Psychosocial Well-being and Genetic Predisposition Shape Responsiveness to Lifestyle Coaching: Insights from a Risk-Stratified Intervention Trial

Psychosocial Well-being and Genetic Predisposition Shape Responsiveness to Lifestyle Coaching: Insights from a Risk-Stratified Intervention Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Psychosocial Well-being and Genetic Predisposition Shape Responsiveness to Lifestyle Coaching: Insights from a Risk-Stratified Intervention Trial Heikki V. Sarin, Patrik Borg, Kirsi H. Pietiläinen, Kati Kristiansson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7638506/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Lifestyle modifications are known to improve cardiometabolic outcomes, however, their effectiveness in modulating metabolic signatures and interacting with genetic susceptibility and behavioral determinants of health remains incompletely understood. Psychosocial well-being may both reflect underlying genetic predisposition and influence responsiveness to lifestyle interventions, yet this interplay remains underexplored, thus elucidating these relationships is essential for advancing personalized and precision health approaches. Methods: This 10-week randomized controlled trial (RCT) assessed the impact of lifestyle coaching on cardiometabolic health among working-age adults at elevated risk (N = 709 screened). Risk stratification was based on apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio. Participants in the highest risk category (~15%, n = 104) were randomized to either personal coaching (intervention: n = 53; control: n = 51), while those at medium risk (~30%, n = 213) were randomized to group coaching (intervention: n = 107; control: n = 106) branch. Low-risk individuals (n = 394) were excluded after baseline. Interventions followed a standardized curriculum and included personalized or group-based behavioral guidance targeting diet, physical activity, and stress management. Statistical analyses were performed using generalized estimation equations (GEE) for primary analyses. Results: At baseline, higher polygenic risk scores (PRS) for body mass index (BMI) associated with greater psychosocial burden, higher adiposity, and more adverse metabolic profiles (FDR < 0.05), including elevated high-sensitivity C-reactive protein (hs-CRP), uric acid, and alanine aminotransferase (ALT) levels. The 10-week lifestyle intervention did not yield major differential effects on cardiometabolic status between coaching modalities within different risk strata but did result in incremental improvements (FDR < 0.05) in psychosocial well-being. Participants with multidomain challenges showed the least responsiveness (FDR < 0.05) in adiposity and metabolic signatures compared with those with more favorable psychosocial profiles. In addition to psychosocial well-being, baseline cardiometabolic status (i.e., adiposity, blood pressure, biomarker profile) and polygenic risk predisposition for BMI and coronary heart disease (CHD) shaped (FDR < 0.05) intervention responsiveness, influencing adiposity and metabolic signature trajectories. Conclusions: These findings underscore the importance of personalized, multidimensional approaches to cardiometabolic health. Genetic risk together with psychosocial well-being shape both baseline status and potential for change and intervention responsiveness. Integrating and accounting both factors is essential for optimizing prevention strategies. Trial registration: ClinicalTrials.gov NCT04633876. Registration date: 18/11/2020. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Biological sciences/Genetics Health sciences/Medical research Health sciences/Risk factors Polygenic Risk Score (PRS) Psychosocial Well-being Cardiometabolic Risk Lifestyle Intervention Gene–Environment Interaction Personalized Medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION The global prevalence of obesity, metabolic syndrome, type 2 diabetes (T2D), cardiovascular and coronary heart disease (CVD / CHD) continues to escalate, driven by complex interactions between genetic susceptibility and adverse lifestyle exposures ( 1 – 6 ). These noncommunicable diseases now account for the majority of global morbidity and mortality, posing an urgent public health burden. Lifestyle interventions—targeting diet, physical activity, and psychosocial stress—remain foundational strategies for mitigating cardiometabolic risk and improving metabolic health trajectories. Yet, the capacity of such interventions to induce sustained changes in phenotype, biomarker profile, and attenuate the expression of polygenic risk remains incompletely characterized. Emerging evidence suggests gene–environment interactions may shape the penetrance of genetic predisposition, highlighting the need for integrative, precision-based prevention frameworks ( 7 – 9 ). The complex interplay between genetic architecture and modifiable lifestyle factors in shaping metabolic health has emerged as a critical focus of contemporary biomedical research. At the population level, body mass index (BMI) serves as a robust proxy for adiposity and cardiometabolic risk, governed by both polygenic inheritance and environmental exposures. Advances in genome-wide association studies have facilitated the development of polygenic risk scores (PRS), which aggregate the effects of numerous common variants to estimate inherited susceptibility to traits such as elevated adiposity and related metabolic disorders ( 9 – 13 ). While PRS enhances risk stratification, especially for early-onset obesity and T2D, its interaction with behavioral determinants, such as diet quality, physical activity, and psychosocial stress, remains under-characterized and mechanistically unresolved ( 8 , 14 – 20 ). Beyond its direct metabolic consequences, an elevated adiposity is often associated with a higher burden of physical, psychosocial, and occupational challenges ( 1 , 4 ). Individuals with a high BMI PRS may experience greater difficulty in weight management, increased inflammation, altered metabolic biomarker profiles, and a heightened risk of obesity-related comorbidities, such as CVD and all-cause mortality ( 8 , 11 ). Moreover, weight-related stigma, mental health challenges, and socioeconomic determinants further complicate health outcomes, underscoring the need for comprehensive, evidence-based lifestyle interventions ( 19 ). Recent advances in personalized medicine and precision healthcare have highlighted the importance of personalized and group-based interventions in modifying disease trajectories ( 11 , 14 ). Both approaches have demonstrated effectiveness in promoting sustainable behavioral changes, yet their comparative impact on biomarker profiles, phenotypic outcomes, and polygenic risk stratification remains largely unexplored ( 20 ). The present study aims to examine the physiological and molecular effects of personal and group coaching modalities on key cardiometabolic biomarkers, alongside changes in phenotypic characteristics such as body composition and cardiovascular parameters. Additionally, PRS for adiposity (e.g., BMI) and metabolic disorders, such as T2D and CHD are evaluated to determine whether lifestyle interventions can influence risk stratification beyond genetic predisposition. By integrating biochemical, anthropometric, and genetic analyses, this study provides a comprehensive evaluation of how structured lifestyle coaching interventions modulate cardiometabolic health indicators. Given the increasing reliance on PRS in preventive medicine, understanding whether targeted lifestyle interventions can modify disease risk profiles could have profound implications for personalized healthcare strategies. The findings will contribute valuable insights into optimizing lifestyle interventions for individuals at varying levels of genetic susceptibility to metabolic disorders, thereby informing the development of more effective, precision-based health programs. METHODS Study objective and participants. The Virta Research Study is a longitudinal, 10-week lifestyle intervention designed to enhance the well-being, workability, and overall health of individuals at elevated cardiometabolic risk in Finland ( 21 , 22 ). The trial is reported in accordance with the CONSORT statement, and the completed checklist is provided as Supplementary file 1. The primary objective of the study is to assess the impact of personalized lifestyle coaching on key physiological health markers, circulating blood metabolites, and risk stratification based on PRS. The study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki, and informed consent was obtained from all participants prior to enrollment. Ethical approval was obtained from the Helsinki and Uusimaa Hospital District (HUS/2093/2019) prior to study initiation. The study cohort comprised working-age adult volunteers, recruited from employees insured by major insurance service provider and receiving occupational healthcare from Aava Medical Ltd. Eligibility criteria included: (i) provision of signed informed consent, (ii) age 18–65 years, (iii) inclusion of both male and female participants, with a minimum representation of one-third per gender, and (iv) sufficient proficiency in Finnish or English to comprehend study-related instructions and complete questionnaires. Exclusion criteria were established to ensure participant safety and data integrity. Individuals were ineligible if they met any of the following conditions: (i) anticipated frequent travel exceeding one workday per week during the intervention period, as assessed by the investigator; (ii) history of a major cardiovascular event within the preceding six months; (iii) a diagnosis of diabetes requiring pharmacological management; (iv) history of malignant disease, such as cancer, within five years prior to recruitment; (v) use of lipid-lowering medications or anti-obesity pharmacotherapy; (vi) pregnancy; or (vii) presence of a cardiac pacemaker. Study design. All eligible and willing participants who met the inclusion criteria were permitted to enter the screening phase (n = 711). Sample size calculation and participant allocation was based on cholesterol balance, measured as the apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio ( 23 ) (Fig. 1 ). This biomarker was selected as a proxy for cardiometabolic risk, given its well-established role as a strong predictor of CVD in the general population. Following the screening phase, individuals whose ApoB/ApoA1 ratio ranked within the highest 45% were selected to proceed to the 10-week intervention phase. No interim analyses or formal stopping guidelines were specified due to the short intervention period. Within the intervention cohort, participants with the highest one-third of ApoB/ApoA1 ratios (~ 15% of all screened participants, n = 104) were classified as the high-risk group. These individuals were randomized using block randomization generated by a computer into either a personalized lifestyle coaching intervention or a corresponding control group. Study personal did not have access to the random allocation sequence. Meanwhile, participants whose ApoB/ApoA1 ratios fell within the lower two-thirds of the high-risk threshold (~ 30% of all screened participants, n = 213) were categorized as the medium-risk group. They were randomized to receive group-based lifestyle coaching or to a corresponding control group. No blinding was performed, and all parties were aware of intervention assignments. All study data was be collected to research database by Aava Medical Ltd, where data is stored as pseudonymized with separate code keys. Participants in the high-risk intervention arm engaged (start: 15/02/2020) in a structured individualized lifestyle coaching program, while those in the medium-risk intervention arm took part in group-based coaching sessions in cohorts of 8–10 individuals. Throughout the 10-week intervention period, participants in both intervention arms followed a standardized lifestyle coaching curriculum, which consisted of eight structured sessions covering identical topics and protocols. The coaching framework emphasized four key domains: (i) fundamentals of lifestyle modification; (ii) nutritional optimization for metabolic health and weight management; (iii) physical activity and movement strategies; and (iv) sleep hygiene and recovery mechanisms. Coaching was delivered by qualified and experienced personal health coaches affiliated with Aava Medical Ltd. In contrast, participants in the control groups did not receive any structured coaching but continued to have access to standard occupational healthcare services as per usual. Regarding the lifestyle coaching curriculum, the content was identical but allowed for prioritizing focus according to individual motives. The participants in the personal coaching branch had relatively balanced coaching strategy with modest emphasis on physical activity and movement strategies [Nutrition (29.7%); Physical activity (36.9%); Sleep, recovery, and stress (29.4%), Other (4.0%)]. In contrast, a greater proportion of individuals in the group coaching branch emphasized sleep hygiene and recovery mechanisms [Nutrition (26.0%); Physical activity (27.9%); Sleep, recovery, and stress (42.7%), Other (3.4%)]. Harms were assessed non-systematically through staff observations and spontaneous participant reports, and severity and trial relatedness were evaluated by a designated assessor. Serious adverse events were documented in patient records. Laboratory assessments and sample analysis. Blood sampling was conducted at three time points: during the screening phase at week 0, at the midpoint of the intervention at week 5, and upon completion of the intervention at week 10. To ensure accessibility and convenience for participants, all laboratory assessments were performed in mobile assessment hubs located near their workplaces. Participants were required to fast for 10 to 12 hours overnight before each blood draw. Additionally, they were advised to avoid strenuous physical activity and maintain their regular diet for at least 24 hours before sampling to minimize physiological variability. All blood samples were collected by a trained phlebotomist to ensure precision and consistency in sample handling. Following collection, blood samples were pseudonymized by Aava Medical Ltd personnel to protect participant identities and were then transported to the National Institute for Health and Welfare for further biochemical and molecular analyses. Laboratory assessments included both standard biomarkers and omics-based profiling. Biomarker analyses included measurements of apoA1, apoB, high-sensitivity C-reactive protein (hs-CRP), alanine aminotransferase (ALAT), triglycerides, uric