting for fat mass. To put this in a comparable scale as the previous study, we observed a 13% lower risk of diabetes per 1 MET higher fitness adjusting for BMI. Based on 2-sample MR analyses, we observed 11% lower risk of diabetes per standard deviation higher genetically predicted fitness.When applying the instrument to examine the association with type 2 diabetes risk in MR analysis, we observed an association that was compatible with a causal role for fitness. In a recent meta-analysis among 40,286 incident cases of type 2 diabetes and a total of 1,601,490 participants, each 3.5 ml O36. However, the direct effect of fitness on type 2 diabetes risk was only slightly attenuated but still nominally significant in the multivariable MR model, which suggests that the association between fitness and fasting insulin did not fully explain the causal effect of fitness on type 2 diabetes risk and that other mechanisms may be involved.A further investigation of intermediate traits for type 2 diabetes suggested that genetically predicted higher fitness was also significantly associated with lower fasting insulin, a marker of insulin resistance. The association between fitness and insulin sensitivity is biologically plausible based on their common underlying physiological function linked with the oxidative capacity of the skeletal muscle16.In contrast with fasting insulin, we did not find convincing evidence supporting a causal relationship between fitness and other glycaemic parameters, although the association with the 2-h glucose levels was close to nominal significance. The effect size was comparable to that for fasting insulin but may not have been statistically significant because the GWAS cohort for the 2-h glucose level is so much smaller than for other traits. In the multivariable MR analyses, adjustment for BMI did not attenuate the apparent causal effect of fitness on type 2 diabetes risk, which is concordant with the apparent BMI-independent observational association of fitness on type 2 diabetes risk observed both here and in a recent large meta-analysis of prospective cohort studies37. The observation in this study of a possible mediating role for sex hormone binding globulin is also supported by strong genetic evidence between sex hormone binding globulin and type 2 diabetes in previous reports38. The results point to novel pathways beyond glycaemia and insulin resistance which may link fitness and type 2 diabetes and are relevant not only to future studies of the biological basis of fitness but would also be of relevance as potential targets for therapies that mimic the metabolic health benefits of improved fitness.We explored other potential pathways that might mediate the association of fitness on type 2 diabetes risk, given the observed strong link between fitness and fasting insulin levels did not wholly explain the causal association between fitness and diabetes. In an analysis capitalising on the availability of proteomics alongside fitness measurements in the Fenland study, we observed a potential mediating role of N-terminal pro B-type natriuretic peptide, which is compatible with reports of a previous MR analysis which suggests that one standard deviation genetically predicted higher N-terminal pro B-type natriuretic peptide levels was associated with a 21% reduction in type 2 diabetes risk18. Despite the extensive search centred on biological relevance, the sample size for the original genetic discovery was small. In this study, we did not replicate any of the genes suggested by their study. Nonetheless, this study identified multiple genes that encode proteins that play key roles in cardiac and smooth muscle development and function , which was also supported by gene-set enrichment analyses and tissue-specific expression patterns.A previous genetic study of fitness in 497 sedentary individuals from the HERITAGE Family Study highlighted some genes involved in the underlying mechanisms in the cardiovascular system, skeletal muscle function, haematopoiesis, and metabolism based on bioinformatic analyses and evidence from knockout mouse models40.This current study has certain limitations. For instance, we applied the Radial method to filter outliers in the association between resting heart rate and cardiorespiratory fitness to construct a more robust genetic instrument for fitness. Despite the notable improvement in the enhanced instrument, the causal assessment using this instrument needs to be interpreted with caution. It is possible that the association was driven by the selected resting heart rate-associated variants, given both observational and MR studies have found a significant positive association between resting heart rate and type 2 diabetesIn conclusion, we developed an enhanced genetic instrument for cardiorespiratory fitness by leveraging the strong genetic overlap between fitness and resting heart rate. We applied this to confirm that the observed strong association between fitness and type 2 diabetes is likely to be causal and partly mediated by the effect of fitness on insulin resistance. The study provides insights into the biological mechanisms that may explain between-individual differences in fitness and also into the pathways that might mediate the beneficial metabolic effects of higher fitness. The nature of the relationship demonstrated in this study should reignite interest in fitness as a quantifiable parameter that is causally linked to type 2 diabetes risk and which could be assessed not only in future prevention trials, but also in clinical practice in patients at risk and in public health surveillance studies, as an important determinant of metabolic health.https://www.ukbiobank.ac.uk/). In brief, the UK Biobank is a large-scale population-based cohort study including 503,325 participants (aged 40–69 years) recruited through the general practitioners within the UK National Health Service. Participants were enroled in 22 study centres in England, Scotland and Wales, and provided extensive data on their demographic information, medical history and health behaviour through questionnaires. Blood samples and physical measurements were taken at baseline. The study was approved by the North West Multi-Centre Research Ethics Committee. All participants provided written informed consent.Our primary analyses were conducted in the UK Biobank study. The study design and details of the study have been described previously  protocol for people deemed in the ‘high’ risk group. The test protocol was specified according to the participants’ age, sex, height, weight, and resting heart rate. During the test, heart rate response was monitored and recorded using a 4-lead ECG device. All participants assigned to the bike test exercised for 6 min, followed by a 1-min motionless recovery-phase on the cycle ergometer. The derivation and validation of fitness estimates have been reported by Gonzales et al.13. Fitness was defined using estimated maximum oxygen consumption (VO2max) values . After quality control, a total of 76,872 participants had available fitness data and 73,574 participants were included in an observational analysis of the association between fitness and incident type 2 diabetes risk, following exclusion of prevalent diabetes41 and missing covariates. The genome-wide association analyses of exercise test-based fitness were conducted on a sub-group of 69,416 individuals after excluding individuals without genotype data and those of non-White European ancestry. In the full cohort, the automated pulse rate reading during seated blood pressure measurement was used as resting heart rate values. Participants who did not have a resting heart rate measurement  and those who were taking beta-blockers  for pre-existing heart conditions that could affect their heart rate were excluded. After excluding individuals without genotype data and those of non-White European ancestry, a total of 452,941 participants were included in the analyses for resting heart rate.Cardiorespiratory fitness was assessed in a subsample using heart rate response to a submaximal ramped cycle ergometer test that was individualised for participant characteristics, including cardiovascular disease risk. The protocol for measuring fitness has been described in detail in the UK Biobank Cardio Assessment manualThe Fenland Study is a population-based prospective cohort study that aims to investigate the associations between genetic and environmental factors and the risk of obesity, diabetes and related metabolic traits in adults. Eligible participants were born between 1950 and 1975, and resided in Cambridgeshire and were registered at a participating General Practices in Cambridge, Ely, Wisbech and the surrounding Cambridgeshire region between 2004 and 2014. Exclusion criteria include clinically diagnosed diabetes mellitus, inability to walk unaided, terminal illness with prognosis ≤1 year at the time of recruitment, clinically diagnosed psychotic disorder, pregnancy or lactation. A total of 12,435 participants completed the baseline phenotype assessments and