Effect of a Dietary Intervention on Weight Loss in Adults with High vs Low Genetic Predisposition to Higher BMI: A Randomized Diet Intervention Trial

Effect of a Dietary Intervention on Weight Loss in Adults with High vs Low Genetic Predisposition to Higher BMI: A Randomized Diet Intervention Trial | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Effect of a Dietary Intervention on Weight Loss in Adults with High vs Low Genetic Predisposition to Higher BMI: A Randomized Diet Intervention Trial View ORCID Profile Rodosthenis S. Rodosthenous , View ORCID Profile Leena E. Viiri , Anne Carson , View ORCID Profile Tatiana Cajuso , View ORCID Profile Andrea Corbetta , View ORCID Profile Samuel Jones , View ORCID Profile Ettore Brenna , FinnGen , Enni Makkonen , Minttu Virolainen , Niina Korkeala , View ORCID Profile Elisabeth Widén , View ORCID Profile Samuli Ripatti , View ORCID Profile Andrea Ganna doi: https://doi.org/10.1101/2025.10.06.25337395 Rodosthenis S. Rodosthenous 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rodosthenis S. Rodosthenous Leena E. Viiri 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland 2 Finnish Clinical Biobank Tampere, Tampere University Hospital, Wellbeing Services County of Pirkanmaa , Tampere, Finland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leena E. Viiri Anne Carson 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tatiana Cajuso 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland 3 Department of Pathology, Dana-Farber Cancer Institute and Harvard Medical School , Boston, MA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tatiana Cajuso Andrea Corbetta 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland 4 MOX - Laboratory for Modeling and Scientific Computing, Department of Mathematics , Politecnico di Milano, Milan, Italy MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Corbetta Samuel Jones 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samuel Jones Ettore Brenna 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ettore Brenna FinnGen 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Enni Makkonen 2 Finnish Clinical Biobank Tampere, Tampere University Hospital, Wellbeing Services County of Pirkanmaa , Tampere, Finland MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Minttu Virolainen 2 Finnish Clinical Biobank Tampere, Tampere University Hospital, Wellbeing Services County of Pirkanmaa , Tampere, Finland BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Niina Korkeala 2 Finnish Clinical Biobank Tampere, Tampere University Hospital, Wellbeing Services County of Pirkanmaa , Tampere, Finland MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elisabeth Widén 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elisabeth Widén Samuli Ripatti 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samuli Ripatti Andrea Ganna 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki , Finland 5 Broad Institute of MIT and Harvard , Cambridge, MA, USA 6 Analytic and Translational Genetics Unit, Massachusetts General Hospital , Boston, MA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Ganna For correspondence: andrea.ganna{at}helsinki.fi Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT It is unclear whether the effectiveness of weight loss interventions differs based on genetic predisposition to higher body mass index (BMI). Genome-wide polygenic scores (PS) for BMI are the strongest genetic predictors of elevated body weight, but no prospective trials have evaluated weight loss responses based on BMI PS. To assess whether adults with high or low genetic predisposition to higher BMI experience differential weight loss during a 6-month dietary intervention, we designed the GENEROOS study, a single-site, randomised controlled trial conducted in Finland from October 2023 to November 2024. Participants (N=223), aged 30–65 years, with a BMI of 23–36 kg/m² and no diabetes, were recruited from 38,621 genotyped individuals in the Finnish Clinical Biobank Tampere. They were selected from the top or bottom 5th percentile of the BMI PS distribution and randomized to either a six-month dietary coaching program aimed at a 500 kcal/day energy deficit or to a control group receiving no dietary intervention. The main outcome was change in body weight (%) from baseline to 6 months and the interaction between BMI PS and intervention status. At baseline, those in the top 5% of BMI PS weighed on average 8.4 kg (95% CI, 5.1 to 