acid, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, and glucose. Omics-based analyses included genome-wide association studies (GWAS), and PRS calculation for obesity (BMI), CHD, and T2D. The majority of the laboratory results were intended exclusively for research purposes. However, participants who consented to receiving personalized genetic risk insights were provided with their PRS for obesity (BMI), CHD, and T2D, allowing them to gain a deeper understanding of their genetic predisposition to these conditions. Physiological measurements. At the beginning of the intervention (screening phase) and upon completion of the 10-week intervention, key physiological parameters were assessed to evaluate participants’ health status and potential changes over time. These included measurements of body weight, height, waist circumference, neck circumference, and blood pressure measures. All assessments were conducted by experienced health coaches, ensuring consistency and accuracy in data collection. Clustering of Respondents Based on Well-being Profiles. To identify distinct subgroups for targeted well-being interventions, we clustered respondents based on their thematic well-being profile ( 21 ). First, theme scores were computed for each individual by averaging responses to questions within each previously defined theme, derived from a hierarchical clustering of 129 questionnaire items using Spearman correlations and Ward’s method. Pairwise Euclidean distances between individuals’ theme scores were then calculated. These distances served as input for Partitioning Around Medoids (PAM) clustering (via the pam function in the R cluster package), which grouped respondents into five clusters based on thematic similarity. Cluster profiles were interpreted using average theme scores and validated visually via principal component analysis (PCA, capscale function, R vegan package). This methodology enabled the identification of five distinct respondent groups reflecting varied patterns of well-being strengths and challenges, that were labeled based on dominant patterns of strengths and challenges: i) the Well-functioning cluster (Good) exhibited high scores across all themes and low presenteeism, ii) the Lifestyle challenges cluster (Life) showed lower scores in lifestyle-related themes despite good mental well-being, iii) the Hard on oneself group (Hard) had high physical activity but low psychological flexibility and performance orientation, iv) the Recovery challenges cluster (Recovery) reported medium-to-low scores across most themes with elevated presenteeism, and v) the Multidomain challenges group (Mult) showed low scores across all domains, indicating broad well-being difficulties. Quality control and statistical analysis. Prior to conducting statistical analyses, a multi-step quality control (QC) procedure was implemented to ensure data integrity and minimize noise. First, univariate outliers were excluded based on a ± 4 standard deviation (SD) threshold, applied within predefined risk groups and across all time points, to remove extreme values unlikely to reflect biological variability. Next, individuals exhibiting extreme response patterns defined by Z-scores of outcome change were identified and excluded (| Z-score | >3) separately within intervention and control groups. This step aimed to reduce undue influence of potentially non-representative while preserving group-specific variance structure. Baseline multivariate outliers were then assessed across three domains: biomarkers, phenotypic traits, and PRS. Within each domain, principal component analysis (PCA) was conducted, retaining components that together explained > 85% of the total variance. Mahalanobis distances were computed in the reduced feature space, and individuals exceeding the 99th percentile of the chi-square distribution were flagged as multivariate outliers. Participants identified as outliers in more than one domain were excluded from all analyses. If an individual was an outlier in only one domain, the corresponding domain-specific variables were excluded for that individual to retain usable data in other domains. To analyze phenotypic changes, biomarker profiles, and PRS over the course of the study, generalized estimating equations (GEEs) with a linear link function were applied, using a working i) independence structure for cross-sectional analyses and ii) an exchangeable structure for longitudinal data. The primary objective was to assess whether phenotypic traits and biomarker profiles differed significantly between the intervention and control groups within either study arm at any time point. A secondary focus examined whether i) genetic predisposition, as captured by PRS, or ii) psychosocial wellbeing contributed to baseline differences or to responder status over time. Furthermore, psychosocial wellbeing cluster-specific changes between baseline and follow-up were assessed using McNemar’s test for paired binary outcomes. For each cluster, a 2×2 table was constructed comparing membership status at baseline versus follow-up. We used the continuity-corrected χ² version of McNemar’s test (df = 1, two-sided), reporting counts moved into and out of each cluster. All models were adjusted for relevant covariates, including age, sex, educational attainment, smoking, geographic region, language, baseline BMI, and use of antihypertensive medication, to account for between-subject variability and enhance robustness. To control for multiple testing, false discovery rate (FDR) adjustment was applied across all statistical analyses, grouped by biologically relevant domains: phenotypes (i) adiposity and (ii) blood pressure, and biomarkers grouped into (i) lipids, (ii) glucose/liver function, and (iii) renal/inflammatory markers. All computations were performed using R statistical software (version 3.3.3 or later, https://www.r-project.org ). RESULTS Baseline Cardiometabolic Profiles Differ by Risk Group, with No Evidence of Differential Intervention Effects Over Time. As anticipated, baseline phenotypic, biomarker, and PRS profiles differed significantly (FDR < 0.05) across predefined cardiometabolic risk groups by apoB/apoA1-ratio (Table 1; Fig. 2 ; Supplementary Tables 1–3). Within these cardiometabolic risk groups, no major global differences were observed at baseline between allocated intervention and control arms in phenotype, biomarker, or PRS profiles. As an exception, in the high-risk arm the intervention group exhibited higher central adiposity (FDR < 0.05) at baseline compared to the control group (Table 2; Supplementary Tables 4–5). Longitudinal analysis of the intervention and control arms, of either medium- or high-risk strata revealed no significant Time × Group interaction effects (FDR > 0.05) for phenotypic or biomarker profiles (Table 2; Fig. 2 ; Supplementary Tables 6–7). Although, participants in the personal coaching arm self-assigned approximately 30% more tasks across the lifestyle intervention modules than those in the group coaching arm, this greater engagement did not translate into broader between group differences. Post-hoc within-group analyses showed modest temporal improvements (FDR < 0.05) in adiposity indices and blood pressure within both intervention and control arms in the medium- and high-risk strata, with more pronounced changes in the intervention groups (Table 2; Supplementary Table 8). Apolipoprotein trajectories improved similarly across strata and study arms; however, reductions in LDL and total cholesterol levels were more pronounced (FDR < 0.05) in the intervention groups (Fig. 2 ; Supplementary Tables 9–10). Consistent with modest reduction in adiposity and improved lipid profiles, ALAT levels also declined (FDR < 0.05) in all groups except the high-risk intervention arm following the 10-week lifestyle intervention. Polygenic Risk Modifies Longitudinal Responses to Lifestyle Intervention in Metabolic Traits. Consistent with existing literature, polygenic risk scores (PRS) were significantly associated with baseline variation in cardiometabolic risk profiles (FDR < 0.05). Specifically, i) the BMI PRS was linked to increased adiposity, elevated blood pressure, and unfavorable levels of metabolic markers, including hs-CRP, glucose, ALAT, and uric acid, ii) the CHD PRS was associated with higher levels of atherogenic lipids, such as apoB, apoA1/apoB ratio, triglycerides, and LDL cholesterol, and iii) the T2D PRS associated with elevated glucose and uric acid levels (Supplementary Tables 11–12). Next, we focused on how these baseline differences genetic risk and cardiometabolic state translated into differential responses over the 10-week lifestyle intervention. At first, evaluation of Baseline x Time interactions revealed consistent negative associations (FDR < 0.05) across all measured circulating biomarkers, blood pressure measures, and central adiposity parameters (i.e., neck and waist circumference), indicating that individuals with more adverse baseline values exhibit more widespread improvements over time (Fig. 3 ; Supplementary Table 13–14). In longitudinal analyses, higher BMI PRS was nominally associated with greater waist circumference reduction over time (β = − 0.70 ± 0.34, P = 0.04) (Fig. 3 ; Supplementary Table 15), suggesting promoted central fat loss in genetically predisposed individuals. Stronger over time associations were seen in lipid profile, where individuals with higher CHD PRS showed smaller improvements or even increases in LDL cholesterol (β = 0.11 ± 0.03, FDR = 1.5 × 10⁻ 3 ), total cholesterol (β = 0.10 ± 0.04, FDR = 0.02), apoB (β = 0.02 ± 0.008, P = 0.044) and the apoB/apoA1 ratio (β = 0.01 ± 0.006, P = 0.02), suggesting that CHD genetic risk may limit lipid profile improvements over time (Fig. 3 ; Supplementary Table 16). To further understand the PRS x Time interactions, we tested whether they trajectories influenced by participants' baseline cardiometabolic status (Baseline x Time) (Supplementary Table 17–18). After adjusting for the Baseline × Time interaction, the previously observed association between BMI PRS and waist circumference change was no longer significant ( P > 0.05), suggesting that the initial effect was most likely driven by higher baseline central adiposity and intervention effect in genetically predisposed individuals (Supplementary Table 17). In contrast, the associations between CHD PRS and the atherogenic lipid profile on total cholesterol, LDL cholesterol, apoB, the apoB/apoA1 ratio persisted following adjustment for corresponding baseline interactions, indicating that genetic risk for CHD independently limited lipid improvements during the intervention regardless of Baseline x Time effect (Supplementary Table 18). Collectively, cautious interpretation of these findings suggest that polygenic burden can affect baseline-dependent responses, shaping individual variability in lifestyle intervention outcomes (Supplementary Table 17–18). Genetic Burden and Physiological Correlates of Metabolic Risk Across Well-being Groups. Among individuals classified as well-functioning (Good), baseline BMI PRS were significantly lower compared to those in the recovery-challenged (Reco) (β = 0.32 ± 0.13, FDR = 0.03) and multidomain challenged (Mult) (β = 0.45 ± 0.14, FDR = 0.004) clusters (Fig. 4 ; Supplementary Table 19). Overall, those with multidomain challenges showed the highest CHD and BMI PRS, reflecting greater genetic susceptibility to higher adiposity and cardiometabolic conditions (Fig. 4 ). These genetic patterns at baseline were paralleled by phenotypic difference, with individuals in the well-functioning (Good) and high-functioning with high demands (Hard) clusters exhibiting (FDR < 0.05) lowest levels of adiposity compared to other clusters, whereas the multidomain challenges (Mult) cluster exhibited the highest levels of adiposity (Fig. 4 ; Supplementary Table 20). After adjusting for BMI, systolic blood pressure emerged significantly lower in the multidomain challenge group (Mult) compared with the well-functioning group (Good) (β = − 5.58 ± 1.81, FDR = 0.004). This indicated that elevated adiposity in the multidomain challenged (Mult) cluster masked underlying between-cluster differences in blood pressure, which only became evident after adjustment for BMI. (Fig. 4 ; Supplementary Table 20). Biomarker profiles mirrored the genetic and phenotypic differences at baseline between the well-functioning (Good) and multidomain challenges (Mult) clusters. Individuals in the well-functioning cluster exhibited the most favorable metabolic signatures, while the multidomain challenged individuals showed elevated levels of hs-CRP, uric acid, triglycerides, glucose, and apoB/apoA1 ratio, along with lower levels of apoA1 an HDL (FDR 0.05) after adjusting for BMI, suggesting that observed adiposity differences mediated the observed biomarker disparities (Supplementary Table 21). Together, these findings highlight a coherent biological gradient across genetic, phenotypic, and metabolic domains linked to psychosocial well-being, where adiposity level is key mediator of biomarker profile differences. Well-functioning Individuals Show the Strongest Phenotypic and Biomarker Profile Improvements Over Time. Longitudinal analyses of phenotypic change (Time × Well-being cluster) showed that individuals in the well-functioning (Good) cluster had the greatest reductions in adiposity over time compared to other clusters (Fig. 5 ; Supplementary Tables 22). In line with these groupwise findings, baseline status of the well-functioning (Good) cluster predicted the most consistent improvements across all adiposity measures, including body weight (β = -1.57 ± 0.33, FDR = 5.88 x 10 − 6 ), BMI (β = -0.50 ± 0.10, FDR = 1.74 x 10⁻ 6 ), and waist circumference (β = -2.45 ± 0.65, FDR = 2.37 x 10 − 4 ) (Supplementary Table 23). In contrast, however, most distinct reductions for systolic blood pressure (β = -4.27 ± 0.79, FDR = 1.38 x 10 − 7 ), and diastolic blood pressure (β = -3.20 ± 0.62, FDR = 2.15 x 10 − 7 ) were observed within the flexible health behavior difficulties (Life) cluster (Fig. 5 ; Supplementary Table 23). Across all other well-being clusters, similar changes across time were detected in phenotype profile, but narrower more inconsistent responses, especially in the multidomain difficulty (Mult) cluster (Fig. 5 ; Supplementary Table 23). Biomarker responses paralleled phenotypic improvements, with individuals in the well-functioning (Good) cluster showing the most favorable changes in metabolic signatures (Fig. 5 ; Supplementary Tables 24–25). These included significant reductions (FDR < 0.05) in apoB/apoA1 ratio, apoB, total cholesterol, triglycerides and ALAT (Supplementary Table 25). Similar beneficial shifts in atherogenic lipid profiles were also