provided written informed consent. The study was approved by the Cambridge Local Research Ethics Committee.2max values  derived from heart rate response during a submaximal treadmill test. Participants attended one of three clinical centres at the MRC Epidemiology Unit at the University of Cambridge for a progressive treadmill test protocol consisting of three phases; a 3-min walk at 3.2 km/h followed by a 6-min increasing speed walk (phase 1), a 6-min brisk walk with an increasing gradient (phase 2) and a 4.5-min flat level jog/run phase with increasing speed up to 12.5 km/h (phase 3). The test was terminated early if the participant (1) wanted to stop, (2) reached 90% of age-predicted maximal heart rate (208 − 0.7 × age)41 or (3) had exercised above 80% of age-predicted maximal heart rate for >2 min. The treadmill protocol and derivation of the VO2max values have been described in detail elsewhere42. The procedure of VO2max estimation has been validated against directly measured VO2max44.Cardiorespiratory fitness was defined using estimated VO45 before baseline, were classified as having likely prevalent diabetes (any type); these participants were excluded from the present analysis. Participants having Hospital Episode Statistics (HES) or death records indicative of type 2 diabetes, and with an inferred diagnosis date after baseline, were classified as incident type 2 diabetes cases. Specifically, ICD10 codes used to identify likely type 2 diabetes were the presence of E11 without E10, or the presence of E14 without E10-E13.In the UK Biobank, participants self-reporting any diabetes other than gestational diabetes only, or self-reporting diabetes medication, at either touchscreen or nurse interview, or having hospital episode statistics records or death records with ICD10 codes E10-E14 and recorded or inferred diagnosis dateWe used logistic regression to estimate odds ratios for incident type 2 diabetes according to fitness level in UK Biobank. Logistic regression models were sequentially adjusted for age and sex (Model 1), ethnicity , hypertension , history of stroke, history of heart failure, history of heart disease, history of atrial fibrillation, history of chronic obstructive pulmonary disease, history of cancer, medication use , smoking , alcohol consumption , meat intake , oily fish intake , fruit and vegetable intake , salt intake , employment , and Area Deprivation Index (Model 2), and adiposity . Participants with prevalent type 2 diabetes at baseline were excluded from analyses.46. In this study, the ‘v3’ release of the genetic data was used, which was imputed to the full set of HRC reference panel47 and the merged UK10K and 1000 Genomes Phase III reference panels48. Approximately 93 million directly genotyped and imputed autosomal genetic markers were available after quality control.In the UK Biobank study, genotyping was performed using the UK BiLEVE and UK Biobank Axiom arrays. Initial quality control was performed by the UK Biobank with details described previouslyn = 8994), the Affymetrix SNP5.0 Array (n = 1402) and the Illumina CoreExome-24 v1 (n = 1060). For the quality control of Axiom array, samples were excluded if they had failed channel contrast (DishQC <0.82), low call rate (<95%), gender mismatch between reported and genetic sex, heterozygosity outlier, unusually high number of singletons or impossible identity-by-descent values. Missing genotypes and those not directly measured were pre-phased using SHAPEIT249. Genetic variants were removed if they had a call rate <95%, clusters failed Affymetrix SNPolisher standard tests and thresholds, MAF was significantly affected by plate, were a duplicate based on chromosome, position and alleles (selecting the best probe set according to Affymetrix SNPolisher), deviated from Hardy-Weinberg equilibrium (p < 5 × 10-6), did not match the reference or had MAF = 050. The QC procedures were similar for the other arrays. Remaining variants were imputed using IMPUTE251 based on the HRC reference panel47, as well as the merged UK10K and 1000 Genomes Project Phase III reference panels48 for additional variants not available in the HRC reference panel. Post-imputation quality control of SNPs was carried out50, and the exclusion criteria include: (1) monomorphic and singleton variants; (2) imputation quality (INFO) < 0.4 or (3) Hardy-Weinberg equilibrium p < 5 × 10-6. Approximately 20 million variants were available after quality control.In the Fenland study, genotyping was performed using one of three genotyping arrays; the Affymetrix UK Biobank Axiom Array 53 to identify additional secondary signals using a collinearity threshold of 0.05, and selected the output variants that were at a genome-wide significance level (p < 5 × 10−8) both before and after conditional analyses, had <10% effect estimate change before and after conditional analyses, had MAF ≥ 0.01, and had LD correlation <0.05. Once potential secondary signals were detected, all the selected variants at the same locus were jointly tested using a joint model to confirm their independent associations with fitness.We performed distance-based clumping to identify genome-wide significant independent signals with a MAF ≥ 0.01, imputation quality score > 0.4 and at least 1 megabase (Mb) apart (assuming they would be at linkage equilibrium). We also used GCTA to perform conditional and joint analysisTo leverage the shared genetic basis between cardiorespiratory fitness and resting heart rate and the large sample size available for resting heart rate, we conducted a series of analyses to derive and assess the validity and quality of genetic scores constructed by triangulating the genetic data of both traits.N = 452,941 after excluding participants who were taking beta-blockers, without genotype data and of non-White European ancestry). Covariates included in the linear mixed model were age at recruitment, sex, genotyping array and first 10 principal components.First, we tested our hypothesis that resting heart rate could be a viable proxy trait for fitness in genetic settings. We conducted a GWAS of resting heart rate in the full UK Biobank cohort  with those with resting heart rate.We estimated the genetic correlation between fitness and resting heart rate using LD score regressionBidirectional Mendelian Randomisation analysis between resting heart rate and fitness was conducted using previously identified, distinct genome-wide significant variants as the instrumental variable for each trait. We then compared the effect estimates of the genome-wide significant fitness-associated SNPs and their look-ups from the resting heart rate GWAS results and vice versa (comparing the effects of resting heart rate-associated SNPs with those in the fitness GWAS).27 (resting heart rate as exposure and fitness as outcome) to select eligible resting heart rate-associated genetic variants to construct the proxy genetic instruments for fitness by excluding heterogenous outliers. We constructed a total of four genetic instruments for fitness using various criteria. Each instrument, a weighted genetic risk score generated from each of the four sets of genetic variants, was calculated by summing the number of fitness-increasing alleles carried by each individual at each of the selected loci in the Fenland Study, weighted by its corresponding per-allele effect estimate on fitness from the GWAS summary statistics in UK Biobank. The four genetic instruments for fitness wereWe utilised the Radial-plot method(Instrument 1) Genome-wide significant variants independently associated with exercise-based fitness;(Instrument 2) Genome-wide significant resting heart rate-associated variants that passed the Radial test mentioned above and not identified as outliers;p < 0.05);(Instrument 3) Radial-filtered resting heart rate-associated variants that were also nominally significant in the fitness GWAS (r2 > 0.01) were identified and fitness variants from (1) were prioritised.