11.7 kg) more than those in the bottom 5%. In the intervention group, mean weight change at 2, 4, and 6 months was −3.89%, −5.32%, and −4.70%; in the control group it was −1.10%, −0.85%, and 0.14%, respectively. No significant interaction was found between BMI PS and intervention on weight change (β = 0.06 [95% CI, −1.33 to 1.44]; P = .94). In this randomised trial, genetic predisposition as measured by BMI PS did not modify response to dietary intervention, indicating that leveraging polygenic scores may not enhance the effectiveness of personalized weight loss strategies. Trial Registration ClinicalTrials.gov Identifier: NCT06092372 INTRODUCTION The prevalence of overweight and obesity is increasing globally, and it is a major public health concern. By 2035, 54% of the global adult population is estimated to be either overweight (body mass index (BMI) ≥ 25 kg/m2) or obese (BMI ≥ 30 kg/m 2 ) 1 . High BMI is a major risk factor for several chronic diseases including cardiovascular disease 2 , diabetes mellitus, chronic kidney disease 3 and certain cancers 4 which can lead to premature mortality. Overweight and obesity are complex, multifactorial conditions influenced by both genetic and environmental factors. Environmental factors contributing to a high BMI include unhealthy diet, low physical activity, and sedentary behaviors 5 , 6 . Despite environmental factors playing an important role, the heritability of BMI is estimated to be moderate to high, ranging from 0.23 to 0.90 depending on study design, age group, and population 7 – 9 . Twin and family studies consistently demonstrate substantial genetic contribution to BMI variability, with higher heritability estimates in children compared to adults, suggesting that genetic factors may exert a stronger influence early in life 7 . While rare monogenic forms of obesity exist, most genetic susceptibility arises from the cumulative effects of common single nucleotide polymorphisms (SNPs). To date, over a thousand SNPs have been associated with BMI 10 – 12 . Polygenic scores (PS), which aggregate the effects of thousands of SNPs, explain approximately 8.4% of BMI variation 8 , capturing only a fraction of the heritability estimated from twin and family studies 7 . In the UK Biobank, individuals in the top PS decile had a 25-fold higher risk of severe obesity (BMI > 40 kg/m²) compared to those in the bottom decile 8 . However, PS is not deterministic, emphasizing that while genetic susceptibility plays a significant role, the interplay with the environment is crucial in shaping BMI over a lifetime. Numerous dietary intervention studies have aimed to reduce body weight in overweight and obese individuals. A meta-analysis of clinical trials reported a mean weight loss of 5 to 8.5 kg (5% to 9%) during the first 6 months following reduced-energy diet interventions 13 . While the role of genetic variation in diet response remains unclear, some studies have suggested that genetic factors may influence individual responses to dietary and lifestyle interventions, while others not 14 . For example, a meta-analysis including retrospective analysis of diet interventions, found that individuals carrying the homozygous FTO obesity-predisposing allele experienced greater weight loss through diet and lifestyle changes compared to non-carriers 15 . On the contrary, a meta-analysis by Livingstone et al., found that carriers of the FTO minor allele had a similar response to diet, drug or lifestyle interventions compared to non-carriers 16 . To the best of our knowledge, the DIETFITS study is the only randomized trial that prospectively tested whether a three-SNP genetic score modified weight change in response to a healthy low-fat versus a healthy low-carbohydrate diet, but no significant differences were observed 17 . Genome-wide PS offer a substantially more powerful measure of genetic risk for BMI than individual SNPs such as those in the FTO gene. Two large behavioral intervention trials have investigated BMI polygenic scores or obesity-associated SNPs in relation to weight loss outcomes. Papandonatos et al. evaluated 91 BMI-associated SNPs from the Diabetes Prevention Program (DPP) and Look AHEAD trials, finding no consistent evidence that genetic risk predicted weight loss or regain 18 . More recently, the ONTIME study by Dashti et al. employed a genome-wide polygenic score and identified that higher BMI PS was associated with a slower rate of weight loss during a structured