observed (FDR < 0.05) in the high-functioning with high demands (Hard) and flexible behavior difficulties (Life) clusters. In line with previous observations, the multidomain challenges (Mult) cluster exhibited the least responsiveness over time also across biomarker profiles (Fig. 5 ; Supplementary Table 25; Supplementary Fig. 1). These distinct physiological, phenotypic, and biomarker profile differences across lifestyle clusters translated into measurable shifts in lifestyle cluster membership over the 10-week intervention (Fig. 5 ; Supplementary Tables 26–28). Across the study population, membership shifted markedly (P < 0.05) towards the well-functioning (Good) cluster (N = + 38) and away from the flexible health behavior difficulties (Life) cluster (N = -25) (Supplementary Table 28). The flexible health behavior difficulties (Life) and high-functioning with high demands (Hard) clusters exhibited the greatest capacity for psychosocial improvement, whereas the well-functioning (Good) and multidomain challenges (Mult) clusters remained largely stable (Supplementary Table 26–28). Strikingly, participants in the multidomain challenges (Mult) cluster assigned themselves the fewest intervention tasks (~ 11.6 tasks/individual), consistent with a diminished capacity for behavioral adaptation, and displayed smallest gains in both phenotypic and biomarker indices (Fig. 5 ). DISCUSSION This study examined the interplay between polygenic risk for adiposity and metabolic disease traits in relation to cardiometabolic phenotypes, biomarker profiles, and psychosocial well-being within the context of a structured 10-week lifestyle intervention. We demonstrated that across allocated risk strata by apoB/apoA1 ratio, baseline cardiometabolic profiles displayed marked global differences on phenotypic, biomarker, and genetic factors. However, no differential intervention effects were observed regardless of intervention allocation to study groups within different risk strata, although, modest within-group improvements were observed in measures of adiposity and lipid profiles. Polygenic risk profiles also associated with baseline metabolic states, modified individual responses to the intervention, and interacted with baseline status to further to shape the magnitude and direction of individual responses. Furthermore, baseline patterns of psychosocial well-being reflected underlying genetic and metabolic risk. Individuals in the well-functioning cluster at baseline demonstrated the most pronounced improvements during the intervention, and lifestyle cluster transitions overall reflected a shift toward this favorable profile. In contrast, individuals with multidomain difficulties not only exhibited greater baseline adiposity and genetic risk for increased body mass but also showed reduced responsiveness to intervention. These findings highlight the complex and interconnected roles of baseline metabolic state, genetic predisposition, and psychosocial functioning in determining health trajectories and modulating intervention efficacy. At baseline, individuals with higher genetic risk for adiposity, as indicated by elevated BMI PRS, exhibited greater adiposity and elevated blood pressure, consistent with established links between genetic predisposition, obesity, and cardiometabolic risk ( 7 , 10 , 14 ). These associations extended beyond anthropometric traits to encompass metabolic disturbances reflective of systemic dysfunction, including markers of hepatic stress, impaired glucose metabolism, dyslipidemia, and low-grade inflammation, collectively aligning with a metabolic syndrome–like phenotype ( 11 , 14 , 15 ). In contrast, PRS for T2D and, CHD were only associated with distinct blood-derived metabolic signatures. Elevated CHD PRS was linked to increased levels of atherogenic lipid markers consistent with a lipid-centric cardiovascular risk profile ( 8 , 9 , 12 ). Similarly, higher T2D PRS was associated with elevated fasting glucose and uric acid levels, in line with the known T2D pathophysiology of impaired glucose metabolism ( 3 , 13 ). Together, these findings underscore the role of polygenic risk in shaping specific metabolic pathways, even when not overtly expressed in the anthropometric measures and broader phenotype. The observed negative interaction between BMI PRS x Time for waist circumference suggested a diminishing genetic influence on central adiposity over time, potentially reflecting intervention effects or behavioral adaptation ( 20 ). A similar negative trend was noted for both systolic and diastolic blood pressure, reinforcing the notion that the beneficial impact of lifestyle changes may manifest first in reductions in visceral fat, blood pressure, and metabolic biomarkers—prior to observable changes in overall weight ( 14 , 20 , 22 ). These findings were, however, attenuated following baseline level adjustment, indicating that the initial effect was largely driven by higher baseline central adiposity in genetically predisposed individuals. This pattern aligns with evidence from cardiometabolic, exercise, and behavioral intervention studies demonstrating that baseline status frequently moderates the magnitude and rate of physiological adaptation—a phenomenon often referred to as the ‘law of initial value’ ( 24 , 25 ). Individuals with more adverse baseline profiles, particularly in central adiposity and blood pressure, tend to exhibit proportionally greater short-term improvements, irrespective of genetic background. Such baseline-driven effects can overshadow polygenic influences in early phases of lifestyle change, with genetic modulation becoming more apparent in the longitudinal evolution of biomarker networks rather than in gross anthropometric measures. Overall, our findings together with these observed notions support the concept that i) baseline levels seem to have greater influence on intervention responsiveness over polygenic risk, while ii) polygenic risk effects over time are more sensitively reflected in dynamic biomarker trajectories than in slower-shifting anthropometric outcomes, particularly in the context of short- to medium-term lifestyle modification ( 14 , 20 ). While PRS for BMI, T2D, and CHD showed minimal associations with longitudinal changes in anthropometric traits, more distinct time-dependent effects emerged within the metabolic domain—most notably for CHD PRS ( 8 , 9 , 12 , 20 ). These patterns suggest that genetic predisposition to cardiometabolic disease may unfold subtly and progressively through specific biochemical pathways, particularly through well-characterized alterations in lipid metabolism, rather than through gross changes in physical measures ( 3 , 9 , 12 ). Moreover, early metabolic status may modulate the expression of genetic risk over time, consistent with evidence that baseline metabolic profiles can shape long-term disease trajectories ( 13 , 14 , 20 ). Collectively, these biomarker dynamics underscore a key insight that polygenic risk may remain latent at the phenotypic level, yet become increasingly apparent through targeted metabolic shifts, offering a more sensitive lens into the early expression and progression of complex disease risk ( 14 , 20 ). A novel aspect of this study is the integration of psychosocial well-being with genetic and metabolic risk, highlighting the complex interplay between mental, behavioral, and biological domains ( 14 , 19 , 21 ). Individuals facing multidomain psychosocial difficulties tended to have higher genetic predisposition to adiposity and displayed more adverse metabolic profiles ( 14 , 19 ). These patterns suggest that psychosocial stressors and mental well-being may not only coexist with genetic risk but potentially exacerbate its physiological expression ( 18 , 19 ). This aligns with literature from behavioral medicine showing that chronic stress and social adversity can contribute to dysregulated metabolic function through behavioral and neuroendocrine pathways ( 26 ). Psychosocial well-being has been shown to be a key mediator in behavior change processes, affecting adherence, motivation, and physiological outcomes, and that resilience and self-regulation facilitate sustained lifestyle changes ( 21 , 22 ). Psychosocial well-being was dynamic over the 10-week intervention period, with many participants in the intervention arm showing gradual shifts toward more favorable functioning states ( 22 ). However, these changes were largely incremental, suggesting that while lifestyle interventions can initiate positive movement, longer durations of support or coaching may be needed to facilitate more profound improvements ( 21 , 22 ). Moreover, capacity to shift in psychosocial well-being may itself be person-specific. For some, well-being may represent a relatively stable state, making transitions from high-burden to well-functioning profiles more gradual and effortful. These findings underscore both the potential and limits of short-term interventions, highlighting the need for extended, personalized strategies that account for individual differences in adaptability ( 14 , 21 , 22 ). This perspective aligns with existing literature emphasizing that multidimensional well-being is shaped by both genetic and psychosocial contexts, and that durable and more distinct improvements are more likely when interventions are tailored and temporally sustained ( 14 , 18 , 22 ). Participants with more favorable psychosocial profiles at baseline demonstrated the most pronounced improvements in adiposity and cardiometabolic markers over time. These changes were reflected in both physical and biochemical domains, supporting the idea that psychosocial well-being may reflect the capacity for positive health change ( 14 , 21 , 22 ). Psychosocial resilience and adaptive coping mechanisms likely play a role in maintaining health behaviors and responding to intervention efforts, underscoring the importance of addressing mental and emotional well-being in lifestyle programs ( 19 , 22 ). In contrast, individuals with greater psychosocial challenges exhibited limited changes in health outcomes, suggesting a form of physiological inertia that may stem from the cumulative effects of stress and adversity ( 19 , 21 ). These individuals might benefit from more personalized or integrative interventions that concurrently target psychosocial and behavioral domains as well as account for potential higher genetic burden of diseases ( 14 , 21 , 22 ). Several limitations should be considered when interpreting the findings of this study. First, the observation that control group participants also exhibited improvements in health-related markers, though less nuanced—raises the possibility of a Hawthorne effect, whereby participation alone may independently influence behavior through multiple mechanisms, irrespective of intervention content ( 27 ). In addition to the Hawthorne effect, factors such as measurement reactivity, social desirability bias, and expectancy effects may lead participants to adopt healthier behaviors ( 27 ). Increased health awareness and regression to the mean may also contribute to improvements unrelated to the intervention itself ( 21 , 22 ). These factors may partially account for the lack of statistically significant differences between intervention and control arms. Additionally, while the intervention was multifaceted and aimed to promote overall lifestyle improvement rather than focusing solely on weight loss, the relatively short duration of the intervention and the minimal effect on body weight may have limited the potential for meaningful physiological adaptations and differential responses, particularly in metabolic markers ( 22 ). Second, the deliberate allocation of higher-risk individuals to the personal coaching group introduces a potential confounding factor. Individuals with elevated baseline cardiometabolic risk may be more responsive to behavioral interventions, potentially amplifying observed effects in this subgroup ( 21 ). Unequal group sizes further complicate interpretation, as they may limit statistical power and reduce the sensitivity to detect meaningful groupwise differences. Additionally, the 10-week duration of the intervention may have been insufficient to induce measurable changes in phenotypes or biomarkers among individuals with higher genetic cardiometabolic risk, as reflected by the apoB/apoA1 ratio ( 20 ). The absence of differential responses across genetically stratified risk groups highlights a key limitation of short-term interventions in modifying genetically embedded metabolic traits ( 14 , 20 ). While lifestyle modification remains a central strategy for cardiometabolic risk reduction, our findings suggest that individuals with a higher genetic burden may require longer or more intensive interventions to achieve significant physiological improvements. These insights underscore the importance of tailoring both the duration and intensity of lifestyle programs to individual risk profiles in future precision prevention efforts ( 14 , 20 , 22 ). In conclusion, this study adds to the growing body of evidence supporting the need for integrated, personalized strategies in the prevention and management of obesity and related metabolic disorders. While genetic predisposition defines a baseline level of risk, it does not act in isolation, whereas rather, it shapes individual responsiveness to lifestyle interventions, potentially amplifying or constraining their effectiveness. Psychosocial well-being emerges as a critical, and often underappreciated, determinant of health trajectories, modulating both where individuals begin, engagement, and how much they can change. The interplay between genetic risk, behavioral context, and psychosocial state underscores the multifactorial nature of cardiometabolic health and calls for tailored approaches that reflect this complexity. Future research should focus on disentangling these interdependencies through longitudinal, multidimensional designs, enabling more precise and adaptive intervention strategies that account for the diverse biological and psychosocial realities of individuals. Abbreviations ALAT = alanine aminotransferase apoA1 = apolipoprotein A1 apoA1 = apolipoprotein A1 apoB = apolipoprotein B BMI = body mass index CHD = coronary heart disease hs-CRP = high-sensitivity C-reactive protein CVD = cardiovascular disease HDL = high-density lipoprotein LDL = low-density lipoprotein PRS = polygenic risk score SD = standard deviation T2D = type 2 diabetes Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and applicable national regulations governing research involving human participants. Ethical approval was obtained from the Helsinki and Uusimaa Hospital District (HUS/2093/2019) prior to study initiation. All participants received detailed