(Instrument 4) Independent variants combining the list of variants from (1) and (3); variant pairs in LD  to assess these four instruments by regressing the effect estimates of these variants from UK Biobank fitness GWAS results on the effect estimates of these variants from Fenland fitness GWAS.Subsequently, we evaluated the validity and strength of these instruments by comparing the significance and proportion of phenotypic variance explained in observed fitness in an independent study, the Fenland study (one-sample MR method). As a sensitivity analysis, we also conducted a fat-free mass weighted fitness GWAS in the Fenland Study. After excluding participants without fat-free mass weighted fitness phenotype and genotype data, those not of European ancestry, and related individuals, a total of 9512 individuals were included in this GWAS. QUICKTEST was used to perform GWAS among unrelated participants genotyped in each genotyping array, using an additive model adjusted for age, sex and first 5 principal components. METALCombining the results from both approaches, the genetic instrument with the strongest effect on fitness which explained the largest proportion of variance in fitness was selected and taken forward to the analyses of association with health outcomes.55. The effect estimates on FI, FPG, 2hrPG and HbA1c were extracted from meta-analysis summary statistics for these traits among European population acquired from the MAGIC investigators working group56. FPG, 2hrPG, HbA1c and natural-log transformed FI levels were first regressed on study-specific covariates including age, age2, sex, genetic principal components and BMI (except for HbA1c) to obtain residuals, which were then rank-based inverse-normal transformed before used as the phenotypes for GWAS analyses. The effect estimates on BMI were obtained from the publicly available meta-analysis results of BMI GWAS from Genetic Investigation of Anthropometric Traits (GIANT) consortium and UK Biobank57. Proxies in high LD  were found for index SNPs not available in the outcome GWAS using the genotyped and imputed data of a random sample of 25,000 unrelated UK Biobank White-British participants as the reference panel, among which the common variant with the smallest p-value was used as the appropriate proxy. The effect alleles from the exposure and outcome GWAS were aligned to the fitness-increasing allele.We conducted two-sample Mendelian randomisation analyses to examine whether genetically predicted fitness levels were causally associated with type 2 diabetes related intermediate traits, including fasting insulin (FI), fasting plasma glucose (FPG), the 2-h post-75 g oral glucose load glucose level (2hrPG), glycated haemoglobin (HbA1c) and BMI. The effect estimates of the optimised genetic instruments on type 2 diabetes were extracted from the meta-analysis of GWAS summary statistics from the DIAMANTE consortium excluding UK Biobank participants 27 to filter out outliers, then took the remaining set of instruments forward for analyses. The inverse variance weighted method (MR-IVW)26 with the random-effects model was used as the primary method. The Cochran’s Q test58 was used to assess heterogeneity between instruments, the MR-Egger regression59 was conducted with the regression intercept examined to assess unbalanced horizontal pleiotropy, and the weighted median and penalised weighted median methods29 which were designed to be robust with the presence of some invalid instruments were also performed. As further sensitivity analyses, Steiger filtering60 was applied to identify reverse directionality (the association between a genetic instrument and the exposure was stronger than its association with the outcome) and MR-PRESSO30 was also applied as an alternative approach to identify horizontal pleiotropic outliers. Subsequently, to assess the direct effect of CRF on T2D, we conducted multivariable MR to account for potential residual horizontal pleiotropic effects by adjusting for the effects of glycaemic traits or BMI in the model individually. Additionally, all intermediate traits were added to the model jointly as covariates in a full multivariable model.For each MR analysis, we first applied the Radial methodhttp://www.mrbase.org), ‘TwoSampleMR’ R-package61 or the ‘MendelianRandomization’ R-package developed by Yavorska and Burgess et al.62.MR analyses were conducted with MR-Base , forced vital capacity (FVC), FEV1/FVC and peak expiratory flow (PEF)) were publicly available and acquired from the study by Shrine et al.52 in UK Biobank. The isometric handgrip strength of UK Biobank participants was measured in an upright sitting position using a Jamar J00105 hydraulic hand dynamometer at the assessment centre at baseline65. The participants were asked to rest their forearms on armrests and squeeze the device as strongly as they could for about 3 s, and the maximum value reached was recorded on the device. One measurement was taken from each hand, and the higher value of the measurements from two hands or the single value for those who only had one measurement was taken forward for analyses. People who did not have either measurement or had a measurement value of ‘0’ for any reason were excluded (n = 1360). The relative grip strength (absolute grip strength divided by body mass or size measures) better reflects overall physical fitness than absolute grip strength66 and is a stronger predictor of cardiometabolic risk68. For this study, relative maximum handgrip strength in UK Biobank was derived as the absolute maximum grip strength divided by fat-free mass measured by bioelectrical impedance analysis (Tanita BC418MA). Another 7926 participants were excluded due to missing fat-free mass measurement. A total of 478,624 individuals were included in the GWAS for fat-free mass weighted handgrip strength. The linear mixed model was adjusted for age at recruitment, sex, genotyping array and first 10 principal components.To obtain genome-wide association results for handgrip strength, a GWAS was performed using BOLT-LMM v2.3z-score (beta/s.e.) >8.9 from the GWAS results. A Bonferroni corrected p-value was used as the significance threshold for genetic correlations (p = 0.05/number of pairs of traits tested).All genetic corrections were tested after removing SNPs with MAF < 0.01, imputation quality info <0.4, located in the MHC region and with a 69. We analysed two behavioural outcomes; total volume of movement and time spent in at least moderate intensity activity (defined as time spent above a movement-related acceleration of 150 mg)71.We also examined the association between genetically predicted fitness and observed physical activity, as measured by wrist accelerometry in a subsample of 71k unrelated White European participants with at least 3 days of valid data as previously describedWe examined association between the 160-SNP fitness genetic risk score for cardiorespiratory fitness and protein targets assessed by the aptamer-based technology (SomaScan©) in 10,707 individuals from the Fenland study using linear regression models. The model was constructed using aptamers as outcome, weighted genetic risk score of fitness as the exposure, and included age, sex, test site (a proxy measure for sample handling which can influence the SomaLogic measures), genotyping array and the first 10 genetic principal components as covariates.72 web-based ‘SNP2GENE’ function to search for candidate variants to select genome-wide significant independent signals  and all the variants that were in LD with the identified independent signals . These candidate variants were then annotated using the Ensembl Variant Effect Predictor (VEP)73 for functional consequences, among which the missense variants were annotated using sorting intolerant from tolerant (SIFT) score and polymorphism phenotyping (PolyPhen) for predicted deleterious effects. As implemented in VEP, LoFtool was used to assess the genic intolerance and consequent susceptibility to diseases based on the ratio of Loss-of-function (LoF) to synonymous mutations, and CADD scale score was also evaluated for relative pathogenicity. Finally, we searched GWASCatalog with the candidate variants for previously associated phenotypes (p < 5 × 10−8).We used Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA)72.Subsequently, to identify potentially relevant genes, the previously identified candidate variants were mapped to protein-coding genes (1) located in or in proximity to (within 10 kb) significant fitness-associated loci defined by the independent signals, (2) significantly associated with expression quantitative trait loci (eQTLs) among the candidate variants (FDR ≤ 0.05) based on a range of database  or (3) showed significant chromatin interaction activities with candidate variants in the CRF-associated loci www.ncbi.nlm.nih.gov/omim), drug-gene pairs were identified from GeneCards v5.0 database  and T2D Knowledge Portal (contains 135 datasets and 261 traits) (www.type2diabetesgenetics.org) for any type 2 diabetes related functions.To find genes that are likely to be involved with biological mechanisms underlying fitness, we also used MAGMA and DEPICT (see details below). We combined the lists of prioritised genes from these two methods with previously mapped genes by FUMA to constitute a prioritised list of genes, which was then used as the input for FUMA ‘GENE2FUNC’ analyses. A systematic search for genes linked with monogenic and Mendelian diseases was completed using OMIM database  EN-TEx and 396 from Roadmap Epigenomics database were used respectively for validation. False Discovery Rate (FDR) ≤ 0.05 was used as the significance threshold for enrichment.We used the stratified LDSC applied to specifically expressed genes (LDSC-SEG) method32 was developed by Pers et al., who utilised information on co-regulation of genes based on expression data and existing annotated gene sets to predict gene functions and generate ‘reconstituted’ gene sets. Each gene is functionally characterised by its membership probabilities across all reconstituted gene sets, and each reconstituted gene sets contain genes with various membership probabilities across the genome.DEPICT (Data-driven