behavioral intervention, though no association with total weight loss or interaction with intervention intensity was found 14 . However, existing studies have been limited by observational designs, absence of control groups or lack of targeted recruitment based on genetic risk. The GENEROOS study was designed to fill this gap by prospectively testing whether BMI polygenic risk affects weight loss following a dietary intervention. To further maximize power, we used a biobank-based genetic-first recontact strategy, recruiting participants at the extremes of BMI genetic risk from a pool of 38,621 already-genotyped individuals as part of FinnGen study 19 . The primary objective of the GENEROOS study was to test whether overweight or mildly obese non-diabetic individuals in the top and bottom 5 th percentile of BMI PS show differential success in 6-month weight change due to dietary and lifestyle intervention, compared to controls. RESULTS Among 38,621 genotyped participants, 959 met initial eligibility criteria and were invited to the study, 529 in the Top 5% and 430 in the Bottom 5% of the BMI PS. Out of those invited, 642 (66.9%) did not respond, 79 (8.2%) responded but did not meet the study eligibility criteria, and 15 (1.6%) did not enroll in the study, resulting in 223 participants (23.3%) completing the first in-person visit and being randomized (mean age, 51.1 [SD, 10.4] years; 74.9% women; mean body mass index, 30.3 [SD, 3.2]). The study enrolment rate was very similar to the 23.1% we observed in a recent biobank-based recall study in Finland 20 . The flow of the study population through the study is shown in Figure 2 . Download figure Open in new tab Figure 1: GENEROOS study design. Download figure Open in new tab Figure 2: Flow diagram for GENEROOS study participants. There was no difference in demographic, anthropometric and biomarkers characteristics between study arms at baseline ( Table 1 ). While we observe overall more individuals belonging to the top 5% of BMI PS than in the bottom 5% (N=141 [63.2%] vs N=82 (36.8%)), the distribution of two groups was similar between study arms ( Figure 2 ). At baseline, individuals in the top 5% of BMI PS had on average, 3.0 kg/m 2 (95% CI, 2.2 to 3.8 kg/m 2 ) higher BMI and 8.4 kg (95% CI, 5.1 to 11.7 kg)) higher body weight than those in the bottom 5% of BMI PS ( Suppl. Figure 2 ). View this table: View inline View popup Table 1: Baseline descriptive characteristics of the GENEROOS study participants. Primary Outcome Of all individuals that participated in the baseline visit, 13 (5.8%) did not return to the 6 months follow-up visit. The proportion of lost at follow-up was similar between the diet intervention and control group (6.2% vs 5.5%). In the diet intervention arm, the mean change (%) in body weight at 2 months was −3.89 (95% CI, −4.40 to −1.97, n=109), at 4 months was −5.32 (95% CI, −6.18 to −4.46, n=108), and at 6 months (end of study) was −4.70 (95% CI, −5.78 to −3.61, n=106), whereas in the control group the mean change (%) in body weight at 2 months was −1.10 (95% CI, −1.49 to −0.70, n=107), at 4 months was −0.85 (95% CI, -1.41 to −0.29, n=106), and at 6 months (end of study) was 0.14 (95% CI, −0.54 to 0.82, n=104) ( Figure 3 ). At 6 months, mean weight change (kg) was −4.02 kg (95% CI, −5.96 kg to −3.08 kg) for the diet intervention and 0.04 kg (95% CI, −0.57 kg to 0.65 kg) for the control group, which was statistically significantly different (p<0.001). On the contrary, the mean 6-month weight change (kg) by BMI PS group was not significantly different with −1.9 kg (95% CI, −3.1 kg to −1.3 kg) in the top 5% BMI PS and −2.2 kg (95% CI, −2.8 kg to −1.1 kg) in the bottom 5% BMI PS. Further, we did not observe a statistically significant interaction (P=0.94) between the BMI PS group and diet intervention when using linear mixed models including self-reported weight measurements at month 2 and 4 ( Table 2 ). Download figure Open in new tab Figure 3: Change in body weight (%) between the control and diet intervention by the PS group (N=223). P-value at each time point was calculated using Mann-Whitney U (Wilcoxon rank-sum) non-parametric test. View this table: View inline View popup Download powerpoint Table 2: Association between bodyweight change (%) and BMI PS among study participants from linear mixed effects models (N individuals = 223 and N observations = 863). All covariates used in the model are reported. Non-prespecified exploratory analysis Individuals