written and verbal information about the study procedures, potential risks, and benefits. Informed consent was obtained from all participants prior to their enrollment, and participation was entirely voluntary. Participants were informed of their right to withdraw from the study at any point without any consequences to their medical care or other entitlements. Consent for publication All participants provided informed consent for the publication of anonymized data generated during the study. No identifiable personal information is included in this manuscript. Consent procedures adhered to institutional and ethical guidelines regarding data confidentiality and participant rights. Availability of data and materials The datasets generated during and/or analyzed during the current study are not primarily available for third party individuals. To gain access to datasets used in this study, individuals need to apply, agree and sign on the necessary requirements and terms of data distribution protocols set by the National Institute for Health Welfare, Helsinki, Finland. The bioinformatics scripts/codes generated for statistical analyses purposes during the current study are available from the corresponding author on reasonable request. Competing interests The authors of this manuscript declare no competing interests. Funding This study was funded by Business Finland (grant no. 2874/31/2019). Additional support was provided through personal grants awarded to the investigators: KK was funded by Juhani Aho Medical Research Foundation; HVS received funding from the Finnish Medical Foundation ; KP was supported by the Research Council of Finland (grant nos. 266286, 272376, 314383, 335443, 342747), the Finnish Medical Foundation , the Gyllenberg Foundation , the Novo Nordisk Foundation (grant nos. NNF20OC0060547, NNF17OC0027232, NNF10OC1013354, NNF25SA0103783), the Finnish Diabetes Research Foundation , the Paulo Foundation , the Sigrid Jusélius Foundation , the University of Helsinki and Helsinki University Hospital , and by Government Research Funds ; MP was funded by The Research Council of Finland no. 359072. Authors' contributions. HVS was responsible for carrying out analysis and majority of writing. MP, KK and PB supervised the project and contributed significantly to the writing and editing of the manuscript. PB was responsible for the data collection. KP contributed to the writing and editing of the manuscript and offered key expertise in obesity-related physiology, significantly advancing the conceptual framework of the study. All authors participated in revision and editing of the manuscript in the final phase before submission. Acknowledgements We sincerely thank all study participants for their time, commitment, and valuable contributions. We also gratefully acknowledge the study personnel for their essential roles in participant recruitment, data collection, and overall coordination, which were critical to the successful implementation of the study. References Abarca-Gómez, L. et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 390 (10113), 2627–2642 (2017). Zhou, B. et al. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 387 (10027), 1513–1530 (2016). Suzuki, K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 627 (8003), 347–357 (2024). GBD & Adult BMI Collaborators. Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. 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The utility of obesity polygenic risk scores from research to clinical practice: A review. Obes. Rev. 25 (11), e13810 (2024). Agbaedeng, T. A. et al. Polygenic risk score and coronary artery disease: A meta-analysis of 979,286 participant data. Atherosclerosis 333 , 48–55 (2021 Sept). Bonnefond, A., Florez, J. C., Loos, R. J. F. & Froguel, P. Dissection of type 2 diabetes: a genetic perspective. Lancet Diabetes Endocrinol. 13 (2), 149–164 (2025). Kim, M. S. et al. Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities. Cell. Metab. 2024 July 2 ;36(7):1494–1503e3 . Han, H. Y., Masip, G., Meng, T. & Nielsen, D. E. Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies. J. Nutr. 154 (12), 3521–3543 (2024). Sutoh, Y. et al. Healthy lifestyle practice correlates with decreased obesity prevalence in individuals with high polygenic risk: TMM CommCohort study. J. Hum. Genet. 70 (1), 9–15 (2025). Brittain, E. L. et al. Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity. JAMA Netw. Open. 7 (3), e243821 (2024). Dashti, H. S. et al. Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank. BMC Med. 20 (1), 5 (2022). Cuevas, A. G., Mann, F. D. & Krueger, R. F. Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions. Lifestyle Genom . 16 (1), 90–97 (2023). McCaffery, J. M. et al. Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention: The Trans-NIH Consortium for Genetics of Weight Loss Response to Lifestyle Intervention. Diabetes 71 (4), 669–676 (2022). Kauppi, K., Borg, P., Roos, E., Torkki, P. & Korpela, K. Utility of an online well-being assessment in targeting employee well-being programmes: a cross-sectional survey study in Finland. BMJ Open. 2024 June 26 ;14(6):e079708 . Shiri, R., Väänänen, A., Mattila-Holappa, P., Kauppi, K. & Borg, P. The Effect of Healthy Lifestyle Changes on Work Ability and Mental Health Symptoms: A Randomized Controlled Trial. Int. J. Environ. Res. Public. Health . 19 (20), 13206 (2022). Yusuf, S. et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 11 (9438), 937–952 (2004 Sept). Berkman, L. F. et al. Employee Cardiometabolic Risk Following a Cluster-Randomized Workplace Intervention From the Work, Family and Health Network, 2009–2013. Am. J. Public. Health . 113 (12), 1322–1331 (2023). Edwards, J. J. et al. Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials. Br. J. Sports Med. 57 (20), 1317–1326 (2023). Cohen, S., Janicki-Deverts, D. & Miller, G. E. Psychological stress and disease. JAMA 298 (14), 1685–1687 (2007). McCambridge, J., Witton, J. & Elbourne, D. R. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J. Clin. Epidemiol. 67 (3), 267–277 (2014). Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table2.xlsx Additionalfile1SupplementaryTables128.xlsx Additional file 1 (Supplementary Tables 1-28).xlsx Additionalfile2SupplementaryFigure1.pdf Additional file 2 (Supplementary Figure 1).pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 26 Sep, 2025 Editor assigned by journal 26 Sep, 2025 Editor invited by journal 26 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 25 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7638506","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525291181,"identity":"d2bd7b56-b6f5-4c1f-ba59-fff2470b09f3","order_by":0,"name":"Heikki V. 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Key steps include genotyping and calculation of polygenic risk score (PRS) for metabolic and cardiovascular traits, phenotypic and biomarker profile assessments, and stratification based on genetic and lifestyle factors before statistical analyses.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/2361293c6fa4d507085960cf.png"},{"id":93240796,"identity":"4ef9fefe-16fd-434f-862c-a861f5673606","added_by":"auto","created_at":"2025-10-10 14:47:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBaseline and longitudinal differences in cardiometabolic profiles across risk and intervention groups.\u003c/strong\u003ePanel A illustrates longitudinal changes in biomarker profiles within and between intervention arms over time. In panel B, combined principal components (PC 1-2) of polygenic risk scores (PRS) (i), biomarker (ii), and phenotype (iii) highlights stratification by cardiometabolic risk and intervention group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/620b18ee83b5f7f47caedc0e.png"},{"id":93239909,"identity":"4710c9c0-a091-42c7-84ee-669fc8576a5b","added_by":"auto","created_at":"2025-10-10 14:39:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between polygenic risk scores (PRS) and longitudinal changes in phenotypic and biomarker profiles. \u003c/strong\u003ePanel A shows a heatmap of overall changes in phenotypic and biomarker variables over time, stratified by PRS tertiles (Low, Medium, High). Red indicates increases and blue indicates decreases over time. Asterisks mark variables with significant PRS*Time interactions (P \u0026lt; 0.05). Z-scores were used to construct the heatmap, with baseline levels of the lowest PRS tertile serving as the reference. Panel B-D presents associations between PRS for body mass index (BMI) and coronary heart disease (CHD), baseline levels, and longitudinal changes in phenotypic and biomarker component levels. Variables included were those showing at least nominal associations (P \u0026lt; 0.05) with baseline values and PRS (PRS x Time). The slope (r) indicates the direction and strength of association; P \u0026lt; 0.05 (*) and false discovery rate (FDR) \u0026lt; 0.05 (**) denote statistical significance.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/8069ace79d6a443ca473025e.png"},{"id":93242977,"identity":"fe5c6fe4-7f1c-4e31-b3bc-3ba18739f5ef","added_by":"auto","created_at":"2025-10-10 14:55:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":323492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBaseline differences between well-being clusters across phenotypic, biomarker, and polygenic risk score (PRS) domains. \u003c/strong\u003ePanels A show principal component (PC) plots of well-being clusters based on all variables within the PRS (i), biomarker (ii), and phenotype (iii) domains, highlighting domain-specific multivariate differences. Panel B display radar plots of individual variable-level differences across well-being clusters, within the PRS (i), biomarker (ii), and phenotype (iii) domains. Raw values were rescaled to a 0–10 range to enable visual comparison of magnitude and patterns across groups. Panel C presents detailed boxplots of selected phenotypic and PRS variables, including significance that is denoted by \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (*), false discovery rate (FDR) \u0026lt; 0.05 (**), and FDR \u0026lt; 0.001 (***).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/6997b258c05fc5154ea7dc56.png"},{"id":93240797,"identity":"af9b4cba-9a82-4d7d-a6af-11165f58d0ad","added_by":"auto","created_at":"2025-10-10 14:47:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":359427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between well-being clusters and longitudinal changes in phenotypic and biomarker profiles. \u003c/strong\u003ePanel A illustrate number of chosen lifestyle tasks within psychosocial wellbeing clusters. Panel B shows directional transitions with intervention group strata between well-being clusters, with arrows indicating the direction of change. Panels C illustrate shifts in wellbeing status over time. Panel D displays a polar bar plot of overall biomarker profile changes over time across wellbeing clusters. Z-scores were used to standardize values, with baseline levels of each biomarker averaged across all clusters serving as the reference. Increases are shown in red, decreases in blue, and the height of each bar reflects the magnitude of change. Bars with non-significant changes are outlined in black; statistically significant changes (false discovery rate, FDR \u0026lt; 0.05) are highlighted with lighter blue or red outlines. Panel D presents boxplots of phenotypic variable changes within and between clusters, with statistical significance of within-cluster changes denoted by \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (*), FDR \u0026lt; 0.05 (**), and FDR \u0026lt; 0.001 (***). Well-being clusters are defined as: Well-functioning (Good), Multidomain Challenges (Mult), Challenges in Flexible Eating and Physical Activity (Life), Well-functioning with High Demands (Hard), and High-functioning with Recovery Challenges (Reco).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/67550ed3eb6d1c356a1c7bbc.png"},{"id":93244298,"identity":"b6df4f93-1af1-45e6-8d90-300ccfc0889f","added_by":"auto","created_at":"2025-10-10 15:03:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1861880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/163d234d-af0c-4ec6-83a6-44ecd84970b9.pdf"},{"id":93239905,"identity":"47781063-86ac-43a3-9715-9eb052be7481","added_by":"auto","created_at":"2025-10-10 14:39:57","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10236,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/ec073cd4e1dea6580747ed92.xlsx"},{"id":93239904,"identity":"0e8b2e19-a279-49fe-8293-4ef8c101a44c","added_by":"auto","created_at":"2025-10-10 14:39:57","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11226,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/b8975ab05d1cb1a531dd906a.xlsx"},{"id":93240799,"identity":"b9f91954-8713-4061-a228-dc8e69cedc73","added_by":"auto","created_at":"2025-10-10 14:47:57","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":87225,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1 (Supplementary Tables 1-28).xlsx\u003c/p\u003e","description":"","filename":"Additionalfile1SupplementaryTables128.