Expression Prioritized Integration for Complex Traits)p < 10–5 in the CRF GWAS results. Associated genes were selected using positional mapping, either within or overlap with the CRF-associated loci (LD lock defined by variants have r2 > 0.5 with the independent variants) or closest to the independent variants if no genes were positionally mapped. Genes were prioritised if they share predicted functions with genes from the other fitness-associated loci more often than expected by chance. We also assessed whether any of the reconstituted gene sets of specific biological pathways and cellular processes were significantly enriched for CRF-associated genes. To gain more biological insights, we also utilised DEPICT to identify tissue or cell types where the expressions of fitness-associated genes were enriched based on the data from a set 37,427 expression microarray samples from a total of 209 human tissues and cell types.We applied DEPICT (version 1 rel194) on independent genetic variants pre-clumped using distance clumping (>1 Mb) on SNPs with a significance level of 75. Rather than using a permutation-based method, MAGMA’s gene analysis applies a multiple regression principal component approach, which takes into account of the LD structure between the SNPs, to compute gene-level p-values using an F-test. When only using summary-level data as input, the gene-analysis test was conducted using the mean of the χ2 statistics for the SNPs in the gene.MAGMA  utilised the full genome-wide association summary statistics and data from the GTEx project to identify gene-sets that are enriched based on 5500 ‘Curated gene sets’ and 9,996 Gene Ontology (GO) terms obtained from MsigDB v7.0p < 0.05/19,208) were added the prioritised gene list mentioned previously. The competitive gene-set analysis was then performed using the gene analysis results, to test whether the genes in a gene-set are more strongly associated with CRF than genes not in the gene set using a one-sided two-sample t-test. We also used MAGMA to identify tissue-specific enrichments in 30 general tissue types and 54 specific tissue type expression data from GTEx v8.We applied MAGMA v1.6 embedded in FUMA (SNP-wide mean model) on GWAS results of CRF to perform gene-based and gene-set analyses, where SNPs were assigned to all the protein-coding genes from Ensemble build 92. The genes that were genome-wide significant after Bonferroni-correction  sets. The DEG sets were pre-calculated by two-sided The prioritized genes were also tested against existing pre-defined gene sets obtained MSigDB  and WikiPathway for overrepresentation of biological functions and cellular processes using hypergeometric tests (FDR ≤ 0.05).76 infers the results of PrediXcan77, which applies a gene-based approach to test the mediating effects of predicated gene expression levels (using models trained with eQTL data from GTEx) on phenotype, to prioritise genes with regulatory mechanisms on the phenotype using summary-level GWAS statistics instead of individual-level data. In addition, MetaXcan can also perform tissue-specific analysis using models made available through PredictDB (http://predictdb.org)76.MetaXcan76 to identify genes whose predicted transcription levels were significantly associated with fitness in specific tissue types. Up to 25,834 genes were tested in 48 different tissue types. Bonferroni correction was applied to the MetaXcan test p-values  to select genes that show tissue-specific altered gene expressions.In this study, we used MetaXcanFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Chronic myeloid leukemia (CML) is a hematological tumor derived from hematopoietic stem cells. The aim of this study is to analyze the biological characteristics and identify the diagnostic markers of CML. We obtained the expression profiles from the Gene Expression Omnibus (GEO) database and identified 210 differentially expressed genes (DEGs) between CML and normal samples. These DEGs are mainly enriched in immune-related pathways such as Th1 and Th2 cell differentiation, primary immunodeficiency, T cell receptor signaling pathway, antigen processing and presentation pathways. Based on these DEGs, we identified two molecular subtypes using a consensus clustering algorithm. Cluster A was an immunosuppressive phenotype with reduced immune cell infiltration and significant activation of metabolism-related pathways such as reactive oxygen species, glycolysis and mTORC1; Cluster B was an immune activating phenotype with increased infiltration of CD4 + and CD8 + T cells and NK cells, and increased activation of signaling pathways such as interferon gamma (IFN-γ) response, IL6-JAK-STAT3 and inflammatory response. Drug prediction results showed that patients in Cluster B had a higher therapeutic response to anti-PD-1 and anti-CTLA4 and were more sensitive to imatinib, nilotinib and dasatinib. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO) and Random Forest (RF) algorithms identified 4 CML diagnostic genes , and the risk score model constructed by these genes improved the diagnostic accuracy. We further validated the diagnostic value of the 4 genes and the risk score model in a clinical cohort, and the risk score can be used in the differential diagnosis of CML and other hematological malignancies. The risk score can also be used to identify molecular subtypes and predict response to imatinib treatment. These results reveal the characteristics of immunosuppression and metabolic reprogramming in CML patients, and the identification of molecular subtypes and biomarkers provides new ideas and insights for the clinical diagnosis and treatment.The online version contains supplementary material available at 10.1186/s12935-023-02905-x. Chronic myeloid leukemia (CML) is a hematological tumor with malignant proliferation of hematopoietic stem cells . The PhiWith the development of next-generation sequencing (NGS) technologies, studies based on the levels of gene expression and regulation have shed light on the pathogenesis and malignant phenotypes of many diseases –10. The In this study, we focused on multiple CML transcriptome sequencing cohorts, and found more downregulated genes and significantly fewer immune cells in CML samples compared to normal samples. CML patients can be further classified into immune activating and immunosuppressive phenotypes and have different predictive responsiveness to treatment with TKI drugs and immune checkpoint inhibitors. We further identified 4 genes with diagnostic value by multiple machine learning methods and validated them in validation cohorts.The datasets GSE13159, GSE144119, GSE100026 were downloaded from the Gene Expression Omnibus (GEO) database. For GSE13159 , we downloaded the original “cel” files and normalized it using the robust multiarray averaging (RMA) method. For GSE144119 , we downloaded the count data and converted it to transcripts per kilobase million (TPM) values. GSE100026  is our own CML data, and we converted the fragments per kilobase of transcript per million (FPKM) values to TPM values for subsequent validation. In this study, GSE13159 was used as the discovery cohort for systematic analysis, GSE144119 and GSE100026 were used as the validation cohorts. In addition, GSE13159 contained samples from 750 patients with acute lymphoblastic leukemia, 542 patients with acute myeloid leukemia, 448 patients with chronic lymphocytic leukemia, and 206 patients with myelodysplastic syndromes, which were further used in the differential diagnosis of CML.We performed differential expression analysis using the R package “limma” to identify DEGs between CML and normal samples in the dataset. The threshold for DEGs was set at an absolute value of log2 fold change |log2FC|> 1 and an adjusted P value < 0.05.https://cn.string-db.org/) was used to construct PPI networks, and Cytoscape software was further used to calculate and identify sub networks with the highest connectivity.Functional enrichment analysis was performed by the R package “clusterProfiler”. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used for the identification of biological functions associated with DEGs; Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) algorithms quantified the activity of corresponding signaling pathways or biological behaviors by calculating the enrichment scores of gene sets, and the GSVA score was calculated based on the overall position of the gene set genes in the expression ranking of all genes, and the higher the overall expression level of these genes, the higher the GSVA score. The STRING website  was used to validate the classification.CIBERSORT is a deconvolution algorithm that quantifies the proportion of immune cell infiltration in a sample (22 different cell types) based on gene expression profiles. We further compare the immune cell infiltration characteristics in CML samples and normal samples to reveal differences in the immune microenvironment between the two.https://amp.pharm.mssm.edu/X2K/) for the construction of upstream regulatory network, where regulatory correlations between transcription factors (TFs), kinases and intermediate proteins were calculated based on hypergeometric P values.We used the eXpression2Kinases website (X2K) (https://cancer.sanger.ac.uk/cosmic) via the "pRRophetic" package. Tumor Immune Dysfunction and Exclusion  was considered a good predictor of immunotherapeutic response for molecular subtypes [https://cloud.genepattern.org/gp) was used to predict immunotherapy response to anti-PD-1 and anti-CTLA4 immune checkpoint inhibitors across different molecular subtypes.The half-maximal inhibitory concentrations (IC50) of different patient samples to therapeutic drugs were predicted based on drug response data of blood cell lines from the Cancer Genome Project (CGP) database  and screened for interactions of miRNA-target demonstrated by at least three predicting programs.We identified miRNAs with binding sites to CML diagnostic genes using the ENCORI database  was used to perform RT-PCR on the ABI7500 instrument to determine the expression of 4 diagnostic genes. The results were calculated using 2R software and corresponding packages were used for calculations and analysis. We used Wilcoxon rank sum test and Kruskal–Wallis test to determine differences between two or more groups. Spearman’s method used to analyze the correlation of variables. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of biomarkers. A two-sided P value of < 0.05 was considered statistically significant.To better reveal the differences in gene expression levels between CML and normal samples, we analyzed two cohorts of CML sequencing data. We identified a total of 378 DEGs in the GSE13159 cohort and 3937 in the GSE144119 cohort  Fig. A. The PCWe further predicted the treatment response of different molecular subtypes. The TIDE score reflects the immune escape ability of tumor cells, and we observed a higher TIDE score for Cluster B than Cluster A Fig. F, suggesTo further explore the diagnostic value of DEGs in CML. We used LASSO, RF and SVM-RFE algorithms to identify 13, 30 and 110 feature variables related to CML from DEGs, respectively Fig. A–E. FinaNext, we used LASSO regression analysis to construct a risk score model based on four diagnostic genes to explore the diagnostic value of combining these biomarkers. Figure To explore the relationship between these diagnostic biomarkers and biological characteristics of CML, we analyzed the correlation between their expression and the level of immune cell infiltration and the activity of cancer-related signaling pathways in CML samples, respectively. Among them, IRF4 and SMPDL3A were positively correlated with CD8 + T cells and resting NK cells, but negatively correlated with monocytes; HDC was positively correlated with resting mast cells and negatively correlated with monocytes Fig. A. In addhttps://www.dgidb.org/) to improve clues for related studies, in which omeprazole and lixivaptan may have regulatory effects on HDC  were retrieved. Subsequently, we obtained the differentially expressed miRNAs between CML samples and normal samples from the GSE90773 dataset. Based on these data, we performed the construction of miRNA regulatory network Fig. C. Many sHDC Fig. D.To validate the bioinformatics results, we performed validation in our own sequencing data. The results showed upregulated expression of HDC, significantly downregulated expression of SMPDL3A, IRF4, AQP3, and significantly higher risk scores in CML samples compared to normal samples Fig. A, B, andThe GSE13159 cohort contained samples from 750 patients with ALL, 542 patients with AML, 448 patients with CLL, and 206 patients with MDS, which were further used in the differential diagnosis of CML. PCA analysis was based on the expression of four diagnostic genes for clustering. The results showed that CML patients could be clearly distinguished from other patients except some AML patients overlapped , infiltration of 22 immune cells (B) between the two molecular subtypes.Additional file 2. Table S1. Model genes and “Coef” value.


Editorial on the Research TopicPrecision medicine for antithrombotic therapy in patients after percutaneous coronary interventionsPrecision medicine is an innovative medical model aiming at providing the right treatment to the right patient at the right time . After pKwan Young Lee et al. sought to explore the effectiveness of extended (>24 months) dual antiplatelet therapy (DAPT) in high ischemic risk acute coronary syndrome (ACS) patients undergoing PCI who had no major bleeding after at least 1 year of DAPT. Ischemic risk was defined according to the PEGASUS-TIMI 54 criteria. They found extended DAPT was associated with a lower risk of mortality without increasing the risk of major bleeding among 2 years survivors after ACS who met the PEGASUS criteria and had no major bleeding events before 24 months. Similar results were also observed in the report from the Korean nationwide registry including 273,670 Korean PCI patients by Seung-Jung Lee et al. that suggests prolonged (1–3 years) DAPT may be particularly beneficial in diabetic patients. The further contribution by Ana Lucrecia Marcano et al. explore the pathophysiological mechanisms at the basis of these clinical findings in diabetes mellitus (DM) patients. In their pharmacodynamic, crossover, study randomizing Mediterranean patients with DM to either ticagrelor (n = 20) or clopidogrel (n = 20), the authors found that ticagrelor was associated with greater platelet inhibition after a loading dose and at 1 week, compared with clopidogrel. These finding are in line with previous reports . Their findings suggest both a strategy of de-escalation of P2Y12 inhibitor intensity and a strategy of P2Y12 inhibitor monotherapy may be associated with better outcomes compared to standard DAPT among ACS patients undergoing PCI. Moreover, they speculate that the former strategy may be more effective in reducing ischemic events while the latter strategy may be more effective in reducing bleeding, compared to standard DAPT.The ever growing understanding of the prognostic impact of bleeding events, the availability of less thrombogenic stent platforms and the notion that the ischemic risk is highest during the first 1–3 months after PCI/ACS, have fueled interest in implementing the so-called “de-escalation” strategies , 7. A coLaborante et al. provide a comprehensive summary of the evidence on gender-differences in antithrombotic therapy in ischemic heart disease, discussing the future perspectives for tackling the Yentl syndrome. Demographic characteristics, especially those concerning the different ischemic and bleeding risks across Asian versus Non-Asian patients, are one of the leading confounding factors in the appraisal of RCTs on antithrombotic therapy  C219  C219 . In factYP) C219 . HoweverYP) C219 . InteresZhitong Li et al. adds important information on the additive role of atrial cardiomyopathy, assessed by B-type natriuretic peptide, P-wave terminal force in ECG lead V1, and left atrium diameter, on top of the standard CHA2DS2-VASc score for the prediction of the risk of cerebrovascular events in ACS patients.Finally, the management of antithrombotic therapy in patients with atrial fibrillation and concomitant ACS or PCI still represents a clinical conundrum . In this


Intensive lifestyle interventions (ILI) improve weight loss and cardiovascular risk factors, but health systems face challenges implementing them. We engaged stakeholders to cocreate and evaluate feasibility of primary care implementation strategies and of a pragmatic randomization procedure to be used for a future effectiveness trial.The study setting was a single, urban primary care office. Patients with BMI ≥ 27 and ≥ 1 cardiovascular risk factor were sent a single electronic health record (EHR) message between December 2019 and January 2020 offering services to support an initial weight loss goal of about “10 pounds in 10 weeks.” All patients who affirmed weight loss interest were pragmatically enrolled in the trial and offered “Basic Lifestyle Services” (BLS), including a scale that transmits weight data to the EHR using cellular networks, a coupon to enroll in lifestyle coaching resources through a partnering fitness organization, and periodic EHR messages encouraging use of these resources. About half (n = 42) of participants were randomized by an automated EHR algorithm to also receive “Customized Lifestyle Services” (CLS), including weekly email messages adapted to individual weight loss progress and telephonic coaching by a nurse for those facing challenges. Interventions and assessments spanned January to July 2020, with interference by the coronavirus pandemic. Weight measures were collected from administrative sources. Qualitative analysis of stakeholder recommendations and patient interviews assessed acceptability, appropriateness, and sustainability of intervention components.Over 6 weeks, 426 patients were sent the EHR invitation message and 80 (18.8%) affirmed interest in the weight loss goal and were included for analysis. EHR data were available to ascertain a 