in the diet intervention group compared to controls showed lower levels (mean, 95% CI, mmol/l) of total cholesterol (−0.205, 95% CI: -0.329 to -0.081 vs -0.009, 95% CI: -0.141 to 0.122), LDL cholesterol (−0.237, 95% CI: -0.351 to -0.122 vs -0.056, 95% CI: -0.171 to 0.059), triglycerides (−0.218, 95% CI: -0.360 to -0.075 vs -0.080, 95% CI: -0.236 to 0.076), HbA1C (−0.623, 95% CI: -0.988 to -0.257 vs -0.058, 95% CI: -0.691 to 0.575), glucose (−0.142, 95% CI: - 0.257 to -0.026 vs -0.003, 95% CI: -0.113 to 0.107), but not HDL cholesterol (−0.018, 95% CI: - 0.053 to 0.017 vs -0.008, 95% CI: -0.042 to 0.026) ( Suppl. Fig. 3 ). DISCUSSION It remains unclear whether genetic factors contribute to individual differences in weight loss following dietary interventions. In this prospective randomized study of overweight and mildly obese, non-diabetic individuals with extreme high or low genetic risk for BMI, a six-month dietary intervention effectively reduced body weight (−4.7%) and improved key cardiometabolic biomarkers, including LDL cholesterol, triglycerides, glucose, and HbA1c, regardless of genetic risk group. However, weight loss was similar across genetic risk groups, suggesting that genetic predisposition to high BMI, as measured by a genome-wide polygenic score does not significantly modify the effectiveness of diet and lifestyle interventions for weight reduction. Two previous meta-analyses 15 , 16 have reported conflicting results on the differential impact that diet interventions have on FTO risk allele carriers. BMI PS capture a substantially larger proportion of genetic risk for BMI compared to single variants such as FTO – which is also included in the PS - providing greater power to detect differential dietary effects. In our study, the differences in BMI between high and low PS groups (31.3 kg/m 2 vs. 28.4 kg/m 2 ) were substantially larger than what reported between carriers and non-carriers of the homozygous FTO risk allele (an increase of 0.31 kg/m 2 (0.14 to 0.47); P<0.001) for each additional copy) 16 . It remains possible, however, that the biological pathways linked to specific variants like FTO may influence diet responsiveness differently from the broader genetic effects captured by BMI PS. Our findings are concordant with prior large-scale studies suggesting limited or no influence of genome-wide BMI polygenic scores on weight loss response to behavioral interventions. In the Look AHEAD and Diabetes Prevention Program (DPP) trials, only one of 91 known BMI-associated variants (MTIF3-rs1885988) showed an association with total weight loss, underscoring a minor role of individual genetic variants in modifying intervention outcomes. Similarly, in the ONTIME study—a genome-wide analysis of behavioral weight loss in over 1,200 individuals—a higher BMI polygenic score was associated with a slightly attenuated rate of weight loss, but not with total weight loss or final outcomes. It is noteworthy that the weight loss achieved through the dietary intervention (−4.02 kg) was approximately half of the baseline difference in body weight between BMI PS extremes (8.23 kg). This suggests that, although the intervention was equally effective across genetic risk groups, it was not sufficient to overcome the underlying genetic differences in body weight between the PS extremes. Our study has several limitations. First, there was a higher proportion of women (74.9%) than men, likely due to known sex differences in volunteer participation, though diet effectiveness was similar across sexes. Second, only 36.8% of participants were from the bottom 5th percentile of BMI PS, as many were excluded for having BMI below 23 kg/m² at screening. Third, we did not collect detailed calorie intake data, limiting insights into behavioral mechanisms of weight loss; however, improvements in cardiometabolic biomarkers suggest meaningful physiological changes beyond weight loss. Fourth, although we used the most advanced BMI PS available at the time, it does not fully capture genetic contributions to BMI. Future studies should test whether newer, more predictive scores replicate these findings. Finally, generalizability may be limited to populations with the resources, interest, and digital literacy to engage in a structured dietary intervention. A major strength of this study is its prospective, randomized design, in contrast to retrospective analyses often used to evaluate genetics in dietary response. Using a genetic-first