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/6f06d6d13b86b69ff4dcb392.xlsx"},{"id":93240801,"identity":"4913433b-59ae-43a6-ada3-9a6f9a90c9ba","added_by":"auto","created_at":"2025-10-10 14:47:57","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":114453,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2 (Supplementary Figure 1).pdf\u003c/p\u003e","description":"","filename":"Additionalfile2SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7638506/v1/ab3a41285e572d1a3cac7f9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychosocial Well-being and Genetic Predisposition Shape Responsiveness to Lifestyle Coaching: Insights from a Risk-Stratified Intervention Trial","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global prevalence of obesity, metabolic syndrome, type 2 diabetes (T2D), cardiovascular and coronary heart disease (CVD / CHD) continues to escalate, driven by complex interactions between genetic susceptibility and adverse lifestyle exposures (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These noncommunicable diseases now account for the majority of global morbidity and mortality, posing an urgent public health burden. Lifestyle interventions\u0026mdash;targeting diet, physical activity, and psychosocial stress\u0026mdash;remain foundational strategies for mitigating cardiometabolic risk and improving metabolic health trajectories. Yet, the capacity of such interventions to induce sustained changes in phenotype, biomarker profile, and attenuate the expression of polygenic risk remains incompletely characterized. Emerging evidence suggests gene\u0026ndash;environment interactions may shape the penetrance of genetic predisposition, highlighting the need for integrative, precision-based prevention frameworks (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe complex interplay between genetic architecture and modifiable lifestyle factors in shaping metabolic health has emerged as a critical focus of contemporary biomedical research. At the population level, body mass index (BMI) serves as a robust proxy for adiposity and cardiometabolic risk, governed by both polygenic inheritance and environmental exposures. Advances in genome-wide association studies have facilitated the development of polygenic risk scores (PRS), which aggregate the effects of numerous common variants to estimate inherited susceptibility to traits such as elevated adiposity and related metabolic disorders (\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). While PRS enhances risk stratification, especially for early-onset obesity and T2D, its interaction with behavioral determinants, such as diet quality, physical activity, and psychosocial stress, remains under-characterized and mechanistically unresolved (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond its direct metabolic consequences, an elevated adiposity is often associated with a higher burden of physical, psychosocial, and occupational challenges (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Individuals with a high BMI PRS may experience greater difficulty in weight management, increased inflammation, altered metabolic biomarker profiles, and a heightened risk of obesity-related comorbidities, such as CVD and all-cause mortality (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Moreover, weight-related stigma, mental health challenges, and socioeconomic determinants further complicate health outcomes, underscoring the need for comprehensive, evidence-based lifestyle interventions (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Recent advances in personalized medicine and precision healthcare have highlighted the importance of personalized and group-based interventions in modifying disease trajectories (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Both approaches have demonstrated effectiveness in promoting sustainable behavioral changes, yet their comparative impact on biomarker profiles, phenotypic outcomes, and polygenic risk stratification remains largely unexplored (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study aims to examine the physiological and molecular effects of personal and group coaching modalities on key cardiometabolic biomarkers, alongside changes in phenotypic characteristics such as body composition and cardiovascular parameters. Additionally, PRS for adiposity (e.g., BMI) and metabolic disorders, such as T2D and CHD are evaluated to determine whether lifestyle interventions can influence risk stratification beyond genetic predisposition. By integrating biochemical, anthropometric, and genetic analyses, this study provides a comprehensive evaluation of how structured lifestyle coaching interventions modulate cardiometabolic health indicators. Given the increasing reliance on PRS in preventive medicine, understanding whether targeted lifestyle interventions can modify disease risk profiles could have profound implications for personalized healthcare strategies. The findings will contribute valuable insights into optimizing lifestyle interventions for individuals at varying levels of genetic susceptibility to metabolic disorders, thereby informing the development of more effective, precision-based health programs.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy objective and participants.\u003c/b\u003e The Virta Research Study is a longitudinal, 10-week lifestyle intervention designed to enhance the well-being, workability, and overall health of individuals at elevated cardiometabolic risk in Finland (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The trial is reported in accordance with the CONSORT statement, and the completed checklist is provided as Supplementary file 1.\u003c/p\u003e\u003cp\u003eThe primary objective of the study is to assess the impact of personalized lifestyle coaching on key physiological health markers, circulating blood metabolites, and risk stratification based on PRS. The study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki, and informed consent was obtained from all participants prior to enrollment. Ethical approval was obtained from the Helsinki and Uusimaa Hospital District (HUS/2093/2019) prior to study initiation.\u003c/p\u003e\u003cp\u003eThe study cohort comprised working-age adult volunteers, recruited from employees insured by major insurance service provider and receiving occupational healthcare from Aava Medical Ltd. Eligibility criteria included: (i) provision of signed informed consent, (ii) age 18\u0026ndash;65 years, (iii) inclusion of both male and female participants, with a minimum representation of one-third per gender, and (iv) sufficient proficiency in Finnish or English to comprehend study-related instructions and complete questionnaires.\u003c/p\u003e\u003cp\u003eExclusion criteria were established to ensure participant safety and data integrity. Individuals were ineligible if they met any of the following conditions: (i) anticipated frequent travel exceeding one workday per week during the intervention period, as assessed by the investigator; (ii) history of a major cardiovascular event within the preceding six months; (iii) a diagnosis of diabetes requiring pharmacological management; (iv) history of malignant disease, such as cancer, within five years prior to recruitment; (v) use of lipid-lowering medications or anti-obesity pharmacotherapy; (vi) pregnancy; or (vii) presence of a cardiac pacemaker.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy design.\u003c/b\u003e All eligible and willing participants who met the inclusion criteria were permitted to enter the screening phase (n\u0026thinsp;=\u0026thinsp;711). Sample size calculation and participant allocation was based on cholesterol balance, measured as the apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This biomarker was selected as a proxy for cardiometabolic risk, given its well-established role as a strong predictor of CVD in the general population. Following the screening phase, individuals whose ApoB/ApoA1 ratio ranked within the highest 45% were selected to proceed to the 10-week intervention phase. No interim analyses or formal stopping guidelines were specified due to the short intervention period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin the intervention cohort, participants with the highest one-third of ApoB/ApoA1 ratios (~\u0026thinsp;15% of all screened participants, n\u0026thinsp;=\u0026thinsp;104) were classified as the high-risk group. These individuals were randomized using block randomization generated by a computer into either a personalized lifestyle coaching intervention or a corresponding control group. Study personal did not have access to the random allocation sequence. Meanwhile, participants whose ApoB/ApoA1 ratios fell within the lower two-thirds of the high-risk threshold (~\u0026thinsp;30% of all screened participants, n\u0026thinsp;=\u0026thinsp;213) were categorized as the medium-risk group. They were randomized to receive group-based lifestyle coaching or to a corresponding control group. No blinding was performed, and all parties were aware of intervention assignments. All study data was be collected to research database by Aava Medical Ltd, where data is stored as pseudonymized with separate code keys.\u003c/p\u003e\u003cp\u003e Participants in the high-risk intervention arm engaged (start: 15/02/2020) in a structured individualized lifestyle coaching program, while those in the medium-risk intervention arm took part in group-based coaching sessions in cohorts of 8\u0026ndash;10 individuals. Throughout the 10-week intervention period, participants in both intervention arms followed a standardized lifestyle coaching curriculum, which consisted of eight structured sessions covering identical topics and protocols. The coaching framework emphasized four key domains: (i) fundamentals of lifestyle modification; (ii) nutritional optimization for metabolic health and weight management; (iii) physical activity and movement strategies; and (iv) sleep hygiene and recovery mechanisms. Coaching was delivered by qualified and experienced personal health coaches affiliated with Aava Medical Ltd. In contrast, participants in the control groups did not receive any structured coaching but continued to have access to standard occupational healthcare services as per usual.\u003c/p\u003e\u003cp\u003eRegarding the lifestyle coaching curriculum, the content was identical but allowed for prioritizing focus according to individual motives. The participants in the personal coaching branch had relatively balanced coaching strategy with modest emphasis on physical activity and movement strategies [Nutrition (29.7%); Physical activity (36.9%); Sleep, recovery, and stress (29.4%), Other (4.0%)]. In contrast, a greater proportion of individuals in the group coaching branch emphasized sleep hygiene and recovery mechanisms [Nutrition (26.0%); Physical activity (27.9%); Sleep, recovery, and stress (42.7%), Other (3.4%)].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHarms were assessed non-systematically through staff observations and spontaneous participant reports, and severity and trial relatedness were evaluated by a designated assessor. Serious adverse events were documented in patient records.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory assessments and sample analysis.\u003c/b\u003e Blood sampling was conducted at three time points: during the screening phase at week 0, at the midpoint of the intervention at week 5, and upon completion of the intervention at week 10. To ensure accessibility and convenience for participants, all laboratory assessments were performed in mobile assessment hubs located near their workplaces. Participants were required to fast for 10 to 12 hours overnight before each blood draw. Additionally, they were advised to avoid strenuous physical activity and maintain their regular diet for at least 24 hours before sampling to minimize physiological variability. All blood samples were collected by a trained phlebotomist to ensure precision and consistency in sample handling.\u003c/p\u003e\u003cp\u003eFollowing collection, blood samples were pseudonymized by Aava Medical Ltd personnel to protect participant identities and were then transported to the National Institute for Health and Welfare for further biochemical and molecular analyses. Laboratory assessments included both standard biomarkers and omics-based profiling. Biomarker analyses included measurements of apoA1, apoB, high-sensitivity C-reactive protein (hs-CRP), alanine aminotransferase (ALAT), triglycerides, uric acid, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, and glucose. Omics-based analyses included genome-wide association studies (GWAS), and PRS calculation for obesity (BMI), CHD, and T2D.\u003c/p\u003e\u003cp\u003eThe majority of the laboratory results were intended exclusively for research purposes. However, participants who consented to receiving personalized genetic risk insights were provided with their PRS for obesity (BMI), CHD, and T2D, allowing them to gain a deeper understanding of their genetic predisposition to these conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhysiological measurements.\u003c/b\u003e At the beginning of the intervention (screening phase) and upon completion of the 10-week intervention, key physiological parameters were assessed to evaluate participants\u0026rsquo; health status and potential changes over time. These included measurements of body weight, height, waist circumference, neck circumference, and blood pressure measures. All assessments were conducted by experienced health coaches, ensuring consistency and accuracy in data collection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClustering of Respondents Based on Well-being Profiles.\u003c/b\u003e To identify distinct subgroups for targeted well-being interventions, we clustered respondents based on their thematic well-being profile (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). First, theme scores were computed for each individual by averaging responses to questions within each previously defined theme, derived from a hierarchical clustering of 129 questionnaire items using Spearman correlations and Ward\u0026rsquo;s method. Pairwise Euclidean distances between individuals\u0026rsquo; theme scores were then calculated. These distances served as input for Partitioning Around Medoids (PAM) clustering (via the pam function in the R cluster package), which grouped respondents into five clusters based on thematic similarity. Cluster profiles were interpreted using average theme scores and validated visually via principal component analysis (PCA, capscale function, R vegan package). This methodology enabled the identification of five distinct respondent groups reflecting varied patterns of well-being strengths and challenges, that were labeled based on dominant patterns of strengths and challenges: i) the \u003cb\u003eWell-functioning\u003c/b\u003e cluster (Good) exhibited high scores across all themes and low presenteeism, ii) the \u003cb\u003eLifestyle challenges\u003c/b\u003e cluster (Life) showed lower scores in lifestyle-related themes despite good mental well-being, iii) the \u003cb\u003eHard on oneself\u003c/b\u003e group (Hard) had high physical activity but low psychological flexibility and performance orientation, iv) the \u003cb\u003eRecovery challenges\u003c/b\u003e cluster (Recovery) reported medium-to-low scores across most themes with elevated presenteeism, and v) the \u003cb\u003eMultidomain challenges\u003c/b\u003e group (Mult) showed low scores across all domains, indicating broad well-being difficulties.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuality control and statistical analysis.\u003c/b\u003e Prior to conducting statistical analyses, a multi-step quality control (QC) procedure was implemented to ensure data integrity and minimize noise. First, univariate outliers were excluded based on a\u0026thinsp;\u0026plusmn;\u0026thinsp;4 standard deviation (SD) threshold, applied within predefined risk groups and across all time points, to remove extreme values unlikely to reflect biological variability. Next, individuals exhibiting extreme response patterns defined by Z-scores of outcome change were identified and excluded (| Z-score | \u0026gt;3) separately within intervention and control groups. This step aimed to reduce undue influence of potentially non-representative while preserving group-specific variance structure.