6-month weight value for 77 (96%) patients. Overall, 62% of participants lost weight; 15.0% exhibited weight loss ≥ 5%, with no statistically significant difference between CLS or BLS arms (p = 0.85). CLS assignment increased participation in daily self-weighing (43% versus 21% of patients through 12 weeks) and enrollment in referral-based lifestyle support resources (52% versus 37%).This preliminary study demonstrates feasibility of implementation strategies for primary care offices to offer and coordinate ILI core components, as well as a pragmatic randomization procedure for use in a future randomized comparative trial. Health systems also have incentives to intervene before cardiovascular risk factors cause symptoms, functional impairments, or costly health complications.9 However, health systems face challenges implementing ILI,12 which require dedicated personnel, space, supplies, and technologies that generally do not “fit” within the existing structure and routines of healthcare delivery.U.S. health systems are a natural partner for ILI implementation because 5 in 6 adults complete office visits each year,17 These strategies have slightly lower effectiveness than more intensive interventions studied in randomized controlled trials (RCTs),18 but also cost less and reach more people.20 Despite increasing numbers of ILI programs nationally, clinicians refer very few patients to ILI, and most people who may benefit remain unaware.19 There is an immense need for research that identifies feasible and acceptable strategies for health systems to implement ILI.Past research has investigated strategies to simplify the role of health systems in ILI delivery. Examples include reducing the frequency or duration of behavioral coaching contacts or shifting the source of coaching to outside healthcare settings, such as via wellness professionals employed by community organizations or virtual coaching delivered using a computer or smartphone.22 Such an approach may expand reach to patients who are initially unable or unwilling to enroll when referred to a full-scale program offered in a single format or location. Core components of ILI include support for: an initial weight loss goal of at least 5 to 10 kg (11 to 22 pounds) at a pace of about 0.5 to 2 pounds per week;1 resources and training for individuals to self-monitor dietary, activity, and weight changes;23 and longitudinal coaching . Coaching serves to create accountability,24 develop self-regulation and problem-solving skills,26 suggest refinements to behavioral strategies, and build self-efficacy and mastery over behaviors;23 coaching can be effective when offered face-to-face or via a technology platform, with individual participants or groups.1One approach used to improve implementation of multi-component behavioral interventions is to distill them to their core components, and devise simpler implementation strategies for each component that both fit within health system constraints and offer a broader array of ways for patients to engage.Over the past 3 years, we engaged with health system and fitness industry leaders, managers, and service professionals to co-create and evaluate the feasibility of an array of practical implementation strategies for health systems to support ILI core components. To our knowledge this is the first such effort in which all intervention components are imbedded within or coordinated directly by an Electronic Health Record management system. We report on the collaborative approach used for creation of the ILI implementation strategies, as well as preliminary data for feasibility and acceptability of this approach.Our research involves both stakeholder-engaged “co-creation”  of implementation strategies using collaborative design principles and a small pilot trial to demonstrate feasibility and acceptability of the strategies in a single general internal medicine practice setting in downtown Chicago, Illinois. The trial involved pragmatic recruitment, enrollment, and data collection strategies. Eligible patients were enrolled from December 2019 – January 2020; data were collected through January 2021. The experimental services were offered to patients beginning January 2020, and access to some services (details below) were interrupted by the coronavirus pandemic stay-at-home order in March 2020.23 and co-create implementation strategies for sustainable delivery of each core component in busy primary care practice settings. Recurring meetings of a “design team” included behavioral and social scientists, health system nursing supervisors, a practicing primary care physician, two wellness program leaders from the partnering fitness club network, and health system employees who develop and manage new tools and applications within the electronic health record (EHR) system. Individual meetings were held with additional stakeholders, including health system leaders, medical group and practice leaders, and the CEO and wellness program leaders from a regional fitness club network. Research staff members organized meetings, kept detailed notes, verified recommendations and action steps with participants, and integrated ideas into an array of design themes and concepts that informed intervention prototypes, pretests, and refinements.11From September 2016 to December 2019, we engaged stakeholders to consider research evidence for ILI core componentsA clinical data manager utilized EHR reporting tools to generate lists of eligible patients. Inclusion criteria were: age 18–75 years; ≥1 log-in within the past 6 months to MyChart ; completion of ≥ 1 office visit in the past 6 months at the participating primary care practice site; measured weight and height at the most recent office visit indicating body mass index (BMI) ≥ 27.0 kg/m2; and ≥ 1 cardiometabolic risk factor . Patients were ineligible if their last blood pressure was ≥ 180/105 mmHg or last hemoglobin A1c was ≥ 10.9%. Patients were also ineligible if they received cancer treatment in the past 2 years, were hospitalized in the past 3 months, were pregnant, or had personal history of an eating disorder or serious mental illness.The data manager used EHR messaging tools to send each physician a list of their own eligible patients, asking them to reply within 1 week if they wished to remove patients whom they believed offering support for lifestyle change may be inappropriate. After 1 week, the data manager used the EHR's batch patient messaging tool to transmit a standardized outreach “goal setting” message to each patient remaining on the list. The message encouraged healthy eating and physical activity changes with a weight loss goal, and it offered free-of-charge access to technologies and coaching support to help recipients achieve the goal. The message also prompted patients to click an embedded link if they wished to adopt a weight loss goal and receive support. Because limited resources were available to support intervention costs during feasibility testing, we limited recruitment by releasing MyChart messages to 100–200 patients every 2 weeks until a target sample size of 80 patients had affirmed interest in receiving the intervention services. All 80 patients were included in the pragmatic trial evaluation; all received a bundle of Basic Lifestyle Support (BLS) services (below), and about half (N = 42) of patients were randomly assigned by a “silent” EHR algorithm to also receive additional, more Customized Lifestyle Support (CLS) services (below).2 and perceived by stakeholders as technically feasible, acceptable, and financial sustainable.27 This approach followed collaborative design principles,11 and produced ILI implementation strategies for support of weight loss goal setting, self-weighing, and recurring coaching support. Stakeholders advised using the EHR to identify eligible patients, to message them to encourage weight loss goal setting, inspire accountability, and increase engagement in support services, as well as to track and monitor progress as means to offer alternative, higher intensity services to patients facing challenges with automated support alone . Stakeholders cautioned that encouragement of a weight loss goal in a MyChart message could be perceived as inappropriate by some patients; they advised framing the goal and offering resources as means to improve cardiovascular risk factor control. They also advised recommending a relatively simple initial goal of “about 10 pounds over 10 weeks,” believing this would be acceptable, appropriate, and achievable for most patients. Stakeholders advised exploring the feasibility of receiving data from a wireless scale and analyzing that data directly within the EHR as means to inform more personalized feedback about self-weighing, as well as to increase accountability and to identify and target patients needing alternative forms of intervention support. They advised providing all patients with free-of-charge access to coaching services offered by a partnering fitness organization and assigning a practice nurse to provide telephonic coaching to patients who do