recontact strategy from a cohort of over 38,000 genotyped individuals, we recruited participants at the extremes of BMI genetic risk, maximizing power to detect differential effects. Although our hypothesis was not supported, this approach shows how biobank-based genetic recruitment can accelerate precision medicine trials. Another strength is the inclusion of a non-intervention control group, the gold standard for testing dietary intervention efficacy. Despite the potential for higher dropout in control groups, follow-up loss was <5% and similar across groups, supporting the robustness of our findings. Finally, whereas most weight loss trials target individuals with obesity and co-morbidities (e.g., type 2 diabetes), we focused on overweight participants without diabetes (BMI 23–36), a population more representative of those who might benefit from preventive interventions. In conclusion, this 6-month randomized dietary intervention study found no evidence that BMI polygenic scores predict differential weight loss outcomes among overweight and mildly obese non-diabetic individuals. Our findings suggest that, in this population, current genome-wide BMI polygenic scores are not helpful in identifying who would benefit most from dietary and lifestyle interventions aimed at reducing body weight and improving cardiometabolic health. These findings underscore the importance of critically evaluating genetic tools before integrating them into clinical decision-making. Data Availability De-identified individual participant data underlying the results reported in this article, including data dictionaries, will be made available via the European Genome-Phenome Archive (EGA; https://ega-archive.org ) by searching for the study ID (to be provided), requesting access, and getting approval by the GENEROOS study research team. The shared dataset includes all primary and secondary outcomes, baseline characteristics, and other variables used in the analysis. Data will be available beginning at the time of publication and for a period of five years. Access requests must include a methodologically sound proposal, will be subject to review within four weeks, and require a valid EGA account. Data reuse for commercial purposes or inclusion of external collaborators not named in the original proposal requires Steering Committee approval. All data will be de-identified in accordance with relevant regulations. Secondary users must agree not to attempt re-identification of participants and must cite the original trial ( ClinicalTrials.gov : NCT06092372 ) and specify how their analyses differ from previously reported findings. AUTHOR CONTRIBUTIONS Drs Rodosthenous and Ganna had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Rodosthenous, Widén, Ripatti, Ganna Acquisition, analysis, or interpretation of data: Rodosthenous, Viiri, Corbetta, Jones, Brenna, Makkonen, Virolainen, Korkeala Administrative, technical, or material support: Carson, Cajuso, Viiri, Makkonen, Virolainen, Korkeala Statistical analysis: Rodosthenous, Brenna Drafting of the manuscript: Rodosthenous, Cajuso, Brenna, Ganna Critical review of the manuscript for important intellectual content : All authors. Obtained funding: Ganna Supervision: Widén, Ripatti, Ganna Final approval of the version to be published: All authors. COMPETING INTERESTS Dr. Andrea Ganna is the founder of Real World Genetics Oy, Finland. Ettore Brenna is an employee of Eli Lilly and Company, Indianapolis, Indiana USA. All other authors declare no potential conflicts of interest. METHODS Study design The Role of Genetics in Weight Loss Study (GENEROOS, ClinicalTrials.gov: NCT06092372 ) was a 6-month, single-site, parallel-arm diet intervention trial ( Figure 1 , created in BioRender. Rodosthenous, R. (2025) https://BioRender.com/y4ujzed ). The approved protocol, including the study design and statistical analysis plan can be found in Supplementary Material File 1. In brief, participants were enrolled between October 2, 2023, and November 8, 2024. Under the Finnish Biobank Act (688/2012), biobanks may recontact individuals who have consented to such contact. Eligible individuals for recontact had to be alive, have consented to recontact via the Finnish Clinical Biobank Tampere, have genetic data from FinnGen release 11, be in the top or bottom 5th percentile of the BMI PS distribution, aged 30–65 years, and have a BMI of 23–36 based on their latest electronic health record (EHR) from Tampere University