\u003c/p\u003e\u003cp\u003eBaseline multivariate outliers were then assessed across three domains: biomarkers, phenotypic traits, and PRS. Within each domain, principal component analysis (PCA) was conducted, retaining components that together explained\u0026thinsp;\u0026gt;\u0026thinsp;85% of the total variance. Mahalanobis distances were computed in the reduced feature space, and individuals exceeding the 99th percentile of the chi-square distribution were flagged as multivariate outliers. Participants identified as outliers in more than one domain were excluded from all analyses. If an individual was an outlier in only one domain, the corresponding domain-specific variables were excluded for that individual to retain usable data in other domains.\u003c/p\u003e\u003cp\u003eTo analyze phenotypic changes, biomarker profiles, and PRS over the course of the study, generalized estimating equations (GEEs) with a linear link function were applied, using a working i) independence structure for cross-sectional analyses and ii) an exchangeable structure for longitudinal data. The primary objective was to assess whether phenotypic traits and biomarker profiles differed significantly between the intervention and control groups within either study arm at any time point. A secondary focus examined whether i) genetic predisposition, as captured by PRS, or ii) psychosocial wellbeing contributed to baseline differences or to responder status over time. Furthermore, psychosocial wellbeing cluster-specific changes between baseline and follow-up were assessed using McNemar\u0026rsquo;s test for paired binary outcomes. For each cluster, a 2\u0026times;2 table was constructed comparing membership status at baseline versus follow-up. We used the continuity-corrected χ\u0026sup2; version of McNemar\u0026rsquo;s test (df\u0026thinsp;=\u0026thinsp;1, two-sided), reporting counts moved into and out of each cluster.\u003c/p\u003e\u003cp\u003eAll models were adjusted for relevant covariates, including age, sex, educational attainment, smoking, geographic region, language, baseline BMI, and use of antihypertensive medication, to account for between-subject variability and enhance robustness. To control for multiple testing, false discovery rate (FDR) adjustment was applied across all statistical analyses, grouped by biologically relevant domains: phenotypes (i) adiposity and (ii) blood pressure, and biomarkers grouped into (i) lipids, (ii) glucose/liver function, and (iii) renal/inflammatory markers. All computations were performed using R statistical software (version 3.3.3 or later, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eBaseline Cardiometabolic Profiles Differ by Risk Group, with No Evidence of Differential Intervention Effects Over Time.\u003c/b\u003e As anticipated, baseline phenotypic, biomarker, and PRS profiles differed significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across predefined cardiometabolic risk groups by apoB/apoA1-ratio (Table\u0026nbsp;1; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Tables\u0026nbsp;1\u0026ndash;3). Within these cardiometabolic risk groups, no major global differences were observed at baseline between allocated intervention and control arms in phenotype, biomarker, or PRS profiles. As an exception, in the high-risk arm the intervention group exhibited higher central adiposity (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at baseline compared to the control group (Table\u0026nbsp;2; Supplementary Tables\u0026nbsp;4\u0026ndash;5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLongitudinal analysis of the intervention and control arms, of either medium- or high-risk strata revealed no significant Time \u0026times; Group interaction effects (FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for phenotypic or biomarker profiles (Table\u0026nbsp;2; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Tables\u0026nbsp;6\u0026ndash;7). Although, participants in the personal coaching arm self-assigned approximately 30% more tasks across the lifestyle intervention modules than those in the group coaching arm, this greater engagement did not translate into broader between group differences. Post-hoc within-group analyses showed modest temporal improvements (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in adiposity indices and blood pressure within both intervention and control arms in the medium- and high-risk strata, with more pronounced changes in the intervention groups (Table\u0026nbsp;2; Supplementary Table\u0026nbsp;8). Apolipoprotein trajectories improved similarly across strata and study arms; however, reductions in LDL and total cholesterol levels were more pronounced (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the intervention groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Tables\u0026nbsp;9\u0026ndash;10). Consistent with modest reduction in adiposity and improved lipid profiles, ALAT levels also declined (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in all groups except the high-risk intervention arm following the 10-week lifestyle intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolygenic Risk Modifies Longitudinal Responses to Lifestyle Intervention in Metabolic Traits.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsistent with existing literature, polygenic risk scores (PRS) were significantly associated with baseline variation in cardiometabolic risk profiles (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, i) the BMI PRS was linked to increased adiposity, elevated blood pressure, and unfavorable levels of metabolic markers, including hs-CRP, glucose, ALAT, and uric acid, ii) the CHD PRS was associated with higher levels of atherogenic lipids, such as apoB, apoA1/apoB ratio, triglycerides, and LDL cholesterol, and iii) the T2D PRS associated with elevated glucose and uric acid levels (Supplementary Tables\u0026nbsp;11\u0026ndash;12). Next, we focused on how these baseline differences genetic risk and cardiometabolic state translated into differential responses over the 10-week lifestyle intervention.\u003c/p\u003e\u003cp\u003eAt first, evaluation of Baseline x Time interactions revealed consistent negative associations (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across all measured circulating biomarkers, blood pressure measures, and central adiposity parameters (i.e., neck and waist circumference), indicating that individuals with more adverse baseline values exhibit more widespread improvements over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;13\u0026ndash;14). In longitudinal analyses, higher BMI PRS was nominally associated with greater waist circumference reduction over time (β = \u0026minus;\u0026thinsp;0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34, P\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;15), suggesting promoted central fat loss in genetically predisposed individuals. Stronger over time associations were seen in lipid profile, where individuals with higher CHD PRS showed smaller improvements or even increases in LDL cholesterol (β\u0026thinsp;=\u0026thinsp;0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, FDR\u0026thinsp;=\u0026thinsp;1.5 \u0026times; 10⁻\u003csup\u003e3\u003c/sup\u003e), total cholesterol (β\u0026thinsp;=\u0026thinsp;0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, FDR\u0026thinsp;=\u0026thinsp;0.02), apoB (β\u0026thinsp;=\u0026thinsp;0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008, P\u0026thinsp;=\u0026thinsp;0.044) and the apoB/apoA1 ratio (β\u0026thinsp;=\u0026thinsp;0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006, P\u0026thinsp;=\u0026thinsp;0.02), suggesting that CHD genetic risk may limit lipid profile improvements over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;16).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further understand the PRS x Time interactions, we tested whether they trajectories influenced by participants' baseline cardiometabolic status (Baseline x Time) (Supplementary Table\u0026nbsp;17\u0026ndash;18). After adjusting for the Baseline \u0026times; Time interaction, the previously observed association between BMI PRS and waist circumference change was no longer significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the initial effect was most likely driven by higher baseline central adiposity and intervention effect in genetically predisposed individuals (Supplementary Table\u0026nbsp;17). In contrast, the associations between CHD PRS and the atherogenic lipid profile on total cholesterol, LDL cholesterol, apoB, the apoB/apoA1 ratio persisted following adjustment for corresponding baseline interactions, indicating that genetic risk for CHD independently limited lipid improvements during the intervention regardless of Baseline x Time effect (Supplementary Table\u0026nbsp;18). Collectively, cautious interpretation of these findings suggest that polygenic burden can affect baseline-dependent responses, shaping individual variability in lifestyle intervention outcomes (Supplementary Table\u0026nbsp;17\u0026ndash;18).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic Burden and Physiological Correlates of Metabolic Risk Across Well-being Groups.\u003c/b\u003e Among individuals classified as well-functioning (Good), baseline BMI PRS were significantly lower compared to those in the recovery-challenged (Reco) (β\u0026thinsp;=\u0026thinsp;0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13, FDR\u0026thinsp;=\u0026thinsp;0.03) and multidomain challenged (Mult) (β\u0026thinsp;=\u0026thinsp;0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, FDR\u0026thinsp;=\u0026thinsp;0.004) clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table\u0026nbsp;19). Overall, those with multidomain challenges showed the highest CHD and BMI PRS, reflecting greater genetic susceptibility to higher adiposity and cardiometabolic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese genetic patterns at baseline were paralleled by phenotypic difference, with individuals in the well-functioning (Good) and high-functioning with high demands (Hard) clusters exhibiting (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) lowest levels of adiposity compared to other clusters, whereas the multidomain challenges (Mult) cluster exhibited the highest levels of adiposity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table\u0026nbsp;20). After adjusting for BMI, systolic blood pressure emerged significantly lower in the multidomain challenge group (Mult) compared with the well-functioning group (Good) (β = \u0026minus;\u0026thinsp;5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81, FDR\u0026thinsp;=\u0026thinsp;0.004). This indicated that elevated adiposity in the multidomain challenged (Mult) cluster masked underlying between-cluster differences in blood pressure, which only became evident after adjustment for BMI. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table\u0026nbsp;20).\u003c/p\u003e\u003cp\u003eBiomarker profiles mirrored the genetic and phenotypic differences at baseline between the well-functioning (Good) and multidomain challenges (Mult) clusters. Individuals in the well-functioning cluster exhibited the most favorable metabolic signatures, while the multidomain challenged individuals showed elevated levels of hs-CRP, uric acid, triglycerides, glucose, and apoB/apoA1 ratio, along with lower levels of apoA1 an HDL (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table\u0026nbsp;21). These differences were dissipated (FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.05) after adjusting for BMI, suggesting that observed adiposity differences mediated the observed biomarker disparities (Supplementary Table\u0026nbsp;21). Together, these findings highlight a coherent biological gradient across genetic, phenotypic, and metabolic domains linked to psychosocial well-being, where adiposity level is key mediator of biomarker profile differences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWell-functioning Individuals Show the Strongest Phenotypic and Biomarker Profile Improvements Over Time.\u003c/b\u003e Longitudinal analyses of phenotypic change (Time \u0026times; Well-being cluster) showed that individuals in the well-functioning (Good) cluster had the greatest reductions in adiposity over time compared to other clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Tables\u0026nbsp;22). In line with these groupwise findings, baseline status of the well-functioning (Good) cluster predicted the most consistent improvements across all adiposity measures, including body weight (β = -1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33, FDR\u0026thinsp;=\u0026thinsp;5.88 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), BMI (β = -0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, FDR\u0026thinsp;=\u0026thinsp;1.74 x 10⁻\u003csup\u003e6\u003c/sup\u003e), and waist circumference (β = -2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65, FDR\u0026thinsp;=\u0026thinsp;2.37 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) (Supplementary Table\u0026nbsp;23). In contrast, however, most distinct reductions for systolic blood pressure (β = -4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, FDR\u0026thinsp;=\u0026thinsp;1.38 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), and diastolic blood pressure (β = -3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62, FDR\u0026thinsp;=\u0026thinsp;2.15 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) were observed within the flexible health behavior difficulties (Life) cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;23). Across all other well-being clusters, similar changes across time were detected in phenotype profile, but narrower more inconsistent responses, especially in the multidomain difficulty (Mult) cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;23).