not make progress towards their weight goal. They emphasized that we evaluate the quality of all new resources, if patients perceive them as appropriate, and if their costs are sustainable.Design goals were to co-create ILI implementation strategies that are evidence-based,Additional File 1) were all deployed or coordinated by the EHR. Interventions commenced when a patient received the introductory MyChart goal-setting message (above) and clicked the link to affirm interest in a weight loss goal. All 80 patients received a bundle of Basic Lifestyle Support (BLS) services. Specifically, they were shipped a wireless weight scale , which had been programmed to transmit weight data back to their electronic chart using cellular networks. Each patient received a follow-up MyChart message, with simple instructions for using the scale to weigh themselves daily, as well as a “coupon code” to enroll free-of-charge in longitudinal lifestyle coaching and support resources offered by a regional fitness organization partner. Patients could choose to access the referral services in 2 formats: face-to-face access to a dietician and fitness coach at 11 facility locations or fully “virtual” access to the same professionals via video chat with a smartphone, tablet, or personal computer. The face-to-face option provided full health club facility access, and the virtual option provided a suite of healthy food preparation and physical activity tips, tracking tools, and videos. Each patient also received instructions for messaging a dedicated nurse-coach at the primary care practice if they had problems using the support services or if they wanted more personalized support for reaching dietary, physical activity, or weight loss goals.The resulting intervention components  of patients who had been randomized to receive “Customized Lifestyle Support” (CLS) services. Specifically, for the first 12 weeks, each patient in the CLS arm was sent an automated weekly MyChart message with coaching tips customized to their use of the eScale in the prior week, whether they had made weight loss progress (defined as ≥ 0.5 pounds of weight loss from the first to last weight measure during the prior week), and whether they had enrolled in referral-based services offered by the fitness partner. A practice nurse used an EHR dashboard to monitor the entire CLS group and attempted to call patients to offer customized “step-up” telephone coaching if either: (1) they had not used their scale in the prior week; or (2) for 2 consecutive weeks they had not achieved ≥ 0.5 pounds of weight loss.28We collected quantitative data from administrative sources and qualitative information from interviews with randomly selected subsets of representative patients. Beginning approximately 12 weeks after launch of the trial, we conducted semi-structured telephone interviews with 15 representative patients to assess perceptions of the appropriateness, ease of access, ease of use, acceptability, desirability, credibility, and usefulness of the intervention components in helping them achieve health related goals.Several electronic sources provided data for patient characteristics and outcomes. EHR data spanning +/− 15 months of the launch of the trial provided information on the baseline characteristics, service utilization patterns, and metabolic and biometric outcomes. The partnering fitness network provided data about patient participation in the two coaching support options and facility use. Analysis of body weight changes utilized data from the EHR and electronic scales . The last EHR weight measure prior to each patient’s randomization date served as the baseline body weight, and the EHR weight measure closest to the date of randomization plus 6 ± 3 months served as the follow-up weight. For patients who did not have such a follow up EHR weight value, we estimated the 6-month value as one-half the difference between the baseline value and the EHR weight value closest to 12 ± 3 months. For participants missing both a 6- and 12-month weight value in the EHR, we used the eScale weight value closest to 6 ± 3 months. Finally, if individuals were missing all those weight values, we assumed that the participant failed to reach 5% weight loss.Multiple data sources were used to describe intervention activities and activity-related costs. Input from health system leaders and study team members identified the specific personnel, supplies, contract services, and facility and administrative inputs needed to implement each intervention component. Administrative data systems captured the number and type of contacts made between nurses and study participants. Staff members were interviewed to estimate the amount of time they spent supporting different intervention activities over 6 and 12 months. Costs of eScales, community intervention program fees, and development costs for health IT components were determined from invoice amounts. Maintenance costs for IT support was estimated from interviews of IT staff members, who were asked to estimate hours per week spent supporting the intervention components.We examined univariate and bivariate descriptive statistics and conducted chi-square tests to compare all patients randomized to CLS and BLS arms on the proportion that reached the dichotomous primary outcome of ≥ 5% weight loss at 6 months. Outcome assessors were blind to the randomized study group assignment at the time of analysis. Transcripts of interviews with patients were analyzed to summarize perspectives raised about acceptability, appropriateness, and feasibility of ILI implementation strategies. Example themes and representative quotes were selected by group deliberation and consensus.The eligibility list for the feasibility trial included 2,146 patients. Over 6 weeks, the clinical data manager sent names of 442 patients to 34 primary care clinicians for pre-review; clinicians asked that only 16 (3.6%) of these patients not be contacted. A single outreach Mychart message was sent to each of the remaining 426 patients, of whom 315 (73.9%) opened the message within 4 weeks, and 80 (18.8%) affirmed interest in the weight loss goal .Demographic characteristics of the 80 participants  approximated the characteristics of the overall target population, with slight over-representation of women and those identifying as Hispanic/Latinx, non-Hispanic Black, or non-Hispanic Asian . In the All but 3 (3.8%) patients were able to initialize their eScale and register a baseline weight. Over the 12-week MyChart messaging phase of the intervention, 11 (14%) of participants did not use their scales for self-weighing, 7 (9%) participated in weighing in some but not all weeks, and 62 (78%) engaged in self-weighing during all 12 weeks. Self-weighing daily (5 to 7 days per week) was performed by 26 (33%) of patients during all 12 weeks; more patients assigned to CLS (customized weekly messaging) performed daily self-weighing in all weeks (43% versus 21% of those offered BLS).Overall, 36 (45%) patients (37% of the BLS arm and 52% of the CLS arm) enrolled in one of the two referral-based coaching platforms offered by the fitness industry partner; 22 (27.5%) elected the face-to-face option and 14 (17.5%) elected fully virtual access. Patients enrolled in these services between 15 and 77 days after receiving the initial outreach MyChart message. Access to fitness facilities  was disrupted by a coronavirus pandemic stay-at-home order beginning the third week of March 2020. At that time, patients had been enrolled in the referral-based services for 19 to 75 days; the 22 patients in face-to-face support had completed an average of 3 (range 1 to 6) individual coaching sessions and attended a partnering fitness facility 0 to 24 times; the 14 patients receiving “virtual” support had completed an average of 4 (range 2 to 5) individual coaching sessions prior to the pandemic but were able to complete an average of 3 additional (range 0 to 5) coaching sessions after the pandemic began.Among patients randomized to CLS, 41 (98%) received at least one step-up telephonic nurse coaching call; 19 (45%) received ≥ 5 nurse calls  over the 12-week intensive support phase.Despite lower than usual office visit rates during the early phase of the coronavirus pandemic, 44 (55%) of feasibility trial patients had an office weight measure recorded in the EHR at 6 ± 3 months from their trial enrollment. Among the remaining 36 patients, 26 (72%) had a weight recorded at 12 ± 3 months, and of the remaining 10 patients, 7 (70%) had an eScale weight received at 6 ± 3 months. Thus, our pragmatic data capture strategy enabled estimation of a 6-month weight change for 77 (96%) of all trial participants. The average time between trial enrollment and the weight used for 6-month data analysis was 184.5 days. Overall, 62% of participants had lower weight at follow up than at baseline; 15.0% exhibited a weight loss of ≥ 5% of baseline weight, with no statistically significant difference between CLS or BLS arms (p = 0.85).some point during the 12-week automated messaging phase. Thus, half