Hospital. Those without a recorded BMI were also eligible. Using nationwide inpatient and outpatient registers, we excluded individuals with a diagnosis of Type I or II Diabetes (ICD-10: E10–E14), prior bariatric surgery (Operation codes: JDF00–01, JDF10–11, JDF20–21, JDF96–97, JFD03–04, JFD20, JFD96), schizophrenia or intellectual disability (ICD-10: F20–F29, F70–F79), dementia or Parkinson’s (ICD-10: F00–F09, G20, G30), visual impairment (ICD-10: H54), or multiple sclerosis (ICD-10: G35). Ethics statement The GENEROOS study was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS/583/2022). No adverse effects, risks, or significant discomfort were anticipated for participants, aside from minor discomfort from blood draws. In accordance with Ethics Committee requirements, participants were informed about procedures for addressing any significantly abnormal laboratory findings that might require medical attention. Study subjects were also included in FinnGen and provided informed consent for biobank research, based on the Finnish Biobank Act. Details on the FinnGen study can be found in Supplementary Material File 2. Re-contacting approach Eligible individuals were invited via physical letter from the Finnish Clinical Biobank Tampere. Interested participants logged into the study portal ( https://omabiopankki.fingenious.fi/login ), provided electronic informed consent, and completed an eligibility survey in RedCap 21 , which collected self-reported height, weight (for BMI calculation), pregnancy status, motivation to lose weight, and smartphone ownership (see Supplementary Material File 3) Those with a BMI between 23–36, a smartphone, interest in weight loss, and—if female—not pregnant or breastfeeding, were eligible to enroll and book a clinical lab visit. All eligible participants were randomized to the control or diet intervention group using computerized randomization (randomizeR package, R version 2024.12.0+467). Group assignment was revealed by the research nurse only after the baseline lab visit. Participants were blinded to their PS group, and outcome assessors were blinded to PS group and lab measures, though not to intervention assignment. Blood sample collection and biomarker analysis Fasting whole blood samples (3 x 3 mL) were collected by a trained bioanalyst in EDTA tubes (BD Vacutainer, catalog number 368499), heparin tubes (BD Vacutainer, catalog number 368497), and Vacuette glucose tubes (Greiner Bio-One, catalog number 454514), both at baseline and follow-up visits and shipped to Fimlab (Tampere, Finland) for biomarker analyses (i.e., total cholesterol, LDL, HDL, triglycerides, glucose and HbA1c). A non-prespecified exploratory analysis compared changes in all measured biomarkers between the diet and control groups. The results of biomarker analyses from both baseline and follow-up were returned to all study participants who provided informed consent and agreed to receive them at the end of the study. All biomarkers were analyzed in Assessment of the primary outcome We calculated the participants’ BMI four times during the study: at baseline, two months and four months from enrollment and at the end of the study ( Figure 1 ). At baseline and end of the study, participants’ BMI was calculated using the body weight and height as measured by a trained nurse in the clinic, whereas at months two and four was calculated using self-reported body weight and height via a text message. Participants were advised to weigh themselves in the morning, before breakfast, and after emptying their bladder. To ensure validity of self-reported BMI data at month two and four, we compared the baseline self-reported BMI collected from the online eligibility survey and in-person BMI measurements. The correlation between the two BMI measurements was high (Spearman’s rho = 0.93, Suppl. Fig 1 ). Polygenic score for body mass index We derived a polygenic score (PS) for BMI using summary statistics from two sources: (1) Yengo et al., which performed a GWAS of BMI in approximately 700,000 genotyped individuals 12 , and (2) a GWAS of BMI in 315,152 individuals from FinnGen release 11, excluding participants from the Tampere biobank. The summary statistics from these two studies were meta-analyzed using METAL 22 . We then applied PRS-CS 23 , following the pipeline available at https://github.com/FINNGEN/CS-PRS-pipeline , to generate the PS weights from the meta-analyzed summary statistics. The PS was based on 1,097,993 