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBiomarker responses paralleled phenotypic improvements, with individuals in the well-functioning (Good) cluster showing the most favorable changes in metabolic signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Tables\u0026nbsp;24\u0026ndash;25). These included significant reductions (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in apoB/apoA1 ratio, apoB, total cholesterol, triglycerides and ALAT (Supplementary Table\u0026nbsp;25). Similar beneficial shifts in atherogenic lipid profiles were also observed (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the high-functioning with high demands (Hard) and flexible behavior difficulties (Life) clusters. In line with previous observations, the multidomain challenges (Mult) cluster exhibited the least responsiveness over time also across biomarker profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;25; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eThese distinct physiological, phenotypic, and biomarker profile differences across lifestyle clusters translated into measurable shifts in lifestyle cluster membership over the 10-week intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Tables\u0026nbsp;26\u0026ndash;28). Across the study population, membership shifted markedly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) towards the well-functioning (Good) cluster (N\u0026thinsp;=\u0026thinsp;+\u0026thinsp;38) and away from the flexible health behavior difficulties (Life) cluster (N = -25) (Supplementary Table\u0026nbsp;28). The flexible health behavior difficulties (Life) and high-functioning with high demands (Hard) clusters exhibited the greatest capacity for psychosocial improvement, whereas the well-functioning (Good) and multidomain challenges (Mult) clusters remained largely stable (Supplementary Table\u0026nbsp;26\u0026ndash;28). Strikingly, participants in the multidomain challenges (Mult) cluster assigned themselves the fewest intervention tasks (~\u0026thinsp;11.6 tasks/individual), consistent with a diminished capacity for behavioral adaptation, and displayed smallest gains in both phenotypic and biomarker indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the interplay between polygenic risk for adiposity and metabolic disease traits in relation to cardiometabolic phenotypes, biomarker profiles, and psychosocial well-being within the context of a structured 10-week lifestyle intervention. We demonstrated that across allocated risk strata by apoB/apoA1 ratio, baseline cardiometabolic profiles displayed marked global differences on phenotypic, biomarker, and genetic factors. However, no differential intervention effects were observed regardless of intervention allocation to study groups within different risk strata, although, modest within-group improvements were observed in measures of adiposity and lipid profiles. Polygenic risk profiles also associated with baseline metabolic states, modified individual responses to the intervention, and interacted with baseline status to further to shape the magnitude and direction of individual responses. Furthermore, baseline patterns of psychosocial well-being reflected underlying genetic and metabolic risk. Individuals in the well-functioning cluster at baseline demonstrated the most pronounced improvements during the intervention, and lifestyle cluster transitions overall reflected a shift toward this favorable profile. In contrast, individuals with multidomain difficulties not only exhibited greater baseline adiposity and genetic risk for increased body mass but also showed reduced responsiveness to intervention. These findings highlight the complex and interconnected roles of baseline metabolic state, genetic predisposition, and psychosocial functioning in determining health trajectories and modulating intervention efficacy.\u003c/p\u003e\u003cp\u003eAt baseline, individuals with higher genetic risk for adiposity, as indicated by elevated BMI PRS, exhibited greater adiposity and elevated blood pressure, consistent with established links between genetic predisposition, obesity, and cardiometabolic risk (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These associations extended beyond anthropometric traits to encompass metabolic disturbances reflective of systemic dysfunction, including markers of hepatic stress, impaired glucose metabolism, dyslipidemia, and low-grade inflammation, collectively aligning with a metabolic syndrome\u0026ndash;like phenotype (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In contrast, PRS for T2D and, CHD were only associated with distinct blood-derived metabolic signatures. Elevated CHD PRS was linked to increased levels of atherogenic lipid markers consistent with a lipid-centric cardiovascular risk profile (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Similarly, higher T2D PRS was associated with elevated fasting glucose and uric acid levels, in line with the known T2D pathophysiology of impaired glucose metabolism (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Together, these findings underscore the role of polygenic risk in shaping specific metabolic pathways, even when not overtly expressed in the anthropometric measures and broader phenotype.\u003c/p\u003e\u003cp\u003eThe observed negative interaction between BMI PRS x Time for waist circumference suggested a diminishing genetic influence on central adiposity over time, potentially reflecting intervention effects or behavioral adaptation (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A similar negative trend was noted for both systolic and diastolic blood pressure, reinforcing the notion that the beneficial impact of lifestyle changes may manifest first in reductions in visceral fat, blood pressure, and metabolic biomarkers\u0026mdash;prior to observable changes in overall weight (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These findings were, however, attenuated following baseline level adjustment, indicating that the initial effect was largely driven by higher baseline central adiposity in genetically predisposed individuals. This pattern aligns with evidence from cardiometabolic, exercise, and behavioral intervention studies demonstrating that baseline status frequently moderates the magnitude and rate of physiological adaptation\u0026mdash;a phenomenon often referred to as the \u0026lsquo;law of initial value\u0026rsquo; (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Individuals with more adverse baseline profiles, particularly in central adiposity and blood pressure, tend to exhibit proportionally greater short-term improvements, irrespective of genetic background. Such baseline-driven effects can overshadow polygenic influences in early phases of lifestyle change, with genetic modulation becoming more apparent in the longitudinal evolution of biomarker networks rather than in gross anthropometric measures. Overall, our findings together with these observed notions support the concept that i) baseline levels seem to have greater influence on intervention responsiveness over polygenic risk, while ii) polygenic risk effects over time are more sensitively reflected in dynamic biomarker trajectories than in slower-shifting anthropometric outcomes, particularly in the context of short- to medium-term lifestyle modification (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile PRS for BMI, T2D, and CHD showed minimal associations with longitudinal changes in anthropometric traits, more distinct time-dependent effects emerged within the metabolic domain\u0026mdash;most notably for CHD PRS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These patterns suggest that genetic predisposition to cardiometabolic disease may unfold subtly and progressively through specific biochemical pathways, particularly through well-characterized alterations in lipid metabolism, rather than through gross changes in physical measures (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Moreover, early metabolic status may modulate the expression of genetic risk over time, consistent with evidence that baseline metabolic profiles can shape long-term disease trajectories (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Collectively, these biomarker dynamics underscore a key insight that polygenic risk may remain latent at the phenotypic level, yet become increasingly apparent through targeted metabolic shifts, offering a more sensitive lens into the early expression and progression of complex disease risk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA novel aspect of this study is the integration of psychosocial well-being with genetic and metabolic risk, highlighting the complex interplay between mental, behavioral, and biological domains (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Individuals facing multidomain psychosocial difficulties tended to have higher genetic predisposition to adiposity and displayed more adverse metabolic profiles (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These patterns suggest that psychosocial stressors and mental well-being may not only coexist with genetic risk but potentially exacerbate its physiological expression (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This aligns with literature from behavioral medicine showing that chronic stress and social adversity can contribute to dysregulated metabolic function through behavioral and neuroendocrine pathways (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Psychosocial well-being has been shown to be a key mediator in behavior change processes, affecting adherence, motivation, and physiological outcomes, and that resilience and self-regulation facilitate sustained lifestyle changes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePsychosocial well-being was dynamic over the 10-week intervention period, with many participants in the intervention arm showing gradual shifts toward more favorable functioning states (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, these changes were largely incremental, suggesting that while lifestyle interventions can initiate positive movement, longer durations of support or coaching may be needed to facilitate more profound improvements (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Moreover, capacity to shift in psychosocial well-being may itself be person-specific. For some, well-being may represent a relatively stable state, making transitions from high-burden to well-functioning profiles more gradual and effortful. These findings underscore both the potential and limits of short-term interventions, highlighting the need for extended, personalized strategies that account for individual differences in adaptability (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This perspective aligns with existing literature emphasizing that multidimensional well-being is shaped by both genetic and psychosocial contexts, and that durable and more distinct improvements are more likely when interventions are tailored and temporally sustained (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParticipants with more favorable psychosocial profiles at baseline demonstrated the most pronounced improvements in adiposity and cardiometabolic markers over time. These changes were reflected in both physical and biochemical domains, supporting the idea that psychosocial well-being may reflect the capacity for positive health change (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Psychosocial resilience and adaptive coping mechanisms likely play a role in maintaining health behaviors and responding to intervention efforts, underscoring the importance of addressing mental and emotional well-being in lifestyle programs (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In contrast, individuals with greater psychosocial challenges exhibited limited changes in health outcomes, suggesting a form of physiological inertia that may stem from the cumulative effects of stress and adversity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These individuals might benefit from more personalized or integrative interventions that concurrently target psychosocial and behavioral domains as well as account for potential higher genetic burden of diseases (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral limitations should be considered when interpreting the findings of this study. First, the observation that control group participants also exhibited improvements in health-related markers, though less nuanced\u0026mdash;raises the possibility of a Hawthorne effect, whereby participation alone may independently influence behavior through multiple mechanisms, irrespective of intervention content (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In addition to the Hawthorne effect, factors such as measurement reactivity, social desirability bias, and expectancy effects may lead participants to adopt healthier behaviors (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Increased health awareness and regression to the mean may also contribute to improvements unrelated to the intervention itself (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These factors may partially account for the lack of statistically significant differences between intervention and control arms. Additionally, while the intervention was multifaceted and aimed to promote overall lifestyle improvement rather than focusing solely on weight loss, the relatively short duration of the intervention and the minimal effect on body weight may have limited the potential for meaningful physiological adaptations and differential responses, particularly in metabolic markers (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, the deliberate allocation of higher-risk individuals to the personal coaching group introduces a potential confounding factor. Individuals with elevated baseline cardiometabolic risk may be more responsive to behavioral interventions, potentially amplifying observed effects in this subgroup (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Unequal group sizes further complicate interpretation, as they may limit statistical power and reduce the sensitivity to detect meaningful groupwise differences. Additionally, the 10-week duration of the intervention may have been insufficient to induce measurable changes in phenotypes or biomarkers among individuals with higher genetic cardiometabolic risk, as reflected by the apoB/apoA1 ratio (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The absence of differential responses across genetically stratified risk groups highlights a key limitation of short-term interventions in modifying genetically embedded metabolic traits (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). While lifestyle modification remains a central strategy for cardiometabolic risk reduction, our findings suggest that individuals with a higher genetic burden may require longer or more intensive interventions to achieve significant physiological improvements. These insights underscore the importance of tailoring both the duration and intensity of lifestyle programs to individual risk profiles in future precision prevention efforts (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn conclusion, this study adds to the growing body of evidence supporting the need for integrated, personalized strategies in the prevention and management of obesity and related metabolic disorders. While genetic predisposition defines a baseline level of risk, it does not act in isolation, whereas rather, it