of patients who achieved a 5% weight loss during the 12 weeks of active support maintained the loss at 6 months.Using eScale data and assuming that the 14% of patients who did not self-weigh also did not lose weight, 28 (35%) of all participants achieved ≥ 10 lbs of weight loss  and 24 (30%) achieved ≥ 5% of weight loss at Additional File Table 3. Mean health system costs to offer the intervention as delivered during the feasibility trial were estimated at $335 per person over the first 6 months  and $150 per person over the second 6 months .Direct medical costs associated with offering each intervention component are summarized in Additional File Table 2.Among the 80 feasibility trial participants, 30 were randomly selected and approached before 15 agreed to complete the telephone interview; 11 were unreachable, 5 declined participation, 2 asked to be recontacted at a future time. Respondents included 5 men and 10 women, with demographic and clinical characteristics generally representative of the overall target population . PatientTrust in the person or organization providing lifestyle support services emerged as an important theme. Importantly, some patients expressed being distrustful when offered resources for “free” from a commercial partner, unless it was clear that their doctor or the health system were recommending and coordinating the activities. Others indicated that they had not initially realized they could access dietary and physical activity coaching at the partnering fitness organization and were unsure how to engage with the practice nurse if they had problems.Most patients described the Mychart messages and website resources as generally easy to understand and navigate. However, some reported feeling frustrated or less engaged by not having a clear understanding upfront for how the different intervention components were coordinated or should be utilized. weight loss goal was motivated by other health or social goals, such as reducing the need for more pills to treat high blood pressure.Nearly all patients reported that an initial weight loss goal of about 10 pounds in 10 weeks was appropriate and achievable. Some expressed interest in larger weight loss goals and access to services beyond 10 to 12 weeks. Many patients reported that their critical importance of supportive accountability in successfully changing behavior. They emphasized the practice nurse, registered dietitian, and physical trainers as key sources of supportive accountability, more so than the automated messages. Patients expressed strengths and limitations of the automated messages, highlighting that the messages were a helpful reminder or source of encouragement when making progress but were insufficient or even demoralizing when not making progress.Several patients highlighted the frustration when they viewed eScales (and self-weighing) as a direct source of support, rather than as a means for healthcare providers and coaches to track and customize their support. Importantly, some viewed daily weighing as a source of stress and felt that no matter how closely they followed diet and physical activity plans, their daily weight remained unpredictable. Several patients highlighted failure expectations stemming from negative experiences with prior weight loss attempts. Those patients recommended a greater focus on recognizing and addressing failure expectations, as well as de-emphasizing a focus on the daily weight readings as a sign of success or failure, while offering more immediate support beyond automated messaging. Relatedly, some patients highlighted how emotions have a larger influence on dietary behaviors than reminders to eat healthy. Those patients recommended greater focus on acknowledging and addressing emotions both as drivers and consequences of attempts to lose weight and change diet.Many patients viewed self-weighing as an additional source of accountability. Some expressed interest in more elaborate electronic scales or integration with other technologies, such as smartphone apps that provide immediate and customized support. Other patients indicated that they preferred support coming from a member of the healthcare or fitness center team. Generally, patients expressed the coronavirus pandemic presented a profound barrier to healthy behaviors and weight loss. Many indicated frustration with becoming more sedentary, adopting ‘stress eating’ behaviors, and gaining weight. The pandemic restrictions eliminated access to fitness facilities, which added to frustration.Finally, all patients indicated that the abrupt start of gain was common.32This preliminary work demonstrates the feasibility of several pragmatic implementation strategies for primary care offices to support the core components of intensive lifestyle interventions. After a single outbound MyChart message was sent to encourage a weight loss goal by overweight or obese adults with cardiovascular risk factors, 3 in 4 recipients opened the message, and 25% of those who opened it affirmed interest in receiving support services lose weight. Among those patients, 30% achieved ≥ 5% weight loss in the first 12 weeks and, despite the disruptive impacts of a pandemic, about 15% still exhibited ≥ 5% weight loss at 6 months, at a time when weight Weekly “nudging” of patients with a customized MyChart message plus step-up telephone coaching by a practice nurse increased participation in daily self-weighing (43% versus 21% of patients through 12 weeks) and enrollment in referral-based lifestyle support resources offered by a partnering fitness organization (52% versus 37%). Though this small feasibility trial did not demonstrate whether patients are more likely to reach weight loss goals when provided these more customized services, this approach warrants further study.17 but we are unaware of other examples where the intervention components have been fully embedded within an EHR system. Our approach employed design thinking with service professionals, health system leaders, health payers, and researchers to create practice components that not only are evidence-based but also desirable to implementers, technically feasible, and financially sustainable.11 Design prototypes were iteratively pre-tested and refined with stakeholder collaboration, and a small feasibility trial demonstrated strong promise for both effectiveness and sustainability.Our work is consistent with other recent studies of pragmatic, technology-facilitated lifestyle interventions in primary care settings,36 However, most past research focused on referrals to a comprehensive program bundle, such as the National Diabetes Prevention Program (NDPP), often offered in a single format or location.37 Offering a multi-component program like NDPP requires considerable community organizational capacity and financial investment, which imposes implementation barriers,39 particularly when enrollment is low or third party payment is limited. Another unique aspect of our approach was to distill ILI programs down to core components and to develop implementation strategies for each component to minimize demands placed on the implementing organization.22 We also considered howto offer the resulting services using an array of different channels  to maximize reach by appealing to different segments of the target population.Our work also builds on past research evaluating referral linkages between health systems and community-delivered intensive lifestyle interventions.41 It will be important for future research to evaluate these dimensions of implementation in the context of a larger and more definitive effectiveness trial.Conceptually, the development of discreet implementation strategies for individual ILI components has appeal if they successfully minimize workflow disruptions, distribute costs of the new activities across different partners, and map costs to existing pathways for health payer reimbursement. Possible disadvantages are that the individual components may not prove as effective in isolation, and additional strategies must be developed to coordinate staff roles, technologies, and patient transitions among the practice components. Some patients may still face access barriers if, for example, referral-based resources are limited or health payers do not provide reimbursement for remote weight monitoring or lifestyle counseling activities by non-physicians.This preliminary research indicates that strategies to implement ILI core components are feasible for primary care practices and generally acceptable to patients. Further research should seek to optimize implementation strategies to maximize feasibility, acceptability, sustainability, and reach, as well as to demonstrate effectiveness and cost-effectiveness compared with alternative service models. Engaging stakeholders in co-creation of these strategies holds promise to improve both implementation and reach of ILI components in new ways that achieve population health and health equity for millions of Americans who are currently overweight or obese and already engaged by the healthcare sector.