SNPs. The polygenic scores were calculated for individuals in the Finnish Clinical Biobank Tampere using the -- score function in PLINK v2.00a4LM 24 . Among the 25,941 genotyped individuals with available BMI from the Finnish Clinical Biobank Tampere, the BMI PS explained 12.7% of the variance in BMI as measured by the coefficient of determination (R 2 ). Diet intervention The diet intervention coaching program, provided by ViaEsca Oy (Helsinki, Finland, viaesca.com ), supported participants in reducing body weight and adopting a sustainable, balanced approach to healthy eating. It emphasized calorie intake and expenditure, using guidance based on official dietary recommendations. Interventionists were blinded to participants’ PS group and lab measures. A standardized approach ensured all participants began from the same baseline, based on the principle that behavioral change is required for outcomes like body weight reduction. The foundational program, regardless of prior habits, addressed eating rhythm, portion control, balanced meals, caloric needs tied to activity levels, and preparation of nutritious, simple meals. Participants received structured meal plans of five daily meals, personalized to individual caloric needs and set to 500 kcal/day below estimated energy expenditure. Plans followed macronutrient targets of 45–60% carbohydrates, 25–35% fat (at least two-thirds unsaturated), and 15–25% protein, including at least 35g fiber and 500– 800g of vegetables and fruits daily. Caloric needs were estimated from registration data (age, height, weight, gender, and activity level), and refined with linked activity trackers using a rolling 7-day calorie average whenever possible. Participants could follow or adapt the plan to meet their energy target, by voluntarily logging meals in the ViaEsca mobile app to monitor intake. A food diary allowed adjustments for meals outside the plan. Two automated messaging types supported participants: time-based messages with daily tips, educational content, and progress check-ins, and activity-based messages providing real-time feedback on intake and prompting coach interaction if targets weren’t met. Participants also had online coaching access via chat for support and guidance, if needed. Statistical analysis Based on the original study design with 62 participants in each of the 4 relevant groups (Top/Bottom 5% BMI PS and dietary intervention), and assuming normally distributed weight change at 6 months, there was 80% power to detect a 15% difference in diet intervention effect by BMI PS group (i.e., whether diet intervention affected weight change between BMI PS groups). This was based on simulations and a 2-sided Wald test at the .05 significance level. The primary outcome was body weight change (%) at 6 months; all other measures were secondary. Outcomes are presented as mean and 95% confidence intervals (CI). An intent-to-treat principle was applied, including all randomized participants with baseline data in the analysis according to original assignment, regardless of adherence or follow-up. This assumes missing follow-up data are unrelated to unobserved weight values, conditional on treatment assignment and baseline/intermittent weights. Linear mixed-effects models for repeated measures were used to assess the effects of diet intervention, PS group, and their interaction, using the lme function from the nlme package in R. Participants had random intercepts to account for individual-level variability. The primary test of interest was the interaction effect. To model within-subject correlation over time, unstructured, compound symmetry (CS), first-order autoregressive, and Toeplitz structures were evaluated. The final structure (CS) was selected based on convergence and highest Bayesian Information Criterion (BIC). Parameters were estimated via restricted maximum likelihood (REML), and missing data were handled under the missing at random (MAR) assumption using na.exclude. Standard diagnostics assessed normality and homoscedasticity of residuals. Differences in body weight and BMI, between Top 5% vs Bottom 5% BMI PS at baseline were compared using the Mann-Whitney U (Wilcoxon rank-sum) test. Non-prespecified exploratory analyses comparing changes in total cholesterol, LDL, HDL, triglycerides, glucose, and HbA1c between diet and control groups were conducted using the Mann-Whitney U (Wilcoxon rank-sum) test. All analyses were conducted in RStudio (version 2024.12.0+467). Changes