shapes individual responsiveness to lifestyle interventions, potentially amplifying or constraining their effectiveness. Psychosocial well-being emerges as a critical, and often underappreciated, determinant of health trajectories, modulating both where individuals begin, engagement, and how much they can change. The interplay between genetic risk, behavioral context, and psychosocial state underscores the multifactorial nature of cardiometabolic health and calls for tailored approaches that reflect this complexity. Future research should focus on disentangling these interdependencies through longitudinal, multidimensional designs, enabling more precise and adaptive intervention strategies that account for the diverse biological and psychosocial realities of individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALAT =\u0026nbsp;alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eapoA1 = apolipoprotein A1\u003c/p\u003e\n\u003cp\u003eapoA1 = apolipoprotein A1\u003c/p\u003e\n\u003cp\u003eapoB = apolipoprotein B\u003c/p\u003e\n\u003cp\u003eBMI = body mass index\u003c/p\u003e\n\u003cp\u003eCHD = coronary heart disease\u003c/p\u003e\n\u003cp\u003ehs-CRP = high-sensitivity\u0026nbsp;C-reactive protein\u003c/p\u003e\n\u003cp\u003eCVD = cardiovascular disease\u003c/p\u003e\n\u003cp\u003eHDL = high-density lipoprotein\u003c/p\u003e\n\u003cp\u003eLDL = low-density lipoprotein\u003c/p\u003e\n\u003cp\u003ePRS = polygenic risk score\u003c/p\u003e\n\u003cp\u003eSD =\u0026nbsp;standard deviation\u003c/p\u003e\n\u003cp\u003eT2D = type 2 diabetes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical principles of the Declaration of Helsinki and applicable national regulations governing research involving human participants. Ethical approval was obtained from the Helsinki and Uusimaa Hospital District (HUS/2093/2019) prior to study initiation. All participants received detailed written and verbal information about the study procedures, potential risks, and benefits. Informed consent was obtained from all participants prior to their enrollment, and participation was entirely voluntary. Participants were informed of their right to withdraw from the study at any point without any consequences to their medical care or other entitlements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent for the publication of anonymized data generated during the study. No identifiable personal information is included in this manuscript. Consent procedures adhered to institutional and ethical guidelines regarding data confidentiality and participant rights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not primarily available for third party individuals.\u0026nbsp;To gain access to datasets used in this study, individuals\u0026nbsp;need to apply, agree\u0026nbsp;and sign\u0026nbsp;on the necessary requirements and terms of data distribution\u0026nbsp;protocols set by\u0026nbsp;the National Institute for Health Welfare, Helsinki, Finland.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;bioinformatics scripts/codes\u0026nbsp;generated for statistical analyses purposes during\u0026nbsp;the\u0026nbsp;current\u0026nbsp;study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by\u0026nbsp;Business Finland\u0026nbsp;(grant no. 2874/31/2019). Additional support was provided through personal grants awarded to the investigators: KK was funded by Juhani Aho Medical Research Foundation;\u0026nbsp;HVS\u0026nbsp;received funding from the \u003cem\u003eFinnish Medical Foundation\u003c/em\u003e;\u0026nbsp;KP\u0026nbsp;was supported by the \u003cem\u003eResearch Council of Finland\u003c/em\u003e (grant nos. 266286, 272376, 314383, 335443, 342747), the \u003cem\u003eFinnish Medical Foundation\u003c/em\u003e, the \u003cem\u003eGyllenberg Foundation\u003c/em\u003e, the \u003cem\u003eNovo Nordisk Foundation\u003c/em\u003e (grant nos. NNF20OC0060547, NNF17OC0027232, NNF10OC1013354, NNF25SA0103783), the \u003cem\u003eFinnish Diabetes Research Foundation\u003c/em\u003e, the \u003cem\u003ePaulo Foundation\u003c/em\u003e, the \u003cem\u003eSigrid Jusélius Foundation\u003c/em\u003e, the \u003cem\u003eUniversity of Helsinki and Helsinki University Hospital\u003c/em\u003e, and by \u003cem\u003eGovernment Research Funds\u003c/em\u003e; MP was funded by The Research Council of Finland no. 359072.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHVS was\u0026nbsp;responsible for carrying out analysis and majority of writing. MP, KK and PB\u0026nbsp;supervised the project and contributed significantly to the writing and editing of the\u0026nbsp;manuscript.\u0026nbsp;PB was responsible for the data collection.\u0026nbsp;KP contributed to the writing and editing of the manuscript and offered key expertise in obesity-related physiology, significantly advancing the conceptual framework of the study.\u0026nbsp;All authors participated in revision and editing of the manuscript in the final phase\u0026nbsp;before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all study participants for their time, commitment, and valuable contributions. We also gratefully acknowledge the study personnel for their essential roles in participant recruitment, data collection, and overall coordination, which were critical to the successful implementation of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbarca-G\u0026oacute;mez, L. et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128\u0026middot;9 million children, adolescents, and adults. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e390\u003c/b\u003e (10113), 2627\u0026ndash;2642 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, B. et al. 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Polygenic risk score and coronary artery disease: A meta-analysis of 979,286 participant data. \u003cem\u003eAtherosclerosis\u003c/em\u003e \u003cb\u003e333\u003c/b\u003e, 48\u0026ndash;55 (2021 Sept).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBonnefond, A., Florez, J. C., Loos, R. J. F. \u0026amp; Froguel, P. Dissection of type 2 diabetes: a genetic perspective. \u003cem\u003eLancet Diabetes Endocrinol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (2), 149\u0026ndash;164 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, M. S. et al. Association of genetic risk, lifestyle, and their interaction with obesity and obesity-related morbidities. \u003cem\u003eCell. Metab. 2024 July\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e;36(7):1494\u0026ndash;1503e3 .\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, H. Y., Masip, G., Meng, T. \u0026amp; Nielsen, D. E. Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies. \u003cem\u003eJ. Nutr.\u003c/em\u003e \u003cb\u003e154\u003c/b\u003e (12), 3521\u0026ndash;3543 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSutoh, Y. et al. Healthy lifestyle practice correlates with decreased obesity prevalence in individuals with high polygenic risk: TMM CommCohort study. \u003cem\u003eJ. Hum. Genet.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e (1), 9\u0026ndash;15 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrittain, E. L. et al. Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (3), e243821 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDashti, H. S. et al. Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 5 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuevas, A. G., Mann, F. D. \u0026amp; Krueger, R. F. Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions. \u003cem\u003eLifestyle Genom\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e (1), 90\u0026ndash;97 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCaffery, J. M. et al. Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention: The Trans-NIH Consortium for Genetics of Weight Loss Response to Lifestyle Intervention. \u003cem\u003eDiabetes\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (4), 669\u0026ndash;676 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKauppi, K., Borg, P., Roos, E., Torkki, P. \u0026amp; Korpela, K. Utility of an online well-being assessment in targeting employee well-being programmes: a cross-sectional survey study in Finland. \u003cem\u003eBMJ Open. 2024 June\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e;14(6):e079708 .\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShiri, R., V\u0026auml;\u0026auml;n\u0026auml;nen, A., Mattila-Holappa, P., Kauppi, K. \u0026amp; Borg, P. The Effect of Healthy Lifestyle Changes on Work Ability and Mental Health Symptoms: A Randomized Controlled Trial. \u003cem\u003eInt. J. Environ. Res. Public. Health\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (20), 13206 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusuf, S. et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (9438), 937\u0026ndash;952 (2004 Sept).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerkman, L. F. et al. Employee Cardiometabolic Risk Following a Cluster-Randomized Workplace Intervention From the Work, Family and Health Network, 2009\u0026ndash;2013. \u003cem\u003eAm. J. Public. Health\u003c/em\u003e. \u003cb\u003e113\u003c/b\u003e (12), 1322\u0026ndash;1331 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards, J. J. et al. Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials. \u003cem\u003eBr. J. Sports Med.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e (20), 1317\u0026ndash;1326 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, S., Janicki-Deverts, D. \u0026amp; Miller, G. E. Psychological stress and disease. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e298\u003c/b\u003e (14), 1685\u0026ndash;1687 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCambridge, J., Witton, J. \u0026amp; Elbourne, D. R. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. \u003cem\u003eJ. Clin. Epidemiol.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (3), 267\u0026ndash;277 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Polygenic Risk Score (PRS), Psychosocial Well-being, Cardiometabolic Risk, Lifestyle Intervention, Gene–Environment Interaction, Personalized Medicine","lastPublishedDoi":"10.21203/rs.3.rs-7638506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7638506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eLifestyle modifications are known to improve cardiometabolic outcomes, however, their effectiveness in modulating metabolic signatures and interacting with genetic susceptibility and behavioral determinants of health remains incompletely understood. Psychosocial well-being may both reflect underlying genetic predisposition and influence responsiveness to lifestyle interventions, yet this interplay remains underexplored, thus elucidating these relationships is essential for advancing personalized and precision health approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This 10-week randomized controlled trial (RCT) assessed the impact of lifestyle coaching on cardiometabolic health among working-age adults at elevated risk (N = 709 screened). Risk stratification was based on apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio. Participants in the highest risk category (~15%, n = 104) were randomized to either personal coaching (intervention: n = 53; control: n = 51), while those at medium risk (~30%, n = 213) were randomized to group coaching (intervention: n = 107; control: n = 106) branch. Low-risk individuals (n = 394) were excluded after baseline. Interventions followed a standardized curriculum and included personalized or group-based behavioral guidance targeting diet, physical activity, and stress management. Statistical analyses were performed using generalized estimation equations (GEE) for primary analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAt baseline, higher polygenic risk scores (PRS) for body mass index (BMI) associated with greater psychosocial burden, higher adiposity, and more adverse metabolic profiles (FDR \u0026lt; 0.05), including elevated high-sensitivity C-reactive protein (hs-CRP), uric acid, and alanine aminotransferase (ALT) levels. The 10-week lifestyle intervention did not yield major differential effects on cardiometabolic status between coaching modalities within different risk strata but did result in incremental improvements (FDR \u0026lt; 0.05) in psychosocial well-being. Participants with multidomain challenges showed the least responsiveness (FDR \u0026lt; 0.05) in adiposity and metabolic signatures compared with those with more favorable psychosocial profiles. In addition to psychosocial well-being, baseline cardiometabolic status (i.e., adiposity, blood pressure, biomarker profile) and polygenic risk predisposition for BMI and coronary heart disease (CHD) shaped (FDR \u0026lt; 0.05) intervention responsiveness, influencing adiposity and metabolic signature trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e These findings underscore the importance of personalized, multidimensional approaches to cardiometabolic health. Genetic risk together with psychosocial well-being shape both baseline status and potential for change and intervention responsiveness. Integrating and accounting both factors is essential for optimizing prevention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u0026nbsp;\u003c/strong\u003eClinicalTrials.gov NCT04633876. Registration date: 18/11/2020.\u003c/p\u003e","manuscriptTitle":"Psychosocial Well-being and Genetic Predisposition Shape Responsiveness to Lifestyle Coaching: Insights from a Risk-Stratified Intervention Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 14:39:52","doi":"10.21203/rs.3.rs-7638506/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"335686394669531712393325141164739208136","date":"2025-09-26T10:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T10:00:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-26T09:56:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-26T09:48:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T19:22:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-25T19:19:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2aba06fb-f7b8-4a23-b520-458c47f1debd","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55822151,"name":"Health sciences/Biomarkers"},{"id":55822152,"name":"Health sciences/Cardiology"},{"id":55822153,"name":"Health sciences/Diseases"},{"id":55822154,"name":"Health sciences/Endocrinology"},{"id":55822155,"name":"Biological sciences/Genetics"},{"id":55822156,"name":"Health sciences/Medical research"},{"id":55822157,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-10T14:39:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 14:39:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7638506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7638506","identity":"rs-7638506","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}