compared to the registered trial protocol Compared to the submitted trial protocol, there have been two major changes. First, we originally planned to recruit 300 participants per PS group across three biobanks. Due to logistical challenges, we instead focused on a single biobank and recruited fewer participants than originally planned. The updated power calculations reflect this revised scenario. Second, while the original protocol stated that we would measure clinical biomarkers, we did not explicitly specify them as a “secondary outcome.” Because of this, we refer to these analyses in the manuscript as a “non-prespecified exploratory analysis.” DATA AVAILABILITY STATEMENT De-identified individual participant data underlying the results reported in this article, including data dictionaries, will be made available via the European Genome-Phenome Archive (EGA; https://ega-archive.org ) by searching for the study ID (to be provided), requesting access, and getting approval by the GENEROOS study research team. The shared dataset includes all primary and secondary outcomes, baseline characteristics, and other variables used in the analysis. Data will be available beginning at the time of publication and for a period of five years. Access requests must include a methodologically sound proposal, will be subject to review within four weeks, and require a valid EGA account. Data reuse for commercial purposes or inclusion of external collaborators not named in the original proposal requires Steering Committee approval. All data will be de-identified in accordance with relevant regulations. Secondary users must agree not to attempt re-identification of participants and must cite the original trial (ClinicalTrials.gov: NCT06092372 ) and specify how their analyses differ from previously reported findings. CODE AVAILABILITY STATEMENT R code used for data preparation and analysis can be found at the GENEROOS GitHub repository: https://github.com/dsgelab/GENEROOS_Public . ACKNOWLEDGMENTS This study has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant number 945733). We also want to acknowledge the participants and investigators of the FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., Merck Sharp & Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc, Novartis AG, and Boehringer Ingelheim International GmbH. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank ( www.auria.fi/biopankki ), THL Biobank ( www.thl.fi/biobank ), Helsinki Biobank ( www.helsinginbiopankki.fi ), Biobank Borealis of Northern Finland ( https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx ), Finnish Clinical Biobank Tampere ( https://www.pirha.fi/en/web/english/for-professionals/finnish-clinical-biobank-tampere ), Biobank of Eastern Finland ( www.ita-suomenbiopankki.fi/en ), Central Finland Biobank ( www.ksshp.fi/fi-FI/Potilaalle/Biopankki ), Finnish Red Cross Blood Service Biobank ( www.veripalvelu.fi/verenluovutus/biopankkitoiminta ), Terveystalo Biobank ( www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/ ) and Arctic Biobank ( https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank ). 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Share Effect of a Dietary Intervention on Weight Loss in Adults with High vs Low Genetic Predisposition to Higher BMI: A Randomized Diet Intervention Trial Rodosthenis S. Rodosthenous , Leena E. Viiri , Anne Carson , Tatiana Cajuso , Andrea Corbetta , Samuel Jones , Ettore Brenna , FinnGen , Enni Makkonen , Minttu Virolainen , Niina Korkeala , Elisabeth Widén , Samuli Ripatti , Andrea Ganna medRxiv 2025.10.06.25337395; doi: https://doi.org/10.1101/2025.10.06.25337395 Share This Article: Copy Citation Tools Effect of a Dietary Intervention on Weight Loss in Adults with High vs Low Genetic Predisposition to Higher BMI: A Randomized Diet Intervention Trial Rodosthenis S. Rodosthenous , Leena E. Viiri , Anne Carson , Tatiana Cajuso , Andrea Corbetta , Samuel Jones , Ettore Brenna , FinnGen , Enni Makkonen , Minttu Virolainen , Niina Korkeala , Elisabeth Widén , Samuli Ripatti , Andrea Ganna medRxiv 2025.10.06.25337395; doi: https://doi.org/10.1101/2025.10.06.25337395 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4436) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (542) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3333) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00f85a018241640',t:'MTc3OTY1OTQ4OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();